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[email protected] Follow us: www.facebook.com/ijtra INTERNATIONAL JOURNAL OF TECHNICAL RESEARCH AND APPLICATIONS (IJTRA) (IJTRA is a high-quality scientific journal devoted to fields of Science, Technology & Engineering. It is publish online/print format with the e-ISSN 2320-8163 & p-ISSN 2321-7332.) . Introduction: The Editorial Board is very committed to build the Journal as one of the leading international journals in The field of sciences in the next few years. With the support of our member of editorial board and Team IJTRA, it is expected that a heavy resource to be channelled into the Journal to establish its international reputation. The Journal's reputation will be enhanced from arrangements with several organizers of international conferences in publishing selected best papers of the conference proceedings. Aims and Scope: International Journal of Technical Research and Applications (IJTRA) is a refereed international journal to be of interest and use to all those concerned with research in various fields of, or closely related to, Science, Technology & Engineering disciplines. : International Journal of Technical Research and Applications (IJTRA) aims to provide a highly readable and valuable addition to the literature which will serve as an indispensable reference tool for years to come. The coverage of the journal includes all new theoretical and experimental findings in the fields of Science, Technology & Engineering or any closely related fields. The journal also encourages the submission of critical review articles covering advances in recent research of such fields as well as technical notes. Guide for Authors Manuscript Submission High-quality submissions to this new journal are welcome now and manuscripts may be either submitted online or mail. Online: For online submission upload one copy of the full paper including graphics and all figures at the online submission site, accessed via E-mail.
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Through the publication, we hope to establish and provide an international platform for information exchange in different fields of science, engineering and technology. International Journal of Technical Research and Applications (IJTRA) recently achieves the Impact factor: 4.39 (sjif); and IC Value: 5.79 which is the great achievement for us. We aims to provide a highly readable and valuable addition to the literature, which will serve as an indispensable reference tool for years to come. We are also organizing International conference on : 3-5 November , 2014 Kuala Lumpur 6-9 November, 2014 Singapore Interested author may register themselves at www.grdsweb.org The coverage of the journal includes all new theoretical and experimental findings in all aspects of concerning science, engineering and technology or any closely related fields. The journal also encourages the submission of critical review articles covering advances in recent research of such fields as well as technical notes. The Editorial Board is very committed to build the Journal as one of the leading international journals in concerning science, engineering and technology in the next few years. it is expected that a valuable resource to be channelled into the Journal to establish its international reputation. We have received an excellent response to the previous issues of IJTRA from both academics and practitioners. We are pleased by this response and proud to report that IJTRA is achieving its mission of promoting research and applications in science, engineering and technology. IJTRA will bring you top quality research papers from an international body of contributors and a team of distinguished editors from the world's leading institutions engaged in all aspects of mechanical and industrial engineering. Now, the IJTRA invites contributions from the entire international research community. The new journal will continue to deliver up to date research to a wide range of science, engineering and technology professionals. We would like to thank all members of the editorial board and the international advisory board members for their continued support to IJTRA with their highly valuable advice. Additionally, we would like to thank the manuscript reviewers for providing valuable comments and suggestions to the authors that helped greatly in improving the quality of the papers. My sincere appreciation goes to all authors and readers of IJTRA for their excellent support and timely contribution to this journal. We would be delighted if the IJTRA could deliver valuable and interesting information to the worldwide community of science, engineering and technology. Your cooperation and contribution would be highly appreciated. More information about the IJTRA guidelines for preparing and submitting papers may be obtained from www.ijtra.com Regards Editor_in_Chief www.ijtra.com Email:
[email protected] Follow us: www.facebook.com/ijtra Members of Editorial Board Dr P.K. Trivedi CSIR Scientist(NBRI, Lucknow,India) Post Doc University of Maryland, USA Dr. Virendra Kumar Scientist UP Remote Sensing Application Centre Land Use and Land Cover Divisional Head Prof (Dr.) Amarika Singh Dean Institute Of Engineering & Technology, Lucknow A Constituent College Of G.B. Technical University, Lucknow Prof (Dr.) S. Qamar Abbas Director And Professor; AIMT Department of Computer Science Engineering Silvia Riva Department of Health Sciences Interdisciplinary centre for Research and Intervention on Decision (IRIDe Centre). Via Festa del Perdono,7 20122 Milan (Italy) Prof (Dr.) M. I. Khan (IITK) (IITK) Professor; Integral University,India Previously; Assistant Professor University of Basrah, IRAQ Professor Univ. of Garyounis, Libya Prof(Dr.) Mohamed Hussein Director/Professor; Jahangirabad Institute of Engineering & Technology Department of Computer Science Engineering Ali I.Al-Mosawi Lecturer in Technical Institute-Babylon,Machines Depart, IRAQ M.Sc. in Materials Engineering Prof.(Dr.) Vikas Misra Dean / Director / Principal Allen House Institute of Technology Department of Mechanical Engineering Dr. Abdulrahman S. Alanazi Consultant Clinical Pharmacist Saudi Arabia Dr. Neelam Pathak Professor; Integral University,India Department Of Bio-Technology Post-Doc University of Maryland, USA Prof (Dr.) Vinodini Katiyar Professor & Dean at Shri Ramswaroop Memorial Group of Professional Colleges Department of Computer Science & Engineering Dr. Taghreed Hashim Al-Noor Professor; Chemistry Department, Faculty of Science of Ibn-Al-Haitham Education College University of Baghdad, Baghdad Dr. K. M. Moeed Associate Professor Integral University, India Department of Mechanical Engineering Dr. Sudhish Kumar Shukla (IIT;BHU) Professor; Manav Rachna College of Engineering,Faridabad Department of Applied Science Post-Doc North West University, South Africa. Dr Ravi Shankar Mishra Professor,HOD;Sagar Institute Of Science & Technolgy Bhopal Department of electronics and communication Engineering Maulana Azad National Institute of Technology, Bhopal, India Prof Yudhishthir Raut Department of electronics and communication Engineering Professor;Sagar Institute of Research & Technology Bhopal Ph.D ;Rajiv Gandhi Proudyogiki Vishwavidyalaya Bhopal, India Mr. Pyie Phyo Maung Department of Biotechnology, Technological University Kyaukse, Kyaukse Township, Mandalay Division, Myanma Mrs Ritu Gulati Faculty of Architecture GBTU, Lucknow. Specialization in Environment and Energy CEPT University Ahemdabad, India * Mr Faizan Sayed Ali Asst. Manager Bureau of Research on Industry & Economic Fundamentals Specialization in Department of CIM Engineering Visvesvaraya Technological University, Karnataka Dr. Maneesh Kumar Srivastav Asst. Prof V. L. College of Pharmacy, Raichur- Karnataka Department of Medicinal Chemistry CONTENTS SL.NO. Page No. Manuscript Title IDENTIFICATIONS OF ELECTRICAL AND ELECTRONIC EQUIPMENT 1. 01-03 POLYMER WASTE TYPES USING MFI, DENSITY AND IR (Anas Mohammed Elhafiz Dafa Alla, Ahmed Ibrahim Seedahmed) ASSESSMENT OF HEAVY METALS CONCENTRATION IN INDIAN AND 2. 04-08 PAKISTANI VEGETABLES (Osama Sarwar Khan, Farooq Ahmad, Adnan Skhawat Ali, Rana Muhammad Kamal, Umar Ashraf) ENERGY AWARE INFORMATION DISSEMINATION STRATEGIES TO 3. 09-11 IMPROVE LIFETIME OF A WSN (Madhu G.C, J. Jhansi) A NETWORK DATA AND COMMUNICATION ANALYSIS BASED COMBINED 4. 12-15 APPROACH TO IMPROVE VIDEO TRANSMISSION IN MANET (Madhu G.C, J. Jhansi) DIFFUSER ANGLE CONTROL TO AVOID FLOW SEPARATION 5 16-21 (Vinod Chandavari, Mr. Sanjeev Palekar) SYNTHESIS, PHYSICO-CHEMICAL AND ANTIMICROBIAL PROPERTIES OF 6. SOME METAL (II) -MIXED LIGAND COMPLEXES OF TRIDENTATE SCHIFF 22-28 BASE DERIVES FROM Β-LACTAM ANTIBIOTIC {(CEPHALEXIN MONO HYDRATE)-4-CHLOROBENZALDEHYDE} AND SACCHARIN (Taghreed. H. Al-Noor, Amer. J. Jarad, Abaas Obaid Hussein) CAUSES AND EVALUATION OF CRACKS IN CONCRETE STRUCTURES 7. 29-33 (Syed Mohd Mehndi, Prof. Meraj Ahmad Khan & Prof. Sabih Ahmad) DEVELOPMENT OF A FRAMEWORK FOR PRESERVING PRIVATE DATA IN 8. 34-36 WEB DATA MINING (Sabica Ahmad, Shish Ahmad, Jameel Ahmad) RELATIONSHIP BETWEEN HEAVY METAL AND TRANSFER FACTOR FROM 9. SOIL TO VEGETABLE GROWN IN WASTE WATER IRRIGATED AREA OF 37-41 REWA (M.P.) INDIA (Geetanjali Chauhan & Prof. U.K. Chauhan) STRESS AND COPING STYLE OF URBAN AND RURAL ADOLESCENTS 10. 42-45 (Samata Srivastava, Dr. J. P. Singh, Dr. Om Prakash Srivastava) THE EFFECT OF SPERM PARAMETERS AND BOTH MATERNAL AND 11. PATERNAL AGE ON OUTCOME OF INTRACYTOPLASMIC INJECTION (Milat Ismail Haje, Christopher Barrett, Kameel M Naoom) SPERM 46-51 BIO-REMEDIATION OF HEAVY METALS FROM DRINKING WATER BY THE 12. 52-60 HELP OF MICROORGANISMS WITH THE USE OF BIOREACTOR (Arpit Srivastava, Dr. Pradeep Srivastava, Ms. Rupika Sinha, Sarada P. Mallick) KINETIC AND STATIC STUDY ON BIOSORPTION OF HEXAVALENT 13. CHROMIUM USING TAMARIND POD SHELL AND CARBON AS ADSORBENT 61-66 (Sudhanva.M.Desai, NCLN Charyulu, Satyanarayana V. Suggala) ANAEROBIC DIGESTION OF MUNICIPAL SOLID WASTE USING FUNGI 14. 67-70 CULTURE (ASPERGILLUS FLAVUS ) WITH METHANOGENS (Mahesh Kumar Shetty, Ravishankar R, Ramaraju H K, Jagadish H Patil, Sunil H, Mamatha B.Salimath) AN 15. EXPERIMENTAL STUDY OF PERFORMANCE AND EMISSION CHARACTERISTICS OF CI ENGINE FUELLED WITH HYBRID BLENDS OF 71-74 BIODIESELS (Shankarappa Kalgudi, K V Suresh) ADAPTIVE FUZZY PID CONTROLLER FOR SPEED CONTROL OF PMSM 16. 75-77 DRIVE SYSTEM (Rajnee Bala Minz, Rajesh Thinga, Supriya Tripathi) MINIMUM DELAY BASED ROUTING PROTOCOL IN MANET 17. (Abhishek Jain, Ashish Jain, Rohit Thete, Akshay Shelke, Harshada Mare, Prof. S.A. 78-81 Jain) SPLIT BLOCK SUBDIVISION OMINATION IN GRAPHS 18. 82-86 (M.H. Muddebihal, P.Shekanna, Shabbir Ahmed) MONITORING FIXTURES OF CNC MACHINE 19. 87-88 (Pingale Namrata Namdev, Prof. Hate S.G) FUELS FROM PLASTIC WASTES 20. 89-90 (Prajakta Sontakke) MEDICAL DECISION MAKING IN SELECTING DRUGS USING COMPUTER- 21. 91-93 GENERATED VIRTUAL ENVIRONMENTS (Silvia Riva, Gabriella Pravettoni) PRODUCTION OF HARD SHEETS FROM MUNICIPAL SOLID WASTE 22. 94-96 (Mohamed Magzoub Garieb Alla, Amel G. Elsharief) THROUGHPUT ANALYSIS OF MOBILE WIMAX NETWORK UNDER 23. 97-99 MULTIPATH RICIAN FADING CHANNEL (Sunil Kumar Gupta, Jyotsna Sengupta) ANALYSIS 24. OF GROUNDWATER QUALITY USING STATISTICAL TECHNIQUES: A CASE STUDY OF ALIGARH CITY (INDIA) 100-106 (Khwaja M. Anwar, Aggarwal Vanita) HOCSA: AN EFFICIENT DOWNLINK BURST ALLOCATION ALGORITHM TO 25. ACHIEVE HIGH FRAME UTILIZATION (Rabia Sehgal, Maninder Singh) 107-112 TRACKING AND CHECKING CARGO CONTAINERS PILFERAGE USING 26. 113-116 ELECTRONIC LOCK (Sandeep Singh R, Feroz Morab, Sadiya Thazeen, Mohamed Najmus Saqhib) RESEARCH FRONTS OF WEB PERSONALIZATION: A SURVEY 27. 117-121 (Deepti Sharda, Sonal Chawla) MULTIPATH ROUTING PROTOCOLS FOR MOBILE AD HOC NETWORK 28. A 29. 122-125 (Amit Sharma, kshitij shinghal, Pushpendra Vikram Singh, Himansu Verma) WEB-BASED EDUCATIONAL SYSTEM FOR LEARNING DATA STRUCTURES (Valentina S. Dyankovaa, Stoyan N. Kapralovb, Milko I. Yankovc and Yumit N. Ismailovd) 126-132 International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 01-03 IDENTIFICATIONS OF ELECTRICAL AND ELECTRONIC EQUIPMENT POLYMER WASTE TYPES USING MFI, DENSITY AND IR Anas Mohammed Elhafiz Dafa Alla1, Ahmed Ibrahim Seedahmed2 1, 2 Department of Plastic Engineering, College of Engineering, Sudan University of Science and Technology, Khartoum P. O. Box: 72, Sudan
[email protected] Abstract:- The main objective of this work is to reduce polymer Waste from Electrical and Electronic Equipment (WEEE) disposal to landfill and hence reduce their negative impacts to environment, and to produce useful products suitable for demanded application using WEEE waste as raw materials and to reduce virgin materials used in production of Electrical and Electronic equipment by using WEEE through Identifications the materials used in manufacturing of the EEE. Four type of (EEE) (Keyboard, colored and Black printer and Mouse) were chosen and the analytical result (Density, Melt Flow Index and Infer Red) showed that the polymer type was used in manufacturing of this samples was ABS material for three type and the forth one Mouse is recycled material. This result emphasizes and achieves the three goals of this paper that the recycling processes can solve the problem of polymer waste. Keywords: Waste from Electrical and Electronic Equipment, IR, MFI, ABS. I. INTRODUCTION According to modern systems of waste management, waste may be classified to different types including: 1. Municipal waste includes: households waste, commercial waste, and demolition waste 2. Hazardous waste includes industrial waste 3. Bio-medical waste includes clinical waste 4. Special hazardous waste includes radioactive waste, explosives waste, electrical and electronic waste. Considering the fourth type, electric and electronic equipment including personal computers, Compact Disks, TV sets, refrigerators, washing machines, and many other dailylife items is one of the fastest growing areas of manufacturing industry today. This rapidly advancing technology together with the increasingly short product life cycles have led to huge volumes of relatively new electronic goods being discarded. This has resulted in a continuous increase of Waste of Electric and Electronic Equipment (WEEE) with estimates of more than 6 million tones annual production or up to 10 kg per person per year in 2005. It has been estimated to be as high as 12 million tons in 2015. Since 1980, the share of plastics in Electrical and Electronic Equipment (EEE) has continuously increased from about 14% to 18% in 1992, 22% in 2000 and estimated 23% in 2005. In 2008, the plastics share from European waste electrical and electronic equipment (WEEE) over all categories was estimated to amount to 20.6 %. Figure1: Wasted Electronic Devices such as computer Monitors Figure2: A collection of WEEE Waste Despite of all advantages of Plastic in different uses, but wastes represent a significant environmental impact necessitate some measures to get rid of it. Although, a variety of techniques have been developed for the recycling of polymers in general and particularly for WEEE, the high cost associated with these methods usually leads to a disposal of plastics from WEEE to sanitary landfills. The main drawback that obstructs material recovery from plastics contained in WEEE is the variety of polymers that are being used, resulting to a difficulty in sorting and recycling. Another relevant drawback in dealing with treatment of WEEE is that very often they contain brominated aromatic compounds, used as fire retardants. So there are many procedures to recycle polymer waste one of it is thermal treatment of such chemicals is likely to produce extremely toxic halogenated dibenzodioxins and dibenzofurans. During last years some work has been carried out on the development of different methods to recycle or give added-value to WEEE. II. Materials A. WEEE materials Four different samples of WEEE viz.: keyboard, mouse, colored printing cartridge and black printing cartridge were selected based on the rate of their consumption. 1|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 01-03 was measured. The pycnometer was emptied and filled it with distilled water only. Filter paper was used to dry the spare water again and weight was measured. The experiment was then repeated for all samples 3. MFI (Melt Flow Index): Approximately (6 gm) of sample was loaded into the barrel of the melt flow apparatuses, which has been heated to a temperature 190 . A weight specific for the material was applied to a plunger and the molten material was forced through the die. Melt flow rat values were calculated in 8 mg/10 min using the following model: Figure3: Selected WEEE samples B. Preparations of samples Selected samples were cleaned with soap and water, then dismantled to separate polymer materials from the other materials ,then crushed and grinded for a laboratory analysis to determine and identify the type of the polymer and additives used in the samples under question. Figure4: Preparation of Samples C. Testing and Identification Methods: The type of polymers was determines using relevant chemical and instruments. 1. IR Spectrophotometer Finely crushed solid samples were and grinded with KBr, then pressed to make a disc, the disc was used as background in the IR spectrophotometer. Then the device was operated as required for the intended test. Figure 6: MFI apparatus III. Results The results for both MFI and density were shown in table 2 and the results of the IR analysis where shown in figures 812: Melt Flow Index Sample Density MFI 1 1.053 17.82 2 1.129 21.23 3 0.538 18.05 4 0.649 16.91 Figure 5: Shimatzu IR Spectrophotometer device 2. Density Samples densities were tested using Pycnometer the empty weight of the pycnometer was determined . , then filled to about 1/3 with sample and measure the weight . Water was then added to pycnometer as well as capillary hole in the stopper was filled with water. Water that leaked through the capillary hole was dried with a filter paper and total weight Figure 7: MFI Keyport sample 2|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 01-03 (1.04). This means the material used is not one of these materials because the density is lower than the range of all materials used in manufacturing EEE. MFI According to literature the MFI of PE, PC and ABS is range from (2 – 60), the Results show that the MFI of all samples in the range of (16.908 -21.228) for all samples (Keyboard, Colored and Black printer and Mouse) compared to (2 – 60) in the literature. This means the material used in manufacturing is not one of these materials because the cutting time for all samples was confirmed with standard 15 second but Mouse sample has cutting time 10 second even the MFI in the range. Figure 8: MFI colored printer IR Results show that the IR of all samples in the range of (459.7 3892.48) for three samples (Keyboard, Colored and Black printer) and (555.52 – 3042) for Mouse, that means there was some bands or groups not available in material used in manufacturing Mouse or it was cracking in re- process. Figure 9: MFI kcalB printer sample Figure 10: MFI Mouse sample IV. Discussions Density According to the current work results the density of all samples in the range of (1.1 -1.05) for three samples (Keyboard, Colored and Black printer) and 0.64 for Mouse. Compared to literature which points the main polymer used to manufacturing EEE is PE with density range (0.88 – 0.94), PC with density range (1.2 – 1.22) and ABS with density of V. Conclusions The analytical results confirmed that the material used in manufacturing three samples (Keyboard, Colored and Black printer) have similar properties and the fourth one Mouse has different properties; this results confirmed by re-analysis for allsamplesandrepeatitseveraltimesforthefourthoneMouse. Mentioned results confirm that the material used in the manufacture of the three samples was ABS material according to similarity obtained between the properties of samples with the properties of ABS material, but sample of Mouse showed a difference results for all materials used in the manufacture of EEE, suggesting that the material may be recycled material. REFERENCES [1] http://en.wikipedia.org/wiki/Waste Definitions. [2] Wäger, P., Schluep M. and Müller, E. Substances in Mixed Plastics from Waste Electrical and Electronic Equipment. Swiss Federal Laboratories for Materials Science and Technology September 17, 2010. [3] D.S. Achilias et al. / Journal of Hazardous Materials (2007). [4] Electric And electronic waste, Received 3 September 2008; accepted 2 March 2009 DOI 10.1002/app.30533 Published online 2 June 2009. [5] Dimitris S. Achilias et al.* Laboratory of Organic Chemical Technology, Department of Chemistry, Aristotle University of Thessaloniki, Thessaloniki Greece.Recent Advances in the Chemical Recycling of Polymers (PP, PS, LDPE, HDPE, PVC, PC, Nylon, PMMA) [6] Chemical recycling of plastic wastes made from polyethylene (LDPE and HDPE) and polypropylene (PP) D.S. Achilias a,∗, C. Roupakias a, P. Megalokonomosa, A.A. Lappas b, E.V.Antonakou b Available online 29 June 2007. [7] Facts and Figures on E‐Waste and Recycling February 21, 2012. [8] Recycling of acrylonitrilebutadiene styrene from used refrigerator material,Aminu, Omar ArokeAhmadu Bello University, ZariaFebruary, 2012. [9] Recycling and disposal of electronic waste Health hazards and environmental impacts report 6417 march 2011. 3|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 04-08 ASSESSMENT OF HEAVY METALS CONCENTRATION IN INDIAN AND PAKISTANI VEGETABLES Osama Sarwar Khan, *Farooq Ahmad, Adnan Skhawat Ali, Rana Muhammad Kamal and Umar Ashraf Sustainable Development Study Centre, GC University, Lahore, Pakistan.
[email protected] ABSTRACT: The current research was conducted to quantify the heavy metals accumulation in vegetables imported from India and compared with same vegetables collected from vegetable market in Pakistan. Green chili, capsicum, tomato and ginger were selected to analyze their heavy metal contents by atomic absorption spectrophotometer. Samples were prepared by dry ash method and wet digestion method to find out the efficient method for heavy metals analysis. Maximum concentration of heavy metals detected by dry ash method in Indian vegetables were of Cu (0.34ppm) in capsicum, Cd (0.0ppm) in capsicum, Cr (0.22ppm) in Ginger, Pb (0.22ppm) in ginger and Ni (0.14ppm) in Ginger while in Pakistani vegetables, it were of Cu (0.62ppm) in Tomato, Cd (0.04ppm) in Capsicum, Cr (0.17ppm) in Tomato, Pb (0.36ppm) in Ginger. Heavy metal contents determined by wet digestion method were of Cu (0.57ppm) in Ginger, Cd (0.01ppm) in capsicum, Cr (0.17ppm) in Ginger, Pb (0.27ppm) in capsicum while in Pakistani vegetables these were of Cu (0.19ppm) in Ginger, Cd (0.04ppm) in green chili, Cr (0.09ppm) in Tomato, Pb (0.25ppm) in Ginger. It was found that the concentrations of these heavy metals in vegetables of both the countries were within WHO/FAO permissible limits so at present these are not hazardous but long term use of these vegetables can magnify heavy metals contents in human body. For statistical analysis two factor ANOVA was run, which indicated that almost all the vegetables had accumulated heavy metals but there was a difference in the uptake of Indian and Pakistani vegetables. Keywords: Heavy metals, Vegetables, Green chili, Capsicum, Ginger. I. INTRODUCTION In Pakistan industrial effluent and untreated sewage are being discharged into surface water bodies. The water deficiency in country, forces the farmers to use wastewater for irrigation of their crops and vegetables fields. Sewage water disposal in big cities of Pakistan and its hazardous effects are worsen with the passage of time because untreated sewage water is used for growing crops in the surroundings of urban areas [1]. Sewage and industrial wastewater contains high level of organic matter and nutrients along with heavy metals like Fe, Mn, Cu, Zn, Pb, Cr, Ni, Cd and Co. Plants have high capacity for accumulation of the heavy metal contents, some species accumulate specific heavy metals while other accumulate all heavy metals, which cause detrimental effects on human health. Leafy vegetables accumulate more concentration of heavy metals when grown in contaminated soil and water [2].It has been widely reported that health problems occurred due to heavy metals contamination of soil [3]. Metals such as iron, copper, zinc and manganese are essential metals but they may produce toxic effects when their levels exceed certain limits in organisms. High level of copper may produce toxic effects such as dermatitis and liver cirrhosis when consumed in excessive amounts in foods [4]. The objectives of this study were to estimate heavy metal concentration in vegetables imported from India and to compare the heavy metal contents of Indian vegetable with Pakistani vegetables collected from vegetable and fruit market. II. MATERIALS AND METHODS The present study was carried out to analyze the contamination of the heavy metals concentration in vegetables imported from India and compared with vegetables grown in Pakistan. A. Collection of samples: The samples of vegetables i.e. Tomatoes (lycopersicon esculentum), Ginger (Zingiber officinale), Green chilli (Capsicum frutescens), Capsicum (capsicum annum) that imported from India to Pakistan were collected from trucks which were shifting the vegetables from Wagha border to vegetable market. Same Pakistani vegetable samples were collected from Lari Adda Mandi and Iqbal Town market Lahore. The samples were taken in winter season in the month of December. B. Pretreatment of vegetable samples: The vegetables that collected from different sites coming from Wagha border were individually washed by distilled water to remove dust particles and non-edible parts were removed from them. After washing and cutting, the vegetables were dried in open air and then these vegetables were placed in an oven for 2-3 days at 80 °C. The hard dried vegetables were broken into small pieces by hammer and then these pieces were grinded into a fine powder (80 mesh) using a commercial blender (TSK-West point, France). The powdered material was stored in polythene bags and placed aside until further procedure was done. C. Heavy metal analysis: Samples were prepared for the analysis of heavy metals by dry ash method and wet digestion method to measure the process efficiency. D. Dry ash method: Electronic balance was used to weigh 1g powered sample of each vegetable in boron free silica crucibles then these samples were placed in the muffle furnace at 450 0C for at least two hours until ash was formed. Furnace was left for some time to get cool. Samples were removed from furnace and added 10ml of 0.7N H2SO4. Mixed the samples thoroughly and left the samples for one hour. A conical flask of 500ml was used to filter the samples with Whatmann filter paper no.42 and washed two to three times by using 5.0ml of 0.7N H2SO4. Samples were filtered again in volumetric flasks were used for heavy metal analysis. 4|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 04-08 II. RESULTS AND DISCUSSION Deposition of heavy metals are associated with a wide range of sources such as brick kilns, small level industries (metal smelting, metal products, battery production, cable coating industries), suspended road dust, vehicular emission, diesel generators and coal combustion. Indian coal has poor quality and high concentration of heavy metals. These are all important contributor of heavy metals present in vegetables. Another source of heavy metal contamination in vegetables is the wastewater produced from domestic and industrial areas and used for irrigation purpose. This wastewater not only contaminates soil but also contaminate crops and vegetables grown in those fields containing contaminated soil. Other sources include excessive use of pesticides, fertilizers and sewage sludge. Industrial wastewater used for irrigation could be the major reason of heavy metal accumulation in vegetables. Cadmium can easily be taken up by the food crops especially leafy vegetables. Different vegetable species contain different heavy metals concentration depending on environmental conditions such as plant availability, metal species and type of irrigation practice. Heavy metal concentrations of plants is directly associated with their concentrations in soils, but their levels significantly differ with plant species [5]. A. Comparison of Pakistan and Indian Vegetables by Dry Ash Method: 1. Heavy metal concentration in Green chili The heavy metal concentration of Cu and Pb obtained by dry ash method in Indian vegetables was 0.29ppm and 0.11ppm respectively and value of Cd, Cr and Ni were below detection limit. The value of Cu and Pb obtained in Pakistani vegetables by dry ash method was 0.07ppm and 0.18ppm and value of Cd, Cr and Ni were below detection limit (Fig. 1). It have been reported that local residents of an area near a smelter in Nanning, China have been exposed to Cd and Pb through consumption of vegetables but no risk was found for Cu and Zn [6, 7]. Heavy metal concentration Pak dry ash method India dry ash method 0.35 0.28 0.21 0.14 0.07 0 Cu Cd Cr Pb Ni Heavy metals in Green chilli Fig. 1: Heavy metal concentration in Indian and Pakistani Green chili by dry ash method. 2. Heavy metal concentration in Capsicum: The heavy metal concentration of Cu, Cd, Cr, Pb and Ni obtained by dry ash method in Indian vegetables was 0.34ppm, 0.01ppm, 0.09ppm, 0.12ppm and 0.13ppm respectively and values of Cu, Cd, Cr, Pb and Ni obtained in Pakistani vegetables by dry ash method were 0.08ppm, 0.04ppm, below detection limit, 0.07ppm and below detection limit respectively (Fig. 2). It have been studied the heavy metal contents in different vegetables grown in the lands irrigated by wastewater and noted the concentration of Cr to be within the safe limits [8]. Pak dry ash method Heavy metal concentration E. Wet digestion method: In wet digestion method, 0.5g sample of each vegetable was weighed by weighing balance and 5ml of concentrated HNO3 was added into digestion flasks. Same quantity of HNO3 was also added into empty digestion flask to run the blank sample. Kjeldahl digestion unit was used to digest samples at 80-90ºC for two hours. Temperature increased to 150ºC (boiling point) and 3 to 5ml of 30%H2O2 along with concentrated HNO3 were added to start and continued the digestion until the clean solution obtained. Samples were cooled at room temperature. Solutions were filtered by Whatmann filter paper no. 42. Final volume was made to 25ml by using distilled water. Samples of both dry ash method and wet digestion method were put in an analyzer for the analysis of heavy metals by Atomic Absorption Spectrophotometer (FAAS, Shimadzu AA-7000F). F. Statistical Analysis: Concentrations of metals in various vegetables dry ash method and wet digestion method were compared by SPSS version-19. India dry ash method 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 Cu Cd Cr Pb Ni Heavy metals in Capsicum Fig. 2: Heavy metal concentration in Indian and Pakistani Capsicum by dry ash method. 3. Heavy metal concentration in Tomato: The heavy metal concentration of Cu, Cd, Cr, Pb and Ni obtained by dry ash method in Indian vegetables was 1.27ppm, below detection limit, 0.18ppm, 0.13ppm and 0.05ppm respectively and values of Cu, Cd, Cr, Pb and Ni obtained in Pakistani vegetables by dry ash method were 0.62ppm, below detection limit, 0.17ppm, 0.23ppm and below detection limit respectively (Fig. 3). It have been analyzed various vegetables (cucumber, tomato, green pepper, lettuce, parsley, onion, bean, eggplant, pepper mint, pumpkin and okra) and reported that the Zn concentration (3.56–4.592 mg kg-1) was within the recommended international standards[9]. 5|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 04-08 India dry ash method Pak wet method Heavy metal concentration Heavy metal concentration Pak dry ash method 1.2 1 0.8 0.6 0.4 0.2 0 Cu Cd Cr Pb Indian wet method 0.3 0.25 0.2 0.15 0.1 0.05 0 Cu Ni Cd Cr Pb Ni Heavy metals in Green chilli Heavy metals in Tomato Fig. 3: Heavy metal concentration in Indian and Pakistani Tomato by dry ash method. Fig. 5: Heavy metal concentration in Indian and Pakistani Green chili by wet digestion method. 4. Heavy metal concentration in Ginger: The heavy metal concentration of Cu, Cd, Cr, Pb and Ni obtained by dry ash method in Indian vegetables was 1.13ppm, below detection limit, 0.22ppm, 0.22ppm and 0.14ppm respectively and values of Cu, Cd, Cr, Pb and Ni obtained in Pakistani vegetables by dry ash method were 0.39ppm, below detection limit, 0.09ppm, and 0.36ppm and below detection limit respectively (Fig. 4). 2. Heavy metal concentration in Capsicum: The heavy metal concentration of Cu, Cd, Cr, Pb and Ni obtained by wet digestion method in Indian vegetables was 0.43ppm, 0.01ppm, below detection limit, 0.27ppm and below detection limit respectively and values of Cu, Cd, Cr, Pb and Ni obtained in Pakistani vegetables by wet digestion method were 0.10ppm, 0.01ppm, 0.08ppm, 0.17ppm and below detection limit respectively (Fig. 6). Pak wet method India dry ash method Heavy metal concentration 1.2 1 0.8 0.6 0.4 Indian wet method 0.5 0.4 0.3 0.2 0.1 0 Cu Cd 0.2 Cr Pb Ni Heavy metals in Capsicum 0 Cu Cd Cr Pb Ni Heavy metals in Ginger Fig. 4: Heavy metal concentration in Indian and Pakistani Ginger by dry ash method. B. Comparison of Pakistan and Indian Vegetables by wet digestion Method: 1. Heavy metal concentration in Green chili: The heavy metal concentration of Cu, Cd, Cr, Pb and Ni obtained by wet digestion method in Indian vegetables was 0.24ppm, below detection limit, below detection limit, 0.13ppm and below detection limit respectively and values of Cu, Cd, Cr, Pb and Ni obtained in Pakistani vegetables by wet digestion method were 0.06ppm, 0.02ppm, 0.07ppm, 0.20ppm and below detection limit respectively (Fig. 5). The estimated intake rates of Cu and Zn suggested that the contribution of vegetables to the intake of these heavy metals is low and does not pose potential health risk to consumers of vegetables [10]. Fig. 6: Heavy metal concentration in Indian and Pakistani Capsicum by wet digestion method. 3. Heavy metal concentration in Tomato: The heavy metal concentration of Cu, Cd, Cr, Pb and Ni obtained by wet digestion method in Indian vegetables was 0.28ppm, below detection limit, 0.10ppm, 0.15ppm and below detection limit respectively and value of Cu, Cd, Cr, Pb and Ni obtained in Pakistani vegetables by wet digestion method were 0.14ppm, 0.01ppm, 0.09ppm, 0.25ppm and below detection limit respectively (Fig. 7). Pak wet method Heavy metal concentration Heavy metal concentration Pak dry ash method Indian wet method 0.3 0.25 0.2 0.15 0.1 0.05 0 Cu Cd Cr Pb Ni Heavy metals in Tomato Fig. 7: Heavy metal concentration in Indian and Pakistani Tomato by wet digestion method. 6|Page Dry Ash Method International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 04-08 4. Heavy metal concentration in Ginger: Table 2: Relationship of copper, cadmium, chromium, lead The heavy metal concentration of Cu, Cd, Cr, Pb and Ni and nickel uptake in various vegetables of India and obtained by wet digestion method in Indian vegetables was Pakistan by dry ash method. 0.57ppm, below detection limit, 0.08ppm, 0.15ppm and below Concentration SS df MS F P-value F crit detection limit respectively and values of Cu, Cd, Cr, Pb and of metals in Ni obtained in Pakistani vegetables by wet digestion method vegetables were 0.14ppm, 0.02ppm, 0.17ppm, 0.25ppm and below Copper 0.901938 3 0.300646 8.489822 0.056199 9.276628 detection limit respectively (Fig. 8). Heavy metals determined in different vegetables showed that the concentrations of Cu, Cadmium 0.00015 3 0.00005 2.77 1.17 9.276628 Zn and Cd have often exceeded the safe limits of both Indian Chromium 0.043338 3 0.014446 7.687361 0.063962 9.276628 and FAO/WHO standards [11- 13]. However the concentration of Cu, Cd, Cr, Pb and Ni were lowered than Lead 0.039438 3 0.013146 4.412587 0.127078 9.276628 given standard of WHO by both dry ash method and wet digestion methods but consumption of contaminated Nickel 0.0067 3 0.002233 1 0.5 9.276628 vegetables may pose risk to human health. Heavy metal concentration Pak wet method Indian wet method 0.6 0.5 0.4 0.3 0.2 0.1 0 Cu Cd Cr Pb Ni Heavy metals in Ginger Fig. 8: Heavy metal concentration in Indian and Pakistani Capsicum by wet digestion method. A. Statistical Analysis: It was analyzed that the F value of Cu, Cd, Cr, Pb and Ni in vegetables were less than F Crit hence there were no difference in the uptake of Cu, Cd, Cr, Pb and Ni in different vegetables which means that all the vegetables contained Cu, Cd, Cr, Pb and Ni in it but the F value among the countries was more than F Crit value so there was a significant difference of concentration of Cu, Cd, Cr, Pb and Ni in vegetables of India and Pakistan (Table 1& 2). Wet Digestion Method Table 1: Relationship of copper, cadmium, chromium, lead and nickel uptake in various vegetables of India and Pakistan by wet digestion method. Concentrati on of metals in vegetables Copper Cadmium Chromium Lead Nickel SS d MS f F Pvalu e F crit 0.059 5 3.75 3 0.01 9833 3 1.25 3.48 9736 1 0.16 6006 0.5 1.06 4 0.18 0212 6553 5 0.48 03 0.90 3481 ---- 9.27 6628 9.27 6628 9.27 7 9.27 6628 9.27 6628 0.010 8 0.002 55 0 3 0.00 36 3 0.00 085 3 0 III. Conclusions This research was conducted to quantify the heavy metals concentration in Pakistani and Indian vegetables. Samples were collected from the market. Pakistani vegetables were collected from vegetable market of Iqbal Town. Samples of Indian vegetables were collected from “lari adda mandi” from the trucks coming from Wagha Border. Samples of Indian and Pakistani vegetables were analyzed by using dry ash and wet digestion method. Heavy metals were analyzed both in Pakistani and Indian vegetables by using atomic absorption spectrophotometer. It was found that both Pakistani and Indian vegetables were contaminated with heavy metals but the concentration of these metals was not higher than WHO/FAO standards limits. As the long term usage of these contaminated vegetables may cause their accumulation in human body which can cause hazardous effects later in their lives. It was noted from the results that heavy metal contents were detected to be similar with both dry ash and wet methods except few in which concentration was detected to be more by dry ash method. IV. Acknowledgements The authors acknowledge Government College University Lahore for providing funding for the current study. The complete research work was done in laboratories of Sustainable Development Study Centre and Department of Chemistry, GC University Lahore which were equipped with all the materials necessary for this study. The authors also acknowledge Muhammad Tariq, Scientific Officer of PCSIR laboratories Lahore, Pakistan. REFERENCES [1] Yamin M T, Ahmed N (2007). Influence of hudiara drain water irrigation on trace elements load in soil and uptake by vegetables.Journal of Applied Science Environmental Management 11(2): 169-172 [2] Farooq M, Anwar F, Rashid U (2008). Appraisal of heavy metal contents in different vegetables grown in the vicinity of an industrial area.Pakistan Journal of Botany40(5): 2099-2106 [3] Singh A, Sharma R K, Agrawal M, Marshall F M (2010). Risk assessment of heavy metal toxicity through contaminated vegetables from waste water irrigated area of Varanasi,India.Tropical Ecology51: 375-387 [4] Aktan N,Tekin-Özan S (2012). Levels of some heavy metals in water and tissues of chub mackerel (Scomberjaponicus) compared with physico-chemical parameters, seasons and size 7|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 04-08 [5] [6] [7] [8] [9] [10] [11] [12] [13] of the fish. The Journalof Animal & Plant Science 22(3): 605613 Öztürk E, Atsan E, PolatT, Kara K (2011). Variation in heavy metal concentrations of potato (Solanum tuberosum L.) cultivars.The Journal of Animal & Plant Science 21(2): 235-239 Cui Y J, Zhu Y G, Zhai R H, Chen D Y, Huang Y Z, QiuandY, Liang J Z (2004). A comparative evaluation of heavy metals in commercial wheat flours sold in calabar-Nigeria. Environment International 30: 785-791 Begum H S,Abida I K (2009).Analysis of heavy metal in water ,sediments and fish samples of madivala lake of Bangalore, Karnataka. International Journal of Chemical Technology Research 1(2): 245-249 Sharma R K, Agrawal M, Marshall F (2008).Transport and fate of copper in soils.Environmental Pollution 154: 254-263 Demirezen D, Ahmet A (2006).Seasonal changes of metal accumulation and distribution in shining pondweed (potamogetonlucens). Journal of Food Quality 29: 252-265 Mapanda F, Mangwayana E N, NyamangaraJ, Giller K E (2007). Uptake of heavy metals by vegetables irrigated using wastewater and the subsequent risks in Harare, Zimbabwe. Physical Chemical and Earth Sciences Parts A/B/C, 32(15-18): 1399–1405 Awashthi S K (2000). Prevention of Food Adulteration Act No. 37 of 1954. Central and State Rules as Amended of 1999.3rdEdition. Ashoka Law House, New Delhi Wei M, Yanwen Q, BinghuiZ, Lei Z (2008).Heavy metal pollution in Tianjin Bohai Bay. Journal of Environmental Science 20: 814-819 Rahman A K M R, HossainS M, Akramuzzaman M M (2010).Distribution of heavy metals in rice plant cultivated in industrial effluent receiving soil. Environment Asia 3(2): 15-19 8|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 09-11 ENERGY AWARE INFORMATION DISSEMINATION STRATEGIES TO IMPROVE LIFETIME OF A WSN Madhu G.C1, J. Jhansi2 1 Assistant Professor, Dept.of EConE 2 Dept.of ECE Sree Venkatesa Perumal College of Engineering and Technology, Puttur, India
[email protected],
[email protected] Abstract— The wireless sensor node can only be equipped with a limited power source. In some application scenarios, replenishment of power resources might be impossible. Sensor node lifetime, therefore, shows a strong dependence on battery lifetime. Hence, power conservation and power management take on additional importance. The main task of a sensor node in a sensor field is to detect events, perform quick local data processing, and then transmit the data. Power consumption can hence be divided into three domains: sensing, communication, and data processing. One of the most commonly used Power management techniques is to allow a node to follow sleep-wake up-sample-compute-communicate cycle. Based on the amount of the battery availability, by adopting the proper information dissemenitation schemes, the network life time can be extended. This process relies on hardware support for implementing sleep states, permits the power consumption of a node to be reduced by many orders of magnitude. Keywords—WSN, Energy Consumption, Information Dissemination Schemes, Mobile Sensor Node. I. INTRODUCTION A wireless sensor network consists of thousands of sensor nodes, deployed according to some predefined pattern, over a region of interest. A sensor node has many stringent resource constraints, such as limited battery power, signal processing, computation and communication capabilities, and a less amount of memory. Group of sensor nodes are collaborated with each other to achieve a bigger task efficiently. A sensor node is made up of four basic components: a sensing unit, a processing unit, a transceiver unit and a power unit. Sensing units are composed of two subunits: sensors and analog to digital converters (ADCs). The analog signals produced by the sensors are converted to digital signals by the ADC, and then fed into the processing unit. A transceiver unit connects the node to the network. One of the most important components of a sensor node is the power unit. There are also other subunits, which depends on the application. In many applications, the sensor nodes are often difficult to access, the lifetime of a sensor network depends on the life time of the power resources of the nodes. However, designing energy efficient and low duty cycle radio circuits is still technically challenging task. The main task of a sensor node in a sensor field is to detect events, perform quick local data processing, and then transmit the data. The total power will be consumed to perform the three important tasks: sensing, data processing and communication. Figure 1.0 The functional blocks of a Sensor Node Sensing power varies with the nature of applications. The complexity of event sensing also plays an important role in determining energy expenditure. A sensor node consumes maximum energy in data communication. This involves both data transmission and reception. It can be shown that for shortrange communication with low radiation power, transmission and reception energy consumption are nearly the same. A sensor node consists of a short range radio which is used to communicate with neighbouring nodes and the outside world. Radios can operate under the Transmit, Receive, Idle and Sleep modes. It is important to completely shut down the radio rather than put it in the idle mode when it is not transmitting or receiving because of the high power consumed in this mode. A sensor node must have computational abilities and be capable of interacting with its surroundings. The limitations of cost and size lead us to choose complementary metal oxide semiconductor (CMOS) technology for the micro-processor. A CMOS transistor pair draws power every time it is switched. This switching power is proportional to the switching frequency, device capacitance. Reducing the supply voltage is hence an effective means of lowering power consumption in the active state. When a micro-processor handles time-varying computational load, simply reducing the operating frequency during periods of reduced activity results in a linear decrease in power consumption. Communicating one bit over a wireless medium at short ranges consumes more energy than processing that bit. With the current technology, the energy consumption for communication is several magnitudes higher than the energy required for computation. One of the power management strategies is to practice the energy aware information dissemination. 9|Page International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 09-11 II. INFORMATION DISSEMINATION SCHEMES base node should consist of the amount of the battery power remained and the temperature value. There are four established techniques for information If the available battery power of static node is greater than dissemination in WSNs: 50% but less than 75%, it transmits the averaged value of the Continuous/periodic dissemination: The sensor node sensed samples for every 2 seconds. During the processing continuously reports data in a periodic manner. In this way, time of the data, the transceiver of the base node and the packets are pushed from the network even when the sensed mobile node should be maintained in sleep state. parameter has not significantly changed, hence containing Event driven Data Dissemination: little useful information since the previous transmission. If the available battery power of the mobile node is Query-driven dissemination: The user initiates data transfer greater than 25% but less than 50%, the temperature sensor by querying data from the network. Qualifying nodes reply to continuously sense the temperature values and it will be stored these queries with packets. in the controller and the present sensed value will be compared Event-driven dissemination: The sensor node decides for with the previously stored value in the microcontroller. If there itself what data are worth reporting to a sink node. In that way, occurs a significant deviation in temperature, say 20 C then the redundant transmissions can be minimized. In continuous mobile node will transmit the information to the base node. If reporting, the choice of period duration has a considerable the present temperature is 280 C, let us suppose the present effect on network performance. If a short period is chosen, a sensed value is 290C, the transceiver of the mobile node will large proportion of the packets are likely to be redundant not transmit this information to the base node. It has to containing little useful information, while still consuming transmit only when the temperature is 300C or greater values. energy. If a long period is chosen, the network is likely to When the microcontroller recognized a deviation, an interrupt suffer from the missing of events. While the missing of events will be generated to awaken the transceiver of the mobile can be avoided by locally aggregating the average sensed node. During this time the transceiver of the mobile node is values. In this paper we designed an experiment to implement maintained in sleep state. all the above strategies by using the two mobile sensor nodes Query driven Strategy: which are used to transmit the sensed information from the If the available battery power of the mobile node is greater region of interest to the base station. Recent progress in low than 5% but less than 25%, a request will be sent from the power embedded systems has led to the creation of mobile base node directly to the mobile node to transmit the sensor nodes. Autonomous node mobility brings with it its temperature present at that time. Based on the request own challenges, but also alleviates some of the traditional generation only, the mobile node has to sense the temperature problems associated with static sensor networks. Mobility is and should be transmitted to the base node. Along with the the ability of a sensor node to move intentionally, and without request an interrupt awakens the transceiver and the human assistance. Methods have been suggested to use mobile microcontroller of the mobile node from sleep state to the sensor nodes to physically transport energy in the network active state. If the available battery power of the node is less from areas where it is available in plenty to other regions than 5%, the redundant mobile node has to move towards the where energy availability is scarce. present mobile node to sense the temperature from the region. This mobile node continues the temperature sensing and can III. PROPOSED SYSTEM also be operated in all the above specified strategies. Mean A system is designed to sense a temperature in a region and while the first mobile node battery can be recharged for the will be transmitted to the base node by using a WSN having further use. two manually deployed mobile nodes and one base node. To optimize the power consumption of a WSN, depending upon the available amount of the battery, this system is free to be operated in one of the three strategies. Periodic data Dissemination: If the available battery power of the mobile node is greater Power IC Temperature Sensor Microcontroller & Mobilizer platform IV. RESULTS If the available battery power of the mobile node is greater than 75%, it will transmit the data to the base node in a periodic manner. The information received by the base node should consist of the level of the battery power and the temperature value. Zigbee IR Sensor Fig.2.0 Construction of a Mobile Sensor Node than 75%, it will transmit the data to the base node in a periodic manner. The temperature sensor connected to the mobile node senses the temperature from a region and it will be processed by using the microcontroller and will be transmitted to the base node. The information received by the Figure 3.0 Continuous transmissions of data from the static node to the base node. 10 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 09-11 If the battery power of mobile node is greater than 50% but REFERENCES less than 75%, it transmits the averaged value of the sensed [1] Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, samples for every 2 seconds.If the battery power of the mobile “A survey on sensor networks,” IEEE Commun. Mag., node is greater than 25% but less than 50%, the sensor vol. 40, no. 8, pp. 102–114, Aug. 2002. continuously sense the temperature and it will be stored in the [2] C. Y. Chong and S. Kumar, “Sensor networks: Evolution, controller. If there occurs a deviation of 20 C then the mobile opportunities, and challenges,” Proc. IEEE, vol. 91, no. 8, node will transmit the information to the base node. pp. 1247–1256, Aug. 2003. Figure 3.2 Event driven data transmission strategy If the available battery power of the mobile node is less than 5%, the redundant mobile node has to move towards the sensing field and present node has to move away from the field. The new mobile node can also be operated in a similar manner as the previous node. [3] Ren C.Luo, Ogst Chen,”Mobile sensor node deployment and asynchronous power management for wireless sensor networks” IEEE Transactions on Industrial Electronics, Vol. 59, No. 5, May 2012. [4] G. T. Sibley, M. H. Rahimi, and G. S. Sukhatme, “Robomote: A tiny mobile robot platform for large-scale ad-hoc sensor networks,” in Proc.IEEE Int. Conf. Robot. Autom. 2002, vol. 2, pp. 1143–1148. [5] K. Dantu, M. Rahimi, H. Shah, S. Babel, A. Dhariwal, and G. S. Sukhatme, “Robomote: Enabling mobility in sensor networks, “Center Robot. Embedded Syst., Viterbi School Eng., Univ. Southern California, Los Angeles, CA, Tech. Rep. CRES-04-006, 2004. [6] Nojeong Heo and Pramod K. Varshney,” EnergyEfficient Deployment of Intelligent Mobile Sensor Networks” IEEE Transactions on systems, man and Cybernatics—Part A: Systems and Humans, Vol. 35, No. 1, January 2005. Figure 3.3 Switching of the data transmission task to the second mobile node. 11 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 12-15 A NETWORK DATA AND COMMUNICATION ANALYSIS BASED COMBINED APPROACH TO IMPROVE VIDEO TRANSMISSION IN MANET Reena Boora#, Veepin Kumar* # M. Tech Scholar, CSE Deptt. Om Institute of Technology &Management, Guru Jambheshwar University, Hisar (Haryana), India * H.O.D. In CSE Deptt. Om Institute of Technology &Management, Guru Jambheshwar University, Hisar (Haryana), India #
[email protected], Abstract –Video transmission over wireless network requires link reliability. Videos are having more data to be transmitted during communication. The criticality and load of the network increases when some video data is communicated over the network. Firstly, describes the characteristics of Mobile Ad hoc Networks and their Routing protocol, and second a mobile ad hoc network (MANET) which consists of set mobile wireless nodes and one fixed wireless server are design using ns-2. In this research we will simulate three MANET routing protocols such as AODV against three different parameters i.e. delay, network load, throughput and retransmission. Keywords- Multi-media Communication, MANET, QOS, MANET routing protocol (i.e. AODV), NS-2(Network Simulator-2). I. INTRODUCTION Wireless networks are getting popular due to their convenience of use. Consumer or user is no more dependent on wires where he or she is, easy to move and enjoy being connected to the network. One of the great features of wireless network that makes it fascinating and distinguishable amongst the traditional wired network is its mobility. This feature gives the ability to move freely, while user being connected to the network. The Wireless networks comparatively easy to install, on other hand wired network don’t. Video transmission over wireless networks to multiple mobile users has remained a challenging problem due to potential limitations on bandwidth and the time-varying nature of wireless channels. Video transmission is one of the part in multimedia communication system. As we know that the multimedia has become an essential part of any presentation. The evolution of internet has also increased the demand for multimedia content. Multimedia is the media that uses multiple forms of information content and information processing (e.g. text, audio, video, graphics, animation, interactivity) to inform or entertain the user. Mobile ad hoc networks (MANETs) consist of multiple wireless mobile nodes which dynamically exchange data among themselves. MANETs nodes are distinguished by their memory resources, processing as well as high degree of mobility.[1] I. MANETS ROUTING PROTOCOLS Routing protocols in MANETs (Murty and Das, 2011) are a challenging and attractive tasks, researchers are giving tremendous amount of attention to this key area (Bouke, 2011). MANETs routing protocols are categorized into three different categories according to their functionality. 1. Reactive protocols (i.e. AODV,DSR and DYMO) *
[email protected] 2. Proactive protocols (i.e. DSDV,OLSR,FSR) 3. Hybrid protocols (i.e. ZRP) 1. Reactive protocols - Reactive routing protocols are only search for a route to a destination when they need to send data to that host. 1(a). AODV - AODV is an on-demand routing protocol used in ad hoc networks. This algorithm facilitates a smooth adaptation to changes in the link conditions. 1(b). DSR - Dynamic Source Routing is a reactive routing protocol for manet ad hoc wireless network. Its characteristics has also on-demand like AODV but it’s not table driven. It based on source routing. A node wishing to send a packet specifies the route for that packet. 1(c). DYMO – DYMO is a routing protocol that was created for situations where clients are mobile and communications will be transported through several different clients over a wireless medium Mobile ad-hoc Network (MANET). When a node initiates communication with another host a routing path is found, on demand, and this will result in a bidirectional unicast communication path, if a path is found to the destination. DYMO was created to dynamically handle changes in the network. II. RELATED WORK Extensive research work has been done in the field of MANET routing protocols. Different routing protocols were simulated in different kind of simulators. Here we will discuss different research papers about MANET routing protocols performance. In this we will simulate three MANET routing protocols such as DSR, DYMOUM and AODV against three different parameters i.e. delay, network load, throughput and Retransmission. Due to this characteristic (Keshtgary and Babaiyan, 2012), there are some challenges that protocol designers and network developers are faced with. These challenges include routing, service and frequently topology changes. Therefore routing discovery and maintenance are critical issues in these networks. There are also limited battery power and low bandwidth available in each node. In this paper, we evaluate the performance of four MANET routing protocols using simulations: AODV, OLSR, DSR and GRP.[2] Our evaluation metrics are End-to-End delay, network load, throughput and media access delay. Most of the papers consider the first three parameters, but here we also consider MAC delay. Path routing and protocol selection are the primary strategies to design any wireless network. In Mobile Ad hoc Network 12 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 12-15 (MANET) the selected protocol should have best in terms of third stage, the route optimization is done, by analyzing the data delivery and data integrity (Mohapatra and Kanungo, participation of each node in network communication. The 2011). nodes having the heavy participation are ignored for The throughput performance in Mobile Ad Hoc Networks communication whereas the nodes having the lesser (Fazeli and Vaziri, 2011) and compares emulated tested communication participation are considered to perform the results with simulation results from OPNET (Optimized communication over the network. The network design for the Network Engineering Tool). presented work is given here in figure 3.1. This paper presents a performance analysis of two Mobile Ad The presented work is about to provide the effective Hoc Network (Hosek, 2011) routing protocols - Ad Hoc On communication in case of congested network or the DOS Demand Distance Vector (AODV)[8] and Optimized Link infected network. The frame analysis provide the work to State Routing (OLSR). take the earlier decision so that the effective communication. They address the on-demand routing protocols by focusing The effective route generation provides the dynamic analysis on dynamic source routing (DSR) protocol and ad hoc on on network traffic to provide effective network demand distance vector (AODV) routing protocol in WMNs communication. (Guo and Peng, 2010). Wireless Ad Hoc Networks Han L mentioned [3] that the wireless ad hoc networks were first deployed in 1990’s Mobile Ad-hoc networks have been widely researched for many years. Mobile Ad-hoc Networks are collection of two or more devices equipped with wireless communications and networking capability. These devices can communicate with other nodes that immediately within their radio range or one that is outside their radio range. An Analytical Model Of Tcp Performance Debessay Fesehaye Kassa mentioned [4] that the Transmission Control Protocol (TCP) is the dominant transport layer protocol for the end-to-end control of information transfer. Accurate models of TCP performance are a key and basic step for designing, dimensioning and planning IP (Internet Protocol) networks. TCP Performance Over Mobile Ad Hoc Network B. Sikdar et. al. described [5] that TCP is a transport protocol that guarantees reliable ordered delivery of data packets over wired networks. Although it is well tuned for wired networks, TCP performs poorly in mobile ad hoc networks (MANETs). This is because TCP’s implicit assumption that any packet loss is due to congestion is invalid in mobile ad hoc networks where wireless channel errors, link contention, mobility and multi-path routing may significantly corrupt or disorder packet delivery. TCP Performance Over Multipath Routing In Mobile Ad Hoc Networks Haejung Lim Kaixin Xu, at all mentioned [6] that in mobile ad hoc networks (MANET), TCP performance is not as stable as in wired networks. TCP performance over a multipath routing protocol is given. Multipath routing can improve the path availability in mobile environment. Thus, it has a great potential to improve TCP performance in ad hoc networks under mobility. III. PROPOSED WORK The presented work is about to perform the effective video communication over the mobile network by performing the three stage work. The first stage of this work is to analyze the video frames so that the frame type of route diversion will be performed. The high quality frames will be transferred from different route. In second stage, the contention window decision is taken by analyzing the network parameters. In Define Network with N Mobile Nodes Initialize the video transmission with source and destination node specification Divide the video in smaller frames Perform frame analysis to provide route diversion based on frame analysis Optimize the contention window size to optimize the communication Analyze the node participation vector to prioritize the nodes Perform multipath communication on low participating nodes Analyze the communication under different parameters Figure 3.1: Flow of Work A. ALGORITHM Algorithm(Nodes,N) /*A Mobile Network is defined with N Number of Nodes*/ { Define as Source Node Src and Destination Node Dst Set curNode=Src [Set Src as Current Node] While curNode<>Dst [Process All Nodes, till Destination Node not occur] { NNodeList=FindNeighbors(Nodes,CurNode) [Identify the Neighbor Node List for CurNode] For i=1 to N NodeList.Length [Process all Neighbor List] { if(Communication(CurNode,NNodeList(i))>0) [If the Neighbor Node is Communicating Node] { 13 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 12-15 CommuCount=CommuCount+1 Load=Load+DataCount [Perform the Communication Analysis on NeighborNode }} AvgComm=CommCount/NNodeList.length [Find the Average Communication Analysis on neighbor node list] For i=1 to NNodeList.Length [Process all Neighbor List] { if (CommuCount(curNode,NNodeList(i))>AvgComm) { set Participation(i)=1 }else if (CommuCount(curNode,NNodeList(i))>AvgComm*2) { set Participation(i)=2 } else { Set Participation(i)=0 }} Disable All Nodes for Communication having Participation Value 2 nextHop=FindPartipentNode(NNodeList,1) [Identify the Effective Neighbor having Partipation value 1 and set it as Effective Neighbor] Set curNode=nextHop [Set Effective communication node as Next communicating Hop] }} IV. SIMULATION RESULTS The work mainly defines an effective video communication routing in energy effective mobile network. The possible outcomes are the simulation results obtained from NS2 which shows the performance parameters such as transmission rate, loss rate, end-to-end delay, packets send and packets received for the both proposed solution and existing network with attack. The simulation results also provide graphical comparison of the networks.The parameters taken in this work for network generation are given here under Table 4.1. Table 4.1 : Simulation Parameters Parameters Values Communication channel Wireless Number of Nodes Area 10 800x800 Routing Protocol AODV MAC Protocol 802.11 Topology Random Communication Delay Energy Adaptive 50 MicroSec Yes Here figure 4(a) is showing the simulation scenario for mobile network. The figure is showing the network with 10 nodes. The communication is here performed between a node pair and the large circles here shows the communication range of each mobile node. Figure 4(a): Network Design Here figure 4(b) shows the outcome of data packet transmission in video file communication. Here X- axis represents simulation time and Y-axis represents packet transmission over the network. It shows, the presented work has improved the network throughput. Figure 4(b) : Packet Transmission Analysis (Existing Versus Proposed Approach) Here figure 4(c) is showing the packet loss analysis as the video data is communicated. Here X-axis represents simulation time and Y-axis represents number of packet lost over the network. Figure 4(c) : Packet Loss Analysis (Existing Versus Proposed) 14 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 12-15 Here figure 4(d) is showing the communication delay VI. FUTURE WORK The presented work is to provide the effective video analysis as the video data is communicated. Here X-axis communication over mobile network by using three stage represents simulation time and Y-axis represents packet delay analysis. The work can be improved under different aspects over the network. The presented work is about to optimize the network communication by performing the dynamic analysis over the network nodes. The work can be improved by using some session based approach to save the path so that the path identification work will be reduced. The work is based on the statistical analysis. The work can be improved by using some optimization approach. [1] [2] [3] [4] Figure 4(d): Communication Delay Analysis (Existing Versus Proposed Approach) Here figure 4(e) is showing the packet lossrate analysis as the video data is communicated. Here X-axis represents simulation time and Y-axis represents packet delay over the network. [5] [6] [7] [8] [9] [10] [11] Figure 4(e) : Packet lossrate analysis (Existing Versus Proposed Approach) V. CONCLUSION The presented work is about to provide the video communication over the mobile network. The work is divided in three main stages. In first stage, the video frame analysis is done. In second stage, the optimized contention window specification is performed. At third stage, the route optimization is done by observing the node participation dynamically. In this work a multipath communication is defined to generate the effective route over the network. The work is implemented in NS2 environment. The obtained results show the effective route generation in congested video communication over the mobile network. The results shows that the work has reduce the communication loss and improved the network communication. The results are here shown using XGraph. [12] REFERENCES http://www.isi.edu/nsnam/ns/tutorial http://www.isi.edu/nsnam/ns/ns-documentation.html http://dev.scriptics.com/scripting V. C. Frias, G. D. Delgado, and M. A. Igartua, “Multipath routing with layered coded video to provide qos for video streaming over manets," in Proceedings - 2006 IEEE International Conference on Networks, ICON 2006 Networking-Challenges and Frontiers 1, vol. 1, pp. 1-6, September 2006. Fahim Maan, Nauman Mazhar, “Analysis of Performance of widely used MANET routing protocols DSDV, AODV, OLSR , DYMO and DSR with mobility models”{978-1-45771177-0/11/©2011 IEEE} Asad Amir Pirzada, Ryan Wishart and Marius Portmann, “an Congestion-Aware Routing In Hybrid Wireless Mesh Network “{ 1-4244-1230-7/07/© 2007 IEEE} Uyeng trang and Jin Xu,”Fundamental approaches to multicast routing “{0163-6804/07/$20.00 © 2007 IEEE}. Youiti Kado, Azman Osman Lim, and Bing Zhang, “Analysis Of Wireless Mesh Network Routing Protocol For Push-to-Talk Traffic “{1-4244-1251-X/07/©2007 IEEE}. Chen Lijuan, “Research On Routing protocol Applied To Wireless Mesh Network”{ 978-0-7695-3989-8/10© 2010 IEEE}. G. A. Pegueno and J. R. Rivera, “Extension to MAC 802.11 for performance Improvement in MANET”, Karlstads University, Sweden, December 2006. C.Parkins, E.B.Royer, S.Das, A hoc On-Demand Distance Vector (AODV) Routing. July 2003, [Online]. Available: http://www.faqs.org/rfcs/rfc3561.html. [Accessed: April. 10, 2010] C.M barushimana, A.Shahrabi, “Comparative Study of Reactive and Proactive Routing Protocols Performance in Mobile Ad-Hoc Networks”, Workshop on Advance Information Networking and Application, Vol. 2, pp. 679-684, May, 2003. 15 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 16-21 DIFFUSER ANGLE CONTROL TO AVOID FLOW SEPARATION Vinod Chandavari1, Mr. Sanjeev Palekar2 M.Tech (APT), Department of Aerospace Propulsion Technology Visvesvaraya Technological University - CPGS Bangalore, Karnataka, India 1
[email protected],
[email protected] Abstract— Diffusers are extensively used in centrifugal compressors, axial flow compressors, ram jets, combustion chambers, inlet portions of jet engines and etc. A small change in pressure recovery can increases the efficiency significantly. Therefore diffusers are absolutely essential for good turbo machinery performance. The geometric limitations in aircraft applications where the diffusers need to be specially designed so as to achieve maximum pressure recovery and avoiding flow separation. The study behind the investigation of flow separation in a planar diffuser by varying the diffuser taper angle for axisymmetric expansion. Numerical solution of 2D axisymmetric diffuser model is validated for skin friction coefficient and pressure coefficient along upper and bottom wall surfaces with the experimental results of planar diffuser predicted by Vance Dippold and Nicholas J. Georgiadis in NASA research center [2]. Further the diffuser taper angle is varied for other different angles and results shows the effect of flow separation were it is reduces i.e., for what angle and at which angle it is just avoided. Keywords: Planar Diffuser, CFD, Taper angle, Flow Separation. I. INTRODUCTION Diffusers are integral parts of jet engines and many other devices that depend on fluid flow. Performance of a propulsion system as a whole is dependent on the efficiency of diffusers. Identification of separation within diffusers is important because separation increases drag and causes inflow distortion to engine fans and compressors. Diffuser flow computations are a particularly challenging task for Computation Fluid Dynamics (CFD) simulations due to adverse pressure gradients created by the decelerating flow, frequently resulting in separation. These separations are highly dependent on local turbulence level, viscous wall effects, and diffuser pressure ratio, which are functions of the velocity gradients and the physical geometry. The diffuser is before the combustion chamber that ensures that combustion flame sustenance and velocities are small [2]. 1.1 What is the meaning of Separation or Reverse Flow? The designing of an efficient combustion system is easier if the velocity of the air entering the combustion chamber is as low as possible. The natural movement of the air in a diffusion process is to break away from the walls of the diverging passage, reverse its direction and flow back in the direction of the pressure gradient, as shown in figure 1.1 air deceleration causes loss by reducing the maximum pressure rise [4]. Fig: 1.1 Diffusing Flow Buice, C.U. and Eaton, J.K [1], was carried out the Experimental work using a larger aspect ratio experimental apparatus, paying extra attention to the treatment of the endwall boundary layers. They are titled as “Experimental Investigation of Flow through an Asymmetric Plane Diffuser,” The results of this experiment are compared to the results of different calculations made for the same diffuser geometry and Reynold number. One of the calculation is Large Eddy Simulation (LES). The other is a Reynold Averaged Navier Stokes (RANS) calculation using v2-f turbulence model. Both calculations captured the major features of the flow including separation and reattachment. Vance Dippold and Nicholas J. Georgiadis[2], they have been performed “Computational Study of Separating Flow in a Planar Subsonic Diffuser” in National Aeronautics and Space Administration is computed with the SST, k-ε, SpalartAllmaras and Explicit Algebraic Reynolds Stress turbulence models are compared with experimentally measured velocity profiles and skin friction along the upper and lower walls. Olle Tornblom[3], repeated the experimental work of Buice, C.U. and Eaton, J.K, “Experimental study of the turbulent flow in a plane asymmetric diffuser”, the flow case has been concentrated on in an uniquely composed wind-tunnel under overall controlled conditions. A similar study is made where the measured turbulence data are utilized to assess an explicit algebraic Reynolds stress turbulence model (EARSM) and coefficient of pressure is measured. In this study diffuser gives an idea of choosing the turbulence model and to avoid separation flow by varying the taper angle (7º, 8º, 9º and 10º). The diffuser model and Fluent 14.5 are used, to study the diffuser characteristics with the effect of various factors like Pressure coefficient and Skin friction coefficient. Obtained results are validated against the known experimental results carried out by Vance Dippold and Nicholas J. Georgiadis [2]. II. PHYSICAL MODEL AND MESH Diffuser geometric configuration with the height of the inlet channel H = 0.015 meters and the diffuser has a 10ᴼ expansion taper angle and is 21H in length. At the end of the expansion, the diffuser channel is 4.7H in height. Figure 2.1 shows the schematic diagram of diffuser [1]. 16 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 16-21 study because the flow through the diffuser user is steady in the mean. In this study, SST-K-ω turbulence models were used with varying complexities and formulations. Understandably, the increased complexity (i.e., increased number of equations) requires more computational time. Thus, the selection of turbulence models with varying complexities provides the opportunity to observe a correlation between modeling accuracy and computational time [4]. Identical boundary conditions were used for all turbulence models. In particular, the inlet conditions were specified as a constant velocity profile corresponding to the bulk inlet velocity, Ub = 20 m/s. Fig: 2.1 Shows the schematic diagram of Diffuser Figure 2.2 shows the computational domain of 2D that mimics the physical model. The diffuser apparatus can be divided into three sections: an inflow channel, the asymmetric diffuser, and an outflow channel. Figure 2.3 shows the 2D structured mesh for computational domain. Mesh having 41511 nodes and 41000 elements. Mesh Quality: Orthogonal Quality is ranges from 0 to 1, where values close to 0 correspond to low quality. Hence the Minimum Orthogonal Quality = 0.945334629648056 Y plus value= 1.03 All turbulence models implemented a COUPLED scheme to couple the pressure and velocity. Furthermore, the spatial discretization was accomplished by a second-order accurate upwind scheme for the momentum and a FLUENT standard scheme for the pressure. Any additional closure equations for the various turbulence models were spatially discretized by second-order accurate upwind schemes. In all cases, the corresponding calculation residuals were monitored to convergence at 1*10-05. These residuals included continuity, xvelocity, and y-velocity for all turbulence models. Beyond these generic residuals, any additional closure equations gave additional terms to monitor. The fluid properties were carefully chosen to ensure a matched Reynolds number with the experimental data. Specifically, the fluid density was chosen to be 1.225 kg/m3 and the dynamic viscosity was selected to be 1.789*10-05 kg/m-s. The combination of these values yields the appropriate Reynolds number based on inlet channel height, ReH = 20,000. IV. Fig: 2.2 Computational domain VALIDATION The suitability of solver selection, turbulence model, numerical scheme, discretisation method and convergence criteria used in the present study is validated by comparing the skin friction coefficient and pressure coefficient along the X/H with the experimental data of Vance Dippold and Nicholas J. Georgiadis[2]. Among various turbulence models available in the fluent code, SST-k-ω model are tested with different taper angle (7º, 8º, 9º and 10º). The figures 4.1.1 and 4.1.3 shows skin friction coefficient is 0.006 of Bottom_wall and Top_wall respectively, figure 4.2.1 shows the Pressure coefficient and the figure 4.1.2, 4.1.4 and 4.2.2 shows computational results obtained are in better agreement with the known experimental results as follows. Table: 4.2 Comparison of Experimental Results with Computational Results Fig: 2.3 Computational Domain with Mesh III. NUMERICAL PROCEDURE This project implemented steady Reynolds Averaged NavierStokes equations (RANS) in the ANSYS FLUENT flow simulation program. For all cases, a two-dimensional, doubleprecision flow solver was used. It was assumed that the application of steady RANS equations was sufficient for this Parameters Experimental Taper Angle 10 º 7º 8º 9º 0.73 to 0.85 0.882 0.880 0.873 0.85 0.006 to 0.0063 0.0064 0.0065 0.0066 0.006 Pressure coefficient Skin friction coefficient Velocity (m/s) Computational 10 º Min -1.156 0 -0.146 -0.723 -1.156 Max 22.845 22.845 22.845 22.845 22.845 17 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 16-21 Table 4.2 demonstrates the correlation distinctive parameters of experimental results with computational results. This table demonstrates the how the taper angle decreases pressure coefficient expands and skin friction coefficient diminishes this implies the flow separation is bit by bit diminishes. Fig: 4.1.4 Comparison of Computational Results with Experimental Results of Top_wall skin friction along with the X/H using the SST model at 10ᴼ taper angle Fig: 4.1.1 Experimental results of Bottom_wall skin friction along with the X/H using the SST model at 10ᴼ taper angle [2] Fig: 4.2.1 Experimental results of pressure coefficient (Cp) at 10ᴼ taper angle along with the X/H Fig: 4.1.2 Comparison of Computational Results with Experimental Results of Bottom_wall skin friction along with the X/H using the SST model at 10ᴼ taper angle Fig: 4.2.2 Computational Results of Pressure Coefficient, bottom and Top wall at 10ᴼ taper angle V. Fig: 4.1.3 Experimental results of Top_wall skin friction along with the X/H using the SST model at 10ᴼ taper angle [2] RESULTS AND DISCUSSION The results are obtained from the CFD by applying the experimental condition to the computational model with variation of taper angle 7ᴼ, 8ᴼ, 9ᴼ, and 10ᴼ, were measured for different contours plots, figure 5.1.1, 5.1.2, 5.1.3, and 5.1.4 shows the contours of velocity, figure 5.2.1, 5.2.2, 5.2.3, and 5.2.4 shows the separation for streamline functions and figure 5.2.5, 5.2.6, 5.2.7, and 5.2.8 shows contours of separation flow. 18 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 16-21 Fig: 5.1.1 Contours of Velocity at 10ᴼ taper angle Fig: 5.1.4 Contours of Velocity at 7ᴼ taper angle Fig: 5.2.1 Separation for Streamline Function at 10ᴼ taper angle Fig: 5.1.2 Contours of Velocity at 9ᴼ taper angle Fig: 5.1.3 Contours of Velocity at 8ᴼ taper angle Fig: 5.2.2 Separation for Streamline Function at 9ᴼ taper angle 19 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 16-21 Fig: 5.2.3 Separation for Streamline Function at 8ᴼ taper angle Fig: 5.2.6 Contours of Separation at 9ᴼ taper angle Fig: 5.2.4 Separation for Streamline Function at 7ᴼ taper angle Fig: 5.2.7 Contours of Separation at 8ᴼ taper angle Fig: 5.2.8 Contours of Separation at 7ᴼ taper angle Fig: 5.2.5 Contours of Separation at 10ᴼ taper angle Identification of separation within diffusers is important because separation increases drag and causes inflow distortion to engine fans and compressors. Figure 5.1.1, 5.1.2 and 5.1.3 shows the contours of velocity 10ᴼ, 9ᴼ and 8ᴼ taper angle respectively, blue color shows the negative value. Figure 5.1.4 taper angle at 7ᴼ contours of 20 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 16-21 velocity doesn’t shows negative value it means that the separation flow is avoided at 7ᴼ taper angle. VI. CONCLUSION From the present study it is evident that when the taper angle is decreased, the skin friction coefficient drops & pressure coefficient rises, as result the flow separation follows a diminishing trend The optimum taper angle is 7ᴼ below which there is no flow separation at all but going beyond it gives rise to flow separation VII. SCOPE FOR FUTURE WORK The proposed next work for the present configuration is, simulating for 3D structured mesh configuration to these taper angle varieties and as in the present work the contrast in 2D taper angle we can figure it for variety taper angle, and 3D configuration simulation is possible for the impact of expectation taper angle. REFERENCES [1] Buice, C.U. and Eaton, J.K., “Experimental Investigation of Flow Through an Asymmetric Plane Diffuser,” 1997 [2] Vance Dippold and Nicholas J. Georgiadis., “Computational Study of Separating Flow in a Planar Subsonic Diffuser,” NASA, October 2005 [3] Olle Tornblom., “Experimental study of the turbulent flow in a plane asymmetric diffuser,” 2003 [4] Reid A. Berdanier., “Turbulent flow through an asymmetric plane diffuser”, Purdue University, April-2011 [5] Arthur H Lefebvre and Dilip R. Ballal., “Gas Turbine Combustion-Alternative Fuels and Emissions”, CRC Press Taylor & Francis Group, Third Edition pp.79 – 112 – 2010 [6] Gianluca Iaccarino., “Predictions of a Turbulent Separated Flow Using Commercial CFD Codes,” 2001 [7] Obi, S., Aoki, K., and Masuda, S., “Experimental and Computational Study of Turbulent Separating Flow in an Asymmetric Plane Diffuser,” Ninth Symposium on Turbulent Shear Flows, Kyoto, Japan, August-1993. [8] Dheeraj Sagar, Akshoy Ranjan Paul Et al., “Computational fluid dynamics investigation of turbulent separated flows in axisymmetric diffusers,” 2011 [9] E.M. Sparrow and J.P. Abraham., Et al. “Flow separation in a diverging conical duct: Effect of Reynolds number and Divergence angle,” 2009. 21 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 SYNTHESIS, PHYSICO-CHEMICAL AND ANTIMICROBIAL PROPERTIES OF SOME METAL (II) -MIXED LIGAND COMPLEXES OF TRIDENTATE SCHIFF BASE DERIVES FROM Β-LACTAM ANTIBIOTIC {(CEPHALEXIN MONO HYDRATE)-4CHLOROBENZALDEHYDE} AND SACCHARIN Taghreed. H. Al-Noor, Amer. J. Jarad, *Abaas Obaid Hussein Department of Chemistry. Ibn -Al-Haithem College of Education Baghdad University
[email protected], *
[email protected] Abstract— A new Schiff base 4-chlorophenyl)methanimine (6R,7R)-3-methyl-8-oxo-7-(2-phenylpropanamido)-5-thia-1azabicyclo[4.2.0]oct-2-ene-2-carboxylate= (HL)= C23H20 ClN3O4S) has been synthesized from β-lactam antibiotic (cephalexin mono hydrate(CephH)=(C16H19N3O5S.H2O) and 4chlorobenzaldehyde . Figure(1) Metal mixed ligand complexes of the Schiff base were prepared from chloride salt of Fe(II),Co(II),Ni(II),Cu(II),Zn(II) and Cd (II), in 50% (v/v) ethanol –water medium (SacH ) .in aqueous ethanol(1:1) containing and Saccharin(C7H5NO3S) = sodium hydroxide. Several physical tools in particular; IR, CHN, 1H NMR, 13C NMR for ligand and melting point molar conductance, magnetic moment. and determination the percentage of the metal in the complexes by flame(AAS). The ligands and there metal complexes were screened for their antimicrobial activity against four bacteria (gram + ve) and (gram -ve) {Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus and Bacillus}. The proposed structure of the complexes using program, Chem office 3D(2006). The general formula have been given for the prepared mixed ligand complexes Na2[M(Sac)3(L)], M(II) = Fe (II), Co(II) , Ni(II), Cu (II), Zn(II) , and Cd(II). HL= C29H24 ClN3O4S, L= C29H23 ClN3O4S -. Key words— (Cephalexin antibiotics, Saccharin, Schiff base, Spectral studies drugs mixed ligand complexes, and antibacterial activities. containing hetero atoms like O,N ,S and P are found to work as very effective corrosion inhibitors [2-3] Schiff bases have been studied extensively because of their high potential chemical permutation. Magnetic susceptibility, absorption spectra, elemental analysis, molecular weight determination, conductivity, thermal analysis of many Schiff bases and their complexes have been reported.[4–5]Several workers also studied their biological properties, such as antibacterial, antifungal, activities.[6–7] Saccharin (C7H5NO3S), also called o-sulfobenzoimide, is widely used as an artificial sweetening agent. Saccharin is a weak acid [8]. The structures of Co(II) [7], Ni(II) [8], Cu(II) [9] and Cd(II) [10] imidazole saccharinates were reported. In this paper we present the synthesis and study of Fe(II),Co(II),Ni(II), Cu(II), Zn(II),and Cd(II) complexes with tridentate Schiff base derives from β-lactam antibiotic { (cephalexin mono hydrate)4-chlorobenzaldehyde } as a primary ligand and Saccharin as secondary ligand. Their structures were confirmed by Uv-Vis . IR and NMR spectral analysis. Further, their antibacterial activity towards some clinically important bacteria was evaluated. II. EXPERIMENTAL Figure(1):structural of the HL (3D) A. Chemicals All chemical reagents and solvents used were of analytical grade and were used without further purification and were used as received, CuCl2.H2O, CdCl2.H2O, ZnCl2, FeCl2.9H2O.MnCl2.2H2O, CoCl2.6H2O,NiCl2 .6H2O, NaOH (supplied by either Merck or Fluka) ethanol, methanol dimethylforamaide, and KBr, acetone , benzene, 4chlorobenzaldehyde, and chloroform from (B.D.H).Cephalexin powder DSM (Spain). I. INTRODUCTION Metal complexes of the Schiff bases are generally prepared by treating metal salts with Schiff base ligands under suitable experimental conditions. However, for some catalytic application the Schiff base metal complexes are prepared in situ in the reaction system. [1].Generally the organic compounds B. Instrumentals Elemental micro analysis for the ligands was performed on a (C.H.N.) Euro EA 3000. In Ibn Al-Haitham College of Education, University of Baghdad, Iraq. 1H NMR spectra were recorded using Brucker DRX system 500 (500 MHz) and 13 C-1H hetero nuclear 2D correlation 22 | P a g e NaOH MeOH Stirring 2hours International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 spectroscopy (COSY), HETCOR), in the Department of complexes precipitated were filtered and washed with distilled Chemistry Sharif University, Tehran, Iran. water, then with methanol and recrystallized using acetone solvent. Na2 [M (L)(Sac)3] (Scheme 2) . Yields: 82-90%. UV-Vis spectra were recorded on a (Shimadzu UV- 160A) Ultra Violet-Visible Spectrophotometer. IR- spectra were taken O OH H H NNa + MCl on a (Shimadzu, FTI R- 8400S) Fourier Transform Infrared S + 3 2 N S O 1 N CH3 Spectrophotometer (4000- 400) cm-1 with samples prepared as N O O KBr discs. Metal contents of the complexes were determined O HO by atomic absorption (A.A) technique using a Shimadzu AA Cl 620G atomic absorption spectrophotometer. The Chloride contents of complexes were determined by potentiometric titration method using (686-Titro processor-665. Dosimat Metrohn Swiss). Conductivities were measured for 10-3M of H complexes in DMSO at 25оC using (conductivity meter, O H HS N Jewnwary, model 4070). Magnetic measurements were N CH N Farady’s method. In addition melting points were obtained using (Stuart Melting Point Apparatus). The proposed molecular structure of the complexes were drawing by using chem. office prog 3DX (2006). C. SYNTHESIS OF SCHIFF BASE (HL) The Schiff base ligand was prepared by condensation of (2.92 gm,8mmol) of Cephalexin mono hydrate in (15ml) methanol and of (1.12 g m , 8mmol) of 4-chlorobenzaldehyde in (15ml) methanol was refluxed on water bath for 3-4 hours in presence of few drops of glacial acetic acid. The yellow coloured solid mass formed during refluxing was cooled to room temperature, filtered and washed thoroughly with methanol, washed with hot acetone and recrystallized from acetone to get a pure sample. Yield: 83%, m p: 205-210o C. M.W= 469. 94, (C23H20N3 ClO4 S). see scheme (2-1) . % Calculated: 58. 78 , H: 4.92, N: 8;94 % Found: C: 57.55, H: 5.093, N: 8.627. S H HN H2O + N Cl OH O O drop acetic acid O O methanol H2N Reflux 3-4h S H HN O N O OH O N Cl (6R,7R)-7-(2-((Z )-4-chlorobenzylideneamino)-2-phenylacetamido)-3-methyl-8-oxo-5-thia-1-azabicyclo[4.2.0]oct-2-ene-2-carboxylic acid Scheme (1): The synthesis route of ligand (HL) D. General preparing of the mixed ligands metal complexes The complexes were prepared by a similar method of synthesis using the reagents in molar ratio of 1:3:1. Of M: L: 3Sac. A methanolic solution (15 mL, 1m mol) of the appropriate FeCl2.6H2O. (0.180gm, 1mmol), CoCl2.6H2O (0.237gm, 1mmol), NiCl2.6H2O (0.238gm, 1mmol), CuCl2.2H2O (0.176gm, 1mmol), ZnCl2(0.136gm, 1mmol),CdCl2 (0.183gm, 1mmol); was added to a methanolic solution (15ml) of the Schiff base, primary ligand [HL] (1m mol) and methanolic solution (0. 549g, 3mmol) ) of the secondary ligand sodium saccharinate was added to the previous solution and the reaction mixture was refluxed for about 2-3 h on a water bath and then aqueous alcoholic solution of Na OH (V: V) was added to the mixture to adjust the pH 6 to 8 and further refluxed for about an hour with constant stirring . The 3 O O O M Cl O N O Na2 N S O O O S O O O N S O M(II) = Fe (II),Co(II),Ni(II),Cu(II), Zn(II), and Cd (II) Scheme (2): The synthesis route of Metal(II) -(Schiff base HL –Sac) Mixed Ligand Complexes III. RESULTS AND DISCUSSION The data obtained from analytical and physico-chemical studies have been correlated in a logical way to explain the properties, bonding and structures of the compounds. A. Characterization of the ligand, Generally, the complexes were prepared by reacting the respective metal salts with the ligands using 1:1:3 mole ratios.[M: L3 :3(Sac)], i.e. one mole of metal salt : one mole of Schiff base(HL) and three moles of sodium Saccharinate The synthesis of mixed ligand metal complexes may be represented as follows 3SacH +3NaOH→ 3 Sac Na + 3H2O 3 SacNa + HL+ MCL2 .n H2O → [M(Sac)3(L)]+ n H2O + NaCl (where HL is Schiff base derives from selected β-lactam antibiotic (cephalexin monohydrate) with 4chlorobenzophenone, and Sac H is Saccharin). M (II) = Fe (II), Co(II),Ni(II),Cu(II), Zn(II), and Cd (II) B. Physical properties The formula weights and melting points, are given in table (1).Based on the physicochemical characteristics, it was found that all the complexes were non- hygroscopic, All complexes are insoluble in most organic solvent, but soluble in ethanol, DMF and DMSO. The complexes were dissolved in DMSO and the molar conductivity values of 10-3 M solution at 25 o C of the complexes are in the range 63.55-77.36 ohm–1mol-1 cm2. It is obvious from these data that complexes are electrolytes types 1: 2 [11]. The test for halide ion with AgNO3 solution was negative indicating that halide ion is inside the coordination sphere of the central metal [12]. The ligand, HL was yellow in color with a melting point of 162oC. The analytical data showed closed agreement with the suggested formula of C23H20 ClN3O4S. It was further characterized by 1H NMR,13C NMR and FT-IR 23 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 C. spectral data all the metal complexes indicates that, these groups are not involved in coordination. Some new bands of weak intensity The (FT-IR) spectrum for the starting material saccharin observed in the regions around (526-474)cm-1 and (418-486) (Sac H) Table(3). In saccharin the bands for stretching cm-1 may be ascribed to M-N and M-O vibrations, respectively vibration of N-H and (CNS) are found at 3402 and 966 cm-1 [14-15].It may be noted that, these vibrational bands are absent respectively. The absorption band for stretching vibration of in the spectra of the ligands.[15-16] (C = O) appeared at 1705 cm-1 .The absorption bands in the (U.V-Vis) Spectral data for the Schiff bases mixed ligands region 1333 to 1553 cm-1 is for C = C in the aromatic ring, complexes [Fe(L)(Sac)3], [Co (L)(Sac)3], [Ni (L)(Sac)3], [Cu 1292 cm-1 for C-N single bond, and at 1692 cm-1 for C-O (L)(Sac)3 , [Zn (L)(Sac)3] and [Cd (L)(Sac)3]. single bond. The two SO 2 stretching vibrations appear at The UV-Vis spectrum of the ligand (saccharin) shows similar frequencies(1292 and 1178 cm-1 for υ asmy(SO2) asym peaks at 275 nm (36363 cm-1)(εmax=142 molar-1.cm-1), 340 and υ smy (SO2) sym, respectively. [13-14] The (FT-IR) nm (21422 cm-1) (εmax=168 molar-1.cm-1) assigned to (π–π*) spectrum for the ligand (HL), displays bands at (3211, 3045) and (n–π*) electronic transitions. [17] cm-1 due to υ (N–H) secondary amine stretching vibration, and The UV-Vis spectrum of the ligand (HL) shows peaks at disappeared the band for the υ (N–H) primary amine stretching 300 nm (33333 cm-1) (εmax=880 molar-1.cm-1), assigned to vibration. (n–π*) electronic transitions within the organic ligand, [17- 18] The spectrum displays a new band at (1689) cm-1 is due υ The absorption data for complexes are given in Table (5). (HC=N-) group of the azomethine stretching vibrations of the Na2[Fe(L)(Sac)3] ligand [125] .Where The band at (1759) cm-1 is due to Stretch The magnetic moment table (3-27) of the Fe (II) d6 grouping υ(C=O) for (COOH) and strong _ (OH) stretching at complex is 4.72 B.M. 3423cm-1 corresponding to carboxylic group. The (U.V- Vis) Fe (II) spectrum, exhibits four peaks. The The band at (1689) cm-1 stretching vibration is due to υ assignment of the electronic spectral bands, their positions, and (C=O) for β-Lactam group overlapping with υ (-HC=N-); The the spectral parameters for Fe (I1) which is in agreement with bands at (1593) and (1398) cm-1 were assigned to stretching data reported by several research workers [24,7], the first high vibration (COOH) asymmetric and symmetric stretching intense peak at (273 nm)( 36630 cm-1)(εmax =1189 molarvibration, respectively. , Δυ = [υ asym (COO-) - υsym (COO-)] 1.cm-1) is due to the (L.F), while the second peak at (299nm)( is (195 cm-1) .These values are quite agreeable with the values 334442 cm-1)(εmax =1208 molar-1.cm-1) and third peak at reported earlier [124-125]. (345 nm)( 28985 cm-1)(εmax =1208 molar-1.cm-1) are due to The bands at (1502), (3045), (1163), and (2813) were the (C-T) .The fourth peak at (757 nm)( 13210 cm-1) (εmax assigned to υ(C=C) aromatic, υ(C–H) aromatic,( υ(C–C) aliphatic., and υ (C–C) aromatic ) stretching vibration =42 molar-1.cm-1) is due to the 5T2g→5Eg transition. respectively. The band at (1315) cm-1 is due to υ(C–N) cm-1 [5,18].These results reveal the distorted octahedral geometry stretching vibration. The band at (1282) cm-1 was assigned to for these complex.[17] υ(C–O) stretching vibration [123]. The band at (582) cm-1 was Na2[Co(L)(Sac)3] assigned to υ(C–S) stretching vibration [13-14]. The electronic absorption spectrum of Co (II) d7 complex The assignment of the characteristic bands (FT-IR) spectra showed five absorption bands as shown in table (5). The for the free ligand (HL), are summarized in Table (2) and (3) assignment of the electronic spectral bands, their positions, and respectively. the spectral parameters for Co (I1) which is in agreement with FT-IR of Na2 [Fe( L)(Sac)3] (1), Na2[Co ( L)(Sac)3] (2), data reported by several research workers [124,127], the first Na2[Ni ( L)(Sac)3](3), Na2[Cu( L)(Sac)3] (4) ,Na2[Zn( high intense peak at (273 nm)( 36630 cm-1)(εmax =1340 L)(Sac)3] (5) and Na2[Cd ( L)(Sac)3] (6) complexes: molar-1.cm-1) is due to the (L.F) , while the second peak at The FT-IR spectra for complexes (1) , (2) , (3) , (4) , (5), (299 nm)( 334442 cm-1) (εmax =1379 molar-1.cm-1) and and (6), are summarized in table (4) . The spectrum of the (HL) third peak at (345 nm)( 28985 cm-1)(εmax =1383 molardisplays a new band at (1689) cm-1 is due to υ (HC=N-) group 1.cm-1) are due to the (C-T). The fourth peak at(862nm)( of the azomethine stretching vibrations of the ligand [125,128]. 11600 cm-1)(εmax =28 molar-1.cm-1) and fifth peak at (981 on complexation these band has been shifted to lower nm)( 10193 cm- 1)(εmax =145 molar-1.cm-1) are due to frequencies (1620), (1629, (1629), (1585), (1629) and (1585) the4T1g→4T1g (P) (ν3) and 4T1g → 4A2g (ν2) cm-1for complexes (1), (2), (3), (4), (5) and (6).This bands respectively. The magnetic moment table (3-26) of the Co (II) gets shifted to lower frequency in the complexes, complex is 3.51B.M suggesting octahedral geometry for the Co indicating the coordination through azomethine nitrogen to (II) complexes. [5,17] metal atom. [5, 14, 15]. Na2[Ni(L)(Sac)3] The bands at (1593), and (1398) cm-1 were assigned to The electronic absorption spectrum of Ni (II) d8 complex stretching vibration (COOH) asymmetric and symmetric showed five absorption bands as shown in table (5).The stretching vibration, respectively. on complexation these bands assignment of the electronic spectral bands, their positions, and have been shifted to lower frequencies [(1581), (1587), the spectral parameters for Ni (I1) which is in agreement with (1527), (1527), (1587) and (1558) cm-1 for Δ (-COO)asy], and data reported by several research workers[124,127],the first [(1336), (1334), (1394), (1380), (1334), and (1358) cm-1,for Δ high intense peak at (272 nm)( 36764 cm-1)(εmax =1188 (-COO) sy] for the compounds (1) , (2) , (3) , (4) ,(5) and (6), molar-1.cm-1) is due to the (L.F) , while the second peak at that the coordination with metal was occurred through the (344 nm)( 29069 cm-1)(εmax =2073 molar-1.cm-1) and third oxygen atom of carboxylate ion. Moreover, Δ(aυs (COO–)peak at (358 nm)( 27932 cm-1) (εmax =1383 molar-1.cm-1) υs(COO–) values of complexes below 200 cm−1 would be are due to the (C-T).The fourth peak at(885 nm)( 11299 cmexpected for bridging or chelating carboxylates but greater than 1)(εmax =12 molar-1.cm-1) and fifth peak at (980 nm)( 200 cm−1 for the monodentate bonding carboxylate anions 10204 cm-1)(εmax =104 molar-1.cm-1) are due to the 3A2g [6,13]. The un altered position of a band due to ring υ(C-S) in 24 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 (C6; C7; C8; β-lactam);135 .65; 131. 63; 130.18).The (F) → 3T1g (P) (ν3) and 3A2g(F) → 3T1g(F) four resonance at (δ=140.87, δ= 140.42, δ= 140.12, δ= 139.92 (ν2)respectively . The magnetic moment table (3-2276) of ppm) assigned to carbon atoms of aromatic ring (C1, C2, C4, the Ni (II) complex is 2.77 B.M suggesting octahedral C3) respectively. (–HC=N); 146.14. geometry for the Ni (II) complexes.[ 17 , 19]. Na2[Cu (L)(Sac)3] F. The proposed molecular structure forNa2[M The electronic absorption of Cu(II) d8 complex showed (L)(Sac)3] three absorption bands as shown in table (5). The first high Studying complexes on bases of the above analysis, the intense peak at (271 nm)( 36900 cm-1)(εmax =1039 molarexistence of Hexa coordinated [M( L) (Sac) 3] were, M= 1.cm-1) is due to the (L.F) , while the second peak at (348 Fe(II),Co(II),Ni(II),Cu(II),Zn(II),and Cd(II).proposed models nm) (28735 cm-1)(εmax =485 molar-1.cm-1) and third peak is of the species were built with chem.3D shows in figure(2 ) observed multiple absorption band at 11682 cm-1 – 16500 cm1 but they are overlapped. Because, octahedral complexes of Cu(II) are observable distorted by Jahn-Teller effect and the structure of complex is to name pseudo-octahedral. It was to taken notice of top of the peak as absorption band and d–d transition at about 11682 cm-1 (2Eg→2T2g) for Cu(II) complex. The complex has a room temperature magnetic moment of 1.71 B.M. which corresponds to distorted octahedral structure for the Cu (II) ion,[ 19-.20]. Na2[Zn (L)(Sac)3] and Na2[C d (L)(Sac)3] The electronic spectra of d10[Zn(II) and C d(II)]complexes do show the charge transfer . The magnetic susceptibility shows that two complexes have diamagnetic moments., because d-d transitions are not possible hence electronic spectra did not give any fruitful information. in fact this result is a good Figure (2): 3D molecular modeling proposed complexes agreement with previous work of octahedral geometry Na2[M(L)(Sac)3] [16,19,21]. M= Fe(II),Co(II),Ni(II),Cu(II),Zn(II) and Cd(II) D. Magnetic susceptibility The observed magnetic moment values of the prepared complexes are summarized in table (6).Examination of these data reveals that magnet moment of 0.0 B.M for Cd (II) and Zn complexes confirms that the complexes are essentially diamagnetic. The magnetic moment found for Fe(II),Co (II), Ni (II), Cu (II), 4.72, 3.51, 2.77, 1.71 B.M respectively these values suggest octahedral geometry which is in good agreement with data of electronic transition . The electronic spectra and the magnetic moments support the stereochemistry of the complexes [12-120] E. NMR Spectral studies The integral intensities of each signal in the 1HNMR spectrum of ligand was found to agree with the number of different types of protons present. In the 1H NMR spectrum of the ligand, the formation of Schiff base is supported by the presence of a singlet at (δ 8.21) ppm corresponding to the azomethine proton (–N=CH–).The signal obtained in range (δ 7.77-7.92) ppm was assigned for doublet due one proton of aromatic ring of phenyl. Three groups of double peaks given by (CO–CH) and (N–CH) on the beta-Lactam ring and (NH sec.) amide appeared at (δ 4.48), (δ 5.06) and (δ 8.08) ppm, respectively. This confirms the formations of imine ligand. This observation was also supported by the FTIR data of the ligand discussed earlier. One group of four resonance signals attributed to (S-CH) on the dihydrothiazine ring was observed in the (δ 2.92-3.27) ppm. and 9.53 ppm (1H, s, –NH–CO); This observation was also supported by the FTIR data of the ligand discussed earlier. [23, 24].The NMR spectral data of HL was compared with the spectral data for the similar ligands reported in literatures [23, 24]. The 13C NMR spectrum of the ligand [HL] in DMSO-d6 solvent shown Antibacterial Activities studies: [25-26] The effectiveness of an antimicrobial agent in sensitivity is based on the zones of inhibition. The synthesized metal complexes were screened for their antimicrobial activity by well plate method in nutrient agar . The invitro antibacterial activity was carried against 4 hold cultures of pathogenic bacteria like gram (+) and gram (-) at 37o C. In order to ensure that solvent had no effect on bacteria, a control test was performed with DMSO and found inactive in culture medium. Antimicrobial activity was evaluated by measuring the diameter of the inhibition zone (IZ) around the hole. Most of the tested compounds showed remarkable biological activity against different types of gram positive and gram negative bacteria. The diameter of the susceptibility zones were measured in mm and the results are presented in Table (7) [2627]Compounds were considered as active when the (IZ) was greater than 6 mm. The zone of inhibition of the complexes against the growth of bacteria were given In table (6), figure(3 ) *The antibacterial activity results revealed that the ligand (HL) and its complexes shown weak to good activity. Complexes Na2[M (L)(Sac)3], M =Co(II) ,Ni(II) ,Cu(II) and Zn(II) show negative against all bacteria. *The complex Na2[Fe (L)(Sac)3] show very good antibacterial activity agains towards 3- organisms except pseudomas. *The complex Na2[Cd(L)(Sac)3] show good antibacterial activity against towards 4- organisms. The inhibition antibacterial property of complexes can be explained as follows. The positive charge of the metal ion is shared antibacterial between the donor atoms of the ligand. There is the possibility of delocalization of the π electron density of aromatic ring also. These two factors positively contribute to increase the lipophilic character. Upon complexation , polarity of metal ion get reduced due to the overlap of ligand orbital and 25 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 [14] Silverstein R. M., Spectrophotometric Identification of Organic the sharing of positive charge of the metal ions with donor Compounds, 2009.John Wiley, New York, NY, USA. groups. [5,16] REFERENCES [1] Cozzi. P.G, Chemical Society Reviews, 33 (2004) 410-421. [2] Blanc C., Gastaud S., J. Electrochem.Soc. 150, 396, 2003. [3] Ebenso E. E,. Okafo P. C r, U. J. Eppe,Anti Corr. Meth. and Mat, 50,414,2003. [4] Taghreed H. Al-Noor, Sajed. M. Lateef and Mazin H. Rhayma, J.Chemical and Pharmaceutical Research,( 2012), 4(9):41414148 [5] Taghreed H. Al-Noor, Ahmed. T. AL- Jeboori , Manhel Reemon , J. Chemistry and Materials Research ,( 2013), Vol.3 No.3, 114124 [6] Taghreed H.Al-Noor,Ahmed T.AL- eboori , Manhel Reemon,( 2013 ) J. Advances in Physics Theories and Applications Vol.18, 1-10. [7] Zhang,. J. Li, Y. Lin, W. Liu, S., J. Huang, Polyhedron, (1992), 11, 419. [8] Zhang, J. Li Y., Lin, W. Liu, J. S. Huang, J. Cryst. Spec. Res. (1992)., 22, 433 [9] Liu, J. Huang, J. Li, W. ., Lin, J. Acta Crystall ogr. (1991), C47, 41. [10] Ke, J. Li, Y. Wang, Q. Wu X., J. Cryst. Res. Technol. (1997), 32, 481. [11] Geary, W. J. Coord. Chem. Rev. 1971, 7, 81-122. [12] Vogel A. (1978).Text Book of Quantitative Inorganic Analysis (Longman, London). 3Ed th 694. [13] Nakamoto; K. (1996).Infrared spectra of Inorganic and coordination compounds “4Ed th ; J. Wiely and Sons, Newyork. [15] Sharma, R.C Giri P.P, Devendra Kumar and Neelam, J. Chem. Pharm. Res(.2012), 4(4): 1969-1973. [16] Fayad N.K., Taghreed H. Al-Noor and Ghanim F.H, Journal of Advances in Physics Theories and Applications, (2012) , Vol. ( 9), 1-12. [17] Lever A.B.P., “Inorganic Electronic spectroscopy“,2rd Ed Elsevier, New York. (1984). [18] Taghreed H. Al-Noor, Manhel Reemon Aziz and Ahmed T. ALJeboori, Journal of Chemistry and Materials Research, 2013 Vol.3 No.3, 114-124. [19] Taghreed H. Al-Noor, Ahmed. T. AL- Jeboori, Manhel Reemon, Journal Advances in Physics Theories and Applications ( 2013) Vol.18, 1-10. [20] Dutta. R. L and Syamal A., Elements of Magnatochemistry , 2nd Ed., East west press, New Delhi, (1996). [21] Manchand W. ConardFernelius W., Journal of Chemical Education (1961). 38 (4) 192-201, [22] Fouziarafat M. Y. Siddiqi and Siddiqi., k. S. J. Serb. Chem. Soc.(2004), 69 (8–9) 641–6649 [23] Chohan, ZH.Daniel L.M. Aguiak DE, Rosane A.S. San GIL, Leandro B. Borre, Monica R.C. Marques, Andre L. Gemal , J. Appl Organomet Chem, (2011) 20: 112- 118. [24] Reddy V., Patil N. and. Angadi S.D, E-J. Chem., (2008), 5(3), 577-583. [25] Seely H.W ,and Van Demark P J, Microbes in Action, Laboratory of Microbiology, 3rd Ed., W H Freeman and Co. U.S.A, 1981, 38 [26] Awetz J., Melnick, And Delbrgs A,( 2007), “Medical Microbiology” 4th ed McGraw Hil-USA. Table (1): The physical properties of the Schiff base mixed ligand Na2 [M(L)(Sac)3]complexes M. wt = Molecular Weight, Lm = Molar Conductivity, dec. = decomposition Table(2):Data from the Infrared Spectrum for the Free Ligand Ceph (cm-1) and Schiff base HL Table (3): Infrared spectral data (wave number ύ) cm-1 for the Saccharin (Sac H) Sym: symmetric, asy: asymmetric, am: amide, v.s: very strong, s: strong, m: medium, w: week, sh: shoulder , arom. = aromatic, aliph = aliphatic 26 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 Table (4): Infrared spectral data (wave number ύ) cm-1 for the ligand HL, and their complexes Table (5): Electronic Spectral data, magnetic moment, of the mixed ligands complexes Table (6): The magnetic measurements data of the prepared complexes 27 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 22-28 Table (7): The antibacterial activity (Zone of inhibition) (mm) data of Schiff base (HL) and its complexes Na2 [M(L)(Sac)3] Figure(3) :Chart of biological effects of the Na2[M( L)(Sac)3] 28 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 29-33 CAUSES AND EVALUATION OF CRACKS IN CONCRETE STRUCTURES Syed Mohd Mehndi Prof. Meraj Ahmad Khan & Prof. Sabih Ahmad (Guide) Dept. of Civil Engineering Integral University Lucknow, India
[email protected],
[email protected],
[email protected] Abstract-This research work focused on checking the cause and evaluation of cracks at every stage in R.C.C structures. This paper will describe how to find out cracks size and cause of cracks. Cracks generally occur both in plastic and elastic state of concrete. I have selected this topic because less work is being done in this area in India. The repair materials and repair technique are different depending upon forms of cracks according to their positions in structure. Good crack repair methods depends on knowing the cause of cracks and selecting appropriate repair method that take these causes into account otherwise the repair would not last long. This report serves as a tool in process of cracks evaluation and causes of cracks in concrete structures. So we can say if crack repair is assumed to be building of structure than this report can be assumed as foundation of it. Keywords— Thermal expansion, alkali-silica reactions, alkalicarbonate reactions, corrosion; cracking; drying shrinkage, heat of hydration, mass concrete, plastic & precast concrete, prestressed concrete, reinforced concrete, shrinkage. II. REASONS OF CRACKING A. CRACKING WHICH OCCUR IN PLASTIC CONCRETE 1. PLASTIC SHRINKAGE CRACKING It arise when the rate of evaporation of water from top layer of freshly laid concrete is greater than bleed water provided by underlying concrete due to this surface concrete contracts. Due to the restraint shown by the concrete below the drying surface concrete layer the tensile stresses are develop in the weak and stiffening plastic concrete. Due to this shallow crack of variable depth are formed at different locations whose shape can be random, polygonal pattern, or be essentially parallel to one another. These cracks may be fairly wide and can be observed the surface. The size of these cracks would vary from few inches to feet in length. Plastic shrinkage cracks begin as narrow cracks, but can become full-depth cracks later on. I. INTRODUCTION Concrete encompasses certain type of cracks in prehardening stage and develops some other types of cracks in post hardening stage in life of structure due to various reasons, even with our extreme care in prevention of cracks. When concrete becomes older cracks become causes of leakages and seepages and give entree to the moisture, oxygen, chloride, carbon dioxide etc. and other aggressive chemicals and gases into the concrete causing serious degradation of the structure and causing corrosion of steel and damage in the concrete and at a same time causing structural failure of the member. Cracking are early indications of failure of structure. Lightweight concrete shrinks more. It is vital to note that concrete does crack and this is usual. What is not normal is too much of cracks. “Cracks can be treated as cancer in R.C.C structure, as cancer which in its primary stage is curable to a certain extent but becomes danger to life in later stage; same happens with cracks” Depending on types and importance cracks can be of two types:- Structural Cracks Non Structural Cracks Structural cracks are of more important and have to be dealt more carefully because neglect to this leads to un-safe structure. Non-structural cracks are not of so much significance as far as safety is considered but it deals more with aesthetic point of view. Fig.1 Above Presenting Typical View of Plastic Shrinkage Crack Plastic shrinkage cracking occur due to: When temperature of air above concrete is high. When there is low relative humidity When wind velocity above concrete is high. Preventive measures of plastic shrinkage include use of: to saturate the air above concrete Fog nozzles Plastic sheeting to cover concrete to decrease the wind velocity Windbreaks to decrease the surface temperature Sunshades 2. SETTLEMENT CRACKING Concrete has general tendency to settle down after initial placing of concrete and when this settlement are blocked by reinforcement, framework etc. then settlement cracks will develop. Due to restraints; cracks develops in structure which are adjacent to restraining element. Settlement cracking increase with increase in bar size, inadequate vibration and increase in slump and decreases with increase in size of cover and addition of fibers in concrete. 29 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 29-33 concrete can be due to water filled inside water retaining structure, foundation that came in contact with soil or due to air pollutant which react with concrete. Concrete get cracked when concrete react with aggregate containing active-silica and alkalis resulting from cement hydration. When the alkalis in cement react with aggregate particles a reaction film of alkalisilica gel is produced around the aggregate. If this gel is exposed to moisture it will expands causing an increase in the volume of the concrete mass which finally results in cracking. Remedial measures include use of aggregates which do not take part in reaction. Certain carbonates rocks take part in reactions with alkalis produce expansion and cracking. Sulfates from soil when react with cement paste Calcium Sulfoaluminate is formed, which may be root cause in increase in volume of concrete. This increased in volume of concrete causes development of closely spaced cracks and ultimately deterioration of the concrete. Sulfate- resistant cements are very beneficial in reducing this problem. Using concrete with a low w/c ratio is important to have adequate protection against severe sulfate attack. 4: WEATHERING Weathering is wear and tear of structures caused by freezing, drying and wetting of concrete. Concrete can be easily get damaged by freezing of water both in elastic stage and plastic stage. Freeze water inside concrete result in increase in volume of concrete. The increased volume of concrete results in cracking of concrete. Concrete can be protected against weathering by use of the Fig above Presenting Typical View of Settlement Crack low w/c ratio, tough aggregate and adequate curing of concrete. 5: CORROSION OF REINFORCEMENT B: CRACKING OF HARDENED CONCRETE Corrosion to reinforcement is signs rather than reason for 1: DRYING SHRINKAGE concrete damage. Corrosion occurs due to electrochemical Concrete has greater volume when it is in dried form and it oxidation of reinforcement bars in existence of moisture and volume decreases on drying; decrease in volume is due to loss electron flow inside metal. After corrosion the volumes of of water. When decrease in volume of concrete is restrained by reinforced bars get increased. Due to increase in volume of reinforcement bars then cracks is established called Plastic reinforced bars a bursting radial stresses are produced around shrinkage cracks. Tensile stresses are developed within structure bars which result in local radial cracks around bars. due to combination of shrinkage and restraint provided by Remedial technique comprises of epoxy coating of bars, use another part of the structure. As we know that concrete are of richer grade of concrete and by use of corrosion inhibitors. weak in tension so when tensile stress which is developed 6: POOR CONSTRUCTION PRACTICES during restraint exceeds tensile strength of concrete then cracks When construction is not done correctly cracks started to started to develop. These cracks are detected at the surface originate in structure called cracks due to wrong construction which go deep later on as time passes. Factors which affect practice. In this the most common is additional of water to drying shrinkage are type of aggregate and W/C ratio. Stiff increase workability. Addition of water plays an important role aggregate offer more resistance to shrinkage. Contraction in decreasing concrete strength, increasing concrete settlement joints and correct detailing of the reinforcement reduces and increasing drying shrinkage of concrete. Another problem shrinkage cracking. which comes under this is when less curing is done or curing is 2: THERMAL STRESS eliminated early stages. Thermal stresses are produced when there is normal 7: STRUCTURAL OVERLOADS expansion and contraction of concrete due to surrounding Concrete gets damaged due to structural overload which are change in air temperature. It was observed that concrete length very easy to detect. Precast member like beam and are variations is about 0.5 inch per 1000 linear feet at an generally subjected to this type of load. Most unfortunate atmospheric temperature of about 80 °F. When there is no things about cracks is due to structural overload are that cracks provision of thermal expansion concrete will crack. This type are detected at early stages. of cracks forms as a source of seepage in water retaining These types of cracks can be prevented if designer limit the structures. Cracks developed from tensile stresses get load on structure. accelerated by consumption of Portland cement. 8: ERRORS IN DESIGN AND DETAILING Method to reduce thermal induced cracking involve Errors in detailing & designing result in cracking of concrete. practice of jute bags to cover concrete and keep watering it at These problems are mostly seen in re-entrant corners near door least three times a day in hot countries like India. and windows opening in building. Problems which also came in 3: CHEMICAL REACTION consideration include incorrect detailing of reinforcement steel Chemical reactions which occur due to reaction of concrete bars and others problems like restraint of members, lack of in its firm state with materials used to make concrete or by materials that came in contact with it. Chemical reaction inside 30 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 29-33 adequate contraction joints and incorrect design of foundations the surface. A hollow sound specifies one or more cracks below etc. and parallel to the surface being hammered. Infrared imaging equipment although expensive but found effective in III. EVALUATION OF CRACKING recognizing regions in which concrete has cracks. The presence of reinforcement bars can be determined using a pachometer A: DIRECT AND INDIRECT OBSERVATION (Fig. 3.2.1). In this method first we note thickness of crack on a sketched of structure. Then grid are marked on the surface of the structure and crack widths are measured by this instrument to an accuracy of about 0.025 mm .This instrument comprises of a small hand-held microscope with a scale on the lens closest to the surface being viewed as shown in (Fig. 3.1.1) below. However it is generally more convenient to estimate crack thicknesses using a clear card which have lines of specified thickness marked on it, as shown in (Fig. 3.1.2) below. Fig. 3.1.1—Comparator for measuring crack thicknesses Fig. 3.1.2—Card used to measure crack thickness Any movement of the surface across the crack should also be documented. Observations such as reinforcement which exposed to environment, surface wear and tear and rust mark on reinforcement bars should be noted down on the sketch. Internal conditions of the crack at definite locations can be observed with the use of flexible shaft fiber- scopes or rigid bore scopes. B: NON-DESTRUCTIVE TESTING Nondestructive tests can be performed to estimate the presence of internal cracks and voids and the depth of penetration of cracks detectable at the surface. Tapping the surface with a hammer is simple method to recognize laminar cracking near Fig. 3.2.1—Pachometer reinforcing bar indicator Pachometers show the presence of steel bars and allow the experienced user to determine depth and the size of reinforcing steel. In some cases however it required to remove the concrete cover to pinpoint the bar sizes or to measure cover especially in areas of congested reinforcement. Results of Pachometers are observed by use computer algorithms and magnetic fields to provide a visual picture of the reinforcing bars layout in the scanned area. This device is very useful in detecting reinforcement bars, measure concrete cover, and estimate the position and reinforcement size. If cracking is due to Corrosion then concrete above bars are removed and bars are saw directly. Corrosion potential of steel bars is measured by half-cell. Generally copper-copper sulfate half-cell is used to measure extent of corrosion in reinforcing steel. By use of ultrasonic non-destructive test equipment it is possible to detect cracks. A mechanical wave is transmitted to one face of the concrete member and received at the opposite face as shown in (Fig. 3.2.2). The time taken by wave to travel through the member is measured electronically. Pulse velocity can be evaluated if the distance between the transmitting and receiving transducers is known. When it is not possible to place transducers on opposite face then it can be placed on the same face (Fig. 3.2.2(a)). In this technique analysis of results is not so easy. If more time is taken by wave to travel from transducer to receiver then section is said to be cracked one. Higher the wave velocity shows the good quality of the concrete. The interpretation of result can be improved to great extent by use of an oscilloscope that provides a visual representation of the received signal (Fig. 3.2.2(b)).In fully flooded crack section interpretation of result is difficult hence this instrument is of no use. 31 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 29-33 concrete can be find out from compressive strength tests but cores containing cracks should not be used to conclude concrete strength. Photographic test result of cracked concrete can tell us about material that causes cracking, w/c ratio relative paste volume and distribution of concrete components, age of cracks, secondary deposits on fracture surfaces. D: REVIEW OF DRAWINGS AND CONSTRUCTION DATA Construction drawing and detailing of reinforcement bars should be studied to confirm that the concrete thickness and quality. Serviceability requirement check is also necessary so that non-structural cracks are evaded in structure. The actual loads which are coming on structure should be checked against designed load. If actual loads coming on structure exceeds design load then we have to either re-design section or look in the direction of restoration of structure. IV. PROPOSED FILTRATIONS AND SUGGESTIONS Fig. 3.2.2—Ultrasonic testing: through-transmission C: TESTS ON CONCRETE CORES Concrete cores give necessary information about cracks which are taken at different positions. It also gives correct information about thickness and depth of cracks. Strength of The first step involves visual observation of cracks. In second step we find location and pattern of cracks. In third we find out root cause of cracks. Fourth steps involves cracks measurements for which different instruments are used such as Ultrasonic Pulse Velocity—To identify Void and measure Cracks depth, Cracks Microscope and Digital Crack Measuring Gauge—To locate and find width of cracks, Crack Monitor, Concrete Endoscope and Fiber Scope—To monitors the changes in cracks, Petrography—Evaluate crack due to fire damage, and Thermal imaging camera—To detect leakage and voids inside concrete. In all the technique mentioned above Cracks Compactor is most efficient in measuring small cracks. Ultrasonic testing is more costly than Crack Compactor and used for measuring all types of cracks. 32 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 29-33 V. CONCLUSION The paper is divided into three parts. First Part contains different causes of cracks, Second part contains evaluation of cracks and the Last part contains my inference drawn on cracks. This paper on a whole focuses on possible causes and evaluation of cracks in R.C.C structures. Evaluation of cracks can be done by different technique like Crack Compactor and by ultrasonic Testing. In all these mentioned technique Crack Compactor technique is most efficient technique for measuring small cracks, Ultrasonic Testing device is more costly than Crack Compactor and should be used for slightly big evaluation of cracks. Pachomerer is used in determining concrete cover, size and location of reinforcement. In evaluating material causes of cracking Photographic examination is used. [5] [6] [7] [8] [9] [10] [11] REFERENCES [1] Concrete Technology by M. S. Shety, Publication of S. Chand & Company Ltd, Delhi, 2005 [2] IS 456:2000, “Indian Standard of Plain and Reinforced Concrete Code of Practice. [3] ACI 224.1R-07, “Causes, Evaluation, and Repair of Cracks in Concrete Structures” [4] Pattanaik Suresh Chandra, “Repair of Active Cracks of Concrete Structures with a Flexible Polyurethane Sealant for Controlled Movement” (2011), Proceed of the National Conference on [12] [13] Advances in Materials and Structures, ‘AMAS - 2011’, Pondicherry Hand book HB 84-2006: Guide to Concrete Repair and Protection, A joint publication of ACRA, CSIRO and Standards Australia ASTM C881 “Standard Specification for Epoxy-Resin-Base Bonding Systems for Concrete” ACI 224.3R-95: Joints in Concrete Construction (Reapproved 2013) ACI 224.2R-92: Cracking of Concrete Members in Direct Tension (Reapproved 2004) ACI 231R-10 Report on Early-Age Cracking: Causes, Measurement and Mitigation Causes, Mechanism, And Control Of Cracking In Concrete, ACI Publication Cracking, Deflection, and Ultimate Load of Concrete Slab Systems (ACI Publication SP-30) Guide to concrete repair U.S Department of the Interior Bureau of Reclamation Technical service center. Appendix E Avoiding Coating Failures Due to Cracking of Concrete Coating Manual 33 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 34-36 DEVELOPMENT OF A FRAMEWORK FOR PRESERVING PRIVATE DATA IN WEB DATA MINING Sabica Ahmad1, Shish Ahmad2, Jameel Ahmad3 Dept. CSE & IT Integral University Lucknow, U.P. 1
[email protected],
[email protected],
[email protected] Abstract- The main aspire of this research work is, to develop proficient methodology to find privacy preserving association rule mining in centralized environment without infringement of any privacy constraints. The issue of privacy constraints for centralized database environment is entirely different from distributed database environment. The goal of attaining privacy in centralized database environment is, to obtain a distorted database which hides the sensitive item sets. When mining task is performed on distorted database all the sensitive rules should be hidden without any side effects. Based on heuristic approach, a new me-thodology is proposed by incorporating suggested Criteria1 and Criteria2 to identify the victim item and selecting suitable supporting transactions efficiently for sanitization purpose to hide the sensitive item sets. Index Terms — preserving private data, frequent item sets, privacy preserving association rule mining. I. INTRODUCTION Data mining has been view edasa risk to privacy because of the widespread propagation of electronic data maintained by organizations. This has initiated augmented concerns about the privacy of the under-lying data .The matter of privacy plays a crucial role when several genuine people share their resources in order to obtain mutual profit but no one is interested to reveal their private data .In the process of data mining, how to determine the problem of privacy preserving has become a hot research topic in the field of data mining. Hence, privacy preserving data mining research area is evolved. The privacy preservation data mining algorithms are generally classified into three categories namely reconstruction based, heuristic based and cryptog-raphy based II. PRIVACY PRESERVING ASSOCIA-TION RULE MINING We consider a method for finding privacy pre-serving association rule mining based on heuris-tic approach in centralized environment for dis-covering solution for hiding sensitive rules by fulfilling association rule hiding goals accurately or approximately. A new method is proposed in this paper re-lated to heuristic approach to hide sensitive association rules specified by users with min-imum side effects. The Criteria1 specifies the competent selection of victim item and Criteria2 helps to find the appropriate supporting transactions for victim item in the sanitization process to minimize side effects. Criteria 1: Victim item can be selected based on the follow-ing condition. If number of times <Ai> appears in non sensitive frequent item set is greater than number of times <Aj> appears in non sensitive frequent item sets then Aj be the victim item. If number of times <Ai> appears in non sensitive frequent item set is less than number of times <Aj> appears in non sensitive frequent item sets then Ai be the victim item. Criteria 2: The minimum number of transactions required to hide item set is based on the value of <Ai,Aj>.supp – MinTrans +1.. For each support-ing transactions for item set <Ai,Aj>, weight is computed by using the following: W(Tg) = No. of dependant items with victim item number of infrequent item sets associated with victim item. III. PROPOSED FRAMEWORK In this paper a procedure is suggested in which all the sensitive item sets whose length is greater than two are considered to find the pairs of sub patterns. From this pair only significant pair-sub patterns are considered as sensitive to hide sensitive patterns. This procedure is very significant in a way that it avoids the difficulty of forward inference attack. In order to avoid forward inference attack problem, at least one such sub pattern with length of two of the patterns should be hidden. The split pattern procedure helps to accelerate up the hiding process. S.No. 1 Symbols DBASE = {t1,t2,..tN} Explanatio nA original database consisting of N number of transactions 2 I ={i1,i2,…iM} 3 Lk An item set of length k 4 Tnm The n 5 S ={ s1, s2, …sr} Set of sensitive item sets 6 MinS 7 Supp(J) User specified Minimum support threshold Number of transactions supporting item set J 8 MinTrans 9 MCT 10 N 11 12 FDBASE L3,… Lk} B 13 FS 14 FNS An item set of length M th th transaction of m item Based on MinS, number of transactions required to support an item set to be frequent User specified Minimum confidence threshold Size of original database, DBASE ={L1, L2, A set consists of all frequent item sets Association rule between item sets A and B The set consisting of sensitive item sets The Set consisting of non sensitive frequent item sets 34 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 34-36 consisting of pairs Consider the victim item (Ai or Aj) based on the Criteria1 by the procedure split Step 8 Find the intersection of supporting transactions for AiAj and AjAk as follows: 15 F2S The set determined pattern. 16 <Ai,Aj> The sensitive item set pair 17 TAiAj 18 DBASE' Set of supporting transactions for item set <Ai,Aj> Distorted database which hides all sensitive item sets. 19 Victim item An item which is selected from the sensitive item pair which produces least side effects or no side effects when modification is done over it. 20 Victim transactions Selected transactions to modify the victim item value. 21 MinT 22 Count A set consisting of suitable number transactions, which are to be modified to hide the sensitive item set Count gives number of times the victim item value has to be modified to hide sensitive item set pair. 23 W(Tg) Weight for transaction Tg Table 3.1: Symbols Used in Proposed Model IV. ALGORITHM The algorithm for the proposed model is as fol-lows: Step 1 For a given database DBASE and set of sen-sitive item sets Fs, generate frequent item sets and store with their support values in FDBASE. Step 2 Let the sensitive item sets are stored in Fs then the non sensitive frequent item sets are obtained by subtracting FS from FDBASE. i.e., FNS = FDBASE - FS. Step 3 If any item sets in FS are having more than length of two, call the procedure split pat-tern to identify the prominent pairs which are to be hidden in order to hide all the item sets whose length is greater than two. Step 4 After step 3 a vector F2S is prepared which consists of all two pair sensitive items. Step 5 The generated all pairs sensitive fre-quent item sets with their support values along with their supporting transactions ID’s are stored in a Table TS. Step 6 All the non sensitive frequent item sets that is F- F2S are stored along with their support values in a Table TNS. Step 7 For each item set in F2S If any non overlapping item set exists go to step 12. Else the patterns <Ai,Aj><Aj,Ak> are chosen Step 9 Obtain the value for Count1 and Count2 as follows: Count1 for AiAj = <Ai,Aj>.Supp - MinTrans + 1 Count2 for AjAk = <Aj,Ak>.Supp - MinTrans + 1 Step 10 Find minimum number of supporting transactions to be modified by applying Crite-ria2. Select smaller one from both Count1 and Count2 and many transactions are chosen from MinT and the victim item (Aj) values are re-placed with 0 values. By this, item set lower count value will be hidden. To hide the item set, which is having higher count value, Count1 – Count2 no of transactions which are not yet processed will be chosen from MinT for the process of sanitization. To protect this item set, the victim item set can be chosen based on their dependencies with the item sets in non sensitive item set FNS. Accordingly the victim item value will be replaced with zero in the selected trans-actions. After performing this, the item set which is having higher count value is also hidden. Step 11 Modify F2S by removing the pairs <Ai, Aj> and <AjAk> from it. Go to step18. Step 12 For the sensitive item set pair <Ai, Aj> in F2S find victim item by using criteria 1. Step 13 After identifying the victim item, find the supporting transactions for <Ai, Aj>. Step14 Obtain the value for Count1 and Count2 as follows: Count1 for AiAj = <Ai, Aj>.Supp - MinTrans + 1 Step15 Select Count1 no of transactions to be modified from a set MinT obtained by the Crite-ria2. Step 16 The value of victim item in the selected transactions is replaced with value zero. Step 17 Update F2S by removing <Ai, Aj> from it. Step 18 Repeat the above steps from step 7 until no more pair in the F2S to hide. Step 19 Finally distorted database, DBASE´ is obtained in which all sensitive item sets in F2S are hidden. Step 20 Stop the process. V. CONCLUSION This study has been carried out to develop method-ology in centralized as well as in distributed envi-ronment to find privacy preserving association rule mining without revealing any private data or infor-mation. This methodology is proposed in this thesis work to hide the sensitive item sets in centralized database environment. My methodology is related to heuris-tic based approach which utilizes suggested criteria to efficiently find the victim item and its supporting transactions. The proposed methodology efficiently performs sanitization process. REFERENCES [1] Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth, “From Data Mining to Knowledge Discovery in Databases”, American Association for Artificial Intelligence, pp. 37-54,1996. [2] J. Han and M.Kamber, Data Mining Con-cepts and Techniques, Elsevier 2001. [3] R. Agrawal and R. Srikant, “Mining Sequential patterns”, Proc.1995 International Conference on Data Engineering (ICDE‟95), pp 3-14, Taipei, Taiwan, March 1995. [4] Verykios, V.S., Bertino, E., Nai Fovino, I., Parasiliti, L., Saygin, Y., and Theodoridis, Y. “State-of-the-art in privacy 35 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 34-36 [5] [6] [7] [8] [9] [10] preserving data mining”. SIGMOD Record, 33(1):50– 57,2004. Ahmed HajYasien, “Preserving Privacy In Association Rule Mining”, Ph D.,thesis, Griffith University, June 2007. Ming-Syan Chen, Jiawei Han,Yu, P.S., “Data mining: an overview from a database per-spective”, IEEE Transactions on Knowledge an Data Engineering, Vol. 8 No. 6, pp 866 – 883,1996. Yongjian Fu, “Data mining: Tasks, tech-niques and Applications”, Department of Computer Science”, University of Missouri- Rolla,1997 Michael Goebel, Le Gruenwald, “A Survey Of Data Mining And Knowledge Discovery Software Tools”, SIGKDD Explorations, ACM SIGKDD, Vol: 1, Issue 1, pp 20- 33, June 1999. Thair Nu Phyu ,”Survey of Classifica-tion Techniques in Data Mining”, Proceedings of the International MultiConference of Engineers and Computer Scientists 2009, Vol I,IMECS-2009, Hong Kong, 2009. Yongjian Fu, Distributed data mining: Overview, University of Missouri- Rolla, 2001. [11] R Agarwal, T Imielinski and A Swamy, “Mining Association Rules between Sets of Items in Large Databases”, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, page 207-210, 1993. [12] R. Agrawal and R. Srikant. “Fast, algo-rithms for mining association rules in large data-bases”, Proceedings of the 20th VLDB Conference Santiago, Chile, pp 487-499, 1994. [13] R. Srikant and R. Agrawal, “Mining Generalized Association Rules”, Proc. 21st VLDB Conference, Zurich, Swizerland., 1995. [14] Mohammed J. Zaki, “Parallel and Distrib-uted Data Mining: An Introduction”, Large-Scale Parallel Data Mining Lecture Notes In Computer Science, Vol. 1759, 2000. [15] Qinghua Zou, esley hu, Johnson,Chiu, . A pattern decomposition (PD) algorithm for finding all frequent patterns in large datasets”, International Conference on Data Mining, ICDM 2001, Proceedings IEEE, 673 – 674, 2001 36 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 37-41 RELATIONSHIP BETWEEN HEAVY METAL AND TRANSFER FACTOR FROM SOIL TO VEGETABLE COLLECTED FROM WASTE WATER IRRIGATED AREA OF REWA (M.P.) INDIA Geetanjali Chauhan1* & Prof. U.K. Chauhan2 Department of Environmental biology, A.P.S. University Rewa-486003, Madhya Pradesh, India
[email protected] Abstract— The study examined the concentration of heavy metals in water, soil and vegetables growing wildly on cement-polluted soil of Rewa city, India. Accumulation of HMs in vegetables occurs by various sources but soil is considered the major one. In this study, soil to vegetable transfer factor (TF) for various HMs were also calculated and data showed that TF values differed significantly between soil and vegetable, the difference in TF values among different vegetables may be attributed to differences in element uptake by different vegetables. However TF values obtained for all vegetables were below (1) at all sites. TF were computed to quantify relative differences in bioavailability of metals to vegetables to identify the efficiency of a vegetables species to accumulate a HM(s). These factors were based on roots uptake of metals and discount the foliar absorption of atmospheric metal deposits. However TF does not present the risk associated with the metal in any form. I. INTRODUCTION The clean and safe environment is the basic requirement of human existence. Rapid urbanization and industrialization releases enormous volumes of wastewater, which is increasingly utilized as avaluable resource for irrigation in urban and peri-urban agriculture. It drives significant economic activity, supports countless livelihoods particularly those of poor farmers, and substantially changes the water quality of natural water bodies (Marshall et al., 2007). Wastewater from industries may contain various heavy metals including Fe, Zn, Cu, Pb, Cd, Mn, Ni, Cr, Cd, depending upon the type of activities it is associated with. Continuous irrigation of agricultural land with industrial wastewater may cause heavy metal accumulation in the soil and vegetables (Chaney et al., 2000; Sharma et al., 2007; Marshall et al., 2007). Soil to plant transfer of heavy metals is the major path way of human exposure to metal contamination. Food is the major intake source of toxic metals by human beings. Vegetables take up heavy metals and accumulate them in their edible and non-edible parts at quantities high enough to cause clinical problems to both animals and human beings. main source of human exposure. A convenient way for quantifying the relative differences of bioavailability of metals to plants is the transfer coeficient. The higher transfer coefficient of heavy metal indicates the stronger accumulation of the respective metal by that vegetable. Transfer coeficient of 0.1 indicates that plant is excluding the element from its tissues (Thornton and Farago, 1997). The greater the transfer coefficient value than 0.50, the greater the chances of vegetables for metal contamination by anthropogenic activities will be and so the need for environmental monitoring of the area will be required (Sponza and Karaoglu, 2002). Thus accumulation of heavy metals in consumable vegetables has been well linked with soil heavy metal and irrigation water from long back; atmospheric deposition has now been identified as one of the principal source of heavy metals entering into plants and soil especially around urban-industrial areas (Pandey et al., 2009). Atmospheric heavy metals may deposit by rain and dust, and contributed to elevated metal concentrations in surface layer of soil (Sharma et al., 2008). Atmospheric metals may be absorbed directly on leafy surface, or entered through stomatal openings and accumulated within plant tissue. Metal accumulation in different plant parts depends on chemical form of metals, their translocation potential, and individual species with their stage of maturity (Salt et al., 1995). Heavy metal contamination in agricultural soil and vegetables through industrial wastewater and atmospheric source are of great concern because of metal translocation in soil-plant system and ultimately to the food chain (Khan et al., 2008; Rattan et al., 2005). Thus accumulation of heavy metals in the edible parts of vegetables represents a direct pathway for their incorporation into the human food chain (Florijin et al., 1993); and therefore has drawn the attention of researchers to health risk assessment of population exposed to contaminated foodstuffs. The aim of this research work was to determine the level of some heavy metals from soil that is transferred to the plants collected from waste water as well as clean water irrigated area of Rewa (M.P.), India and to correlate potential health effect of the people those who consumes those vegetables. Transfer of Heavy Metals from Soil to Vegetables Transfer factor expressed the bioavailability of a metal at a particular position on a species of plants (vegetables). This is however, dependant on different factors such as the soil pH and the nature of plant itself. As the vegetables are the source of human consumption so the soil-to-plant transfer quotient is the II. MATERIALS AND METHODS A. Experimental Sites Rewa is a city in the northern-eastern parts of the state of Madhya Pradesh, India. It is the administrative centre of Rewa District and Rewa Division. The cities lie about 420 km. (261 mi) north east of the state capital Bhopal, Madhya Pradesh and Key words— Heavy metal, soil contamination, Transfer Factor (TF), Health Risk (Hazardous), Waste Water. 37 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 37-41 130 km. (81 mi) south of the city of Allahabad, Uttar Pradesh. It is situated at 24.530 North latitude and 81.30 East longitudes and covers an area of 6,240 km2 (2,410 sq mi). It has an elevation of 304 m. (997 ft) above mean sea level. The average rainfall is 980 mm (39 inches) per year. The average temperature is around 250C (770 F) and the humidity is quite high. Experimental sites of different irrigation sources J.P. Cement Plants Bela, Naubasta (waste water irrigated sites) &, Bhiti village (clean water irrigated site) were selected. Cultivated land of these two industrial areas (Bela & Naubasta) received waste water discharge from industries, manufacturing cement while third site of rural area (Bhiti) received clean (ground) water from deep bore well. Thus all sites of different irrigation sources were selected and the sampling of water, soils and vegetables of the surrounding areas were carried out in May month, to estimate heavy metals contamination from soil to vegetables (TF). Sampling and laboratory analyses B. Collection and digestion of water samples At each site, both waste water and clean water samples collected randomly from different location. As soon as the samples were brought to the laboratory, they were acidified with HNO3 (Merck), filtered and stored in dark at ambient temperature (40C) before analysis. Both waste water and clean water samples were digested according to APHA, (2005); the irrigation water sample, 50 ml. was transferred into beaker and 10 ml. of concentrated nitric acid (HNO3) was added. The beaker with the content was placed on a hot plate and evaporated down to about 20 ml at 800C .The beaker was cool and another 5 ml. concentrated HNO3 was also added. The beaker was covered with watch class and returned to the hot plate. The heating was continued, and then small portion of HNO3 was added until the solution appeared transparent. The beaker wall and watch glass were washed with distilled water and the solution was filtered through whatman NO. 42 filter paper and the total volume were maintained to 50 mL with distilled water. C. Collection and digestion of soil samples Waste Water Irrigated soil samples were collected from the cultivated fields near the J.P. Cement Plant (Bela and Naubasta) along a distance of 100m from the Plants. Soil samples taken from each sites were separately labelled and transferred into air tight polythene bags and brought into laboratory. Before its transported to the research laboratory, care was taken, to the extent possible, to ensure that there were no other sources of contamination at the site of investigation such as motor vehicle emission, dumpsite garbage, sewage water, grey water, domestic waste, slurry, or compost to mask the effect of waste water irrigation. Soils were sieved through a 2 mm sieve to remove coarse particles and stored at ambient temperature prior to analysis. Soil samples were digested according to Allen et al., (1986). To 5g of each of the air dried and sieved soil samples was thoroughly grinded, 1.0g of each of the ground soil samples were placed in 100 ml beaker. 15 ml of HNO, H2SO4 and HCl mixture (5:1:1) of tri-acid were added and the content heated gently at low heat on hot plate for 2 hrs at 800C until a transparent solution was obtained. After cooling, the digested sample was filtered using whatman NO. 42 filter paper. It was then transferred to a 50 mL volumetric flask by adding distilled water. D. Collection and digestion of vegetable samples Vegetable samples were taken in the agricultural fields around the commune where they were known to be affected by waste water and where they were not (control). Samples of seven different kinds of vegetables; leafy vegetables included Table 1. Description of vegetable samples analyzed Common Name Spinach Designation Scientific Name Edible Parts SP Betavulgaris L. CV. Leaf Cabbage CA Leaf Cauliflower CF Lady’s Finger Brinjal LF Tomato TO Radish RA Brassica oleracea L. Var. Capatuta Brassica oleracea L. Var. botrytis Abelmoschus esculentus L. Solanum melongena L. Lycopersicon esculentum L. Raphanus sativus L. BR Inflorescence Fruit Fruit Fruit Root Spinach (SP) (Betavulgaris L. CV. All green), and Cabbage (CA) (Brassica oleracea L. Var. Capatuta). Inflorescence vegetable included Cauliflower (CF)(Brassica oleracea L. Var. botrytis), Fruit vegetables included Lady’s Finger (LF) (Abelmoschus esculentus L.), Brinjal (BR)(Solanum melongena L.), Tomato (TO) (Lycopersicon esculentum L.) and Root vegetable included Radish (RA) (Raphanus sativus L.) were taken from the same experimental sites where waters and soils samples were taken . The detailed of the vegetable samples collected from the experimental sites are given in Table 1. Vegetable sample were collected randomly by hand using vinyl gloves carefully packed into polyethylene bags and the whole plant body was brought to the laboratory from each experimental site in order to estimate heavy metals. Cleaning (soil removal) of vegetable plant samples was performed by shaking and also by means of a dry pre-cleaned vinyl brush. Only edible parts of different vegetables were randomly taken from each experimental site. Freshly collected mature vegetable samples from each experimental site were brought to the laboratory and washed primarily with running tap water, then in distilled water and finally rinsed carefully in demonized water to remove any attached dust pollen particles (Burton and Patterson, 1979). Vegetable samples were also digested according to Allen et al., (1986) as described above. E. Analysis of samples Concentrations of Fe, Zn, Cu, Pb, Cd, Mn and Cr in the filtrate of digested soil, water and different kind of vegetables samples were estimated by using an Atomic Absorption Spectrophotometer (AAS, Perkin Elmer analyst 400). The instrument was fitted with specific lamp of particular metal. The instrument was calibrated using manually prepared standard solution of respective heavy metals as well as drift blanks. Standard stock solutions of 1000 ppm for all the metals were obtained from Sisco Research Laboratories Pvt. Ltd., India. These solutions were diluted for desired concentrations to calibrate the instrument. Acetylene gas was used as the fuel and air as the support. An oxidising flame was used in all cases. 38 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 37-41 F. Quality Control Analysis Quality control measures were taken to assess contamination and reliability of data. For this Blank samples (zero metal concentration) were analyzed after seven samples. Concentrations were calculated on a dry weight basis. All analysis was replicated three times. The accuracy and precision of metal analysis were checked against NIST (National institute of standard and Technology)-SRM (Standard Reference Material) 1570 for every heavy metal. BIOCONCENTRATION CALCULATION A. Transfer Factor (TF) Metal concentrations in the extract of soils and vegetables were calculated on the basis of dry weight (mg/kg). TF was calculated as follows (Cui et al., 2004): Where, C (Vegetable) represent the heavy metal concentration (mg/kg) in extract of edible parts of vegetables & C (Soil) represent the heavy metal concentration (mg/kg) in soils from where the vegetable was grown. B. Statistical analysis Statistical analysis of data was done by SPSS 17. For water, soil, vegetable and site, two-way ANOVA was used. Pearson’s Correlations between the vegetable and the soil were also worked out. Statistical significance of means was computed using Pair Samples t-test, with a significance level of P < 0.001 (Steel and Torrie, 1980). while lowest was in Cd in Cauliflower (0.015) at CWI-Bhiti village. The higher value of TF suggests poor retention of metals in soil and/or more translocation in vegetables. Because metal with high TF are easily transferred from soil to the edible parts of vegetables than ones with low TF. The higher uptake of heavy metals in leafy vegetables may be due to higher transpiration rate to maintain the growth and moisture content of these plants (Gildon and Tinker (1981). The present result agrees with the investigation made by (Zhuang et al. 2009) in the food crops in the vicinity of Dabaoshan mine, South China where the Bioaccumulation factors for heavy metals were significantly higher for leafy than non-leafy vegetables. Similarly high transfer factor value for Cu in Spinach from WWI site of Beijing, China, reported by YongGuan et al., (2004). Due to the high conc. of exchangeable Cu in vegetable soils, the Cu in edible parts of Spinach probably came from the root uptake from soils. The lowest values of the Cu in Cauliflower may be the absence of Cd concentration in soil of CWI-site. Thus a major pathway for Cd to enter the above- ground edible parts of Cauliflower, from vegetable soils, may be through application of fertilisers by farmers. III. RESULTS AND DISCUSSIONS A. Level of heavy metals in water, soil & vegetables The present study had generated data on heavy metals (Fe, Zn, Cu, Pb, Cd, Mn and Cr) in water, soil and different kind of vegetables (edible parts) from waste water irrigated sites of Rewa, India and associated risk assessment for consumer’s exposure to heavy metals. Pb, Cd, Mn and Cr concentration in waste waters; Cd concentration in waste water irrigated soils and Pb, Cd and Cr concentration in all tested vegetables (from WWI sites) were above the national and international permissible limits. These accumulated heavy metals from Waste Water Irrigated area of Rewa (J.P.Cement Plant of Bela &Naubasta) had affected soil and water for a long time. People living in the contaminated area are at greater risk for health issues than individuals in the reference area. Children are at somewhat higher risk than adults. Heavy metal concentrations were several fold higher in all the collected samples from waste water irrigated sites compared to clean water irrigated ones. B. Transfer Factor of heavy metals from soil to vegetables In all sites of WWI &, CWI, TF of the heavy metals from soil to vegetables are presented in Fig 1, 2 & 3. These factors were based on roots uptake of the metals and discount the foliar absorption of atmospheric metal deposits (Lokeshwari and chandrappa 2006; Awode et al., 2008). The TF values between waste and clean water irrigated soils were not significantly different. The values of TF obtained from each sites were below (1). The TF values of Fe, Zn, Cu, Pb, Cd, Mn and Cr for various vegetables varied greatly between plant species and location. From the results, the highest TF value was observed for Cu in Spinach (0.634) at WWI-Bela site Fig.1. Transfer Factor of heavy metals for vegetables of WWI site of Bela Fig.2. Transfer Factor of heavy metals for vegetables of WWI site of Naubasta Fig.2. Transfer Factor of heavy metals for vegetables of CWI site of Bhiti 39 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 37-41 C. Pearson’s Correlation Coefficient for Transfer Factor The Pearson’s correlation coefficient of heavy metals in soils and different kind of vegetables are summarised in table 2. VE G TFFe TFZn SP -0.389 CA -0.502** TFCu TFPb TFCd TFMn TFCr FOR WWI SITE OF BELA 0.395* -0.130 * -0.499 * LF -0.217 * TO -0.225* RA -0.358 * SP -0.382* CF BR -0.011 NS -0.643 ** -0.577 ** -0.208 * -0.211 -0.711** 0.837** -0.502** 0.972** 0.370* 0.204* 0.947** -0.226 * 0.942** -0.005 NS * 0.362 * * -0.144 -0.412* -0.070 NS * 0.409 * -0.190 -0.0522 NS * -0.232* 0.182 * -0.021 * -0.207 -1.35 -0.001 -0.570** NS 0.010 NS NS 0.002 NS -0.009 NS 0.850** -0.660 ** 0.842** -0.498 * 0.590** 0.758** 0.883** 0.334 * -0.996** FOR WWI SITE OF NAUBASTA -0.710 ** CF -0.529 ** BR CA -0.447* 0.999** * ** -0.201 ** 0.946 * -0.988** * -0.246* ** 0.332 1.00 ** ** 0.996** 0.315* 0.936 ** -0.760** ** 0.858** 0.535 0.285 -0.511 -0.974 0.972 -0.437* -0.971** 0.971** -0.837** -0.710** 0.789** 0.542** LF -0.326* 0.992** 0.169* -0.989** 0.946** -0.061 NS 0.356* TO ** RA 0.793 -0.094 ** -0. 689 -0.979 ** * -0. 214 ** -0.572 ** -0. 888 ** -0.954 ** 0. 683 ** 0.893 -0. 991 -0.239 ** * -0.751** -0.629** study area had received minimum rainfall in the recent years. This may also have contributed to the higher concentration of metals in the soil. It may be expected that during the summer season the relatively high decomposition rate of organic matter is likely to release have metals in soil solution for possible uptake by vegetables. Soil to vegetable transfer is one of the key components of human exposure to metals through food chain. In this study, the soil to vegetable transfer factor (TF) for various heavy metals and for most common vegetables consumed by human being were calculated and data showed that the TF values differed significantly between soil and vegetable concentrations the difference in TF values among different vegetables may be attributed to differences in element uptake by different vegetables.**Present studies on uptake of heavy metals through vegetables and the correlation between the heavy metals content in soil and vegetables are necessary to further understand the problem and to plan remedial measures with public participation. ACKNOWLEDGMENT The author would like to place on record their sincere thanks to prof. U.K.Chuahan (Prof. & Head) Dept. of Environmental Biology, A.P.S. University Rewa (M.P.), for so much advice & guidance to complete this research. NS FOR CWI SITE OF BHITI REFERENCES SP 0.683** -0.912** -0.939** -0.210* -0.818** 0.971** 0.539** CA 0.479* 0.845** -0.569** -0.236* -0.972** -0.318* 0.421* CF -1.48 ** ** ** BR 0.970** LF -0.169 * TO -0.277 * RA 0.429* -0.963 ** -0.421* * ** 0.980 0.738** * 0.437 -0.371 ** ** * 0.451 0.516** 0.016 NS ** -0.689 0.656 0.168* -0.044 NS 0.046 NS -0.139* * 0.283 * ** 0.721 ** -0. 999 0. 923 0. 844 0. 283 0. 989 0.956** 0.449* -0.065 -0.689** 0.257* 0.377* -0.986** -0.884** NS Computation of Pearson’s correlation coefficient of heavy metals between soils and vegetables showed that for some vegetables; there were positive but not significantly correlation found while for other vegetables it was positively and significantly correlated. Positive correlation suggested that the metal in different kind of vegetables were translocated efficiently from the soil through root system (Agbenin et al., 2009). However most vegetables showed negatively and significantly while other showed negative but not significantly correlation (Table 2). Negative correlation indicated that higher concentration of heavy metals present in soils but in comparison much lower concentration were found to be in vegetables of that soils. This was due to poor retention capabilities of different edible parts of vegetables. TF values decreases with increasing respective metal concentration in soils, indicating an inverse relationship between transfer factor and metal concentration such inverse relationship were also reported by Wang et al., (2006). IV. CONCLUSIONS/RECOMMENDATION This study indicated that long term and indiscriminate application of waste water or letting of waste water directly to agricultural field without prior treatment which contain heavy metals in association with sludge particles may cause accumulation of toxic metals in surface and sub surface soils. And build up of heavy metals in soil profile may prove not only to plants and animals but also to consumers of harvested crops and vegetables. The vegetable samples were taken in the month of May when the temperature was high and also, the [1] Agbenin, J.O., Danko, M. and Welp, G. 2009. Soil and vegetable compositional translocation relationships of eight potentially toxic metals in urban agricultural fields from northern Nigeria. J. Sci. Food Agri. 89(1): 49-54. [2] Allen, S.E., Grimshaw, H.M., Rowland, A.P., 1986. Chemical analysis. In: Moore, P.D., Chapman, S.B. (Eds.), Methods in Plant Ecology. Blackwell, Scientific Publication, Oxford, London, pp. 285–344. [3] APHA (American Public Health Association) (1985). Standard methods for the examination of water and wastewater. Washington DC: American Public Health Association. [4] Cai S, Yue L, Hu ZN, Zhong XZ, Ye ZL, Xu HD, et al. Cadmium exposure and health effects among residents in an irrigation area with ore dressing waste water. 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Integrated Risk Information System- database, US Envrion. Protec. Agency. [11] Khan, S., Cao, Q. Y. Z., Huang, Y. Z., & Zhu, Y. G. (2008). Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. Environmental Pollution, 125, 686–692. doi:10.1016/j.envpol.2007.06.056 PMID:17720286. [12] Pandey, J., & Pandey, U. (2009). Accumulation of heavy metals in dietary vegetables and cultivated soil horizon in organic farming system in relation to atmospheric deposition in a seasonally dry tropical region of India. Environmental 40 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 37-41 [13] [14] [15] [16] Monitoring and Assessment, 148(1), 61–74. doi:10.1007/s10661- 007-0139-8 PMID:18202901. Pandey, J., Pandey, R., & Shubhashish, K. (2009). Air-borne heavy metal contamination to dietary vegetables: A case study from India. Bulletin of Environmental Contamination and Toxicology, 83, 931–936. doi:10.1007/s00128-009-9879-1 PMID:19771380 Rattan, R.K., Datta, S.P., Chhonkar, P.K., Suribabu, K., Singh, A.K., 2005. Long-term impact of irrigation with sewage effluents on heavy metal content in soils, crops and groundwater-a case study. Agriculture. Ecosystem and Environment 109, 310e322. Salt, D.E , Blaylock M, Kumar P.B.A.N, Dushenkov S, Ensley B.D, Chet I and Raskin I (1995): Phytoremediation. A novel strategy for the removal of toxic metals from the environment using plants. Biol. Techol. 13: 468 – 474. Sharma, R. K., Agrawal, M., & Marshall, F. (2007). Heavy metal contamination of soil and vegetables in suburban areas of [17] [18] [19] [20] Varanasi, India. Ecotoxicology and Environmental Safety, 66, 258–266. doi:10.1016/ j.ecoenv.2005.11.007. Sponza D and Karaoglu N. 2002. Environmental geochemistry and pollution studies of Aliaga metal industry district. Environ Internat 27:541–53 Steel, R. G. D. and Torrie, J. H. (1980), Principles and Procedures of Statistics, Second Edition, New York: McGrawHill. Zhuang P, McBride MB, Xia H, Li N, Li Z (2009) Health risk from heavy metals via consumption of food crops in the vicinity of Dabaoshan mine, South China. Sci Total Environ 407:1551–1561 Wang, G., Su, M.Y., Chen, Y.H., Lin, F.F., Luo, D., Gao, S.F., 2006. Transfer characteristics of cadmium and lead from soil to the edible parts of six vegetable species in southeastern China. Environmental Pollution 144, 127e135. 41 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 42-45 STRESS AND COPING STYLE OF URBAN AND RURAL ADOLESCENTS Samata Srivastava1, Dr. J. P. Singh2, Dr. Om Prakash Srivastava1 1 Department of psychology, P. G College Gazipur Uttar Pradesh (India) 2 Department of Psychology, Rashtriya P.G. College, Jamuhai (Jaunpur)
[email protected] Abstract— This study aimed to assess the nature of stress, and coping styles among rural and urban adolescents. Methods: 200 students in 10+2 and graduation first year of both genders in the age range of 16-19 years were assessed with the Adolescent Stress Scale, and a self-report coping scale. Results: The Result of present study reveals that in both environmental settings male reported more stress than their counterparts girls, however, to utilize coping strategies female adolescents are in higher in number than male adolescents. Conclusions: It is important for research to examine how adolescents suffering from typical stressors such as school examination, family conflict and poor peer relations. Social support is likely one of the most important resources in their coping process. Key words— Adolescents; Stress; Coping; Environmental setting. I. INTRODUCTION Adolescence is conceptualized as a transitional period, which begins with the onset of puberty and ends with the acceptance of adult roles and responsibilities. Of all life-stages, except childhood, adolescence is the one most marked by rapid and potentially tumultuous transition (Williams, Holmbeck, & Greenly, 2002). This is to be seen in the domain of biological development where the changes are physically externally manifest as well as in the progression of both cognitive and psychosocial maturity from that of childhood to that of the fully functioning adult (Byrne, Davenport, & Mazanov, 2007). While the transition through adolescence is inevitable the speed and magnitude of these changes overtax the capacity of many young people to cope and the resulting phenomenon of adolescent stress is now well recognized (Byrne, et al., 2007). The adolescent period involves a number of different intensities biological, cognitive, and psychosocial changes (Susman & Dorn, 2009). The biological changes involve physical changes in an Individual’s body with extraordinary growth and change in physical appearance and biological functioning. The pubertal changes also affect the adolescents psychologically, in different ways, and with and timing. The cognitive processes are one of the most striking changes to take place during adolescence and involve the development of far more sophisticated thinking abilities and reasoning ability. The rapid development of psychosocial processes during adolescence involve changes in emotions, personality, relationships with others, and social contexts (McElhaney, Allen, Stephenson, & Hare, 2009). A critical task of adolescence is the establishment of a stable sense of identity as a part of achieving autonomy. Adolescents must learn to deal with an expanding social universe and must develop the social skills to find friendship, romance, employment, and social standing within multiple social spheres (Cote, 2009). Adolescents must therefore develop a range of mechanisms, which allow them to function effectively in the face of the stress, which comes about from the transition of adolescence (Byrne et al., 2007). A. Stress and adolescence period Stress has traditionally been conceptualized in three ways; as a stimulus (an event or accumulation of events); as a response (a psychophysiological reaction); or as a transactional process, in which a person and the environment interact to produce an appraisal of threat or loss (Caltabiano, Sarafino, & Byrne, 2008). “Stress” is used to describe the subjective experience of pressure, implying an evaluation of the outcome of a process. This is in line with the transactional view of stress as a relationship between environmental events or conditions, and the individual’s cognitive appraisals of the degree and type of challenge, threat, harm or loss (Lazarus & Folkman, 1984). The most widely accepted definition of stress is the transactional definition offered by Lazarus and Folkman (1984): “Psychological stress involves a particular relationship between the person and the environment that is appraised by the person as taxing or exceeding his or her resources and endangering his or her well-being” (p. 19). According to this definition, stress is subjective by nature, since it involves an appraisal of individual experiences. Many adolescents today experience numerous potential stressors throughout the process of growth and Development (Compas & Reeslund, 2009). Stressors of both an acute and chronic nature are important in the course of normal as well as disrupted development during adolescence. The types of stressors experienced in adolescence can broadly be divided into three categories. These categories are normative events, non-normative events and daily hassles (Suldo, Shaunessy, & Hardesty, 2008). Normative events refer to events that are experienced by most adolescents, but usually within a relatively predictable timescale. Examples of these includes internal and external changes related to pubertal development, psychosocial changes related to school, family, peers and academically demands. One important aspect here is that these are events, which all young people have to confront, but usually within a relatively predictable timescale (Coleman & Hendry, 1999; Suldo et al., 2008). Non-normative events are different in the way that they are events affecting one adolescent or only a smaller group of adolescents, and can occur at less predictable points in the life course (Grant et al., 2003). Such events can include for example, divorce, illness, injury or natural disasters. The last category is daily hassles. Daily hassles differ from major events in life that they are defined as minor, irritating, and frustrating events that are typical of daily interactions between individuals and their environments. B. Coping style and adolescence period Coping has been defined as the constantly changing cognitive and behavioral effect to manage specific external and /or internal demand that has been evaluated as taking up or exceeding the resources of the person (Lazarus & folkman, 42 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 42-45 Materials: - In the present investigation, two tools have been 1984). Research recognizes two functions of coping: regulating used to measure two dependent variable. The detailed stressful emotions and altering the person-environment relation description of these has given below. causing the distress (Folkman, Lazarus, Dunket-schetter, Delongis & Gruen, 1986). I.Psychological stress scale (Prof. A. K. Srivastava)The questionnaire was designed to assess the extended of Coping is thus expending conscious effort to solve personal individual’s feelings of basic components of psychological and interpersonal problems and seeking to master, minimize or stress (such as pressure tension anxiety, conflict, frustration, tolerate the stress of conflict. Psychological coping etc.) resulted from perceived stress situations (such as mechanisms are commonly termed coping strategies or coping adversities, hardships, threats, affliction, failures, constraints skill. The tern coping generally refers to adaptive or excessive demands, conflicting roles etc.) in various spheres of constructive coping strategies, i.e., the strategies reduce stress his social life. The Questionnaire altogether consisted of 40 levels. However; some coping strategies can be considered items representing following seven categories of the social maladaptive, i.e. stress levels increase. Coping response are situation of stress. partly controlled by personality trait, but also by the social contexts of person, particularly the nature of the stressful S.No. Psycho social stressors No. of environment. Coping is also an important mediator of items experience that shapes personality development and influences 1 Tense or strained interpersonal 5 adaptability and resilience in difficult situations (Garmezy, relationship 1987). Conceptualization of children’s/adolescent’s coping was 2 Economic constraints ; Extra 8 derived from the adult coping work. However, growing economic burden evidence indicates that coping abilities of children/adolescents 3 Excessive/ demanding responsibilities 5 may differ from those of adults in some very important ways and Liabilities and expectations of (Arnold, 1990; Compas, Banez, Malcarne, & Worsham, 1991; others Elias, Gara, & Ubriaco, 1985; Omizo, Omizo, & Suzuki, 1988). 4 Marriage related problem (of own or/ 4 Adolescents/Children may be limited in their coping repertoire and of family members) by cognitive, affective, expressive, or social facets of 5 Health related problems (of own or/ 3 development and by lack of experience. The adolescent’s and family member or near relations environments are quite different from adults’ environments, 6 Social situation; legal or property 10 particularly because children have less control over related disputes or problems. circumstances. Adolescents/Children are limited by realistic 7 Perceived or imagined threats to social 5 constraints, such as restricted freedom to actively avoid and economic status or prestige stressors (though restricted freedom also limits their exposure to some stressors), and a state of personal and financial II.Coping Strategies scale (Prof. A.k. Srivastava)dependence on parents. Thus, aspects of development and the The present measure of coping strategies comprises so items, to environment may limit the coping responses, adolescents are be rated on five point scale, 0 to 4 describing varieties of capable of making, and the coping strategies promoting coping behavior underlying following five major categories of adjustment in adolescents may differ from those promoting coping strategies based on the combinations of ‘operation’ and adjustment in adults. Teenagers are also at the stage of ‘Orientation’ of Coping. developing their personal styles of coping. The coping ACTIVE / APPROACH strategies can be reviewed, modified if needed and crystallized COPING (Problem- Focused from one experience of using certain mechanisms of coping coping) with another, during adolescent years. Miller and Kirsch (1987) they found that many studies report differences in how women Behavioral Approach Coping Strategies and men cope with stress, with men tending to deal with stress by problem-focused coping, while women tend to use strategies Cognitive Approach Coping Strategies that modify their emotional response, although these tendencies Cognitive Behavioral coping can change in certain circumstances. Several authors (i.e., Strategies Almeida & Kessler, 1998; Barnett et al., 1987) have suggested AVOIDANCE COPING that the impact of gender on the stress process could be (Emotion Focused coping) conditioned by traditional socialization patterns. The traditional female gender role prescribes dependence, affiliation, Behavioral Avoidance coping Strategies emotional expressiveness, a lack of assertiveness, and the subordination of one’s own needs to those of others. On the Cognitive Avoidance coping strategies other hand, the traditional male role prescribes attributes such as autonomy, self-confidence, assertiveness, instrumentality Procedure: For purpose of the study two groups of subjects and being goal-oriented. . As a summary, we can conclude that were undertaken, one belonging to extreme rural and the other there are some gender differences as well as similarities in from extreme urban environment. To meet the requirement, it adolescents' coping. was decided to adopt schools situated in two entirely different II. METHOD environments giving education to two category of students, one Sample: - The sample consisted of 200 adolescents from rural belonging to very-very poor family background and were less and urban population residing in the eastern district of U.P 100 privileged from the view point of socio-economic status, while adolescents were from rural background (50 male and 50 the other school chosen were from well-developed? female) and 100 were from urban backgrounds (50 male and 50 environment where the student from one of the richest families female). The age ranges of the subjects were 16 to 19 years. were taking education. After selection of the institutions 43 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 42-45 researcher contacted to the principle of all institutions for *p <.05 permission to collect the data from their institution. After **p<.01 seeking permission from principle of respective institution main procedure has started, in the process all the participants Environmental setting Gender Mean SD were contacted personally and provided a consolidated Rural Male 55.92 20.57 questionnaire have psychological stress scale and coping Female 80.95 20.93 strategies scale. The data were collected individually. The Urban Male 72.12 27.47 subjects were interviewed to make the observations more Female 90.99 20.39 precise and accurate. The filled questionnaires were reTable.2.3.Mean and S.D of coping strategies as a function of examined and the scoring was done as per manual instructions environmental setting and gender. for each questionnaire. Scored data were analyzed by using statistical package for the social sciences (SPSS) version 20.0 Source Sum of Df Mean F for windows. square square III. RESULT The results of the present study are presented in two sections. The statistical procedure used is descriptive and twoway analysis of variance. The first section reports the mean value and S.D. Of dependent variables as a function of environmental setting (Rural and urban) and Gender, the second section report the main effect and interaction effect of the environment setting (urban and rural) gender. Environmental 17213.440 1 17213.440 33.879** setting Gender 48180.250 1 48180.250 94.827** Environmental 948.640 1 948.640 1.867 setting*Gender Table.2.4.summary of two-way Anova for the score of coping strategies. *p <.05 **p<.01 Section-1 Descriptive statistics for stress and coping strategies in rural and urban subjects. Dependent Variable Rural Urban Mean SD Mean SD Result of present study reveals that in both environmental settings male reported more stress than their counterparts girls, however utilization of coping strategies in numbers are higher in female adolescents than male. Stress 51.62 22.25 73.97 29.98 Coping strategies 76.17 32.97 81.80 24.70 Table-1.1 Mean and S.D. of stress and coping strategies in rural and urban setting. Dependent Variable Male Female Stress Mean 76.81 SD 29.00 Mean 48.78 SD 20.22 Coping Strategies 61.27 26.20 96.70 19.89 Table-1.2 Mean and S.D. of stress and coping strategies in Male and female adolescents. Section-2 Two-way analysis of variance between stress and coping as function of environmental setting (Urban and Rural) and Gender. Environmental setting Gender Mean SD Rural Male 57.60 25.69 Female 45.64 16.22 Urban Male 96.02 16.90 Female 51.93 23.21 Table-2.1 Mean and S.D of stress as a function of environmental setting and Gender. Source Sum of square 49974.603 Df Mean square 49974.603 F Environmental 1 114.332** setting Gender 78540.063 1 78540.063 179.685** Environmental 25808.423 1 25808.423 59.045** setting*Gender Table.2.2. summary of two-way Anova for the score of stress. IV. DISCUSSION The objective of the present investigation is explored rural and urban differences in the level of, and relation between stress and coping in adolescents. The findings indicated that male reported more stress than females in both settings (rural-urban) This result was similar with the result of Vijayalakshmi and Lavanya (2006), Kumar and Jejurkar (2005), Carlson and Grant (2008), Pastey and Aminbhavi (2006) which indicated that boys tend to have significantly higher stress. Next finding of present study reveals that girls are more likely to utilize coping strategies their counterparts boys. Our finding is consistent with Barusch and Spaid’s (1989) research which revealed that women caregivers tend to use a greater variety of coping styles overall than men. Other findings of the present investigation, explore that urban adolescents reported more stress than their counterparts’ rural adolescents. The results were similar to the results of Vijayalakshmi and Lavanya (2006) which revealed that urban students experienced more stress as compared to rural students, but contrary to the results of Elgar et al. (2003). In the context of coping strategies, urban adolescents use more coping strategies than rural adolescents. Reason behind this is may be the urban adolescents have many options to solve the problem or cope with stress, but rural adolescents have little amount of option to cope with stress. Rural adolescents are deprived in many aspects of their lives e.g. They have not enough money to buy things in comparison to urban adolescents.in rural India adolescents affected by lots of environmental problems such as lack of electricity, lack of drinking water, lack of healthy academic atmosphere and so many things, these are the factors that directly or indirectly affects the personality of the adolescents. 44 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 42-45 REFERENCES [1] Almeida, D. M., & Kessler, R. C. Everyday stressors and gender differences in daily distress. Journal of Personality and Social Psychology, 1998, 75, 670–680. [2] Arnold, L. E. (Ed.). Childhood stw.s.s New York: John Wiley & Sons, 1990. pp. 192-264. [3] Barnett, R. C., Biener, L., & Baruch, G. K. Gender & stress. New York: The Free Press, 1987. [4] Byrne, D.G., Davenport, S.C., & Mazanov, J. Profiles of adolescent stress: The development of the adolescent stress questionnaire (ASQ). Journal of Adolescence,2007, 30, 393-416. [5] Caltabiano, M.L., Sarafino, E.P., & Byrne, D. Health Psychology (2nd ed). Australia: John Wiley & Sons Ltd,2008. [6] Carlson, G.A. and Grant, K.E. The roles of stress and coping in explaining gender differences in risk for psychopathology among African American urban adolescents. The J. Early Adol.,2008, 28(3): 375-404. [7] Coleman, J.C., Hendry, L.B. The Nature of Adolescence. (3rd ed). London: Routledge. Carter, J.S., Garber, J., Ciesla, J.A., & Cole, D.A. Modelling relations between hassles and internalizing and externalizing symptoms in adolescents: A fouryear prospective study. Journal of Abnormal Psychology, 1999,115, 428-442. [8] Compas, B. E., Banez, G. A., Malcarne, V., & Worsham, N. Perceived control and coping with stress: A developmental perspective. Journal of Social Issues, 1991, 47, 23-34. [9] Compas, B.E., & Reeslund, K.L. Processes of risk and resilience during adolescence. In: R.M. Lerner & L. Steinberg (Eds.) Handbook of adolescent psychology, 3rd ed, New Jersey: John Wiley and Sons, Inc, 2009, pp. 561-588. [10] Cote, J.E. Identity formation and self-development in adolescence. In: R.M. Lerner & L. Steinberg (Eds.) Handbook of adolescent psychology ,3rd ed, New Jersey: John Wiley and Sons, Inc,2009, pp.266-304. [11] Elgar, F.J., Arlett, C., & Groves, R . Stress, coping, and behavioural problems among rural and urban adolescents. Journal of Adolescence,2003, 26, 574-585. [12] Elias, M. J.. Gara, M., & Ubriaco, M. Sources of stress and support in children’s transition to middle school: An empirical analysis. Journal of CZinical Child Psychology,1985,14, 112-l 18. [13] Folkman, S., Lazarus, R. S., Dunkel-Schetter, C., Delongis, A., & Gruen, R. J. Dynamics of a stressful encounter: Cognitive appraisal, coping, and encounter outcomes. Journal of Personality and Social Psychology, 1986, 50, 992–1003. [14] Grant, K.E., Compas, B.E., Stuhlmacher, A., Thurm, A.E., McMahon, S., & Halpert, J.Stressors and child/ adolescent psychopathology: Moving from markers to mechanisms of risk. Psychological Bulletin, 2003,129, 447-466. [15] Gwmezy, N. Stress, competence; and development: (continuities in the study of schizophrenic adults, children vulnerable to psychopathology, and the search for stress-resistant children. Orthopsychology,1987, 57.159-174. [16] Kumar, S. and Jejukar, K. Study of stress level in occupational therapy students during their academic curriculum. The Indian J. Occup. Therapy, 2005, 3,1,11-14. [17] Lazarus, R.S., & Folkman, S. Stress, appraisal and coping. New York: Springer Publication, 1984. [18] McElhaney, K.B., Allen, J.P., Stephenson, J.C., & Hare, A.L. Attachment and autonomy during adolescence. In: R.M. Lerner & L. Steinberg (Eds). Handbook of adolescent psychology, 3rd ed, New Jersey: John Wiley & Sons, Inc., 2009,pp.358-403. [19] Miller, S. M., & Kirsch, N. Sex differences in cognitive coping with stress. In R. C. Barnett, L. Biener, & G. K. Baruch (Eds.), Gender & Stress New York: The Free Press,1987,pp. 278–307. [20] Omizo, M. M., Omizo, S. A., 8c Suzuki, I.. A. Children and stress: An exploratory study of stressors and symptoms. The School Counselor; March,1988, 267-274. [21] Pastey, G.S. and Aminbhavi, V.A. Impact of emotional maturity on stress and self-confidence of adolescents. J. Indian Acad. Appl. Psychol.,2006, 32(1): 66-70. [22] Suldo, S.M., Shaunessy, E., & Hardesty, R. Relationships among stress, coping and mental health in high-achieving high school students. Psychology in the Schools, 2008, 45, 273-290. [23] Susman, E.J., & Dorn, L.D. Puberty: Its role in development. In: R.M. Lerner & L. Steinberg (Eds). Handbook of adolescent psychology,3rd ed, New Jersey: John Wiley & Sons, Inc,2009, 116-151. [24] Vijaylakshmi, G. and Lavanya, P. Relationship between stress and mathematic achievement among intermediate students. Edutracks, 2006.7,7, 34-37. [25] Williams, P.G., Holmbeck, G.N., & Greenley, R.N. Adolescent health psychology. Journal of Consulting and Clinical Psychology,2002, 70, 828-842. 45 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 46-51 THE EFFECT OF SPERM PARAMETERS AND BOTH MATERNAL AND PATERNAL AGE ON OUTCOME OF INTRACYTOPLASMIC SPERM INJECTION Milat Ismail Haje1, Christopher Barrett2, Kameel M Naoom3 1 Postgraduate student in College of Medicine, Hawler Medical University, Erbil/Iraq 2 Professor in Embryology, Dundee University/U.K 3 Professor in Embryology, College of medicine, Hawler Medical University, Erbil/Iraq
[email protected] Abstract— The purpose of this study was to investigate any influence of maternal and/or paternal age, three sperm parameters (sperm count/ml, motility and morphology) on pregnancy outcomes in intracytoplasmic sperm injection (ICSI) cycles. In all, 785 ICSI cases were analyzed retrospectively. Pregnancy outcome were influenced by the age of the maternal , paternal partners and sperm count x10⁶. The clinical pregnancy rate with respect to the age of female partner and male partner was revealed a significant inverse correlation between them with (P = <0.001) for each partner. The relationship between clinical pregnancy rate and sperm count x10⁶/ml was revealed a significant difference between the groups (P= 0.046). On the other hand no basic semen parameters (motility and normal morphology) influence on ICSI pregnancy outcome was found in the subgroup of patients. We conclude that the influence on pregnancy outcome after ICSI is related mostly to maternal and paternal age. Keywords: Intracytoplasmic sperm injection, maternal age, paternal age, semen parameters, pregnancy outcome. I. INTRODUCTION The outcome of assisted reproductive technology (ART) procedures is known to be influenced by multiple factors, including the etiology of infertility, patient age, semen parameter quality, the type of ovarian stimulation, and the level of follicular phase estradiol (E2). The subsequent number and quality of oocytes and the number of embryo transferred are affected by the different regimens of ovarian stimulation. The negative impacts of advancing female age are well known. A classic study of the Hutterites observed a rise in sterility first noted at 35 years of age, with a sharp increase after the age of 39 years, reaching an almost complete inability after the age of 44 years (Tietze, 1957), the same trend has been noted in women undergoing ART. In women aged >35 years, success rates after ART start to decline. By the age of 40 years a marked decline is noted (Navot et al, 1991and AlShawaf et al 1992). On the other hand, male reproductive function does not cease abruptly as in women, but become fundamentally changed with age (Sartorelli et al, 2001). Some investigators have associated a decline in pregnancy rates after ART with advancing paternal age in couples in which the women is younger (Klonoff-Cohen and Natarajan, 2004), on the other hand, Spandorfer et al( 1998) suggested that the pregnancy is not affected by male age. Sperm morphology is consistently the most significant parameter that relates to fertilization. In this context Coetzee and colleagues (1998) have performed a metaanalysis of the data that confirmed the importance of sperm morphology for IVF success. Similar results were found by Liu et al 1994 and Nikolettos et al 1999. Other studies revealed little or no relationship between semen quality and fertilization with ICSI ((Nagy et al, 1995; Mercan et al, 1998). However, in extreme cases of defective spermatozoa fertilization and pregnancy rate reduced (Nagy et al, 1995). Chen et al (2009) conceded that the percentage of normal forms was no different in pregnant and non-pregnant groups. Mercan et al, 1998 and Karpuz et al, 2007 found that there was no significant difference for the ICSI outcome indicating pregnancy rate. In this study the main objectives were therefore to analyze the pregnancy rates retrospectively in 785 cycles, in relation to the age of both partners and to the three conventional sperm parameters (sperm count/ml, motility and morphology). II. MATERIAL AND METHODS: This study was performed retrospectively in Fertility and IVF center in Maternity Teaching Hospital in Erbil City-Iraq between (January 2011-December 2012). Out of 1055 infertile couples 785 underwent ICSI cycles. The average age of infertile men was (33.1±7.3) years. Seminal fluid analysis was done for each patient; analyses were done in the Andrology room according to WHO criteria (1992). Before sample collection patient were informed about the relevance of abstinence time (3-6) days and about importance of collecting the complete ejaculate and not using any soap during collection. A. Ovarian stimulation, oocyte collection, and oocyte preparation for microinjection: Controlled ovarian stimulation was done using downregulation with gonadotropin-releasing hormone GnRH (Zoladex or decapeptyle 0.1mg) ) agonist protocol with urinary FSH (Gonal F) or recombinant (Merional) or GnRH antagonist protocol with urinary or recombinant FSH. When at least two follicle reached a mean diameter of 18 mm, using transvaginal ultrasonography, 1000 IU HCG (Ovitrelle, pregnyl) was administered and oocyte retrieval was done by vaginal ultrasound- guided puncture of ovarian follicles, 36 hour after HCG administration. At the end of oocyte retrieval, the cumulus-corona cell complexes were removed enzymatically; immediately before micromanipulation; by incubating the oocyte in HEPES- buffered intracytoplasmic 46 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 46-51 sperm injection (ICSI) medium(Hyalourindase IVC) for up to twice in HTF-IVC, then incubated at 37°c in a 50 µl drop of 2 min, enzymatic removal was enhanced mechanically by G1.2 medium under mineral oil in a CO² incubator (Galaxy aspirating the oocyte in and out of hand-drawn the pipettes. CO² ). The denuded oocytes were examined to assess integrity and maturity. Inspected under the inverted microscope (olympus) E. Luteal phase support after Intra uterine insemination ( IUI) at 20 x magnification and classified as mature metaphase 2 and ICSI: (MII), mature metaphase 1 (MI) and immature prophase (GV). Luteal phase support was started with progesterone vaginal Only MII oocytes with extruded first polar body were pessaries (cyclogest 400 mg bd) on the day after retrieval and microinjected. was continued till 12wk of gestation if pregnancy positive. B. Semen preparation for ICSI: For ICSI semen preparations were performed according to WHO (1992) using freshly ejaculated spermatozoa. The semen specimens were collected by masturbation, Semen was diluted 1-2(volume/volume) with (HTF, HEPES, IVC). After centrifugation at 250 xg for 10 min, and the pellets were resuspended and combined in 3 ml of the medium. The sperm suspension was centrifuged for 10 min; the pellet was resuspended in 0.7 ml of the medium and placed in incubator for 30-50 min. in this study, approximately 10 ul was used to determine the concentration and motility of the sperm. The remaining sample was used for ICSI. C. Testicular sperm extraction (TESE): TESE was performed under general anesthesia. The scrotal skin and tunica vaginalis were opened, a 0.5 cm incision was made in the tunica albuginea and one or two pieces of the extruding testicular tissue were excised using a pair of curved scissors. The specimen was then transferred into a Petri dish filled with ~ 2 ml modified HEPES-buffered Earle’s medium and heparin 0.4% ( HTF-HEPES-IVC). D. Intracytoplasmic Sperm Injection (ICSI): The 3-5 µl sperm- polyvinylpyrrolidone (PVP-IVC) droplet was placed in the center of a Petri dish (Falcon type 1006) and was surrounded by eight 5µl droplets of HEPES-buffered IVC medium. These droplets were covered by ~ 3.5 ml of lightweight paraffin oil. The ICSI procedures were carried out on the CRI-UK micromanipulation system attached to inverted microscope Olympus Ixs1 (Olympus company). Injection of oocytes was performed in microdroplets of HEPES-buffered ICSI medium covered with lightweight paraffin oil (IVC). A single spermatozoon with apparently normal morphology was immobilized by cutting across its tail with the injection pipette. After securing the oocyte onto the holding pipette, with the polar body at the 6 or 12 o’clock position, the injection pipette was pushed through the zona pellucida and the oolemma into the ooplasm at the 3 o’clock position. When penetration of the oolemma was verified by aspirating some cytoplasm, the spermatozoon was slowly ejected. The injection pipette was withdrawn gently and the oocyte released from the holding pipette. After injection oocytes were washed F. Definition of pregnancy: Serum β-human chorionic gonadotropin was measured after 12 days after embryo transfer. After 10 days clinical pregnancy was indicated by doing transvaginal sonography for detection of fetal sac. G. Statistics: All statistics were performed using the Statistical Package for the Social Science (SPSS- version 19). Difference in the pregnancy rate between the groups was tested using the chi square test. III. RESULTS Out of 1055 infertile couples, 785 underwent ICSI cycles revealed an overall clinical pregnancy rate of 34.8%. Table 1 shows the evaluation of the clinical pregnancy rate with respect to the age of female partner. The results revealed a significant inverse correlation between them with (P = <0.001). The highest group was for women age <25 years, the clinical pregnancy rate ( PR) was 46.6%, and lowest rate was the group +40 years which was 12.3%. Evaluation of the clinical pregnancy rate with respect to the age of men partner revealed a significant inverse correlation between them with (P = <0.001), for men age <25 years the clinical PR was 41.2%, for the group 25-29 years were 56.6% ( Table 2). Semen parameters (count, motility and morphology) obtained on the day of the aspiration procedure was evaluated to determine if semen quality has an impact on ICSI outcome. The relationship between clinical pregnancy rate and sperm count x10⁶/ml is shown in Table 3. There was significant difference between the groups (P= 0.046), higher rate was with count x10⁶/ml (1.9-9) x10⁶ group which PR was 50%. The relationship between clinical pregnancy rate and sperm total motility % is illustrated in Table 4. There was no significant difference between the groups (P= 0.107). The relationship between clinical pregnancy rate and sperm normal morphology % is shown in Table 5. Also the results showed no significant difference between the groups (P= 0.185). 47 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 46-51 women age Pregnancy negative Pregnancy positive Total <25 39 34 73 53.4% 46.6% 100.0% 93 71 164 56.7% 43.3% 100.0% 122 72 194 62.9% 37.1% 100.0% 102 50 152 67.1% 32.9% 100.0% 93 13 106 87.7% 12.3% 100.0% 449 240 689 65.2% 34.8% 100.0% 25-29 30-34 35-39 +40 Total Pearson Chi-Square P< 0.001 Table (1): The correlation of clinical pregnancy rates after ICSI with respect to the women age Men age Pregnancy negative Pregnancy positive total < 25 10 7 17 58.8% 41.2% 100.0% 36 47 83 43.4% 56.6% 100.0% 86 54 140 61.4% 38.6% 100.0% 108 59 167 64.7% 35.3% 100.0% 159 53 212 75.0% 25.0% 100.0% 399 220 619 64.5% 35.5% 100% 25-29 30-34 35-39 40+ Total Pearson Chi-Square P< 0.001 Table (2): The correlation of clinical pregnancy rates after ICSI with respect to the men age 48 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 46-51 Sperm countx10⁶/ml Pregnancy negative Pregnancy positive Total <1 85 62 147 57.8% 42.2% 100.0% 17 17 34 50.0% 50.0% 100.0% 16 8 24 66.7% 33.3% 100.0% 85 35 120 70.8% 29.2% 100.0% 94 37 131 71.8% 28.2% 100.0% 108 55 163 66.3% 33.7% 100.0% 405 214 619 65.4% 34.6% 100.0% 1.9-9 10-14.9 15-39.9 40-69.9 +70 Total Pearson Chi-Square P=.0.46 Table (3): Sperm count x10⁶/ml according to pregnancy outcome following ICSI Total motility% Pregnancy negative Pregnancy positive Total <1 100 66 166 60.2% 39.8% 100.0% 3 5 8 37.5% 62.5% 100.0% 11 9 20 55.0% 45.0% 100.0% 43 21 64 67.2% 32.8% 100.0% 251 114 365 68.8% 31.2% 100.0% 408 215 623 65.5% 34.5% 100% 1-9.9 10-19.9 20-39.9 40-99.9 Total Pearson Chi-Square P=.01.7 Table (4): Sperm total motility percentage according to pregnancy outcome following ICSI 49 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 46-51 Normal morphology% Pregnancy negative Pregnancy positive Total <1 130 87 217 59.9% 40.1% 100.0% 29 14 43 67.4% 32.6% 100.0% 52 23 75 69.3% 30.7% 100.0% 197 90 287 68.6% 31.4% 100.0% 408 214 622 65.6% 34.4% 100% 2-3.9 4-9.9 10+ Total Chi-Square P=.0185 Table (5): Sperm normal morphologic percentage according to pregnancy outcome following ICSI IV. DISCUSSION As the study of Hutterites has demonstrated normal fertility is highly dependent on female ageing (Tietze, 1957). It is also well established that the outcome of couples treated by IVF is significantly influenced by advancing female age. It is therefore, a logical assumption that the ICSI would be similarly affected by female age. In current study, the rate of pregnancy after ICSI inversely with women age , and this correlation was significant with (P<0.001) (Table 1). This result is in consistent with results of (Devroey et al, 1996 and Spandorfer et al 1998). Some investigators have associated a decline in pregnancy rates with advancing paternal age in couples in which the women is younger (Klonoff- Cohen and Natarajan, 2004), Some studies have suggested a negative trend in fertility with advanced male age (De La Rochebrochard et al, 2006; Ferreira et al 2010; Tsai et al, 2013).). On the other hand, Gallardo et al.,1996 and Spandorfer et al., 1998 suggested that the age of male partner didn’t affect fertilization, embryo development or implantation. In this study there was a significant inverse correlation between pregnancy rate after ICSI and age of male partner with (P = <0.001). The combination of sperm morphology, progressive motility percent and sperm count has been demonstrated to be the best parameter to evaluate the fertility capacity of sperm in IVF (Lundin et al., 1997). In this study the effect of semen parameters on PR after ICSI were examined. The results showed that there was significant difference between the groups (P= 0.046), higher rate was with count x10⁶/ml (1.9-9) x10⁶ (Table 3). It means that pregnancy rate (PR) after ICSI was better when sperm count was low (Table 3). The relationship between clinical pregnancy rate and sperm total motility % are shown in Table (4). The results showed no significant difference between the groups (P= 0.107), higher rate was with sperm total motility % (1-9.9%) group. Moreover, in the current study, the relationship between clinical pregnancy rate and sperm normal morphology % is shown in Table (5). There was no significant difference between the groups (P= 0.185), higher rate was with sperm normal morphology %(<1%) group. It means that PR after ICSI didn’t depend on total sperm motility and normal morphology This result is in agreement with (Nagy, 1995; Karpuz et al., 2007) and Mansour et al, (1995) found no significant difference in the incidence of fertilization between patients with <5% normal forms and patient with >5% normal forms, . Chen et al (2009) conceded that the percentage of normal forms was no different in pregnant and non-pregnant groups. ICSI is also independent on sperm motility (Verheyen et al, 1999; Mercan et al, 1998) but fertilization rate was significantly higher in patients with more adequate sperm parameters. The only ultimate criterion for successful ICSI is the presence of at least one living spermatozoa per oocyte in the semen preparation used for microinjection. Nikolettos et al (1999) have concluded that the chance of a successful pregnancy is low with sever anomalies of the sperm head shape. So according to previous reports and this study in ICSI, fertilization may be achieved, even in the presence of a few motile sperm, because natural selection steps are skipped in the presence of abnormal sperms (Van Steirteghem et al., 1993). REFERENCES [1] Al-Shawaf T., Nolan A., Guirgis R. et al. (1992). The influence of ovarian response on gamete intra-fallopian transfer outcome in older women. Hum Reprod: 7; 1106-111o. [2] Chen X., Zhang W., Luo Y., Long X., and Sun X. (2009). 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Intracytoplasmic sperm injection does not require special treatment of the spermatozoa. Hum Reprod: 9; 1127-1130. [12] Lundin K., Soderlund B., and Hamberger L. (1997). The relationship between sperm morphology and rates of fertilization, pregnancy and spontaneous abortion in an in-vitro fertilization/intracytoplasmic sperm injection program. Hum Reprod: 12; 2676-2681. [13] Mansour RT., Aboulghar MA., Serour GI., Amin YM., Ramzi AM. (1995). The effect of sperm parameters on the outcome of intracytoplasmic sperm injection. Fertil Steril: 64; 982-986. [14] Mercan R., Oehninger SJ., Tober JP., Mayer J., and Lanzendorf SE. (1998). Impact of fertilization history and semen parameters on ICSI outcome. J Assisst Reprod Genet: 15; 39-45. [15] Nagy ZP., Liu J., Joris H., Verheyen G., Tournaye H., Camus M., Derde MC., Devroey P., and Van Steirteghem AC. (1995). The result of intracytoplasmic sperm injection is not related to any of the three basic sperm parameters. Hum Reprod: 10; 11231129. [16] Navot D., Bergh PA., Williams MA.et al (1991). Poor oocyte quality rather than implantation failure as a cause of related decline in female fertility. Lancet: 337; 1375-1377. [17] Nikolettos N., Kupker W., Demirel C., et al. (1999). Fertilization potential of spermatozoa with abnormal morphology. Hum Reprod: 14; 47-70. [18] Sartorelli EM., Mazzucatto LF., and de Pina-Neto JM. (2001). Effect of paternal age on human sperm chromosomes. Fertil Steril: 76;1119-1123. [19] Spandorfer SD., Avrech OM., Colombero LT., Palermo GD., and Rosenwaks Z. (1998).Effect of paternal age on fertilization and pregnancy characteristics in couples treated by intracytoplasmic sperm injection. Hum Reprod:13 (2); 334-338. [20] Tietze C. (1957). Reproductive span and rate of reproduction among Hutterite women.FertilSteril: 8; 89-97. [21] Tsai YR., Lan KC., Kung FT., Liin PY., Chaing HJ., Lin YJ., and Haung FJ. (2013). The effect of advanced paternal age on the outcomes of assisted reproductive techniques among patients with azoospermia using cryo preserved testicular spermatozoa. Taiwanese J of Obstet and Gynecol; 52: 351-355. [22] Verheyen G., Tournaye H., Staessen A., et al. (1999). Controlled comparison of conventional in-vitro fertilization and intracytoplasmic sperm injection in patients with asthenozoospermia. Hum Reprod: 14; 2313-2319. [23] World Health Organization WHO.(1992). WHO Laboratory Manual for the Examination of Human Semen and Semencervical Mucus Interaction. Cambridge, University Press, Cambridge. 51 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 BIO-REMEDIATION OF HEAVY METALS FROM DRINKING WATER BY THE HELP OF MICROORGANISMS WITH THE USE OF BIOREACTOR Arpit Srivastava1, 2, Dr. Pradeep Srivastava2, Ms. Rupika Sinha2, Sarada P. Mallick2 1 Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow (U.P.) India 226010 2 Technology Business Incubator, Malaviya Centre for Innovation, Incubation & Entrepreneurship Indian Institute of Technology Banaras Hindu University Varanasi (U.P.) – India 221005 Abstract— On Earth water has too many forms and variety which are necessary specifically for particular geographical as well as environmental surroundings. Below 1% of the world's fresh water (0.007% of all water on earth) is reachable for direct human uses. Water pollutions now become a part of concern and disquiet in country like India. Large parts of water which are life supportive get contaminated because of illegal activities of human beings. Water effluence is a major problem globally. It is the leading worldwide cause of deaths and diseases, and that it accounts for the deaths of more than 14,000 people daily. In addition to the acute problems of different problems in developing countries, industrialized countries continue to struggle with water pollution problems as well. There are many inorganic metals which are contaminating water bodies which serve life to large part of India, Arsenic (As) is one of the biggest threats for water bodies. High toxicity of Arsenic poses a serious risk not only to ecological systems but also for human health. There is availability of sophisticated techniques for arsenic removal from contaminated water, development of new laboratory based techniques along with cost reduction and enhancement of conventional techniques are essential for the benefit of common people. This paper is based on the future aspects, for removal of Arsenic from drinking water or the water of different rivers like Ganga, Gomti and Yamuna etc which humans are consuming for domestic purpose. Demograph estimate that around 52 millions peoples are drinking ground water with arsenic concentrations above the guidelines of World Health Organization. WHO proposed a parameter or MIC for Arsenic i.e. of 10 parts per billion (ppb) or 0.010 Mg/L, it is found that level of Arsenic has been increased vigorously in many rivers. Objective is to apply Bioremediation technique with the help of batch culture that needs Bioremediators to detoxify contaminated water and helps in maintaining the original quality of water. Key words— Arsenic, MIC, Bioremediation, Batch culture, Bioremediators I. INTRODUCTION A. Water Our Earth is called a ―blue planet‖ as it contains a large and enormous percentage of water than land. On earth water has too many forms and variety which are necessary specifically for particular geographical as well as environmental conditions. It was found that only < 1% of water is available for drinking or domestic use and rest of the part is saline. Human being, plants & animals i.e. living being needs water for their survival. < 1% of the world's fresh water (~0.007% of all water on earth) is accessible for direct human uses. Rivers, lakes, ponds, estuaries, waterfalls, sea, oceans, deltas these are the different types of varieties of water bodies and have different percentage of water in such forms as it covers 71% of the Earth's surface. It is vital for all known forms of existence. On Earth, 96.5% of the total water is found in seas and oceans, 1.7% in groundwater, 1.7% in glaciers and the ice caps of Antarctica also small fraction in other large water bodies and 0.001% in form of haze, (water cycle), and rainfall. Domestic water (fresh water) constitutes only 2.5% of the Earth's water and from that 98.8% of water is in the form of ice and ground water. [1] Less than 0.3% of all freshwater is in rivers, lakes, and the atmosphere, and smaller amount of the Earth's freshwater (0.003%) is contained within biological bodies and manufactured products. It‘s a fact that origin of life takes place in water on earth ―Oparin- Haldane theory‖. Long voyage from Unicellular to Multicellular organisms‘ water is necessary either for their survival or for their basic needs. As unicellular get developed into multicellular organisms, this development took place in water itself, different chemical as well as biological reactions took place this accident had occur millions of years ago this also defines origin of life due to water and just because of that all living being needs water for their survival[2]. For the subsistence of life on Earth the existence of liquid form of water and some extent its gaseous and solid forms are vital. Our Earth has geographical relationship with water so it is necessary to know the features of water on earth. The Earth is located in the ―zonal habitat‖ of the solar system, if it were slightly closer to or farther from the Sun (about 5%, or about 8 million km.), the conditions which allow the forms of water to be present simultaneously would be far less likely to exist. Gravity exists on earth that allows it to hold an atmosphere. Carbon dioxide and Water vapour in the atmosphere provide a temperature buffer (greenhouse effects) which helps to maintain a relatively steady surface temperature. If Earth were smaller, temperature extremes would allow by a thinner atmosphere, thus the accumulation of water except in ice caps of polar region get prevented [3]. Water is necessary in many geological processes. In most of the rocks Groundwater is present, and the pressure of this groundwater affects Faulting patterns. Water present in the mantle play a very major role as it is responsible for the melt that produces volcanoes at subduction zones. On the surface of the Earth, water is obligatory in both physical and chemical weathering processes. For a large amount of sediment transport that occurs on the surface of the earth‘s water, and to a lesser but still significant extent, ice, are also responsible. Many types of sedimentary rocks are formed by deposition of transported sediment which makes up the geological record of earth‘s history [4]. Water scarcity is the major concern in most part of 52 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 the world, and its availability is a major social and economic The Ganges River contains the utmost freight of silt than any issue. Currently, about a billion people around the world other river in the world and the deposition of this material in consume contaminated as well as unhealthy water. Up to 2015 the delta region resulting in the largest river delta in the world the number of people worldwide who do not have access to (400 km from north to south and 320 km from east to west). safe water and sanitation, many countries accepted the goal of The affluent mangrove forests of the Gangetic delta called as halving the rate during the 2003 G8 Evian summit [5]. [6] Sunderban delta contain very incredible and valuable species Water, however, cannot be a finite resource, but rather it will of plants and animals which are consummate among many be re-circulated as potable water in precipitation in quantities, forest ecosystems existing on the earth [10]. The expansion of many degrees of magnitude higher than consumption by groundwater in different parts of the country has not been human beings. If this difficult goal is met then also it will still uniform. Highly exhaustive development of ground water in leave more than an estimated half a billion people without certain areas in the country had resulted in over exploitation access to adequate sanitation and over a billion without access leading to decline in the levels of ground water and sea water to safe drinking water. Bad or poor water quality and bad instruction in coastal areas. There is unremitting growth in sanitation are deadly; some five- six million deaths a year are dark and overexploited areas in the country [11]. caused by polluted or contaminated drinking water. Developing world scenario said that, 90% of all waste water still goes into local rivers and streams without proper treatment. The strain not only affects surface freshwater bodies like rivers and lakes, but it also degrades groundwater resources. Demograph of Some 50 countries show that roughly a third of the world's population also suffer from medium or high water scarcity stress, and 17 of these extract more water annually than is recharged through their natural water cycles this is the statically proved data. Due to water pollution there are different types of diseases occurring and are also getting increase day by day like Diarrhea, Cholera, Jaundice and other Gastro problems are the major issue now these days. Not only this, there are also harmful chemicals which are polluting water; these pollutants are release as nonbiodegradable waste product which is easily thrown to the river. There are many heavy metals who show their presence in our rivers which are boon to life and prove that these are also contributing in contamination of water. Improper sewage, The Ganges has to suffer from extreme pollution conditions, a industrial waste, illegal human activities all are responsible for demographic study said that this affects near about 400 million water pollution [8]. people who exists close to the river. Suspended Particulate B. India and Water matter is also responsible for contamination of water quality of Indian scenario is also not good as we all had got highest the rivers. This major problem is exacerbated due the fact that rank amongst the top polluted water countries because we did many people who live their life to poverty line and their life not know how to make water safe, because we don‘t know dependently rely on the river on a daily basis for different how to conserve water, because we are not concern about domestic purpose like bathing, cooking and washing [12]. sustainable development and for our mistake the upcoming The world economy representative ―WORLD BANK‖ generation has to pay for it. Condition of Major River like estimates that three percent of India's GDP is equal to that the Ganga Yamuna and their substitutes are not good, taking the health costs of pollution of water in India It has been example of river Yamuna, in New Delhi the river is actually proposed statically that 80% of all illnesses in India and onedead because of 0.0 B.O.D the water which is now serving the third of deaths can be contributed to water-borne diseases. capital of India is just a flow of sewage which is a mix up of Rapid deforestation in the last few decades, increment in silt sewage and industrial waste and that water is consumed by the deposits occur due to topsoil erosion in the lose area which citizens of New Delhi that‘s why the water scarcity problem in lead to the raise the river bed and also lead to devastating New Delhi is the biggest issue, think that when the condition floods in the rainy season and stagnant flow in the dry season of capital of India is worst what is the condition of rest of the [13]. Along the main river course there are 25 towns with a country[9]. population of more than 100,000 and about another 23 towns II. THE GANGES with populations above 50,000. In addition there are 50 River Ganga is a national river of India serving maximum smaller towns with populations above 20,000. There are also Northern Plains of India in many ways like for domestic about 100 identified major industries located directly on the purpose, for ethical purpose, for industries, for irrigation, for river, of which 68 are considered as grossly polluting. Fiftyhydroelectricity that is in the form of energy resource but can five of these industrial units have complied with the anybody ask that Ganga is clean river or not, river Ganga regulations and installed effluent treatment plants (ETPs) and exists on earth since from that era when there was no life on legal proceedings are in progress for the remaining units. The earth in form of human beings. River Ganga originates from natural assimilative capacity of the river is severely stressed Gangotri glaciers over Himalayas and ends her passage to Bay [14]. Cultural, Historical, Educational capital of India of Bengal it covers about 2,525 km distance overall, but before ―Varanasi‖ , a city of more than one million people that many that this river has taken so many turns as well as burden in pilgrims explore this holy place to take a "holy dip" in Ganga, form of waste, toxic products and buried parts of human body. statics shows around 200 million litres of untreated human 53 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 sewage flows into the river each day, which leads to large concentrations of ―coliform‖ bacteria. According to official principles, it is estimated that water is only safe for bathing should not contain more than 500 ―faecal coliforms‖ per 100ml, and upstream of ghats of Varanasi, the water of Ganga river already contains 120 times as much, sixty thousands ―faecal coliform‖ bacteria per 100 ml which is now comes in major concern by many non- governmental organizations. The basin of Ganges is significally influential to the agricultural economies of India and Bangladesh just because of this India is called for Agriculture Based Country. River Ganga and its tributaries provide for irrigation, a perennial source by covering a large area. Chief crops cultivated in the area include rice, wheat and potatoes [15]. There are also many fishing opportunities to many small scale industries along the river, though it remains highly polluted. The major In November 2008, the Ganges, alone among India's rivers, industrial towns like Varanasi, Kanpur etc situated on the was declared a "National River", facilitating the formation of banks of the river with the preponderance of tanning industries a “National Ganga River Basin Authority (NGRBA)” that add to the pollution [16]. A rich growing area for crops such as would have greater powers to measures aimed at protecting the legumes, chillies, mustard, sesame, sugarcane, and jute along river as well as set plan, implementation is also necessary and the banks of the river because of the presence of swamp and finally a monitoring cell has to form which look after all the lakes provided naturally. A plan is made seeing the worst central issues [18]. The incidence of water borne condition of Ganga i.e. ―The Ganga Action Plan‖, which was and enteric diseases such as intestinal diseases, different types taken on priority and with much enthusiasm but unfortunately of infections like typhoid, cholera etc significally among it was delayed for two years. The result was not very people who are using the water of river Ganga for bathing, appreciable after the expenditure was almost doubled. The washing dishes and brushing teeth is high and other domestic concerning governments and the agencies which got the purpose, demographically 66% per year. Recent studies responsibility for the plan were not very prompt to make it a by Indian Council of Medical Research (ICMR) proved that success. Social Audit was not taken into consideration which the river is now full of pollutants which are highly toxic that can help to reduce the time in surveying. The releasing of those are living along its banks in Uttar Pradesh, Bengal and industrial and urban wastes in the river was not controlled Bihar [19]. Bacteriophages are also found in river ganga fully. The flowing of dirty water through drains and sewers designated as ―super bugs‖, helpful in phage therapy against were not adequately diverted as they are diverted to directly patients of Diarrhea, phages play a medicinal role in formation into the river. The continuing customs of throwing carcasses, of phage therapy which will be very helpful for the treatment burning dead bodies, washing of dirty clothes by washer men, of patients of Diarrhea [20]. It has now be proven that as The immersion of idols cattle, wallowing etc were not checked Ganges came to northern plains region of India it start properly. Ignorance and avoidance to all these points all these accumulating itself with lots of unpredictable stats and results made the Action Plan a failure, has also been variously in form of toxic contamination but as the fact says that Ganga attributed to "environmental issue without proper flow with very high speed so all the contaminated also flow to understanding of the interaction or relationship between the deeper regions and West Bengal as Indian state suffers a human and environment the failure of the Ganga Action Plan lot by this. Bangladesh a sub continental country a very close occur. Customs, tradition, culture ethical issues, beliefs, to West Bengal also suffers the devastating features of river corruption and a lack of technical acquaintance and lack of Ganga in Bangladesh called as ―JAMUNA‖. Ministry of support from religious authorities. These are some highlighted Health and ruling authorities for Ganga proved that points which are now leading to destroy the quality of water of accumulation of heavy metals in the water of river Ganga river Ganga. In 2009 December the World Bank agreed to pass made it toxic in lower regions of India [21]. The figure shown loan for India of US$1 billion over the next five years, for above proved that not only the river but nearby places are also achieving the objective of cleaning The Ganges. According to contaminated due to Arsenic poisoning. If we scrutinize the Budget 2010 Planning Commission estimates, an investment figure it proves that the eastern part is highly contaminated of almost Rs. 70 billion ( approximately US$1.5 billion) is than Northern part but both the regions are toxic but eastern needed to clean up the river[17]. part is showing maximum toxicity rate because at northern plains of India Ganga flow with a continuous speed that means water of river Ganga is not in rest form but in Maximum part of India as well as in Bangladesh water of river Ganga is in state form basically in Sunderban delta region that‘s why the accumulation of contaminated water is higher in eastern region rather than Northern Plains of India, Biochemical Oxygen Demand start decreasing as we start moving from higher to eastern part eventually proves that Ganga is contaminated in Eastern India [22]. 54 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 Arsenic is formed which is a poor conductor of electricity. One stable isotope of natural occurring Arsenic is 75As, this is said to be monoisotopic element but many radio isotopes have also been synthesized and their atomic mass ranges from 60 to 92. Out of this the most stable form is known as 73As has half life of 65.30hrs. , 72As has half life of 26.00hrs. , 77As has a half life of 38.83hrs. Isotopes which are lighter than the stable [31]. 75As tend to decay by β+ decay, and those that are heavier and tend to decay by β- decay with some expectations. Least ten nuclear isomers have been described, ranging in atomic mass from 66 to 84. The most stable form of Arsenic One more report by the government of India shows that Royal isomer is 68mArsenic with a half life of 111 seconds. These Bengal Tigers of Sunderban delta are also anguish from values define atomic properties as many times these metals or different diseases or genetic disorder because their body is metalloid are exist in different forms not in their native state so consuming that Arsenic as heavy metal and they are also atomic properties helps to determine that these forms are new getting dead in that toxic environment [23]. Loss of Dissolved elements or they are the isotopes of any known element [32] Oxygen, Loss of Biochemical Oxygen Demand etc also leads [33]. Arsenic in form of sulphur compounds is also found. to a threatening for aquatic life, for example Gangatic Realgar (As4S4) and Orpiment (As2S3) are abundantly found in Dolphins are very famous all over world but not as single nature and formerly used as in paint industry. In As 4S10, authority is considering that a great loss to the Dolphins going arsenic has exclusive oxidation state of +2 in As4S4 which to occur which are living in river Ganga. The dolphins of river descript bond between the As atoms so that the valancy of As Ganga, which used to exist in near to urban centres in the river is fixed as 3 [34]. Industries waste management program has Ganga, is now seriously in danger of extinction by pollution to be made so to control and check the level of waste which is and dam constructions. Their population have now dwindled toxic in nature and provide a specific area so that these toxic to a quarter of their numbers of fifteen years before, and now resources are thrown into that place [35]. These toxic they have become extinct from the main streams of river compounds which are used to make highly toxic materials like Ganga [24]. A recent survey by the WWF reported only 3,000 in pesticides poison etc. The proton acceptation steps between dolphins left in the water catchment of Ganga river system. the Arsenate and Arsenic acid are similar to the Indirectly or directly these contaminated water is responsible Phosphate and Phosphoric acid as somewhat structures are for many losses, it is now found that the rice or wheat products also same. Unlike phosphorous acid, Arsenous acid is which is growing by the farmers contains parts of heavy genuinely basic in nature, with the formula As(OH) 3. The total metals included and when it serves to us then our body volume of Arsenic makes up about 1.5 ppm (0.00015%) of consumes those poisonous food which is really toxic and can the total Earth‘s crust and just because of 53rd most abundant destroy us [25]. element in Earth. Soil contains 1–10 ppm of arsenic which is III. ARSENIC an inactivated or normal range for Arsenic also recognized by Arsenic (As) is a chemical element with atomic number 33. It WHO. Statistics said that water in form of oceans and sea has occurs in many minerals and in a pure form of existing only 1.6 ppb Arsenic (As). Many lower Arsenic containing elemental crystal. Albertus Magnus was the first person to minerals are known. An organic form of Arsenic also occurs in documented Arsenic in the year 1250 [26]. Actually Arsenic is the environment [36]. Arsenic is obtained mainly as a a metalloid and can be found in the form of different peripheral product from the Copper purification. Furthermore allotropes. There are many fundamental uses of Arsenic like it purification from chalcogens and sulfur which is achieved is used in underpinning alloys of copper [27]. In the field of by Sublimation process in vacuum or in a hydrogenic semi conductors Arsenic is an N- type dopant. In doping atmosphere or simply by distillation from Lead-Arsenic techniques Arsenic is used like ―Gallium arsenide‖ is a mixture present in molten form. Dust from gold, copper, common semiconductors used after doped silicon. In and lead smelters Arsenic is just a part of that smelter only formation of herbicides, pesticides Arsenic are used but now a [37]. day‘s its use are banned in many fields. Arsenic is a poison for multicellular life existing on the earth, it‘s a fact that some species of bacteria can able to use arsenic compounds as primary metabolites or authoritarian metabolites. The trioxide form of Arsenic is used in the pesticides industry, industry of insecticides as well as herbicides [27]. Arsenic being a heavy metal it is a metalloid and poisonous to living being this came to know by understanding the analysis of physio-chemical properties because sometime objective is hiding in these properties only and without understanding the physiochemical properties it is not possible to cure it [28]. It is stable in nature; [29] Yellow arsenic is tetraphosphorus (P 4) is very soft as well as waxy as both have 4 atoms precise in a tetrahedral type in which each atom is bound to each of the other 3 atoms by a single bond. An unstable allotrope, being Arsenic in inorganic form is considered the most potential molecular, is the most volatile, low density as well as high form of human carcinogen, and humans are exposed to it from toxicity. One more form Black Arsenic very similar to red water, soil, air and food. In the system of arsenic metabolism, phosphorus [30]. By cooling vapour state at 100-2200C Black 55 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 inorganic arsenic is get methylated to monomethylarsonic acid A research study on rats in the field of immunology has done (MMA) and finally to dimethylarsinic acid (DMA), which get in which rats are daily dosed with arsenic in their water every excreted through urine. Thus, DNA hypomethylation may month the level of arsenic get increases by the researches, it is cause due to arsenic exposure resulting continuous methyl found that arsenic is not effecting the rats and rats are curved depletion, initiating aberrant gene expression that results in towards the garlic, this experiment is done in levels equivalent carcinogenesis. Further, though arsenic is non mutagenic, it to those found in groundwater in West Bengal in India and interacts significally with gene based toxic agents in the Bangladesh and the consequences were found to respond assembly of different types of mutation, and also different towards garlic extracts. It was found that rats which are tested chromosomal disorders which get induces and proliferation of for arsenic are recover with 40 percent less arsenic in their cell also occur. In the arsenic endemic regions of West Bengal blood and liver, and 47 percent more arsenic get passed from (India) due to arsenic contamination this region is declared as their urine. The conclusion is conforming that sulphurepidemic and epidemiological research have established that containing substances in garlic, eradicate arsenic from blood inorganic arsenicals have the capability to cause lung and skin and tissues. The whole observation concludes that people who cancers in human being. Research on the genetic are living in areas at risk of arsenic contamination in any kind polymorphism like SNP in the arsenic methyltransferases of water supply should eat one to three peels of garlic per day enzyme with the population exposed to arsenic, and which will really help out human to be safe, garlic is also quantification in the arsenic-induced mutational spectrum may helpful for the patients of blood pressure. be necessary for the development of molecular markers and Removal Methods therapeutics and for simplifying the knowledge and features of Natural as well as feasible methods are necessary to remediate arsenic-induced carcinogenesis [38][39]. Arsenic it on a large scale keeping in view that those removal methods contamination is understood to be of geo-genic origin that may not be disadvantageous for humans as well as different means released from soil under conditions inductive to living beings. Water pollution and its remediation are now a dissolution of Arsenic from solid phase on grains of soil to part of major concern and cost effective techniques could help liquid phase in water and fertilizer percolation of residues it out [43]. must played a modifying and significant role in imminent Chelation Processing exaggeration. There are large number of hypotheses and Chemical and synthetic- Artificial methods are used to treat predictions about the source of Arsenic and sufficient reasons arsenic poisoning or Arsenicosis. Chelating agents such as of occurrence in groundwater [40]. Symptoms of arsenic Dimercaprol and Dimercaptosuccinic acid are help in poisoning begin with headaches, confusion and drowsiness. As removing the arsenic away from blood proteins in the human the poisoning develops, convulsions and changes in fingernail body and are also used in treatment of acute arsenic poisoning. pigmentation may occur. When the poisoning becomes acute, But it has too many side-effects also; most important side symptoms may include diarrhea, vomiting, blood in the urine, effect is Hypertension. Hypertension may lead to death of cramping muscles, hair loss, stomach pain, and more human being also so its use is not in medical field. convulsions. Inorganic arsenic show the mechanism of action Dimercaprol is considerably more toxic than succinic acid but of leading to cancer remains a mystery. There is no proved DMSA monoesters e.g. MiADMSA are helpful antidotes for report which can justify that inorganic arsenic species can arsenic poisoning. It is found that over dose of such drugs may react covalently with DNA just like organic carcinogens. But, lead to serious infection in human body. Synthetic materials to act as carcinogens (which cause cancer) it has to be act in are not always suitable for human body. some way to alter or restrict the regulation of cell replication. In the study with human keratinocyte cultures, increase in cell IV. Bioremediation proliferation arsenite was shown the formation of It is a technique of waste management which involves the keratinocyte-derived growth factors [41]. This significant significant use of different types of organisms to remove activation process of Arsenic and its substitute salt appeared to neutralizes and eradicates pollutants from the site of generate Oxidative Stress which initiate cardiac problem as the contamination and toxification. According to official as well free radical scavenger N-acetylcysteine inhibit the activation as governmental authorities bioremediation is a ―biological of the kinases. Directly or indirectly Arsenic also plays an treatment that utilize naturally occurring organisms or microimportant role in increasing oxidative stress by lowering the organisms to break down hazardous as well as catastrophic Nitric Oxide in human body [42]. substances into less toxic or non-toxic as well as reaches to inactive state; substances‖. Bioremediation technologies can be generally classified into two categories as 1st In situ bioremediation and 2nd ex situ bioremediation. In situ bioremediation means involving treatment of the contaminated material at the site itself, whereas ex situ means ―involves the removal of the contaminated material to be treated somewhere else. There are many examples of bioremediation related techniques are phytoremediation, biostimulation, rizofertilizers, biopesticides etc [44]. Current advancements and development have also proved successful by the addition of similar microbial strains having such genes which can deliberately help in removing toxicity from environment to the medium and to enhance the population of microbe which has ability to break down contaminants and toxic properties. Microorganisms which are used for the 56 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 intensive function of bioremediation are called inhibition of repair polymerization and hence is a genotoxicas Bioremediators [44]. However, all contaminants are not enhancing process. It is thus likely that arsenic-mediated able to treat through bioremediation by using microorganisms. DNA–protein interactions may play a major role in arsenic For example, different heavy metals such as lead and carcinogenesis and the induced protein associated DNA-strand cadmium are not readily absorbed or disrupt by breaks could provide an explanation for chromosome microorganisms. A dynamic question arises as the primary aberration [47]. Studies on the basis of Molecular biology, detoxification process regarding the determination of Genetics, Biochemical studies, microbiological, methylation, since some of the mammals are deficient in the biotechnological, bio-informatical and structure biological are primary enzyme of this metabolic process. In case of human going on but yet the consequences and conclusions are not beings who have been exposed to a high levels of arsenic in verified up to that clinical extent [48]. There are so many drinking water due to many unsolved reasons, during techniques already implemented for remediation of arsenic but treatment with sodium 2,3- dimercapto-1-propane sulfonate all cannot as useful for removal of such toxic metal in mass. It DMPS urinary MMA levels were found to increase several is very necessary to find such micro-organisms which are turns, a chelating agent is also used to treat metal intoxication helpful in removing arsenic but their estimation in different from natural resources [45]. It is uncommon that over river is not yet proved though it is found that bacteria which accumulation of arsenic exposure had predisposed abruptly the have the property of methylation can methyl arsenic in methylation of MMA to DMA which is very important to detoxify form. It is mandatory to found such bacteria that will know, though it is also a fact that the actual physiological and not able to spread disease [49][50]. It is a contradiction or may metabiological cause of reduced rate of methylation due to be exceptional case that such detoxifying bacteria are found arsenic exposure is yet unclear. Arsenite also have a high only those places which are arsenic affected. But their affinity as well as compatibility for ―Thiol‖ groups in many populations are very low at that place and are not able to proteins which can form various complexes with vicinal detoxify on a mass level. National Institute of Technology implicative thiols and directly inhibit more than 200 enzymes Rourkela has isolated some unknown strains of bacteria which at a time. So, inhibition or restriction of methyltransferase by are helpful in removing Arsenic from water especially in this arsenite could affect arsenic metabolism positively [46]. report we have to focus on those remediators which provide a Second aspect, genetic polymorphism in the arsenite MMA least cost effective approach [52][53]. (monomethylarsonic acid) methyltransferases might contribute There are many microorganisms found on earth since from to the observed disruption in arsenic metabolism and sharp very long time. The fact provided against arsenic is that not a variations in degree of susceptibility and accountability of single vision is applied to bioremediate it from water. exposure to arsenic within a population or of different Metabolic pathway of Arsenic is based on the two enzymes geographic regions. So, research in the genetic polymorphism, arsenic reductase and methyltransferase. It is found via KEGG using different amino acid sequences or the coding gene of genome that Arsenic inhibits most of the enzyme regulation but arsenite methyltransferases can be taken as a probe in the being nontoxic form it can be excrete through Urine. It is found arsenic-endemic large population of West Bengal should that the pentavalent metabolites MMA V and DMA V are less provide just round the corner knowledge into the genetic study toxic than arsenite or arsenate Excretion of Arsenic in the urine of arsenic metabolism [47]. primarily through the kidneys occurs. Humans excrete a mixture of inorganic, monomethylated, and dimethylated but not the trimethylated forms of arsenic as it is very toxic and this form cause disease [54]. Identification of such microorganisms is necessary so to compare the metabolic pathway of such organism as well as metabolic pathway of Arsenic in Human body as we are searching towards the basic aspects which can prove that this bacterium will help in bioremediating heavy and toxic metals from water. It also to find that this bacteria is found in water or not and if it is not found in water then what are the specialization to associate both of them at a very low cost. It is also necessary to find different techniques which can prove that in human body the detoxification would occur in As shown in figure that arsenic reductase is working in similar manner. conversion of Arsenate V to Arsenite III similarly Arsenic V. EXPERIMENT oxidase is responsible for the conversion from arsenite III to arsenate V. Arsenic is also defined as pro-oxidant and thus may cause lipid peroxidation, protein and enzyme oxidation and GSH depletion, DNA oxidation and DNA adducts. Further, arsenic generates reactive oxygen species like nitric oxide; reactive oxygen species are known to induce poly ADPribosylation which is implicated in DNA repair, signal transduction and apoptosis. As a result, arsenite may induce DNA strand-breaks and NAD depletion. Hence the genotoxic effects of arsenic compounds may be connected with an inhibition of DNA repair or the induction of oxidative stress. In fact, metabolic methylation of inorganic arsenic to DMA is involved in induction of DNA damage and DNA single-strand breaks resulting from the Bio-remediation of heavy metals from drinking water by the help of micro-organisms with the use of bioreactor Brief Study-- World Health Organization. WHO proposed a parameter or MIC for Arsenic i.e. of 10 parts per billion (ppb) or 0.010 Mg/L, it is found that level of Arsenic has been increased vigorously in many rivers. Objective is to apply Bioremediation technique with the help of batch culture that needs Bioremediators to detoxify contaminated water and helps in maintaining the original quality of water. Aim is to identify that will this strain of E.coli K12 sub strain MG-1655 can be act suitable for detoxification process of water which is 57 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 contaminated due to arsenic. This strain theoretically proved M. On a colony counter it is found that in 1 section 6 colonies for detoxification process against arsenic but yet regarding are formed. practical analysis has not been proven so for justification it is N. One more method can be applied as it is a gram negative necessary to follow some new procedure to analyze that it is bacterium therefore ―Gram- staining technique‖ has been confirmed or not. Bioreactor can be further used for mass applied to identify the bacteria which have been done after production of such micro-organisms which would surely help centrifugation process. Centrifuge the culture with 3ml water in improving treatment plant so that our rivers get also purified discard the supernatant and dry the pellet then stain the pellet with Gram‘s stain and identify how many colonies are there from such harmful effects. present the pink colored one will tell no. of colonies present in the serial dilution. O. Arsenic quantification test has been done also to get the exact value of level of Arsenic in water with the help of Arsenic quantification system in ppb. P. Further this bacterial strain is now taken into consideration as work is going on to continuation; different reactors can be used to grow this strain on a large scale. VI. MATERIAL REQUIRED Petri plates, L.B. media, Agar, conical flask, DO probe detector, sterile condition, Incubator, Inoculation loop, Test tubes, cotton, paper, sprit lamp. VII. PROCEDURE A. Collection of water sample of river Ganga from different cities like Varanasi, Gazipur, Allahabad and Kanpur. B. Sample received from MTCC- IMTECH was provided in a glass tube sealed from all side. It is packed in a very sophisticated manner in such way that it cannot be broke by chance. C. Sealed sample is crushed safely under sterile condition; this sterile condition will help to protect culture safe free from contamination. D. Now it was the time to analyze the culture, E. Formation of L.B. media of Broth nature and inoculate this bacterial strain into the media and incubate it for 24hrs at 37ᵒC. F. After incubation up to 24hrs it is found that this strain of E.coli gives a translucent formation in conical flask containing L.B. media; Preparation of different aliquots of enriched culture. Stored at 15-20ᵒC so that its properties remain sustained. G. Now preparation of water sample with Arsenic trioxide that means 0.001gm of Arsenic trioxide has been taken in 100ml of water. H. Measurement of Dissolved Oxygen in different water sample collected from different ghats of Varanasi and from other cities like Gazipur, Allahabad and Kanpur. I. Preparation of solidified L.B. media and this solidified media pour on different Petri plates this method is applied for screening test of strain producing or following which pathway. J. With the help of micropipette at same concentration of water sample has been taken and enriched culture has been spread over four solidified LB media plate and two controls are also formed out of that one contains the untreated water sample and other contains enriched culture. K. To find the original concentration it is necessary to find the CFU, which means how many colonies are necessary to convert toxic into detoxification. L. for that ready all apparatus for Serial dilution technique, take 9ml water which is distilled and take 1ml enriched sample serially dilute it for 10-12 times and finally spread 1ml of that to new solidified plate. VIII. OBSERVATION & RESULT 1. It is observed that this strain of E.coli is giving Arsenic oxidase as an enzyme. 2. Arsenic oxidase gives yellowish color in the process of detoxification. 3. Arsenic oxidase or arsenite oxidase convert Arsenite into Arsenate which is further methylated into less toxic forms with the help of enzyme Arsenite methyltransferase. 4. It is also confirm that there are approx. 6 colonies are responsible for this particular conversion. 5. There are 4 samples and 2 controls have been observed result shown that no color is observed in both the controls; controls are of water containing arsenic trioxide and other contains enriched sample. 6. It has been proven that appearance of color is like 1st < 2nd <3rd <4th that is 4th sample on plate of concentration 100µl untreated water and 40µl of enriched sample showing biggest change on the plate which is finally screened out. 7. Consequences said that arsenic in river Ganga is very low in Stretch of Varanasi and Allahabad but then also there are some places where B.O.D is very low as shown in table 2, this is because of other reasons but Assam, West Bengal and Bangladesh as shown in figure 3 is highly affected from such heavy metals. 8. Other medicinal properties of river Ganga nearby West Bengal has been lost and West Bengal royalty by Royal Bengal Tigers are also getting died just because they are consuming this toxic water. 58 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 9. As now it is confirmed that E.coli K12 sub strain MG 1655 Arsenic detoxification from water and from human body is responsible for Arsenic detoxification therefore it is from different metabolic pathways. necessary to increase the production of such microorganism is so that they can act as bioremediator in large purification or X. FUTURE ASPECT & ANALYSIS water treatment plants. The detail characterization of the material synthesized 10. Different In silico study can also help in getting that how before & after the experiment. drugs can be design or can be improved in a better way if we Studies of various Mass production models. find the molecular data of such enzymes. Effect of competitive anion present in the removal 11. The results which proved that keeping the concentration of efficiency of arsenic untreated water same and varying the volume of enriched Practical applicability of the material culture of bacterial strain proves that as we are moving in Calculation for the cost of the implementation of this increasing order from lower to higher concentration the plates technique. are observed to be give a yellow color appearance as shown in figure 11 . REFERENCES 12. Finally it has been practically proved that E.coli K12 sub [1] Gleick, P.H., ed. (1993). Water in Crisis: A Guide to the strain MG 1655 is responsible for detoxification of water from World's Freshwater Resources. arsenic and can be awarded as good bioremediation agent or [2] Water Vapor in the Climate System, Special Report, December Bioremediator. Hence it has been proved that two important 1995. [3] Gleick, P.H., ed. (1993). Water in Crisis: A Guide to the enzymes namely—Arsenite Oxidase and Arsenite World's Freshwater Resources. Methyltransferase are going to provide a significant marker in [4] Water Vapor in the Climate System Special Report, December Arsenic detoxification from water and from human body from 1995. different metabolic pathways. [5] IX. CONCLUSION Large parts of water which are life supportive get contaminated because of illegal activities of human beings. Water pollution is a major problem globally. It is the leading worldwide cause of deaths and diseases, and that it accounts for the deaths of more than 14,000 people daily. On earth water has too many forms and variety which are necessary specifically for particular geographical as well as environmental conditions. Less than 1% of the world's fresh water is accessible for direct human uses. Water pollutions now become a part of concern in country like India. In addition to the acute problems of different problems in developing countries, industrialized countries continue to struggle with water pollution problems as well. There are many inorganic metals which are contaminating water bodies which serve life to large part of India, Arsenic (As) is one of the biggest intimidations for water bodies. High toxicity of Arsenic poses a serious risk not only to ecological systems but also for human vigor. There is availability of sophisticated techniques for arsenic removal from contaminated water, enlargement of new laboratory based techniques along with cost reduction and augmentation of conventional techniques are indispensable for the benefit of common people. Demograph estimate that around 52 millions peoples are drinking ground water with arsenic concentrations above the guidelines of World Health Organization. WHO proposed a parameter or MIC for Arsenic i.e. of 10 parts per billion (ppb) or 0.010 Mg/L, it is found that level of Arsenic has been increased vigorously in many rivers. This paper is based on the future aspects, for removal of Arsenic from drinking water or the water of different rivers like Ganga, Gomti and Yamuna etc which humans are consuming for domestic purpose. Objective is to apply Bioremediation technique with the assist of batch culture that needs Bioremediators to detoxify contaminated water and helps in maintaining the original quality of water. Hence it has been proved that two important enzymes namely—Arsenite Oxidase and Arsenite Methyltransferase are going to be a significant marker in [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] "G8 "Action plan" decided upon at the 2003 Evian summit".2003-06-02. "World Health Organization. Safe Water and Global Health". Who.int. 2008-06-25. UNEP International Environment (2002). Environmentally Sound Technology for Wastewater and Stormwater Management: An International Source Book. Ravindranath, Nijavalli H.; Jayant A. Sathaye (2002). Climate Change and Developing Countries. Springer. Ministry of ―human resource and development‖ MHRD- New Delhi, table-1,YPCA-2013 Alter, Stephen (2001), Sacred Waters: A Pilgrimage Up the Ganges River to the Source of Hindu Culture. AIR AND WATER QUALITY MONITORING NETWORKNew Delhi 2013, table-2- report from Uttarakhand and Uttar Pradesh, official record-2014. Abraham, Wolf-Rainer. "Review Article. Megacities as Sources for Pathogenic Bacteria in Rivers and Their Fate Downstream". International Journal of Microbiology 2011. Akanksha Jain (23 April 2014). "‗Draw plan to check Ganga pollution by sugar mills'". The Hindu. Bharati, Radha Kant (2006), Interlinking of Indian rivers, "India and pollution: Up to their necks in it", The Economist, 27 July 2008. "Ganga can bear no more abuse"-Times of India. 18 July 2009. Caso, Frank; Wolf, Aaron T. (2010), Freshwater Supply Global Issues, Infobase Publishing. Pp. 350, "Chronology: 1985 India launches Phase I of the Ganga Action Plan to restore the Ganges River; most deem it a failure by the early 1990s.(page 320). "Ganga gets a tag: national river – Vote whiff in step to give special status", The Telegraph, 5 November 2008 Ganga is now a deadly source of cancer, report: Anirban Ghosh 17 October 2012. Prof. Gopal Nath- IMS-BHU; Dept of Microbiology- isolation of phage for clinical testing and medicinal used-2009. MHRD- New Delhi, 2014. Jal Nigam &YPCA Delhi report. AIR AND WATER QUALITY MONITORING NETWORKNew Delhi 2013, figure-2- report from Uttarakhand, Uttar Pradesh, Bihar & West Bengal official record-2014. "Lower Gangetic Plains moist deciduous forests". Terrestrial Ecoregions. World Wildlife Fund. Retrieved 6 May 2011. Puttick, Elizabeth (2008), "Mother Ganges, India's Sacred River", in Emoto, Masaru, The Healing Power of Water, Hay House Inc. Pp. 275, pp. 241–252 "WWF – Ganges River dolphin". Wwf.panda.org. Retrieved 4 July 2012. 59 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 52-60 [26] Emsley, John (2001). Nature's Building Blocks: An A-Z Guide to the Elements. Oxford:Oxford University Press. pp. 43, 513, 529. [27] Sabina C. Grund, Kunibert Hanusch, Hans Uwe Wolf (2005), "Arsenic and Arsenic Compounds", Ullmann's Encyclopedia of Industrial Chemistry, Weinheim: Wiley-VCH [28] Norman, Nicholas C (1998). Chemistry of Arsenic, Antimony and Bismuth. Springer. p. 50 [29] Holleman, Arnold F.; Wiberg, Egon; Wiberg, Nils (1985). "Arsen". Lehrbuch der Anorganischen Chemie (in German) (91–100 ed.). Walter de Gruyter. pp. 675–681. [30] Madelung, Otfried (2004). Semiconductors: data handbook. Birkhäuser. pp. 410–. [31] http://www.chemicool.com/elements/arsenic.html [32] Georges, Audi; Bersillon, O.; Blachot, J.; Wapstra, A.H. (2003). "The NUBASE Evaluation of Nuclear and Decay Properties". Nuclear Physics A (Atomic Mass Data Center) 729: 3–128. [33] Chisholm, Hugh, ed. (1911). "Arsenic". Encyclopædia Britannica (11th ed.). Cambridge University Press. [34] Uher, Ctirad (2001). "Chapter 5 Skutterudites: Prospective novel thermoelectrics". Recent Trends in Thermoelectric Materials Research I. Semiconductors and Semimetals 69. p. 139 [35] Tanaka, A (2004). "Toxicity of indium arsenide, gallium arsenide, and aluminium gallium arsenide".Toxicology and Applied Pharmacology 198 (3): 405–11. [36] Ossicini, Stefano; Pavesi, Lorenzo; Priolo, Francesco (1 January 2003). Light Emitting Silicon for Microphotonics. [37] Din, M.B.; Gould, R.D. (1998). "High field conduction mechanism of the evaporated cadmium arsenide thin films". [38] Pradosh Roy and Anupama Saha Department of Microbiology, Bose Institute- 10 October 2001. [39] Cervantes, C., Ji, G., Ramirez, J. L. and Silver, S., FEMS. Microbiol. Rev., 1994, 15, 355–367,11. Knowles, F. C. and Benson, A. A., Trends Biochem. Sci., 1983, 8,178–180. [40] Guha Mazumder, D. N. et al., ibid, pp. 113–123.25. Samanta, G. et al., Curr. Sci., 1998, 75, 123–137. [41] Kaltreider, R. C., Pesce, C. A., Ihnat, M. A., Lariviere, J. P. and Hamilton, G. W., Mol. Carcinogen., 1999, 25, 219–229. [42] Srivastava.A & Singh.A ―Nitric Oxide- significant marker in Oxidative Stress‖ June 2014. [43] Arsenic removal technologies for drinking water treatment) Kuan-Seong Ng1, Zaini Ujang1 & Pierre Le-Clech2, 2004. [44] Bioremediation-an approach, Mann, D. K., T. M. Hurt, E. Malkos, J. Sims, S. Twait and G. Wachter. 1996. [45] Brim H, McFarlan SC, Fredrickson JK, Minton KW, Zhai M, Wackett LP, Daly MJ (2000). "Engineering Deinococcus radiodurans for metal remediation in radioactive mixed waste environments".Nature Biotechnology 18 (1): 85–90. [46] Singh.R ―Study of transferases‖- methyltransferase for DNA, 2013. [47] Aposhian, H. V. et al., J. Pharmacol. Exp. Ther., 1997, 282, 192–200. [48] Schlenk, D., Wolford, L., Chelius, Steevens, J. and Chan, K. M.,Comp. Biochem. Physiol. Pharmacol. Toxicol. Endocrinol., 1997, 118, 177–183. [49] Lynn, S., Lai, H. T., Gurr, J. R. and Jan, K. Y., Mutagenesis, 1997, 12, 353–358. [50] Lynn, S., Shiung, J. N., Gurr, J. R. and Jan, K. Y., Free Radical Biol. Med., 1998, 24, 442–449. [51] Gebel, T., Chem. Biol. Interact., 1997, 107, 131–144. [52] Yamanaka, K., Hayashi, H., Tachikawa, M., Kato, K., Hasegawa, A., Oku, N. and Okada, S., Mutat. Res., 1997, 394, 95–101. Hartwig, A., Groblinghoff, U. D., Beyersmann, D., Natarajan, A. T., Filon, R. and Mullenders, L. H., Carcinogenesis, 1997, 18,399–405. [53] Uong, J. I. and Luo, X. M., Mutat. Res., 1993, 302, 97–102. [54] Caldwell et al. 2008, National Health and Nutrition Examination Survey 2003-2004. 60 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 61-66 KINETIC AND STATIC STUDY ON BIOSORPTION OF HEXAVALENT CHROMIUM USING TAMARIND POD SHELL AND CARBON AS ADSORBENT Sudhanva.M.Desai1, NCLN Charyulu2, Satyanarayana V. Suggala3* 1 Chemical Engineering Department, Dayananda Sagar College of Engineering, Bangalore, India. 2 Chemical Engineering Department, C.B.I.T. Hyderabad, India. 3 Chemical Engineering Department, J.N.T.U.A.C.E.Anantapuramu, India.
[email protected] Abstract— This study aims to employ low-cost agro waste biosorbent tamarind (Tamarindus indica) pod shells and activated carbon prepared by complete and partial pyrolysis of tamarind pod shell for the removal of hexavalent chromium ions from aqueous solution. The effect of parameters namely, initial metal ion concentration, pH, temperature, biomass loading on chromium removal efficiency were studied. More than 96.9% removal of Chromium was achieved using crude tamarind pod shells as biosorbent. The experimental data obtained were fitted with Langmuir, Freundlich, Temkin and Redlich-Peterson adsorption isotherm models. The experimental data fits well to Langmuir, Freundlich and Temkin isotherms with regression coefficient R2 more than 0.9. For Redlich-Peterson adsorption isotherm the experimental data does not fit so well. The crude tamarind had maximum monolayer adsorption capacity of 40 mg/g and a separation factor of 0.0416 indicating it as best adsorbent among the three tested adsorbent. Further, an attempt is made to fit sorption kinetics with pseudo first order and pseudo second order reactions. Pseudo second order kinetics model fits well to the experimental data for all three adsorbents. Key words— Chromium, Tamarind pod shell, pyrolysis, isotherms, kinetics. * Corresponding author. Email:
[email protected] I. INTRODUCTION Industrialization has led to increased disposal of Chromium [Hexavalent Chromium] into the environment and hence effluent treatment is one of the most important targets for industry to remove chromium from waste water. Chromium found in wastewater is harmful to environment and their effects on biological systems are very severe. Unlike organic pollutants, the majority of which are susceptible to biological degradation, Chromium ions do not degrade into harmless end products [1]. Chromium have been extensively studied and their effects on human health are regularly reviewed by international bodies such as the WHO. Chromium may enter the human body through food, water, air, or absorption through the skin when they come in contact with humans in manufacturing, industrial, or residential settings. Industrial exposure accounts for a common route of exposure for adults [2]. Chromium is found naturally in the soil in trace amounts, which pose few problems. Exposure may occur from natural or industrial sources of chromium. Chromium (III) is much less toxic than chromium (VI). The average daily intake from air, water, and food is estimated to be less than 0.2 to 0.4 µg, 2.0 µg, and 60 µg, respectively [3]. Chromium is a heavy metal that is commonly found at low levels in drinking water. It can occur naturally but can also enter drinking water sources by historic leaks from industrial plant’s hazardous waste sites. Various other sources also contribute to the amount of chromium in ground water. Chromium is known to be a potent carcinogen when inhaled [4]. It is very difficult for anyone to avoid exposure to chromium that is so prevalent in our environment. Hence there is a strong need to reconsider our consumption patterns especially the concentration level and the way we use our water resources. Chemical approaches are available for chromium remediation, but are often expensive to apply and lack the specificity required to treat target metals against a background of competing ions. In addition, such approaches are not applicable to a cost-effective remediation of large-scale subsurface contamination in situ. In view of this biological methods are becoming more popular [5]. Different alternatives for treating effluents are described in literature, including chemical precipitation, ion exchange and membrane separation process. These processes are either expensive or produce sludge [6]. Therefore the search for new technologies to remove chromium from wastewater has directed attention to biosorption especially using agro waste biomass, which is based on metal binding to various biological materials. Biosorption is a fast and reversible reaction of the chromium with biomass. Many biosorbents were tried for chromium removal as seen in literature. Microorganisms including algae, fungi and bacteria were used and studied as biosorbents [7-8]. Among the agro waste used are Bengal gram husk (Cicerarientinum) [9], treated sawdust (acacia Arabica) [10], activated tamarind seed [11], walnut, hazelnut and almond shell [12], pods of Gulmohar (delonixregia) and activated carbon from Gulmohar pods [13] corn cob and coconut husks [14] and animal waste like crab shells [15]. Agro waste as biosorbent is promising because of low cost, abundance in availability and reasonably high efficiency. In this study tamarind pod shell and activated carbon prepared from tamarind shell were used for chromium removal because of its proven efficiency for other metals and also abundance availability in India. Apart from this tamarind pod shell tamarind pod shell cheap low grade fuel, it is not used for any other useful work The objective of the present work is to study the effect of pH, initial chromium concentration, adsorbent dosage and temperature were studied on chromium removal efficiency by conducting batch adsorption experiments. Further, experimental results were tested using Langmuir, Freundlich, Temkin and Redlich-Peterson adsorption isotherm models. In addition the experimental data were fitted to Pseudo-first order equation or Lagergren's kinetics equation and Pseudosecond order equation. II. MATERIALS AND METHODS A. Preparation of Biosorbent [16 -18] i. Collection of tamarind pod shells Natural agro waste biosorbent Tamarindusindica pod shells collected from Kolar area Karnataka were used for batch and kinetic studies for removal of chromium. Natural biosorbent along with two types of pyrolysis was employed 61 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 61-66 E. Adsorption kinetics. for comparative metal removal efficiency. Adsorption experiments are conducted by taking 100 ml of known concentration of adsorbent and fixed amount of ii. Crude (Untreated) tamarind pod shells (T) adsorbate doses in 250 conical flasks. As many as conical Tamarind pod shells were sun dried, powdered, sieved flasks are taken. All these flasks are maintained at fixed pH using 60/80 mesh BSS Standard sieve to get uniform sized and the bottles were kept in the temperature controlled particles. Powder so obtained washed thoroughly with mechanical shaker at a constant temperature. Samples are distilled water and dried in the hot air oven for 2 hours at 80 collected for every 10 min until equilibrium is reached. ºC. iii. Preparation of Activated carbon by pyrolysis. The activated carbons used in this study were prepared by Complete Pyrolysis and Partial Pyrolysis using Crude Tamarind pod shell in a muffle furnace. The complete pyrolysis tamarind (TCP) adsorbent is obtained by just keeping the crucible in the muffle furnace whereas partial pyrolysed tamarind (TPP) is obtained by keeping the lid on the crucible. B. Adsorbate i. Preparation of chromium stock solution Synthetic chromium solution was prepared by dissolving potassium dichromate (K2Cr2O7) in double distilled water. 1000 ppm of stock chromium solution was prepared by dissolving 2.83 mg of potassium dichromate in one litre of double distilled water. Other required concentrations were prepared by diluting the stock solution. The pH of the solution was adjusted to the required value. ii. Preparation of diphenylcarbazide (DPC) solution Diphenylcarbazide (DPC) solution was prepared by dissolving 250mg of DPC in 50ml of acetone in a 100 ml volumetric flask. III. RESULTS AND DISCUSSION A. Effect of pH The solution pH has significant influence for the removal of chromium ions. Experiments were conducted over a range of pH values (1-7) keeping other conditions constant and the obtained values are shown in Figure 1 As seen from the figure 1 that biosorption capacity of chromium is maximum at around pH 1 for all the adsorbents. Paptri.Rao.et.al. [16] reported similar results for the biosorption of chromium using tamarind pod shell. Some functional groups, such as NH, are positively charged when protonated and may electrostatically bind with negatively charged metal complexes. At lower pH, the biosorbent is positively charged due to protonation and dichromate ion exists as anion leading to an electrostatic attraction between them [20]. As pH increases, deprotonation starts and thereby results in decrease of adsorption capacity. Maximum chromium removal of 99.86% was found for crude tamarind pod shell [20]. As pH increases, deprotonation starts and thereby results in decrease of adsorption capacity. Maximum chromium removal of 99.86% was found for crude tamarind pod shell 100 C. Analysis of chromium [19] 0.25ml of phosphoric acid was added to 1ml of standard sample containing known concentration of chromium, pH was adjusted to 1.0±0.3 using 0.2N sulphuric acid. The solution was mixed well and then diluted to 100 ml in a volumetric flask using double distilled water. Further 2ml of DPC solution was added and mixed well. After full colour development for 10 min, 4ml of this solution was used in an absorption cell and the concentrations were measured spectrometrically at 540nm in UV-double beam spectrophotometer [Shimadzu- UV Visible 1700]. The calibration curve is prepared by measuring the absorbance of different known concentrations of chromium solutions and plotting a graph between concentrations versus absorbance. A straight line is obtained with R2 of 0.994. D. Batch Experiments-Adsorption isotherms Batch adsorption studies were performed by Shaking 100 ml of solutions in 250 ml conical flasks fitted with cork lid kept in constant temperature shaker. Experiments are conducted by varying one of the parameter and keeping other parameters at constant values. All experiments were performed in triplicate and the results were averaged. The adsorption of chromium ions were calculated from the change in metal concentration in the aqueous solution before and after equilibrium sorption by using the following equation. qe= [V(Co−Ce)] /W (1) where qe is adsorbed metal (mg/g adsorbent), V is the solution volume (l), W is the amount of sorbent (g), and Co and Ce (mg/l) are the initial and equilibrium chromium concentrations of the solution, respectively. The chromium percent removal (%) was calculated using the following equation: Chromium Removal (%) = [(Co-Ce)/ Co] *100 (2) T TCP TPP 80 60 40 Initial conc.-82mg/l Biomass-7.8 g/100ml temp.-46oC 20 0 1 3 pH of 5 7 Figure 1: Effect of pH on removal of chromium B. Effect of initial concentration of chromium ions. The equilibrium time required for the biosorption of chromium with three forms of tamarind pod shell was studied varying initial concentrations from 50-250 mg/l keeping other conditions at constant values. Figure 2. shows the effect of initial concentration on % chromium removal. As expected, adsorption capacity decreases with increase in initial concentration. It can be inferred from the figure that maximum removal is achieved at the initial concentration of 50 to 100 ppm. This is due to the fact that at lower initial concentration sufficient adsorption sites are available for adsorption of chromium ions and at higher concentrations the chromium ions will be more than the available adsorption sites [10]. It is observed that 99.9% of chromium removal is achieved at 50 ppm for crude tamarind pod shell. All the adsorbents showed the similar type of behaviour 62 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 61-66 pH-2.5 biomass-7.8 g/100ml 0 Temperature-46 C Figure 2: Effect of initial concentration of chromium on % removal C. Effect of adsorbent dosage To study the effect of adsorbent dosage on removal of chromium, adsorbent dosage is varied from 1 g/100ml to 10 g/100 ml keeping other conditions at constant values. The obtained results are shown in Figure 3.. There is an increase in removal of chromium ion with increase of adsorbent dosage as exhibited by all the adsorbent. It is apparent that the chromium ion removal increases with increase in adsorbent dosage due to the greater availability of the exchangeable active sites or the surface area for adsorption. Moreover the percentage of metal ion adsorption on adsorbent is determined by adsorption capacity of the adsorbent [10]. The maximum removal of 99.89% is observed at the dosage of 10 g/100ml for T among all three adsorbents. PH-2.5 Biomass-7.8 g/100ml 0 Temperature-46 C Figure 3: Effect of adsorbent dosage on removal of chromium D. Effect of Temperature Temperature effect on biosorption of chromium was studied by varying the temperature between 30oC to 45oC for all the three adsorbents. It is observed from Figure 4 that there is a slight increase in adsorption from 30oC to 35oC and there is a decrease in percentage removal of chromium with increase in temperature. This behavior may be due to the slight exothermic behavior of adsorption process. Initial conc-82 mg/l pH-2.5 biomass-7.8 g/100ml Figure 4: Effect of Temperature on removal of chromium E. Adsorption Isotherms. Sorption equilibrium provides fundamental physicochemical data for evaluating the applicability of sorption process as a unit operation. Sorption equilibrium is usually described by an isotherm equation whose parameter expresses the surface properties and affinity of the sorbent at fixed temperature, pH and initial metal concentration. Adsorption isotherms are mathematical models that describe the distribution of the adsorbate species among liquid and solid phases, based on a set of assumptions that are related to the heterogeneity or homogeneity of the solid surface, the type of coverage, and the possibility of interaction between the adsorbate species. In this study, equilibrium data were analyzed using the Freundlich, Langmuir, Temkin and Redlich-Peterson isotherms expression. i. The Langmuir isotherm [21] The Langmuir model suggests, as a hypothesis, that uptake occurs on a homogeneous surface by monolayer adsorption without interaction between sorbed molecules. This model is described by the equation = (3) Where qeq (mg/g) and Ceq (mg/l) in the above equation are the amount of adsorbed metal per unit weight of biosorbent and un adsorbed metal concentration in solution at equilibrium respectively. Q0 (mg/g) is the maximum amount of metal per unit weight of biomass to form a complete monolayer on the surface bound, b(l/mg) is the Langmuir constant related to the energy of adsorption. The equation (3) may be written as (4) As per eq. (4) a plot of qeq-1 and Ceq-1 provides Q0 and b are constants which are related to the affinity of the sites. The Langmuir constants obtained are presented in Table 1. They indicate that Langmuir isotherm model fits best for all the adsorbents as seen by high R2 value. T exhibits highest maximum monolayer adsorption capacity of 40 mg/g which is in agreement with work done by others in literature [20] In addition, the effect of isotherm shape can also be used to predict whether an adsorption system is “favourable” or “unfavourable”. According to Hall K.Ret.al.,[22], the essential features of the Langmuir isotherm can be expressed in terms of a dimensionless constant separation factor or equilibrium parameter KR, which is defined by the following 63 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 61-66 relationship adsorption of all the molecules in layer decreases linearly with coverage due to adsorbent-adsorbate interactions, and (5) that the adsorption is characterized by a uniform distribution Where KR is a dimensionless separation factor, C0 is of the bonding energies [25]. The Temkin isotherm is initial ion concentration (mg/l), and b is the Langmuir represented by the following equation in the linear form as constant (l/mg). The calculated KR values are also reported (8) in the Table 1 and the value of KR is in the range of 0 <KR < Where Ce is the equilibrium concentration of the adsorbate 1 for all the adsorbents. Further, the value of KR also in mg/l, qe is the amount of adsorbate adsorbed at indicates the shape of the isotherm. According to Table 2 the equilibrium (mg/g), A (mg/g) = RT/b lna and B (l/mg) = adsorption of chromium is favorable for all three adsorbents. RT/b where T is the temperature (K), R is the ideal gas constant, A and B are constants. A plot of qe against lnCe Table 1.Langmluir Isotherm Constants enables the determination of constants A and B. The 2 Biosorbe R Separati Langmuir constant B is related to the heat of adsorption and A is the nt on factor isotherm equilibrium binding constant corresponding to the maximum KR constants binding energy. From Table 4 it is evident that the Q0(mg/g) b(l/mg) adsorption also follows Temkin model as regression T 40.0 0.012 0.994 0.0417 coefficient (R2) is high and it is highest for TPP at 0.99. It TCP 18.2 0.015 0.990 0.0666 has been reported [26] that the typical range of bonding TPP 20.1 0.022 0.981 0.6902 energy (value of A) for ion-exchange mechanism is 8-16 kJ/mol. The low values in this study indicate a weak Table 2.Isotherm Separation Factor [22] interaction between sorbate and sorbent Values of KR Type of isotherm KR > 1 Unfavourable Table 4.Temkin isotherm constants KR = 1 Linear Temkin constants R2 Biosorbent 0 <KR < 1 Favourable A (mg/g), B (l/mg) KR = 0 Irreversible T 0.637 2.5106 0.951 TCP 1.551 0.5559 0.976 ii. The Freundlich isotherm [23] TPP 1.446 0.8693 0.990 The Freundlich model proposes a monolayer adsorption with a heterogeneous energetic distribution of active sites, and with interactions between sorbed molecules, as described by the equation (5) (6) Where Ceq (mg/l) is the equilibrium concentration and qeq (mg/g) is the amount of adsorbed metal ion per unit mass of the adsorbent. The constant n is the Freundlich equation exponent that represents the parameter characterizing quasiGaussian energetic heterogeneity of the adsorption surface [24]. Freundlich constants KF and n are the indicators of adsorption capacity and adsorption intensity, respectively. Equation (5) can be linearized in logarithmic form which is presented in eq (6) (7) A plot is made between ln qeq and ln Ceq provides the Freundlich isotherm constants and calculated values are presented in Table 3. The high R2 value indicates that Freundlich isotherm model also fits well with all the three adsorbents. The high value of KF of 0.882 and n of 0.855 for crude tamarind shell indicates it is best adsorbent among the all three adsorbents. Table 3. Freundlich Isotherm Constants Biosorbent Freundlich constants R2 KF(L/g) n T 0.882 0.85 0.957 iv. Redlich–Peterson model. Redlich–Peterson model is used as a compromise between Langmuir and Freundlich models, which can be written as [27]. (9) Equation 9. can be expressed in its linear form as: (10) Where KR (l/g), αR (l/m.mol) and β are Redlich-Peterson constants. The value of β lies between 0 and 1. The Redlich– Peterson isotherm constants can be predicted from the plot between Ce/qe versus Ce. However, this is not possible as the linear form of Redlich–Peterson isotherm equation contains three unknown parameters αR, KR and β. Therefore, a minimization procedure is adopted to maximize the coefficient of determination R2, between the isotherm equation and the experimental data. The Redlich–Peterson isotherm constants for the chromium ions are presented in Table 5. It is seen from the Table 5 that R2 value are low indicating this model does not fit the as accurately as other models. Table 5: Redlich-Peterson isotherm constants Biosorbent Α β R2 T 87.619 -0.066 0.746 TCP 0.360 1.41 0.971 TCP 38.78 0.512 0.801 TPP 0.513 1.65 0.988 TPP 38.015 0.399 0.873 iii. Temkin Isotherm Equation. The Temkin isotherm equation assumes that the heat of 64 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 61-66 F. Adsorption kinetics. (13) Equilibrium study is important in determining the This on integration for boundary conditions when t=0 to >0 efficacy of adsorption. It is also necessary to identify the and q=0 to>0 and further simplifications of equation 13 adsorption mechanism for a given system. Kinetic models gives have been exploited to test the experimental data and to find (14) the mechanism of adsorption and its potential ratecontrolling step that include mass transport and chemical Or the equation 14 can be written as reaction. In addition, information on the kinetics of metal (15) uptake is required to select the optimum conditions for full scale batch or continuous metal removal processes Where h = k2qe2 and is known as initial sorption rate. Where In order to analyze the rate of adsorption and possible k2 is the rate constant of second order adsorption adsorption mechanism of chromium onto biomass, the (g/mg/min). Values of K2 and qe were calculated from the pseudo first order and pseudo second order kinetic models linear plots of t/qt versus t. The obtained rate constant value were applied to adsorption data. K2, qe and R2 values are also reported in the Table 6. Pseudo-first order equation or Lagergren's kinetics equation It is seen from Table 6 that the adsorption of chromium [28] is widely used for the adsorption of an adsorbate from with all the adsorbent is following pseudo second order an aqueous solution. kinetics (high R2). This indicates adsorption may be chemisorption. (11) After integration and applying boundary conditions to above equation, t = 0 to t = t and qt = 0 to qt = qt, the integrated form of above equation 11.becomes (12) Where qt is the amount of metal adsorbed per unit of adsorbent (mg/g) at time t, kp1 is the pseudo-first order rate constant (l/min), and t is the contact time (min). The adsorption rate constant (kp1) was calculated from the plot of ln (qe - qt) against t. The obtained rate constant value K1, qe and R2 values are reported in the Table 6. The low values of R2 indicating that the adsorption data does not follow Pseudo first order kinetics. Pseudo- second order model [29] presented the pseudosecond order kinetic. The pseudo-second- order kinetic model which is based on the assumption that chemisorption is the rate-determining step we have the equation as follows [30]. IV. CONCLUSION The agro waste biomass, tamarind and carbon from tamarind demonstrated a good capacity of chromium biosorption, highlighting its potential for effluent treatment processes. High chromium removal is possible at low pH, high adsorbent dosage and low initial concentrations. Further, highest chromium removal was possible at moderate temperature of 35oC. Among all the adsorbents crude form of tamarind pod shell is the best with a highest removal capacity. Langmuir, Freundlich and Temkin isotherm models were in good agreement with experimental results. The biosorption of chromium obeyed the pseudo second-order biosorption kinetic model as it fitted the experimental data with a high correlation coefficient, R2 of 0.995 Table 6: Parameters of the kinetic models for the adsorption of Chromium on different adsorbents V. NOMENCLATURE T-tamarind crude. TCP-tamarind crude completely pyrolysed. TPP- tamarind crude partially pyrolysed. VI. REFERENCES [1] [2] R.A. Goyer, Toxic effect of metals, the basic Science of Poisons, 4th edition, Pergamon press, New York. (1991) E.J. Roberts. and S.P. Rowland., Removal of mercury from aqueous solutions by nitrogen-containing chemically modified cotton, Environmental Science and Technology, Vol. 7 (6), (1973), pp. 552 -555. [3] [4] [5] [6] U.S. EPA, Environmental Pollution Control Alternatives, Economics of Wastewater Treatment Alternatives for the Electroplating Industry. EPA – 625/5-79-016, Cincinnati, Ohio. (1979). N.R Bishnoi, M. Bajaj, N. Sharmar. and A. Gupta, , Adsorption of Cr (VI) on activated rice husk carbon and activated alumina, Bioresource Technology, Vol. 91, (2004), pp.305 –307. P. Valeria, Z. Mirco, R. Daniele, T. Valeria, A. Antonella, C.V. Giovanna, Chromium removal from a real tanning effluent by autochthonous and allochthonous fungi, Bioresource Technology, Vol. 100, (2009), pp.2770 – 2776. N. Ahalya, T.V. Ramachandra, and R.D. Kanamadi, Biosorption of Heavy metals, Research Journal of Chemistry and Environment, Vol.7 (4), (2003), pp. 189 – 192. 65 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 61-66 [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] G. Rani, A. Prerna, K. Seema, R.K. Saxena, and M. Harapriya, Microbial biosorbents meeting challenges of heavy metal pollution in aqueous solutions, current science, Vol. 78 (8), (2000), pp. 234 - 238. M. Rajasimman, K. Murugaiyan, Optimization of process variables for the Biosorption of Chromium using HypneaValentiae, Nova Biotechnologica, Vol. 10 (2), (2010),pp. 107 - 115. N. Ahalya, T.V. Ramachandra, and. R.D. Kanamadi, Biosorption of chromium (VI) from solution by the husk of Bengal gram (Cicerarientinum), Eloctronic Journal of Biotechnology, (2006), pp.61 - 64. K. Ajay, Meena, and K. Kadirvelu, Adsorptive removal of heavy metels from aqueous solution by treated sawdust (Acacia Arabica), Journal of Hazardus Material, Vol. 150, (2008), pp. 604 - 611. B.V. Babu, S. Gupta, Removal of Cr (VI) from waste water using activated tamarind seeds as an adsorbent, Journal of Environmental Engineering and Science, Vol. 7, (2008), pp. 553 - 557. P. Erol, A. Turkan, Biosorption of chromium (VI) ion from aqueous solutions using walnut, hazelnut and almond shell, Journal of Hazardous Materials, Vol. 155, (2008), pp.378 – 384. D.N. Renuga, K. Manjusha, and P. Lalitha, Removal of Hexavalent Chromium from aqueous solution using an ecofriendly activated carbon adsorbent, Advances in Applied Science Research, Vol. 1 (3), (2010), pp. 247 - 254. A. Nigam, and O.P. Rama, Corncob -A promising adsorbent for the removal of chromium (VI) from wastewater, Indian Journal of Environmental Protection, Vol. 22 (5), (2002), pp. 550 553. H.N.Catherine, V.Bohumil, Modeling Chromium (VI) Biosorption by Acid Washed Crab Shells, AIChE Journal, Vol. 53 (4), (2007), pp.1056 - 1059. R. Paptri, J. Srinivisa, Naga. Ajithapriya, S.R. Krishnaih, Biosorption of Hexavalant chromium using tamarind (Tamarindusindicus) fruit shell-A Comparitive study, Electronic Journal of Biotechnology, Vol. 10, (2009), pp. 358 367. S.S. Ahluwalia, and D. Goyal, Microbial and plant derived biomass for removal of heavy metals from wastewater, Bioresource Technology, Vol. 98, (2007), pp.2243 – 2257. [18] N. Ahalya, T.V. Ramachandra, and. R.D. Kanamadi, Biosorption of chromium (VI) by Tamarindus indica pod shells, Journal of Environmental Science Research International, Vol. 1 (2), (2004), pp. 77 - 81. [19] APHA, AWWA, Standard method for examination of water and waste water, 19th edition, Washinton DC (1994). [20] Y.E.Hala, and M.E. Eman, Optimization of Batch Process Parameters by Response Surface Methodology for Mico remediation of Chrome-VI by a Chromium Resistant Strain of Marine TrichodermaViride, American-Eurasian Journal of Agriculture & Environmental Science, Vol. 5 (5), (2009), pp. 676 - 681. [21] V.M. Boddu, K. Abburi, . J.L.Talbott, and E.D.Smith, Removal of hexavalent chromium from wastewater using a new composite chitosan biosorbent, Environmental Science and Technology, Vol. 37 (19), (2003), pp. 4449 – 4456. [22] I. Langmuir, The adsorption of gases on plane surfaces of glass, mica and platinum, Journal of American Chemical Society, Vol. 40, (1918), pp. 1361 – 1403. [23] K.R.Hall, L.C.Eagleton, A. Acrivos, T. Vermeulen, Pore- and solid-diffuion kinetics in fixed-bed adsorption under constantpattern conditions, Industrial and Engineering Chemistry Fundamentals, Vol. 5, (1966), pp. 212 - 223. [24] T. Robert, Adsorption, Mass transfer operations, McGraw-Hill Book Company, 1st edition, Singapore, (1955). [25] R.C. Bansal, and M. Goyal, Activated Carbon Adsorption, Boca Raton, Crc Press Taylor Francis Group, (2005). [26] F.Jonethan, A.N.Kosasih, J.Sunarso, Y. Jua, N. Indraswati, S. Ismadji, Equilibrium and kinetic studies in adsorption of heavy metals using biosorbent: A summary of recent studies, Journal of Hazardous Material, Vol. 162 (2-3), (2009), pp. 616 – 645. [27] Y.S Ho, D.A.J. Wase, and C.F. Forster, Removal of lead ions from aqueous solution using sphagnum moss peat as adsorbent, Water SA, Vol. 22 (3), (1996), pp. 219 - 224. [28] O.Redlich,and D.L. Peterson, A useful adsorption isotherm, Journal of Physical Chemistry, Vol. 63, (1959), pp. 1024 – 1029. [29] S.Lagergren.; About the theory of so-called adsorption of soluble substances, Kungliga Svenska Vetenskapsakademiens, Handlingar, Vol. 24, (1898), pp. 1 – 39. [30] Y.S Ho, and G.McKay, Pseudo-second order model for sorption processes, Process Biochem, Vol. 34, (1999), pp. 451 – 465. 66 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 67-70 ANAEROBIC DIGESTION OF MUNICIPAL SOLID WASTE USING FUNGI CULTURE (ASPERGILLUS FLAVUS ) WITH METHANOGENS Mahesh Kumar Shetty1, Ravishankar R2, Ramaraju H K3, 1,2 Department Chemical Engineering, 3Department of Civil Engineering, 1,2,3 Dayananda Sagar College of Engineering Bangalore, Karnataka, India
[email protected],
[email protected],
[email protected] Jagadish H Patil4 4 Assistant professor, Department of Chemical Engineering, R.V. College of Engineering Bangalore, Karnataka, India
[email protected] Sunil H5, Mamatha B.Salimath6 5 Department of Chemical Engineering, 6Department of Microbiology, Dayananda Sagar College of Engineering Bangalore, Karnataka, India
[email protected],
[email protected] Abstract— Municipal Solid Waste (MSW), mainly Kitchen Waste (K) with Cow Dung (C) and Fungi Culture (F) can be used to generate energy which could save on the fossil fuels conventionally used as source of energy. In this study, the possibility was explored to mix Cow Dung with Fungi Culture for anaerobic digestion, so that energy can be generated as biogas and at the same time digested sludge can be used as fertilizer for agricultural applications. Pre-treatment of Kitchen Waste was done by alkali method. Anaerobic digestion (AD) was carried out in mesophilic temperature range of 30°C to 37°C with different fermentation slurries of 8 % total solids. Digestion was carried for a retention period of 60 days. The gas produced was collected by the downward displacement of water and was subsequently measured and analyzed. The overall results showed that blending of Kitchen waste with cow dung and fungi culture (Aspergillus flavus) had significant improvement on the biogas yield. Keywords— Anaerobic Digestion, Cumulative Biogas Production, Kitchen waste, Fungal Culture, Cow dung, Inoculums, Aspergillus flavus. I. INTRODUCTION The country’s economy mainly depends on the energy resources available and utilized. Energy has been exploited since the prehistoric times. With the advent of industrial revolution use of fossil fuels began growing and increasing till date. The dependence on fossil fuel as primary energy source has led to global climate change, environment degradation and human health problems [1]. With increasing prices of oil and gas the world looks towards alternative and green energy resources. Anaerobic digestion (AD) offers a very attractive route to utilize certain categories of biomass for meeting partial energy needs. AD is a microbial decomposition of organic matter into methane, carbon dioxide, inorganic nutrients and compost in oxygen depleted environment and presence of the hydrogen gas. This process is also known as biomethanogenesis. Anaerobic digestion has the advantage of biogas production and can lead to efficient resource recovery and contribution to the conservation of non-renewable energy sources. AD can successfully treat the organic fraction of biomass [2]. AD is the controlled degradation of biodegradable waste in absence of oxygen and presence of different consortia of bacteria that catalyze series of complex microbial reactions [3]. The process is one of the most promising for biomass wastes as it provides a source of energy while simultaneously resolving ecological and agrochemical issues [4]. Fungi culture (Aspergillus flavus): A. flavus as well as some other fungi, proved to have capacity of maturing in 3 days in an anaerobic jar [5]. Fungi are found to be the major decomposers of cellulose and lignin [6]. The production of cellulose enzyme is a major factor in the hydrolysis of cellulosic materials [7]. Aspergillus flavus is capable of producing endoglucanase even from sawdust and corncob. Aspergillus flavus also possess the capacity to degrade the non- starch polysaccharide in the substrate to soluble sugar [8]. Most of the cellulolytic microorganisms belong to eubacteria and fungi can degrade cellulose. Cellulolytic microorganisms can establish synergistic relationships with non cellulolytic species in cellulosic wastes. The interactions between both populations lead to complete degradation of cellulose, releasing carbon dioxide and water under aerobic conditions and carbon dioxide, methane and water under anaerobic condition[9] The Strain Aspergillus flavus can be recommended for bioremediation programmes to clear cellulosic wastes [10]. II. MATERIALS AND METHODS Sample Collection Kitchen waste (K) was obtained from the canteen of Dayananda Sagar College of Engineering, Bangalore. Fresh cow dung was collected from a local cow yard in Yarab Nagar, Bangalore. Fungal culture Aspergillus flavus was procured from Department of Microbiology Culture Collection, Bangalore University, Bangalore. A. 67 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 67-70 B. Materials / Instruments The materials/instruments used for the purpose of this research are as follows: Weighing balance (Systronics), Gas Chromatography (CHEMITO), pH meter (Systronics), thermometer (range 0°C to 100°C), Borosilicate desiccators, silica glass crucibles, oven, grinding mill, temperature controlled water bath, water troughs, graduated transparent glass gas collectors and biogas burner fabricated locally for checking gas flammability. AR grade sodium hydroxide and acetic acid manufactured by Ranbaxy laboratories were used as procured without further purification. C. Analytical Methods The following parameters of Kitchen Waste and cow dung were analyzed: pH analysis: A glass electrode pH meter (Systronics) was used to monitor the pH of the sample. Total Solids (TS) and total volatile solids (VS) analysis: TS were determined at 103°C to constant weight and VS were measured by the loss on ignition of the dried sample at 550°C. Biogas analysis: Gas Chromatograph (Chemito 1000) equipped with a thermal conductivity detector was used to analyze the biogas sample. Hydrogen was used as a carrier gas (25 ml/min) with porapak Q column. Standard calibration gas mixture was used for calibration. The oven temperature of 40°C, detection temperature of 80°C and the detector current of 180 mA were used. D. Biomethanation Unit A schematic diagram of biomethanation unit is shown in Fig. 1 and water bath in Fig. 2. It consists of a temperature controlled thermo bath which is maintained at 35°C [11] and has a bio digester. Each bio digester is connected to a means of connecting tube. A stand holds all the gas collectors. Biogas evolved is collected by downward water displacement. Fig. 1 Schematic picture of biomethanation unit Fig. 2 Schematic picture of water bath Fig. 3 Schematic diagram of water bath E. Solid Analysis Total solid (TS) and Volatile solid (VS) were analyzed for Kitchen Waste and Cow Dung according to standard methods [12]. Table.1 gives the solid analysis and pH data of Kitchen Waste and Fungi Culture. TABLE I SOLID AND PH ANALYSIS Digester pH % TS % VS 6.7 75.55 93.36 Kitchen Waste (K) 6.4 64.7 93.83 Cow Dung (C) F. Fermentation Slurry Preparation Fresh Kitchen Waste was initially collected and it was grounded to paste in the mixer. Material balance was made and different slurries with 8 % total solids were prepared by varying the amount of paste (grounded kitchen waste) and Water (W) [14]. The contents of each digester are shown in Table 2. Each digester was checked for neutral pH (i.e., 7.0), since the optimal pH for methanogenesis was found to be around 7.0 [15] When measured, each digester was found to have acidic pH (i.e., < 7.0), hence the contents were treated with 1 % NaOH (by volume) solution to bring them to neutral pH. TABLE III CONTENTS OF DIGESTERS Cow Kitchen Fungi Digesters Water (g) dung waste (g) (g) (g) DK 19.2 160.8 DKF 19.2 160.8 5 - DKC 19.2 160.8 - 2 DKCF 19.2 160.8 5 2 III. RESULTS AND DISCUSSION A. The Influence of Inoculums to Cumulative Biogas Productions Anaerobic digestion of Kitchen Waste / Canteen Waste: The quantity of cumulative biogas production with time for all the digesters is given in Table 3. As shown in Fig. 3, Digesters DK, DKF, DKC and DKCF commenced biogas production from 5th day and evolved flammable biogas from 9th day. While digester Kitchen Waste Blank which serves as blank for Kitchen waste 68 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 67-70 commenced biogas production after 10 days and evolved The gas chromatogram obtained is shown in Fig. 4. The flammable biogas on 20th day. The highest biogas yield was for amounts of CH4, CO2 and CO in the biogas were found to be digester DKCF (0.28 l/gVS). This performance could be 59.3 %, 40.6 % and 0.1 % respectively. The biogas because of optimum balance between the anaerobic bacteria compositions from the other digesters were also found to be in consortium and amount of VS (23.76 g). This indicates the same range. digestion of Kitchen Waste and cow dung with fungi culture improves biogas yield significantly. TABLE IIIII CUMULATIVE BIOGAS PRODUCTION Days DK DKF DKC DKCF (l/g VS) (l/g VS) (l/g VS) (l/g VS) 0 0 0 0 0 5 0 0.006 0.005 0.01 10 0.001 0.011 0.01 0.02 15 0.005 0.045 0.04 0.06 20 0.008 0.075 0.07 0.08 25 0.012 0.115 0.1 0.12 30 0.021 0.15 0.14 0.17 35 0.045 0.18 0.17 0.21 40 0.075 0.2 0.19 0.24 45 0.115 0.22 0.21 0.26 50 0.15 0.23 0.22 0.28 55 0.16 0.235 0.23 0.28 60 0.17 0.24 0.23 0.28 Fig. 4 Gas chromatogram for the digester DKCF Fig. 3 Daily biogas production B. Analysis of Biogas With Biogas analysis was done for chief components CH4 and CO2 for biogas evolved from the digester DKCF. Biogas was sampled in a rubber bladder carefully. Gas chromatograph (Chemito 1000) equipped with a thermal conductivity detector was used to analyze the biogas sample. Hydrogen was used as carrier gas (25 ml/min) with Porapak Q column. Standard gas mixture was used for calibration. A fixed 500 μl volume was taken each time using a gastight syringe. The sample was then injected to gas chromatograph to analyze for methane and carbon dioxide. Following are the characteristics of the GC gas composition method: Column : Porapak Q Gas : Hydrogen with flow rate of 25 ml/min Oven : 40°C Detector : TCD at 80°C and 180 mA The concentrations of methane and carbon dioxide were calculated using IV. CONCLUSIONS Kitchen Waste is a very good biogas producer, needs minimal pre-treatment (soaking in NaOH solution and grinding) to enhance the biogas yield. The use of Cow dung with Fungi culture (Aspergillus flavus) for biogas generation therefore, will be a good energy source. The result of the study has shown that anaerobic digestion of ground Kitchen waste with cow dung and Aspergillus flavus improved biogas yield. This performance confirms the earlier reports by other researchers that combining cow dung with fungi culture catalyzes the biogas production with consequent increased yield [16]. ACKNOWLEDGEMENT We thankfully acknowledge the help from Prof. Karthik K.V, Prof. D.C Sikdar, Prof. G.K Mahadevaraju, Prof. B.S. Thirumalesh, Prof. Pradeep H.N, Prof. S.C Maidargi, Prof. S.M. Desai, Department of Chemical Engineering, DSCE, Bangalore and the entire staff of the Department of Chemical Engineering, DSCE, Bangalore and authorities of Dayananda Sagar College of Engineering, Bangalore. REFERENCES [1] Budiyano, Widiasa I N, Johari and Sunarso S Increasing biogas production rate from cattle manure using rumen fluid as inoculums, International Journal of Chemical and Basic & Applied Sciences, 10(1), 68-75, 2010 [2] Hill DT Simplified Monod kinetics of methane fermentation of animal wastes, Agricultural Wastes, 5, 1–16, 1983 [3] McInerney M J. Bryant M P and Stafford D A Metabolic stages and energetics of microbial anaerobic digestion. In: Stafford DA, Wheatley BI, Hudges DE (eds) Anaerobic digestion, Applied Science, London, 91-98, 1980. 69 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 67-70 [4] Budiyano, Widiasa I N, Johari and Sunarso S The kinetic of biogas production rate from cattle manure in batch mode, International Journal of Chemical and Biomolecular Engineering, 3(1), 39-44, 2010 [5] Gunnel Clevstrom, Hans Ljunggren,Seven Tegelstrom, and Kerstin Tideman Production of aflattoxin by an A. Flavus Isolate culture under limited oxygen supply. Applied and Environmental Microbiology Aug Vol 46 No.2, 400-40. 1983 [6] Bennett JW., A Childress and K Wunch Fungi in bioremediation. Int. Biodet Biodeger 37: 244-255,1996. [7] Ghose, TK Measurement of cellulose activities. Pure Applied Chem 59: 257-268,1987. [8] Hamyln, P.F Fungal biotechnology. Br.Mycol.Soc Newslett 30: 930-934,1998 [9] Leschine, S.B Cellulose degradation in anaerobic environments Annu. Rev Microbiol 49: 399-426 [10] Betty Anitha B, Thatheyus A.J and Ramya D [2013] Biodegradation of Caroboxymethyl Cellulose using Aspergillus flavus. Science international DOI 10.5567/sciintl.85.91, 8591,1995. [11] Chae, K.J., Jang, A., Yim, S.K., Kim, I.S., The effects of digestion temperature and temperature shock on the biogas yields from the mesophilic anaerobic digestion of swine manure. Bioresour. Technol. 99, 1–6. 2008 [12] APHA, AWWA and WPCF Standard methods for the examination of water and waste water, Washington D.C, 19,1995 [13] Jagadish. Patil. H, Antony Raj MAL and Gavimath CStudy on effect of pretreatment methods on biomethanation of water hyacinth. International Journal of Advanced Biotechnology and Research, 2(1), 143-147,2011. [14] Jagadish Patil H, Antony Raj MAL, Shankar BB, Mahesh Kumar Shetty, and Pradeep Kumar B P. Anaerobic Co-Digestion of Water Hyacinth and Sheep Waste. International Conference on Alternative Energy in Developing Countries and Emerging Economies. 216-220,2013 [15] Yang, S.T, Okos, M.R Kinetic study and mathematical modeling of methanogenesis of acetate using pure cultures of methanogens. Biotechnol Bioeng. 30, 661–667,1987 [16] Huber, H., Thomm, M., Konig, H., Thies, G., Stetter, K.O Methanococeus thermolithotrophicus, a novel thermophilic lithotrophic methanogen. Arch. Microbiol. 132, 47–50, 1982 AUTHORS First Author – Mahesh Kumar Shetty, B.E, M.Sc, (M.Sc (Engg)), DSCE, Bangalore and
[email protected]. Second Author – Ravishankar R, Ph.D., DSCE, Bangalore and
[email protected]. Third Author – Ramaraju H K, Ph.D., DSCE, Bangalore and
[email protected]. Fourth Author – Jagadish H Patil, Ph.D., RVCE, Bangalore and
[email protected] Fifth Author – Sunil H, M.Tech, DSCE, Bangalore and
[email protected] Sixth Author – Mamatha B.Salimath, M.Sc, M.Ed, M.Phil, (Ph.D) DSCE, Bangalore and
[email protected] 70 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 71-74 AN EXPERIMENTAL STUDY OF PERFORMANCE AND EMISSION CHARACTERISTICS OF CI ENGINE FUELLED WITH HYBRID BLENDS OF BIODIESELS Shankarappa Kalgudi, K V Suresh Dept. of Mechanical Engineering Alva’s Institute of Engineering & Technology Moodbidri, Karnataka, India
[email protected],
[email protected] Abstract—Due to increase demand of energy, increasing price of petroleum fuels, depletion of petroleum fuels, and environmental pollution by these fuel emissions, it is very necessary to find the alternative fuels. This work focused on use of hybrid blends of Karanja and Cottonseed oil Biodiesels. In this work 20% and 25% blends are used and the performance and emission tests were conducted on single cylinder, 4-stroke, water cooled CI engine by running the engine at a speed of 1500rpm, at a compression ratio of 16.5:1 and at an injection pressure of 205bar and performance parameters like BP, BSFC, BTE and the emissions like CO, HC and NOx are compared. It was found that the blends gave comparatively good results in respect of performance and emissions. Index Terms—Petroleum fuel, Alternate fuels, Biodiesel, CI engine. I. INTRODUCTION Biodiesel, the most promising alternative diesel fuel, has been received considerable attention in recent years due to its following merits: biodegradable, renewable, non-toxic, less emission of gaseous and particulate pollutants with higher cetane number than normal diesel. In addition, it meets the currently increasing demands of world energy that, in a large degree, is dependent on petroleum based fuel resources, which will be depleted in the foreseeable future if the present pattern of energy consumption continues. Biodiesel is derived from vegetable oils or animal fats through transesterification. Transesterification is also called alcoholysis, which uses alcohols in the presence of catalyst (e.g., base, acid or enzyme depending on the free fatty acid content of the raw material) that chemically breaks the molecules of triglycerides into alkyl esters as biodiesel fuels and glycerol as a by-product. The commonly used alcohols for the transesterification include methanol, ethanol, Methanol particularly due to its low cost. Commonly used feed stocks (vegetable oil) for transesterification include pea nut oil, jatropha oil, soybean oil, rapeseed oil, cotton seed oil, honge oil etc. In recent years, there exist active researches on biodiesel production from cottonseed oil, of which the conversion between 72% and 94% was obtained by methanol catalyzed transesterification when the refined cottonseed oil reacted with short-chain primary and secondary alcohols. The results showed that the yield of methyl ester by alkali based transesterification was above 90% after 4 hours of reaction leaving a small amount of glycerine. No matter what kind of catalysts or approaches were applied, all those studies aimed to produce high yield of biodiesel by optimized reaction conditions based on optimized parameters in terms of alcohol/oil molar ratio, catalyst concentration, reaction temperature, and time. However, nearly in all studied cases, there existed complex interactions among the variables that remarkably affected the biodiesel yield. In this study the methanol process gave high yield of bio diesel as more than 90% i.e. for 1 litre of cotton seed oil we got more than 900 ml of biodiesel. II. MATERIALS AND METHODOLOGY OF BIODIESEL PRODUCTION Materials Washed cotton seed oil has purchased from Farmer’s Cooperative Society, Binkadakatte, Gadag. Honge oil has purchased from the Bio-diesel Plant, Tontadarya College of Engineering, Gadag. The chemicals like methanol (CH3OH), sodium hydroxide (NaOH), sulphuric acid (H2SO4), phenolphthalein indicator and batch reactor for producing biodiesel in 1 liter batch and also the required chemical laboratory has been taken from TCE Gadag. Methodology Basically all vegetable oil are triglycerides having saturated and unsaturated fatty acids. These triglycerides break into glycerin and the corresponding alcoholic esters. The chemical reaction for the transesterification is given as shown in the figure1. Figure1: Chemical reaction of transesterification The vegetable oil reacts with methanol or ethanol in presence of sodium hydroxide (NaOH) as a catalyst it gives the bio-diesel with formation of glycerine as byproduct. Before the transesterification process, one has to know the FFA (free fatty acid) content in the raw oil. Depending on the FFA value the transesterification process involves into two viz. alkali base catalyst process or acid base and alkali based catalyst process. FFA will be determined by simple chemical titration and by using the following simple formula, If the FFA is less than 4% alkali based process is carried. And if FFA is more than 4% then acid + alkali based process is carried 71 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 71-74 Table1: Properties of biodiesels and their blends. Fuel type TRANSESTERIFICATION Transesterification is carried in a 3-neck flask with reflex condenser, take 1litre of cottonseed oil into a 3-neck flask. Heat the oil to 60°C with magnetic stirrer and reflex condenser fixed. Take 300 ml of methanol and required quantity of sodium hydroxide (NaOH) and mix these properly. This solution is called methoxide mixture. When the temperature of the oil reaches 630 C add the methoxide mixture to the 3-neck flask. Allow the mixture to heat and stir for to 2 hours to mix properly. Figure2: batch reactor (3-neck flask) Figure3: separating funnel Then transfer the mixture into a separating funnel and allow to settle for 2-3 hours so that the glycerin is settled at the bottom and is drained off carefully. Again allow the same for ½ hour and observe if any glycerin settles. These processes are shown in figure2 and figure3. Once the glycerine is removed, the biodiesel is to be washed i.e. transfer the prepared bio-diesel into washing funnel, spray 300-500 ml of warm water slowly into bio-diesel and allow to settle for diesel 10-15 minutes so that soap water starts to form, drain the soap water slowly and again spray warm water and remove soap content, repeat the procedure 4-5 times. The last step is drying of bio-diesel i.e. transfer the washed bio-diesel to a beaker and heat the beaker to 1000 C with magnetic stirrer. Allow the bio-diesel to cool gradually. Store it in a clean and dry container. III. PROPERTIES OF BIODIESEL Some of the properties needed for using the bio-diesel as vehicular fuel like flash point, viscosity, density, specific gravity and calorific value are determined as follows. The flash & fire point were determined by closed cup apparatus, the kinematic viscosity was determined by cannon-fenske viscometer with tube number 100, and the calorific value was determined by bomb calorimeter. We determined the density at 25°C and the kinematic viscosity at 40°C Bio-diesel can be used in diesel engines either as a standalone or blended with diesel. H100 stands for neat honge biodiesel, C100 for neat cotton seed oil bio diesel. We used two blends in the proportion of 5H+15C+80D and 5H+20C+75D. After finding the properties of each fuel they are tabulated as shown in the Table1. Kinematic viscosity (cst) 2.42 Specific. gravity D100 Flash point (°C) 38 0.823 Calorific value (kJ/kg) 43033 H100 206 5.89 0.852 34902 C100 5H+15C+80D 5H+20C+75D 170 43 42 5.79 3.182 3.353 0.840 0.827 0.828 36565 41657 41333 IV. EXPERIMENTAL PROCEDURE The engine tests were conducted on a computerized single cylinder four-stroke, water-cooled diesel engine test Rig. It was directly coupled to an eddy current dynamometer that permitted engine motoring either fully or partially. The engine and the dynamometer were interfaced to a control panel, which is connected to a digital computer, used for recording the test parameters such as fuel flow rate, temperatures, air-flow rate, load etc and for calculating the engine performance characteristics such as brake thermal efficiency, brake specific fuel consumption, volumetric efficiency etc. computerized CI engine is used. The calorific value and the density of the particular fuel is fed to the software for calculating the above said performance parameters. The photographic view of the engine used is shown in figure4 and the engine specifications are given in Table2. The components of the experimental setup of the present work as detailed below. A four stroke, single cylinder, water cooled diesel engine is used for the experiment and the engine specifications are provided below. The engine speed runs up to 1500 rpm. The engine is attached at one end to the eddy current type dynamometer with a drive shaft coupling flange for loading. A throttle is used to control and increase the speed of the engine as the control variable. The dynamometer and engine is cooled by continuous supply of water to dissipate the generated heat. The other end of the dynamometer is hooked up to the digital readout system which contains the digital RPM meter, flow meter, oil sump temperature and so that experimental readings can be obtained. Figure4: Single cylinder 4-stroke CI engine with computerized control panel The engine was started by hand cranking with diesel fuel supply, and it is allowed to get its steady state (for about 10 minutes). Water to engine cooling jacket is maintained about 80lph and water flow pressure to Eddy current dynamometer is maintained between 1 to 1.5 bar throughout the experiments, this water flow pressure is maintained by means of ¼ HP external water pump. 72 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 71-74 Figure6: Comparison of BSFC of blends with Diesel Table 2: Engine specifications The software is run and operated in ONLINE mode with a specific filename. To record the data online, software is logged every time and data will be stored in the computer hard disk, which can be retrieved as and when required. The set of experiments were conducted at the designed speed of 1500 RPM and compression ratios of 16.5:1. The experiments were conducted at no-load, 10% of full load, 30% of full load, 50% of full load and 70% of full load, with neat diesel and blends of Methyl ester of Cotton seed oil and honge oil with Diesel as fuel. Data such as fuel flow, cylinder pressure, indicated power, brake power, air flow, exhaust temperature, specific fuel consumption, break thermal efficiency, volumetric efficiency, PV-diagram, pressure v/s crank angle diagrams were recorded at this condition. Figure7: Comparison of BTE of blends with Diesel V. RESULTS AND DISCUSSIONS The results from the experiments performed on the four stroke, single cylinder, diesel engine at an engine speed of 1500 rpm and compression ratio of 16.5:1 for various load operating conditions (likewise no load, 10%, 30%, 50%, and 70% and 100%). Initially the experiments are performed for diesel, H100, and C100 fuels. Then for different blends like 5H+15C+80D, and 5H+20C+75D was carried. The engine performance, like brake power, indicated power, brake specific fuel consumption, brake thermal efficiency, volumetric efficiency are obtained and then compared the performance of blends with those of D100, H100 and C100. The comparison graphs are shown in figure from figure5 to figure9. Figure8: Comparison of CO emission of blends with Diesel Figure9: Comparison of HC emission of blends with Diesel VI. CONCLUSION From this study it is found that the performance characteristics like break power (BP), break specific consumption (BSFC), break thermal efficiency (BTE) and emissions characteristics for the hybrid blends are better than the D100. Hence these two blend blends of Karanja oil biodiesel and Cotton seed oil biodiesels can be used as a vehicular fuel in CI engines. Figure5: Comparison of BP of blends with Diesel ACKNOWLEDGEMENT I express my gratitude to Prof. Dayanand Goudar, Head of the Department of Mechanical Engg. Tontadarya College of Engg. Gadag. for providing the facility and support during the development of this project. 73 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 71-74 REFERENCES [1] A Shivkumar, D Maheshwar, K Vijaya Kumar Reddy (2009) “Comparison of Diesel engine performance And emissions from neat and transesterified cotton seed oil”, JJMIE, Vol. 3, Sept 2009, pp. 190-197. [2] A Haiter Lenin, K Thyagarajan (2012) “Performance evaluation of disel engine fuelled with methyl ester of pongamia oil”, International Journal of Energy and Environment, Vol. 3, issue 6, 2012, pp.939-948 [3] Hanumanth Mulimani, Dr.O D Hebbal, M C Navindgi,(2012) “Extraction of biodiesel from vegetable oils and their comparisons”Intarnational Journal of Advanced Scientific Research and Technology. Vol.2. Issue 2. April 2012. [4] Xiahu Fan, Xi Wang, Feng Chen, Daniel P Geller, Peter J Wan.(2008) “Engine Performance Test of Cottonseed Oil Biodiesel”The Open Energy and Fuels Journals. Vol 1, pp.40-45 [5] Lohith.N, Dr .R Suresh , Yathish K V (2012) “Experimental Investigation of CI Engine using Karanja oil Methyl Ester as Alternative Fuel.” IJERA, Vol.2 Issue 4, August 2012, pp. 1172-1180. [6] S Kirankumar, Prof. K Apparao, Prof. R Nagendra Babu. “Experimental Investigation on Performance, Combustion Characteristics of Diesel Engine by using Fish Oil” IJERA, Vol.2, December 2012, pp. 1258-1263 [7] Sanjay Patil,” Theoretical Analysis of CI Engine Performance Fuelled with Honge Oil and its Blends with Ethanol”. IJMET, vol.4, August 2013, pp. 366-372 [8] Jinlin Xue, Tony E grift, Alan C Hansen (2011). “Effect of Biodiesel on Engine Performance and Emissions”. Renewable and Sustainable Energy Reviews, ELSEVIER, vol.15, pp. 10981116. [9] J M Marchetti, V U Miguel, A F Errazu,(2007). “Possible Methods for Biodiesel Production” Science Direct, vol.11, pp.1300-1311. [10] S P Singh, Dipti Singh.(2010) “Biodiesel Production through the use of different sources and characterization of oils and their esters as the substitute of diesel” ELSEVIER (rser), vol.14, pp.200-216 [11] T Elango, T Shenthilkumar (2011), "Performance and Emission Characteristics of CI Engine Fuelled with Non Edible vegetable oil and Diesel blends". JEST, vol. 6, No.2,(2011), pp.240-250. [12] Lokesh Adhappa Chandrashekar, N S Mahesh, Balakrishna Gowda, Willium Hall, (2012). "Life Cycle assessment of bio diesel production from pongamia oil in rural Karnataka". CIGR Journal, vol.13 No.3 pp 67. [13] S V A R Sastry, Ch.V Ramachandra Murthy (2012). "Prospectus of biodiesel for future energy security". Elexir,Chemical Engg., 53 (2012) 12029-12034. 74 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 75-77 ADAPTIVE FUZZY PID CONTROLLER FOR SPEED CONTROL OF PMSM DRIVE SYSTEM Rajnee Bala Minz1, Rajesh Thinga2, Supriya Tripathi3 PG Scholar/Deptt. of EE Samrat Ashok Technological Institute Vidisha(M.P.) 1
[email protected],
[email protected],
[email protected] Abstract- The modelling of adaptive fuzzy based speed controller for PMSM is presented in this paper. This detailed modelled system is derived in Simulink, which includes the tuning of the controller for the improvement of performance of the PMSM drive and the avoiding of the load disturbances. This adaptive fuzzy based speed controller is efficient not only in controlling linear plants but also in non-linear plants. Keywords- Adaptive fuzzy controller, PMSM drive, PID Controller. Abbreviations used: PMSM- Permanent Magnet Synchronous Motor, PID- Proportional Integral Derivative, AFLC- Adaptive Fuzzy Logic Controller. I. INTRODUCTION The PMSM like other drive system needs its speed to be controlled. There are various control techniques for controlling the parameters of the motor including the speed. In this paper adaptive fuzzy control is used for controlling the speed of PMSM. II. PMSM DRIVE The field- winding of synchronous machine is energised from a dc source and its armature winding is connected to an ac source therefore poly-phase-synchronous machines are known as doubly excited ac machine. When synchronous machine is used as a motor it takes in active power from an ac source and when it works as a generator, it delivers or exports ac power from a dc source. Since a synchronous generator delivers ac output, it is also known as an alternator. III. ADAPTIVE FUZZY BASED SPEED CONTROLLER The control of a permanent-magnet synchronous motor (PMSM) in AC drives is an important issue, because of its nonlinearity in the dynamics and time-varying parameters. This paper introduces the application of a new adaptive logic fuzzy controller (AFLC) for the speed control of field oriented PMSM. A model-referenced adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based PID controller on the error and changes of error measured between the motor speed and the output of a reference model. To reduce the dependency of controller on the quality of the expert knowledge, the fuzzy logic based speed controller is augmented by the model following error driven fuzzy adaptive mechanism so as to obtain fast and robust control for various operating conditions. The fuzzy logic controller is used for controlling the speed of this type of motor. The dynamic response with the Fuzzy based controller is more accurate as compared to the conventional controllers. The proposed controller is used in order to overcome the nonlinearity problem of PMSM and also to achieve faster settling response time. IV. SYSTEM MODEL The Simulink model consists of various blocks from the Simulink library so as to the desired designed speed controller. The various blocks in the controller performs according to the need in the controller. V. PID CONTROLLER A proportional-integral-derivative controller (PID controller) is a feedback control loop mechanism which is widely used in control systems in industries. A PID controller calculates the difference between a measured process variable and a desired set-point which is considered as an error. By manipulating the variables the error can be reduced by the controller. The controller can provide control action designed for specific process required by tuning the three parameters in the PID controller. Some of the applications may require using only one or two actions to provide the appropriate system control. This is achieved by keeping the other parameters to zero. A PID controller will be called as PI, PD, P or I controller in the absence of the respective control actions. PI controllers are quite common, since the derivative action is sensitive to measurement of noise, whereas the absence of an integral term may prevent the system from reaching its desired value due to the control action. VI. SPEED CONTROL OF PMSM DRIVE The speed control of PMSM drive can be done by various methods. But here adaptive fuzzy technique is used in the speed controller which uses the PID type controller. The control techniques are based on the fuzzy rules. The fuzzy rules can be formed by hit and trial error method. These rules so created for the speed controller depend on the nature of the output. 75 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 75-77 NL NM NS ZE PS PM PL E ----CE NL NVL NV NVL NL NM NS ZE B NM NVL NVL NL NM NS ZE PS NS NVL NL NM NS ZE PS PM ZE NL NM NS ZE PS PM PL PS NM NS ZE PS PM PL PVL PM NS ZE PS PM PL PL PL PL ZE PS PM PL PVL PVL PVL The above figure shows the membership functions of the input. The membership function of either input or output defines the range of values of the input or output in which they lie. The rule viewer of the fuzzy controller: ABBREVIATIONS USED IN FUZZY RULE BOX NVL- NEGATIVE VERY LARGE NL- NEGATIVE LARGE NM- NEGATIVE MEDIUM NS- NEGATIVE SMALL ZE- ZERO PS- POSITIVE SMALL PM- POSITIVE MEDIUM PL- POSITIVE LARGE PVL-POSITIVE VERY LARGE VII. CLOSED LOOP SPEED CONTROL IMPLEMENTATION For the implementation of closed loop speed control the PID controller is used with different gain values for the respective controllers. The fuzzy controller calculates the error between the reference value and the value from the output as a feedback. The advantage of the AFLC over controllers is that it eliminates the error in the system instead of reducing it. Rule in fuzzy controller is done using the ‘AND’ and ‘OR’ operation between the inputs and the output. There are mainly two techniques for forming these rules i.e. either by Generalised Modus Pones(GMP) or Generalised Modus Tollens(GMT). The rules so formed VIII. SIMULATION OUTPUT By the implementation of AFLC the stating current, starting is reduced and controlled and we obtain symmetrical waveforms of the motor parameters. Phase current in the motor: Rotor speed of the motor: 76 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 75-77 [II] [III] [IV] Output of Electromagnetic Torque (Te): [V] [VI] [VII] O.Kraa, A Aboubou and A.A .Taleb. Courrier du Savoir – N°15, Mars 2013, pp.97-104. Adaptive Fuzzy Logic Based Speed Control of Permanent Magnet Synchronous Motor by R.Venkatesh kumar, Dr.T.Govindaraj MNRE Sponsored National Conference AITHRE 2013 March 21st -22nd,2013. A New Adaptive Fuzzy Vector Control for Permanent Magnet Synchronous Motor Drive by K. Hakiki, A. Meroufel, V. Cocquempot, M. Chenafa, 18th Mediterranean Conference on Control & Automation Congress Palace Hotel, Marrakech, Morocco June 23-25, 2010. Efficiency optimization of an open loop controlled permanent magnet synchronous motor drive using adaptive neural networks by Munaf S. N. Al-Din, PhD, Assistant Prof and Majid A. AlTaee, PhD, Prof, January 2014 edition vol.10, No.3 ISSN: 1857 – 7881 (Print) e - ISSN 1857- 7431. Modelling and simulation of PMSM drives using fuzzy logic controller by Praveen kumar and Anurag Singh Tomer, vol.3 issue 4,july-aug 2013 pp-2492-2497,ISSN 2249-6645. Fuzzy Adaptive Controllers for speed control of PMSM drive by N.J.Patil, Dr. R.H.Chile and Dr. L.M. Waghmarre, vol. 1, no. 11 (0975-8887). Design, Development & Simulation of fuzzy logic controller to control the speed of Permanent Magnet Synchronous Motor Drive System. By Davendra Yadav, Sunil Bansal and Munendra Kumar, vol.1 issue 5,pp 101-106 aug 2012. IX. CONCLUSION By the implementation of this adaptive fuzzy control the speed of the PMSM drive is controlled and the output waveforms of the speed and torque produced in the motor is shown and they can be studied accordingly. As we can see in simulation output speed of the PMSM drive is not only controlled but also the starting current is reduced effectively. REFERENCES [I] Hybrid fuzzy logic and vector control of permanent magnet synchronous motor drive for electric vehicle by H.Ghodbane , 77 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 78-81 MINIMUM DELAY BASED ROUTING PROTOCOL IN MANET Abhishek Jain, Ashish Jain, Rohit Thete, Akshay Shelke, Harshada Mare BE, Department of Computer Science, MIT Academy of Engineering, Pune, India
[email protected],
[email protected],
[email protected],
[email protected],
[email protected] Prof. S.A. Jain Asst. Professor, Department of Computer Science, MIT Academy of Engineering, Pune, India
[email protected] Abstract—Broadcasting is a fundamental and effective broadcasting technique in mobile ad hoc networks (MANET). Simple flooding technique is used in conventional ad hoc protocols for route discovery in which the mobile node blindly rebroadcast the packets until route to destination is established. But this causes redundant transmission of control packets leading to collision and contention in network. This problem is referred as broadcasting storm problem. To overcome this problem, neighbor coverage based probabilistic rebroadcasting protocol is used which combines the merits of neighbor coverage knowledge and probabilistic method. In order to effectively exploit the neighbor coverage knowledge, rebroadcast delay is used to determine the forwarding order and then we calculate rebroadcast probability by combining additional coverage ratio and connectivity factor. This approach can significantly reduce the end-to-end delay by reducing the routing overhead and increasing packet delivery ratio to improve routing performance of the network. Index Terms—MANET, Rebroadcast Probability, Control Packets, Neighbor Coverage Knowledge, Broadcasting, Routing Overhead. I. INTRODUCTION MANET is a self-configuring, infrastructure-less network of mobile nodes which are connected without wires. But due to high mobility of nodes, link breakages may occur which will lead to frequent path failures and route discoveries. This increases the overhead of routing protocols, effectively increasing end-to-end delay and reduces the packet delivery ratio [1]. So reducing the routing overhead in MANET is essential problem. In conventional ad hoc on-demand distance vector routing protocol (AODV) [2] method, simple flooding is used for route discovery where nodes blindly rebroadcast received route request (RREQ) packet until route to the destination is established. While this method has many advantages, but due to redundant retransmission causes broadcast storm problem [3]. Some methods have been proposed to optimize broadcast problem and Williams and Camp [4] has categorized broadcasting protocol into four classes: “simple flooding, probability-based methods, area based methods and neighbor knowledge methods.” Since limiting the number of rebroadcast can effectively optimize the broadcasting [3]; and the neighbor knowledge methods perform better than the area-based method and the probability based method [6]. Combining merit of neighbor knowledge and probabilistic based method we propose neighbor coverage based probabilistic rebroadcast (NCPR) protocol. So, (1) In order to effectively exploit the neighbor coverage knowledge, rebroadcast delay is used to determine the forwarding order, (2) with the help of uncovered neighbor (UCN) set, additional coverage ratio and connectivity factor is calculated the determine the rebroadcast probability. Additional coverage ratio is a ratio of the covered node by single broadcast to the total number of neighbor and connectivity factor is relationship of network connectivity and number of neighbor of given node. The rest of this paper is organized as follows: Section 2 describes the proposed system and implementation Detail. Section 3 is Simulation Results. In Section 4 we have concluded with our observations. II. PROPOSED SYSTEM In this section, first calculate rebroadcast delay to determine it forwarding order and set the timer according to the delay. Second, calculate rebroadcast probability with the help of neighbor knowledge method by multiplying additional coverage ratio and connectivity factor, which requires that each node needs its 1-hop neighborhood information. A. Uncovered Neighbor Set and Rebroadcast Delay When source node S send RREQ packet to the node ni, it attaches its neighbor list along with RREQ packet. Node ni uses the neighbor list in the RREQ packet to estimate the number of neighbor nodes that are not covered by RREQ packet of node S. The uncovered neighbor set U(ni) of node ni is given as: (1) U (ni ) N (ni ) [ N (ni ) N (S )] {S} Where N(ni) and N(S) are neighbor set of node S and ni. S is the node which send the RREQ packet to node ni. But due to broadcasting characteristics, node may receive duplicate RREQ from its neighbor. So when node receives RREQ packet, a rebroadcast delay is set according to the neighbor list in RREQ packet and its own neighbor list. The rebroadcast delay T(ni) is defined as follows: Tr (ni ) 1 N ( S ) N ( ni ) N (ni ) T (ni ) MaxDelay * Tr (ni ) (2) Where Tr (ni) is delay ratio of node ni and MaxDelay is a small constant delay in the network.|.| is the number of elements in a set. 78 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 78-81 The rebroadcast is used to determine the forwarding order. During the actual implementation of NCPR, every node The node which has mode common node with the sender, will receive different RREQ and on reception of RREQ, they according to Eq. 2, will have lower delay. Therefore, this will calculate their UCN set and set the timer. rebroadcast delay enables the information that the nodes have 1. If receives the RREQs for the first time from s then transmitted the packet, spread to neighbors more quickly. This 2. Find the initial UCN set U(s, Rs.id) for RREQs is performed by using the Neighbor Knowledge Probabilistic 3. Calculate rebroadcast delay T(ni) Rebroadcast (NCPR) protocol based on the neighbor 4. According to delay T(ni), set the Timer (ni, Rs.id) knowledge method. After determining the rebroadcast delay, 5. End if the node can set its own timer. 6. While ni receives duplicate RREQj from before B. Neighbor Knowledge and Rebroadcast Probability Timer(ni, Rs.id) expires do 7. Adjust UCN set U(S, Rs.id) The node which has larger rebroadcast delay may receive 8. Discard RREQj RREQ packets from nodes which have lower delay. Suppose, if 9. End while ni receives duplicate RREQ request from its neighbor node nj, it 10. When timer is expires, we get the final UCN set will check how many neighbors had been covered by RREQ of 11. Calculate additional coverage ratio Ra(ni) node nj. Thus ni could further adjust the UCN set according to 12. Calculate connectivity factor Fc (ni) neighbor list in the RREQ of the node nj, i.e. U(nj), and is 13. Compute Rebroadcast probability P (ni) adjusted as follows: 14. Check if (Random(0,1) <= P (ni)) 15. Broadcast RREQs (3) U (ni ) U (ni ) [U (ni ) N (nj )] 16. Else After adjusting U(ni) , the RREQ packet received from node nj 17. Discard RREQs is discarded. 18. End if When the timer of rebroadcast delay expires, the node ni obtains final UCN set, which is used to calculate the additional III. PROTOCOL IMPLEMENTATION AND SIMULATION RESULT coverage ratio Ra(ni) for node ni: A. Protocol Implementation The NCPR protocol is implement NS-2.34 using AODV as U (ni ) Ra (ni ) base protocol. The NCPR uses Hello protocol to get (4) N (ni ) neighborhood information and then carry the neighbor list along with RREQ packet. To reduce overhead the Hello packet This metric indicate the number of nodes that are do not use periodical Hello mechanism but checks if the last additionally covered by the node ni. The higher value of Ra broadcasting time of control packets is greater than indicates that more nodes will be covered by this rebroadcast HelloInterval, the node will send the Hello packet. The control and hence more nodes should receive and process the RREQ packets such as RREQ and route error (RERR) can also act as packet. Thus, the value of rebroadcast probability will be Hello packet. higher. But, Ra does not consider the node density and the An additional field nb_count is added to the RREQ packet overall network connectivity. header to maintain the count of neighbor in the received RREQ Xue and Kumar [10] has derived that if each node connects packet. Since the node are mobile so there are three to more than 5.1774logn of its nearest neighbors, then the possibilities: probability of the network being connected is approaching 1 as Node ni may receive duplicate RREQ packet (checked n increases, where n is the number of nodes in the network. So, by comparing sequence number of RREQs) or new 5.1774logn can be used as the connectivity metric of the RREQ packet may be received so that node is to be network. The connectivity factor Fc (ni) for the node ni is : added to neighbor list Some node may move out of coverage area of node ni Nc so that node is removed from neighbor list Fc(ni ) (5) N (ni ) No node is added or removed from the neighbor list of node ni. Where Nc = 5.1774logn, and n is the number of nodes in the The nb_count is set with a positive integer when the node is network. added and its value is equal to the number of new node added Multiplying the additional coverage ratio and connectivity to neighbor list. Similarly when node are removed, nb_count is factor, we obtain rebroadcast probability P (ni) for node ni: a negative integer and is equal to number of nodes deleted neighbors but if no node is added or removed is nb_count is set P(ni) Fc(ni ) * Ra(ni ) zero. Thus according to the value of nb_count the node updates If P (ni) is greater than 1, then we set it to 1. the neighbor cache of node ni. C. Algorithm of NCPR Let RREQs is the route request packet received from node s, Rs.id is unique identifier of RREQs ,N(u) be the neighbor set of node u, U(u, x) is UCN set and Timer(u, x) is timer of node u for RREQ whose id is x. B. Simulation Environment The performance of the protocols is evaluated using following parameters: a. Average End-to-end delay: The average delay experienced by constant bit rate (CBR) packets to reach from source to destination successfully. 79 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 78-81 b. Packet Delivery Ratio: It is the ratio of total number of packets reaching the destination to the total packet sent by the source. c. Normalized Routing Overhead: It is the ratio of total packet size of control packets to the total packet size of data packets delivered to destination. The performance of three protocols AODV, Load Balancing Single Path Routing (LBR) and NCPR is compared in this paper. LBR and NCPR are the protocols modified using the source code of AODV. For the simulation, we have considered CBR data traffic and the selection of sourcedestination is done randomly. The simulation field will be 1000 Fig. 1. Normalized Routing Overhead Vs Number of Nodes (nodes are m 1000m and transmission range of every node is 250. Every static) source will send four CBR packets whose size is 512 byte/sec Fig 1 shows the graph of normalized routing overhead in the multi-hop fashion. The net performance is evaluated by against the number of nodes; it can be seen that NCPR has varying the number of nodes and the mobility of the nodes. The lowest overhead among all. MaxDelay for rebroadcast delay is set to 0.01 sec. The simulation parameter and scenarios for evaluating performance of protocols is given in the table below. TABLE I. SIMULATION PARAMETERS Simulator NS 2.34 MAC Type 802.11 Channel Type Wireless Channel Transmission Range 250 m Traffic CBR Routing Protocol AODV, LBR NCPR Antenna Model Omni Number of Nodes 5, 10, 20, 50, 100 Simulation Area 1000 m Traffic Type CBR / TCP Data Payload 512 Bytes/Packet Network Loads 4 Packet/Sec Simulation Time 100 sec Mobility 0, 5, 10, 25, 50 m/s Connection 1,3,5,10 Interface Queue length 5, 10, 20, 50, 100 Fig. 2. Packet Delivery Ratio Vs Number of Nodes(nodes are static) 1000 m C. Simulation Result 1) Performance in Static Environment Static environment is scenario in which mobility of the nodes of zero, i.e. the node are static. The performance of the protocol is analyzed for the traffic load between the nodes is varied as 1, 3, 5 and 10 connection. For each connection, the interface queue length (ifqlen) by varied as 5, 10, 20, 50 and 100. And for each queue length, we find the value by for 10, 20, 50 and 100 nodes. The Fig.1, Fig.2 and Fig.3 below shows the normalized routing overhead, packet delivery ratio and end-to-end respectively for ifqlen = 5 and connection = 3. Fig 2 shows the graph of packet delivery ratio against the number of nodes; as the number of node increases, the packet delivery ratio highest in NCPR, lower in LBR and least in case of AODV. Fig. 3. End to End Delay Vs Number of Nodes (nodes are static) Fig 3 shows the graph of end to end against the number of nodes; as the NCPR has smaller delay as the rebroadcast of RREQs is limited unlike AODV. Though LBR has delay close to the delay of NCPR. 2) Performance in Dynamic Environment Unlike static, in dynamic environment the nodes are mobile. The nodes have mobility of 5, 10, 25 and 50 m/sec. The simulation scenarios for dynamic environment are similar to the static environment. The below figure represent the normalized routing overhead, packet delivery ratio and end-to-end delay of the 80 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 78-81 network in dynamic environment where ifqlen = 20 and IV. CONCLUSION connection = 3 and the mobility of nodes is 10 m/sec: The conventional on-demand routing protocol suffers from broadcasting storm problem. So to overcome this problem, we propose NCPR protocol which combines the merits of neighbor coverage knowledge and probabilistic method. The NCPR will first calculate the rebroadcast delay to determine the forwarding order and then we calculate the rebroadcast probability by combing. Thus the overhead in the network has also reduced which the additional coverage ratio and connectivity factor. The simulation result shows the proposed system reduces the end-to-end delay effectively since we have limited the rebroadcast of RREQs to only those nodes who receives RREQ it for the first time will eventually increase the packet delivery ratio. The simulation result also shows that since the control packets are reduced, the NCPR has good Fig. 4. Normalized Routing Overhead Vs Number of Nodes(nodes are performance as the number of node increase. dynamic) Fig 4 shows the graph of normalized routing overhead against the number of nodes; as the node are mobile the overhead will increase as the number of node increases but in comparison to AODV and LBR, NCPR has the lowest overhead due to reduced number of controls packets unlike conventional which suffers from broadcast storm problem. V. ACKNOWLEDGMENT We would like to express our sincere gratitude to Prof. S.A Jain, Department of Computer Engineering, MIT Academy of Engineering for his valuable guidance that he provided us at various stages throughout the project work. He has been source of motivation enabling us to give our best efforts in this project. REFERENCES Fig. 5. Packet Delivery Ratio Vs Number of Nodes(nodes are dynamic) Fig 5 shows the graph of packet delivery ratio against the number of nodes; LBR performs well for the lower number of node but as the number of nodes increases, the merits of NCPR are quiet visible from the graph. Fig 6 which shows the graph of end to end against the number of nodes also shows the same observation as in the static environment and in the case also the end to end is lowest in the NCPR compared to conventional AODV. Fig. 6. End to End Vs Number of Nodes(nodes are dynamic) [1] X. Wu, H.R. Sadjadpour, and J.J. Garcia-Luna-Aceves, “Routing Overhead as a Function of Node Mobility: Modeling Framework and Implications on Proactive Routing,” Proc. IEEE Int’l Conf. Mobile Ad Hoc and Sensor Systems (MASS ’07), pp. 1-9, 2007. [2] C. Perkins, E. Belding-Royer, and S. Das, Ad Hoc On-Demand Distance Vector (AODV) Routing, IETF RFC 3561, 2003. [3] S.Y. Ni, Y.C. Tseng, Y.S. Chen, and J.P. Sheu, “The Broadcast Storm Problem in a Mobile Ad Hoc Network,” Proc. CM/IEEE MobiCom, pp. 151-162, 1999. [4] B. Williams and T. Camp, “Comparison of Broadcasting Techniques for Mobile Ad Hoc Networks,” Proc. ACM MobiHoc, pp. 194-205, 2002. [5] J.D. Abdulai, M. Ould-Khaoua, L.M. Mackenzie, and A. Mohammed, “Neighbour Coverage: A Dynamic Probabilistic Route Discovery for Mobile Ad Hoc Networks,” Proc. Int’l Symp. Performance Evaluation of Computer and Telecomm. Systems (SPECTS ’08), pp. 165-172, 2008. [6] J. Kim, Q. Zhang, and D.P. Agrawal, “Probabilistic Broadcasting Based on Coverage Area and Neighbor Confirmation in Mobile Ad Hoc Networks,” Proc. IEEE GlobeCom, 2004. [7] C. E. Perkins and P. Bhagwat, ”Highly dynamic destinationsequenced distance vector routing (DSDV) for mobile computers,” Proceedings of ACM SIGCOMM’94, pp. 234-244, September1994. [8] W. Lou and J. Wu, On reducing broadcast redundancy in a hoc wireless networks, IEEE Transactions on Mobile Computing, vol. 1, no. 2, pp. 111123, Apr.-June 2002. [9] Y. Sasson, D. Cavin, A. Schiper, Probabilistic broadcast for flooding in wireless mobile ad hoc networks, EPFL Technical Report IC/2002/54, Swiss Federal Institute of Technology (EPFL),2002. [10] A. Mohammed, M. Ould-Khaoua, L.M. Mackenzie, C. Perkins, and J.D. Abdulai, “Probabilistic Counter-Based Route Discovery for Mobile Ad Hoc Networks,” Proc. Int’l Conf. Wireless Comm. AndMobile Computing: Connecting the World Wirelessly (IWCMC ’09), pp. 1335-1339, 2009. [11] F. Xue and P.R. Kumar, “The Number of Neighbors Needed for Connectivity of Wireless Networks,” Wireless Networks, vol. 10, no. 2, pp. 169-181, 2004. 81 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 82-86 SPLIT BLOCK SUBDIVISION DOMINATION IN GRAPHS M.H. Muddebihal 1, P.Shekanna2, Shabbir Ahmed3 Department of Mathematics, Gulbarga University, Gulbaarga-585106. 1
[email protected],
[email protected],
[email protected] Abstract: A dominating set set in is a split dominating . If the induced subgraph is isolated vertices. The cototal domination number of is denoted by , is the minimum cardinality of is the minimum cardinality of a cototal dominating set. See [4] The following figure illustrate the formation of a split dominating set in . In this paper, some results on of a graph disconnected in The split domination number of were obtained in terms of vertices, blocks, and other different parameters of but not members of Further, we develop its relationship with other different domination parameters of Key words: Block graph, Subdivision block graph, split domination number. [I] INTRODUCTION All graphs considered here are simple, finite, nontrivial, undirected and connected. As usual denote the number of vertices, edges and blocks of a graph respectively. In this paper, for any undefined term or notation can be found in F. Harary [3] and G .Chartrand and PingZhang [2]. The study of domination in graphs was begin by O.Ore [5] and C.Berge [1]. As usual, The minimum degree and maximum degree of a graph are denoted by respectively. A vertex cover of a graph edges of is a minimum cardinality of a vertex cover in The vertex independence number is the maximum cardinality of an independent set of vertices. A edge cover of is adjacent to some vertex in of .The if the induced subgraph MAIN RESULTS Theorem A [4]: A split dominating set each vertex There of is minimal for one of the following condition holds. exists a vertex such that is an isolated vertex in is connected. Theorem B [4]: For any graph . is the minimum cardinality of Now we consider the upper bound on a dominating set in . A dominating set [II] is a set of edges that covers all the vertices. The edge covering number of is minimum cardinality of a edge cover. The edge independence number of a graph is the minimum cardinality of an independent set of edges. A set of vertices is a dominating set. If every Domination number We need the following Theorems for our further results: is a set of vertices that covers all the The vertex covering number vertex in The domination of split subdivision block graph is denoted by . In this paper, some results on where obtained in terms of vertices, blocks and other parameters of of a graph is a split dominating set in terms of blocks in is disconnected. The split domination number of a graph is the minimum cardinality of a split dominating set .This concept was introduced by A dominating set of is a cototal dominating set if the induced subgraph Theorem 2.1: For then any graph with . has no 82 | P a g e Proof: For any graph International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 82-86 Case2: Suppose each block of is a complete graph , a split domination with does not exists. Hence we required blocks. Let be the number of blocks of and be the vertices in with corresponding to Subcase2.1: Assume , that . not ,1 vertices cut .Clearly Let which such that is a dominating set is disconnected graph. Then Then is and where is an isolates. Hence be a set of cut vertices. Again consider a such are . Again we consider the sub cases of the blocks of Also be the set of vertices in Let 1 subset with case 2. a Hence gives which . Sub case 2.2: Assume every block of ) is . Let then and = which gives In the following Theorem, we obtain an upper bound for in terms of vertices added to where is Theorem 2.2: For any connected blocks, then is the number of vertices added to Proof: For any nontrivial connected graph . If the graph Then by the definition, split domination set does not isolate. Hence graph with where has an exists. Hence be the blocks of which gives We establish an upper bound involving the Maximum degree and the vertices of for split block sub division domination in graphs. Let Theorem 2.3: For any graph and then . be the vertices in which corresponds to the blocks of .Now we consider the following cases. Case1: Then be Suppose the each set of block . Let of is vertices consider of an corresponding to the blocks of be the vertices in be a Again there exists a subset property adjacent to disconnected of graph . is vertex of Hence By Theorem 1, dominating Theorem . Let set is a split in Thus Since by with the where atleast one Let By Theorem A, each vertex there exist a vertex are adjacent to end vertices of and be the vertices in is a set of cut vertices in consider the graphs with Let be the blocks of edge. Now , Proof: For split domination, We the property B which gives is a is a The following lower bound relationship is between split domination in and vertex covering number in 83 | P a g e Theorem 2.4: For any graph , where International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 82-86 A relationship between the split domination in and ,then with is a vertex covering number of Proof: We consider only those graphs which are not Let which be the blocks of to the set be the vertices in B(G). Let correspondes be the vertices in Again such that , independence number of a graph following theorem. Theorem2.6: For any connected graph then disconnected, which Now corresponds to the .Let set of vertices in be the e have the following cases. Suppose is are a cut tree. vertices Let in Again . Hence which gives Let be the blocks of which vertices of the set in . Let is adjacent to atleast one vertex in Clearly the be the vertices in Case1: and each edge in is Proof: By the definition of split domination, and is . with independence number of . such that . Hence gives is established in the and . ,were . Then we consider , The following result gives a upper bound for in terms of domination and end blocks in with the property Theorem 2.5: For any connected graph and where and with in then is a set of all end vertices . Again where every is an isolates.Thus . Proof: Suppose graph is a block .Then by definition, the split domination does not exists. Now assume with at least two blocks. Let Subcases2.1: be the vertices in Now which corresponds to the blocks of . be the vertices . Suppose D is a of whose vertex set is size vertex of the . More over, any component of is a block. Then , minimal the number of isolates in Hence can see that for . One the as in case , We have which gives . is of which gives Sub case 2.2: Assume has atleast two blocks.Then as in subcase 2.1,we have vertices in and Every Suppose there exists a vertex such that every vertex of to at least Thus which gives one vertex . . The next result gives a lower bound on in terms of the diameter of is adjacent to at least one vertex of is not adjacent is Thus . Note that atleast two. Thus . Again be Assume in and at least one is not a tree. Again we consider sub is a graph be the set of blocks in in Case 2: Suppose cases of case 2 Theorem 2.7: For any graph ,then with blocks . Hence . 84 | P a g e Proof : International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 82-86 be the Further which Suppose blocks of ,Then corresponding block be the vertices in B(G). Suppose be the set of edges which constitutes the diameteral path in .Let Suppose where are non end blocks in which gives cut vertices in Suppose and be the vertices in gives . Next, the following upper bound for split domination in is interms of edge covering number of . Theorem2.9: For any connected ( graph with where is the edge covering number. where such that vertices in are cut is a . Clearly Suppose is cyclic then there exists atleast one block which contains a block diametrical path of length atleast two. In the block as a singleton and if atmost two elements of diameter of then gives . is acyclic then each edge of , where . have is a block of gives Clearly we domination set does not exists. Hence Let which be the vertices in such that We have the following cases. Case 1: Suppose each block is an edge in .Then where is the set of end edges, vertex. If every cut vertex of Then 2.8: For , . . Let then there exist atleast one cut vertices in any graph with . Proof: Suppose the graph is adjacent with an end Then .Otherwise The following result is a relationship between Theorem . be the blocks of to the set be the vertices in Let correspondes . domination and vertices of with then by definition of split domination, the split Since they are non end blocks in . Then Suppose Now Proof: For any non trivial connected graph Let which are non cut vertices in and . Again The is a split dominating set. has one block, then split Hence . Since has more than one component. domination does not exists. Hence Hence Suppose which gives be the blocks of Then be corresponding block vertices in the Let be the set of vertices in . Case2: Suppose has atleast one block which is not an edge. Let , and be the set of cut vertices such that Also , and is adjacent to atleast one vertex of be the set of vertices in Now such that which corresponds to the elements of forms a minimal dominating set of Since each element of in be the set of cut such that , and where are non end blocks in . Then we have vertices . Again Hence is disconnected , which gives As in case 1 , will increase. Hence which gives . is a cut vertex, then 85 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 82-86 The following lower bound for split domination in is and are cut vertices in interms of edge independence number in is adjacent to at least one vertex in Then Theorem 2.10: For any graph gives disconnected graph. Thus with gives Proof: By the definition of Split domination, we need We have the following cases. Case 1: Suppose each block in . Case 2: Suppose each block in be is an edge. Let .Further be a set of alternative edges in we vertices such where Clearly Consider in that is disconnected. which gives be the vertices in , again be the cut vertices which are adjacent to at least one vertex of and are the end vertices in disconnected. Then is Case2: Suppose there exists at least one block which is not an edge. Let be the set of edges in Again edges in Finally, the following result gives an lower bound on in terms of Theorem . 2.12: For any nontrivial . Proof: We consider only those Let H which gives , Suppose be the vertices of Then where is a set of cut vertices is a set of non cut vertices. Now we consider such that which are end vertices in with has more than one component. Hence is a and which gives For any connected then vertices in split domination, graph where with is the cut . Proof: Suppose graph and has . Let the Let vertices no in isolates, consider be the set of all vertices of with the property , is a set of all end vertices in gives minimum split domination in In the following theorem, we expressed the lower bound for in terms of cut vertices of 2.11: that then be with be a subset of such tree graphs which are not is the set of alternative which gives Theorem cut consider Then and the Again are the non cut vertices in be the set of edges in Also is not an edge. Let is a block. Then by the definition, of consider the following cases. Case 1: Suppose each block of is an edge. Then we consider be the cut vertices in . Clearly gives which . REFERENCES [I] C Berge, Theory of graphs and its applications, Methuen, London, (1962). [II] G. Chartrand and Ping Zhang, “Introduction to graph Theory”, Newyork (2006). [III] F.Harary, Graph Theory, Adison Wesley, Reading Mass (1972). [IV] V.R.Kulli, Theory of domination in Graphs, Vishwa international Publications, Gulbarga, India. (2010). [V] O.Ore, Theory of graphs, Amer. Math. soc., Colloq. Publ., 38 Providence, (1962). be the vertices in 86 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 87-88 MONITORING FIXTURES OF CNC MACHINE Pingale Namrata Namdev1, Prof. Hate S.G2 1 ME(VLSI & Embedded System), G.H. Raisoni College of Engineering, Ahmednagar, India Electronics and Telecommunication Department, G.H. Raisoni College of Engineering & Technology, Pune, India 2
[email protected],
[email protected] Abstract – This paper introduce a system that used ARM basedmicrocontroller and wireless sensors to control the various devices and to monitor the information regarding the CNC machines parameter using WI-FI technology .If there is any error in machine it can’t be recognized by the person sitting in the office.The existing system is difficult to maintain. This consumes lot of time on communication between technical persons. To overcome this problem we are trying to develop the system. This system will give a informatioto the respective technical person according to the error detected. Keyword: ARM (Advanced RISC Machines); WI-FI(wireless fidelity) I. INRODUCTION CNC machine is having three sections Hydraulic Section Mechanical Section and electrical section. So while working with so much of machines this EMI section gets problem. When any of the machines stop working because of any internal problem, it gives an alarm so that worker should know that there is some problem with that machine. But if worker can’t recognise the problem then he should inform this to concerned technical person. But worker will inform this to all technical person from mechanical section, Hydraulic section and also to electrical section. This consume a lot of time. Vibration is one of the most concentrated problems in CNC machine tools, which can significantaly reduce the machining precision [1].This lead to decrease in production because of difficulty in tracing the error by technical person. To reduce this time delay we are implementing one system. This system help will technical person instantly as error will be introduced through the text message. This text message will include machine number and actual error occurred in that machine. And this text message will be send to only the section related that technical person. While message is sending to that respective person machine will be switched off..mesh clients, mesh routers and gateways often consist by Wireless mesh networks [3]. A WMN is offers redundancy[3]. II. LITRATURE SURVEY CNC was invented by John T. Parsons while making helicopter blades for the military. His numerical control used a rudimentary computer to move the cutting spindle along the x and y axes. The CNC machine first appeared when John Runyon managed to produce punch tapes under computer control. This showed dramatic results in terms of time, reducing the normal production duration of 8 hours to 15 minutes. the Air Force accepted the proposal to produce a generalized “programming” language for NC In June 1956.. Eventually, the Air Material Command at the WrightPatterson Air Force Base and the Aircraft Industries Association (AIA) collaborated with MIT in 1957 to generate a fully computer controlled NC system. The CNC machines invenstion paved the way for automated tools that meant cost efficient production for manufacturers. The computer automation of manufacturing now uses very sophisticated programs, including the original x and y grid to cut parts on several axes. CNC mills can now cut at various angles, and even have moving tables that turn the part to access areas previously impossible to reach. III. SYSTEM BLOCK DIAGRAM Fig. 1:System Block diagram We have used a ARM7 based LPC2148 microcontroller. LPC2148 is a 16bit/32bit microcontroller with a high speed flash memory ranging from 32kbit to 512kbit. Serial communications interfaces ranging from a USB 2.0 Full-speed device, multiple UARTs, SPI, SSP to I2C-bus and on-chip SRAM of 8 kB up to 40 kB, make these devices very well suited for communication gateways and protocol converters, soft modems, voice recognition and low end imaging, providing both large buffer size and high processing power.PIC 18f458 is used outside the CNC machine PIC 18f458 is used to monitor the lcdand indicator and wi-fi module.PIC 18f458 is having 10 bit 8 channel ADC.PIC 18f458 is also used at the slav side to monitor the wi-fi module and the pc.we can see the data of CNC with the help of labview software. A. Hall Effect Sensor: HALL EFECT SENSOR is a device which converts magnetic or magnetically encoded information into electrical signals is called. A Hall Effect device/sensor is a solid state device that is becoming more and more popular because of its many uses in different types of applications this devices are immune to vibration, dust and water.TheBasic Principle of Hall Effectis the activation by an external magnetic field. As we are familiar that there are two important characteristics of a magnetic field.Viz.Flux density, (B) and polarity (North & South Poles).the HallVoltage,VH produces When the magnetic flux density around the sensor exceeds a certain preset threshold, the sensor detects it and generates an output voltage [6] 87 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 87-88 Certified product can use any brand of access point with any other brand of client hardware that is also "Wi-Fi Certified". Products that pass this certification are required to carry an identifying seal on their packaging that states "Wi-Fi Certified" and indicates the radio frequency band used (2.5GHz for 802.11b, 802.11g, or 802.11n, and 5GHz for 802.11a).[3] Fig2: Working of Hall Effect B. Limit switch An electromechanical device that consists of an actuator mechanically linked to a set of contacts is called as limit switch .When an object comes into contact with the actuator, the device operates the contacts to make or break an electrical connection.It can determine the presence or absence of an object. It was first used to define the limit of travel of an object; hence the name "Limit Switch." Actuator: The portion of the switch that comes in contact with the object being sensed. Head: It is the mechanism that translates actuator movement into contact movement. When the actuator is moved as intended, the mechanism operates the switch contacts. Contact Block: It is the electrical contact elements of the switch. It typically contains either two or four contact pairs. Terminal Block: It contains the screw terminations. This is where the electrical (wire) connection between the switch and the rest of the control circuit is made. Switch Body: The switch body is the contact block in a plug-in switch. It and terminal block in the nonplug-in switch. Base: The base is the terminal block in a plug-in switch do not have a separate base[5] D. Liquid Crystal Display: LCD is used in a project to visualize the output of the application. We have used 16x2 lcd which indicates 16 columns and 2 rows. So, we can write 16 characters in each line. So, total 32 characters we can display on 16x2 lcd. LCD can also used in a project to check the output of different modules interfaced with the microcontroller. Thus lcd plays a vital role in a project to see the output and to debug the system module wise in case of system failure in order to rectify the problem. E. PC: PC is used to analysis the various parameters of CNC machine using a labview software developed window. Using pc we can analysis the intensity of errors happened and the necessary solution can be started well on time. IV. In this paper, we can use this system in various industries. This system will definitely help us to remove errors as early as possible. So, because of this system production rate of the industry will increase. As human communication errors are removed, communication between worker and technical person will be very fine. This system will also keep record of errors and technical person dealing with that error. So, this will be helpful for company while analysis. ACKNOWLEDGMENT A work of such a great significance is not possible without the help of my guide Prof. Hate S. G and ME Coordinator Prof. Bhope V.P for the valuable suggestions, co-operation and continuous guidance.. It’s my pleasure to thank to my principal who is always a constant source of inspiration and always provided joyful atmosphere. [I] . Fig3: limit switch C. Wi-Fi module: Wi-Fi provide wireless high-speed Internet and network connections this is the popular wireless networking technology that uses radio waves. Wi-Fi is supported by many applications including video game consoles, home networks, PDAs, mobile phones, major operating systems, and other types of consumer electronics. Any products that are tested and approved as "Wi-Fi Certified" (a registered trademark) by the Wi-Fi Alliance are certified as interoperable with each other, even if they are from different manufacturers. For example, a user with a Wi-Fi CONCLUSION [II] [III] [IV] [V] [VI] REFERENCES Ming Zhao1, Jing Lin1,2*,Xiufeng Wang1,2, Yuhe Liao1,2”Dynamic Transmission Error Analysis for A CNC Machine Tool Based on Built-In Encoders ”2011IEEE Zhai Wen-zheng1,2, HU Yue-li1,21. Key Laboratory of Advanced Display”Design and Implementation of CNC Machine Remote Monitoring and Controlling System Based on Embedded Internet” 978-0-7695-4212-6/10 $26.00 © 2010 IEEE DOI 10.1109/ISDEA.2010.283 506 wi-fitechnology”National telecom regulatory authority.2003. SONG Wen, WANG Fei, DAI Jianbo” A Emergency Communication System Based on WMN in Underground Mine” 978-1-4244-7237-6/10/$26.00 .2010 IEEE http://www.insysacorp.com/catalogs/cromptongreaves/Limit%2 0Switches.pdf http://www.electronicstutorials.ws/electromagnetism/halleffect.html 88 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 89-90 FUELS FROM PLASTIC WASTES Prajakta Sontakke Dist-Thane, Mumbai, India.
[email protected] Abstract- Originally, plastic is made from petroleum or natural gas in a chemical process that combines smaller molecules into a large chainlike molecule, often with other substances added to give it particular qualities. Processes like gasification of granulated plastic and catalytic pyrolysis can be used to convert plastic, the long hydro-carbon chain back into smaller hydra-carbon chains of naphtha, diesel, heavy diesel, kerosene etc. These fuels can then be used anywhere from boiler fuel in power generation to use in automobiles. This paper aims to provide the best possible review of this much needed conversion with the hope of visiting lowered fuel prices in the near future by improvements in the design. Keywords: Waste-energy, diesel, gasification, pyrolysis, ecofriendly, fuels. I. INTRODUCTION According to the United Nations Environment Programme, global plastic consumption has gone from 5.5 million tons in the 1950s to 110 million tons in 2009. Due to the technical limitations or inconvenience of recycling, only a fraction of that material resurfaces in new plastic products. This leads to extra-ordinary amounts being dumped in landfills for thousands of years. The Pacific Ocean is home of the world's biggest landfill: the Great Pacific Garbage Patch. The Plastics Division of the American Chemical Council asked the Earth Institute’s Earth Engineering Centerto explore ways of recovering the energy inherent in non-recycled plastics. The resulting report, released in August 2011, determined that the amount of energy contained in the millions of tons of plastic in U.S. landfills is equivalent to 36.7 million tons of coal, 139 million barrels of oil, or 783 billion cubic feet of natural gas. If all this plastic were converted into liquid fuel, it could power all the cars in Los Angeles for a year. And the fact is there are now technologies that can put all this waste plastic to good use. As stated earlier, plastic is a long chain hydro-carbon made from smaller chained hydro-carbons like oil, diesel, kerosene etc. Following are the processes in detail which yield the maximum efficiency in this conversion: A. Gasification of granulated waste plastic Industrial makers of plastic parts generate a lot of plastic wastes, which sometimes is granulated before being dumped into a landfill so companies are not paying to dump airspace. This process involves complete gasification; there is no melting or slagging. The burner takes the granulated plastic, sized in diameter between 2 and 10 millimetres, from a solid to a liquid to a gas immediately in the combustion chamber. That gas is actually producing the heat we need to transfer into the boiler system. During the gasification of the granulated waste plastic, temperatures are very high-1,850 degrees Fahrenheit. The studies indicate emissions profiles cleaner than that of natural gas. Stack tests conforming to U.S. EPA standards were conducted on the burner unit by an independent testing company. The emissions testing evaluated the burner fueled with pelleted No. 4 low-density polyethylene (LDPE) from Korea; granulated No. 2 high-density polyethylene from discarded plastic barrels; and granulated, dirty No. 4 LDPE mulch- film. Three main categories of pollutants were tested: particulate matter; gases (sulphur dioxide, nitrogen oxide and carbon monoxide); and dioxins/furans. Test results proved that this is an extremely clean-burning system. Fig1. Stages of plastic particles undergoing gasification B. Catalytic Pyrolysis of waste plastic While interest in combusting and gasifying plastic appears to be growing, there is another route to making practical use of all the waste plastics modern society produces. Through catalytic pyrolysis, a system was devised to convert waste plastics into liquid hydrocarbons, coke and gas, which can then be used as boiler fuel for power generation. The technology uses lower temperatures than gasification-significantly lower, so it's more energy efficient to produce. Through "random depolymerisation," or selective breaking of carbon-to-carbon bonds, in addition to feeding in proprietary catalytic additives, the reactor melts and vaporizes waste plastic in one step at temperatures between 840 and 1,020 degrees F. On average, 78 percent of every pound of plastic fed into the system is converted to liquid hydrocarbons, coke and gas. The resultant coke can be further processed to produce additional fuel oil. This catalytic pyrolysis system processes polyolefins like polyethylene and polypropylene with up to 5 percent other plastic materials, plus up to 25 percent additional nonplastic waste, such as paper, glass, sand and water-making it ideal for processing municipal wastes. The output oil contains no chlorine, sulphur, nitrogen or heavy metal. Fig2. Process of pyrolysis of waste plastic 89 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 89-90 II. PLASTIC TO FUEL CONVERSION TECHNOLOGY III. CONCLUSION ACROSS THE GLOBE This conversion as we have already seen is nothing but A series of plants adopting either one of the above beneficial. The gas emissions and ash produced during the mentioned processes have been set up across many countries process also falls under the permitted conditions against by a range of leading companies. Their fuel production pollution. A huge lot of terra-firma is also saved with such capacity ranges from a mere 4 litres of fuel to lakhs of undertakings. In addition, the following table shows the tonnes, they are obviously related to the size of waste plastic fuels so obtained are better than normal boiler fuels too. The feeder and the time taken for 1 cycle of the process to fuel quality from our P2O process is notably better than the complete. Following are some of the already set up and fully typical boiler fuel purchased by large industrial users, as shown below. functional plant datas: 1. 2. 3. 4. 5. 6. 7. 8. UK: Cynar produces a synthetic fuel suitable for all internal combustion engines.20 tonnes per day per module. Washington, DC: Boosts easy installation, high efficiency, no second-time pollution.Plant converts 6,000 tons of plastic into nearly a million barrels yearly. Circle Pines, MN and International: They have a modular unit that produces 775 litres of fuel for every ton of plastic waste processed. System capacity is rated at 185 tons per month. New York/Canada: JBI, Inc. 20-ton processor, 4,000 lbs. of plastic feedstock per machine per hour. Philippines: www.polygreen.com.ph 5,000 kilos of fuel per day. Hong Kong: Ecotech Recycling Social Enterprise Prototype machine can process three tons of plastic waste into 1,000 litres of fuel oil per day. Pune, India: Rudra Environmental Solutions, the yield is claimed to be 50 to 55% of the plastic disintegrated. Annamalai University, Tamilnadu, India: Produces 50 litres of the petroleum products in two hours. The fuel obtained is ultra-low sulphur fuel. Ultra-low sulphur diesel is a type of diesel fuel that contains 15 parts per million (ppm) or lower sulphur content. This diesel fuel is often referred to as “clean diesel” because the sulphur content has been reduced by more than 95%. In 2010, the U.S. Environmental Protection Agency (EPA) mandated that 100% of highway diesel imported into or refined in the U.S. must meet this low sulphur standard. The reduction of sulphur in diesel enables and preserves the operation of advanced emissions control systems on light- and heavyduty diesel vehicles. The use of advanced emissions controls leads to an environmental benefit where oxides of nitrogen and other pollutant particulates are drastically diminished. REFERENCES [I] Feedstock refining and pyrolysis of waste plastics by John Scheirs and Walter Kaminsky. [II] http://biomassmagazine.com/articles/2067/power-and-fuelfrom-plastic-wastes. [III] http://www.inspirationgreen.com/plastic-waste-as-fuel.html. [IV] Articles from Times Of India. Fig3. A plastic to fuel conversion plant at JBI, Inc., NY. 90 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 91-93 MEDICAL DECISION MAKING IN SELECTING DRUGS USING COMPUTER-GENERATED VIRTUAL ENVIRONMENTS Silvia Riva1, Gabriella Pravettoni1-2 1 Department of Health Sciences, University of Milan (Italy) 2 Psycho-Oncology Unit, European Institute of Oncology (IEO), Milan, Italy
[email protected] ABSTRACT Introduction: Many people rely on non-prescription drugs therapy to treat common medical conditions. Health technology can be a valid support to help people in selecting and choosing an appropriate treatment. Aim: This study examined how common people make their decisions to select a non-prescription drug, evaluating comprehensibility and satisfaction of a virtual tool that could propose and sell different types of non-prescription drugs therapy. Methods: Fifty voluntary participants were enrolled to conduct both the experiment with the virtual tool and a short structured interview which included comprehensibility and satisfaction questions, about the task performed. Results: All participants performed the task quickly and easily. Most of them focused their attention only on specific cues (91%) of the drugs, namely side effect (61%) and doctor’s advice (39%). Moreover participants evaluated the tool as comprehensible and satisficing. Conclusion: The use of non-prescription drugs therapy shift different responsibilities onto the individuals. A dedicated virtual tool can represent a valid support to help people in these type of decisions. These findings have implications both for the cognitive psychology that studies the cognitive process behind the choice and the selection of a drug and for technology and computer science that studies how to create concrete support for improve people’s quality of life. KEYWORDS: Decision making, health outcomes, health technology, virtual task, cognitive psychology, psychology I. INTRODUCTION Nonprescription drug therapy is an increasingly important element of everyday-life contexts and it is becoming tightly woven into the self-care system for several common health problems [1-6]. Clearly, it is important for both patients and healthcare providers to discuss how nonprescription drug therapy are chosen and used because there is evidence that patients can be often uncertain about these type of treatment that are being consumed [3; 5-6]. Furthermore, in line with this, it is important to understand how the process of choice works and which strategies plays a role in this process. Nowadays, there are several modalities to study people’ preferences and to evaluate habits and choice styles [7]; one of these is offered by technology, especially by virtual technology [8-9] which is becoming more and more popular in psychological sciences [10] II. COMPUTER-GENERATED VIRTUAL ENVIRONMENTS Computer-generated virtual environments have reached a high level of usability in several area of psychology. In recent times, the creation of virtual environment simulations (VES) has reached a sophisticated level in terms of graphic display and interaction with the user [8-9]. In psychology VES may be used to create realistic scenarios which simulate the real situations in the real world. Moreover, these virtual situations may be designed to reflect natural situations. What do we mean by VES? One definition is that it is a state of affairs, a depiction of objects or a space which has no physical basis. In this environment people can interact or even touch these objects [10-11]. The ontology of a VES includes its actors or players, an environment or geometry in which the actors behave and a set of rules of behavioural dynamics attached to the actors [11]. In essence a VES requires three differentiable components: the motion input or interactive control devices, the simulated environment itself and the rendering of the environment. There are a number of ways in which using a VES may be beneficial. Using a VES gives one the opportunity to eliminate or control for unwanted cues while preserving the attention on the study-stimulus [11]. VES also allows one to look at the dynamics and behavioural aspects of learning and encoding. Another benefit of VES is the ability to create specific environment for specific application. There are however a number of barriers to using VES [9]. Many researchers are skeptical regarding the validity of obtained results under such artificial conditions. One remedy for this appears to be bringing experiments in which the performance of subjects in a particular task in compared in both VES and equivalent ecological task. Another remedy is to clarify and deep the results that can be obtained in a VES task with other measures like questionnaires or interview or observational methodologies. III. AIM In a previous published study [5], we have evaluated the role of VES in supporting people’s choice for selecting different type of nonprescription drug therapy. Using the previous architecture [5], we proposed a new virtual task in a new community of people with the intent to evaluate the level of comprehensibility and satisfaction of the task through a short semi-structured interview. IV. METHODS A. Participants The analysis was based on semi-structured tests devised in Java language with 50 participants. Tests were conducted by a cognitive researcher trained into this research (SR, first author). Participant signed an Informed Consent to declare their participation to this experiment. The research was conducted in Milan (Northern Italy) with the collaboration of the University of Milan and at the Catholic University of Sacred Heart of Milan from January 2014 to April 2014. Participants were not remunerated. They voluntarily participated in the tasks and showed great 91 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 91-93 enthusiasm, viewing their participation as a contribution to V. RESULTS the quality of their medical assistance. Characteristics of participants are given in Table 1. The B. The VES Task sample included 31 (62%) female and 19 (38%) female, the The data treated here consist of test results which track mean age was 25 (range 21-32; SD=1,5). information lookups and decisions in a hypothetical situation To investigate the level comprehensibility and in which participants were asked to pretend to assume a satisfaction, we considered three factors: the ability to nonprescription drug therapy. The interviewer read the perform the task choice, the type of information that people instructions to each participant and also explained the aim of considered before choosing the treatment and the results from the test. Each experimental session lasted approximately 15 the short structured interview. minutes. About the first point, all the participants (100%) were Each subject was placed in front of the touch-screen and able to use the tool and perform the VES task from the trained on how to manage each single task. A personal beginning to the end with the selection of one or more computer ran a Java Virtual Machine which recorded all the appropriate nonprescription drugs. data. Tests were conducted on a touch-screen-based interface About the type of information, the 91% (46 out of 50 programmed in Java language in order to facilitate the participants) of participants looked at only specific pieces of interaction with dynamic information provided by the information revealing a clear preference for smaller computer. information sets to act upon. Participants probably focused on Each subject was placed in front of the touch-screen and those subsets of medical products that mostly captured their trained on how to manage each single task. A personal interest because more known and used in case of need computer ran the Java Virtual Machine which recorded all the without a deep attention for differences and similarities data. among drugs. The cue of the highest interest was again side As well described in the original paper of the VES Task effects, followed by the doctor’s advice. Side effects was [5], the test began similarly by asking participants to choose explored in the 61% of times, and doctor’s advice was between hypothetical nonprescription drugs commonly explored in the 39% of times. available in a Pharmacy. Participants were invited to explore Finally, about the interview’s questions, participants a 6 x 2 matrix displaying in each of the two rows the two showed a high level of support, clearness and satisfaction alternative treatments (Drugs 1, Drugs 2) and in each column, with the tool, reporting highly satisficing interactions with the six treatment features: price, doctor’s advice, daily dose, instrument and a clearness in usability with ratings of 8 or 9 availability, brand and side effects. There were no constraints on the 10-point scale, where 8 and 9 represented a very high on how participants should look up feature information even degree of support, clearness and satisfaction (see Fig. 2). if there was a constraint on the number of possible features looked up. Fig.2. Tool evaluation expressed by participants Legend: green: support red: clearness blue: satisfaction Fig 1. The VES task After the exploration phase, we asked participants how they evaluate this test in terms of comprehensibility and satisfaction in selecting drugs and make an appropriate by the following questions: -How much did the tool support your process of choice? (support) -How much was the tool clear and comprehensible? (clearness) -How much were you satisfied in using this tool? (satisfaction) Answers were given on a 10-point scale from “not at all” to “completely.” The level of support, clearness and satisfaction were measured according to the scores given by participants. VI. DISCUSSION The aim of this study was to investigate how common people judged a virtual device as an appropriate tool make medical decisions for selecting a non-prescription drug. We analysed three factors: the ability to perform the task choice, the type of information that people considered before choosing the treatment and the results from the short structured interview. For this study, we designed naturalistic environments in which participants had to choose common non-prescription drugs therapy for common health problems, frequently experienced by people. We observed that all the participants were able to perform the task revealing a direct expression of comprehensibility and clearness of the device. Moreover, we observed that, during the experiment, participants consulted very little information at their disposal, confirming 92 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 91-93 [III] Covington T (2006) Nonprescription Drug Therapy: Issues and preliminary data published before [5]. Two features were Opportunities. American Journal of Pharmaceutical Education systematically explored: side effects in the 61% of times, and 70: 137–141. doctor’s advice in the 39% of times. The process of choice [IV] Riva S, Schulz P, Staffoni L, Shoeb V (2014) Patient was also very quick reflecting the use of some smart participation in discharge planning decisions in the frame of strategies (eg. cognitive heuristics) that helped participants in Primary Nursing approach: A conversation analytic study, selecting the most important pieces of information easily [5Studies in Communication Sciences 14: 61-67 2]. The behaviour of participant in performing the task in a [V] Riva S, Monti M, Antonietti A (2011) Simple heuristics in oversuch way can represent a direct indicator of clearness and the-counter drug choices: a new hint for medical education and comprehensibility of the tool. practice, Advances in Medical Education and Practice 2: 59 -70. doi 10.2147/AMEP.S13004 Finally, the semi-structured interview confirmed our [VI] Riva S, Monti M, Iannello P, Antonietti A (2012) The initial hypothesis showing that participants evaluated the tool Representation of Risk in Routine Medical Experience: What as highly clear, understandable and they reported highly Actions for Contemporary Health Policy? PLoS ONE 7(11): satisfaction in the task. e48297. doi:10.1371/journal.pone.0048297 This is a preliminary and exploratory study, and the [VII] Schulz PJ, Hartung U, Riva S (2013) Causes, Coping, and present findings require further investigation. The study has Culture: A Comparative Survey Study on Representation of several limitations. First, the size of our sample, composed of Back Pain in Three Swiss Language Regions. PLoS ONE 8(11): 50 participants, is clearly a small sample not highly e78029. doi:10.1371/journal.pone.0078029 representative of certain group of population like elderly [VIII] Schönbrodt FD, Asendorpf JB (2011) Virtual social environments as a tool for psychological assessment: dynamics people that might be have more difficulties in performing a of interaction with a virtual spouse. Psychology Assessment VES task. Second, we acknowledge that the choice of a non23:7-17 prescription drug can be affected by other variables not [IX] Loomis JM, Blascovich JJ, Beall AC (1999) Immersive virtual examined in this study such as past experience, the opinion of environment technology as a basic research tool in psychology. other people, and commercial advertisement. Behavioural Research Methods Instruments Computation Last but not least, there were limitations in using this 31:557-64 type of methodology. We tried our best to design a tool that [X] Riva S, Camerini AL, Allam A, Schulz PJ (2014) Interactive could reveal real-life situations with very common treatment Sections of an Internet-Based Intervention Increase used in Italy. Nonetheless, we may expect different outcomes Empowerment of Chronic Back Pain Patients: Randomized Controlled Trial Journal of Medical Internet Research, in real situations. VII. CONCLUSION Even though this research does not claim absolute generalisations, we can describe some interesting findings in a context-bound sense that come from an active process of reflection given by the experimental phase and quantitative data analysis. First, the use of VES have the potential to support the ability of individuals to judge and participate in decisions concerning their self-care. Second, the education to cooperation, along with the entire team of health professionals, technicians and computer specialists, will permit to overcome problems of communication and obstacles, so that the use of VES technology will help people in choosing the more appropriate drug for specific healthproblems. Finally, the future research in psychology, especially cognitive psychology, should work together with technology and computer science to find new strategies to improve the education of society regarding appropriate use of non-prescription drugs. 16(8):e180 [XI] Blascovich J, Loomis J, Beall, AC, Swinth, KR, Hoyt, CL, and Bailenson, JN (2002) Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology, Psychological Inquiry 13:103–124 VIII. ACKOWLEDGMENT The first author wants to thank dr. Marco Monti and Prof. Alessandro Antonietti for their support in design and conceptualize the first version of the tool that helped us to project the current version. REFERENCES [I] Berry DC (2006) Informing People about the Risks and Benefits of Medicines: Implications for the Safe and Effective Use of Medicinal Products. Current Drug Safety 1: 121–126. [II] Riva S, Monti M, Iannello P, Pravettoni G, Schulz P, Antonietti A (2014) A Preliminary Mixed-Method Investigation of Trust and Hidden Signals in Medical Consultations. PlosOne 9(3): e90941. doi:10.1371/journal.pone.0090941 93 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 94-96 PRODUCTION OF HARD SHEETS FROM MUNICIPAL SOLID WASTE Mohamed Magzoub Garieb Alla, Amel G. Elsharief
[email protected] Abstract: Waste is actually the biggest feed stock available for processing, to produce useful and usable products. The increasing amount of waste is a characteristic of the modern human, though this discloses a more luxury life it presents an environmental hazard that cannot be ignored. Following this understanding we decided to impact on method of utilizing waste and converting it into useful and usable products as well as reducing the nuisance of waste. The methodology followed comes in steps; first a random trial to detect the use of unsorted waste and evaluating the equipment design, secondly improvement of blending of different components of waste, thirdly improvement of facilities for uniformity of heat and pressure, finally arriving at suitable formula regarding the ratio of the different waste components to give uniformity and better hold of the product. Key words: waste, aseptic carton, plastic waste, hard Sheets. I. INTRODUCTION Human activities create waste, and the ways that waste is handled, stored, collected, and disposed of can pose risks to the environment and to public health. Solid waste management includes all activities that seek to minimize health, environmental, and aesthetic impacts of solid waste. In urban areas, especially in the rapidly urbanizing cities of the developing world, problems and issues of municipal solid waste management (MSWM) are of immediate importance. Most governments have acknowledged the importance of MSWM; however, rapid population growth overwhelms the capacity of most municipal authorities to provide even the most basic services. According to a United Nations Development Programmer survey of 151 mayors of cities from around the world, the second most serious problem that city dwellers face (after unemployment) is insufficient solid waste disposal (UNDP 1997). Typically one- to two-thirds of the solid waste that is generated is not collected. The uncollected waste is dumped indiscriminately in the streets and in drains, contributing to flooding, breeding of insect and rodent vectors, and spreading of diseases. II. LITERATURE REVIEW In recent years, environmental problems and recycling issues are being discussed with more popularity in most of the developed and developing countries. In 2006, 313000 tons of beverage carton were recycled within a total capacity of 12 billion tons recycled material that represents a recycling rate of 30% in Europe. Recycling is not only increasing at a high rate but also combining with recovery of material reaching to almost 636000 tons with an approximate value of 61% rate in European Union. It is expected that more than 70% of municipalities will have enhanced opportunities for recycling household packaging. Recycled food carton also has substantial amount of market share within recycling industry. Nyström(2000) In Sudan, recently the discussion of environmental problems and recycling issues is started with more popularity. Recovering waste material from used beverage cartons (UBC) to manufacture a value-added product with an economical and efficient method is an important issue from the perspective of environmental pollution. Recycling of beverage cartons is a relatively new developing industry in Sudan. The material obtained from recycled UBC carton in world is predominantly used for the manufacture of paper and carton based products including shopping bags, cores for paper reel, sheets of cardboard, disposable kitchen towels, printing paper, plaster board lining and corrugated board. FIGURE 1: Aseptic carton Throughout the manufacturing process, the cartons are treated in such a way that no other material including toxic adhesives are needed. Cardboard also offers excellent soundproofing and insulation qualities UBC carton can be recycled using a thermal compression process to manufacture home and building products. The dominating structure of a used beverage carton is seen in Fig.1. As an alternative to repulping for paper applications, an additional process converts shredded cartons into thermally compressed to make a high strength bio-composite panel alternative to traditional wood based panels such as particleboard, medium density fiberboard (MDF) and oriented strand board (OSB). This type of panel product was developed by Tetra Pak® and is produced in various countries under different brand names. Which is composed of 70 - 90% paper, 10 - 25% low density polyethylene (LDPE) and about 5% aluminum which are existing components of UBC used. UBC cartons collected from consumers are shredded and then molded together under 94 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 94-96 high temperature and pressure. The process uses the whole B. Hot press design: waste package without leaving any waste. UBC cartons are The hot press should give:shredded into 5 mm particles and formed in a layer to get a 1- High temperature desired thickness. The mat is then compressed under pressure 2- High pressure and heat in a hot press. In this process, there is no need for the 3- Uniformity addition of an adhesive element due to 20% polyethylene in The first trials using steel plates were very tedious UBC carton raw material. The polyethylene content in the mat and did not give the required results due to: melts and binds fiber and aluminum pieces together in the 1- High temperature burning the stock. form of a compact elastic matrix. Aluminum at the rate of 5% 2- Non uniformity of selvedges heating. causes the heat to spread more uniformly. Nyström(2000) The design changed using high resistant bricks similar to bricks use in bakeries and those gave the required results. A. Production of panel board: The next step was to improve the selection of the It started as a project in Lund, Sweden, in mid-eighties. feed batches, in such a way that each batch contain similar The first production site was built at Tetra Pak in Kenya 1987. ratio of different component, we took notice of the fact that to Since then, other manufacturing sites operating in countries give repeatability of the production, we also made note of the across the world have developed. Today’s manufacturing fact that the plastic component under action of heat and plants are located in Argentina, Brazil, China, Germany, pressure will act as binder to increase the composure and solid Kenya, Pakistan, Slovakia South Korea and Turkey. properties of the sheet. Most plants are small-scale business with production Having obtained sheets of reasonable characteristics operating at 1-2 shifts, 5 days/week. Capacities vary but are in then proceeded to reduce the thickness to facilities production general from 2-5 tons/day. The world production for 1999 is of corrugated sheets which are popular as ceiling. summarized in the table below. Nyström(2000). . Trials: Location Commercial Tones/year The trails were done at different conditions such as different name recipes of feed stock, different temperatures. But most of them Argentina T-PLAK 910 were done at time between 10 to 15 minutes. Brazil Reciplak 200 China Chiptec 0011 Trial 1:(2 factories) This is entry to the process to get adequacy of self-made Germany Tectan 500 simple equipments to give required results as governed by the Kenya Lamiboard 350 production obtained. Waste was cut in small pieces mixed Pakistan Green Board 001 manually and randomly scattered to give a uniform layer. The Slovakia Tetra K1,K2,K3 645 production obtained:Turkey Yekpan 1400 1- Lack in uniformity. Total 6255 2- Selvage where not adequately heated and pressed. 3- The whole of the final product was doubtful as Production of the panel board is basically the same regards to its use for specific purpose. across the world and includes the following unit operations. Shredding, (washing, drying), forming, hot and cold pressing, Trial 2:handling / trimming. As it was noticed that the feedstock suffered from burning also The modern trend of utilizing natural resources is the heating plates had to be modified from iron plates to heat becoming more and more prime since the resources are limited resistant bricks. This gave safer heating without causing and full utilization is no longer a luxury but a necessity. While change to the stock and modifying pressure. The result was reprocessing of water ,soil remediation , and the hot cakes better as regards to selvage heating and uniformity. other areas can be ignored paper processing has gained a Consistency was still a problem as hold. remarkable momentum as raw material for paper are trees and forest which are getting exhausted. Trial 3:In his endeavors to play a role however small it might In this trial we concentrate on more proper blending of the be , he put in his mind the utilization of feed stock which waste and the result was a visible improvement but lack in nobody wants, and which actually is considered a bother and ability to cement. an environmental hazard, namely waste packaging material including:Trial 4:1- Paper Concentration was on how to give a better hold avoiding as 2- Aluminum foil much as possible any expensive additions so it was thought 3- Plastics that the incorporation of thermoplastic should be consider a 4- Cartons prime factor in the percentage of polyethylene to be used was 5- Shrubbes give more than step child attention, so minium of:Aseptic carton (commercially known as Tetrapak) 95 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 94-96 [III] 3-George Tchobanoglous, Frank Keith, (2002) handbook 1. 25% linear chain polyethylene of the used a cheaper solid Waste management, second edition, McGraw-Hill. packs which always available around is made a rule. [IV] Cheremisinoff, Nicholas P., (2003), Handbook of solid 2. The polyethylene is cut in small pieces and uniform waste management and waste minimization technologies, distributing the blend. Elsevier Science The use of poly ethylene is important as binding agent as a [V] Zhu Da, Asnani P. U., Zurbrügg Chris, Anapolsky dissolve under the reaction of heat and pressure and then Sebastian, Mani Shyamala , (2008),Improving Municipal solidified acting as glue improving the whole and giving a Solid Waste Management in India, The World Bank better light reflection. Washington, D.C. [VI] Trial 5:Was done using Aseptic Carton (terapack) as major component the recipe runs as follows:75% paper 20% polyethylene 5% aluminum foil The purpose behind the use of aluminum is it shiny looks and luster and extra strength it gives to the product. Young, Gary C., (2010) Municipal solid waste to energy conversion processes, John Wiley &Sons. General speaking we made more than twenty trails for plain sheets C. Corrugated trials: A mold was designed to produce corrugated product to be used as fencing and roofing. Results were encouraging even compare to standard light weight international products available in the market. The same recipe was done in the corrugated trials. Plate1. Different trials III. CONCLUSION The best recipes according the trials are: 1. 50% plastic and 50% agricultural waste (Shrubbes). This trial was done under temperature 80˚C. 2. 30% newsprint waste, 30% agricultural waste and 40% plastic. This trial was done under temperature 80˚C. [I] [II] REFERENCES Nyström Tommy,(Aug 2000) Carton Environment, Tetra Pak Carton Packaging Division. Miller, Debra A., (2010) Garbage and recycling, Gale, Engage Learning. 96 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 97-99 THROUGHPUT ANALYSIS OF MOBILE WIMAX NETWORK UNDER MULTIPATH RICIAN FADING CHANNEL Sunil Kumar Gupta, Jyotsna Sengupta Department of Computer Science, Punjabi university Patiala (147002)
[email protected],
[email protected] Abstract — The Mobile WiMAX simulation model is implemented by using MATLAB code. The simulation model consists of different phases which will help us to model the transmitter and receiver section. In the next phase, the data is being modulated by using the modulation methods QPSK and QAM followed by OFDM transmitter. These phases can be used to show the performance of these modulation methods under varying condition. The Multipath Rician fading model is implemented to introduce the fading in the transmitter data. Receiver section is used to receive data from channel will be fed into the OFDM demodulation. In the next phase, Fast Fourier Transform is used to disassemble OFDM frame. After that convolution encoding is applied to data and interleaving is carried on by using MATLAB function. BPSK method is used to change the data in the form of bit information to be symbols. We had used Different functions to modulate and demodulate data. I. INTRODUCTION A. What is WiMAX? World Wide Interoperability for Micro Wave Access is the IEEE 802.16 standard, that specifies a frequency band in the range from 10 GHz to 66 GHz. Basically WiMAX is a wireless internet that's able to covering a wide geographic area by serving a vast selection of users at a very low cost. It particularizes a metropolitan area networking protocol which not only provides a wireless alternative for cable, Digital Subscriber Line (DSL) and T1 level services for last mile broadband access and also provides a backhaul for 802.11 hotspots and automobile higher data rates WiMAX is usually more established in cellular sector [1]. B. Multipath delay spread The channel impulse response of a wireless channel looks like a compilation of pulses, due to the multipath reflections. The volume of pulses that may be eminent is very large, and depends on the time resolution of the communication or measurement system. So because of the non line of vision propagation nature of the WiMAX OFDM, we have to address multipath delay spread in this channel model. To handle the effect of multipath propagation, the delay spread parameter is employed. It depends on terrain, distance, antenna directivity and additional factors [3]. We can show a LOS and multipath scenario. It shows that at different time, multiple reflections of the same signal come to the receiver. This might result in an Inter symbol Interference (ISI) causing noticeable degradation in signal quality [2]. C. Fading Characteristics In multipath fading, the received signal experiences variation in its amplitude, phase and angle of arrival in a multipath propagation environment. As a result they might add either constructively or destructively leading to a complex envelope. Small scale fading has also been addressed in this channel model due to the fixed deployment of transmit and receive antenna. If there is no line of vision signal component and there are multiple reflective paths that are large in number then small scale fading is termed Rayleigh fading [6]. When there is a line of vision component in conjunction with the multiple reflective paths then small scale fading is described by a Rician pdf, so in this channel model Rician distribution is used. The key component of this is the k factor that is the ratio of the direct component power and the scatter component power [5]. II. LITERATURE CITED Lee et. al. [5] Here, all simulations are performed by using Matlab programs. At First, the 88 data bits are randomly generated for BPSK modulation. Next we use the function in Matlab named as rsenc (msg,N,K,varargin) to perform Reed-Solomon encoding with the output of 96 bits (8 code word bits). Later, the 96 bits are input of Viterbi (msg , template , Tx) in order to perform Convolution encoding. At this stage, the output of data stream is 192 bits. Next, the task of interleaving 192 bits is carried on by using intrlv (data, elements). Now, it is ready to change the data in form of bit information to be symbols. For BPSK, the number of bits is equal to the number of symbols. The function to modulate data is called as pskmod (x,M) in which pskdemod (x,M) is a function to demodulate signal. Loutfi et.al. [7] The simulation result shown in this paper infers that mobile WiMAX system using Turbo coding provides BER of 10-5 at Eb/No of 14 dB which is better than LDPC coding by providing BER of 10-4 for QPSK modulation scheme in the presence of Rayleigh fading channel. Ghosh et.al. The performance analysis of WiMAX 802.26e physical layer model, simulation is performed by considering the standard test vectors specified in the WIMAX standard. BER Verses SNR. BER is the number of error bits occurs within one second in transmitted signal. BER define mathematically as follow. When the transmitter and receiver’s medium are good in a particular time and Signal-to-Noise Ratio is high, and then Bit Error rate is very low. In our thesis simulation we generated random signal when noise occurs after that we got the value of Bit error rate. SNR= Signal Power/Noise Power SNR= (Signal Amplitude/Noise Amplitude) 2 97 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 97-99 method remain the same which had been used by other Khan et.al. [11] The WiMAX MAC (Medium Access channels for the comparison. I had used MATLAB software Control) layer simulink model is used; we use AWGN to implement different functions related to this simulation (Additive White Gaussian Noise) and different modulation model. Various parameters which can affect the throughput schemes used like QPSK (Quadrature Phase Shift Keying) of the model are defined beforehand. Different metrics are and QAM (Quadrature Amplitude Modulation). chosen to perform the evaluation and results are represented Chowdhury et.al. [8] The performance of WiMAX in tabular and graphical from [7]. MAC layer is based on the simulation results. Mobile C. Methodology Used WiMAX system using LDPC coding and Turbo coding and The methodology which we have used to develop the MIMO model is simulated for different modulation schemes WiMAX Modelling using ideal Rician Channel for the such as QPSK and 16-QAM under Rayleigh fading channel physical layer is given as follows: with the help of MATLAB. Grewal et.al. [9] It is inferred that mobile WiMAX system using LDPC coding has BER of approximately 10-4 at 9 dB of Eb/No for QPSK modulation. But, the BER is approximately 10-4 at 13 dB of Eb/No for 16-QAM modulation under Rayleigh channel. Neha et.al. [10] BER performance of Mobile WiMAX system using LDPC and Turbo coding is determined and compared for above mentioned modulation schemes in the presence of Rayleigh channel. Further, BER analysis of mobile WiMAX system using MIMO model with STBC and STTC is calculated as compared between STBC and STTC. III. 1. 2. MATERIALS AND METHODS A. WiMAX System Modelling Using Rician Fading Channel The third variation in the modelling of WiMAX product is with the replacement of Rayleigh channel by Rician channel. For sub cities where there can be the prospects for realizing the line of vision path in conjunction with multipath structure, the wireless channel must be modeled as the Rician channel and that is again the real time realization of fading phenomenon of the wireless systems. In the modeling of the Rician channel the multipath variations of signal are superimposed over the line of sight component which increases the overall strength of the whole information at the receiver.[12] Rician fading is characterized by a factor, which is expressed as the power ratio of the secular (los or dominant path) component to the diffused component. This ratio, k, defines how in close proximity to Rayleigh statistics the channel is. In fact when k=infinite, there isn't any and when k=0, this method for Rayleigh fading. The ratio is expressed linearly and not in decibels. While the Average path gain vector parameter manages the overall gain through the channel, the K-factor parameter controls the gain’s partition into line-of-sight and diffuses components [4]. The other blocks and properties of WiMAX model will continue to be the same. [1] B. Implementation We have done the following for implementation and analysis: In this semester, I had implemented the Mobile WIMAX simulation model by using Multipath Rician Fading Channel. The throughput of the model is being analysed and it is compared with the results already given for various other channels. Basic encoding and modulation 3. The first step which had been followed is to generate a random data stream of length 4400 bit which we have used as the input binary data. We have used Matlab 7 version in which data input of bits is done by using inbuilt function of the Matlab. We have used the random function which is used to divide and assemble the data so that it nis possible to convert long sequences of 0's or 1's in a random sequence. If the input is given in a proper way then it is possible to have better coding performance. After the data is input then we have to check the errors in data. There are many inbuilt functions available In Matlab which can used for error checking. The encoded data after error checking is used to perform rated convolutional encoding. The interleaving function is applied on the encoding data. Then various digital modulation techniques like QAM, 16-QAM and 64-QAM, are specified for WiMAX Physical layer so that we can say that it is used to modulate the encoded data. IV. RESULTS AND DISCUSSION In this part of the report, we will presents and discuss all of the results obtained by the computer simulation program written in Matlab7. We have analyzed the wireless communication system considering AWGN, Rayleigh Fading and Rician Fading channel. In the figure given above, we have performed all the calculations with the synthetically generated data. The results are shown in terms of bit energy to noise power spectral density ratio (Eb/No) and bit error rate (BER) for original values of system parameters. By varying SNR, we have plotted Eb/No vs. BER by using the “semiology” function. The Bit Error Rate (BER) plot obtained in the performance analysis showed that simulator is valid for Signal to Noise Ratio (SNR) less than 25 dB. 98 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 97-99 Simulation results in figure above shows the performance of the system over AWGN and fading (Rayleigh & Rician) channels using modulation scheme. In this research work, it has been shown that the performance of an OFDM based WIMAX Communication system adopting different coding schemes and digital modulation scheme. The performance of Rician fading channel is worse than that of AWGN channel and better than that of Rayleigh fading channel. Because Rician fading channel has higher BER than AWGN channel and lower than Rayleigh fading channel. BER of this channel has not been much affected by noise. The elapsed time taken for the implementation of the simulation is reduced as compared to other models. The path gain values for the Rician fading model are given foe certain points as well as a total value. V. CONCLUSION This thesis presents performance over MAC layer under various modulation schemes in Rician channel. A key performance way of measuring a wireless communication equipment is the SNR versus PER. It might be figured for a certain price of SNR at some signal power the performance in relation to BER is less in QAM system compared to a QPSK system. To realize ideal conditions of propagation of WiMAX system, the modeling can be achieved by assuming the international calls highly efficient path i.e. AWGN channel. For understanding the real time multipath structure of WiMAX model, the channel is simulated as Rayleigh channel in worst scenario wherein the performance in terms of fading can be improved by changing the valuation on Doppler shift. With the same criterion, the presence of line of vision is usually justified by modeling WiMAX with Rician channel. Also the performance can be improved by implementing of antenna diversity techniques with BLAST and also STBC with fading channel scenario. Broadband Wireless Access Systems”, Re v. of IEEE 802.162001, 1 Oct.2001. [5] J. El-Najjar, B. Jaumard, C. Assi, “Minimizing Interference in WiMAX 802.16 based Mesh Networks with Centralized Scheduling,” Global Telecommunications Conference, New Orleans, LA, USA, pp.1-6, 30Nov.–4 Dec., 2008. [6] K. Lee and D. Williams, “A space-time coded transmitter diversity technique for frequency selective fading channels, “Proceedings of IEEE Sensor Array and Multichannel Signal Processing Workshop, Cambridge, Mass, USA, pp.149–152, March, 2000. [7] M. Patidar, R. Dubey, and N.K. Jain “Performanc analysis of WiMAX 802.16e Physical Layer model” proceeding 2012 Ninth International conference on, 2012, pp. 1-5. [8] Muhammad Nadeem Khan, Sabir Ghauri , “The WiMAX 802.16e Physical Layer Model” , University of the West of England, United Kingdom. [9] Nuaymi Loutfi, 2007, WiMAX Technology for Broadband Wireless Access, Wiley London. [10] “Performance Analysis of WiMAX PHY” by S.M. Lalan Chowdhury, P. Venkateswaran, IEEE CASCOM Post Graduate Student Paper Conference 2010 jadavpur university, Kolkata, pp.13-16,Nov, 2010. [11] “Simulation of WiMAX 802.16 MAC Layer Model: Experimental Results” by Neha Rathore, IJ ECT Vol. 3 Issue 1, Jan.- March 2012, R.K.D.F. Institute of Technology & Science, Bhopal, MP,India. [12] “The WiMAX 802.16e Physical Layer Model” by Muhammad Nadeem Khan, Sabir Ghauri, University of West of England, United Kingdom. [13] V. Grewal, A. K. Sharma, “On performance Enhancement of WiMAX PHY Layer with Turbo coding for Mobile Environments”, International Journal of Advanced Science and Technology, Volume 31, pp.37-46, June, 2011. REFERENCES [1] Fan Wang, Amitava Ghosh, Chandy Sankaran, Philip J. Fleming, Frank Hsieh and Stanley J. Benes, “Mobile WiMAX Systems: Performance and Evolution”, IEEE Communications Magazine, ISSN: 0163-6804,Volume 46, Issue 10, pp. 41-49, October 2008. [2] IEEE 802.16-2006: “IEEE standard for Local and Metropolitan Area Network- Part 16: Air Interface for Fixed Broadband Wireless Access Systems”. [3] IEEE 802.16e-2005, “IEEE Standard for Local and Metropolitan Area Networks, part 16, Air Interface for Fixed and Mobile Broadband Wireless Access Systems”, IEEE Press, 2006. [4] IEEE 802.16-2004, “IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed 99 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 100-106 ANALYSIS OF GROUNDWATER QUALITY USING STATISTICAL TECHNIQUES: A CASE STUDY OF ALIGARH CITY (INDIA) Khwaja M. Anwar Aggarwal Vanita Research Scholar, Department of Civil Engineering, Maharishi Markandeshwar University, Mullana, Ambala, India
[email protected] Professor, Department of Civil Engineering, Maharishi Markandeshwar University, Mullana, Ambala, India
[email protected] Abstract- The study was conducted to evaluate the groundwater quality of Aligarh city, (India). Groundwater samples were collected from 40 wells and analyzed for 20 water quality parameters in post-monsoon seasons during the year 2013. High coefficient of variance indicates variability of physico-chemical parameters concentrations in ground water. The descriptive statistical analysis was done beside Pearson correlation. From correlation analysis it was observed that very strong correlations exist between total hardness and Mg++ (0.99), TDS and total hardness (0.88), TDS and Chloride (0.87). In 100% of the samples recorded alkalinity and magnesium concentration were found higher than maximum permissible limit prescribed by BIS. Concentration of hardness, cadmium, pH, iron, lead, and total dissolved solids were also found above the standard limits prescribed by BIS. This reveals deterioration of water quality. It is therefore, suggested to take up regular monitoring of groundwater in areas of Aligarh city. Key words: Contamination, Groundwater quality, Multivariate, Physicochemical characteristics, Statistical analysis. I. INTRODUCTION ater is blessing of God and is very precious resource of this planet. It is well known that human health and survival depends upon use of uncontaminated and clean water for drinking and other purposes. Most human activities involve the use of water in one way or other such as food, production, nutrition are dependent on water availability in adequate quantities and good quality (Howari F.M., 2005). It is estimated that approximately one third of the world's population uses groundwater for drinking purposes and today more than half the world's population depends on groundwater for survival (Mohrir A., 2002). Data has shown that groundwater were less susceptible to bacterial re growth (Niquette et al. 2001). The water supply for human consumption is often directly sourced from groundwater without biochemical treatment and the level of pollution has become a cause for major concern (Sinha, 2004). Groundwater resource is under threat from pollution either from human life style manifested by the low level of hygiene practiced in the developing nations (Ikem, A. et al, 2002). With increasing industrialization, urbanization and growth of population, India’s environment has become fragile and has been causing concern (Mohapatra and Singh, 1999). Pollution of water is due to increased human population, industrialization, use of fertilizers in agriculture and man made activity (Rao, et al, 2012). Once the groundwater contaminated, its quality cannot be restored by stopping the pollutants from the source therefore it becomes very important to regularly monitor the quality of groundwater. In this study statistical techniques were used to analyze the water quality data collected from Aligarh City, (India). Correlation coefficient is used to measure the strength of association between two continuous variables. This tells if the relation between the variables is positive or negative that is one increase with the increase of the other. Thus, the correlation measures the observed co-variation. The most commonly used measure of correlation is Pearson‘s correlation (r). It is also called the linear correlation coefficient because r measures the linear association between two variables (Halsel and Hirsch, 2002). II. STUDY AREA The Aligarh is an ancient city in the north Indian state of Uttar Pradesh is situated in the middle of doab-the land between The Ganga and Yamuna rivers, at a distance of 130 Km Southeast of Delhi on the Delhi- Howrah rail route and the Grand Trunk road. Aligarh lies between latitude 27º 54’ and 28º north and Longitude is 78º and 78º 5’ east. The Aligarh city is spread over an area of about 36.7 km2. The area lies between the Karwan River in the west and the Senger River in the east and is a part of central Ganga basin. Aligarh is mostly known as a university city where the famous Aligarh Muslim University is located. The Aligarh city is an important centre of lock smithy and brassware manufacturing.There are a total of 5506 industrial units in Aligarh city, of these; there are 3500 small scale industries, 2000 medium scale 6 large industries. Environmental quality of the area deteriorates mainly as a result of the increasing industrial activities. All segments of environment are being polluted by various ways. However, the study of water pollution is selected as it is not an ordinary liquid but is the elixir of life. Aligarh has a monsoon influenced humid subtropical climate. July is the wettest month. The normal annual rainfall is 760 mm. Maximum temperature shoots upto 47 0C and minimum temperature may fall around 20C. The average relative humidity in the morning is 62.25% and in the evening it is 44.2%. Hydrogeologically there is a three to four tier aquifer system. Aquifers seem to merge with each other, thus, developing a single body’s aquifer. This makes the aquifer vulnerable to contamination (Khan T. A., 2011). 100 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 100-106 B. Temperature The maximum water temperature was observed 240C at S31 and minimum 170C at S14with an average value of 19.250C. The variation in temperature may be due to different timing of collection and influence of season (Jayaraman et al, 2003). Temperature controls behavioral characteristics of organisms, solubility of gases and salts in water, No other factor has so much influence as temperature (Welch 1952). pH III. MATERIAL AND METHODS Forty water samples were collected in post-monsoon (November) seasons during the year 2013. These samples were collected as per the standard methods prescribed for sampling. Plastic bottles of 1.5 liter capacity with stopper were used for collecting samples. Each bottle was washed with 2% Nitric acid and then rinsed three times with distilled water. Samples were analyzed to determine the concentrations of pH, Turbidity, Temperature, Total Dissolved Solids (TDS), Electrical conductivity, Hardness, Chloride, Sulfate, Alkalinity, Fluoride, Iron, Calcium, Magnesium, Nitrate, Zinc, Copper, Lead, Chromium and Cadmium in the laboratory of U.P. Jal Nigam, Aligarh. All the tests were conducted in accordance with the techniques described by American Public Health Association (APHA 2005). pH was measured by digital pH meter micro processor based model no: LPV 2550 t. 97, 2002 make: HACH USA. Electrical conductivity (EC) and total dissolved solids (TDS) were measured with digital EC-TDS analyzer model No: CM 183, make Elico, India. Turbidity was measured by using Nephalo-meter model No: 2100 Q-01 make: Hach USA. Iron, Nitrate, Sulfate, Fluorides, Calcium, Magnesium, Copper, Zinc, ion concentrations were determined by spectrophotometer, using UV-Vis laboratory spectrophotometer (Model No: DR 5000) make Hach, USA. All the general chemicals used in the study were of analytical reagent grade (Merck/BDH). Standard solutions of metal ions were procured from Merck, Germany, Fisher Scientific, Mumbai and Rankem from RFCL limited, New Delhi. Various statistical analyses of the experimental data were performed using Microsoft Excel 2007. The pH of a solution is the negative logarithm of Hydrogen ion concentration in moles per liter. pH is dependent on the carbon dioxide-carbonate-bicarbonate equilibrium. pH values ranged and 7.01 to 8.82 with an average value of 8.38,indicating the alkaline nature of water samples. 62.5% of samples were above the standard limit (6.5 to 8.5) prescribed by BIS. Carbon dioxide in groundwater normally occurs at a much higher partial pressure than in the earth’s atmosphere. When groundwater was exposed to the atmosphere, CO2 will escape and the pH will rise. For consumption point of view, all the samples may be considered fit, as they are neither acidic nor strongly alkaline in nature. C. Total Dissolved Solids (TDS) A total dissolved solid (TDS) is the concentrations of all the dissolved minerals in water. TDS is used as an indication of aesthetics and general nature of salinity of water. Concentration of dissolved solids is important parameter in drinking water; to ascertain the suitability of the groundwater for any purpose, it is essential to classify the groundwater depending upon its hydro chemical properties based on the total dissolved solids values (Freeze and Cherry 1979). The TDS values in all the study area varies from 224 to 987 mg/l with an average value of 541.38 mg/l in post-monsoon period. In the present study, 42.5% of the samples were exceeding maximum permissible limit (500 mg/l) prescribed by BIS. An elevated level of TDS, by itself, does not indicate that the water present a health risk. However, elevated level of specific ions included in the TDS measurement such as Mg++, Ca++, No3-, F- could present health risk. The concentration of dissolved ions may cause the water to be corrosive, salty or brackish taste, result in scale formation. D. Turbidity The turbidity is a measure of the extent to which light is either absorbed or scattered by suspended material in water. The turbidity for all the samples is below the BIS Standards limit 1.0 NTU. The highest value of turbidity is 2.37 NTU. Turbidity in water causes the degradation in the clarity. IV. RESULTS AND DISCUSSION A. Groundwater chemistry Groundwater samples were drawn from deep (> 50 m) and shallow wells (<50 m) and analyzed for physico-chemical parameters. The results obtained were evaluated in accordance with the standards prescribed under Indian standard drinking water specification IS: 10500:2012 of Bureau of Indian Standards. The parameters exceeding the BIS permissible limits along with their permissible limits are presented in table-1. 101 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 100-106 Table-1 Parameters exceeding the permissible limit Serial No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Parameter Temperature in 0C pH TDS in mg/l EC in µ mohs/cm Turbidity in NTU Iron in mg/l Nitrate in mg/l Sulfate in mg/l Fluorides in mg/l Chloride in mg/l Alkalinity in mg/l T. Hardness in mg/l Calcium in mg/l Magnesium in mg/l Copper in mg/l Zinc in mg/l Manganese in mg/l Lead in mg/l Chromium in mg/l Cadmium in mg/l Permissible limit as per BIS IS:10500:2012 - 6.50-8.50 500.00 1500.00 1.00 0.30 45.00 200.0 1.00 250.00 200.00 200.00 75.00 30.00 0.05 5.00 0.10 0.01 0.05 0.003 E. Electrical Conductivity Electrical conductivity is the measure of capacity of a substance to conduct the electric current. Most of the salts in water are present in their ionic form and capable of conducting current and conductivity is a good indicator to assess groundwater quality. EC is an useful parameter of water quality for indicating salinity hazards. In the study area, EC values varied between 378.3 µmohs/cm to 2532.7 µmohs/cm with an average value of 1005.65 µmohs/cm. F. Iron Iron concentrations in this study varied from 0.08 to 0.64 mg/l with an average value of 0.35 mg/l. 65% of samples were found above the standard limit (0.30 mg/l) prescribed by BIS. Iron is a common metallic element found in the earth's crust Iron can affect the flavor and color of food and water. Iron is biologically an important element which is essential to all organisms and present in hemoglobin system. G. Nitrate The highest value of Nitrate concentration was 25.23 mg/l with an average value of 6.52 mg/l. All the samples is below the BIS Standards limit 45.0 mg/l. Nitrate-nitrogen (NO3-N) in groundwater may result from point sources such as sewage disposal systems and livestock facilities, non-point sources such as fertilized cropland. H. Sulfates Sulfates were found in the range from 13.2 to 379.2 mg/l with an average value of 149.05 mg/l. In 35% samples the values were found above the standard limit (200 mg/l) prescribed by BIS. The sulfate content in water is important in determining the suitability of water for public and industrial Analytical results of samples Minimum Maximum 17.0 24.0 7.01 8.82 224.00 987.00 378.30 2532.70 0.05 2.37 0.060 0.61 0.19 25.23 13.20 379.20 0.01 0.71 22.00 421.00 216.00 598.00 197.00 608.00 47.00 122.00 34.02 130.25 0.00 0.19 0.01 1.84 0.00 0.26 0.00 0.21 0.00 0.33 0.00 0.48 Sample exceeding permissible limit Numbers % 25 62.5 17 42.5 13 32.5 01 2.5 26 65 0 0 14 35 0 0 12 30 40 100 39 97.5 20 50 40 100 16 40 0 0 10 25 19 47.5 5 12.5 35 87.5 supplies. Higher concentration of sulfate in water can cause malfunctioning of alimentary canal and shows cathartic effect on human beings (M. Lenin Sunder et al. 2008). I. Fluorides The fluoride values in the study area ranges from 0.01 to 0.71 mg/l with an average value of 0.25 mg/l. The fluorides concentration in all the samples is below the BIS standards limit 1.0 mg/l. Fluoride is beneficial for human beings as a trace element, this protects tooth decay and enhances bone development. J. Chlorides Chloride occurs in all natural waters in widely varying concentrations. The chloride contents normally increases as the mineral contents increases (Dubey, 2003). Chlorides concentrations ranged from 22.0 to 421.0 mg/l with an average value of 156.33 mg/l. In 30% sample wells, the chloride values exceeded the maximum limit (250 mg/l) prescribed by BIS. At concentration above 250 mg/l, water acquires salty taste which is objectionable. However no adverse health effects on humans have been reported from intake of water containing highest content of chloride (Amrita Singh et al., 2011). If the water with high chloride concentration is used for construction purpose, this may corrode the concrete. K. Alkalinity Alkalinity is the measure of the capacity of the water to neutralize a strong acid. The Alkalinity in the water is generally imparted by the salts of carbonates, silicates, etc. together with the hydroxyl ions in free state. Most of the natural waters contain substantial amounts of dissolved carbon dioxide, which is the principal source of alkalinity. The alkalinity varies from 216 to 598 mg/l. 100% samples were 102 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 100-106 found above the standard limit (200 mg/l) prescribed by BIS. sickness and in extreme cases liver damage (Marwari, et al, The alkalinity values were found increasing in the post 2012). monsoon period, compared to the pre-monsoon period in almost all the wells. This may be due to the movement of P. Zinc pollutants into the ground water during rainfall season. The The zinc concentrations were varied from 0.009 to 1.836 most prevalent mineral compound causing alkalinity is mg/l. It can be observed that all the samples having Zinc value calcium carbonate, which can come from rocks such as below 5.0 mg/l fall within the limits. Zinc compounds are limestone or can be leached from dolomite and calcite in the astringent, corrosive to skin, eye and mucus membrane. They soil. Large amount of alkalinity imparts a bitter taste to water. cause special type of dermatitis known as ‘Zinc pox’. Large amount of alkalinity in water imparts a bitter taste to water. Q. Manganese Manganese was one of the most abundant metals in the L. Total Hardness earth’s crust and usually occurs together with iron (Khan M. Total hardness is a measure of the capacity of water to the M. A., et al, 2010). Manganese concentration in water samples concentration of calcium and magnesium in water and is ranged between 0.001 to 0.255 mg/l. In 25% of samples usually expressed as the equivalent of CaCo3 concentration. In Manganese concentration were found above the standard limit the study, the total hardness of the water samples ranges (0.10 mg/l) prescribed by BIS. Manganese concentrations as between 197 to 608 mg/l. 97.5% of samples were found above low as 0.05 mg/L can cause color problems. the standard limit (200 mg/l) prescribed by BIS. Hard water is not a health hazards. In fact, the National Research council R. Lead states that the hard drinking water generally contributes a The lead concentrations in the water samples were ranged small amount toward total calcium and magnesium human between 0.001 to 0.21 mg/l. In 19 sampling locations, the dietary needs. In some instances, where dissolved calcium and value of lead exceeded the limit (0.01 mg/l) prescribed by BIS. magnesium are very high, water could be a major contributor In 47.5% of samples lead concentration were found above the of calcium and magnesium to the diet. Hard water is useful in standard limit (0.01 mg/l) prescribed by BIS. Lead is one of the growth of children, if within the permissible limit. the hazardous and potentially harmful polluting agents. It has However, hard water is a nuisance because of mineral buildup impact on man and animals. Lead poisoning symptoms usually on fixtures and poor soap /detergent performance. The high develop slowly. It inhibits the formation of hemoglobin by degree of hardness in the study area can definitely be reacting with SH group and interfering with many enzyme attributed to the disposal of untreated, improperly treated functions (Sabhapandit P., et al. 2011). sewage and industrial wastes. S. Chromium M. Calcium The chromium concentration in the study area was found The Calcium concentrations were varied from 47 to 122 between 0.001 to 0.328 mg/l with an average value of 0.03 mg/l. 50% of samples were found above the standard limit (75 mg/i. In 12.5% samples the value of chromium exceeded the mg/l) prescribed by BIS. Calcium (Ca2+) is an important limit (0.05 mg/l) prescribed by BIS. Chromium and chromate element to develop proper bone growth. It is found in alkaline are known to be potential carcinogenic and chromate are in nature. Calcium content is very common in groundwater, known to be potential carcinogenic substance for lung and because they are available in most of the rocks, abundantly nose cancer. Chromates act as irritant to the eyes, nose and and also due to its higher solubility. throat in traces and chronic exposure with high concentration lead to liver and kidney damage (Marwari, et al, 2012). N. Magnesium A large number of minerals contain magnesium; T. Cadmium Magnesium is washed from rocks and subsequently ends up in The Cadmium concentration of water samples were varied water. Magnesium has many different purposes and from 0.001 to 0.480 mg/l. In 87.5% samples Cadmium consequently may end up in water in many different ways. exceeded BIS permissible limit (0.003 mg/l). Cadmium in Chemical industries add magnesium to plastics and other high concentration is harmful, but small amounts of cadmium materials as a fire protection measure or as filler. It also ends taken over for a long period also bio-accumulates in the body up in the environment from fertilizer application and from and cause serious illness (Sabhapandit P., et al. 2011). cattle feed. The values of magnesium from groundwater ranged between 36.45 to 118.1 mg/l. In all the well locations, U. Statistical analysis the values of magnesium exceeded the limit (30 mg/l) The data were subjected to normal distribution analysis prescribed by BIS and this indicates the hardness of water. and Pearson correlation Microsoft Excel 2007. Normal distribution analysis (involved mean, median, standard O. Copper deviation, skewness and kurtosis) analysis is an important The Copper concentrations were varied from 0.004 to statistical tool for identifying the distribution patterns of the 0.186 mg/l. 40% of samples were found above the standard different water quality parameters in groundwater samples. limit (0.05 mg/l) prescribed by BIS. Copper is an essential Correlation coefficients of various parameters analyzed element in the human being for metabolism. Human being were calculated. These Correlation coefficients values were especially requires copper as a trace element in the formation used in estimating the values of other parameters at the of R.B.C and some enzymes. 0.05 m/L are not generally particular place without actually measuring them (Mishra, et regarded as toxic as but more than 1.5 mg/L may cause al, 2003). Pearson correlation analysis is an approach, which provides intuitive similarity relationship between any one 103 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 100-106 sample and entire data set. Pearson’s correlation coefficient is correlation, 0.0 is no correlation and 1.0 is a perfect positive usually signified by r (rho), and can take on the values from correlation. The variables having coefficient value (r) > 0.5 or 1.0 to 1.0. Where -1.0 is a perfect negative (inverse) < -0.5 are considered significant. Table-2 Statistical analysis post-monsoon 2013 Temp. pH TDS EC Turb. Iron NO3 SO4 F Cl- Mean 19.85 8.38 541.38 1005.65 0.49 0.35 6.52 149.05 0.25 156.33 Variance 4.03 0.15 67895.4 353810 0.16 0.03 30.58 15161.21 0.05 17634.69 SD 2.01 0.39 260.57 594.82 0.40 0.17 5.53 123.13 0.23 132.80 Skewness 0.32 -1.97 0.56 1.08 2.88 -0.23 1.15 0.62 0.70 0.83 Kurtosis -0.79 4.25 -1.24 0.13 12.62 -1.33 1.98 -1.26 -1.05 -0.82 Median 20.00 8.53 454.50 776.75 0.41 0.38 5.76 90.15 0.13 100.00 Mode 18.00 Alka. 8.62 TH 345.00 Ca++ 0.31 Cu 0.51 Zn #N/A Mn 0.08 Cr+6 54.00 Cd Mean 353.23 397.20 77.25 77.75 0.04 0.39 0.05 0.01 0.03 0.04 Variance 7553.5 18688.0 326.76 911.84 0.00 0.17 0.00 0.00 0.00 0.01 SD 86.91 136.70 18.08 30.20 0.04 0.41 0.07 0.03 0.05 0.09 Skewness 0.57 0.21 0.58 0.27 2.01 2.25 1.95 5.87 4.34 4.13 Kurtosis 0.16 -1.51 -0.32 -1.42 4.55 5.75 3.20 36.05 22.17 19.19 Median 350.00 357.00 72.00 69.01 0.03 0.26 0.02 0.01 0.02 0.01 Mode 380.00 332.00 81.00 46.66 0.02 0.08 0.02 0.00 0.00 0.01 #N/A Mg++ Table 2 indicates the normal distribution analysis pattern of different water quality parameters, where, significant variations between mean and median for parameters, viz. temperature, TDS, EC, Cl-, SO4-- ,F-, alkanity, hardness, Ca++, Mg++, NO3-and Zn++ were observed. It indicated that these parameters were not found to be completely distributed in a normal (almost normal) and symmetric way in the samples. However, small difference of mean and median for parameters pH, turbidity, Fe, Cu, Mn, Pb, Cr and Cd, indicated that these parameters were seemed to be distributed normally in groundwater samples. Parameters temperature, TDS, Fe, Cl-, SO4-- ,F-,pH, TDS, and EC in the collected samples had negative values of Kurtosis, which indicated that, the distribution of these parameter have flat peak compared to normal distribution pattern. The negative values of skewness of pH (-1.97) and Fe (-0.23) indicated that the data were distributed towards the lower values or having a negative tail in the negative direction. The skewness values for Temp. (0.32), TDS (0.56),EC(1.08),turbidity(2.88), NO3(1.15) were positive, indicated their tail distributed towards the higher values which pointed out that data were distributed in the right direction of the tail. Correlation among water quality parameters greatly facilitates the task of rapid monitoring of water quality. Table 3 presents the Pearson correlation coefficient matrix between major chemical parameters of ground water of the study area. The variables having coefficient value (r) > 0.50 are considered significant. #N/A Pb The analytical data showed close significant positive association of TDS with EC(r=0.93), Turbidity (0.50), SO 4-(r=0.81), Cl- (r=0.87), alkalinity(r=0.71), total hardness (r=0.88), Ca++ (r=0.72), Mg++ (r=0.87), Cd (r=0.50). It indicates that TDS was increased with increasing these parameters in ground water samples. EC with Sulfate (r=0.80), Chloride (r=0.83), Alkalinity (r=0.71), Total hardness (r=0.82), calcium (r=0.64), Mg++ (r=0.81). It indicates that EC was increased with increasing these parameters in ground water samples, Turbidity with TDS alkalinity (0.52). SO4— with TDS EC (r=0.80), Cl- (r=0.78), alkalinity (r=0.61), total hardness (r=0.73), Ca++(r=0.64), Mg++ (r=0.71). Cl- with Alkalinity (r=0.61), total hardness (r=0.76), Ca++(r=0.59), Mg++ (r=0.75), Cd (r=0.51). It indicates that Cl- was increased with increasing alkalinity, total hardness, Ca++, Mg++, Cd in ground water samples. Pb and F- content also showed negative correlation with almost all parameters. pH content showed negative correlation with TDS, EC, turbidity, SO4-- , Cl- , alkalinity, total hardness, Ca++ , Cu++,Zn, Pb, Cr and ,Cd. Fe++content showed negative correlation with Temperature, TDS, EC, turbidity, NO3-, SO4--, Cl-, alkalinity, total hardness, Ca++, Mg++ , Cu++, Mn, Pb, ,Cr, Cd. It reflects a decreasing trend in Fe++ values of groundwater due to increasing Temperature, TDS ,EC, turbidity , NO3-,SO4--, Cl- , alkalinity, total hardness, Ca++ ,Mg++, Cu++,Mn, Pb, Cr , Cd . Bangar et al (2008) also observed a highly significant negative correlation coefficient between pH and SO4--,EC, Ca++, Cl-, SO4--. This indicates that these variables have an inverse relation. 104 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 100-106 Table-5.Pearson correlation between different water quality parameters post-monsoon 2013 Temp. pH TDS EC Turbidity Iron Nitrate Sulphate Fluorides Chloride Alkalinity TH Calcium Mg Copper Zinc Mn Lead Chromium Cadmium Alkalinity TH Calcium Mg Copper Zinc Mn Lead Chromium Cadmium Temp. 1.00 0.20 0.18 0.17 0.13 -0.08 0.30 0.20 -0.22 0.16 0.24 0.15 0.18 0.13 -0.10 0.01 0.06 0.06 -0.06 -0.19 Alka. 1.00 0.61 0.66 0.57 -0.15 -0.08 0.14 -0.13 0.37 0.13 pH TDS EC 1.00 -0.20 -0.17 -0.23 0.22 0.22 -0.32 0.01 -0.12 -0.12 -0.11 -0.25 -0.08 -0.19 -0.43 0.01 -0.20 -0.19 -0.05 TH 1.00 0.93 0.50 -0.67 0.37 0.81 -0.37 0.87 0.71 0.88 0.72 0.87 -0.05 -0.004 0.46 -0.12 0.29 0.50 1.00 0.46 -0.62 0.37 0.80 -0.40 0.83 0.71 0.82 0.64 0.81 -0.001 -0.03 0.45 -0.09 0.42 0.42 1.00 0.72 0.99 -0.05 0.07 0.38 -0.11 0.21 0.38 Ca++ 1.00 0.65 -0.09 0.07 0.35 -0.16 0.21 0.42 Mg++ 1.00 -0.04 0.07 0.36 -0.09 0.20 0.36 V. CONCLUSION The present study clearly reveals that all the water sources chosen for study are not managed suitably for the utilization of water. From the present study the following conclusions were drawn: In all places alkalinity were found above the standard limit (200 mg/l) prescribed by BIS, reveals that the groundwater of the study area is alkaline in nature. The Ca++ and Mg++ ion and total hardness values were high in most of the places, reveals that groundwater of the study area is hard to very hard. The Fe++ values were high in most of the places. The correlation matrix indicates that the TDS is mainly controlled by SO4--,Cl-, alkalinity, total hardness, Ca++and Mg++.There is a strong positive relationship between TDS and these parameters. pH content showed negative correlation with TDS, EC, turbidity, SO4-- , Cl- , alkalinity, total hardness, Ca++ , Cu++,Zn, Pb, Cr and ,Cd. There is an immediate and urgent need for the implementation of a better water quality management policy incorporating the following recommendations. Tube wells and other drinking water sources should be installed in a safety place. A proper planning and management is required to mitigate the problem of drinking water contamination in the study area. Turb. Iron No3 So4 F Cl- 1.00 -0.05 0.16 0.44 -0.42 0.40 0.52 0.38 0.42 0.35 -0.09 0.02 0.26 -0.06 0.10 0.14 Cu 1.00 -0.12 -0.61 0.26 -0.69 -0.46 -0.58 -0.46 -0.58 -0.18 0.04 -0.20 -0.19 -0.28 -0.46 Zn 1.00 0.32 -0.24 0.30 0.35 0.45 0.49 0.42 -0.14 -0.10 0.10 -0.18 0.16 0.22 Mn 1.00 -0.47 0.78 0.61 0.73 0.64 0.71 -0.10 0.07 0.36 -0.12 0.28 0.37 Pb 1.00 -0.39 -0.18 -0.42 -0.19 -0.44 -0.16 -0.09 -0.27 -0.12 -0.21 -0.23 Cr+6 1.00 0.61 0.76 0.59 0.75 -0.04 -0.07 0.31 -0.11 0.30 0.51 Cd 1.00 0.42 -0.09 0.61 0.22 0.20 1.00 -0.11 0.11 -0.03 -0.05 1.00 -0.06 0.22 0.51 1.00 0.01 -0.03 1.00 0.39 1.00 ACKNOWLEDGEMENTS The authors are thankful to the Head of Civil Engineering Department, Maharishi Markandeshwar University, Mullana, Ambala for support and facilities provided. They also thankful to Dr Taqveem Ali Khan, Associate Professor Department of Geology, AMU Aligarh India for helping in bringing out the paper in the present form. The author is also thankful to Er Mohammad Owais, Executive Engineer U.P. Jal Nigam Aligarh for providing necessary facilities. REFERENCES [I] APHA Standard Methods for Examination of water and Wastewater (2005) 21st edition, APHA, AWWA & WPCF, Washington DC. [II] Bangar, K. S., S. C. Tiwari, S. K. Verma and U. R. Khandkar (2008) Quality of groundwater used for irrigation in Ujjain districy pf Madhya Pradesh, India", Journal of Environ. Science & Engg. Vol. 50(3), pp 179-186. [III] Dubey, N. A (2003) comparative status of quality of drinking water of Bhopal city filtration plants and ground water with special reference to heavy metals and organo chemical. Ph.D. Thesis, Barkatullah University, Bhopal. [IV] Freeze, R. A., & Cherry, J. A., (1979) Groundwater”. Englewood Cliffs: Prentice Hall. [V] Helsel D. R. and Hirsch R. M. (2002) Statistical methods in water resources”, U.S. Department of the interiorgale, chapter 3 pp218. [VI] Howari F.M., Abu-Rukah Y. and Shinaq R. 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Geneva: World Health Organization, Vol. 2. 106 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 107-112 HOCSA: AN EFFICIENT DOWNLINK BURST ALLOCATION ALGORITHM TO ACHIEVE HIGH FRAME UTILIZATION Rabia Sehgal, Maninder Singh Department Computer Science, Punjabi University Patiala, Punjab
[email protected] Abstract— A Broadband Wireless Access technology known as Worldwide Interoperability for Microwave Access (WiMAX) is based on IEEE 802.16 standards. It uses orthogonal frequency division multiple accesses (OFDMA) as one of its multiple access technique. Major design factors of OFDMA resource allocation are scheduling and burst allocation. To calculate the appropriate dimensions and location of each user’s data so as to construct the bursts in the downlink subframe, is the responsibility of burst allocation algorithm. Bursts are calculated in terms of number of slots for each user. Burst Allocation Algorithm is used to overcome the resource wastage in the form of unused and unallocated slots per frame. It affects the Base station performance in mobile WiMAX systems. In this Paper, HOCSA (Hybrid One Column Striping with Non Increasing Area) algorithm is proposed to overcome frame wastage. HOCSA is implemented by improving eOCSA algorithm and is evaluated using MATLAB. HOCSA achieves significant reduction of resource wastage per frame, leading to more exploitation of the WiMAX frame. Index Terms— Burst allocation, downlink subframe, Mobile WiMAX, OFDMA, MATLAB. I. INTRODUCTION The vendor interoperability organization gave the name Worldwide Interoperability for Microwave Access (WiMAX) to the 802.16-2004 amendment which is an industry name. Main aim of WiMAX is to provide broadband wireless access (BWA). WiMAX is an alternative solution to wired broadband technologies like cable modem access and digital subscriber line (DSL). Mobile WiMAX or 802.16e is known as the mobile version of 802.16. To maintain mobile clients connected to a Metropolitan Area Network (MAN) while moving around, this amendment is done[1]. Point-to-Multipoint (PMP) topology is used for Mobile WiMAX, where the traffic occurs between a Base Station (BS) and its Mobile Stations (MSs). Here, the BS is the centre of the system. Thus BS efficiency highly affects the performance of Mobile WiMAX systems performance. Orthogonal Frequency Division Multiple Access (OFDMA) technology is used by the physical layer (PHY) of Mobile WiMAX. OFDMA can be implemented by Time Division Duplex (TDD) or Frequency Division Duplex (FDD). TDD is the preferred technology for mobile WiMAX. Mobile WiMAX frames uses TDD mode consists of two parts, the downlink subframe and the uplink subframe as shown in figure 1. The upward data is sent from the MS towards the BS through the UL subframe interval and the downward data is sent from the BS towards the MS through the DL subframe interval. The Ratio of downlink-to-uplink-subframe may vary from 3:1 to 1:1. Guard time intervals between successive DL and UL subframes are transmit-receive Transition Gap (TTG) and Receive transmit Transition Gap (RTG) [2]. Mobile WiMAX channel resources frequency and time are used to formulate frames. These frames carry users’ data in the form of data bursts. The frame has a limited size as defined in Mobile WiMAX standards (5ms) [3]. The frame should carry a maximum number of data bursts to satisfy high system performance. A data burst is formed by a number of slots in the form of irregular rectangles. A slot is the smallest resource portion that can be allocated to a single user in a frequency and a time domain. Each slot is defined by one subchannel (frequency) and one to three OFDM symbols (time). The Burst Profile for each data burst is assigned by BS. Burst Fig. 1 TDD frame structure of mobile WiMAX. Profile is used to identify the forward error correction (FEC), combination of modulation, and code rate for individual bursts. A burst allocation algorithm or burst mapping faces a problem to fill up the frame with irregular downlink burst rectangle shapes. This often leads to wastage of resources in the form of unallocated and unused slots due to decision complexity of finding conformity between the rectangular shapes and that of the available area within the frame. This leads to design an efficient algorithm to avoid the resource wastage. When the allocation of bursts to the user data in the DL subframe is bigger than the actual data size it results in Unused slots as shown in figure 1 (the red area). The area which is not assigned to any burst in the DL subframe due to a mismatch of rectangular shapes to the available area within the DL subframe is known as Unallocated slots (the yellow area). Eventually, the frame’s utility will definitely be decreased by the unused and unallocated slots left out vacant and transmitted as blank slots. A part of the frame is assigned to the overhead bursts, which informs the served users 107 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 107-112 about their burst profiles. At the beginning of each frame the of the burst and is known as Downlink MAP Information overhead is compulsory to be allocated and broadcasted to all Element (DL_MAP_IE). users under BS coverage. The overhead bursts include: preamble, The contribution of this paper is to design and develop a burst downlink map (DL-MAP) and uplink map (UL-MAP), Frame allocation algorithm with low complexity and minimum resource Control Header (FCH) as shown in figure 1. wastage. The developed algorithm is based on allocation of data Drawbacks which affect the frame utilization of the standard slots in the form of columns. The algorithm is developed to allocation algorithm can be listed as follows: achieve higher frame utilization, by reducing the unused and (1) The allocation algorithm faces problem in area calculation unallocated slots without violating the agreed QoS guarantee and frequently re-dimensioning the incoming data to find a within the downlink subframe. match between burst rectangles and available area due to lack of The rest of this paper is organized as follows: Section 2 knowledge about the incoming data sizes. (2) Burst size affects describes the related works, section 3 describes the proposed the allocation procedure, which results in more resource wastage. work, section 4 presents the results and discussion and finally in (3) User waiting time inside the Medium Access Control (MAC) section 5 conclusion and future work. layer queue increases, which results in increase in data transmission time. (4) ST algorithm is an NP-complete problem II. RELATED WORK [4]. NP-complete problem is a class of decision complexity WiMAX systems performance depends on the burst allocation problems. It uses a set of rules that prescribes more than one algorithm; therefore it is a very crucial matter for the action to be performed for a given situation, which leads to an manufactures. This paper identifies key factors and tradeoff increase in the computational load. issues associated with the downlink burst packing algorithm through a competitive survey of algorithms. A. Downlink Data Allocation Problem Literature surveys can be divided into many methods of The IEEE 802.16e standard specifies some constraints while designing the Burst Allocation Algorithm, as follows: mapping the user data bursts into downlink sub frame these are A. The first method is to reshape the rectangular burst to get the described below:1. Downlink subframe has a limited size as defined by Mobile appropriate shape that can be inserted into the DL subframe with WiMAX standards. This subframe should carry a maximum minimum wastage of slots, as given in the following literatures :number of user data bursts in order to achieve high system performance. In [19] authors introduced an algorithm which used a physical 2. The overhead is compulsory to be added at the beginning of component called Bucket. A Bucket consisted of a number of each subframe, which informs the served users within a slots allotted to each user in the form of columns. Then the subframe about their burst profiles and significantly effects Buckets with similar profile were combined to construct a single the system performance. burst. This scheme violated QoS, when the packets that did not 3. Mapping of the data bursts into downlink subframe has to match the available burst space and did not meet their be in rectangular form. This constrain results in two- transmission deadline were to be discarded because there was no dimensional rectangular burst mapping problem which is a estimate of how many packets to be dropped. NP complete problem. To shape the selected data bursts in In [10] Hung-Chang Chen et.al. authors presented Efficient rectangular form may require extra allocation of slots, Downlink Bandwidth Allocation (EDBA) algorithm. It moreover to fit those rectangles into subframe may leave calculated the shape, alleviation of unallocated slots, sequence some slots as unutilized. These unutilized slots and and location of the bursts within downlink subframes. In this unallocated slots left out vacant decreases frame utility and algorithm overhead calculation was ignored within the frame as the efficiency of mapping algorithm. well as the results showed that there was much wastage of slots 4. There are many considerations along with two-dimensional with lower number of users per frame. This algorithm increased rectangular burst mapping problem like: (i) to reduce power the computational load. consumption and SS active time minimize the number of Chakchai SO-IN et al. developed OCSA algorithm in which burst time symbols [7], (ii) to efficiently utilize the all the user data to be mapped was sorted in the descending order subchannel minimize the number of burst subchannels [8, [7]. The resource allocations were mapped from bottom to top 9] and (iii) to reduce DL-MAP overhead size reduce the and from right to left in the DL subframe. According to the burst number of bursts [10]. area, there were many possible combinations of height and width out of which we chose the pair which was smallest in width. B. DL-MAP Overhead & Allocation Algorithm Small width lead to save energy as the receiving MS shut down Users are assigned slots in the rectangular form called a burst. its electronic circuit for the remaining of the DL subframe. A burst contains data for a single or multiple CID that share same Chakchai SO-IN et al. presented eOCSA (enhanced One physical parameters. DL_MAP massage is broadcasted with the Column Striping with non increasing Area first mapping) most reliable MCS at the beginning of the DL subframe which algorithm [8]. It was a two-dimensional burst mapping algorithm. informs each user about its burst allocation. The DL_MAP field Its goal was to minimize the unused slots and the energy consists of two main groups. The first group needs 104 bits once consumption as in [11]. It allowed the MAP to grow per DL Subframe. It consists of Message Type, DCD Count, BS dynamically. In this algorithm, allocations were sorted in the ID, PHY Synchronization, and No Symbols. The second group decreasing order, and mapped from right to left and bottom to needs (44+16 No CID) bits once per burst. It consists of No CID, top. It consisted of two phases. In the first phase, vertical CID, Boosting, No Subchannel, No Symbols, Symbol Offset, mapping took place in which the largest allocation was mapped Subchannel Offset, DIUC and Repetition Coding Indication [3]. with the least width or least height. After this the left space above This group is used to define a two-dimensional allocation criteria this allocation was used for the horizontal mapping phase, where 108 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 107-112 eOCSA tries to assign the largest allocation that can be fitted in as multiples of buckets which are fixed sized can be easily that space. Also, in this scheme some slots were left unused, packed into the DL subframe [11]. some were over allocated. Zhu et. al. introduced an algorithm in which the bursts were III. PROPOSED DOWNLINK BURST ALLOCATION allocated in the columns of identical width .Then these allocated ALGORITHM bursts were shuffled to combine the left scattered unused space in The contribution of this work is to develop a new burst the frame. This formed a large space which could accomodate allocation algorithm, a Hybrid One Column Striping with nonmore bursts [10]. . increasing Area first mapping (HOCSA) algorithm with low Ahmed M Husein Shabani et al. presented Improved eOCSA complexity to overcome frame wastage. HOCSA is implemented Algorithm (IOCSA) [3]. The improvement algorithm is same as using MATLAB software. eOCSA algorithm with a little difference in the vertical mapping step. In eOCSA algorithm the requests were mapped based on A. HOCSA (Hybrid OCSA Algorithm) maximum height (H) which minimized the burst width. In case Hybrid OCSA is obtained by improving One Column Striping most of the bursts were large sized, the unused space left above with non-increasing Area first mapping (OCSA) proposed by Sothe allocated burst cannot accommodate any burst in the In et.al. in [13] and it’s enhancement in [14]. HOCSA uses horizontal mapping step. In IOCSA, the vertical mapping step Genetic Algorithm to optimize the allocated space so as to have included slight increment in the burst width so as to fit more better frame utilization. bursts in the horizontal mapping step. Instead of maximum The algorithm can be described in three main steps. height we used 3/2 of the maximum height. This efficiently 1. Sort all the data bursts in decreasing order. utilized the left space. 2. Vertically allocate the largest burst (Bi) with dimensions (Wi,Hi). B. The second method is to fragment the burst to get the (Here mapping is done if the allocated slots are less than required shape that can fit the available allocation space in the unallocated slots. And mapping will take place if user DL subframe, such as in the following papers:data burst is equal to the fitness function. Where Wi=┌Bi/H┐, Hi=┌Bi/Wi┐ where H is maximum Jincao Zhu et al. presented a linear complexity algorithm [13]. height and ┌ ┐is ceiling function.) It included frequent reshaping and fragmentation of the bursts. 3. Allocate the left space in the allocated column The constructed bursts were shifted and combined in the adjacent horizontally. area. Overhead size was not considered especially when there was burst fragmentation which required additional overhead on After this we further optimize the frame utilization by merging the expense of the data slots. the adjacent unallocated columns and map the remaining Zaid G. Ali introduced a low complexity algorithm called appropriate bursts that best fits in it. There by utilizing the left Sequential Burst Allocation (SBA) [25]. SBA was based on space to the maximum. For this step, if the column value is less sequential allocation of data slots in the form of columns. It than the fitness function then merge this column with the reduced the unused slots within a burst to be one slot per burst at adjacent column to allocate the remaining appropriate bursts, and the worst case and eliminated the unallocated slots between the continue this process till all the columns are covered. bursts. It numbered the fragments to be re-assembled in the correct order by the recipients. Figure 4.1 illustrates the algorithm steps. C. The third method consists of the cross layer design between 1st step Sorted_allocations = Sort (resource_allocations) PHY and MAC layers to satisfy the differentiation between FOR each unmapped element in sorted_allocations service types and utilize the QoS information to allocate the 2nd step bursts according to the priorities and constrains to reduce the Vertical_Mapping(&start_strip_GA_i,&end_strip_GA_i, slots wastage. &height_GA_i) FOR each unmapped element in sorted_allocations Authors proposed a cross layer design [14] to achieve the non- If allocated.slot<unallocated.slot real time and real time scheduling in addition to the burst Allocate.slot.initialize.coulumn allocation. It consisted of a two tier framework, the first was for If sorted.structure==fitness.function the priority scheduling and the second was for the burst Allocation.column=true allocation. The burst allocation divided the downlink subframe Else into several slices horizontally, each called a bucket. The Allocation.column=false allocation process ignored the calculation of the unused slots end within a bucket. The overhead reduction depended on the number 3rd step of buckets that can be aggregated. This method enhanced the Horizontal_Mapping(start_strip_GA_i,end_strip_GA_i, system QoS by manipulating the subchannels, distributing the height_GA_i, &sub_height_GA_i) computational load between burst allocation algorithm and the For i=1:column.count QoS scheduler, aggregates similar users conditions in a single If column.value<fitness.value burst. Column.e=column.e+column.e+1 Jia-Ming et. al. combined the problem of burst allocation and end scheduling in the cross-layer manner. The proposed allocation END FOR algorithm scheduled the non real time and real time data traffic END FOR 109 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 107-112 1) Genetic Algorithm used in HOCSA numColNeeded = same previous numColNeeded Genetic algorithm is a method for solving both constrained numSubchannels =┌ (sizeInPs / numColNeeded)┐ and unconstrained optimization problems based on a natural numsymbols = numColNeeded * num symbols per PS selection process. The algorithm repeatedly modifies a For i=1:column.count population of individual solutions. At each step, the genetic If column.value<fitness.value algorithm randomly selects individuals from the current Column.e=column.e+column.e+1; population and uses them as parents to produce the children for end the next generation. Over successive generations, the population Mark these symbols are used on all subchannels "evolves" toward an optimal solution. As proposed in HOCSA, Step 5 return allocated burst info (burstIndex, moduencoding Genetic Algorithm optimizes eOCSA Algorithm to have better type, subchannelOffset, numSubchannels, symbolOffset, frame utilization. Genetic Algorithm uses fitness function. Inputs numSymbols ) to the Genetic Algorithm are provided slots and used slots. Genetic algorithm further optimizes the used slots by comparing IV. SIMULATION RESULTS AND DISCUSSIONS the value of used slots with that of fitness value. Fitness value is Results are the most important part of any research work, they calculated using Fitness Function. If the value of used slots is are used to justify the work. To analyze Downlink Burst equal to the Fitness value then allocation is mapped otherwise it allocation algorithm in a WiMAX network, MATLAB is chosen is ignored. as the simulation tool to reflect the actual deployment of the Fitness function that the GA uses:- f= (1-e)*((1-Fs)/Ft) WiMAX network. Fs= each slot, Ft = total number of slots, e is the classification error rate A. Simulation Parameters The following outline summarizes the genetic algorithm:Table 5.1 shows the parameters that are used to perform the 1. The algorithm begins by creating a random initial evaluation experiments. The data traffic is generated with population. different packet sizes and different time intervals to produce 2. The algorithm then creates a sequence of new different burst sizes. The number of users(SS) are changed from populations. At each step, the algorithm uses the 10 to 50. individuals in the current generation to create the next population. Table.5.1 WiMAX System Simulation Parameters Parameter Value 2) Implementation of HOCSA algorithm in MATLAB Frame length 5 ms Channel BW 10 MHz The algorithm implementation’s main steps are:Duplexing TDD Step1 Multiple Access OFDMA Define frame parameters (number of subchannel (rows), number Permutation scheme PUSC of time symbols (columns ), PS size (1 subchannel X 2 time Number of subchannels 50 symbols) , symbol (0) for preamble, Symbol 1 and symbol 2 on FFT 1024 subchannel, 0 and 1 are for FCH, Burst 0 for( FCH +DL-MAP ), Simulation time 100 ms Burst 1for UL-MAP ) Step2 B. Results (Performance evaluation) Allocate B0 (DL-MAP) &B1 (UL-MAP) as previous allocations. Here different simulation scenarios are used for performance Step3 evaluation and the results are obtained from them. In this section, Sort the scheduled data based in PS required as set by scheduler. we compare the performance of the Hybrid eOCSA with eOCSA Merge Sorting is used here. and IOCSA algorithms using MATLAB simulation software. Step 4 Start allocation from the head of sorted list do Average unused slots- Unused slots are calculated for every Vertical mapping Calculate number of columns needed and allocated frame then divided by number of allocated frames to number of rows needed inside the frame as per the equation: get the average unused slots. numColNeeded = ┌(sizeInPs / numSubchannels)┐ Figure 5.6 illustrates the average unused slots per DL numSubchannels = ┌(sizeInPs / numColNeeded) ┐ subframe and the results show that the average of unused slots numsymbols = numColNeeded * num symbols per PS for the HOCSA algorithm is smaller than that of eOCSA and If allocated.slot<unallocated.slot IOCSA. This is because eOCSA and IOCSA left more unused Allocate.slot.initialize.coulumn space that cannot accommodate any burst. But the HOCSA If sorted.structure==fitness.function Algorithm uses Genetic Algorithm to optimize the frame space Allocation.column=true and merges the unused columns to allocate the data bursts that Else best fits into that space. Results also show that the unused slots Allocation.column=false continuously decrease as the number of users or the load end increases in the given subframe. Mark these symbols are used on all subchannels Horizontal mapping Calculate Empty slots available in Current allocation and Check largest request that can fit in it and Calculate number of columns needed and number of rows needed inside the frame as per the equation 110 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 107-112 of rectangles limits the opportunities of fitting more users in the frame which leads to an increase in slot wastage. Figure 5.6: Comparision of unused slots of eOCSA, HOCSA, IOCSA Average allocation efficiency- Allocation efficiency is calculated for every allocated frame and then divided by the number of allocated frames to get average allocation efficiency. Figure 5.7 illustrates the average allocation efficiency of HOCSA, eOCSA and IOCSA. Here we increase the load upto 30%, the results show that the HOCSA algorithm achieves higher efficiency than eOCSA and IOCSA when the most burst sizes are large and close in size. HOCSA Algorithm uses Genetic Algorithm to optimize the frame space by mapping more number of user data bursts, thus efficiently packs the downlink sub frame further merges the unused columns to allocate the data bursts that best fits into that merged space. Figure 5.7: Comparision of allocation efficiency of eOCSA,HOCSA,IOCSA Figure 5.8 shows the frame utilization versus different MAC PDU sizes. There are two curves, each corresponding to an individual allocation algorithm HOCSA and eOCSA. The figure shows that the algorithm HOCSA outperforms the eOCSA and the percentage difference between them is 22.84%. Moreover the figure depicts that the utilization of the eOCSA algorithm declines comparatively to the HOCSA while increasing the MAC PDU size because in eOCSA, the size of MAC PDU in the form Fig 5.8 Frame utilization vs MAC PDU size Figure 5.9 shows the unallocated slots versus different MAC PDU sizes. In HOCSA the allocation is optimized as the result of Genetic algorithm, further merges the unallocated columns to map appropriate burst into it. Thus has a major effect of reducing the unallocated slots per frame. On the other hand the eOCSA algorithm curve allocates more users when MAC PDU is small size which reduce the unallocated slots, while when MAC PDU become large size the opportunity of allocating more users decreases which increase the unallocated slots per frame. The HOCSA algorithm behavior explains the effect of MAC PDU size to the increment of the unallocated slots. Fig 5.9: Unallocated slots vs MAC PDU size The graph in Fig. 5.10 below shows the average packing capacity of the number of users in HOCSA and eOCSA. Average packing capacity of HOCSA is more than that of eOCSA. As the size of MAC PDU increases average packing capacity of both increases. 111 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 107-112 Fig. 5.10: Packing Capacity vs No of users in HOCSA and eOCSA V. CONCLUSION AND FUTURE WORK A. Conclusion In this paper, an efficient burst allocation algorithm for mobile WiMAX network has been presented. This work discussed the downlink burst allocation algorithm for IEEE 802.16e Mobile WiMAX networks and its implementation in MATLAB, namely HOCSA. Our work shows that the implemented burst allocation algorithm (HOCSA) in MATLAB is obtained by modifying eOCSA (enhanced One Column Striping with non-increasing Area first mapping). HOCSA is a burst allocation algorithm with low complexity and minimum resource wastage. It has been observed from the simulation results, obtained by MATLAB simulation model that the HOCSA exhibit 15% improvement in efficiency as compared to the eOCSA. The proposed algorithm achieves higher frame utilization within the downlink subframe, by reducing the unused and unallocated slots and without violating the agreed QoS guarantee. B. Future Work Future work may be done to optimize the frame utilization and reduce the unused and unallocated slots in the DL_Subframe without violating the agreed QOS. Here, the proposed downlink burst allocation algorithm known as HOCSA reduces the wastage of slots by using genetic algorithm and further by merging the adjacent vacant columns to allocate the remaining bursts that best fits into it. Future work may include various design factors and optimization techniques to achieve significant reduction of resource wastage per frame, leading to more exploitation of the WiMAX frame. Furthermore, the proposed algorithm may be taken forward and BFO optimization algorithm may be used on it to achieve higher frame utilization. REFERENCES [I] Abbas, Hajj, H. Yassine, “Optimal WiMAX Frame Packing for Minimum Energy Consumption”, IEEE IWCMC, pp. 1321 - 1326, 2011. [II] Abbas, Hajj, H. Yassine, “An Energy-Efficient Scheme for WiMAX Downlink Bursts Construction”, IEEE ICEAC, pp. 1-6, 2010. [III] Ahmed M Husein Shabani.et.al, “WiMAX Downlink Burst Allocation Algorithm,” Proceedings published in International Journal of Computer Applications® (IJCA) (0975 – 8887), Mobile and Embedded Technology International Conference, pp. 12-25, 2013. [IV] Bacioccola, Cicconetti, Lenzini, Mingozzi, E.A.M.E., and Erta , A.A.E.A., “A downlink data region allocation algorithm for IEEE 802.16e OFDMA”, 6th International Conference on Information Communications & Signal Processing, pp. 1-5, December 2007. [V] Chakchai So-In, Raj Jain, Abdel-Karim Al Tamimi, “OCSA: An Algorithm for Burst Mapping in IEEE 802.16e Mobile WiMAX Networks,” 15th Asia-Pacific Conference on Communications, pp. 5-10, 2009. [VI] Chakchai So-In, Raj Jain, Abdel-Karim Al Tamimi, "eOCSA: An algorithm for burst mapping with strict QoS requirements in IEEE 802.16e Mobile WiMAX networks," in 9 th International Conference on Information Communications & Signal Processing , pp. 1-5,2009. [VII] Hung-Chang Chen, Sheng-Shih Wang, Chi-Tao Chiang,"An Efficient Downlink Bandwidth Allocation Scheme for Improving Subchannel Utilization in IEEE 802.16e WiMAX Networks," 71st IEEE Vehicular Technology Conference (VTC 2010), pp. 1-5, 2010. [VIII] IEEE 802.16 Working Group, "IEEE Draft Standard for local and metropolitan area networks Part 16: Air Interface for Broadband Wireless Access Systems," IEEE P802.16Rev3/D5, March, pp. 12614, 2012. [IX] Jeffrey G. Andrews, Arunabha Ghosh, Rias Muhamed, Fundamentals of WiMAX United States: Prentice Hall 2007. [X] Jincao Zhu, Hyogon Kim, Hee Hwan Kwak, "A LinearComplexity Burst Packing Scheme for IEEE 802.16e OFDMA Downlink Frames," 69th IEEE Vehicular Technology Conference, pp .1-5, 2009. [XI] Jia-Ming Liang, Jen-Jee Chen, You-Chiun Wang, Yu-Chee Tseng, "A Cross-Layer Framework for Overhead Reduction, Traffic Scheduling, and Burst Allocation in IEEE 802.16 OFDMA Networks," IEEE Vehicular Technology Conference, Vol. 60, pp. 1740-1755, May 2011. [XII] L. Nuaymi, “WiMAX: Technology for Broadband Wireless Access”. The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England: John Wiley & Sons Ltd, 2007. [XIII] T. O. Takeo Ohseki, Megumi Morita, Takashi Inoue, "Burst Construction and Packet Mapping Scheme for OFDMA Downlinks in IEEE 802.16 Systems," IEEE Global Telecommunications Conference, GLOBECOM '07, pp. 43074311, 2007. [XIV] T Wang, H Feng, B Hu, “Two-dimensional resource allocation for OFDMA system”, in Proc IEEE International Conference on Communications Workshops, pp.1–5 ,May 2008. [XV] Xin Jin, Jihua Zhou, Jinlong Hu, Jinglin Shi, Yi Sun, Eryk Dutkiewicz, "An Efficient Downlink Data Mapping Algorithm for IEEE802.16e OFDMA Systems," IEEE Global Telecommunications Conference, GLOBECOM 2008, pp. 1-5, 2008. [XVI] Y. Xiao, WiMAX/MobileFi: advanced research and technology. New York: Taylor & Francis Group, 2008. [XVII] Zaid G. Ali, R. B Ahmad, Abid Yahya, “Burst Fragmentation Model Based on Sequential Burst Allocation Algorithm for Mobile WiMAX,” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Vol. 3, Issue-3, pp. 47-52, July 2013. [XVIII] Zaid G. Ali, R. B Ahmad, Abid Yahya, “ Improve Downlink Burst Allocation to Achieve High Frame Utilization of Mobile WiMAX (802.16e)”, International Journal of computer Science Issues, Vol. 9, Issue-6, No 3, pp. 142-147, November 2012. [XIX] Zesmond J. Higham, Nicholas J. Higham, MATLAB Guide, SIAM, 01-Apr-2005. 112 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 113-116 TRACKING AND CHECKING CARGO CONTAINERS PILFERAGE USING ELECTRONIC LOCK Sandeep Singh R1, Feroz Morab2, Sadiya Thazeen3, Mohamed Najmus Saqhib4 1 [M.Tech] Signal Processing, SJCIT, Chikkaballapur, Karnataka, India. 2,3 [M.Tech] Dept. of Electronics and Communication, RRCE 4 M.Tech, Digital Electronics Bangalore, Karnataka, India. Abstract— Present technologies of locking and monitoring the cargo do not provide effective solution for the situation. A little corruption among the employees can easily deceive whole security system. Since the cargo contains materials of high value and in high quantity, these containers are more prone to the pilferage and hence to protect the material we need a sound technique which minimizes the loss due to involvement of the corrupt employees. The technical work undertaken here aims at providing a sound mechanism to prevent pilferage in the cargo containers by implying an electronic lock and having series of basic security check like swapping the RFID card provided to the customer, biometric sensor and by entering the password which is sent at the time of delivery to the customer. Therefore it minimizes the human interference security of the cargo containers. This paper proposes a cost effective method of tracking cargo mobility using GPS. The system gives current vehicle location whenever needed with reliable accuracy. The system uses GSM and GPS Technology, helping in efficient monitoring of the desired vehicles. The paper also discusses the proposed GPS based vehicle Tracking System using GSM technology in which the coordinates are forwarded to Central monitoring system. The position of the vehicle can be traced on Google / Local maps. The paper gives functional, Technical description and Software implementation for the GPS and GSM based Vehicle Tracking System. Index Terms— GPS (Global Positioning System), GSM (Global System for Mobile Communications), Pilferage, RFID, Vehicle Tracking system. guilty or not comes under the light of suspicion .This results in culminate loss of the industry and people involved in the process of transportation. At present days, during the course of transportation, a scam of pilferage is being done. This results in culminate loss of industry. So, to overcome this type of theft, we aim to provide a sound mechanism to prevent the pilferage in the cargo container by implying an electronic lock and minimizing the human interference in the security of the cargo containers. This mechanism secures the container by an electronic lock which requires a series of security check during opening of the lock. The lock is controlled and monitored by the base station. The paper describes the idea of tracking a vehicle using latest technology of GPS and GSM. The number of industry related vehicles like oil tankers (trucks), vans carrying huge amount of money for ATMs are increasing at a very fast rate and keeping a track of these vehicles is becoming difficult day by day. To keep a check on these kinds of vehicles, these technologies prove to be very useful since in case of theft or missing of vehicles, they can be easily traced and tracked on the website or cell-phones. Mobile technologies such as GSM / GPRS and GPS can be used for displaying the current position of the vehicle indicating the latitude, longitude and height from sea level. This displaying of location of the vehicle can be done by a number of methods. The location can be sent via SMS to a GSM modem kept at the control station or to a cell phone. I. INTRODUCTION “ISLAMABAD: In a bid to hush up Rs. 50 billion scam of pilferage of thousands of containers of Afghan Transit Trade (ATT), the Federal Board of Revenue (FBR) has reinstated over 22 suspended staff in grade 14 to 18 apparently after pressure from the Supreme Court of Pakistan. It is learnt that the apex court of the country has taken suo moto notice of pilferage of containers scam causing billions of rupees loss to the national kitty. The FBR was under pressure at that time that resulted into suspension of officials of customs in grade 14 to 18 mainly at Karachi Port to settle down the dust for the time being. When contacted, Acting Chairman FBR Mehmood Alam who is also looking after the affairs of Customs because Member of Customs went abroad, told The News on Wednesday that legal opinion of FBR’s team were sought before instating the suspended officials because no one could be suspended for an indefinite period”. This news article shows the loss of huge sum of money due to pilferage from the Cargo containers which is used in transporting huge amount of manufactured product or raw material from one place to another. In the course of transportation, the pilferage or employee theft is done and all the authorities involved whether II. PROBLEM DEFINITION As discussed above, in the course of transportation of goods like PETROL, MILK products, RAW IRON, VEHICLE etc, if pilferage happen then every concerned person associated with the task whether guilty or not comes under the light of suspicion. This results in huge loss of money and also the industry’s reputation is at stake causing havoc to the company and the people involved in the process of transportation. This is due to the absence of strong security system. Presently we have Vehicle tracking system where we can only trace the location of the vehicle using GSM & GPS, but we cannot avoid the theft of goods. Another scenario is when the vehicle is reaching the destination much later than the scheduled time but provide a fake delivery time report to the company. During this time the vehicle will be used for other illegitimate purposes like shifting the other company goods or products in local area, pick & drop etc in the company’s expenses. III. PROPOSED SYSTEM We have proposed a sound mechanism to prevent pilferage in the cargo containers by implying an electronic lock and having series of basic security check like swapping the RFID 113 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 113-116 card provided to the customer, biometric sensor and by entering IV. HARDWARE DESCRIPTION the password which is sent at the time of delivery to the The SMS Based Vehicle Tracking System has been customer, thereby minimizing the human interference with implemented using GPS module and GSM / GPRS module regard to the security of the cargo containers. This paper SIM300. Figure above shows the block diagram for the proposes a cost effective method of tracking cargo mobility implementation of a GPS based vehicle tracking and using Global Positioning System (GPS). The system gives monitoring system. GPS module receives signals from a series current vehicle location whenever needed with reliable of satellites and calculates its current geographical location, accuracy. The system uses GSM and GPS technology, helping speed etc. The microcontroller processes this data through in efficient monitoring of the desired vehicles. The paper also MAX232 level converter. GSM module receives request for discusses the proposed GPS based vehicle Tracking System sending vehicle location from system registered cell phone. using GSM technology in which the coordinates are forwarded The GSM module sends reply SMS to same cell phone to Central monitoring system. The position of the vehicle can giving the vehicle location. The information about vehicle be traced on Google / Local maps. The paper gives functional location i.e. latitude, longitude and height from sea level can and technical description and Software implementation for the also be stored for later retrieval or frequently transmitted to a GPS and GSM based Vehicle Tracking control station where it can be displayed on a high-resolution Here we are providing Electromechanical Lock, controlled geographical map or can be directly sent to a data server by a Relay, which is locked after loading the consignment and through GSM module. The LCD at the system end indicates the monitoring system gets activated. Once the door is locked, coordinates and speed of the vehicle. The system consists of it will not get opened in any case until it reaches the following modules. destination. If in case it is tampered, the buzzer which is A. GPS Module provided gets activated for alert. The monitoring system keeps GPS receivers are composed of an antenna, tuned to the sending status message to the vehicle to check and track the frequencies transmitted by the satellites, receiver-processors Cargo location. We have a monitoring front end designed using and a highly stable clock (often a crystal oscillator). They may VB.NET, interfaced with Google map for mapping the cargo. also include a display for providing location and speed Once the vehicle reaches the destination, a message is sent to information to the user. Monitoring system. If the coordinates match then a password is sent to the person who is collecting the consignment. Once the Features: password is reached, LCD displays “SWIPE THE RFID CARD 65 channels to acquire and track satellites AND ENTER PASSWORD”. Upon the card swiping and simultaneously password entering, if these two parameters are accurate only Industry-leading TTFF speed then the door opens, otherwise the monitoring system gets a Tracking sensitivity reaches -161 dBm tampering message. The user gets only 3 attempts to enter the 0.5 PPM TCXO for quick cold start correct password. Integral LNA with low power control By using this technology, we can track the Vehicle to avoid SBAS (WAAS/EGNOS) capable theft of consignments and loss for the company due to over trip Cold start 29 sec under clear Sky and illegal usage of diesel. Figure 1 describes the design of the Hot start 1 sec under clear Sky proposed system. Accuracy 5m CEP Operable at 3.6V-6V Both of RS232 and UART interface at CMOS level Small form factor of 32mm W x 32mm L x 8mm H Mountable without solder process 6 pins wafer connector Applications: Automotive and Marine Navigation Automotive Navigator Tracking Emergency Locator Geographic Surveying Personal Positioning B. MAX 232 MAX232 is an integrated circuit that converts signals from an RS232 serial port to signals suitable for use in TTL compatible digital logic circuits. MAX232 is a dual driver/receiver which typically converts the RX, TX, CTS and RTS signals. Here, in this device, the MAX 232 is expected to serially interface the GPS module with the microcontroller so that the microcontroller can accept the GPS frames sent by the GPS module in an efficient way. Fig.1. Block diagram for GPS and GSM/GPRS based system C. Microcontroller 89S52 In this system, the microcontroller 89S52 plays the most vital role. The code burnt in the microcontroller decodes the data received from the satellite using the concept of counter. Thus converting the GPS frames received from the GPS 114 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 113-116 module in an understandable format. Moreover, the power output and the radio frequency used. When an microcontroller is also responsible to send the required RFID tag passes through the electromagnetic zone, it information through MAX232 and GPRS/GSM to the detects the reader's activation signal. monitoring. The reader decodes the data encoded in the tag's integrated circuit (silicon chip) and the data is passed to D. GSM/GPRS Module SIM 300 the host computer for processing. SIM300 is a Tri-band GSM/GPRS engine that works on frequencies EGSM 900 MHz, DCS 1800 MHz and PCS1900 F. Electromagnetic Lock MHz SIM300 provides GPRS multi-slot class 10 capabilities An electromagnetic lock, magnetic lock, or mag-lock is a and support the GPRS coding schemes CS-1, CS-2, CS-3 and locking device that consists of an electromagnet and an CS-4. armature plate which is used by attaching the electromagnet to The physical interface to the mobile application is made the door frame and the armature plate to the door. through a 60 pins board-to-board connector, which provides all hardware interfaces between the module and customers’ boards Operation: except the RF antenna interface. The magnetic lock relies upon some of the basic concepts The keypad and SPI LCD interface will give you the flexibility to develop customized applications. Two serial ports can help you easily develop your applications. Two audio channels include two microphones inputs and two speaker outputs. This can be easily configured by AT command. GSM300 AT commands: AT+CMGF=1 <ENTER>:To check the modem AT+CPIN="0000" <ENTER> :To check the network AT+CMGF=1 <ENTER>: To send the message in text format AT+CMGS=”NUMBER”<ENTER>: To enter the destination number AT+CNMI=2,2,0,0,0<ENTER>: To receive the message Features of GSM 300: SIM300 Tri-band: EGSM 900, DCS 1800, PCS 1900. The band can be set by AT COMMAND, and default band is EGSM 900 and DCS 1800. Power supply: Single supply voltage 3.4V – 4.5V Normal operation: -20°C to +55°C Supported SIM card: 1.8V ,3V Programmable via AT command E. RFID RFID (Radio Frequency IDentification) is a technology that incorporates the use of electromagnetic or electrostatic coupling in the radio frequency (RF) portion of the electromagnetic spectrum to uniquely identify an object, animal, or person. RFID is coming into increasing use in industry as an alternative to the bar code. The advantage of RFID is that it does not require direct contact or line-of-sight scanning. RFID is sometimes called Dedicated Short Range Communication (DSRC). Components: A basic RFID system consists of three components: An antenna or coil A transceiver (with decoder) A transponder (RF tag), electronically Programmed with unique information The antenna emits radio signals to activate the tag and to read and write data to it. The reader emits radio waves in ranges of anywhere from one inch to 100 feet or more, depending upon its of electromagnetism. Essentially it consists of an electromagnet attracting a conductor with a force large enough to prevent the door from being opened. In a more detailed examination, the device makes use of the fact that a current through one or more loops of wire (known as a solenoid) produces a magnetic field. This works in free space, but if the solenoid is wrapped around a ferromagnetic core such as soft iron the effect of the field is greatly amplified. This is because the internal magnetic domains of the material align with each other to greatly enhance the magnetic flux density. Advantages: Easy to install: Magnetic locks are generally easier to install than other locks since there are no interconnecting parts. Quick to operate: Magnetic locks unlock instantly when the power is cut, allowing for quick operation in comparison to other locks. Sturdy: Magnetic locks may also suffer less damage from multiple blows than do conventional locks. If a magnetic lock is forced open with a crowbar, it will often do little or no damage to the door or lock. V. RESULTS The complete project is implemented using software KIEL UV4 for Programming, debugging and compiling. Once this program is compiled and target is generated, flash the program into the IC using software FLASH MAGIC. KICAD is used for schematic and PCB designing of the complete project and VB6.0 software is used for Front-end design for monitoring system. The below figures depict the software and results of frontend and Hardware implementation. Fig. 2: Board designing using KICAD 115 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 113-116 for designing as well as improving the performance of the security system. REFERENCES Fig. 3: Hardware arrangement of Module Fig. 4: Complete Hardware arrangement of Module Fig. 5: Monitoring System designed using VB 6.0 Once the module is implemented as shown in above figures, Hardware module is initialized and monitoring of front-end which is designed using VB.NET, interfaced with Google map, for mapping the cargo, is executed on the PC. Monitoring system gets initialization message and status message for every request. Once the vehicle reaches the destination, a message is sent to Monitoring system. If the coordinates match, then the Password is sent the person, who is in charge of collecting the consignment. Once the password is reached LCD displays “SWIPE THE RFID CARD AND ENTER PASSWORD”. Once the card is swiped and password is entered, if both matches then the door opens, otherwise the monitoring system gets an alert message about the tampering. The user has 3 attempts to enter the correct password. VI. CONCLUSION A design of providing a sound mechanism to prevent the pilferage in the Cargo containers by implying an electronic lock was carried out in the above explained work. While designing the cargo many factors were considered. These kind of security systems have a lot of advantages in this developing world. They reduce large amount of maintenance problem and improves the security to a great extent. The performance of the checking of cargo pilferage was found to be satisfactory. A similar design principle can be adopted for the security of other vehicles which carry out material of high value and in high quantity. So this technical work can be extended further [I] Fleischer P.B., Nelson A.Y., Sowah R.A., Bremang, A., “Design and development of GPS/GSM based vehicle tracking and alert system for commercial inter-city buses”, Adaptive Science & Technology (ICAST), 2012 IEEE 4th International Conference on:25-27 Oct. 2012Page(s):1 – 6 [II] Ganesh G.S.P, Balaji B, Varadhan T.A.S, “Anti-theft tracking system for automobiles (autogsm)Full textsign-In or Purchase”, Anti-Counterfeiting, Security and Identification (ASID), 2011 IEEE International Conference on24-26 June 2011Page(s):17 – 19 [III] Zhigang Liu, Anqi Zhang, Shaojun Li, “Vehicle anti-theft tracking system based on Internet of things”, Vehicular Electronics and Safety (ICVES), 2013 IEEE International Conference on 2013 , Page(s): 48 – 52 [IV] Hui Song, Sencun Zhu, Guohong Cao, “SVATS: A SensorNetwork-Based Vehicle Anti-theft system”, INFOCOM 2008. The 27th Conference on Computer Communications. IEEE [V] Baburao Kodavati, V.K. Raju, S. Srinivasa Rao, A.V. Prabu, T. Appa Rao, Dr. Y.V. Narayana “GPS Based Automatic Vehicle Tracking Using RFID”, International Journal of Engineering Research and Applications , Vol. 1, Issue 3, pp.616-62. [VI] Devyani Bajaj, Neelesh Gupt, “GSM and GPS based vehicle location and tracking system”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 1, Issue 1, January 2012 [VII] Mrs. Ramyakulandaivel, P. Ponmalar, B. Geetha, G. Saranya, “GPS and GSM based vehicle information system”, International Journal of Communications and Engineering Volume 01– No. 1, Issue: 01 March 2012. [VIII] Deepak Mishra, Apurv Vas, Puneet Tandon, “A novel and cost effective approach to public vehicle tracking system”, International Journal of ubicomp (IJU), Vol. 3, No. 1, January 2012. [IX] Ruchika Gupta and BVR Reddy, “GPS and GPRS Based Cost Effective Human Tracking System Using Mobile Phones”, Volume 2 January-June 2011. [X] Mohammad A. Al-Khedher, “Hybrid GPS-GSM Localization of Automobile Tracking System”, International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 6, Dec 2011. [XI] Francis EnejoIdachaba “Design of a GPS/GSM based tracker for the location of stolen items and kidnapped or missing persons in NIGERIA”, ARPN Journal of Engineering and Applied Sciences VOL. 6, NO. 10, OCTOBER 2011. [XII] Adnan I. Yaqzan, Issam W. Damaj, and Rached N. Zantout “GPS-based Vehicle Tracking System-on-Chip”, Proceedings of the World Congress on Engineering 2008 Vol I WCE 2008, July 2 - 4, 2008, London, U. K. [XIII] T. Krishna Kishore, T. Sasi Vardhan, N. Lakshmi Narayana, “Vehicle Tracking Using a Reliable Embedded Data Acquisition Sytem With GPS and GSM”, IJCSNS International Journal of Computer Science and Network Security, VOL. 10 No. 2, February 2010 [XIV] S.S Pethakar, N Srivastava and S.D Suryawanshi. Article: “GPS and GSM based Vehicle Tracing and Employee Security System”, International Journal of Computer Applications 62(6):37-42, January 2013. Published by Foundation of Computer Science, New York, USA. 116 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 117-121 RESEARCH FRONTS OF WEB PERSONALIZATION: A SURVEY Deepti Sharda1, Sonal Chawla2 1 Assisstant Professor, Department of Computer Science and Application MCM DAV College for Women 2 Chairperson, Associate Professor, Department of Computer Science and Application Panjab University Chandigarh, India
[email protected] Abstract— Web personalization is the process of customizing a Web site according to the needs of specific user. It takes advantage of the knowledge acquired from the analysis of the user’s navigational behavior (usage data) in correlation with other information collected in the Web context, namely structure, content and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial area. A lot of research has been done in this field. This paper is comprised of three parts. First part gives introduction to Web Personalization. Second part tries to summarize the work done in this field and third part does research analysis. This paper tries to throw light on the research areas that are required to be explored. Index Terms—Web Personalization, Customization, Recommendation System, Ranking System, Wisdom Web I. INTRODUCTION With the dramatic, quick and explosive growth of information on the Web, it seems that Web users are drowning in the ocean of information. They are facing the problem of information overload. Apart from the diversity in information, there is diversity in the way users navigate web. A major drawback of generic search engines is that they follow same model and are not adaptable to the needs of an individual user. Personalization of web is a solution to such problems as it can provide different search results based on preferences and information needs of the users. II. WEB PERSONALIZATION Web Personalization is a technique to provide users with the desired information they require, without expecting from them to ask for it. Personalization requires implicitly or explicitly collecting visitor information and leveraging that knowledge in Content Delivery Framework. It then manipulates this information and decides which information should be provided to user and how it should be presented [1]. Different users have different backgrounds and interests. They may have completely different needs and goal when issuing the same query. For example, the query issued “trees” by a Botanist requires totally different information as compared to the same query issued by a programmer. Such queries are termed as ambiguous queries. Mostly, these types of short and ambiguous queries are issued by the users. If the user does not get information related to his interest then he may get frustrated. his preferences web page is constructed. Hence customization is dependent on user’s input where as personalization does not expect from a user to give directions. Personalization mechanism is based on explicit preference declarations made by the user. Iteratively it monitors the user’s navigation, collects its request of ontological objects and stores them in its profile in order to deliver personalized contents [2]. Web Personalization is divided into four distinct phases as follows: A. COLLECTION OF WEB DATA: In this phase, Web data is collected. It can be in implicit or explicit form. Implicit data includes past activities/click streams which are recorded in Web server logs and/or via cookies or session tracking modules. Explicit data usually comes from registration forms or rating questionnaires. Apart from this data, Web content, structure and usage data can also be included to give extra knowledge that can be helpful in next phases. B. PREPROCESSING: In this phase, Web data that is collected from previous phase is preprocessed to put it into a format compatible with the analysis technique to be used in the next step. Preprocessing may include cleaning data of inconsistencies, filtering out irrelevant information and completing the missing links in incomplete click through paths. C. ANALYSIS OF DATA: In this phase, automatic user profile is created after analyzing data processed from the previous step. It is generally done offline so that no extra burden is added on the Web server. This step applies Machine Learning or Data Mining techniques to discover interesting usage patterns and statistical correlations between Web pages and user groups. D. RECOMMENDATION PHASE: The last phase in personalization makes use of the results of the previous analysis step to deliver recommendations to the user. The recommendation process typically involves generating dynamic Web content on the fly, such as adding hyperlinks to the last web page requested by the user. This can be accomplished using a variety of Web technology options such as Common Gateway Interface(CGI) programming. Personalization should not be confused with customization. It is different from customization. In case of customization user is given questionnaire and then based on 117 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 117-121 survey [1] presented the uses of Web Mining and Web Personalization and other highlighted Web usage mining as a tool for personalization [3]. Another survey done by Chhavi Rana (2012), gave a precise and comprehensive understanding of the work done in this sphere from 2007 to 2012. This paper reviews usefulness of different approaches along with the projects associated with some of the techniques [4]. After that there has been lot of up heals that are encountered in this area of Web Personalization. This paper tries to summarize the work done in various subfields of Web Personalization after 2012. Fig.1 Phases of Web Personalization Fig. 1 shows the different phases of Web Personalization. There are numersous startegies for Web Personalization. Developers can use any of these or in combination depending on the personalization requirements. Different strategies used for Web Personalization are as follows: A. MEMORIZATION: Using this strategy, user information such as name and browsing history is stored using cookies. It is later used to recognize and greet the returning user. It is usually implemented on the Web server. B. CUSTOMIZATION: This form of personalization takes as input a user’s preferences from registration forms in order to customize the content and structure of a web page. This process tends to be static and manual. So it is called semi-automatic. It is usually implemented on the Web server. Typical examples include personalized Web portals such as My Yahoo and Google. C. RECOMMENDER SYSTEMS: Recommender systems automatically recommend hyperlinks that are deemed to be relevant to user’s interest. It is usually implemented on the Web server. It relies on data that reflects the user’s interest implicitly or explicitly. Implicitly refers to browsing history as recorded in Web server logs. Explicitly refers to user profile as entered through a registration form or questionnaire. D. TASK PERFORMANCE SUPPORT: In case of Task Performance Support systems, personal assistant executes action on the behalf of the user in order to facilitate access to the relevant information. This approach requires heavy involvement on the part of the user, including access, installation, and maintenance of the personal assistant software. III. PREVIOUS RESEARCH Though Personalization of user data is a recent phenomenon due to the rise of Internet usage, but over the years Web mining techniques have developed a full fledged support to provide personalized experience for user. Lot of work is done to improvise Web Personalization. The two important surveys done by [1, 3], gave a complete overview of the work done in this sphere up to 2003. One of these With the dramatic increase in the number of websites on the Internet, tagging has become popular for finding related, personal and important documents. Onur Yilmaz (2013), presented a tag-based website recommendation method, where similarity measures are combined with semantic relationships of tags. This approach performs well in recommending new websites or catching user's current interests. However, there is no control on the tags provided by users in this system. Although users do not intend to mislead the method while tagging websites, different purposes of tagging can create confusion [13]. Su, Chang and Tseng (2013) present music recommendation system that utilizes social media tags instead of user rating to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that proposed approach in this paper using social tags outperforms the existing ones which are using only ratings in terms of predicting the user’s preferences to music [7]. A.Vaishnavi (2011) proposed a technique for developing Web Personalization system using Modified Fuzzy Possibilistic C Means (MFPCM). The author claims that this approach raises the possibility that URLs presented before a user will be of his interest [6]. Another area that has been explored by many researchers is Research Paper Recommendation System. Different researchers have used different approaches. Hong, Jeon and Jeon (2013) proposed Personalized Research Paper Recommendation System PRPRS (PRPRS) that designed expansively and implemented a user Profile-based algorithm for extracting keyword by keyword extraction and keyword inference. PRPRS calculates the similarity between given topic and collected papers by using Cosine similarity which is used to recommend initial papers for each topic in the information retrieval [8]. Lee, Lee and Kim (2013) have proposed Personalized Academic Research Paper Recommendation System (PARPRS) using collaborative filtering methods, which recommends related articles, for each researcher that may be interesting to her/him [9]. Gao et al. (2014) have suggested novel collaborative filtering approach that will make itembased CF more robust [12]. Jiang et al. (2013) have proposed two novel boosting frameworks for collaborative filtering [15]. Jing Lu et al. (2013) investigated Online collaborative filtering techniques for building live 118 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 117-121 recommender system [16]. Nikos and Katrien (2013) have context. The solution proposes an analytical approach over discussed Layered evaluation applied for multi-criteria the computer aided learning mechanism. It defines the recommendation service. It could be deployed for paper concept of personalized learning and provides an example of recommendation using Mendeley dataset [14]. Mendeley implementation for a software system. This example dataset is in the form of Mendeley Research Catalog with a subsequently offers support and assistance for visually collection of 80 million research documents. impaired computer users [25]. Bedi and Aggarwal (2013) presents Aspect-Oriented Trust Based Mobile Recommender System (AOTMRS) that Yarandi, Jahankhani & Tawil (2013) present an uses the concept of trust and Aspect Oriented Programming ontology-based approach to develop adaptive e-learning for advice-seeking and decision-making process similar to system based on the design of semantic content, learner and real life. The proposed system AOTMRS builds a mobility domain models to tailor the teaching process for individual aspect and generates the trustworthy recommendations learner’s needs. The proposed new adaptive e-learning has based on the user preferences and his demographic the ability to support personalization based on learner’s information such as location, time, need etc [10]. ability, learning style, preferences and levels of knowledge [22]. Preeti, Ankit and Purnima (2014) emphasize visualization in recommendation systems. It proposes an argument based Cakulaa and Sedleniecea (2013) have aimed to identify recommender system which uses hybrid approach in which overlapping points of KM and e-learning phases to improve two results: trustworthy users and arguments occurred the structure and transfer of personalized course knowledge between user agent is visualized, using D3 tool [11]. using effective methods of ontology and metadata standards. This research offers a theoretical background of knowledge Pan and Chen (2013) propose a new Group Bayesian management principle implementation for the development Personalized Ranking (GBPR) system for one-class of a practical personalized e-learning model [23]. collaborative filtering or collaborative ranking which models user’s ranking-related preferences more directly Another major application area of personalization is e[17]. commerce. Personalized recommendation technology in ecommerce is widespread to solve the problem of product S.Geetha rani (2013) suggests click count as well as linkinformation overload. However, with the further growth of click based Ranking algorithm. In this algorithm the count the number of ecommerce users and products, the original of click of each query concept can be evaluated as well as recommendation algorithms and systems face several new the link is also evaluated in the submitted query [18]. Wang challenges such as modeling of user’s interests more et al. (2013) explained General Ranking Model Adaptation accurately, providing more diverse recommendation modes Framework for personalized search is explained [19]. Global and supporting large-scale expansion. To address these RankNet model which is widely adopted ranking model for challenges from the actual demands of e-commerce web search tasks is discussed in another paper [20]. They applications, Dong et al. (2013), designed and implemented have suggested improvements in this model. a personalized hybrid recommendation system, which can support massive data set using Cloud technology [26]. One of the subfield of web personalization is e-learning. Personalization of e-learning is considered as a solution for World Wide Web has undergone three generations from exploiting the richness of individual differences and the Information Web to Social Web to Semantic Web. It has different capabilities for knowledge communication. In started its journey towards the fourth generation which particular, to apply a predefined personalization strategy for expects wisdom from the Web and so termed as the Wisdom personalizing a course, some learner’s characteristics have Web. In present era, where computers and Internet has to be considered. Furthermore, different ways for the course become inseparable parts of our life, user wants the Web to representation have to be considered too. Fathi, Leila, sense their requirements and interests and serve the contents Mohammad (2013) have studied solutions to the question: accordingly. Search engines play major role in information How to automate the E-learning personalization according extraction and delivery and present models of search to an appropriate strategy? The study is about finding an engines that are still struggling for providing personalized answer to this original question by integrating the automatic information to the users. Aarti Singh and Basim Alhadidi evaluation, selection and application of personalization [27] present knowledge oriented personalized search engine strategy [21]. framework which can provide personalized contents to its users. This framework provides a direction for the next D.Suresh, S.Prakasam (2013) suggest the instructors to generation of WWW and contributes towards Wisdom Web. use the combination of e-learning system using Rank-Based In another research paper Aarti Singh and Dr. Singh (2013) Clustering Algorithm (ESURBCA) to get consistency in highlight the technologies contributing towards the next content delivery, quality content in learning materials, generation of WWW and also suggest future direction for students self-learning concept and performance Web Personalization [28]. improvement in their examination [24]. Another upcoming area is being suggested by Hanak et Diana Butucea (2013) presents a theoretical framework al. (2013). They have suggested methodology for measuring for visually impaired persons, followed by a technical personalization in Web search results. They have also implementation of the concept in relation with the e-learning applied this methodology to 200 users on Google Web 119 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 117-121 Search and have deducted the results. The causes of apply simple recommendation approaches that are not based personalization on Google Web Search are also investigated. on any recent research results. So there is a need of research There effort is a step towards understanding the extent and paper recommendation system which incorporates all the effects of personalization on Web search engines today [29]. latest research work done in this field. . Another future scope can be to develop frameworks for IV. RESEARCH ANALYSIS research paper recommender system evaluations. This The wealth of Research projects described in the previous should include an analysis of how suitable offline section indicates that the demand for better Web evaluations are, to what extend datasets should be allowed personalization is high. However, many challenging to be pruned, how many participants user studies shall have research problems must be addressed if this demand is to be at minimum and which factors influence the results of fully met. Issues that cut across all of the applications are evaluations (e.g. user demographics). henceforth described, where progress will consequently have the broader impact. The goal of personalization is to One of the latest areas under Web Personalization is provide users with what they want or need without requiring Wisdom Web. WWW is on a journey of wisdom and them to ask for it explicitly. Following are listed some of the maturity, where it can sense what user wants and serve major issues that needs to be catered to provide a better relevant contents on its own. Transformation of WWW to personalized system. Wisdom Web requires change in the way web is accessed Research on recommendation system is an emerging field. Although it has obtained very good results, but there are still many problems that need to be handled. One of the problems faced is Data acquisition. It is mainly dependent on the user's explicit evaluation. If user does not know about the product it will not be helpful. Another problem that is being faced is Cold start problem also called first evaluation problem. If a new product has no evaluation, then the product will not get a recommendation, hence recommendation system will lose action. Ping, Xiang and Ming (2012) have suggested that the future recommendation research should use the web mining technology to collect the user’s implicit browsing information rather than depending on explicit evaluation information supplied by the user for some product. At present, the recommendation system needs the user to make preference assessments for a product but this method has low degree of automation and many users do not evaluate the product. Web mining technologies can improve the automation degree of the collected information. Another angle for future research has been suggested by Xujuan et al. (2012). Social Networking Sites contain a warehouse of information. It can be mined and analyzed to expand user profiles. It can be used to build complex diagrams and maps of user-to-user and user-to-interest relationships. A lot can be understood about the user using these websites. The research issues on how to make breakthrough on the current recommender system for social networking environment or how to build the trust-based Web personalized recommender systems need to be explored further [31]. Another area which has been researched is Research Paper Recommendation System. Different authors have given different approaches. Some have used Content Based Filtering and some have used Collaborative Filtering. Stereotyping is one of the oldest approaches that are successfully applied by Yahoo. Some authors have used item centric approach and some have taken help of graphs. One generalized approach is required which could handle various issues related to research paper recommendation. Despite the large number of research articles, there is only a hand full of active recommender systems, and most of them and therefore it requires new architecture. A new model of web personalization search engine, which is capable of providing knowledge, based recommendation to an individual is suggested by Singh & Allhadidi (2013). Its implementation and evaluation is still in pipeline. Further new approaches can be adopted and developed [27]. Another area of measuring personalization of web search can be explored. Over the past few years, we have witnessed a trend of personalization in numerous Internet-based services, including Web search. While personalization provides obvious benefits for users, it also opens up the possibility that certain information may be unintentionally hidden from users. Despite the variety of speculation on this topic, to date, there has been little quantification of the basis and extent of personalization in Web search services today. Efforts should be made to provide transparency for users of Web search and other Web based services. Measurement of personalization is in its budding stages. New methodologies can be investigated, generalized and applied on various Web Search engines and Web sites. Personalized e-learning model using effective methods of ontology can also be explored. Dynamic user modeling is another upcoming research area. Few systems have attempted to handle the dynamics within the user profile. The behavior of users varies over time and it may affect the construction of models. In nutshell a Web Personalization system should be able to adapt to the user’s behavior. V. CONCLUSION Web personalization is the process of customizing a Web site to the needs of a specific user. Web personalization is a very important, if not necessary, part of WWW today with many of its application areas. This paper tries to present a comprehensive description of the various web personalization efforts made in the last three years. 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[29] Hua Quan ping, Xiang Ming, “Research on Several Recommendation Algorithms”, Engineering 29 (2012) 2427 – 2431 [30] Xujuan Zhou, Yue Xu, Yuefeng Li, Audun Josang and Clive Cox, “The State-of-the-Art in Personalized Recommender Systems for Social Networking”, Artificial Intelligence Review(2012), 37(2), pp. 119-132. 121 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 122-125 MULTIPATH ROUTING PROTOCOLS FOR MOBILE AD HOC NETWORK #Amit Sharma1, kshitij shinghal2, Pushpendra Vikram Singh3, Himansu Verma4 1,3,4 Deptt. of E&C Engg., KIMT, Moradabad, U.P. Deptt. of E&C Engg., MIT, Moradabad, U.P., #Author 1 is a research scholar at IFTM University Moradabad, UP. India
[email protected] 2 Abstract: Multi-path routing has been studied thoroughly in the context of wired networks. It has been shown that using multiple paths to route messages between any source-destination pair of nodes balances the load more evenly throughout the network. The common belief is that the same is true for ad hoc networks, i.e., multi-path routing balances the load significantly better than single-path routing. In this paper, we introduce The Temporally Ordered Routing Algorithm (TORA) is a highly adaptive loop free distributed routing algorithm based on the concept of link reversal. TORA is proposed to operate in a highly dynamic mobile networking environment. Keywords: TORA, RREQ, DSR, RERR. I. INTRODUCTION Multi-path routing consists of finding multiple routes between a source and destination node. These multiple paths between source and destination node pairs can be used to compensate for the dynamic and unpredictable nature of Ad hoc networks. Multipath routing consists of three components viz. route discovery, traffic allocation and route maintenance. Route Discovery Route discovery consists of finding multiple routes between a source and destination node. Multipath routing protocols can attempt to find node disjoint, link disjoint, or non-disjoint routes. Node disjoint routes[1,2], also known as totally disjoint routes, have no nodes or links in common. Link disjoint routes have no links in common, but may have nodes in common. Non-disjoint routes can have nodes and links in common. Fig 1.1 shows different kinds of multipath routes. Routes SXD, SYD, and SZD in Fig 1.1 (a) have no links or nodes in common and are therefore node disjoint. Routes SXYZD and SYD in Fig1 . 1 (b) have node Y in common and are therefore only link disjoint. Routes SXD and SXYD in Fig1 . 1 (c) have node X and link SX in common and are therefore non-disjoint. II. SPLIT MULTIPATH ROUTING (SMR) It is a multipath version of DSR. Unlike many prior multipath routing protocols, which keep multiple paths as backup routes, SMR is designed to utilize multipath concurrently by splitting traffic onto two maximally disjoint routes. Two routes said to be maximally disjoint if the number of common links is minimum. SMR uses one route discovery process to accumulate as many as possible routes to the destination node. This route discovery process runs in the same way as in DSR. However, there are more steps involved in processing RREQ packets at intermediate and destination nodes[2]. If an intermediate node receives a RREQ packet, it adds its own address and rebroadcasts the RREQ packet. Whenever an intermediate node receives another RREQ from the same source node and with the same request id, i.e. a duplicated RREQ, the node checks the following two things (Fig 1.2(a)). If the first RREQ packet arrives at the destination node, a RREP is generated and sent back on the reverse path, which is the “Shortest delay path”. Then the destination node waits a period of time and selects multiple disjoint routes, acording to the first path, and sends RREP packets back to the source via the selected routes (Fig 1.2(b)). (a) RREQ Propagation (b) Available Paths Fig 1.2: Route Discovery III. Fig1.1: Different Kinds of Multipath Routes (a) Node Disjoint (b) Link Disjoint (c) Non Disjoint DYNAMIC MULTI-PATH SOURCE ROUTING (DMSR) DMSR extends DSR’s routing mechanism to deal with multi-path routing couple with bandwidth constraint. It consists of three major phases, namely routing discovery, multipath route selecting and routing maintenance. In multi-path route selecting phase, the ideal number of multi-path routing is achieved to compromise between load balancing and network overhead[4]. A DMSR RREQ includes Source id, RREQ id, Routing list, Hmax and B min.When a source node initiates a QoS request, it firstly checks whether it has the 1|P age International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 122-125 routing information to the destination node. If not, it begins MP-DSR gives 100% multiple disjoint paths. It provides to broadcast RREQ to its neighborhoods. Once relay nodes QoS in terms of shortest delay path, which in most case receive this RREQ, it can’t respond to the RREQ even it could be the best/shortest path. Nodes require more buffer has the routing information to the destination node. Rather, it space and periodic message exchange leads to more overhead. follows the steps: Step 1- If current node itself is in the Routing list of RREQ, V. AODV MULTIPATH ROUTER APPROACH it will discard the RREQ because of the routing loop. Other(AODVM-R) wise, and it goes to step 2; When performing route discovery, the source and interStep 2- If the tuple (SourceID, RREQID) of RREQ is not mediate nodes maintain multiple routes to the destination. To included in the routing table, which means the current ensure loop freedom the RREQ packet includes path infornode is the first time to receive this RREQ, it calculates mation (path from the source to the router). Primary and the corresponding value of the bandwidth. If the value secondary routes will have the same sequence numbers. can’t meet the requirement denoted by B min, and the RREQ When a link breaks, a node tries to reestablish the route using alternate paths. If still there is an unreachable destiwill be discarded. Otherwise, it goes to step3. nation, the node sends an RERR message to its neighbors. If Step 3- Adds the value of bandwidth to the corresponding the primary route works for a long time, alternate paths fields of the RREQ. Then the RREQ will be continually formight timeout because they are not used. While the primawarded and it goes back to step 1. ry route is being used, send REFRESH message to the alternate routes occasionally to refresh them. The REFRESH IV. MULTIPATH-DISTANCE VECTOR ROUTING packet is sent every active route_timeout /2 seconds.The (MP-DSR) [3]: REFRESH packet is forwarded to the destination, refreshing MP-DSR is an extension of DSR with QoS support. It the routes on the way. If an alternate route is detected to be tries to forward packets on multiple disjoint paths with certain broken, it is simply discarded from the route table[5,6]. end-to-end reliability requirements. This reliability considers the probability of having a successful transmission between the two mobile nodes within the time period from t0 to t0 + t, where t0 is any time instant. The probability successful transmission is shown in the following equation. Where k is a set of node-disjoint paths from the source to the destination. p(k,t) is the path reliability of path k, calculated as the product of link availability of all the links in path k. In other words, P(t) is the probability that at least one path stays connected for the duration of t. After the value of lowest path reliability requirement and the number of paths to be discovered are set, the source node floods a RREQ packet for a set of paths (neighbor nodes), which can satisfy these requirements as shown in Fig1.3. Each RREQ packet contains additional parameters e.g. the reliability requirement, time window, path that a RREQ message has traversed, etc. Whenever an intermediate node receives a RREQ, it checks, whether the RREQ meets the lowest path reliability requirement. If yes, the intermediate node adds itself and sends out multiple copies of this RREQ to its neighbors. Otherwise RREQ packet is dropped. After the reception of first RREQ, the destination waits a period of time. Then it selects multiple disjoint paths out of all received RREQ packets. RREP packets are sent along these paths as shown in Fig 1.3. VI. AD HOC ON-DEMAND MULTIPATH DISTANCE VECTOR ROUTING (AOMDV) AOMDV involves route discovery and route maintenance phases similar to AODV. Noteworthy feature of the AOMDV protocol is the use of routing information already available in the underlying AODV protocol as much as possible. Thus little additional overhead is required for the computation of multiple paths. The AOMDV protocol has two main components[7]: 1. A route update rule to establish and maintain multiple loop-free paths at each node. 2. A distributed protocol to find l disjoint paths. To keep track of multiple routes, the routing entries in each node for each destination contain a list of the nexthops along with the corresponding hop counts. All the next hops have the same sequence number. For each destination, a node maintains the “advertised hop count”, which is defined as the maximum hop count for all the paths. Table 1: Structure of Routing Table Entries for AOMDV: destination sequence number advertised_ hopcount route_ list d {seqnumi {(nexthop 1,hopcount1), (nexthop2,hopcount 2),} expiration timeout Fig 1.3: MP-DSR RREQ & RREP Messages 123 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 122-125 The “advertised hop count” is initialized each time the SI1-ID1, SI2-ID2 or SI1-ID2, SI2-ID1. sequence number is updated. A node i updates its “advertised_hopcount” for a destination d whenever it sends a route advertisement for d. specifically, it is updated as followsAdverd tised_hopcounti :=maxk{hopcountk|(nexthop k,hopcountk)€ d route_listi }The route update rule in algorithm 2 is invoked whenever a node receives a route advertisement. Lines (1) and (9)-(10) of the AOMDV route update rule ensure loop freeFig 1.4 Link-Disjoint Paths dom. AOMDV update rule is used whenever a node i receives a route advertisement to a destination d from a neighAOMDV offers better performance relative to AODV d d under a wide range of mobility and traffic scenarios. It has bor j. The variables seqnumi , advertised_hopcounti , been observed that AOMDV offers a significant reduction in d route_listi represent the sequence number, adverdelay, often more than a factor of two. It also provides up to tised_hopcount and route_list for destination d at node i about 20% reduction in the routing load and the frequency respectively. AOMDV can be used to find node-disjoint or of route discoveries. In general, AOMDV always offers a link-disjoint routes. To find node-disjoint routes, each node superior overall routing performance than AODV in a variety does not immediately reject duplicate RREQs. Each RREQ of mobility and traffic conditions[9]. arriving via a different neighbor of the source defines a node-disjoint path. This is because nodes cannot broadcast VII. TEMPORALLY ORDERED ROUTING duplicate RREQs, so any two RREQs arriving at an interALGORITHM (TORA) mediate node via a different neighbor of the source could The Temporally Ordered Routing Algorithm (TORA) is a not have traversed the same node. In an attempt to get highly adaptive loop free distributed routing algorithm based multiple link-disjoint routes, the destination replies to duplion the concept of link reversal. It is designed to minimize cate RREQs regardless of their first hop[8]. reaction to topological changes. A key design concept in TORA is that it decouples the generation of potentially farreaching control messages from the rate of topological changes. Such messaging is typically localized to a very small set of nodes near the change without having to resort to a complex dynamic, hierarchical routing solution. TORA is also characterized by a multi-path routing capability. Each node has a height with respect to the destination that is computed by the routing protocol. Fig1.5 illustrates the use of the height metric. TORA is proposed to operate in a highly dynamic mobile networking environment. The protocol performs three basic functions as followsa. Route creation b. Route maintenance c. Route erasure Algorithm 2 : AOMDV Route Update Rules To ensure link-disjointness in the first hop of the RREP, the destination only replies to RREQs arriving via unique neighbors. After the first hop, the RREPs follow the reverse paths, which are node-disjoint and thus link-disjoint. The trajectories of each RREP may intersect at an intermediate node, but each takes a different reverse path to the source to ensure link-disjointness. Fig1 . 4 illustrates link-disjoint path computation in AOMDV. S, D and I denote the source, intermediate node and the destination, respectively. There are two possible sets of link-disjoint paths between S and D, Fig 1.5: TORA Height Metric 124 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 122-125 [8] S. J. Lee and M. Gerla, "Split multipath routing with maxWhen a node needs a route to a particular destination, it imally disjoint paths in ad hoc networks," ICC 2001. IEEE broadcasts a QUERY packet containing the address of the International Conference on Communications, Helsinki, destination for which it requires a route. This packet propa2001. gates through the network until it reaches either the destina[9] A. Tsirigos and Z. J. Haas, "Multipath routing in the prestion, or an intermediate node having a route to the destinaence of frequent topological changes," Communications tion[10,11]. Magazine, IEEE, vol. 39, pp. 132- 138, 2001. The node which receives the QUERY then broadcasts [10] Zhi Li and Yu-Kwong Kwok, "A New Multipath Routing an update packet, listing its height with respect to the destiApproach to Enhancing TCP Security in Ad Hoc Wireless nation. As this packet propagates through the network, each Networks," Proc. ICPP Workshops, pp. 372–379, June 2005. node that receives the update sets its height to a value [11] Vincent D. Par and M. Scott Corson, “Temporally-Ordered greater than the height of the neighbor from which the upRouting Algorithm (TORA) Version 1: Functional Specidate has been received. This has the effect of creating a series fication”, Internet-Draft, draft- ietf-manet-tora-spec-01.txt, of directed links from the original sender of the QUERY August 1998. to the node that initially generated the update. VIII. CONCLUSION In this paper we study the different multipath routing protocols for mobile Ad HOC network. The overall summery of above-mentioned multipath routing protocols is shown in the table: Protocol Base Protocol AODVM-R AODV AOMDV AODV (Source routing) Routing Choice Made at Intermediate nodes Source node Motivation /Application Reduces number of route discoveries Reduction in delay, routing load and the frequency of route discoveries Source node Splitting traffic pro(source routing) vides better load distribution Source node QoS applications with MP-DSR DSR (source routing) soft end-to-end reliability Operate in a highly TORA link reversal Source node (Source routing) dynamic mobile networking environment. SMR [1] [2] [3] [4] [5] [6] [7] DSR REFERENCES Xuefei Li and Laurie Cuthbert, “Stable Node-Disjoint Multipath routing with low overhead in Ad Hoc Networks”, MASCOTS-2004, pages 184-191, 2004. Xuefei Li and Laurie Cuthbert, “A Reliable NodeDisjoint Multipath Routing with Low Overhead in Wireless Ad hoc Networks”, Venezia, Italy MSWiM’04, October4–6, 2004. M. K. Marina and S. R. Das “On-Demand Multi Path Distance Vector Routing in Ad Hoc Networks,” Proc. ICNP 2001,Nov. 2001. S.-J. Lee and M. Gerla, “Split Multipath Routing with Maximally Disjoint Paths in Ad Hoc Networks,” Proc. ICC 2001, vol. 10, June 2001. S. Bouam and J. Ben-Othman, “Data Security in Ad Hoc Networks Using Multipath Routing,” Proc. PIMRC 2003, vol. 2, pp. 1331–1335, Sept. 2003. N. Taft-Plotkin, B. Bellur, and R. Ogier, "Quality-ofservice routing using maximally disjoint paths," Quality of Service, 1999. IWQoS '99. 1999 Seventh International Workshop on, London, 1999. Broch J. Jhonson DB, Maltz DA, “The Dynamic Source Routing protocol for mobile Ad Hoc Networks”, IETF Draft, October, 2003. 125 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 A WEB-BASED EDUCATIONAL SYSTEM FOR LEARNING DATA STRUCTURES Valentina S. Dyankovaa1, Stoyan N. Kapralovb2, Milko I. Yankovc3 and Yumit N. Ismailovd4 University of Shumen, “Universitetska” str., 115 Bulgaria, Shumen, 9700 1
[email protected],
[email protected],
[email protected] 1,3,4 Technical University of Gabrovo, “Hadji Dimitar” str., 4 Bulgaria, Gabrovo, 5300
[email protected] 2 Abstract—This article presents DSLearning – a web-based learning system, developed by the authors, for teaching the concepts of the Data Structures field. Oriented towards increasing the independent work of students, the integration of the concepts in a dynamic, self-developing system, allows for students to focus their attention on the logical definitions of the terms. This allows freedom, with regard to the choice of formalization, and determines the adaptability of attained knowledge to the technology used for software realization. In teaching the concepts of data structures, a series of assignments is used. The web-based environment focuses on providing helpful questions and tasks of lower difficulty, in the cases when students give a wrong answer. This process is managed via a developed structural task model. It is used as a basis for the decomposition of a task to auxiliary tasks of lower difficulty. The application of the DSLearning system fosters active independent work and self-paced learning during the formation of skills for modeling real objects and processes. Index Terms—improving classroom teaching; interactive learning environments; programming and programming languages; teaching/learning strategies I. INTRODUCTION In accordance with the ACM curricula (CS2013) [2], the study of fundamental concepts/terms of data structures and the skills, necessary for their use in modeling real-world tasks, are a part of the subject matter of the Fundamental Data Structures (knowledge area Software Development Fundamentals) and Fundamental Data Structures and Algorithms (knowledge area Algorithms and Complexity) courses. The CS2013 curriculum, in regards to the time needed for introduction of new material in a traditional lecture, assumes “students to spend a significant amount of additional time outside of class” [2]. A way of acquiring extracurricular skills and knowledge by the students is using free, publicly-available educational resources. The main guidelines on using such resources, related to the topic of Data Structures education, can be systematized in the following ways: - Publication of text materials: systems of this type contain information on the data structures being studied. Examples of such systems are presented in [8], [18]. - Use of graphics and multimedia for visualization of the organization of the elements of a data structure and the basic operations with such elements [4]. For instance, in [18] there is a description of the possible ways of experimenting with a data structure, while in [7] there are visualizations of specific data structures and the operations performed on them. - Algorithm visualization: systems of this type are described in [7], [17], [24]. A key for them is to offer a good, dynamic visualization of the algorithm stages through the use of multimedia technology. - Tests of programming code. In [25] the test of a studentgenerated program source code is executed on the basis of varying data input. It is impossible to generate (and test) an arbitrary source code, but rather one of predetermined modules available in the system. - Test questions and frequent mistakes. In [1] the system supplies a warehouse of test questions, which can be solved by students and be automatically validated in conditions similar to a real testing environment. - Knowledge testing. In such systems there is an emphasis on the quantitative grading of the students’ answers. It is apparent that openly available educational resources have their limits in terms of capabilities for guiding the learning process and their adaptability based on the students’ answers. In [17] there is an accent on the need for “self-balancing behavior” on part of the students. Such findings provide a natural focus on the creation and realization of a Data Structures educational model, in the context of a reflective approach in an open educational environment. Learning of data structures requires the development of the students’ ability to discover logical relationships among the data structures with the goal of formalization, adaptation of algorithms to a particular problem, logical thinking within the terms of a specific system for the formalization of knowledge. The solutions presented in this paper, on the topic of developing a web-based system, which incorporates a learning process, can be summarized in the following three ways: 1) Organization of the learning process in the context of “Characteristics of Graduates” given in CS2013 [2]: system-level perspective, problem-solving skills, appreciation of the interplay between theory and practice. 2) Choice and definition of a basic learning element, which enables the technological realization of the learning process. Developing a model for such basic learning element makes possible the decomposition of knowledge into learning elements of lower complexity, decreasing the difficulty for the students, and creating the capability for self-testing. The latter follows one of the major suggestions of professor Bergin for active learning “provide exercises of different difficulty levels” [13]. 3) Choice of strategy for defining a system of learning elements, which purposefully influences the students to acquire a certain knowledge element. In this system, every learning element plays a particular role with regard to the complex goal – “your job is not to give the students information. Your real job is to show them ways to build new information structures for the problems of their days.”[13]. 126 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 the group of properties, which can be isolated as primary, thus II. STRUCTURE OF THE TEACHING PROCESS The DSLearning system (www.dslearning.eu) is developed by determining all others. These properties are identified into the the authors (Fig. 1). It is conceived as a learning system with a following three groups: Properties for generalized volume – type of elements and feedback loop, utilizing non-linear algorithms, and organizing the students’ learning process in accordance with the illustration dynamics (i.e. can their number be changed). Properties for characterization of the relationships – specific on Fig. 2. elements (are all elements equal, or are there different tiers), specific fields (are certain element properties defining for the treatment), linearity, connection (whether the elements are equal or linked and what information is carried by the link). Properties for permissible actions – determine the range of possible operations. Defining the concepts of data structures in DSLearning, through their common and key properties, focuses the attention of students on the important characteristics invariant with regard to programming language. In this sense, the system integrates the CS2013 requirement for shifting the emphasis from writing a code to a logical interpretation of concepts (problem-solving skills [2]). The understanding of logically related fundamental concepts, integrated in a self-developing system, allows for the Fig. 1. The DSLearning system accumulation of facts for each new concept to occur in The impact (1) on the students by the system is achieved connection to the rest, and aims for the setting of new goals. This through entry questions. The students’ response (2) reflects the leads to awareness and generalization of the knowledge through degree of understanding of the learning material. The response is the creation of internal, previously-generalized models of the concepts in data structures. In this sense, the system organizes 4) 1) learning activities based on constructive feedback, unified by the idea of electronic education [16], [12], [27]. 2) 3) In the second level of knowledge organization, software formalization (class) of the learned concepts is used. It is devoted to aid the formation of skills for correct use of the concept’s software form. Fig. 2 Management of the Learning Process The third level of organization requires the application of formalized in an appropriate way and is input into the error skills, acquired in the first two levels for modeling of real analyzer (3). Depending on the accepted evaluation criteria for processes. Here, skills for adaptive application of the learned the students’ knowledge, a corresponding influence (4) on part of knowledge are formed. The learning environment offers the system is determined. It is interpreted via a suitable information model, which is presented to the students in terms of problem-solving scenarios, developing the students’ abilities to a particular problem scenario. In addition, some slides may discover unknown properties of the data structures, which may be essential for the solution of a particular problem, capabilities reflect the ‘ ’ emoticon. The main purpose of this is to enable for broadening the functionality of a given data structure, in students to receive help from the system – a direction on accordance with a particular task, methods for modeling a continuing the reasoning process, a control answer, or simply a specific process, logical thinking within the terms of a concrete visualization of the task at hand. system for the formalization of knowledge, as well as DSLearning comprises of components, in accordance with the optimization of the created models. requirements laid out in the Instructional-Design Theory [3]: These three levels reflect the three tiers of mastery clear information, thoughtful practice, informative feedback, and (Familiarity, Usage, Assessment), as per CS2013, associated strong intrinsic or extrinsic motivation. This determines the with the expected results from the students’ education. following model of learning activity in the system: goal, motive, independent work, result. III. A SELECTION OF BASIC TEACHING ELEMENT The management of information flows, in the learning of data The selection of a tool for the realization of the described structures through DSLearning, is in accordance with the systemlevel perspective [2]. Beginning with its essence, the concept is organization of the teaching process is realized, in accordance later developed in subsequent levels of more in-depth with the pedagogical pattern of prof. Joseph Bergin: “How do knowledge, i.e. it is defined at yet another, higher level of you begin the design of a new course?” – “Therefore, first decide organization. Each level is characterized by a system of on what the students will do in the course. Design the student knowledge and a way of formalization of such knowledge. In tasks.”[11]. The tasks in DSLearning are systematized based on accordance with the Patterns for Experiential Learning [14], their structural development as the level of difficulty increases. DSLearning realizes three levels of input of data structure terms, The number of unknown components is assumed to be the objective criterion of difficulty. In this context, the authors have thus allowing for monitoring of the students’ progress. The first level of knowledge organization follows the classical developed a structural model of a data structure task. The correct definition of a term as a system of properties, which defines a choice of a suitable structure requires in-depth analysis of the qualitative determination. The DSLearning system emphasizes relationships among data. A basic model is selected to be the 127 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 mathematical task model of L.M. Freedman [19]. In it, the with more unknown components is harder to solve than a task functional dependencies among objects in the task are contained with less unknown components. as separate structural elements. Freedman’s model is adapted by - Analysis of the information, provided by the known the authors for the purposes of DSLearning, as the data structures structural elements, and the possibilities for its application in task comprises of a system with the following elements: completing particular practical exercises. Objective (C) – what should be reached as a result? - Analysis of the logical correlation between unknown and Subject matter field (P) – the objects as described by the known structural elements of a task and the possibilities for its exercise - consists of an input field (I), output field (O), as well adaptive interpretation, according to the particular situation. The purposeful use of the transformation for decomposition of as potential sets of other objects M (I O P). The output field is related to the objective – this is, in fact, a concrete action in exercises in DSLearning requires theevaluation to be conducted under the following conditions: terms of the required data formatting and the used output stream. a) The objective of the exercise is always known. Relation (F) – the relationship between the objects in the task. b) The unknown component of the exercise is a component, Included here are the relations which connect input data F1(I), relations connecting output data F2(O), relations of dependency for which there is no information, or one which has unknown of output data on the input data F3(O), as well as relations sub-components. c) The type of a given exercise is determined by the type of between other objects in the subject matter F4(M). These relations can be defined through reasoning or predicates. The unknown components of this exercise. Let K be the set of unknown components in an exercise. conjunction of these relations is defined by the component F. d) A mandatory characteristic of each exercise type is their The basis (B) – theoretical basis for formalization of the task at hand, in terms of data structures, in relation to a given difficulty. The elements of the set K determine the difficulty parameters of the specific task. knowledge system. e) The set K uniquely defines the set Q, comprised of The purposeful management of education through tasks ([22], [23]) requires a selection of such tasks with an increasing level of relations MN among the components of an exercise in the difficulty. Each unknown component of the problem requires of following way: Q = {MN: NϵK Mϵ{C, P, F, B}\K}. the students to transfer the acquired knowledge and skills to a Every logical link MN, within the set Q, has specific for its new situation, modeled through the particular task. nature methods of scientific knowledge. They outline the specific The unknown components of a DSLearning exercise are way of determining the unknown component N through the established through questions, which allow for: a single correct known component M. In this context, the form of links MN, in answer (Radio Button); a variable number of correct answers set Q, defines the direction of the thinking process when solving (Check Box); a selection of an option from a predetermined list an exercise. This is a basis for selection of an appropriate of templates (List Box); an option for the arbitrary completion of informational model, in DSLearning, of management action of parts of the complete task solution. the system, in the case of a wrong answer given by students Example 1: Question 36/38 of lesson stack (Fig. 3) (Action 4, Fig. 2). For instance, the link CBϵQ (a known The objective C is the addition of a new element as a bottom objective C and unknown basis B) requires the application of of a stack. The subject matter field B consists of the set of analogy or concretization of a known approach. This implies that integers and stacks, whose elements are integers as well. The the students execute a transfer of their knowledge and skills to a relation F consists of the correlation “element over stack”, i.e. the new task situation, determined by the exercise objective C. In participating objects are connected via links, which define them this instance, DSLearning furnishes students with an animated or as elements of a given stack. This also results in the objects’ static graphic depiction of a similar task’s solution. acceptable applications. The basis B consists of a sequence of operators that realize the, given in the exercise, algorithm. In this task, the unknown component is the basis B, which is set as a sequence of list boxes. Typical for DSLearning is the ability to change the pace of the learning process, based on the students’ responses (Action 4 on Fig.2). This is accomplished through the use of logical relationships among the known and unknown components, to reconstruct the exercise types, based on the number and type of unknowns. The justification of the classification of problems, based on the number of unknowns, may be summarized as follows: - Objective evaluation of the exercises’ difficulty level, according to the type and number of their unknown components. The conclusions of this assessment are used for organization of the exercises in a complete system, used in the DSLearning curriculum for learning data structures. It is assumed that a task 128 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 Example 2: For the exercise from Example 1, the sets K and Q naturally implement the properties in a suitable sequence. The are: K={B}; Q={CB,PB,FB}. CBϵQ and therefore the reasoning purposeful management of this process is achieved in the done is deemed valid. In case of a wrong answer, DSLearning following way: It is assumed that for the internalization of given makes a transition to an equivalent, related to the objective, knowledge the students perform a given action d. Each action exercise with particular data and a particular operator (Fig. 4). includes a set of operations o1, o2 …, on, performed in a Students are required to determine the result of the operator’s particular order and in accordance with given rules. Each action action for the objects provided on the drawing. The answer to the is directed towards an object H(d), which has properties c 1, c2,…,ck. The properties of the object, at which the action is directed, are defined as parameters of the action. Therefore, the parameterization of the action is a basis for the selection of the operations comprising the object. The conformity between an exercise and an operation can be defined via the scheme on Figure 6, where z1, z2,…,zk are the tasks, through which the operations o1, o2 …,ok are realized. The completion of operation oi aims determining the parameter ci of action d, i.e. learning about the property ci. Fig. 4. A problem concretization example control question “Will the elements of stack s be moved to stack sp?” determines the ability of the students to apply the particular algorithmic scheme for the specific data. If the students give a wrong answer here as well, they receive secondary information (Fig. 5) in determining the result of each simple step of the algorithmic scheme. IV. A CHOICE OF STRATEGY FOR DEFINING A SYSTEM OF LEARNING ELEMENTS The exercises integrated in DSLearning allow the decomposition of functions of the Data Structures education process, the definition of relationships among the functional subsystems, the division of the management and informational flows, and the realization of an interactive overview of the teaching process by using the activity approach [6], [9]. The exercises for each concept in DSLearning are divided into two groups – accumulation of knowledge of the logical structure of the concept and accumulation of knowledge of the concept as a software object. Each one of these modules examines the concept as a concrete object with inherent, for the corresponding point of view, properties. Students have to assimilate the required knowledge and skills for the adequate use of these properties in modeling of real processes. This requires for the exercises to Fig. 6 Defining a Task with an Operation The correspondence of an operation oi to its realizing exercise zi can be defined via the following algorithm A: A1. Defined is the parameter of action d, which varies with operation oi. Let this be property ci of H(d). A2. The property ci is interpreted in terms of the accepted exercise model in the following way: task zi is considered, whose goal is the definition of the property ci with given (already identified) properties c1, c2, …, ci-1. A3. For the thus defined exercise zi, the set of unknown structural elements K(zi) is defined. A4. The elements of the set K(zi) determine the set of correlations among known and unknown components Q(zi). The interpretation of the objects’ properties, of the concept to be learned through the described algorithm A, is a basis for defining the series of exercises in each of the two groups. During the execution of a given operation oi, the students possess knowledge Z. With the execution of operation oi, the goal is to define the parameter ci of the action d, i.e. assimilation of knowledge Z(ci) for the property ci. This means that after completing operation oi, the student possesses knowledge Z=ZZ(ci). With the inclusion of knowledge from the „zone of proximal development“ [21] in Z’, an active learning process with a constructive approach is realized: “To improve his skills, the exercise must be located at the upper limit of the participant's current skill level”[13]. The thus systematized problems are focused on „empowering the student through active learning“[13]. V. RESEARCH The criteria and indicators for measuring the effectiveness of the DSLearning system’s use are: K1 – operations with the idiosyncratic properties of the concepts. Indicators: an interpretation of the behavior of an object via the values of the existence criteria (P11); a derivation of new properties of the concept (P12); a perception of properties as inherent to the logical definition of the concept, but not of its software form (P13). 129 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 K2 – a choice of concept for modeling a real process (without (42%) 4 (67%) a software realization). Indicators: identification of a concept Good – 3 Very good – P24 based on sufficient properties (P21); finding of specific (44%) 4 (73%) manifestations of the studied concept in a real process (P22); Average ability to consider real processes as systems, characterized by value for 50.50% 81.25% objects and their interrelations (P23); option for algorithmic K2 modeling of a real-world process in natural language without Good – 3 Very good – P31 programming realization (P24). (56%) 4 (71%) K3 – formalization of real processes through the learned Good – 3 Good – 3 P32 concepts of data structures in the terms of a programming K3 (41%) (53%) language. Indicators: transfer of knowledge based on analogy Average (P31); formalization of given parts of the process via a value for 50.50% 81.25% programming language (P32). K3 K4 – desire to enhance one’s knowledge. Indicators: an ability Good – 3 Very good – P41 to find critical parameters when modeling a process, via which (42%) 4 (63%) the process can be optimized; independent research on the K4 Average likelihood of this topic (P41). value for 42% 63% These criteria and indicators are refined with the help of the K4 “field research” method [20]. Students’ accomplishment level is graded on a 5-point scale of each criterion: poor, satisfactory, Regarding criterion K1: an accumulation of facts about the good, very good, and excellent. For each criterion, the grade is objects and their properties in cause-effect relationships; deriving determined by the percentage ratio of the results exhibited by the consequences of the manifestation of the concept characteristics students. The average of the grades reached on a given criterion in interaction with other objects; accumulation of facts about new is the basis for determining its level as follows: 0%-20% - poor, properties when using experiment, induction, analogy; denoted by 1; 21%-40% - satisfactory, denoted by 2; 41%-60% - organization of the amassed empirical material on the properties good, denoted by 3; 61%-80% - very good, denoted by 4; and via abstraction and summarization. 81%-100% - excellent, denoted by 5. The selected from the sample population are students studying Computer Science at the University of Shumen “Episkop Konstantin Preslavski”. An analysis, done on the study, interprets the achieved results. The results (Table 1) show how the use of cause-effect relationships, among the components of an exercise, influences the learning goals. The analysis of achievements on the first two criteria (Fig. 7) demonstrates that the learners understand the essence and can apply the knowledge of modeling and algorithmization of real processes in the context of “Problem solving skills” [2]. Taking into consideration the grades variance of the target experimental and the control groups on the corresponding criteria, a conclusion can be reached, that the organization of the learning process with DSLearning aids the development of the Regarding criterion K2: a justified choice of a property, following skills: particular to a given data structure, which specifies a given Table 1: Summarized results from the study criterion in a real process; discovery, in various real situations, of Results the characteristic properties of the concepts, however not Without With using Criteria Indicator isolated, but in a system; discovery of a set of sufficient using DSLearning properties, in the behavior of a real object, allowing the DSLearning understanding; application of base properties for modeling of a Good – 3 Excellent – 5 P11 real process; generation of ideas for structuring data with the (47%) (97%) objective of modeling a given relationship. Good – 3 Excellent – 5 P12 (54%) (95%) K1 Good – 3 Excellent – 5 P13 (51%) (99%) Average value for 50.67% 97.00% K1 Good – 3 Excellent – 5 P21 (56%) (96%) Regarding criterion K3: forming an understanding of the K2 Good – 3 Excellent – 5 P22 formalization of a real process via the studied concept; (60%) (89%) formation of critical thinking through the comparison of pros and P23 Good – 3 Very good – 130 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 cons of different realizations of a real process (with and without The empirical value of the t-statistic, t-emp. = 8.92, is greater the studied concept); precision in modeling the process of the than the theoretical value (Table 4), which shows that the studied concept through accenting on typical mistakes; discovery difference between the means of the two groups (EG and CG) is of marginal cases of the model in consideration and the statistically significant. This corroborates the importance and behavioral study of the object in such cases; alternative ways of value of the experimental work and confirms the effectiveness of formalization of a given relation through the studied concept; DSLearning in the learning of data structures. ability for evaluation of different models, using the concept as a The effectiveness of a learning process with DSLearning can given parameter; determination of sub-tasks and reconstruction be determined in the following ways: of the whole from its parts; combination of known ways of Stimulates the independent work of students. In the modeling a new process; ideas for parameterization of real event of a wrong answer to an exercise, students receive helpful problems and management of the parameters via the properties of information, needed for the self-correction of the mistake made. the studied concept. The system aims not the quantitative assessment of a wrong Regarding criterion K4: the possibilities for practical answer, but rather creating a reflective environment for acquiring application foster motivation and are a natural basis for further, knowledge. more in-depth, investigation of this topic. The clarification of the just of each concept is derived To achieve the goal, students’ T criterion is calculated. The from the concept’s applicability and there are no constraints on results from the study of the application of DSLearning are the necessary knowledge of computer science. The burden shifts analyzed via SPSS 9.0. To determine the succession rates (level from the realization of the main operations for one data structure of attained knowledge) of students, a series of tests are carried to their use in real situations. out. They are conducted according to the following methods: 6. CONCLUSION Measurement of main statistical measures: average ( х ) and standard deviation (SD). The statistical measures serve to describe the empirical distribution of the population. They are summary statistics and have a key role in the comparative analysis, based on different conclusions for the state of the population [10]. Hypothesis testing with the use of statistical measures. Students’ T criterion is used. Tables 2, 3 and 4 reflect the average values and measures of spread, for the target student group using DSLearning, as well as the statistical measures for hypothesis testing. From the values in Tables 2 and 3, it can be observed that EG has a higher mean ( х =75.80) compared to KG ( х =47.91), which demonstrates that the test results of students using DSLearning are higher. Another important indicator for proving the effectiveness of the DSLearning application is the standard deviation. The data for SD in Table 2 shows the spread in the corresponding group. A lower spread (lower SD) is observed in the students from the EG group (11.99), as contrasted to the students from the control group CG (20.65). The students from the target group exhibit small deviation in the test scores among the members of the group, which in turn means they are more tightly clustered around the mean, according to which we measure the students’ success. For comparison of the mean values, the Students’ T criterion is used. Proving a hypothesis is linked to a determined difference between the means for the two groups (EG and CG). Keep in mind the level of authenticity of a standard confidence level α = 0,05, and a standard critical value t-theoretical = 1.98 [15]. The observed value of t (empirical) is compared to the t-alpha (critical value). Electronic learning is a process of accumulating knowledge, defined in advance, in accordance with selected pedagogical designs, and is presented in a form of a complete framework [5]. In developing this point through the outlined in [2], [3], [6], [9], [11], [14], and [16], the authors have integrated in DSLearning the following: 6. 1. Purposeful definition of the exercises’ contents in each lecture. Learning of each of the concepts of data structures begins with a general summary, followed by its detailing in levels of hierarchical organization. Each level has a set of character attributes. The developed, by the authors, algorithm allows for conformity of these attributes to the exercises in a particular lecture (Section IV). 6. 2. Systematization of exercises, ensuring their increasing problematic level. For a criterion of difficulty, assumed are the number and type of unknown components in a given task/exercise. With this in mind, the authors have developed an adaptive structural model of data structures’ exercises (Section III). 6. 3. Providing of helpful/secondary information for the logical sequence of knowledge and skills, needed for successfully completing a given exercise. The solution of a given task is a result of the approach, using which the students perform an analysis of the known components, and thus extract information about the unknowns. In this way, the authors have tested and systematized the specific, for the known-unknown pair of components, methods of scientific knowledge (Section III, set Q). The derived, upon testing, results are used in selecting an information slide in case of a wrong answer, submitted by the students. 131 | P a g e International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 2, Issue 5 (Sep-Oct 2014), PP. 126-132 The developed structural model of a task and the [16] Schreurs, J., & Al-Huneidi, A., (2012). 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