Ijebea vol2 print 1 by iasir journals

ISSN (ONLINE): 2279-0039 ISSN (PRINT): 2279-0020

Issue 9, Volume 1 & 2 June-August, 2014

International Journal of Engineering, Business and Enterprises Applications

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

STEM International Scientific Online Media and Publishing House Head Office: 148, Summit Drive, Byron, Georgia-31008, United States. Offices Overseas: India, Australia, Germany, Netherlands, Canada. Website: www.iasir.net, E-mail (s): iasir.journals@iasir.net, iasir.journals@gmail.com, ijebea@gmail.com

PREFACE We are delighted to welcome you to the ninth issue of the International Journal of Engineering, Business and Enterprises Applications (IJEBEA). In recent years, advances in science, engineering, and business processes have radically expanded the data available to researchers and professionals in a wide variety of domains. This unique combination of theory with data has the potential to have broad impact on educational research and practice. IJEBEA is publishing high-quality, peer-reviewed papers covering a number of topics in the areas of business process models, engineering and enterprise applications, knowledge engineering science, modeling and designing, control and deployment techniques, e-Commerce applications, B2B and B2C applications, Protocol management and channel management, Mobility, process, engineering, security and technology management, Semantic Web and interfaces, Enterprise applications for software and web engineering,

open-source

platforms,

Human

resource

management,

Operations

management, Organizational and management issues, Supply chain management, Strategic decision support systems, Cloud computing, Risk management, Information technology, Information retrieval systems, Aspect-oriented programming, e-Libraries and e-Publishing, Data mining and warehousing, Distributed AI systems and architectures, Bioinformatics and scientific computing, Knowledge and information management techniques, and other relevant fields available in the vicinity of engineering, business and enterprise applications. The editorial board of IJEBEA is composed of members of the Teachers & Researchers community who have expertise in a variety of disciplines, including business process models, software and technology deployments, ICT solutions, and other related disciplines of engineering, business and enterprise applications. In order to best serve our community, this Journal is available online as well as in hard-copy form. Because of the rapid advances in underlying technologies and the interdisciplinary nature of the field, we believe it is important to provide quality research articles promptly and to the widest possible audience.

We are happy that this Journal has continued to grow and develop. We have made every effort to evaluate and process submissions for reviews, and address queries from authors and the general public promptly. The Journal has strived to reflect the most recent and finest researchers in the field of emerging technologies especially related to engineering, business and enterprises applications. This Journal is completely refereed and indexed with major databases like: IndexCopernicus, Computer Science Directory, GetCITED, DOAJ, SSRN, TGDScholar, WorldWideScience, CiteSeerX, CRCnetBASE, Google Scholar, Microsoft Academic

Search,

INSPEC,

ProQuest,

ArnetMiner,

Base,

ChemXSeer,

citebase,

OpenJ-Gate, eLibrary, SafetyLit, SSRN, VADLO, OpenGrey, EBSCO, ProQuest, UlrichWeb, ISSUU, SPIE Digital Library, arXiv, ERIC, EasyBib, Infotopia, WorldCat, .docstoc JURN, Mendeley,

ResearchGate,

cogprints,

OCLC,

iSEEK,

Scribd,

LOCKSS,

CASSI,

E-PrintNetwork, intute, and some other databases.

We are grateful to all of the individuals and agencies whose work and support made the Journal's success possible. We want to thank the executive board and core committee members of the IJEBEA for entrusting us with the important job. We are thankful to the members of the IJEBEA editorial board who have contributed energy and time to the Journal with their steadfast support, constructive advice, as well as reviews of submissions. We are deeply indebted to the numerous anonymous reviewers who have contributed expertly evaluations of the submissions to help maintain the quality of the Journal. For this ninth issue, we received 92 research papers and out of which only 23 research papers are published in two volumes as per the reviewersâ&#x20AC;&#x2122; recommendations. We have highest respect to all the authors who have submitted articles to the Journal for their intellectual energy and creativity, and for their dedication to the fields of engineering, business and enterprises applications.

This issue of the IJEBEA has attracted a large number of authors and researchers across worldwide and would provide an effective platform to all the intellectuals of different streams to put forth their suggestions and ideas which might prove beneficial for the accelerated pace of development of emerging technologies in engineering, business and enterprise applications and may open new area for research and development. We hope you will enjoy this ninth issue of the IJEBEA and are looking forward to hearing your feedback and receiving your contributions.

(Administrative Chief)

(Managing Director)

(Editorial Head)

--------------------------------------------------------------------------------------------------------------------------The International Journal of Engineering, Business and Enterprises Applications (IJEBEA), ISSN (Online): 2279-0039, ISSN (Print): 2279-0020 (June-August, 2014, Issue 9, Volume 1 & 2). ---------------------------------------------------------------------------------------------------------------------------

BOARD MEMBERS

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EDITOR IN CHIEF Prof. (Dr.) Waressara Weerawat, Director of Logistics Innovation Center, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Divya Sethi, GM Conferencing & VSAT Solutions, Enterprise Services, Bharti Airtel, Gurgaon, India. CHIEF EDITOR (TECHNICAL) Prof. (Dr.) Atul K. Raturi, Head School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala campus, Suva, Fiji Islands. Prof. (Dr.) Hadi Suwastio, College of Applied Science, Department of Information Technology, The Sultanate of Oman and Director of IETI-Research Institute-Bandung, Indonesia. Dr. Nitin Jindal, Vice President, Max Coreth, North America Gas & Power Trading, New York, United States. CHIEF EDITOR (GENERAL) Prof. (Dr.) Thanakorn Naenna, Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Thailand. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London. ADVISORY BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Fabrizio Gerli, Department of Management, Ca' Foscari University of Venice, Italy. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Vit Vozenilek, Department of Geoinformatics, Palacky University, Olomouc, Czech Republic. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Praneel Chand, Ph.D., M.IEEEC/O School of Engineering & Physics Faculty of Science & Technology The University of the South Pacific (USP) Laucala Campus, Private Mail Bag, Suva, Fiji. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain.

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Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Dr. Cathryn J. Peoples, Faculty of Computing and Engineering, School of Computing and Information Engineering, University of Ulster, Coleraine, Northern Ireland, United Kingdom. Prof. (Dr.) Pavel Lafata, Department of Telecommunication Engineering, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 166 27, Czech Republic. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.) Anis Zarrad, Department of Computer Science and Information System, Prince Sultan University, Riyadh, Saudi Arabia. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Md. Rizwan Beg, Professor & Head, Dean, Faculty of Computer Applications, Deptt. of Computer Sc. & Engg. & Information Technology, Integral University Kursi Road, Dasauli, Lucknow, India. Prof. (Dr.) Vishnu Narayan Mishra, Assistant Professor of Mathematics, Sardar Vallabhbhai National Institute of Technology, Ichchhanath Mahadev Road, Surat, Surat-395007, Gujarat, India. Dr. Jia Hu, Member Research Staff, Philips Research North America, New York Area, NY. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Bindhya Chal Yadav, Assistant Professor in Botany, Govt. Post Graduate College, Fatehabad, Agra, Uttar Pradesh, India. REVIEW BOARD Prof. (Dr.) Kimberly A. Freeman, Professor & Director of Undergraduate Programs, Stetson School of Business and Economics, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Klaus G. Troitzsch, Professor, Institute for IS Research, University of Koblenz-Landau, Germany. Prof. (Dr.) T. Anthony Choi, Professor, Department of Electrical & Computer Engineering, Mercer University, Macon, Georgia, United States. Prof. (Dr.) Yen-Chun Lin, Professor and Chair, Dept. of Computer Science and Information Engineering, Chang Jung Christian University, Kway Jen, Tainan, Taiwan. Prof. (Dr.) Jen-Wei Hsieh, Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taiwan. Prof. (Dr.) Jose C. Martinez, Dept. Physical Chemistry, Faculty of Sciences, University of Granada, Spain. Prof. (Dr.) Joel Saltz, Emory University, Atlanta, Georgia, United States. Prof. (Dr.) Panayiotis Vafeas, Department of Engineering Sciences, University of Patras, Greece. Prof. (Dr.) Soib Taib, School of Electrical & Electronics Engineering, University Science Malaysia, Malaysia. Prof. (Dr.) Sim Kwan Hua, School of Engineering, Computing and Science, Swinburne University of Technology, Sarawak, Malaysia. Prof. (Dr.) Jose Francisco Vicent Frances, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Rafael Ignacio Alvarez Sanchez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Francisco Miguel Martinez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Antonio Zamora Gomez, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Leandro Tortosa, Department of Science of the Computation and Artificial Intelligence, Universidad de Alicante, Alicante, Spain. Prof. (Dr.) Samir Ananou, Department of Microbiology, Universidad de Granada, Granada, Spain. Dr. Miguel Angel Bautista, Department de Matematica Aplicada y Analisis, Facultad de Matematicas, Universidad de Barcelona, Spain. Prof. (Dr.) Prof. Adam Baharum, School of Mathematical Sciences, University of Universiti Sains, Malaysia, Malaysia. Prof. (Dr.) Huiyun Liu, Department of Electronic & Electrical Engineering, University College London, Torrington Place, London.

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Dr. Cristiano De Magalhaes Barros, Governo do Estado de Minas Gerais, Brazil. Prof. (Dr.) Pravin G. Ingole, Senior Researcher, Greenhouse Gas Research Center, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, KOREA. Prof. (Dr.) Dilum Bandara, Dept. Computer Science & Engineering, University of Moratuwa, Sri Lanka. Prof. (Dr.) Faudziah Ahmad, School of Computing, UUM College of Arts and Sciences, University Utara Malaysia, 06010 UUM Sintok, Kedah Darulaman. Prof. (Dr.) G. Manoj Someswar, Principal, Dept. of CSE at Anwar-ul-uloom College of Engineering & Technology, Yennepally, Vikarabad, RR District., A.P., India. Prof. (Dr.) Abdelghni Lakehal, Applied Mathematics, Rue 10 no 6 cite des fonctionnaires dokkarat 30010 Fes Marocco. Dr. Kamal Kulshreshtha, Associate Professor & Head, Deptt. of Computer Sc. & Applications, Modi Institute of Management & Technology, Kota-324 009, Rajasthan, India. Prof. (Dr.) Anukrati Sharma, Associate Professor, Faculty of Commerce and Management, University of Kota, Kota, Rajasthan, India. Prof. (Dr.) S. Natarajan, Department of Electronics and Communication Engineering, SSM College of Engineering, NH 47, Salem Main Road, Komarapalayam, Namakkal District, Tamilnadu 638183, India. Prof. (Dr.) J. Sadhik Basha, Department of Mechanical Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia. Prof. (Dr.) G. SAVITHRI, Department of Sericulture, S.P. Mahila Visvavidyalayam, Tirupati517502, Andhra Pradesh, India. Prof. (Dr.) Shweta jain, Tolani College of Commerce, Andheri, Mumbai. 400001, India. Prof. (Dr.) Abdullah M. Abdul-Jabbar, Department of Mathematics, College of Science, University of Salahaddin-Erbil, Kurdistan Region, Iraq. Prof. (Dr.) ( Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, Tirupati-517502, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Manjulatha, Dept of Biochemistry,School of Life Sciences,University of Hyderabad,Gachibowli, Hyderabad, India. Prof. (Dr.) Upasani Dhananjay Eknath Advisor & Chief Coordinator, ALUMNI Association, Sinhgad Institute of Technology & Science, Narhe, Pune -411 041, India. Prof. (Dr.) Sudhindra Bhat, Professor & Finance Area Chair, School of Business, Alliance University Bangalore-562106, India. Prof. Prasenjit Chatterjee , Dept. of Mechanical Engineering, MCKV Institute of Engineering West Bengal, India. Prof. Rajesh Murukesan, Deptt. of Automobile Engineering, Rajalakshmi Engineering college, Chennai, India. Prof. (Dr.) Parmil Kumar, Department of Statistics, University of Jammu, Jammu, India Prof. (Dr.) M.N. Shesha Prakash, Vice Principal, Professor & Head of Civil Engineering, Vidya Vikas Institute of Engineering and Technology, Alanahally, Mysore-570 028 Prof. (Dr.) Piyush Singhal, Mechanical Engineering Deptt., GLA University, India. Prof. M. Mahbubur Rahman, School of Engineering & Information Technology, Murdoch University, Perth Western Australia 6150, Australia. Prof. Nawaraj Chaulagain, Department of Religion, Illinois Wesleyan University, Bloomington, IL. Prof. Hassan Jafari, Faculty of Maritime Economics & Management, Khoramshahr University of Marine Science and Technology, khoramshahr, Khuzestan province, Iran Prof. (Dr.) Kantipudi MVV Prasad , Dept of EC, School of Engg., R.K.University, Kast urbhadham, Tramba, Rajkot-360020, India. Prof. (Mrs.) P.Sujathamma, Department of Sericulture, S.P.Mahila Visvavidyalayam, ( Women's University), Tirupati-517502, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications, National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. P.R.SivaSankar, Head, Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P. India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science( AIES), Amity University, Noida, India. Prof. Manoj Chouhan, Deptt. of Information Technology, SVITS Indore, India.

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Prof. Yupal S Shukla, V M Patel College of Management Studies, Ganpat University, KhervaMehsana. India. Prof. (Dr.) Amit Kohli, Head of the Department, Department of Mechanical Engineering, D.A.V.Institute of Engg. and Technology, Kabir Nagar, Jalandhar,Punjab (India). Prof. (Dr.) Kumar Irayya Maddani, and Head of the Department of Physics in SDM College of Engineering and Technology, Dhavalagiri, Dharwad, State: Karnataka (INDIA). Prof. (Dr.) Shafi Phaniband, SDM College of Engineering and Technology, Dharwad, INDIA. Prof. M H Annaiah, Head, Department of Automobile Engineering, Acharya Institute of Technology, Soladevana Halli, Bangalore -560107, India. Prof. (Dr.) Prof. R. R. Patil, Director School Of Earth Science, Solapur University, Solapur Prof. (Dr.) Manoj Khandelwal, Dept. of Mining Engg, College of Technology & Engineering, Maharana Pratap University of Agriculture & Technology, Udaipur, 313 001 (Rajasthan), India Prof. (Dr.) Kishor Chandra Satpathy, Librarian, National Institute of Technology, Silchar-788010, Assam, India Prof. (Dr.) Juhana Jaafar, Gas Engineering Department, Faculty of Petroleum and Renewable Energy Engineering (FPREE), Universiti Teknologi Malaysia-81310 UTM Johor Bahru, Johor. Prof. (Dr.) Rita Khare, Assistant Professor in chemistry, Govt. Women’s College, Gardanibagh, Patna, Bihar. Prof. (Dr.) Raviraj Kusanur, Dept of Chemistry, R V College of Engineering, Bangalore-59, India. Prof. (Dr.) Hameem Shanavas .I, M.V.J College of Engineering, Bangalore Prof. (Dr.) Sanjay Kumar, JKL University, Ajmer Road, Jaipur Prof. (Dr.) Pushp Lata Faculty of English and Communication, Department of Humanities and Languages, Nucleus Member, Publications and Media Relations Unit Editor, BITScan, BITS, PilaniIndia. Prof. Arun Agarwal, Faculty of ECE Dept., ITER College, Siksha 'O' Anusandhan University Bhubaneswar, Odisha, India Prof. (Dr.) Pratima Tripathi, Department of Biosciences, SSSIHL, Anantapur Campus Anantapur515001 (A.P.) India. Prof. (Dr.) Sudip Das, Department of Biotechnology, Haldia Institute of Technology, I.C.A.R.E. Complex, H.I.T. Campus, P.O. Hit, Haldia; Dist: Puba Medinipur, West Bengal, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) R.K.Tiwari, Professor, S.O.S. in Physics, Jiwaji University, Gwalior, M.P.-474011. Prof. (Dr.) Deepak Paliwal, Faculty of Sociology, Uttarakhand Open University, Haldwani-Nainital Prof. (Dr.) Dr. Anil K Dwivedi, Faculty of Pollution & Environmental Assay Research Laboratory (PEARL), Department of Botany,DDU Gorakhpur University,Gorakhpur-273009,India. Prof. R. Ravikumar, Department of Agricultural and Rural Management, TamilNadu Agricultural University,Coimbatore-641003,TamilNadu,India. Prof. (Dr.) R.Raman, Professor of Agronomy, Faculty of Agriculture, Annamalai university, Annamalai Nagar 608 002Tamil Nadu, India. Prof. (Dr.) Ahmed Khalafallah, Coordinator of the CM Degree Program, Department of Architectural and Manufacturing Sciences, Ogden College of Sciences and Engineering Western Kentucky University 1906 College Heights Blvd Bowling Green, KY 42103-1066. Prof. (Dr.) Asmita Das , Delhi Technological University (Formerly Delhi College of Engineering), Shahbad, Daulatpur, Delhi 110042, India. Prof. (Dr.)Aniruddha Bhattacharjya, Assistant Professor (Senior Grade), CSE Department, Amrita School of Engineering , Amrita Vishwa VidyaPeetham (University), Kasavanahalli, Carmelaram P.O., Bangalore 560035, Karnataka, India. Prof. (Dr.) S. Rama Krishna Pisipaty, Prof & Geoarchaeologist, Head of the Department of Sanskrit & Indian Culture, SCSVMV University, Enathur, Kanchipuram 631561, India Prof. (Dr.) Shubhasheesh Bhattacharya, Professor & HOD(HR), Symbiosis Institute of International Business (SIIB), Hinjewadi, Phase-I, Pune- 411 057, India. Prof. (Dr.) Vijay Kothari, Institute of Science, Nirma University, S-G Highway, Ahmedabad 382481, India. Prof. (Dr.) Raja Sekhar Mamillapalli, Department of Civil Engineering at Sir Padampat Singhania University, Udaipur, India. Prof. (Dr.) B. M. Kunar, Department of Mining Engineering, Indian School of Mines, Dhanbad 826004, Jharkhand, India. Prof. (Dr.) Prabir Sarkar, Assistant Professor, School of Mechanical, Materials and Energy Engineering, Room 307, Academic Block, Indian Institute of Technology, Ropar, Nangal Road, Rupnagar 140001, Punjab, India.

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Prof. (Dr.) K.Srinivasmoorthy, Associate Professor, Department of Earth Sciences, School of Physical,Chemical and Applied Sciences, Pondicherry university, R.Venkataraman Nagar, Kalapet, Puducherry 605014, India. Prof. (Dr.) Bhawna Dubey, Institute of Environmental Science (AIES), Amity University, Noida, India. Prof. (Dr.) P. Bhanu Prasad, Vision Specialist, Matrix vision GmbH, Germany, Consultant, TIFACCORE for Machine Vision, Advisor, Kelenn Technology, France Advisor, Shubham Automation & Services, Ahmedabad, and Professor of C.S.E, Rajalakshmi Engineering College, India. Prof. (Dr.)P.Raviraj, Professor & Head, Dept. of CSE, Kalaignar Karunanidhi, Institute of Technology, Coimbatore 641402,Tamilnadu,India. Prof. (Dr.) Damodar Reddy Edla, Department of Computer Science & Engineering, Indian School of Mines, Dhanbad, Jharkhand 826004, India. Prof. (Dr.) T.C. Manjunath, Principal in HKBK College of Engg., Bangalore, Karnataka, India. Prof. (Dr.) Pankaj Bhambri, I.T. Deptt., Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India. Prof. Shashikant Shantilal Patil SVKM , MPSTME Shirpur Campus, NMIMS University Vile Parle Mumbai, India. Prof. (Dr.) Shambhu Nath Choudhary, Department of Physics, T.M. Bhagalpur University, Bhagalpur 81200, Bihar, India. Prof. (Dr.) Venkateshwarlu Sonnati, Professor & Head of EEED, Department of EEE, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, Andhra Pradesh, India. Prof. (Dr.) Saurabh Dalela, Department of Pure & Applied Physics, University of Kota, KOTA 324010, Rajasthan, India. Prof. S. Arman Hashemi Monfared, Department of Civil Eng, University of Sistan & Baluchestan, Daneshgah St.,Zahedan, IRAN, P.C. 98155-987 Prof. (Dr.) R.S.Chanda, Dept. of Jute & Fibre Tech., University of Calcutta, Kolkata 700019, West Bengal, India. Prof. V.S.VAKULA, Department of Electrical and Electronics Engineering, JNTUK, University College of Eng.,Vizianagaram5 35003, Andhra Pradesh, India. Prof. (Dr.) Nehal Gitesh Chitaliya, Sardar Vallabhbhai Patel Institute of Technology, Vasad 388 306, Gujarat, India. Prof. (Dr.) D.R. Prajapati, Department of Mechanical Engineering, PEC University of Technology,Chandigarh 160012, India. Dr. A. SENTHIL KUMAR, Postdoctoral Researcher, Centre for Energy and Electrical Power, Electrical Engineering Department, Faculty of Engineering and the Built Environment, Tshwane University of Technology, Pretoria 0001, South Africa. Prof. (Dr.)Vijay Harishchandra Mankar, Department of Electronics & Telecommunication Engineering, Govt. Polytechnic, Mangalwari Bazar, Besa Road, Nagpur- 440027, India. Prof. Varun.G.Menon, Department Of C.S.E, S.C.M.S School of Engineering, Karukutty,Ernakulam, Kerala 683544, India. Prof. (Dr.) U C Srivastava, Department of Physics, Amity Institute of Applied Sciences, Amity University, Noida, U.P-203301.India. Prof. (Dr.) Surendra Yadav, Professor and Head (Computer Science & Engineering Department), Maharashi Arvind College of Engineering and Research Centre (MACERC), Jaipur, Rajasthan, India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences & Humanities Dehradun Institute of Technology, (D.I.T. School of Engineering), 48 A K.P-3 Gr. Noida (U.P.) 201308 Prof. Naveen Jain, Dept. of Electrical Engineering, College of Technology and Engineering, Udaipur-313 001, India. Prof. Veera Jyothi.B, CBIT, Hyderabad, Andhra Pradesh, India. Prof. Aritra Ghosh, Global Institute of Management and Technology, Krishnagar, Nadia, W.B. India Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Sirhind Mandi Gobindgarh, Punajb, India. Prof. (Dr.) Varala Ravi, Head, Department of Chemistry, IIIT Basar Campus, Rajiv Gandhi University of Knowledge Technologies, Mudhole, Adilabad, Andhra Pradesh- 504 107, India Prof. (Dr.) Ravikumar C Baratakke, faculty of Biology,Govt. College, Saundatti - 591 126, India. Prof. (Dr.) NALIN BHARTI, School of Humanities and Social Science, Indian Institute of Technology Patna, India. Prof. (Dr.) Shivanand S.Gornale , Head, Department of Studies in Computer Science, Government College (Autonomous), Mandya, Mandya-571 401-Karanataka, India.

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Prof. (Dr.) Naveen.P.Badiger, Dept.Of Chemistry, S.D.M.College of Engg. & Technology, Dharwad-580002, Karnataka State, India. Prof. (Dr.) Bimla Dhanda, Professor & Head, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India. Prof. (Dr.) Tauqeer Ahmad Usmani, Faculty of IT, Salalah College of Technology, Salalah, Sultanate of Oman. Prof. (Dr.) Naresh Kr. Vats, Chairman, Department of Law, BGC Trust University Bangladesh Prof. (Dr.) Papita Das (Saha), Department of Environmental Science, University of Calcutta, Kolkata, India. Prof. (Dr.) Rekha Govindan , Dept of Biotechnology, Aarupadai Veedu Institute of technology , Vinayaka Missions University , Paiyanoor , Kanchipuram Dt, Tamilnadu , India. Prof. (Dr.) Lawrence Abraham Gojeh, Department of Information Science, Jimma University, P.o.Box 378, Jimma, Ethiopia. Prof. (Dr.) M.N. Kalasad, Department of Physics, SDM College of Engineering & Technology, Dharwad, Karnataka, India. Prof. Rab Nawaz Lodhi, Department of Management Sciences, COMSATS Institute of Information Technology Sahiwal. Prof. (Dr.) Masoud Hajarian, Department of Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, General Campus, Evin, Tehran 19839,Iran Prof. (Dr.) Chandra Kala Singh, Associate professor, Department of Human Development and Family Studies, College of Home Science, CCS, Haryana Agricultural University, Hisar- 125001 (Haryana) India Prof. (Dr.) J.Babu, Professor & Dean of research, St.Joseph's College of Engineering & Technology, Choondacherry, Palai,Kerala. Prof. (Dr.) Pradip Kumar Roy, Department of Applied Mechanics, Birla Institute of Technology (BIT) Mesra, Ranchi- 835215, Jharkhand, India. Prof. (Dr.) P. Sanjeevi kumar, School of Electrical Engineering (SELECT), Vandalur Kelambakkam Road, VIT University, Chennai, India. Prof. (Dr.) Debasis Patnaik, BITS-Pilani, Goa Campus, India. Prof. (Dr.) SANDEEP BANSAL, Associate Professor, Department of Commerce, I.G.N. College, Haryana, India. Dr. Radhakrishnan S V S, Department of Pharmacognosy, Faser Hall, The University of Mississippi Oxford, MS- 38655, USA. Prof. (Dr.) Megha Mittal, Faculty of Chemistry, Manav Rachna College of Engineering, Faridabad (HR), 121001, India. Prof. (Dr.) Mihaela Simionescu (BRATU), BUCHAREST, District no. 6, Romania, member of the Romanian Society of Econometrics, Romanian Regional Science Association and General Association of Economists from Romania Prof. (Dr.) Atmani Hassan, Director Regional of Organization Entraide Nationale Prof. (Dr.) Deepshikha Gupta, Dept. of Chemistry, Amity Institute of Applied Sciences,Amity University, Sec.125, Noida, India. Prof. (Dr.) Muhammad Kamruzzaman, Deaprtment of Infectious Diseases, The University of Sydney, Westmead Hospital, Westmead, NSW-2145. Prof. (Dr.) Meghshyam K. Patil , Assistant Professor & Head, Department of Chemistry,Dr. Babasaheb Ambedkar Marathwada University,Sub-Campus, Osmanabad- 413 501, Maharashtra, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Sudarson Jena, Dept. of Information Technology, GITAM University, Hyderabad, India Prof. (Dr.) Jai Prakash Jaiswal, Department of Mathematics, Maulana Azad National Institute of Technology Bhopal, India. Prof. (Dr.) S.Amutha, Dept. of Educational Technology, Bharathidasan University, Tiruchirappalli620 023, Tamil Nadu, India. Prof. (Dr.) R. HEMA KRISHNA, Environmental chemistry, University of Toronto, Canada. Prof. (Dr.) B.Swaminathan, Dept. of Agrl.Economics, Tamil Nadu Agricultural University, India. Prof. (Dr.) K. Ramesh, Department of Chemistry, C.B.I.T, Gandipet, Hyderabad-500075. India. Prof. (Dr.) Sunil Kumar, H.O.D. Applied Sciences &Humanities, JIMS Technical campus,(I.P. University,New Delhi), 48/4 ,K.P.-3,Gr.Noida (U.P.) Prof. (Dr.) G.V.S.R.Anjaneyulu, CHAIRMAN - P.G. BOS in Statistics & Deputy Coordinator UGC DRS-I Project, Executive Member ISPS-2013, Department of Statistics, Acharya Nagarjuna University, Nagarjuna Nagar-522510, Guntur, Andhra Pradesh, India.

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Prof. (Dr.) Sribas Goswami, Department of Sociology, Serampore College, Serampore 712201, West Bengal, India. Prof. (Dr.) Sunanda Sharma, Department of Veterinary Obstetrics Y Gynecology, College of Veterinary & Animal Science,Rajasthan University of Veterinary & Animal Sciences,Bikaner334001, India. Prof. (Dr.) S.K. Tiwari, Department of Zoology, D.D.U. Gorakhpur University, Gorakhpur-273009 U.P., India. Prof. (Dr.) Praveena Kuruva, Materials Research Centre, Indian Institute of Science, Bangalore560012, INDIA Prof. (Dr.) Rajesh Kumar, Department Of Applied Physics, Bhilai Institute Of Technology, Durg (C.G.) 491001, India. Dr. K.C.Sivabalan, Field Enumerator and Data Analyst, Asian Vegetable Research Centre, The World Vegetable Centre, Taiwan. Prof. (Dr.) Amit Kumar Mishra, Department of Environmntal Science and Energy Research, Weizmann Institute of Science, Rehovot, Israel. Prof. (Dr.) Manisha N. Paliwal, Sinhgad Institute of Management, Vadgaon (Bk), Pune, India. Prof. (Dr.) M. S. HIREMATH, Principal, K.L.ESOCIETY’s SCHOOL, ATHANI Prof. Manoj Dhawan, Department of Information Technology, Shri Vaishnav Institute of Technology & Science, Indore, (M. P.), India. Prof. (Dr.) V.R.Naik, Professor & Head of Department, Mechancal Engineering, Textile & Engineering Institute, Ichalkaranji (Dist. Kolhapur), Maharashatra, India. Prof. (Dr.) Jyotindra C. Prajapati,Head, Department of Mathematical Sciences, Faculty of Applied Sciences, Charotar University of Science and Technology, Changa Anand -388421, Gujarat, India Prof. (Dr.) Sarbjit Singh, Head, Department of Industrial & Production Engineering, Dr BR Ambedkar National Institute of Technology,Jalandhar,Punjab, India. Prof. (Dr.) Professor Braja Gopal Bag, Department of Chemistry and Chemical Technology , Vidyasagar University, West Midnapore Prof. (Dr.) Ashok Kumar Chandra, Department of Management, Bhilai Institute of Technology, Bhilai House, Durg (C.G.) Prof. (Dr.) Amit Kumar, Assistant Professor, School of Chemistry, Shoolini University, Solan, Himachal Pradesh, India Prof. (Dr.) L. Suresh Kumar, Mechanical Department, Chaitanya Bharathi Institute of Technology, Hyderabad, India. Scientist Sheeraz Saleem Bhat, Lac Production Division, Indian Institute of Natural Resins and Gums, Namkum, Ranchi, Jharkhand, India. Prof. C.Divya , Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli - 627012, Tamilnadu , India. Prof. T.D.Subash, Infant Jesus College Of Engineering and Technology, Thoothukudi Tamilnadu, India. Prof. (Dr.) Vinay Nassa, Prof. E.C.E Deptt., Dronacharya.Engg. College, Gurgaon India. Prof. Sunny Narayan, university of Roma Tre, Italy. Prof. (Dr.) Sanjoy Deb, Dept. of ECE, BIT Sathy, Sathyamangalam, Tamilnadu-638401, India. Prof. (Dr.) Reena Gupta, Institute of Pharmaceutical Research, GLA University, Mathura, India. Prof. (Dr.) P.R.SivaSankar, Head Dept. of Commerce, Vikrama Simhapuri University Post Graduate Centre, KAVALI - 524201, A.P., India. Prof. (Dr.) Mohsen Shafiei Nikabadi, Faculty of Economics and Management, Industrial Management Department, Semnan University, Semnan, Iran. Prof. (Dr.) Praveen Kumar Rai, Department of Geography, Faculty of Science, Banaras Hindu University, Varanasi-221005, U.P. India. Prof. (Dr.) Christine Jeyaseelan, Dept of Chemistry, Amity Institute of Applied Sciences, Amity University, Noida, India. Prof. (Dr.) M A Rizvi, Dept. of Computer Engineering and Applications , National Institute of Technical Teachers' Training and Research, Bhopal M.P. India. Prof. (Dr.) K.V.N.R.Sai Krishna, H O D in Computer Science, S.V.R.M.College,(Autonomous), Nagaram, Guntur(DT), Andhra Pradesh, India. Prof. (Dr.) Ashok Kr. Dargar, Department of Mechanical Engineering, School of Engineering, Sir Padampat Singhania University, Udaipur (Raj.) Prof. (Dr.) Asim Kumar Sen, Principal , ST.Francis Institute of Technology (Engineering College) under University of Mumbai , MT. Poinsur, S.V.P Road, Borivali (W), Mumbai-400103, India. Prof. (Dr.) Rahmathulla Noufal.E, Civil Engineering Department, Govt.Engg.College-Kozhikode

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Prof. (Dr.) N.Rajesh, Department of Agronomy, TamilNadu Agricultural University -Coimbatore, Tamil Nadu, India. Prof. (Dr.) Har Mohan Rai , Professor, Electronics and Communication Engineering, N.I.T. Kurukshetra 136131,India Prof. (Dr.) Eng. Sutasn Thipprakmas from King Mongkut, University of Technology Thonburi, Thailand. Prof. (Dr.) Kantipudi MVV Prasad, EC Department, RK University, Rajkot. Prof. (Dr.) Jitendra Gupta,Faculty of Pharmaceutics, Institute of Pharmaceutical Research, GLA University, Mathura. Prof. (Dr.) Swapnali Borah, HOD, Dept of Family Resource Management, College of Home Science, Central Agricultural University, Tura, Meghalaya, India. Prof. (Dr.) N.Nazar Khan, Professor in Chemistry, BTK Institute of Technology, Dwarahat-263653 (Almora), Uttarakhand-India. Prof. (Dr.) Rajiv Sharma, Department of Ocean Engineering, Indian Institute of Technology Madras, Chennai (TN) - 600 036,India. Prof. (Dr.) Aparna Sarkar,PH.D. Physiology, AIPT,Amity University , F 1 Block, LGF, Sector125,Noida-201303, UP ,India. Prof. (Dr.) Manpreet Singh, Professor and Head, Department of Computer Engineering, Maharishi Markandeshwar University, Mullana, Haryana, India. Prof. (Dr.) Sukumar Senthilkumar, Senior Researcher Advanced Education Center of Jeonbuk for Electronics and Information Technology, Chon Buk National University, Chon Buk, 561-756, SOUTH KOREA. . Prof. (Dr.) Hari Singh Dhillon, Assistant Professor, Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, Jalandhar (Punjab), INDIA. . Prof. (Dr.) Poonkuzhali, G., Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, INDIA. . Prof. (Dr.) Bharath K N, Assistant Professor, Dept. of Mechanical Engineering, GM Institute of Technology, PB Road, Davangere 577006, Karnataka, INDIA. . Prof. (Dr.) F.Alipanahi, Assistant Professor, Islamic Azad University,Zanjan Branch, Atemadeyeh, Moalem Street, Zanjan IRAN Prof. Yogesh Rathore, Assistant Professor, Dept. of Computer Science & Engineering, RITEE, Raipur, India Prof. (Dr.) Ratneshwer, Department of Computer Science (MMV), Banaras Hindu University Varanasi-221005, India. Prof. Pramod Kumar Pandey, Assistant Professor, Department Electronics & Instrumentation Engineering, ITM University, Gwalior, M.P., India Prof. (Dr.)Sudarson Jena, Associate Professor, Dept.of IT, GITAM University, Hyderabad, India Prof. (Dr.) Binod Kumar,PhD(CS), M.Phil(CS),MIEEE,MIAENG, Dean & Professor( MCA), Jayawant Technical Campus(JSPM's), Pune, India Prof. (Dr.) Mohan Singh Mehata, (JSPS fellow), Assistant Professor, Department of Applied Physics, Delhi Technological University, Delhi Prof. Ajay Kumar Agarwal, Asstt. Prof., Deptt. of Mech. Engg., Royal Institute of Management & Technology, Sonipat (Haryana) Prof. (Dr.) Siddharth Sharma, University School of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Satish Chandra Dixit, Department of Chemistry, D.B.S.College ,Govind Nagar,Kanpur208006, India Prof. (Dr.) Ajay Solkhe, Department of Management, Kurukshetra University, Kurukshetra, India. Prof. (Dr.) Neeraj Sharma, Asst. Prof. Dept. of Chemistry, GLA University, Mathura Prof. (Dr.) Basant Lal, Department of Chemistry, G.L.A. University, Mathura Prof. (Dr.) T Venkat Narayana Rao, C.S.E,Guru Nanak Engineering College, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Rajanarender Reddy Pingili, S.R. International Institute of Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) V.S.Vairale, Department of Computer Engineering, All India Shri Shivaji Memorial Society College of Engineering, Kennedy Road, Pune-411 001, Maharashtra, India Prof. (Dr.) Vasavi Bande, Department of Computer Science & Engineering, Netaji Institute of Engineering and Technology, Hyderabad, Andhra Pradesh, India Prof. (Dr.) Hardeep Anand, Department of Chemistry, Kurukshetra University Kurukshetra, Haryana, India. Prof. Aasheesh shukla, Asst Professor, Dept. of EC, GLA University, Mathura, India.

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Prof. S.P.Anandaraj., CSE Dept, SREC, Warangal, India. Satya Rishi Takyar , Senior ISO Consultant, New Delhi, India. Prof. Anuj K. Gupta, Head, Dept. of Computer Science & Engineering, RIMT Group of Institutions, Mandi Gobindgarh, Punjab, India. Prof. (Dr.) Harish Kumar, Department of Sports Science, Punjabi University, Patiala, Punjab, India. Prof. (Dr.) Mohammed Ali Hussain, Professor, Dept. of Electronics and Computer Engineering, KL University, Green Fields, Vaddeswaram, Andhra Pradesh, India. Prof. (Dr.) Manish Gupta, Department of Mechanical Engineering, GJU, Haryana, India. Prof. Mridul Chawla, Department of Elect. and Comm. Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. Seema Chawla, Department of Bio-medical Engineering, Deenbandhu Chhotu Ram University of Science & Technology, Murthal, Haryana, India. Prof. (Dr.) Atul M. Gosai, Department of Computer Science, Saurashtra University, Rajkot, Gujarat, India. Prof. (Dr.) Ajit Kr. Bansal, Department of Management, Shoolini University, H.P., India. Prof. (Dr.) Sunil Vasistha, Mody Institute of Tecnology and Science, Sikar, Rajasthan, India. Prof. Vivekta Singh, GNIT Girls Institute of Technology, Greater Noida, India. Prof. Ajay Loura, Assistant Professor at Thapar University, Patiala, India. Prof. Sushil Sharma, Department of Computer Science and Applications, Govt. P. G. College, Ambala Cantt., Haryana, India. Prof. Sube Singh, Assistant Professor, Department of Computer Engineering, Govt. Polytechnic, Narnaul, Haryana, India. Prof. Himanshu Arora, Delhi Institute of Technology and Management, New Delhi, India. Dr. Sabina Amporful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Pawan K. Monga, Jindal Institute of Medical Sciences, Hisar, Haryana, India. Dr. Sam Ampoful, Bibb Family Practice Association, Macon, Georgia, USA. Dr. Nagender Sangra, Director of Sangra Technologies, Chandigarh, India. Vipin Gujral, CPA, New Jersey, USA. Sarfo Baffour, University of Ghana, Ghana. Monique Vincon, Hype Softwaretechnik GmbH, Bonn, Germany. Natasha Sigmund, Atlanta, USA. Marta Trochimowicz, Rhein-Zeitung, Koblenz, Germany. Kamalesh Desai, Atlanta, USA. Vijay Attri, Software Developer Google, San Jose, California, USA. Neeraj Khillan, Wipro Technologies, Boston, USA. Ruchir Sachdeva, Software Engineer at Infosys, Pune, Maharashtra, India. Anadi Charan, Senior Software Consultant at Capgemini, Mumbai, Maharashtra. Pawan Monga, Senior Product Manager, LG Electronics India Pvt. Ltd., New Delhi, India. Sunil Kumar, Senior Information Developer, Honeywell Technology Solutions, Inc., Bangalore, India. Bharat Gambhir, Technical Architect, Tata Consultancy Services (TCS), Noida, India. Vinay Chopra, Team Leader, Access Infotech Pvt Ltd. Chandigarh, India. Sumit Sharma, Team Lead, American Express, New Delhi, India. Vivek Gautam, Senior Software Engineer, Wipro, Noida, India. Anirudh Trehan, Nagarro Software Gurgaon, Haryana, India. Manjot Singh, Senior Software Engineer, HCL Technologies Delhi, India. Rajat Adlakha, Senior Software Engineer, Tech Mahindra Ltd, Mumbai, Maharashtra, India. Mohit Bhayana, Senior Software Engineer, Nagarro Software Pvt. Gurgaon, Haryana, India. Dheeraj Sardana, Tech. Head, Nagarro Software, Gurgaon, Haryana, India. Naresh Setia, Senior Software Engineer, Infogain, Noida, India. Raj Agarwal Megh, Idhasoft Limited, Pune, Maharashtra, India. Shrikant Bhardwaj, Senior Software Engineer, Mphasis an HP Company, Pune, Maharashtra, India. Vikas Chawla, Technical Lead, Xavient Software Solutions, Noida, India. Kapoor Singh, Sr. Executive at IBM, Gurgaon, Haryana, India. Ashwani Rohilla, Senior SAP Consultant at TCS, Mumbai, India. Anuj Chhabra, Sr. Software Engineer, McKinsey & Company, Faridabad, Haryana, India. Jaspreet Singh, Business Analyst at HCL Technologies, Gurgaon, Haryana, India.

TOPICS OF INTEREST Topics of interest include, but are not limited to, the following:                                                    

e-Commerce applications using web services B2B and B2C applications Advanced web service technologies including security, process management and QoS Surveillance technologies and security policies Security for protocol management Resource and channel management Mobility management Network Security management Technology management Information security management Semantic web for e-Business and e-Learning e-Learning design and methodologies Instructional design methodologies Content management and development Knowledge and information management techniques Enterprise Applications for software and web engineering Open-source e-Learning platforms Internet payment systems Techniques for B2B e-Commerce e-Business models and architectures Service-oriented e-Commerce Human resource management Business-oriented and consumer-oriented e-Commerce Development of e-Business and applications Supply chain management Strategic decision support systems Enterprise resource planning and e-Business Intranet and extranet business applications Enterprise-wide client-server architectures Information systems analysis and specification Strategic issues in distributed development Semantic web technologies and cloud computing Legal aspects of e-Government Risk management Methods and tools for e-Government e-Democracy and e-Voting Operations management Information technology Information retrieval systems Aspect-oriented programming e-Libraries and e-Publishing Intelligent tutoring systems Digital libraries for e-learning Web-based learning, wikis and blogs Social networks and intelligence Social science simulation Information retrieval systems Wired and wireless data communication networks Data mining and warehousing Distributed AI systems and architectures Bioinformatics and scientific computing Knowledge and information management techniques

TABLE OF CONTENTS (June-August, 2014, Issue 9, Volume 1 & 2) Issue 9, Volume 1 Paper Code

Paper Title

Page No.

IJEBEA 14-304

Investigating the effects of process parameters on MRR in WEDM using Molybdenum wire Baljit Singh, Dr. B.S. Pabla, Manju Saroha

01-05

IJEBEA 14-308

The Influence of Intellectual Capital towards Firm Value with Independent Commisioner and Audit Committe As Moderating Variables Etty Murwaningsari

06-11

IJEBEA 14-315

The Impact of positive cash operating activities on the Cost of Debt: International Evidence Dr. Saadani Ghali, Harit Satt

12-20

IJEBEA 14-316

Optimisation of RCC Beam Bikramjit Singh, Hardeep Singh Rai

21-34

IJEBEA 14-320

An Investigative Study on Factors Causing Job Choice of Business School Students in Private Universities of Bangladesh Sadia Tangem, Mohammad Ishtiak Uddin

35-39

IJEBEA 14-326

Consumer Perception Regarding Eco-Friendly Fast Moving Consumer Goods in India Sudhir Sachdev, Vinod Mahna

40-43

IJEBEA 14-327

Analysis of Manufacturing Competency for an Automobile Manufacturing Unit Chandan Deep Singh, Jaimal Singh Khamba

44-51

IJEBEA 14-329

The human resources policy of the Health Ministry in Angola - from current practice to the desired praxis Pedro J. M. Gomes, Pedro F. Franque

52-56

IJEBEA 14-334

Investigating Location Differences in Influence of Parameters on Organizational Role Stress among IT Sector Employees Deepa Mohan, Sudarsan N

57-62

IJEBEA 14-337

Review on Concrete Subjected to Elevated Temperature Maya T M, Nivin Philip, Dr. Job Thomas

63-69

IJEBEA 14-338

Global Talent Management Strategies for High Performance Culture Dr. A.Narasima Venkatesh

70-72

IJEBEA 14-341

Role of CRM in Business Sector Dr.T.Vijayaragavan

73-76

Issue 9, Volume 2 Paper Code

Paper Title

Page No.

IJEBEA 14-342

Dual Mechanism to detect DDOS Attack Priyanka Dembla, Chander Diwaker

77-82

IJEBEA 14-357

The influence of the Authentic Leadership in the commitment of Health Professionals in Angola Pedro J. M. Gomes

83-88

IJEBEA 14-362

Experimental Study of Non-Destructive Test on Steel Fibre Reinforced Concrete Mandeep Singh, Inderpreet Kaur

89-95

IJEBEA 14-368

Conceptualisation of Business Rules Rakhi Dewan, Rekha Sachdeva

96-98

IJEBEA 14-371

Research on fluid in an open container with a complex force field applied Penev Mariel, Stoyanov Ivaylo

99-103

IJEBEA 14-373

Analysis of Strategic Success for an Automobile Manufacturing Unit Chandan Deep Singh, Jaimal Singh Khamba

104-111

IJEBEA 14-374

Design of Adder logic cell with XOR gate Chandrahash Patel, Dr. Veena C.S.

112-115

IJEBEA 14-379

Discrete Optimisation of One way Slab using Genetic Algorithm Hatindera Singh, Hardeep Singh Rai, Jagbir Singh

116-121

IJEBEA 14-385

Fuzzy Clustered Speaker Identification Prof. Angel Mathew,Preethy Prince Thachil

122-125

IJEBEA 14-386

Parameter Analysis of Hybrid Power System with UPFC V. K. Bhola, T. Sharma, P. Saini

126-129

IJEBEA 14-387

Impact of Communication in Predicting Adoption of Drugs Treating Hypertension Dipanjan Goswami, D. R. Aggarwal, Neera Jain,

130-139

International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Dual Mechanism to Detect DDOS Attack Priyanka Dembla , Chander Diwaker2 Research Scholar, 2 Assistant Professor CSE Department, U.I.E.T., Kurukshetra University, Kurukshetra University, Haryana, INDIA Abstract: Cloud computing has emerged as computing paradigm which shares resources and services with its customers. In its early days we does not heard of the Denial of service attack. But since 2000, a series of DDOS attacks by multiple nodes is accomplished of blocking the services of cloud servers. The attack can be for many reasons. It became a major threat for cloud environment. Significant problem of DDOS attacks is that they are difficult to detect. The effects of various attacks can shut the organization off from network. The main goal of this attack is to make cloud services unavailable for the legitimate users. This paper aims at proposing an efficient method for security in cloud. We propose an algorithm which modifies the confidence Based Filtering method (CBF) by adding the IP Spoofing filtering method before applying CBF. 1

Keywords: Cloud computing, DDOS attack, Hop count, IP spoofing I. Introduction Cloud computing is one of the most hyped information technology and it has become one of the fastest growing segments of IT. Costumers must only pay for the amount they are using and have not to pay for local resources such as storage or infrastructure. The cloud offers several benefits like fast deployment, pay-for- use, lower costs, scalability and flexibility. Resources such as hardware and software are liable to be outdated soon [1]. Therefore outsourcing of resources is the solution. Cloud computing is basically consist of 4 deployment models and 3 service models. Deployment models arePublic Cloud is cloud model in which services are available for the public and payment is on the basis of pay per use. It is less secure model among all the models. Private Cloud provides services to the particular group of people which may belong to some organisation. So it becomes easy to manage them. Hybrid is an environment in which some of the resources are for private use such as in private cloud and rest is for public use. It is a combination of public and private cloud. Community Cloud model is shared by the organisation or people which have similar cloud requirement. These number of organisation are limited in nature moreover they are trusted ones [2]. Services of the cloud is provided on the basis as Software as a service, Infrastructure as a service and Platform as a service. A cloud application delivers Software as its Service over the internet, thus clients does not have to install the application on its system. Platform as a Service provide a computing platform. It has all the application typically required by the client deployed on it. In Infrastructure as a Service, the client need not purchase the required servers, data center or the network resources. As a result customers can achieve a much faster service delivery with less cost. DDos attacker is one of the most common attacks in cloud computing. Attacker sends a huge amount of packets to a certain service. Each of these requests has to be processed by the server. This increases workload per attack request. This usually causes denial of service to the legitimate users also the performance of network reduces. This attack is also known as flooding attack. Denial of service does not modify data instead it crashes server and networks, making service unavailable to the legal users. DOS can be launched from either a single source or multiple sources. Multiple sources DOS attacks are Distributed denial of service (DDOS) [3]. DDOS is distributed, large scale coordinated attempt of flooding the network with large amount of packets which becomes difficult for victim network to handle and hence the victim sever becomes unable to provide the services to its legitimate user. Various resources such as bandwidth, memory, computing power get wasted in serving flooding packets. It makes services or resources unavailable for indefinite amount of time. The attacker usually spoofs IP address section of a packet header in order to hide their identity from their victim[4]. II. Related Work Kumar et al. [4] presented an approach in which packets with the same hop count passes through the router are assigned some identification number. This number is the combination of the 32 bits of IP address of the router and encrypted value of hop count. The receiver of the packets matches this hop count with the already stored value. This PID is placed in the identification field of the IP header. When the router receives the packet, it checks packet ID number whether it is valid. The advantage of this approach is that if it filters the traffic after receiving just one packet. If it is not valid, it means that the packet is arrived from the sender host or from

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attacker which sends packet with forged mark. After receiving this type of packet, router starts detection process. Attack graph is constructed to filter all packets coming out of the attack source. By the attack path construction, it is easy to identify the source of the attacker. This algorithm lowers false alarm and is executed close to attack source. Overhead of routers are reduced as compared to the IP traceback and packet marking approach. Yu et al. [5] proposed dynamic resource strategy for countering DDOS attack. It clones sufficient Intrusion Detection servers for the victim with the help of resources of other clouds. Before serviced by the server, packets have to pass through the queue and the IPS. The assumption of this paper that the users are stable and resources of clouds are sufficient to overcome DDOS attack. During the attack on any of the individual cloud server, number of attack packets generated by the botnets increases. This algorithm clones multiple IPS to maintain the quality of service. The number of IPS depends upon the volume of the attack. This method focuses on the resources management. The loop point of this approach is that if any of the data center runs out of resources during attack, this method will fails in this case. Huang et al. [6] proposed low reflection ratio mitigation system against DDOS attack. System consists of source checking, counting, attack detection, turing test and question generation module. Turing test is conducted for the possible attackers detected at the detection module. This test can determine the incoming packet is initialized by Zombie host or Human. The packet first reaches checking and counting module. Attack detection module cooperates with source checking module to detect any DDOS attack. It tries to find malicious source and blocks it. Test based turing testing module randomly selects question from question generation module and waits for the requester to answer. Without getting correct answer to the question, it will not be allowed to reach server. The system has low reflection ratio with high efficiency. Megha et al. [7] presented a mechanism to prevent DDos attacks and to improve resource availability of resources. The basic idea behind the proposed system is to isolate and protect the web server from huge volumes of DDos request when an attack occurs. In the proposed algorithm for user friendly in domain and the capacity to store user profiles and profiles and sending them to the server component aided by computer speed high memory capacity and accuracy. This have the advantage of differentiating the clients from the attackers those who tries to affect the server function by posting requests in a large amount for unwanted reasons. This can be used for creating defenses for attacks require monitoring dynamic network activities. III. Proposed Approach The main goal of this paper is to filter the packets received from various source on the basis of the IP spoofing by using TTL field in the packet and then allowing these filtered packets to go through CBF method. This method is based upon the correlation pattern stored in the packets. These patterns are mainly in network and transport layer. DDOS attack is accompanied by IP spoofing. Attackers conceal their identity by changing the Source IP address field of the packet to make it as packet is coming from the legitimate user. But attacker can forge the Hop Count of the packet. This idea is used in this paper to filter the packets. Hop count and SYN flag of the packets detects whether the packet is spoofed one or legitimate. The spoofed packet is rejected and rest the packets which passed this test are collected under filtered list for further test. This filtering has reduced the numbers of packets on which further tests will be applied. Hence it reduced the overhead of applying CBF on all the packets. CBF consist of two concepts- Confidence and Score. Each packet from the filtered list is collected and the frequency of appearance of single attribute is calculated. This is the confidence of that attribute value. If the confidence of single attribute is greater than the minconf (pre defined) are selected to generate attribute value pairs. This step is essential because if the confidence of one attribute value in an attribute value pair is not greater than minconf, the confidence of the combination of this value pair will still not be greater than minconf. We again scan all the packets in the filtered list to count the frequency of appearances of attribute value pairs and count their confidence. Attribute values pairs whose confidence is greater than minconf will update the nominal profile. Nominal profile is a 3 dimensional array. The first dimension is for first attribute pair and the second dimension is for second attribute pair. The third dimension is the confidence value dimension. There is no need to update nominal profile if the confidence of attribute pairs less than predefined confidence value. Score is the weighted average of the confidence of the attribute value pairs in it. Score = â&#x2C6;&#x2018; (weight * confidence [attribute value pairs] )/ â&#x2C6;&#x2018; weight Weights of the attributes are adjusted on the basis of operating system, network structure and other elements. The patterns which are less copied by attackers are generally are given higher weight. This requires looking in the nominal profile for the confidence of the attribute pairs and applying some arithmetic operations. Attributes pairs whose confidence is not on the nominal profile, we will use minconf value instead when confidence values are used in calculating score. Score of the packets is generated by the above method. After calculating CBF

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scores of the packets, we use it to distinguish attack packets from the legitimate ones. Method will only accept the packets with scores greater than discarding threshold. Discarding Threshold can be fixed depending upon the score distribution or dynamic like load shedding algorithm. In our paper we have used fixed discarding threshold. IV. System Model Figure 1 system model

V. Algorithm nspoof==0; count[attribute value]==0; For each packet Calculate hop count and SYN flag ; hop count=Final TTL-Initial TTL; if(packet is in table) if(SYN==1) compare hop count with stored hop count;

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if (same value) add it in table; else update hop count; else compare hop count with stored hop count; if (same value) allow packet; else remove packet; nspoof+1; else

// packet is not in table if (SYN==1) add packet in table; else remove packet; nspoof+1;

for each packet received after filtering spoofed for each attribute value in packet count[attribute value] +1; calculate confidence[attribute value] = count[attribute value]/ no of packets; if (confidence[attribute value] > MinConf) calculate count[attribute value pairs]; if (confidence[attribute value pairs]> MinConf) update nominal profile; else do not update nominal profile; for each value in nominal profile calculate score; score= ∑ (weight * confidence[attribute value pairs] )/ ∑ weight; if (score< Threshold) reject packet ; else accept; VI. Simulation conditions The test environment is intel core i3 processor. The simulation programs is written in NetBeans. The window size is set to 10 packets, and the value of minconf is set to 0.13.Under this circumstance, the storage data at counting period are affordable in normal servers. Our method spends around 0.024 seconds to process data during each non attack phase. The weights in score calculation are set higher in the attribute pairs containing source IP address, TCP server port number or TTL value, and set lower in those only with TCP flag, IP protocol type and packet size. For the fast response at attack period, fixed discarding threshold is adopted. In implementation, discarding threshold is selected as 0.012. The six single attributes used are - total time, time to live, protocol type, source IP address, flag, Destination port number like those in CBF. The filtering is on the basis of the spoofed table which contains IP address and Hop count of all the packets entered in the system. SYN flag and TTL of each packet is extracted from the packet. Hop count is calculated from the TTL. Figure 2 is showing the spoof filtering result. Here, 10 packets are scanned in which 2 of them are found to be spoofed

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and hence filtered out. The remaining 8 packets are collected in the filtered list. We now apply second phase of testing which includes calculating confidence of each attribute pair and updating their value in nominal profiles. As we have 6 attributes on which we are working. So we will have total of 15 nominal profiles. The figure 3 is showing the result of proposed work. It is attack phase in which score is calculated and on the basis of discarding threshold , packets are dropped and accepted. This figure is also showing IP addresses of the packet which are discarded. Figure 2 Spoof filtering

Figure 3 Discarded IP addresses

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Figure 4 shows the comparative analysis of packet discarded over time. Here x axis represents the time (Attack and Non-Attack phase) and y axis represents the number of packets discarded. As it can be seen after implementation the proposed approach, the packet discarded over time increased.

Figure 4 Comparison 6 5 4 Number of packets 3 Discarded

existed proposed

2 1 0 0

Non Attack time

Attack

VII. Conclusion and future scope Cloud computing is one of the most hyped information technology and it has become one of the fastest growing segments of IT. The most serious threat to cloud computing is DDOS attack. It caused a lot of damage to many organizations. Attacker shut down the servers for a period of time. The site became non functional for some time. Dual mechanism approach is used to prevent attack. This method is about to improve the CBF method which is based on the correlation patterns. Our analysis has provided a tool to prevent from attack by using IP Spoofing and correlation pattern among attributes of packet .DDOS attack is mainly associated with spoofed packets. The spoofed packets are dropped in the initial phase so reducing the overhead in calculating confidence and score of the all packets. The simulation result showed that 90 % of the DDOS attack can be dropped. The proposed system can be enhanced in future by other researchers in the following ways:  More flexible strategy for choosing weights for each attribute pair  Discarding threshold can be chosen dynamically based on the load balancing or other factors  Question generated module can be added which ask some questions by possible attackers before discarding packet. These questions can be easily solved by human but not program run by zombies. References [1] [2] [3] [4]

[5] [6]

[7] [8] [9]

Farzad Sabahi, “Cloud Computing Security Threats and Responses”, IEEE 3rd International Conference Comunication Software and Networks,2011, pp.245-249. Ziyuan, Wang, “Security and privacy issues within Cloud Computing”, IEEE International Conference on Computational and Information Sciences, 2011,pp.175-178. Bansidhar Joshi, A. Santhana Vijayan and Bineet Kumar Joshi, “Securing Cloud Computing Environment Against DDos Attacks” IEEE International Conference on Computer communication & Informatics , 2012, pp. 1-5. Bharathi Krishna Kumar , P. Krishna Kumar, and R. Sukanesh, "Hop count based packet processing approach to counter DDoS attacks", IEEE International Conference on Recent Trends in Information, Telecommunication and Computing, 2010, pp. 271273. Yu, Shui, Yonghong Tian, Song Guo, and D. Wu, "Can we beat ddos attacks in clouds?", IEEE International Conference on Transactions on Parallel and Distributed Systems, 2013, pp.1-11. Vincent Shi-Ming Huang., Robert Huang, and Ming Chiang,"A DDoS Mitigation System with Multi-stage Detection and TextBased Turing Testing in Cloud Computing", 27th IEEE International Conference on Advanced Information Networking and Applications Workshops,2013, pp. 655-662. Patel Megha, Arvind Meniya, “Prevent DDOS Attack Using Intrusion Detection System in cloud”, International Journal of Computer Application ,Vol. 2 Issue 3 , 2013 , pp. 95-104. Haining Wang , Cheng Jin, and Kang G. Shin,” Defense Against Spoofed IP Traffic Using Hop-Count Filtering”,IEEE/ACM Transactions on Networking, 2007, pp.40-53. Weili Huang and Jian Yang, “New Network Security Based On Cloud Computing” ,IEEE Second International Workshop on Education Technology and Computer Science, 2010, pp.604-609 [10] Xue Jing and Zhang Jian-jun, “A Brief Survey on the Security Model of Cloud Computing” IEEE Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, 2010 ,pp.475-478.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net The influence of the Authentic Leadership in the commitment of Health Professionals in Angola Pedro J. M. Gomes1 Department of Vocational Guidance Higher Institute of Health Sciences / Agostinho Neto University Address Av January 21, Next to Clinic Multiperfil, Luanda / Angola

Abstract: Several studies carried out over the past few days, that focus on the various types of leadership and is paper in the organization, highlighting is importance and the impact it has on employees in an organizational context. Based on that condition, this article aims to emphasize the influence that authentic leadership in the involvement of health professionals in Angola with the organization and how it is done, highlighting and reinforcing the importance of this type of leadership in the organization. Thereby, a bibliographical research was made based on the fact that hospitals in Angola suffer serious problems suck as lack of medication, the lack of involvement of professionals which sometimes have low commitment with the care quality provided to users of the health care system. Keywords: Leadership, authentic leadership, commitment, organizational behaviour, Angolan health, motivation I. Introduction The effects of globalization are recognized as one of the causes pointed for the changes at a social and organizational level. In this sense, either in society either in organizations a need exists of finding ways to deal and exceed the various resulting problems. There is a a clear need, either at a social level and the organizational level of individuals, to find a reference in order to consolidate their behaviors, attitudes and values. Naturally, in an organizational context, individuals try to be based on the behavior and attitudes of the leader, following is model and example. Being the leadership recognized as an important factor in the organization, is natural that organizations explore and support on it as a way to combat and surpass the problems founded in this new organizational context. In recent years, many authors do developed approaches and studies on this subject, in his eagerness to find a type of leadership capable of dealing with the problems resulting from current context. As a result, it is natural that organizations turn to the leadership and the leader's role as a way of konw, not only what is said in the previous sentence as well as the desired optimism in the organizational context [1]. However, it is not only the organizations that are looking and seeking in leadership and in the leader a solution to their problems, and also subordinated feel the need off finding a reference in the organizational context, to guide them and give some sense to his life [2]. The result of all this and in the current context of health in Angola, emerge the so-called authentic leaders,that according to some authors, have a set of characteristics and values such as honesty and transparency [2] [3], which are key in the new organizational context and may result in value added for everyone involved. It is study object of this article to understand the influence of the Authentic Leadership commitment of Health Professionals in Angola. This study proposes to determine how employees are affected in their commitment to the organization by the authentic leadership. Starting with the next question of discussion: -Does the employee commitment to the organization is influenced by the characteristics of authentic leadership? Due to that, the objective of this study aims to understand specifically and verify until how (1) the authentic leadership can explain the employment commitment to the organization, (2)in what way the relationship employed vs leader translates the commitment to the organization and (3) analyze the quality of Health Units Management, focusing on the question "How to manage Angolans Hospitals? II. The Authentic Leadership Authentic leadership started to be seen as one form of leadership that is based on the capabilities of leader and promotes in a positive manner the capabilities of the team members, being directly related to the development of a positive ethical climate. Also promotes a new type of relationship between leader and follower, that passes for a greater self-awareness, based on ethical and moral values, promoting greater relational development and self development of employees [3]. Following this, Northouse [4] argues that authentic leadership must be open to criticism, so it is important to hear opinions, accept them and implement them when appropriate. Therefore authentic leadership requires communication. According to Schneider [5] effective communication requires that information flows in both directions thereby developing a feedback process. The leader has a dominant role in the internal communication

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in order to mobilize the employees. The leadership communication as well as the existence of and consultation mechanisms and dialogue between employees and leaders, encourage participation and learning, sharing of information, and active involvement of employees in decisions in an improvement action. Therefore, this type of leadership has a strong impact on subordinates, creating positively influences in the relationship between the leader and the organization reflecting positively [6]. In turn, Avolio et al. [7] tell us that this type of leadership is what most influences the positive emotions of employees, and this will influence how employees view their connection to the organization, it plays a pivotal role in the commitment that they may have the organization [6] [7] [8]. Carapeto and Fonseca [9] highlight the crucial role of leadership in creating an environment of trust in the organization, promoting employee satisfaction through the stimulation and development of skills resulting in increased internal cooperation and better performance. According to these authors, associated with employee satisfaction is empowerment. According to Kotter [10] the latter term would aims to create conditions for greater participation of employees in decision making by giving greater control over own work it performs. The delegation of tasks and responsibilities is critical to providing greater autonomy to employees in the execution of their work. For Bass [11] it will be possible to systematize the authentic leadership as follows: - Idealized influence (charisma) - the leader adopts behaviors (e.g. respect and moral elevation) that activate strong emotions in followers, give rise to confidence and identification with him, influencing his ideals and aspects "larger than life"; - The leader communicates an appealing vision, using symbols to encourage the efforts of followers, acts as a model of behavior, instills optimism; - Intellectual stimulation - the leader encourages followers to the awareness of the problems of their own thoughts and imagination. Help them to recognize their own beliefs and values. Encourages them innovative / creative thinking. Encourages them to question their assumptions; - Individualized Consideration - the leader meets the development needs of followers, support them, encourage them, train them, try to develop their potential, providing them feedback, assign them responsibility. In the same way, the authentic leadership must motivate your employees acknowledging the differences in individuals and developing their role in the organization, even praise them publicly, realizing their needs and satisfying them, thus creating proactive relationships, managing conflict and developed empathy with employees. III. The Organizational Commitment The employee develops a sense of commitment, acting to achieve goals and outcomes in the organization. There are many studies that have reported the instrumental basis of organizational commitment differs from the normative and affective bases with respect to desirable behaviors for the organization and for the individual [12] [13]. In this sense, the affective component got the strongest correlations with favorable and positive behaviors for the organization (attendance, performance, organizational citizenship behavior) and the employee (management of stress and lower work-family conflict). Thus, authentic leadership is closely related to the skills of communication and transmission of ideas. The Affective commitment sets out how the worker feels that identifies emotionally with the organization where he belongs. This impairment may result from the perception developed by the individual: the just way it is respected and supported by the organization, by the confidence you have placed in the upper and in the organization, the satisfaction and fulfillment in their work and the existence of congruence between personal and organizational goals [14] . With respect to the commitment Calculative defines how the employee feels connected to the organization by recognizing the costs associated with its output. If the employee recognize that by abandoning the organization will lose the entire investment made therein, or, if not envision alternatives that allow them to change jobs, and the degree the degree of calculative commitment will increase [14]. Finally, the Normative Commitment categorizes how the employee feels to have a sense of moral obligation or duty, the responsible organization face causing him to act competently, though without enthusiasm or commitment [14]. The studies about Organizational Commitment seek to understand the characteristics of individuals and their organizations, in order to facilitate the achievement of strategies able to match business objectives with the goals of workers. In the last years, organizational commitment has emerged as a central concept in the study of working attitudes and behavior [14]. [15] By attracting employee commitment to the organization can increase their interest in achieving organizational goals. So It appeared from involvement as the psychological state in which employees maintain identification and involvement with the organization, worrying about their well-being, incorporating their values and assuming them as their own. Recently, the commitment has been associated with various forms of work and behavior [16] [17]. For employees to commit themselves, the organization needs to perform behaviors that involve and integrate,

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ensuring return on investments made on them, because when the worker has committed better yields. So the organization must internalize in your employees their own goals and objectives, attaching to these goals and objectives of employees to both is necessary that sense of reciprocity, succeeding exchange of benefits between individual and organization, that is, both must match the expectations of one another. Account should be taken of the fact that organizational commitment depends on several variables such as employment stability, performance evaluation, self-evaluation, variable remuneration in line with productivity (commission), freedom of expression, participation in team, internal recruitment, training employees and promotion criteria. Ichniowski et al. [18] consider it necessary to provide benefits, innovation and participation in team training, extended to employees and job security selection, so there is a real commitment. Paauwe and Richardson [19] tell us of practical compromise: satisfaction, commitment, involvement, loyalty and employee motivation and Guthrie [20] discusses the career opportunities, the work environment, the intensity of participation in and access to team information. Ahmad and Schroeder [21] highlight the variables to organizational commitment: satisfaction, loyalty and motivation, plus a competent leadership, and compensation and incentives, training and development and job security, while Park et al. [22] discuss practices merit promotion and access to information. Kundu et al. [23] highlight the performance evaluation, training, internal recruitment, compensation, human resource planning, workforce diversity and flexibility of the organization while Lee and Lee [24] highlight the training and development, teamwork, incentives, human resource planning, performance evaluation and job security. In summary, the need to implement an effective and personalized system in the realities of the business it is essential to promote the continuous improvement of a company and commitment of its employees. Following this, even though organizations through a period of crisis worldwide, affective commitment, plays a key role within organizations, with a view to the involvement of employees and improvement of individual performance to the achievement of organizational objectives [25]. IV. Quality Management of Health Units As indicated in the National Health Plan 2011-2016 [26], the concept of quality as well as the methodologies associated with quality, were disseminated from the industry, by authors such as Deming, Juran and Ishikawa and adapted to healthcare, particularly for Avedis Donabedian. However, concern about the quality of health care through the entire history of medicine, from Hippocrates, passing through Florence Nightingale and Ernest Codman. In industry, the quality of a product or service has been defined as the fitness for use (Juran) or as satisfying the requirements of consumers of that product or service (Ishikawa), in all cases there is a notion of appropriateness to the expectations of those who purchase a product or use a service. “Quality health care is justified for several reasons, among which the social order (there is growing demand and expectations on the part of users), ethics (the requirement level of training and knowledge), professional (development good practice, appreciation and satisfaction of carers) and economic (development and rationalization of resources on the part of a Culture of Quality in Health) organizations”[27]. Sá and Sintra [28] argue that "Quality has been a recurring concern of governments (...). The commitment to quality appear systematically linked to demand from a combination of imperatives of effectiveness, efficiency and equity to satisfy the needs of customers naturally very diverse and, often, conflicting with each other. " Thus, health care facilities must implement, maintain and improve its quality management system, through correct application of key implementation factors, in particular, the involvement of customers / users, opening the organization / institution and commitment towards / leadership. Similarly, management must play an important role in promoting more advanced cultures of quality at all levels of the organization and lead the process of change, both in human resources and strategy, as well as promote their management philosophy, set goals and create an organizational structure capable of achieving the proposed goals. In the study by Reis [29], "health management", the author believes that "the increasing intervention in the delivery cycle, either payers or those assisted contribute to a strengthening of concerns about quality of care and to development of techniques and methods whose purpose is to guarantee it. Also why all health professionals should be prepared in order to master these techniques and methods, almost specific burden of managers favor their installation "[29]. In fact, this study demonstrates how the lack of involvement of the top management of such a unit, it can jeopardize the implementation of a project in the organization. A culture of quality is not possible without the involvement of top management, either at the time of his birth nor along time managing their lifecycle. Despite the foregoing, it can be stated that "In many African countries, despite the efforts they have undertaken to bring health to all individuals, there are enormous difficulties in providing access to numerous population groups to health services . This is due to organizational weakness of health systems, economic crisis, inadequate attention to the principles of primary health care, the scarcity of resources of all kinds (financial, human, technological, etc.) and / or its maldistribution "[30].

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The integration of learning in health professionals in this country should go beyond mere professional context; should cover areas such as culture, society, in short own personal spaces. This multidisciplinary causes resistance to the process of formation itself is a minor, even more, you can also get turn professional and bring out the diverse qualities [29]. According to the article by Oliveira and Artmman [30], the major endemic diseases in Angola are malaria, tuberculosis, HIV, leprosy, among others, and have a high infant mortality rate. From the point of view of human resources, there is a high concentration of nurses and other health professionals in capital, representing an asymmetry in the distribution of professionals in the Angolan provinces. With regard to health care services, is a degraded system, a result of years we've been at war and under-investment in this area. The authors suggest, from the point of view of the efforts to improve the quality there is a better allocation of human resources, which is developed relevant and appropriate to this country legislation that improves the health network, which motivate the workers, who if wages increase, which can invest in public transport (for better access to health services), there is investment and greater investment in health. Thus, "despite the still weak performance of health systems in Africa, the gradual implementation of regionalization, which consider the realities and political-administrative, epidemiological, economic, cultural, managerial, cognitive local circumstances, according to the experiments analyzed, including African countries could represent a suitable strategy for improving access to health care, particularly for economically disadvantaged groups. In addition, it could strengthen the decentralization process underway [30]. Sá and Sintra [28] suggest that there may be a strengthening of the concerns and efforts to improving the quality you: - Integrate a space for collecting comments / suggestions on the required documents, in order to provide channels for single customers / citizens to actively participate in building a more targeted public service for their needs; - Contribute so that customers can know better the mechanisms at its disposal to advise on the services used, as well as providing prompt and personalized feedback to all opinions presented, informing them of the start of any corrective measures, to increase the perception the effectiveness thereof; - Communicate to all employees (and in particular at the service, as they are these that are in direct contact with customers / users and should quickly adjust the service to suit your needs) information and the results of several hearings made to customers / users; and - If you regularly measure the satisfaction of customers / users, because only then can we assess the effectiveness of implemented measures and examine how everyone (and each employee) can best contribute to increased customer satisfaction. A. How to administer Angolans Hospitals? According Serapioni [31] “various reasons contributed to the development of strategies for quality assurance, among them it is worth mentioning: i) the inadequate safety of health systems; ii) inefficiency and excessive costs of some technologies and clinical procedures; iii) the dissatisfaction of users; iv) unequal access to health services; v) the long waiting lists; vi) the unacceptable waste arising from the ineffectiveness. Finally, the finding of high variability of clinical and care practices - is among the same professions or between different geographical areas - as well as the variability of costs not always related to epidemiological or clinical factors, has been an important stimulus to enter the Review systematic quality of health care. " As we study the evaluation of hospital quality, Ferreira [32] states that it is necessary to make adjustments to the dimensions used to evaluate and measure quality of care. “Hospital performance measures based on patients have been defined and essentially designed to answer the following two questions: - What is the experience of a hospital patient? - How can we get reviews of hospital performance that reflect the concerns of patients?” [32]. In the Angolan case, what the current issues to be considered? Recently, the Angolan authorities were accused of incompetence in the management of hospitals. Denounced the lack of material for the surgeries, lack of water, lack of light, the fact of having hospitals that work by candlelight, among others. The real effort involves the action of municipalize health services [33]. In fact, the poor hospital management is a problem that has even caused many weaknesses in the hospitals of Luanda, a situation that has impoverished the public attendance. There is a lack of expertise in hospital management in Angola, mainly because they had few doctors, considering better get an administrator to the hospital and let the doctor make their technical functions. In this sense, the vice governor of Luanda for the social area, Jovelina Imperial says it is concerned by the Provincial Government to improve the working conditions of health professionals and providing means for quality care to the population. Luanda had a population growth that has not been accompanied by the health network. Therefore, the Provincial Government

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is working to build more municipal hospitals to decongest the main hospital units. There is also a shortage of skilled human resources for health, especially in the area of hospital management [34]. V. Conclusion Departing from the understanding that the skills and qualifications of employees of an organization resulting from the application of a set of practices and strategies, and is considered the fact that organizations with higher levels of skills are better prepared to adopt more effective measures for excellence in organizational performance, may be regarded as the leader of an organization must meet potential to influence the ability to achieve higher levels of performance [35] organizations. Following this, the organizational communication in an organization, will have greater chances of getting desired results without taking into account human capital as having a key role in the success of the strategic planning of any organization. Thus, possible deviations that hinder the achievement of established goals will be avoided. Organizational communication allows you to create a link between organizations, take them to integrate with the other and this is only possible through communication and communication [36]. It should be understood that the administrator of an organization must employ the various resources dedicated communication to achieve their goals and understand certain situations, since he must know how to think, act and needs of their customers (internal and external) . It is clear, too, that more and more organizations have been successful through valuation practices and employee involvement, serving thus to meet the objectives set by the organization. In order to improve the performance and communication, a company, even if only in a certain area, can and should improve with time, how to deal with their public. Therefore, you should act according to what each group needs and not according to how each client thinks. Various communication tools that are used to identify and meet customer needs in an organization to make this possible, there. The uncertainties and challenges that organizations face require them to mobilize all their resources, resulting in the Organizational Commitment, focusing on affective as a priority element that glorifies and encourages companies to develop authentic leaders increasingly a desired role . Finally, in the particular case of unit management Angolan health, must pass through managerial accountability in cleaning, planning, preventive maintenance of medical equipment, control of stocks of materials in order to avoid waste. The management also has an obligation to provide appropriate destination hospital waste and to promote campaigns for the control of epidemics. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

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F. Luthans and B. Avolio, “Authentic leadership: A positive developmental approach”. In K. S. Cameron, J. E. Dutton, & R. E. Quinn (Eds.), Positive organizational scholarship (pp. 241 – 261). San Francisco7 Barrett-Koehler, 2003. W. Gardner, B. Avolio, F. Luthans, D. May and F. Walumbwa, “Can you see the real me?” A self-based model of authentic leader and follower development. Leadership Quarterly, 2005, vol. 16, pp. 343-372. F. Walumbwa, P. Wang, H. Wang, J. Schaubroeck and B. Avolio, “'Psychological processes linking authentic leadership to follower behaviors', The Leadership Quarterly, 2010, vol. 21 no. 5, pp. 901-914. P. Northouse, Leadership: Theory and Practice. 5a ed. Thousand Oaks, CA: Sage Publications, 2007. B. Schneider, “Organizational Climate: An Essay”. Personnel Psychology, 1975, vol .28, pp. 447-479. R. Ilies, F. Morgeson and J. Nahrgang, “Authentic leadership and eudaemonic well-being: Understanding leader-follower outcomes. Leadership Quarterly, 2005, vol. 16, pp. 373-394. B. Avolio, F. Luthans and F. Walumbwa, “Authentic leadership: Theory-building for veritable sustained performance. Working paper. Gallup Leadership Institute, University of Nebraska, Lincoln, 2004. J. McColl-Kennedy and R. Anderson, 'Impact of Leadership Style and Emotions on Subordinate Performance', The Leadership Quarterly (Special Issue on Leadership Style and Emotions), 2002, vol. 13, n.º 5 (October), 545-559. C. Carapeto and F. Fonseca, F., Administração Pública – Modernização. Qualidade e Inovação. Lisboa: Edições Sílabo, 2ª ed., 2006. J. Kotter, Liderança e gestão de pessoas: autores e conceitos. São Paulo: Publifolha, 2002. B. Bass, Leadership and performance beyond expectation. New York: Free Press, 1985. J. Meyer, D. Stanley, L. Herscovitch and L. Topolnytsky, “Affective, continuance, and normative commitment to the organization: A meta-analysis of antecedents, correlates, and consequences”. Journal of Vocational Behavior, 2002, vol. 61, n.º 1, pp. 20-52. A. Cooper-Hakim and C. Viswesvaram, “The construct of work commitment: Testing an integrative framework”, Psychological Bulletin, 2005, vol. 131, n. º 2, pp. 241-259. J. Meyer and N. Allen N., Commitment in the Workplace: Theory, Research, and Application, Sage Publications, 1997. J. Mathieu and D. Zajac, “A review of meta-analysis of the antecedents, correlates and consequences of organizational commitment”, Psychological Bulletin, 1990, vol. 108, no. 2, pp. 171-194. J. Wahn, “Organizational Dependence and the Likelihood of Complying with Organizational Pressures to Behave Unethically", Journal of Business Ethics, 1993, vol. 12, pp. 245-251. R. Hackett, P. Bycio, and P. Hausdorf, “Further assessments of Meyer and Allen’s (1991) three-component model of organizational commitment, Journal of Applied Psychology, 1994, vol. 79, no. 1, pp. 15-23. C. Ichniowski, K. Shaw and G. Prennushi, “ The effects of human resource management practices on productivity: a study of steel finishing lines”. The American Economic Review, 1997, vol. 87, no.3, pp. 91-313. J. Paauwe and R. Richardson, Introduction special issue on HRM and performance. The International Journal of Human Resource Management, 1997. J. Guthrie, “The management, measurement and the reporting of intellectual capital", Journal of Intellectual Capital, 2001, Vol. 2 Iss. 1, pp.27 – 41.

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S. Ahmad and R. Schroeder, “The impact of human resource management practices on operational performance: Recognizing country and industry differences”. Journal of Operations Management, 2002, vol. 21, no.1, p. 19. H. Park, H. Mitsuhashi, C. Fey and I. Bjorkman, I. “The effect of human resource management practices on Japanese MNC subsidiary performance: a practical mediating model”. International Journal of Human Resource Management, 2003, vol. 14, no. 8, pp. 1391-1406. S. Kundu, M. Divya and O. Kumar, “Human resource management practices in shipping companies: a study”. Delhi Business Review, 2007, vol.8, no. 1. F.-H. Lee and F.-Z. Lee, “The relationships between HRM practices, leadership style, competitive strategy and business performance in Taiwanese steel industry”. In Proceedings of the 13th Asia Pacific Management Conference 2007 (pp. 953-971). Melbourne, Australia. C. Goodworth, Técnicas da Gestão Pessoal. 2.ª ed. Lisboa: Editorial Presença, 1993. Plano Nacional de Saúde 2011-2016, http://www.portaldasaude.pt/NR/rdonlyres/4DDFFD8C-EB94-4CAE-BCE4D95DE95EA944/0/PNS_Vol1_Estrategias_saude.pdf O. Ribeiro, F. Carvalho, L. Ferreira, and P. Ferreira, “Qualidade dos Cuidados de Saúde”, pp. 1-2, http://www.ipv.pt/millenium/Millenium35/7.pdf P. Sá and O. Sintra, “Modernização administrativa e gestão da qualidade: um estudo empírico nos municípios portugueses”. Notas Económicas, Junho 2008, p. 78. V. Reis, “Gestão em saúde”, Gestão de Serviços de Saúde, 2004, vol. 22, no. 1, p.9 — janeiro/junho, http://www.ensp.unl.pt/dispositivos-de-apoio/cdi/cdi/sector-de-publicacoes/revista/2000-2008/pdfs/1-01-2004.pdf M. Oliveira and E. Artmann. “Regionalização dos serviços de saúde: desafios para o caso de Angola”. Cad. Saúde Pública [online], 2009, vol.25, n.4, pp. 751-760. ISSN 0102-311X. M. Serapioni, M. “Avaliação da qualidade em saúde. Reflexões teórico-metodológicas para uma abordagem multidimensional”. Revista Crítica de Ciências Sociais, 209, vol. 85, pp. 65-82. P. Ferreira, “Definir a medir a qualidade de cuidados de saúde”. Revista Crítica de Ciências Sociais, 1991, no. 33, p.106. M. José, “Nos hospitais angolanos para além da incompetência há falta de tudo”, In Jornal Voz da América, 2013, http://www.voaportugues.com/content/hospitais-mal-adminsitrados-em-angola/1661669.html Jornal de Angola, “Má administração empobrece saúde”, 2011 http://www.portaldeangola.com/2011/09/ma-administracaoempobrece-saude/ M. Mendes, Estratégias organizacionais e práticas de recursos humanos: Estudo de caso na Universidade Estadual do Maranhão (Tese de mestrado não publicada), 2001. M. Kunsch, Planejamento de relações públicas na Comunicação Integrada. 3. ed. São Paulo, Summus, 1986.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net EXPERIMENTAL STUDY OF NON-DESTRUCTIVE TEST ON STEEL FIBRE REINFORCED CONCRETE Mandeep Singh Saroe1, Inderpreet Kaur2 Guru Nanak Dev Engg. College, Ludhiana (Punjab) Punjab Technical University, Jalandhar (Punjab) W.No.9, Vijay colony, Khamano, Distt. Fatehgarh Sahib (Punjab) INDIA __________________________________________________________________________________________ Abstract: Steel fibres are widely used in concrete as additional industrial materials for concrete products. Because of its huge application areas it keeps its popularity to be an academic research on developing the usage of them. As concrete is naturally weak in tension, steel fibres added will improve tensile strength of concrete. A convenient parameter describing the fibre is its aspect ratio which is defined as the fibre length divided by its equivalent diameter. The equivalent fibre diameter is the diameter of a circle having an area equal to the cross-sectional area of the fibre. Hooked steel fibres are used in the present study. This study investigates the effect of adding steel fibres with aspect ratio 80 and length 60mm on concrete properties in fresh and hardened states. The concrete are produced by fibres with varying volume percentages i.e., 0.5%, 1.0% and 1.5% by volume. At the end of the study, the effects of the mixes with the plain concrete and the correlation curve are obtained for compressive strength, flexural strength, rebound hammer and ultrasonic pulse velocity. The results have shown that the adding fibres to the concrete increase the compressive strength, flexural strength and rebound number on the other hand adding fibres decreases the workability and ultrasonic pulse velocity of concrete. Keyword: Steel fibres, Compressive strength, Flexural strength, Rebound hammer, Ultrasonic pulse velocity, Workability. ________________________________________________________________________________________ I. INTRODUCTION Steel fibre reinforced concrete (SFRC) may be defined as a composite materials made with Portland cement, fine and coarse aggregate, and incorporating discrete discontinuous fibres. When steel fibres are added to a concrete mix, they are randomly distributed and act as crack arrestors. Debonding and pulling out of fibres require more energy, giving a substantial increase in toughness and resistance to dynamic loads. SFRC has been used for a wide variety of applications, namely, pavements and overlays, industrial floors, hydraulic and marine structures, repairing and rehabilitation works. Steel fibres may be of different types i.e., straight steel fibre, hooked steel fibre, paddled steel fibre, crimped steel fibre, etc. In this research, mainly hooked steel fibres were used. In fresh state, high percentage of steel fibres reduces workability of concrete but increases the hardened properties of concrete i.e., compressive strength, flexural strength. So admixtures are added in SFRC for high workability. Non-destructive test are those in which there is no damage to the concrete. These tests are used to check the quality control or the resolution of doubts about the quality of materials. These tests are done on both old and new structures of concrete. This test also saves time and money. In this research mainly Schmidt rebound hammer test and Ultrasonic pulse velocity test were done. The Schmidt rebound hammer is principally a surface hardness tester. It works on the principle that the rebound of an elastic mass depends on the hardness of the surface against which the mass impinges. And, the ultrasonic pulse velocity test is a pulse of longitudinal vibrations is produced by an electro-acoustical transducer, which is held in contact with one surface of the concrete under test. According to [1], SFRC is used to improve the strength of concrete. And it has been recognizes that adding steel fibres to the concrete develops the mechanical properties of it. According to [2], proposed an equation to quantify the effect of fibre on compressive strength of concrete in terms of fibre reinforcing parameter. In their model, the compressive strength ranging from 30 to 50 MPa, with fibre volume fraction of 0%, 0.5%, 0.75% and 1% and aspect ratio of 55 and 82 were used. According to [3], were carried out a slump tests to determine the workability and consistency of fresh concrete. In their research slump changed due to different types of fibre content and form. According to [4], when the amount of steel fibres increased, i.e., more than 2%, the workability of concrete is decreased and for 0.5% steel fibres the workability is increased. According to [5], using steel fibres in concrete increases the rebound number. Although, adding two or three different sizes of steel fibres increase the rebound number. Increasing the amount of steel fibres increases the rebound number. According to [6], there is a relation between the rebound number and the quality of concrete

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which says that when rebound number is more than 40 the quality of concrete is very good, between 30 to 40 it is good, between 20 to 30 it is fair, less than 20 it is poor and when it is 0 it is very poor. According to [7], states that adding steel fibres in concrete reduces the pulse velocity, the higher the amount of fibre the lessens the pulse velocity. According to [8], there is a relation between UPV and quality of concrete which says that when UPV (km/s) is more than 4.5 the quality of concrete is excellent, between 3.5 to 4.5 it is good, between 3.0 to 3.5 it is doubtful, between 2.0 to 3.0 it is poor and less than 2.0 it is very poor. II. EXPERIMENTAL WORKS A. MATERIALS Cement: Ordinary Portland cement (O.P.C.) of 43 grade manufactured by Shree Ultra Fine Aggregates: crushed stone sand Coarse Aggregates: Coarse aggregates (angular type of 10mm size Steel Fibres: Hooked steel fibres were of 0.75mm diameter and 60mm in length and aspect ratio of 80. Water: Potable water Superplasticizer: CONPLAST SP-430 manufactured by FOSROC Chemicals (India) Methodology: The effect of hooked steel fibres on the compressive strength, flexural strength and workability of concrete was studied on M20 concrete mix. For compressive strength test, concrete cubes of size 100mm were casted and three samples of each proportion were tested after 3, 7 and 28 days. For flexural strength test, beam specimens of size 100mm x100mm x500mm were casted two samples at each proportion were tested after 3, 7 and 28 days. Workability of concrete mix at each proportion was tested by using slump test. After casting of these specimens it will be remove from moulds after 24 hours and it is cured for 3, 7 and 28 days in a storage tank of water. Different dosages (0.5%, 1.0% and 1.5% by volume fraction) were used for hooked steel fibres for all the above three types of tests. Non destructive test (NDT), i.e., Schmidt rebound hammer test and Ultrasonic pulse velocity test were performed on cubes and beams before the compressive strength and flexural strength were tested after 3, 7 and 28 days. III. RESULTS AND DISCUSSION The Effect of Steel Fiber on the Fresh Properties of SFRC: Workability of concrete decreases considerably by the use of steel fibres because the reason of lower slump is that adding SF can form a network structure in concrete, which restrain mixture from segregation and flow. For dosage of 0.5% of SF there is decrease in workability by 14%. For addition of 1.0% of SF there is decrease in workability by 32% and for addition of 1.5% of SF there is decrease in workability by 55%. Volume fraction (in %) 0 0.5 1.0 1.5

Slump (mm) 2nd 115 100 82 50

1st 120 105 80 55

Average Slump (mm) 118.33 102.33 80.33 53.67

3rd 120 102 79 56

Slump of concrete (mm)

Fig.1 Volume fraction V/s slump of concrete 140 120 100 80 60 40 20 0 0%

0.50%

1.00%

1.50%

Steel fibre content by Volume fraction (%)

The Effect of Steel Fiber on the Hardened Properties of SFRC: Compressive strength: The compressive strength is one of the most important properties of hardened concrete. Table (1) and Figure (2) show the compressive strength test at 3, 7 and 28 days. The results indicate that all specimens exhibited a continuous increase in compressive strength with progress in age. This increase in compressive strength with age is due to the continuity of hydration process which forms a new hydration product within the concrete mass. For dosages of 0.5% of hooked steel fibres there is gain in compressive

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strength of concrete by 13%. For addition of 1.0% there is gain in compressive strength by 15%. And for addition of 1.5% there is gain in compressive strength by 27%. Table 1 Compressive strength of all specimens Fibre content (%) 0 0.5 1.0 1.5

Compressive strength (MPa) 7 days 23.55 28.77 29.43 37.93

3 days 20.44 20.93 22.56 31.06

28 days 33.03 37.61 38.59 44.47

Compressive strength (MPa)

Fig. 2 compressive strength at age (3, 7 & 28) days 50 40 30 For 3 days 20

For 7 days

For 28 days

0 0

0.5

1.5

Steel fibre content by vol. (%)

Flexural strength: The results indicate that all specimens exhibited a continuous increase in flexural strength with progress in age as shown in Table (2) and Figure (3). The result showed that the benefit of steel fibers to improve of flexural strength. For dosage of 0.5% of hooked steel fibres there is gain in flexural strength by 8%. For addition of 1.0% there is gain in flexural strength by 13%. And for addition of 1.5% there is gain in flexural strength by 20%. Table 2 Flexural strength of all specimens Fibre content (%) 0 0.5 1.0 1.5

Flexural strength (MPa) 7 days 5.89 6.14 6.38 6.63

3 days 4.66 4.91 5.16 5.65

28 days 6.38 7.36 7.61 8.10

Fig.3 Flexural strength at age (3, 7 & 28) days Flexural strength (MPa)

10 8 6 For 3 days 4

For 7 days

For 28 days

0 0

0.5

1.5

Steel fibre content by vol. (%)

Rebound number: The results indicate that all specimens exhibited a continuous increase in compressive strength with progress in age as shown in Table (3) and Figure (4). This increase is due to the fibre-matrix bond between the concrete and the steel fibres. Although According to Kamran keikhaei (2012), using steel fibres in concrete increases the rebound number. Table 3 Rebound number of all cube specimens Fibre content (%)

3 days

0 0.5 1.0 1.5

22.95 26.36 27.15 27.06

Rebound number 7 days

24.82 28.15 28.65 29.11

28 days

Quality of concrete (Mishra 2012)

27.17 35.32 35.45 37.21

Fair Fair Good Good

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Fig.4 Rebound number of cubes at age (3, 7 & 28) days

Rebound number

40 30 20

For 3 days

For 7 days For 28 days

0 0

0.5

1.5

Steel fibre content by vol. (%)

The results indicate that all specimens exhibited a continuous increase in flexural strength with progress in age as shown in Table (4) and Figure (5). This increase is due to the fibre-matrix bond between the concrete and the steel fibres. Table 4 Rebound number of all beams specimens Fibre content (%) 0 0.5 1.0 1.5

Rebound number 7 days 30.94 34.82 35.04 35.48

3 days 27.62 29.75 29.86 30.73

Quality of concrete (Mishra 2012) Good Good Good Good

28 days 37.73 42.09 43.42 44.94

Fig.5 Rebound number of beams at age (3, 7 & 28) days

Rebound number

50 40 30

For 3 days

For 7 days

For 28 days

0 0

0.5

1.5

Steel fibre content by vol. (%)

Ultrasonic pulse velocity: Ultrasonic pulse velocity increased with the increase in curing age of SFRC mixes. The results of ultrasonic pulse velocity at (3, 7 and 28 days) are presented in Table (5), (6) and Figure (6), (7). It can be seen that introducing steel fibers negatively affected the ultrasonic pulse velocity. This might be attributed to the increase of the amount of entrapped air voids due to incorporation of fibers into the mixes. Besides, the fibers inside cube were randomly oriented, when the wave pass through the fibers the wave maybe deflected to other directions rather than pass straight forward to the end of the cube. Table 5 UPV of all cubes specimens Fibre content (%) 0 0.5 1.0 1.5

Ultrasonic pulse velocity (km/s) 3 days 7 days 4.65 5.53 4.43 4.84 4.36 4.66 4.32 4.48

Quality of concrete (IS:13311_1-1992) Excellent Excellent Excellent Excellent

28 days 5.98 5.41 5.29 5.27

Fig.6 UPV of cubes at age (3, 7 & 28) days

UPV (km/sec)

8 6 4

For 3 days

For 7 days For 28 days

0 0

0.5

1.5

Steel fibre content by vol.(%)

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Table 6 UPV of all beams specimens Fibre content (%) 0 0.5 1.0 1.5

3 days 4.81 4.73 4.37 4.23

Ultrasonic pulse velocity (km/s) 7 days 28 days 5.21 5.32 5.20 5.27 4.98 5.02 4.94 4.98

Quality of concrete (IS:13311_1-1992) Excellent Excellent Excellent Excellent

Fig.7 UPV of beams at age (3, 7 & 28) days 6 UPV (km/sec)

5 4 3

For 3 days

For 7 days

For 28 days

0 0

0.5

1.5

Steel fibre content by vol.(%)

Relationship curves: The relationship curve between compressive strength and flexural strength is pooled together for all results in Figure (8). The relationship curve between compressive strength and rebound number is in Figure (9). The relationship curve between flexural strength and rebound number is in Figure (10). The relationship curve between compressive strength and UPV is in Figure (11). And the relationship curve between flexural strength and UPV is in Figure (12).

Flexural strength (Mpa)

Fig.8 Relationship between Compressive strength and flexural strength 9 8 7 6 5 4 3 2 1 0

y = -0.0001x4 + 0.0136x3 - 0.6634x2 + 14.161x - 105.88 R² = 0.927

Compressive strength (Mpa)

Fig.9 Relationship between compressive strength and rebound number 50 45 40 35 30 25 20 15 10 5 0

y = 1E-04x6 - 0.0156x5 + 0.9948x4 - 32.915x3 + 592.83x2 - 5458.5x + 19746 R² = 0.8303

Rebound number

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Fig.10 Relationship between Flexural strength and rebound number 9 y = 0.1801x - 0.0999 R² = 0.9461

8 Flexural strength (Mpa)

7 6 5 4 3 2 1 0 0

Rebound hammer

Compressive strength (Mpa)

Fig.11 Relationship between Compressive strength and UPV 50

y = 260.83x5 - 6544x4 + 65425x3 - 325839x2 + 808467x - 799558 R² = 0.6033

45 40 35 30 25 20 15 10 5 0 0

UPV (km/s)

Fig.12 Relationship between Flexural strength and UPV 9 y = 373.73x6 - 10583x5 + 124667x4 - 781942x3 + 3E+06x2 - 5E+06x + 4E+06 R² = 0.6798

Flexural strength (Mpa)

8 7 6 5 4 3 2 1 0 0

UPV (km/s)

The statistical analysis of the regression models are mentioned below: 1. Relationship between compressive strength and flexural strength y = -0.0001x4+0.0136x3-0.6634x2+14.161x-105.88 R2 = 0.927 2. Relationship between compressive strength and rebound hammer y = 1E-04x6-0.0156x5+0.9948x4-32.915x3+592.83x2-5458.5x+19746 3. Relationship between flexural strength and rebound hammer y = 0.1801x-0.0999 R2 = 0.9461 4. Relationship between compressive strength and UPV y = 260.83x5-6544x4+65425x3-325839x2+808467x-799558

R2 = 0.8303

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R2 = 0.6033 5. Relationship between flexural strength and UPV y = 373.73x6-10583x5+124667x4-781942x3+3E+06x2-5E+06x+4E+06 R2 = 0.6798 From these regression models the correlation coefficient R2 i.e., 0.927, 0.8303 and 0.9461are very good polynomial relations and they gives the almost accurate results. Whereas, the correlation coefficient R 2 i.e., 0.6033 and 0.6798 are the highest degree order polynomial relations but they are not gives the accurate results as compared to the R2 i.e., 0.927, 0.8303 and 0.9461 because the UPV results are decreasing due to the steel fibres in concrete because it reflects the waves to the opposite direction rather than passing through the concrete.    

IV. CONCLUSION: The slump decreases with the increase in SF content of the concrete mixtures with respect to plain concrete mixtures by 55%. The addition of 1.5% SF, the maximum increase in compressive strength and flexural strength was observed to be around 27% and 20% with the SF compared to the reference concrete. Rebound number of concrete increases up to 20% by increasing the percentage of steel fibre by 1.5%. UPV decreased with including SF in concrete by 10%. REFERENCES:

[1] [2] [3] [4] [5] [6] [7] [8]

Chalioris, C.E. & Sifri, E.F., 2011. “Shear Performance of Steel Fibrous Concrete Beams”. In The Twelfth East Asia-Pacific Conference on Structural Engineering and Construction. China, 2011. Nataraja M.C., Dhang, N. and Gupta, A. P. (1999), “Stress-strain curve for steel fibre reinforced concrete in compression”, Cement and Concrete Composites, 21(5/6), pp 383- 390. Chen B, Liu J (2000),”Contribution of hybrid fibres on the properties of the control concrete hybrid fibres. Cem. Con. Comp”. 22(4): 343-351. Tayfun, U., 2010. Effect of fibre type and content on bleeding of steel fibre reinforced concrete. Construction and Building Materials, pp.766-772. Kamran Keikhaei, 2012, Properties of concretes produced by single and combined hooked end discontinuous discrete steel fibres. Mishra, G. (2012). Rebound Hammer Test. Retrieved May 10, 2012. From World Wide. Abbas AL-Ameeri (2013),” The effect of steel fibre on some mechanical properties of self compacting concrete”, American Journal of Civil Engineering, Vol. 1, No. 3, 2013, pp. 102-110. Whitehurst E.A. (1951). Soniscope tests concrete structures. J. Am. Concr. Inst. 47. pp 443-444.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net CONCEPTUALISATION OF BUSINESS RULES 1

Rakhi Dewan, 2 Rekha Sachdeva. Department of Business Administration, SUSCET, Tangori Mohali, Punjab, India . 2 MRCE, Sector 43, Surajkund Badhkal Road, Faridabad 121001, Haryana, India. ______________________________________________________________________________________ Abstract. The Business Rule Revolution is happening everywhere, even if it seems invisible. An organization aiming to better manage its important business rules needs a goal, a roadmap, and a plan for action. We propose a four step evolvement of business rules starting from conceptualization to representation to stimulation till validation. First phase named as conceptualization includes how the business rules are to be generated and what is to be the source of rules. Representation discusses what part of rules. The next step would be the simulation of the rules and in the end validation of the rules. This paper concentrates on the conceptualization of the business rules. In this paper we will be discussing the framework of conceptualization of business rules. Keywords: Business rules, Conceptualization, Business Rule Management, _______________________________________________________________________________________________ 1.

I. Introduction The need of business to be more extensible and flexible is the most sought after for the last so many years. To meet these demands two fundamental different approaches emerged on different independent platforms. Both the approaches survived and remained self contained without developing and deploying each other. These two approaches are now days commonly known as Business Process Management and Business Rule. Overtime various authors have produced different definitions of the term business rule. According to the Business Rules Group, the first authoritative definition of the term 'business rule’ appeared in the seminal 1995 report of the GUIDE Business Rules Project, entitled "Defining Business Rules—What Are They Really?" This definition reads: "A business rule is a statement that defines or constrains some aspect of the business. It is intended to assert business structure or to control or influence the behavior of the business" The same definition appeared in the Business Rules Group's Final Report of July 2000. In 2001, von Halle defined a business rule as "a condition that govern(s)…. business event(s) so that (they) occur in such a way that is acceptable to the business." In 2008, the authors of the SBVR chose to elaborate on this definition rather than the more general one and defined a business rule as "a rule that is under business jurisdiction..... The semantic community can opt to change or discard the rule. Laws of physics may be relevant to a company (or other semantic community); legislation and regulations may be imposed on it; external standards and best practices may he adopted. These things are not business rules from the company's perspective" All organizations are subject to the laws of physics. Therefore, since these cannot be violated, it might seem that an organization does not need to document such rules or include code in its application systems to ensure that such rules are not violated. However, many laws of physics need to be taken into account when establishing the rules governing the capture of information about the real world. Consider a college timetable system in which college days are divided into periods of, say, 55 min each and in which groups of students are assigned to a particular classroom. At least two laws of physics need to be taken into account in such a system. First, a person, or for that matter any concrete object, cannot be in more than one location at the one time. Our college timetable system should therefore prevent any teacher being timetabled to be in more than one classroom during the same timetable period. Second, time is unidirectional. Nothing can therefore finish before it starts. Our college timetable system should therefore also prevent a timetable period being defined with an end time earlier than its start time. The college can, of course, "opt to change or discard” the rules that ensure that information representing such impossible situations cannot be entered into the timetabling system; these rules would then qualify as business rules according to the SBVR definition. The business Rules has basically two approaches: technology approach and methodology approach. Where business rule technology approach is many decades old, methodology approach has gained momentum recently. Methodology approach discuss what, why, when and how of business rules. We propose a four step evolvement of business rules starting from conceptualization to representation to stimulation till validation. First phase named as conceptualization includes how the business rules are to be generated and what is to be the source of rules. Representation discusses what part of rules. The next step would be the simulation of the rules and in the end validation of the rules as shown in Figure 1. First phase, which is the subject matter of this paper named as conceptualization includes how the business rules are to be generated and what is to be the source of rules.

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Conceptualizati on

Validation

Representation

Simulation

Figure 1

II. Conceptualization Business rules strongly relate to several concepts of the business. These relations can be seen in the layered structure shown in Figure 2.

Figure 2 For example in an educational institution rule for admission as lateral entry is as follows: Lateral entry to a program shall be allowed after approval of Academic Council of University under following cases: 1. Lateral entry schemes as per AICTE/UGC 2. Transfer cases as approved 3. Migration cases (National) 4. Migration cases (International) 5. Any other case such as branch change etc. Credits and Course Mapping The Academic Council shall look at the courses, successfully completed by the candidate before applying for lateral entry to a program. The courses shall be compared to the existing courses of the program and equivalence shall be drawn. The council shall define the following: 1. Equivalent courses (EC) that the candidate may be declared as having already completed. 2. Overlapping courses (OC) whose contents have major overlapping with the courses for which the candidate has already been granted credits and therefore the same cannot be opted for by the candidate in future. 3. New Elective Courses (NEC) which do not map with any of the courses of the program but can be treated as elective in nature, fulfilling the objective of the program. 4. Bridge Courses (BC) that the candidate should do to bridge the academic gap. All bridge courses prescribed to a candidate shall be

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 

‘Core’ in nature in the sense that the candidate should pass these to become eligible for the award of the degree. Non-credit courses.

The Academic council shall define the complete matrix stating the courses mapped and credits awarded/to be awarded to candidate for EC & NEC. Terms. The above exemplary business rules make use of the words courses, candidates, credit etc. Each business rule associates a particular standardized meaning with this word. Consequently, in order to avoid ambiguities and provide consistency for the whole business, these special words, called terms have to be defined properly. These terms build a foundation for the other concepts, like shown in Figure 2. For example candidate here is defined a person who applies to the institute for a Course. Facts. In exemplary business rules, the term course relates to another term candidate. These relations between terms are called facts. Facts can set arbitrary many terms into relation. Facts do not define any constraints for these relations, but rather a general connection between terms. In this example a fact is for example a course is in relation to a candidate or a candidate has a credit. Instance Model. All the terms and facts are comprised in the fact model or instance model which might be presented textually or visually. For example following may be the instances in an academic institution registration process. A candidate may be internal or external External candidate may be vertical or horizontal If vertical entry, than candidate may have to clear one of three courses to be treated as Bridge Course (BC). If horizontal entry, candidate may or may not have the same line of the courses as in the institution. If candidate has cleared an extra course which is not in the institute syllabus if it is to be treated as Equivalent courses (EC) or Over lapping Course (OC). Business Rules: After defining facts and terms now the business rule come for work upon. Business rules can be defined as possible and logical statements that constrain possible instance models, derive particular parts of the instance models according to a derivation instruction, or advice how the instance model should look like. In the above example one business rule may be emerged as, A candidate of lateral entry has to clear a bridge course which will be core and non credit course. But while discussing business rule it has to take into consideration that business rules should be unambiguous and consistent. In other words it should have one interpretation and should not contradict with another rule. III. Conclusion After conceptualizing the business rule, representation part come into play. Representation can be done through text language or some graphic method. Validation and simulation of the business rule is to be done in a real life situation. A final advantage of the proposed approach concerns rule management. The concept of rule management is closely related to the efficient organization and retrieval of business rule expressions. Bibliography 1. 2. 3.

Business Rules Group, “Business Rules Manifesto The Principles of Rule Independence”, Version 2.0, Ross R. G. (ed.) November 1, 2003, available at www. Business Rules Group.org. Halle, B. v. (2006). The Essential Business Rule Roadmap. In B. v. Halle, The Business Rule Revolution (pp. 3-16). Silicon velly: Happy about. The Business Rule Approach, Eduard Bauer, University of Paderborn.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Research on fluid in an open container with a complex force field applied Penev Mariel, Stoyanov Ivaylo Department “Machine Components” Technical University of Gabrovo, Bulgaria Abstract: This article is a study of the behavior of fluid under the influence of a force field. Examined is the behavior of the fluid during the reciprocal linear movement, by simulating different laws of acceleration of various sizes. Conclusions are made for the application of this study. Keywords: CFD, Simulation, VOF, Slosh, Field of Force. I. Introduction In modern devices for packaging fluids require the use of positioning systems. These in turn are influenced by the behavior of the fluid in the container undergoing complicated force field. In [3] Armenio and La Rocca, is considered modeling of fluid in two-dimensional space by Reynolds Averaged Navier-Stokes Equations (RANSE) for incompressible flows and shallow water equations for surfaces - Shallow Water Equations (SWE). These problems are solved numerically FEM. Digital solutions compared to the experimental show that the model RANSE is closer to the real model than SWE [4], is considered a free surface with a moving range. Digital method requires working with two streams that are incompressible or one is incompressible, and the other - a collapsible. Move grid is used to clarify the boundaries of the field. The technique (volume-of-fluid - VOF) is used to track the boundaries of the two fluids. In [5] describes the potential flow equations using Laplace. The dynamic boundary conditions at the free surface is used so-called damped modified equation of Bernoulli, which is included the viscosity of the fluid. This method is solved by BEM (boundary element method). The boundary of the free surface is adjusted by the movement of the free surface. II. Summary The movement of the fluid is described by the equations of the Navier-Stokes [1, 2]. They are a set of nonlinear partial differential equations, in his first equation represents the law of conservation of mass (equation of the continuous environment): (1) where ρ is the density of the fluid, ν=(νx,νy,νz) - velocity vector of the fluid in the spatial coordinate system, (x, y, x). Here the law of conservation of energy is given with an equation having four unknowns. More conditions are required, assuming that the density is known. These conditions are given by Newton's second law, giving three equations. For viscous fluids, while retaining the currently recorded: (2) where: p is pressure; β coefficient of volume contraction; μ - viscosity, F=(Fx, Fy, Fz) – intensity of mass force. If the fluid is ideal and incompressible, ie: (β = 0 and μ = 0) the equations of Navier-Stokes transform into Euler’s equations: (3) (4) In the expression (4), vector forces, such as is converted in the type:

and taking account of the potential external (5)

The value of the velocity potential

 is determined by the expression: (6)

After substitution of (6) to (3) to be recorded: , which is the familiar equation of Laplace. After substitution of (6) and integration of the equation is obtained Bernoulli’s equation:

(7) × v = 0 in the (5) and subsequent (8)

where: C(t) is a random function of time.

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Figure 1 Diagram of the container

Figure 2 Correlation between the two coordinate systems

Further approximations can be made for the width and depth of the container. Horizontal velocity of the fluid is assumed to be independent of the vertical position. This is described by equations of the thin film fluid. The limit of the fluid are considered walls of the container, the bottom and the free surface. Condition of boundary walls and bottom of the container is zero velocity in the direction normal to them ie: (9) where n normal vector to the wall. Fig.1 shows a diagram of the container. Fluid surface z = η(t, x, y) has two components - one is dynamic, and the other - kinematics. Dynamic component arises from external forces acting on the surface and can be expressed by the equation of Bernoulli: . (10) Kinematic components defines a fluid particle on the free surface, i.e.: (11) Consider the motion of a fluid under the influence of horizontal acceleration in open container. The container has a rectangular cross-section with a length a, width - b and height of the fluid - h and accelerates horizontally, parallel to two of its walls (see Figure 1.). The movement of the container is parallel to two of the walls, it is assumed that the flow velocity is zero in a direction perpendicular to the movement. Then solve the problem can be seen in two-dimensional space. Used a fixed (absolute) coordinate system ( , ) and relative coordinate system (moving together with the container) (x, z), the relationship between coordinate systems is , where: xc position of the container, e* corresponding basic vectors. FIG. 2 shows the relationship between the two coordinate systems. The fluid is accepted as incompressible and ideal. Surface level should be , and defines from . The forces acting on the fluid are gravity and horizontal acceleration. Gravity force , and horizontal acceleration is defined by the expression: . Transformation to relative coordinate system is: (12) Equations (7), (8), (9), (10), (11) и (12) describe the flow of fluid in the container (13) (14) (15) ,

(16)

where the expressions (14) and (15) describe the free surface of the fluid i.e. η(t, x)) и u  t   xc - horizontal acceleration of the container. It’s solved by separating the variables, substituting i.е.:

in (13) and is seeking of type (17)

The following equations are derived from (16) and (17) i.е. (18)

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(19) Solutions of the expressions (18) and (19) are: After entering the boundary conditions:

Boundary conditions are:

determine the harmonic flow: ,

Individual harmonics are determined by the expressions: ; (21)

The potential given in (20) and (21) is defined: (22) wherein all constants are collected together in Tn (t). Function Tn (t) can be found from (14). It is assumed that the speed of the power flow is low, and in the square (14) is even smaller. Furthermore, the pressure is evenly across surface, thus to C(t)=p[t, x, η(t, x)]/ρ: (23) After differentiation of (23) over time After substituting (15) and neglecting all nonlinear members and the assumption that i.е.:

is very small

Replace (22) to give: (24) т.е.:

Decomposes right side baseline function

For the differential equation (24) after decomposition by the cosine function is obtained: при ,

It should be noted that the applied horizontal acceleration excites only odd harmonics. Modification of the surface of the fluid relative to the equilibrium position can be determined by (23), i.e.: (25) Assuming that the increase in the surface of the fluid from the equilibrium position is not significant η(t,x) ≈ 0 and after substitution with potential   t , xm , 0  to give:

(26) where: Combining the input and output equations defines the model: (27) where

is the differential operator

Substitution coefficients

bn и cn  xm  therefore: (28)

The first member of the sum is the order of the cosine in the form is raised above the point for a rectangular section of the container, ie.:

, сtherefore, the surface of the fluid (29)

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III. Numerical experiments In the numerical experiments made a glass container is used in which it is poured fluid (water) – the length of the fluid is , and the height (see fig. 3a). Figure 3. General formulation of the research:

Figure 4. Material characteristics of the fluid and the open container

а) Geometry of the fluid б) Height of Fluid

а)

б)

The material properties of the fluid (water) and the open container (glass) are shown in Fig. 4. To study the sloshing into an open container is necessary that it is partially filled. This must be specified by setting the initial state of the fluid in Height of Fluid (HOF). Define the initial conditions for the height of the fluid (Fig. 3b), requires the use of separate volumes for both it and the volume located above it, where the fluid enters. The open container is moving linearly with velocity , at a distance . For this numerical study of the behavior of interest is fluid due to the different laws of acceleration shown in Fig. 5, The maximum value of the applied acceleration is . Figure 5. Laws of the acceleration applied in the numerical experiment

а)

The results of the numerical experiments are shown graphically in FIG. 6. FIG. 6a has shown in the pressure of application of the acceleration of the type on Fig. 5c The change in pressure depending on the acceleration applied to the type of Fig. 5b compared with the acceleration and the sinusoidal law (8.sin (2π.t) shown in Fig.. 6b. After applying the acceleration to a rectangular law see Fig. 5e,f the pressure change shown in fig.6c, if law of acceleration is linear according to Fig. 5d then the pressure changes according to the graph in Fig. 6d. Figure. 6 The change in pressure during acceleration shown in Figure 5 250

250 150

150 Pressure

Pressure

200

100 50

50 -50 -150

0 -50

-250

100 Step Acc - a)

150

Acc - c)

200

а)

100 Step Acc - b)

Acc - 8.sin(2 π.t)

150

200

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200

Pressure

150 100 50 0 -50 0

100 Step Acc-e)

150

90 80 70 60 50 40 30 20 10 0

200

Acc - f)

100 Step

150

200

Acc - d)

Using the technique of Volume of Fluid (VOF) numerical modeling results can be displayed and shown in Fig. 7. Study was done in “reciprocating with half period time equal 2 seconds” move to distance 0,2 m. with acceleration applied to the type of Fig. 5c. FIG. 8 shows the pressure variation depending on the magnitude of the acceleration during the acceleration of the type on Fig. 5c. Figure 7 Volume of Fluid (VOF) on one of the numerical studies.

200

150

100

Pressure

Figure 8. Pressure change depending on the magnitude of the applied acceleration 200

0 -50

-100

-150

-200

u=1 ms^-2

u=2 ms^-2

100 Step

120

u=3 ms^-2

140

160

180

200

u=5 ms^-2

100 Step u=8 ms^-2

120

140

160

180

200

u=10 ms^-2

IV. Conclusions The analytical conclusions made may be used to study sloshing on fluid located in an open container under the influence of a complex force field, which can be relevant for the packaging machines and systems for finding the optimal amplitudes of splashing depending on the type and magnitude of the applied acceleration, and also the viscosity of the fluid. From the completed studies is seen that the least pressure <78 Pa is observed in the linear acceleration Fig. 5. When applying different sized accelerations (Fig. 8) shows that the peaks of pressure in the time delay, and this most clearly appears when accelerations are greater than . V. References [1] Madzhirski C. H., Hydrodynamics, Sofia, Technics, 1979. [2] Povh I.L. Tehnicheskaya Fluid Mechanics, Mechanical Engineering, L. 1976 [3] Armenio, V. and M. La Rocca (1996): “On the analysis of sloshing of water in rectangular containers: Numerical study and e perimental validation.” Ocean Engineering, 23:8, pp. 705–739. [4] M.Penev, H. Slavchev, N. Marchev, “Conduct Container with Fluid in Comple ity a Fluid of Force”, International Science Conference Unitech’06 Gabrovo, vol. II., ISBN 13: 978-954-683-352-5, 24-25 november 2006, pp. II-422 … II-426 [5] Kelkar, K. M. and S. V. Patankar (1997): “Numerical method for the prediction of free surface flows in domains with moving boundaries.” Numerical Heat Transfer, Part B: Fundamentals, 31:1, pp. 387–399. [6] Romero, V. J. and M. S. Ingber (1995): “A numerical model for 2D sloshing of pseudoviscous liquids in hori ontally accelerated rectangular containers.” In Brebbia et al., Eds., Boundary Elements XVII, pp. 567–583. Computational Mechanics Publications, Southampton, England.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Analysis of Strategic Success for an Automobile Manufacturing Unit Chandan Deep Singh1* and Jaimal Singh Khamba2 Assistant Professor, Department of Mechanical Engineering, Punjabi University, Patiala – 147002, Punjab, India 2 Professor, Department of Mechanical Engineering, Punjabi University, Patiala – 147002, Punjab, India _______________________________________________________________________________________ Abstract: Strategies are actions a business takes to compete more aggressively, to acquire additional customers and to operate the company more profitably. A successful strategic plan provides the information and guidance the management team needs to run the company with greater efficiency and help the business reach its full potential. Strategic planning helps managers make decisions based on logical assumptions and a clearer view of the future. Strategic Success of the industry relegated to the profitability, market share, growth and expansion, quality and reliability, labour intensiveness, etc. For accomplishing the success set parameters, the operations strategy links long- and short- term operations decisions to corporate strategy, which is composed of Core Competencies - these are the unique resources and strengths of the organisation, which include workforce, facilities, market and financial know-how, and systems and technology. This work correlates various strategic success issues and sub issues and their reliability based on responses from various industries. Thus helping in knowing the importance of these issues for an automobile manufacturing unit. 1

Keywords: Strategy, Strategic Success, Manufacturing Excellence, Manufacturing Strategy. __________________________________________________________________________________________ I. Introduction The word "strategy" comes from the Greek word for "generalship". Like a good general, strategies give overall direction for an initiative. A strategy is a way of describing how you are going to get things done. It is less specific than an action plan (which tells the who-what-when); instead, it tries to broadly answer the question, "How do we get there from here?" A good strategy will take into account existing barriers and resources (people, money, power, materials, etc.). It will also stay with the overall vision, mission, and objectives of the initiative. Often, an initiative will use many different strategies--providing information, enhancing support, removing barriers, providing resources, etc.--to achieve its goals. (http://en.wikipedia.org/wiki/strategy). An organization's strategy that combines all of its marketing goals into one comprehensive plan. A good marketing strategy should be drawn from market research and focus on the right product mix in order to achieve the maximum profit potential and sustain the business. The marketing strategy is the foundation of a marketing plan. (http://www.businessdictionary.com/). Developing strategies is really a way to focus your efforts and figure out how you're going to get things done. By doing so, you can achieve the following advantages:  Taking advantage of resources and emerging opportunities  Responding effectively to resistance and barriers  A more efficient use of time, energy, and resources Developing strategies is achieved by VMOSA (Vision, Mission, Objectives, Strategies, and Action Plans) process outlined at the beginning of this chapter. Developing strategies is the essential step between figuring out your objectives and making the changes to reach them. Strategies should always be formed in advance of taking action, not deciding how to do something after you have done it. Without a clear idea of the how, your group's actions may waste time and effort and fail to take advantage of emerging opportunities. Strategies should also be updated periodically to meet the needs of a changing environment, including new opportunities and emerging opposition to the group's efforts. (http://www.businessdictionary.com/) II. Literature Review Strategic Success in present turbulent times increasingly depends on competitiveness. Competitiveness comes through an integrated effort across different manufacturing functions and deployment of advanced manufacturing technologies. Advanced manufacturing technology plays a major role in quality and flexibility improvements in manufacturing organizations (Dangayach et. al, 2006). The authors provided a picture of maintenance management in Italian manufacturing firms supported by empirical evidence (Chinese and Ghirardo, 2010). The relationship between various factors influencing the implementation of TQM and TPM thus the manufacturing strategies for different approaches in an Indian context: TQM alone; TPM alone; both TQM and TPM together (Seth and Tripathi, 2006).

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(Schlie, 2000) raised the issue of company strategies according to regional and global requirements. The presented evidence suggests that there are some valid reasons for companies to follow an eclectic course of regionalization as well as globalization. In the context of the automotive industry, however, the preliminary findings suggest that a car producer should first become a global company, in order to efficiently and selectively regionalize in a second step. Overall, regional strategies could be associated with later, rather than earlier, stages in the evolution of a company’s global strategy. (Reed and Walsh, 2000) stated that a strategic approach to technology acquisition will become increasingly vital to manufacturing SMEs, and needs to be recognized as a key competence. (Dangayach and Deshmukh, 2000) A model is proposed linking the manufacturing competitive priorities and the action plan pursued by manufacturing firms. (Lazim and Ramayah, 2010) The author focused on improving equipment effectiveness, productivity, workplace safety and environmental issues. The manufacturing function can be a formidable weapon to achieve competitive superiority. Maintenance has become more challenging in the current dynamic business environment. (Chang et al., 2005) identified the habitual expression modes used by individuals when conveying their desires for product forms. (Jones and Parker, 2004) the author considered the strategic operations in which the firms have developed and adopted a strategic approach onto how they manage their operations strategically. (Demeter, 2003) described importance of manufacturing strategy (MS) and emphasized many theoretical concepts, frameworks, and models. Intuitively, it seems obvious that a smoothly running production system will have a positive influence on business performance. (Amoako-Gyampah and Acquaah, 2008) examined the relationship between manufacturing strategy and competitive strategy and their influence on firm performance. The test how competitive strategy influences manufacturing strategy and also examine the impact that manufacturing strategy and competitive strategy have on firm performance among Ghanaian manufacturing firms. (Lee & Yang, 2011) discussed the effect of organization structure and competition on the design of performance measurement systems (PMSs) and their joint effects on performance. (Terziovski, 2006) compared the strength of the relationship between quality management practice and two key operational performance measures: productivity improvement and customer satisfaction. (Sharma et al., 2008) proposed a new framework for manufacturing excellence using the comparative analysis of the existing frameworks along with the domain knowledge of the concept of manufacturing excellence. Manufacturing excellence means to be the best in the field at each competitive priority and to demonstrate industry best practices. (Fredriksson, 2004) analysed and compared the internal, supply and customer side conditions that different organizational forms provide for module assembly units’ performances. (Zhang et al., 2003) Manufacturing flexibility is strategically important for enhancing competitive position and winning customer orders. It describes a framework to explore the relationships among flexible competence, flexible capability and customer satisfaction. (Singh et al., 2010) described the status of manufacturing enterprises and examined the roles of government policies and strategy development for competitiveness. (Subramoniam Ramesh et al., 2009) stated that the Remanufacturing is an industrial process whereby used products referred to as cores are restored to useful life. (Laosirihongthong and Dangayach, 2005) focused on competitive priorities of companies in India and Thailand manufacturing strategies implementation. The results indicated that competitive priorities of companies in both countries are improving product and process-related quality and on-time delivery. III. Factors: Based on the literature studied, following factors have been finalized: 1. Strategy Agility 2. Management 3. Teamwork 4. Administration 5. Interpersonal IV. Analysis This section presents the “analysis and results” of strategic success of automobile industry. The following classification of the section is based on the analysis performed for attaining the desired objectives of the research study. SPSS 21.0 has been used as the statistical tool for applying various techniques. Various statistical techniques applied in this analysis are: Croanbach alpha, Percent Point Score, Central Tendency and Correlation. A. Response Analysis 1. Strategy Agility Table– 1 depicts the performance of manufacturing organizations regarding the Strategy Agility.

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Table 1: Response Analysis of the Respondents on Strategy Agility No. of Companies Scoring Points

Total No. of Responses

Total Points Scored

Percent Points Score

(TPS)

(PPS)

Central Tendency TPS/N

S. No

FACTORS

(N)

TPS 100 4* N

Quality conformance

118

316

66.9

2.68

Improving Customer Base

118

294

62.3

2.49

Developing and enhancing Market Share

118

307

65.0

2.60

Achieving higher profit

118

337

71.4

2.86

Competitive Pricing of the products

118

302

64.0

2.56

65.92

2.64

(Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

The close analysis of various issues related to maintenance organization reveals that most of the organizations have generally scored quite low rating (percent point scored ‘PPS’) regarding major strategy agility issues. The data shows that most of the organizations press for achieving various strategic issues like quality conformance, customer base, competitive pricing, market share, profit. The response analysis results showed that the under strategy agility based on the idea that “achieving higher profit” in the organization was given maximum weightage which was followed by the idea based on quality conformance and developing and enhancing market share. In last almost similar extent of weightage was given in the surveyed organization regarding competitive pricing of the products, while least weightage was on the improving customer base. The analysis showed regarding the issues based on the strategy agility i.e. quality conformance and improving customer base, 50.8% and 48.3% of the organizations were implementing them reasonably well whereas 28.0% and 39.8% of the organizations reported that they were implementing them at some extent. On the issue of development and enhancing of market share, 38.1% and 35.6% of the organizations were implementing this concept at either some extent or at reasonable level while 16.9% organizations at great extent. Competitive pricing of the products concept was implemented at some extent in 040.7% of the organizations, while 24.7% and 22.0% of the organizations were implementing this concept at either at reasonable level or at great extent. 2. Management Table – 2 portrays the performance of manufacturing organizations regarding the issues related to Management. Table 2: Response Analysis of the Respondents on Management No. of Companies Scoring Points

Total No. of Responses

Total Points Scored

Percent Points Score

Central Tendency TPS/N

S. No 1

FACTORS

(TPS)

(PPS) TPS 100 4* N

(N)

118

336

71.2

2.85

118

342

72.4

2.89

118

267

56.6

2.26

118

257

54.4

2.18

Enhanced production capabilities and improved control Better Production Planning and Control Functions Information Flow within departments through intranet Information analysis in different departments Risk Management

118

242

51.3

2.05

Crisis Management

118

255

54.0

2.16

Co-ordination between departments

118

330

70.0

2.80

61.41

2.46

2 3 4

(Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

The response analysis results showed that the under management parameter of the strategic success based on the idea that “better production planning and control functions” in the organization was given maximum weightage which was followed by the idea based on enhanced production capabilities and improved control and co – ordination between departments. The scope of the information flow within the departments through intranet was also given preferences in the organizations, while somewhat equal importance’s was also shared in between the concepts based on information analysis in different departments and crisis management. The least weightage was on the risk management. The analysis of the above table showed regarding the issues based on the

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management i.e. enhanced production capabilities and improved control, information analysis in different departments, co – ordination between departments and better production planning and control functions, 41.0 – 46.0% of the organizations were implementing them at reasonably well rate whereas 29.0 – 33.0% of the organizations on the similar issues pertaining to management was being following them at some extent respectively. Also it was followed that 20.0 – 22.0% of the organizations were implementing the issues of enhanced production capabilities and improved control, co–ordination between departments and better production planning and control functions, at great extent. 3. Team Work Table – 3 represents the performance of manufacturing organizations regarding the Team Work issues. The close analysis of various issues related to maintenance organization reveals that most of the organizations have generally scored quite low rating (percent point scored ‘PPS’) regarding team work issues. The data shows that although most of the organizations have better communication between team members (PPS=68.8), better promotion of products (PPS=68.2) and coordinated efforts for fostering next generation technology (PPS=62.1), some improvement can never the less be suggested for other factors as they have quite low PPS. The response analysis results showed that the under team work parameter of the strategic success based on the idea that communication and co – operation among the team members and promotions of developed products in the organization was given maximum weightage which was followed by the idea based on co – ordinate efforts for the development of the next generation technology and effectively managing process capabilities. Table 3: Response Analysis of the Respondents on Team Work

No. of Companies Scoring Points

Total No. of Responses

Total Points Scored

Percent Points Score

Central Tendency TPS/N

S. No

3 4 5

6 7 8

FACTORS

1 2 3 4 (N) Coordinated efforts for 8 45 65 0 118 Development / fostering of next generation technology Transforming a traditional 27 68 22 1 118 hierarchical organization into a boundary-less organization Promotion of developed 14 29 50 25 118 product Culture of Kaizen & 37 37 26 18 118 Continuous Improvement Overall Equipment 30 41 28 19 118 Effectiveness (OEE) improvement Effectively managing 13 57 45 3 118 process capability Enhanced Autonomous 30 48 26 14 118 Maintenance capabilities Communication and Co8 39 45 26 118 operation among team members (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

(TPS)

(PPS)

TPS 100 4* N 293

62.1

2.48

233

49.3

1.97

322

68.2

2.73

261

55.3

2.21

272

57.6

2.31

274

58.0

2.32

260

55.1

2.20

325

68.8

2.75

59.3

2.37

It was further inference that 23.0% - 25.0% of the organizations were not implementing the concept based on the team work i.e. transforming a traditional hierarchical organization into a boundary-less organization, overall equipment effectiveness (OEE) improvement and enhanced autonomous maintenance capabilities, while on same issues 57.6%, 34.8% and 40.7% of the organizations were following them at some extent respectively. The Culture of Kaizen & Continuous Improvement, was either not followed or to some extent in 31.4% of organizations while 22.0% followed at reasonable level. 4. Administration Table – 4 illustrates the performance of manufacturing organizations regarding the Administration. The close analysis of various issues related to maintenance organization reveals that most of the organizations have generally scored quite low rating (percent point scored ‘PPS’) regarding major administration issues. The data shows that most of the organizations have efficient administration and

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management (PPS=71.6), Policy formation (PPS=67.6) and Top level management commitment (PPS=63.1) while some improvement can be suggested for support and encouragement as it has low PPS. The response analysis results showed that the under administration parameter of the strategic success based on the idea that efficient office management and administration in the organization was given maximum weightage which was followed by the idea based on policy formation. Table 4: Response Analysis of the Respondents on Administration No. of Companies Scoring Points

Total No. of Responses

Total Points Scored

Percent Points Score

Central Tendency TPS/N

S. No

FACTORS

Efficient office administration & management Policy Formation Commitment of Top level management

2 3 4

(TPS)

338

(PPS) TPS 100 4* N 71.6

1 5

2 25

3 69

4 19

(N) 118

2.86

8 7

42 61

45 31

23 19

118 118

319 298

67.6 63.1

2.70 2.53

Support and Encouragement from Top 19 54 41 4 118 level management (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

266

56.3

2.25

64.65

2.59

The analysis of the administrative reforms like efficient office administration and management is being followed in 57.6% organization at reasonable level while 21.2% reported it at some extent whereas 51.7% of the organizations reported commitment of top level management at some extent whereas 26.3% of the organization were performing it at reasonable level. It was further assessed that 35.0 - .8.0% of the organizations were implementing the concept based on the support and encouragement from top level management and policy formation at reasonable level while 45.8% and 35.6% of the organization was implementing it at some extent. 5. Interpersonal Table – 5 outlines the performance of manufacturing organizations regarding the interpersonal. The close analysis of various issues related to maintenance organization reveals that most of the organizations have generally scored quite low rating (percent point scored ‘PPS’) regarding interpersonal issues. Table 5: Response Analysis of the Respondents on Interpersonal No. of Companies Scoring Points

Total No. of Responses

Total Points Scored

Percent Points Score

Central Tendency

TPS/N S. No

FACTORS

A 1 0 38

B 2 30 43

C 3 74 25

D 4 14 12

(TPS) (N) 118 118

1 2

Self-Confidence of employees Stress management

3 4 5

Waste Utilization 39 60 7 12 118 Multi skilling of workers 28 39 50 1 118 Safety and Health awareness among 17 55 34 12 118 workers Broader Job Perspectives & Employee 23 46 43 6 118 empowerment Self-managed project teams & Problem 30 31 48 9 118 solving groups (Total Points Scored ‘TPS’ = A x 1 + B x 2 + C x 3 + D x 4)

6 7

(PPS) TPS 100 4* N

338 247

71.6 52.3

2.86 2.09

228 260 277

48.3 55.0 58.7

1.93 2.20 2.34

268

56.8

2.27

272

57.6

2.31

57.19

2.29

The response analysis results showed that the under interpersonal parameter of the strategic success based on the idea that self-confidence of employee in the organization was given maximum weightage which was followed by the idea based on safety and health awareness among workers and self-managed project teams and problem solving groups. The scope of the broader job prospective and employee empowerment and multi skilling of

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workers was also given preferences in the organizations, while somewhat equal importance’s was given to the concepts based on stress management. The least weightage was on the waste utilizations. Also on further analysis it was assessed that about 25.0% of the organizations were not implementing the concept of multi skilling of workers and self-managed project teams and problem solving groups, whereas about 42.0% of the organizations were implementing both these concepts at reasonable levels. B. Correlation Analysis Correlation analysis was performed in this section, the purpose was to identify the relationship between each statements within each parameters of the manufacturing competencies. Moreover, the direction of perception was measured by using correlation by assessing statements as all were measured on the same scale. The correlation process was Karl Pearson Correlation with significances level 0.05. Table 6: Karl Pearson Correlation Matrix for the Strategy Agility Strategy Agility - 1 Strategy Agility - 1

Strategy Agility Strategy Agility - 2 3 Strategy Agility - 4 Strategy Agility - 5 .692**

Strategy Agility - 2 Strategy Agility - 3 Strategy Agility – 4 Strategy Agility – 5

.692

.276

.434

.352

.276**

.434**

.352**

.425

.370**

.618

.672**

.418

.425

.370

1 .618

.672

.519**

1 .519

The correlation analysis results showed that the process of strategy agility based on the idea that quality conformance, improving customer base, developing and enhancing market share, achieving higher profit and competitive pricing of products was well positively correlated with each other i.e. they had strong and significant positive inter correlation between each other. Table 7: Karl Pearson Correlation Matrix for the Management Management – 2

Management - 3

Management - 4

Management - 5

Management - 6

Management - 7

Management – 1

.642**

.547**

.647**

.430**

.566**

.613**

Management – 2

.477**

.438**

.378**

.563**

.441**

.629

.610

.581**

.534

.575

.607**

.699

.577**

Management – 3 Management – 4 Management – 5 Management – 6 Management – 7

.477

.438

.378

.563

.441

.749

.629

.610

.581

1 .534

.575

.607

1 .699

.577

.621**

1 .621

The correlation analysis results showed that the process of management in strategic success parameter based on the idea that Enhanced production capabilities and improved control, Better Production Planning and Control Functions, Information Flow within departments through intranet, Information analysis in different departments, risk management, crisis management and Co-ordination between departments was well positively correlated with each other i.e. they had strong and significant positive inter correlation between each other. Table 8: Karl Pearson Correlation Matrix for the Team Work Team Work – Team Work - Team Work Team Work Team Work Team Work Team Work Team Work 1 2 -3 -4 -5 -6 -7 -8 Team Work – 1

.267**

Team Work – 2

.267

Team Work – 3

.472**

.590**

Team Work – 4

.570

.430

.399

.420

.447

.253

Team Work – 7

.482

Team Work – 8

.391**

Team Work – 5 Team Work – 6

.472**

.570**

.399**

.447**

.482**

.391**

.439**

.590

.221

.439**

.430

.548**

1 .548

.392

.435

.379

.483**

.420

.755 .635

.754

.640**

.221

.392**

.435**

.379**

.483**

.635

.754

.640**

.467

.773

.645**

.448

.685**

.755 **

.253

1 .467

.773

.645**

1 .448

.685**

.509**

The correlation analysis results showed that the process of team work in strategic success parameter based on the idea that Coordinated efforts for Development / fostering of next generation technology, Transforming a traditional hierarchical organization into a boundary-less organization, Promotion of developed product, Culture of Kaizen & Continuous Improvement, Overall Equipment Effectiveness (OEE) improvement, Effectively managing process capability, Enhanced Autonomous Maintenance capabilities and Communication and Co-operation among team members was well positively correlated with each other i.e. they had strong and significant positive inter correlation between each other.

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Table 9: Karl Pearson Correlation Matrix for the Administration Administration - 1 Administration – 1

Administration - 2

Administration – 2

.264

Administration - 3

.264**

1 **

Administration – 3

.236

Administration – 4

.469**

.600

.469**

.363**

.600 **

.363**

Administration - 4

.236* 1

.539**

The correlation analysis results showed that the process of administration in strategic success parameter based on the idea that Efficient office administration & management, Policy Formation, Commitment of Top level management and Support and Encouragement from Top level management was well positively correlated with each other i.e. they had strong and significant positive inter correlation between each other. Table 10: Karl Pearson Correlation Matrix for the Interpersonal Interpersonal – 2 Interpersonal - 3 Interpersonal - 4

Interpersonal - 5 Interpersonal – 6

Interpersonal - 7

Interpersonal – 1

.568**

.382**

.497**

.211*

.349**

.653**

Interpersonal – 2

.727**

.649**

.592**

.465**

.551**

.762

.484

.483**

.590

.398

.535**

.480

.369**

Interpersonal – 3 Interpersonal – 4 Interpersonal – 5 Interpersonal – 6 Interpersonal – 7

.727

.649

.592

.465

.551

.655

.762

.484

.483

1 .590

.398

.535

1 .480

.369

.603**

1 .603

The correlation analysis results showed that the process of interpersonal in strategic success parameter based on the idea that self confidence of employee, safety and health awareness among worker, self managed project teams and problem solving groups, broader job prospective and employee empowerment, multi skilling of workers, stress management and waste utilizations was well positively correlated with each other i.e. they had strong and significant positive inter correlation between each other. V. Conclusion From above analysis it is concluded that the parameters of Strategic Success are highly correlated and they have a high internal consistency. Moreover, from the above analysis it is also shown that the strategic success factors have an important role in performance and achievement of an automobile manufacturing unit.                  

References Anders Drejer (2001), “how can we define and understand competencies and their development?” The journal Technovation. Vol 21, pp. 135-146 Bonavia, T. and Marin, J.A. (2006), ‘‘An empirical study of lean production in the ceramic tile industry in Spain’’, International Journal of Operations & Production Management, Vol. 26 No. 5, pp. 505-31 Cardy, R. L., & Selvarajan, T. T. (2006). Competencies: Alternative frameworks for competitive advantage. Business Horizons. C. McDermott and T. Coates, (2007) “Managing competencies in breakthrough product development: A comparative study of two material processing projects,” IEEE Trans. Eng. Manage, vol. 54, Issue No. 2, pp. 340–350. Eric Bonjour, Jean-Pierre Micaelli (2010) ‘Design Core Competence Diagnosis: A Case From the Automotive Industry’, IEEE Transactions on Engineering Management, Vol 57, pp 323-337. Erik Schlie, George Yip (2000) Regional Follows Global: Strategy Mixes in the World Automotive Industry. European Management Journal. Vol. 18, pp 234-240 F. Belkadi, E. Bonjour, M.Dulmet (2007) Competency characterisation by means of work situation modelling. The journal Computers in Industry. Vol 58. Issue No. 2, pp. 85-91. Felicia Fai, Nicholas von Tunzelmann (2002) Industry-specific competencies and converging technological systems: evidence from patents. Structural Change and Economic Dynamics. Vol. 18, pp. 34 – 57. Freeman, C., Perez, C., (2001): Structural crisis of adjustment, business cycles and investment behavior. In: Dosi, G., Freeman, C., Nelson, R., Silverberg, G., Soete, L. (Eds.), Technical Change and Economic Theory. Pinter, London .Vol. 46 pp. 38–66. G.S. Dangayach, S.C. Pathak, A.D. Sharma.(2006) ‘Advanced Manufacturing Technology: A Way of Improving Technological Competitiveness’,. International Journal of Global Business and Competitiveness, Vol. 2, No 1, pp 1-8. G.S. Dangayach, S.G. Deshmukh,(2000) Manufacturing Strategy: Experiences from Select Indian Organizations. Journal of manufacturing system vol 19 pp 134-147 Hall, R. (1993) A framework linking intangible resources and capabilities to sustainable competitive advantage. Strategic Management Journal Vol. 18 Issue No.8, pp. 607–618. Hua-Cheng Chang, Hsin-Hsi Lai, Yu-Ming Chang (2005): Expression modes used by consumers in conveying desire for product form: A case study of a car. Hu, Q., Yu, D., & Xie, Z. (2008). Neighborhood classifiers. Expert Systems with Applications, Vol.34 Issue No.2, pp.866–876. John P. Millikin, Peter W. Hom, Charles C. Manz (2010) ‘Self-management competencies in self-managing teams: Their impact on multi-team system productivity’, Elsevier Publisher,vol 21, pp 687-702. Krisztina Demeter.(2002) ‘Manufacturing strategy and competitiveness (International Journal of Production Economics’, Elsevier Science B.V,pp 205–213. Kwasi Amoako-Gyampah, Moses Acquaah (2008) Manufacturing strategy, competitive strategy and firm performance: An empirical study in a developing economy environment.Vol. 64, pp. 575-592. Lei, D., Hitt, M. A., & Bettis, R. (1996). Dynamic core competencies through metal earning and strategic context. Journal of Management.Vol. 22, pp.549-569.

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LEYE Consulting (2007). In: Jones, P. (2006) Strategic Operations Management, unpublished. Guildford: University of Surrey. Mark R. Gallon, Harold M. Stillman, David Coates. (1999) ‘Putting Core Competency thinking into practice’, .RTM Journal, pp 1-12. McClelland, D. C. (1973). Testing for competence rather than for intelligence. American Psychologist, Vol. 28 Issue No.1, pp.1– 24. Monideepa Tarafdar . Steven R. Gordon (2009): Understanding the influence of information systems competencies on process innovation.Vol.46, Issue No.6 pp.357-363. Monica Sharma, Rambabu Kodali. (2008) ‘Development of a framework for manufacturing excellence’,Measuring Business Excellence, Emerald Group Publishing Limited, vol 12 no. 4, pp 50-66. Peter Fredrikisson (2004): Modular assembly in the car industry-an analysis of organizational influence on performance. Peter Jones and Alan Parker (2004): Strategy execution and implementation— achieving strategic goals through operations. Vol 42. pp.341-348. Qing Yu Zhang, Mark A. Vonderembse, Jeen-Su Lim (2003) Manufacturing flexibility: defining and analyzing relationships among competence, capability, and customer satisfaction. Journal of Operations Management Vol. 21, pp. 540-547. Ramesh Subramoniam, Donald Huisingh, Ratna Babu Chinnam, (2009) Remanufacturing for the automotive aftermarketstrategic factors: literature review and future research needs. The Journal of Cleaner Production Vol.17, pp. 172-181.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Design of Adder logic cell with XOR gate Chandrahash Patel1, Dr. Veena C.S.2 Research Scholar, ECE Department, Technocrats Institute of Technology, Bhopal, India Email: patel.chandrahash@gmail.com 2 Associate Professor, ECE Department, Technocrats Institute of Technology, Bhopal, India 1

Abstract: Adder logic cell is basically an important part of adder sub-system and is well known by the designers so many researchers are still working in this field for speeding up the circuit process along with power consumption. These cells are used in various application as DSP, microprocessors etc. In today’s modern world Complementary Metal Oxide Semiconductor (CMOS) design techniques are used for designing so that power, area, delay may be reduced. In this paper designing of Adder Logic Cell is done with the help of XOR/XNOR gate using Microwind software. Here 120nm and 70nm technology is used. Keywords: Adders, Area, CMOS Design, Low power I.

Introduction

According to Moore’s law “The number of transistors double once in every eighteen months” which indicates that many transistors are used being integrated to perform function of device thus huge amount of power is being dissipated. This cause a major problems for the designers hence from there researchers are investigating on this part : how the circuit can be modified so that power consumption can be reduced , some techniques being developed for that as leakage power reduction, reduce number of transistors etc. So while designing of Adder Logic Cell same thing is considered that is power consumption, area captured be less, or processing speed be fast. Adder plays a very important role in digital arithmetic as many sub system like multipliers are designed using it so while designing of Adder Logic Cell (a sub-part of adder) XOR gate is used as it is very efficient. In this paper designing is done under Microwind software and XOR gates are designed using 3, 4 & 6T (transistor) shown in figure 2(a-c). Each schematic designing is done under DSCH ( a sub part of Microwind) tool with 120nm technology and with respect to that simulation are performed under Microwind with 70nm technology. II.

Adder Schematic Design with XOR/XNOR gate

As in digital arithmetic system for performing basic mathematical and comparative operation controller needs an ALU (Arithmetic Logical Unit) and Adder is an important part of this ALU. So here an efficient Adder (Adder logic cell) is designed which dissipates less power. Fist a conventional Half Adder is implemented with two inputs say A, B and Sum, Carry as output shown in figure 1(a) and then this Half Adder is implemented with XOR gates shown in figure 3(a-c) its layout diagram shown in figure 4(a-c) And its simulation is shown in figure 5(a-c) which done by Microwind tool.

(a)

(b)

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(a)

(b)

(c)

Figure 2 (a-c): XOR gate with (a) 6T (b) 4T & (c) 3T

(a)

(b)

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(a)

(b)

(a)

(b)

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III.

Result and Discussionion

All results obtained by simulation is shown in table below and found that for low power 3T XOR gate design can be considered but area wise other design to be considered. Based on table a graphical analysis of power and area is shown. Table I: Simulation results on layout of Adder logic cells Design Styles Conventional 6T XOR 4T XOR 3T XOR

Power (µW)

Layout Area(µm2)

4.991 1.811 0.810 Very low (nW)

Transistors count

30.9 69.8 47.1 55.6

Routed wire numbers

6 22 13 16

12 7 5 4

70 60 50 40

Conventional

6T XOR

4T XOR

3T XOR

0 Power (µW)

Layout Area(µm2)

Figure 6: Graphical presentation of Simulation result obtained on power and area IV.

Conclusion

Adders as said an important part of ALU & has biggest role in operation like filtering, signal processing, multiplication etc. In short works as foundation for big system designs. Here XOR/XNOR gate is used for design implementation which reduces power due less number transistors used as shown in table 1 but area wise other is better. Hence this work can be implemented in other fast adder sub system such Look Ahead Carry Adders, Ripple Carry Adder, Multipliers etc. for better performances. References [1]

V. Elamarn. N.B.P. Reddy and K. Abhiram, “Low power prescalor implementation in CMOS VLSI”, Procedings of the International Conference on Emerging trades in Engieering & Energy Management”, Dec13-15 2012, Chennai, India, pp: 16-19.

[2]

T Vigneshwaran and P.S.Reddy,2006 “ A novel low power and high performance 14 transistors CMOS full adder cell” J. Applies Science,6 pp: 1978-1981 design

[3]

S. Kang and Y. Leblebici “CMOS Digital Integrated Circuit, Analysis and Design” (Tata McGraw-Hill, 3rd Edition, 2003).

[4]

A. Bellaouar and Mohamed I. Elmasry “Low Power Digital VLSI Design: Circuits and Systems” (Kluwer Academic Publishers, 2nd Edition, 1995)..

[5]

Anantha P. Chandrakasan and Robert W. Brodersen “MinimizingPower Consumption in CMOS circuits”. Department of EECS, University of California at Barkeleyhttp://bwrc.eecs.berkeley.edu/php/pubs/pubs.php/418/paper.fm.pdf pp.1-64

[6]

M.Morris Mano “Digital Design” (Pearson Education Asia. 3rd Edition, 2002).

[7]

Sung-Mo Kang and Yusuf Leblebici, “CMOS Digita Integrated Circuits Analysis and Design”, TMH 3rd edition.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Discrete Optimisation of One way Slab using Genetic Algorithm Hatindera Singh1, Hardeep Singh Rai2, Jagbir Singh3 Civil Engineering Department, Guru Nanak Dev Engineering College, Ludhiana, Punjab, INDIA Abstract: Optimising most structural systems used in practice requires considering design variables as discrete quantities. The paper presents the Cost optimisation of One Way Slab. The variables used in this research are discrete variables. The optimisation techniques in general enable designers to find the best design for the structure under consideration. The best design of a structure means the most economic structure without impairing the functional purposes the structure is supposed to serve. In this particular case, the principal design objective is to minimise the total cost of a structure. The resulting structure, however, should not only be marked with a low price but also comply with all strength and serviceability requirements for a given level of the applied load as per IS: 4562000. Total cost includes cost of concrete, cost of steel and cost of form work are considered. Heuristic techniques, namely the Genetic Algorithm was carried out in this research. Genetic algorithms (GA) belong to the family of evolutionary algorithms (EA). Since the adjustment of parameters in a genetic algorithm (e.g., population size, crossover and mutation rates, and maximum number of generations) is a significant problem for any application, we present our own methodology to deal with this problem. Keywords: Optimisation, Genetic Algorithm, Matlab, One Way Slab. I. INTRODUCTION Optimum design of structures has been the topic of many studies in the field of structural design. A designer’s goal is to develop an “optimal solution” for the structural design under consideration. An optimal solution normally implies the economic structure without impairing the functional purposes the structure is supposed to serve. The total cost of the concrete structure is the sum of the costs of its constituent materials; these constituent materials are at least: concrete, steel and framework, (Sarma and Adeli, 1998). As there are an infinite number of possible slab dimensions, reinforcement ratios and pre stressing forces that yield the same moment of resistance, it becomes difficult to achieve the least-cost design by conventional iterative methods. It was shown that even for a simple and well-defined RC structure of a small garage; the designs proposed by experienced design engineers can be very different. In such a situation, an optimisation procedure can help designers to find the best design or at least, a good design among different possible designs. There are some characteristics of RC structures, which make design optimisation of these structures distinctly different from other structures. Several cost items including the cost of concrete and reinforcement, influence the cost of RC structures. Therefore, in case of RC structures, the minimum weight design is not necessarily the same as the minimum cost design. In fact, for RC structures the optimum cost design is a compromise between the consumption of concrete, reinforcement, which minimises the total cost of the structure and satisfies the design requirements. In the design optimisation of RC structures the cross-sectional dimensions of elements and detailing of reinforcement, e.g. size and number of steel bars, need to be determined. Consequently, the number of design parameters that need to be optimised for a RC structure can be larger than that for a steel structure. Also cracking and durability requirements are two characteristic properties of RC structures; these increase the number of design constraints of the optimisation problem of RC structures. (Sahab, 2002) II. GENETIC ALGORITHM AS AN OPTIMISATION TECHNIQUE The genetic algorithm (GA) is a heuristic search technique based on the mechanics of natural selection developed by John Holland. The genetic algorithm is a highly parallel mathematical algorithm that transforms a set (population) of individual mathematical objects (typically fixed- length character strings patterned after chromosome strings), each with an associated fitness value, into a new population (i.e. the next generation) using operations patterned after the Darwinian principle of reproduction and survival of the fittest and after naturally occurring genetic operations.  Genetic algorithms use a population of points at a time in contrast to the single-point approach by the traditional optimization methods. That means, at a given time, Genetic algorithms process a number of designs.  Genetic algorithms do not require problem-specific knowledge to carry out a search. For instance, calculusbased search algorithms use derivative information to carry out a search. In contrast to this, Genetic algorithm are in different to problem-specific information.

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 Genetic algorithms work on coded design variables, which are finite length strings. These strings represent artificial chromosomes. Every character in the string is an artificial gene. Genetic algorithms process successive populations of these artificial chromosomes in successive generations.  Genetic algorithms use randomized operators in place of the usual deterministic ones. III. PROBLEM FORMULATION A large number of papers have been published on optimisation of structures. The great majority deals with minimum weight design or academic examples. For structural optimisation algorithms to find widespread usage among practicing engineering ,they must be formulated as cost optimisation and applied to realistic structures subjected to the actual constraints of commonly used design codes such as the Indian code (IS 456:2000). GA MODEL

Call Input Data

Create initial population according to GA parameters

Design the Slab for flexure, shear and deflection for each individual population

Calculate the objective function (cost) for every individual in the population

Check for constraint violation. Is there any violation? Yes

Apply penalty function

Sort population according to their fitness function value

Apply crossover process

Apply mutation process

Produce the set of individuals of the new generation

Are stopping criteria met? No Iterate through generations

Yes END, Display results

Figure 1: Flow chart representing Genetic Algorithm

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Therefore, in present research, a general formulation is presented for cost optimisation of one way RC slabs subjected to all the constraints of the IS 456:2000. The problem is formulated as a mixed integer-discrete variable optimisation problem. Design variables for slab:  Thickness of slab  Bar spacing  Main Reinforcement bar diameter Keeping the above design variables in concern, optimum cost would be calculated for RC slabs. A flow chart has been created which describes the way, the genetic algorithm works in present thesis. A. Objectives The relative objective of the research are further explained as follow 1. Discrete Optimisation of One-way slab with respect to cost. 2. For all research work, multiple grade of concrete and steel will be used according to codal provision IS 456:2000 B. Design of one way slab The general form of an optimisation is as follows 1. Given Constant parameters 2. Find Design variables 3. Minimise Objective function 4. Satisfy Design constraint B.1. Constant parameter The constant parameters specified prior to the solution of the optimisation problem include following: Cost of concrete per m3 for M20 = C = Rs 4500/m3 3 Cost of steel per m for Fe415 = S = Rs 353250/m3 3 Cost of concrete per m for M25 = C = Rs 5000/m3 3 Cost of steel per m for Fe500 = S = Rs 392500/m3 2 Cost of Formwork per m = F = Rs 100/m2 Breadth of slab = B = 3 m, 3.5 m, 4 m, 4.5 m,5 m Live load = 3 kN/m2, 5 kN/m2, 7 kN/m2 Characteristics strength of steel = fy = 415 KN/m2, 500 KN/m2 Characteristics strength of concrete = fck = 20 KN/m2, 25 KN/m2 B.2. Design variables Depth of slab = d = x (1) Spacing of main reinforcement = Sv = x (2) Diameter of reinforcement bar = x (3) Design variable vector B.3. Objective function The objective function to be minimised,

B.4. Constraints a. Constraint on flexural strength The factored moment must be equal to or less than permissible moment.

Constraint on shear strength

Constraint for Spacing of Main Reinforcement

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Constraint for Diameter of bar

Minimum Reinforcement

IV. RESULTS

3 metre Span 2400 2283

Cost in Rs.

2300

2220

2200 2100

2085

2170

2147

2130

2121

2085 1992

2147 2085

1983

2000 1900 1800 3

fck=20 N/mm2 ,fy = 415 N/mm2

Load in kN/m2

fck=20 N/mm2 ,fy = 500 N/mm2

fck=25 N/mm2 ,fy = 415 N/mm2

fck=25 N/mm2 ,fy = 500 N/mm2

Figure 2: Graph b/w Cost and Load for 3 metre span 3.5 metre span 3049

3100 3000

2923

Cost in Rs.

2900

2837 2784

2800 2700 2600

2886

3015

2875 2823

2738

2699

2640 2588

2500 2400 2300 3 fck=20 N/mm2 ,fy = 415 N/mm2 fck=25 N/mm2 ,fy = 415 N/mm2

Load in5 kN/m2

7 fck=20 N/mm2 ,fy = 500 N/mm2 fck=25 N/mm2 ,fy = 500 N/mm2

Figure 3: Graph b/w Cost and Load for 3 metre span

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4 metre span

4500 3704

4000 3342

Cost in Rs.

3500

3431

3547

3639

3827

3633

3812

4137

3892

3887

3660

3000 2500 2000 1500 1000 500 0 3

5 Load in kN/m2

fck=20 N/mm2 ,fy = 415 N/mm2 fck=25 N/mm2 ,fy = 415 N/mm2

7 fck=20 N/mm2 ,fy = 500 N/mm2 fck=25 N/mm2 ,fy = 500 N/mm2

Figure 4: Graph b/w Cost and Load for 4 metre span

4.5 metre span 6000

Cost in Rs.

5000

4171

4474

4146

4456

4439

4729

4360

4655

5152

4991

4843

4682

4000 3000 2000 1000 0 3

Load in kN/m2 fck=20 N/mm2 ,fy = 415 N/mm2

fck=20 N/mm2 ,fy = 500 N/mm2

fck=25 N/mm2 ,fy = 415 N/mm2

fck=25 N/mm2 ,fy = 500 N/mm2

Figure 5: Graph b/w Cost and Load for 4.5 metre span

Cost in Rs.

5 metre span 4000 3500 3000 2500 2000 1500 1000 500 0

3174

3133

3405

3366

3421

3657 3312

3536

3533

3532

3739

3768

3 5 7 Load in kN/m2 Live Load = 3kN/m2 ,fck=20 N/mm2 ,fy = 415 N/mm2 Live Load = 3kN/m2 ,fck=20 N/mm2 ,fy = 500 N/mm2 Live Load = 3kN/m2 ,fck=25 N/mm2 ,fy = 415 N/mm2 Live Load = 3kN/m2 ,fck=25 N/mm2 ,fy = 500 N/mm2

Figure 6: Graph b/w Cost and Load for5 metre span

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V. CONCLUSIONS Having l/d ratio =29-30, gives us the optimum cost for our considerations. M20 and fy 500 to be used for the optimum results. It is not always true that higher grade will always results in minimum cost.

1. 2. 3.

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]

[28] [29] [30]

Babiker S.A., Adam F.M., and Mohamed A.E., (2012) “Design Optimization of Reinforced Concrete Beams Using Artificial Neural Network” International Journal of Engineering Inventions ISSN: 2278-7461, Volume 1, Issue 8 (October2012) PP: 07-13 Babu R., “Neural Network Model For design of One-way RCC Slabs using GA/BPN” Volume 5, Issue 3, March (2014), pp. 100-106 Chou T., “Optimum Reinforced Concrete T- Beam section” Journal of the Structural Division, ASCE, Vol.103, No.ST8, Proc. Paper 13120, Aug., 1977, pp. 1605-1617. Coello C.A., Christiansen A.D., and Hernfindez F.S., (1997) “A Simple Genetic Algorithm for the Design of Reinforced Concrete Beams” Engineering with Computers (1997) 13:185-196 Colin M.Z., and Rae A.J.M., “Optimization of Structural Concrete Beams” Journal of Structural Engineering, Vol.110, No.7, July, 1984 Deb K., “Optimization for Engineering Design Algorithm and Problems”, PHI Publications, 2005, pp. 21-71. Ferreira C.C., Barros M.H.F.M., and Barros A.F.M., “Optimal Design of Reinforced Concrete T-sections in Bending”, Engineering Structures, vol.18, No. 5, 1996, Elsevier Publications, Science Direct, pp.951-964. Galeb A.C., and Atiyah Z.F., (2011) “Optimum design of reinforced concrete waffle slabs” International Journal of Civil and Structural Engineering Volume 1, No 4. Govindaraj V., Ramasamy J.V., “Optimum detailed design of reinforced concrete continuous beams using Genetic Algorithms” Computers and Structures 84 (2005) 34–48) Holland J., (1975) “Adaptation in Natural and Artificial Systems”. University of Michigan Press, Ann Arbour, IS 456-2000, “Code of Practice for Plain and Reinforced Concrete”, Bureau of Indian Standards, New Delhi Kaveh A., and Massoudi M.S., “Cost Optimisation of a Composite Floor System Using Ant Colony System” IJST, Transactions of Civil Engineering, 2012, Vol. 36, No. C2, pp. 139-148. Kaveh A., Behnam A.F., “Cost optimization of a composite floor system, one-way waffle slab, and concrete slab formwork using a charged system search algorithm” Scientia Iranica, Transactions A: Civil Engineering 19 (2012) 410–416 Kaveh A., Shakouri A., and Abadi M., “Cost Optimization of Reinforced Concrete One-Way Ribbed Slabs Using Harmony Search Algorithm” Arab J Sci Eng (2011) 36:1179–1187 Khan F.A., Adeli H., “Optimum cost design of reinforced concrete slabs using neural dynamics model” Engineering Applications of Artificial Intelligence 18 (2005) 65–72 Koza, J.R., (1992) “Genetic Programming. On the Programming of Computers by Means of Natural Selection” The MIT Press, Cambridge, MA Kumar R., (2013) “Cost Optimization of Industrial Building using Genetic Algorithm” International Journal of Scientific Engineering and Technology (ISSN: 2277-1581) Volume 2 Issue 4, pp: 185-191 Mehta Z.S., Desai A.N., and Patel S.B., (2013) “Cost Optimisation of Concrete Beam Element – By Direct Exhaustive Search Method” International Journal of innovations in Engineering and Technology, ISSN: 2319-1058, Volume-2, Issue-2, April 2013 Moharramp H., and Grierson D.E., “Computer-Automated Design of Reinforced Concrete Frameworks” Journal of Structural Engineering, Vol. 119, No. 7, July, 1993 Najem R.M., (2010) “Optimum Cost Design of R.C One-way Slabs” Vol.18 No.6 Nedushan A.B, and Varaee H., (2011) “Minimum Cost Design of Concrete Slabs using Particle Swarm Optimization with time Varying Acceleration Coefficients” World Applied Sciences Journal 13 (12): 2484-2494 Nimtawat A., and Nanakorn P., (2011) “Simple Particle Swarm Optimisation for Solving Beam-Slab Layout Design Problems” Procedia Engineering 14 (2011) 1392–1398 Patil K.S., Gore N.G., and Salunke P.J., (2013) “Optimum Design of Reinforced Concrete Flat Slab with Drop Panel” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-4 Rajeev S. and Krishnamoorthy C.S., (1992) “Discrete Optimization of Structures Using Genetic Algorithms” J. Struct. Eng. 1992.118:1233-1250 Raju K., “Design of Reinforced Concrete Structures”, (IS 456-2000) III Edition CBS Publishers. Rao S.S., “Engineering Optimization Theory and Practice”, New Age International Publisher, 2006, III Edition, pp. 29-336. Saini B., Sehgal V.K., and Gambhir M.L., (2006) “Genetically Optimized Artificial Neural Network based Optimum Design of Singly and Doubly Reinforced Concrete Beams” ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING) VOL. 7, NO. 6 (2006) PAGES 603-619 Sarma K.C and Adeli H., (1998) “Cost Optimisation of Concrete Structures” JOURNAL OF STRUCTURAL ENGINEERING 1998.124: PP: 570-578 Senouci Ahmed B, Al-Ansari Mohammed S. (2009) “Cost optimisation of composite beams using genetic algorithms” Advances in Engineering Software 40 (2009) 1112–1118. Yousif S.T and Najem R.M., (2012) “Optimum Cost Design of Reinforced Concrete Beams Using Genetic Algorithms” The Iraqi Journal for Mechanical and Material Engineering, Vol.12, No.4, 2012

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Fuzzy Clustered Speaker Identification Prof. Angel Mathew1, Preethy Prince Thachil2 Assisant Professor1, M.Tech. Student2 Department of Electronics and Communication, Ilahia College of Engineering And Technology, Kerala, INDIA ______________________________________________________________________________________ Abstract: Speaker identification is a biometric system. In speaker identification the task is to determine the unknown speaker identity by selecting one from the whole population. The key idea is that to use a fuzzy clustering to partition the original large population into subgroups. Clustering is done based on some features of the speeches. For a speaker under test, first conduct the fuzzy clustering based classification. Then apply MFCC + Neural network identification approach to the selected leaf node to determine the speaker identity. Keywords: Fuzzy clustering, MFCC, Neural Networks _________________________________________________________________________________________ I. Introduction Identify a person from the sound of their voice is known as speaker identification [1]. There are two types of identification process. They are closed set identification and open set identification. In the closed set identification process set of registered speakers will be there, whereas in the open set the speaker will not be there in the database. In speaker identification, human speech from an individual is used to identify who that individual is. There are two different operational phases. They are training phase and testing phase. In training the speech from verified speaker need to be identified, is acquired to train the model for that speaker. This is carried out usually before the system is deployed. In testing the true operation of the system is carried out where the speech from an unknown speaker is compared against each of the trained speaker models. There are different techniques used for the identification process [2], [3]. In order to accomplish large population speakers and to identify the speakers in the correct group fuzzy clustering approach [4] has been used. Based on the features, the speakers can be separated into different group. At each level of the tree, we use a speech feature to do speaker clustering, i.e., a node (or a speaker group) splits into several child nodes (or speaker subgroups) at its lower level. In this process, speakers with similar speech feature are put into a same child node whereas speakers with dissimilar speech feature are put into different child nodes. Then, each child node contains a smaller population size than its parent node. Thus, at the bottom level, each speaker group at the leaf node has a very small population size and the population reduction is achieved. At the bottom level, we select one and only one speaker group at the leaf node that the speaker belongs to and apply MFCC + Neural Network to the selected speaker group for speaker identification. The advantage of our approach is that 1) we only apply MFCC + Neural Network to the speaker group at the leaf node with a very small population size instead of applying it to the original large population, 2) less computational complexity, and 3) more accurate. II. Fuzzy Clustering In large population speaker identification, it’s feasible to use hierarchical decision tree for population reduction because human speech does contain many useful features that can be used to cluster speakers into groups. Speaker groups do exist that speakers sharing with a similar speech feature are in a same group whereas speakers having different speech features are from different groups. For example, speakers with different genders can be distinguished by using pitch feature [5]; based on different movement patterns of the vocal cords, different speaker groups could be obtained; Many emerging features which are independent from MFCC may indicate different speaker groups [6]. In summary, human speech has many different attributes and it’s feasible to cluster speaker into groups by using various speech features. At each level of our hierarchical decision tree, we try to find different speaker groups by examining a certain attribute of speech. To achieve good performance, features used in our approach for clustering should meet the following requirements: 1) a good feature should be very capable of discriminating different groups of speakers; 2) features used at different levels of the tree should be independent from each other; 3) all features should be robust to additive noise. A. Feature Description All features we used fall into the category of vocal source feature. The source-filter model of speech production [7] tells us that speech is generated by a sound source (i.e., the vibration of vocal cords) going through a linear acoustic filter (i.e., the combination of the vocal tract and the lip). MFCC mainly represents the vocal tract

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information. The vocal source is believed to be an independent component from the vocal tract and is able to provide some speaker-specific information. This is why we are interested in extracting vocal source features for speaker clustering. The first feature we derived is pitch or fundamental frequency. The rest of five features are all related to the vocal source excitation of voiced sounds. We extract them from the linear predictive (LP) residual signal [8]. B. Feature Extraction In this section, we will specify how the six features are extracted from the speech signal. 1) Pitch Extraction: Pitch is calculated using cross correlation function. The samples are overlapped. By doing the overlapping samples, no information from the samples will be lost. It uses a 30msec segment and it chooses a segment at every 20msec so it overlaps at every 10mses. In the range of 60 Hz to 320 Hz [9] maximum autocorrelation is found out. Continuous speech

Overlapped at every 10msec

Pitch is calculated using cross correlation for each segment

Mean of all segment Figure 1: Pitch Feature Extraction

2) Vocal Source Features Extraction: The vocal source features are only derived from voiced speech frames. Given a continuous speech as the input, it is decomposed into short-time frames. The algorithm for vocal source feature extraction is as follows: Step 1: Read the continuous speech. Step 2: Speech is segmented into frames. Step 3: Initialize frame index i = 1. Step 4: Calculate energy, power and zero crossing. Step 5: Pre- emphasis and windowing is done. Step 6: Linear prediction analysis is done. Step 7: Residual signal is calculated. Step 8: Positive and negative pulse is detected. Step 9: Vocal source features such as PAR (peak average ratio), skewness, and pulse width is calculated. Step 10: If all frames finishes its processing it will terminate else it will jump to step 4. 3) Fuzzy clustering: The algorithm [10] applies to every feature we derived so that it does not specify the feature. We first do feature extraction and obtain the feature. We first calculate the mean and the standard deviation of the feature data. It is fed into Lloyd’s algorithm [11] and a partition vector is obtained. The algorithm for fuzzy clustering is as follows: Step 1: Input number of speeches. Step 2: Input number of leaf nodes. Step 3: Feature is extracted. Step 4: Calculate mean and standard deviation of each feature. Step 5: Apply Lloyd’s algorithm. Step 6: Initialize cluster index. Step 7: Apply fuzzy. Step 8: If cluster size is less than or equal to leaf node it will terminate else it will jump to step 7. III. MFCC + Neural Network After obtaining the features, we have to indentify the speaker. In order to identify the speaker MFCC [12] and neural network approach is applied. Since this approach is applied to the last node of the clustered output, the number of speakers will be reduced as compared to the parent node. So that it will function properly. 1) MFCC: MFCC (mel-frequency cepstrum coefficients) is based on the human peripheral auditory system. The human perception of the frequency contents of sounds for speech signals does not follow a linear scale. Thus for each tone with an actual frequency measured in Hz, a subjective pitch is measured on a scale called the ‘Mel Scale’.The mel frequency scale is a linear frequency spacing below 1000 Hz and logarithmic spacing above

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1kHz.As a reference point, the pitch of a 1 kHz tone, 40 dB above the perceptual hearing threshold, is defined as 1000 Mels. A compact representation would be provided by a set of mel-frequency cepstrum coefficients (MFCC), which are the results of a cosine transform of the real logarithm of the short-term energy spectrum expressed on a mel-frequency scale. Fmel=2595 log10 (1+f/100) Speech signal

Frames

FFT Windowing

Power spectrum

MEL filter

DCT

Figure 2: Block diagram of MFCC

The algorithm for MFCC is as follows: Step 1: Convert time domain into frequency domain Step 2: Convert speech signal into linear scale Step 3: Mel frequency scale is linear till 1000Hz Step 4: Logarithm scale after 1000Hz Step 5: Power spectrum=|fft|2 Step 6: Fmel = 2595 log10 (1+f/100) 2) Neural Network: Neural network [13] is a machine that is designed to model the way in which brain performs a particular task or function of interest and network is usually implemented by using electronic components or is simulated on software in a computer. To achieve good performance neural network employ a massive interconnection of simple computing cells referred to as neurons or processing units. It resembles the brain in two aspects 1) knowledge is acquired by network from its environment through a learning process. 2) Interneuron connection known as synaptic weights are used to acquire knowledge. The procedure used to perform the learning process is called a learning algorithm, the function of which is to modify the synaptic weights of the network in an orderly fashion to attain a desired design objective. The algorithm used in the Neural Network is backpropagation algorithm with adaptive learing Rate.the multilayer perceptrons have been applied successfully to solve some difficult and diverse problems by training them in a supervised manner with a highly popular algorithm known as back propagation algorithm. The network consists of source nodes. The constitute the input layer, one or more hidden layer of computation nodes and an output layer of computation nodes. The input signal propagates through the network in a forward direction, on a layer by layer basis. These neural networks are commonly referred to as multilayer perceptrons. Two kinds of signals are identified in the multilayer perceptron networks. A function signal is an input signal that comes in at the input end of the network, propagates forward through the network and emerges at the output end of the network as an output signal. An error signal originates at an output neuron of the network and propagates backward through the network. Back propagation learning consists of two passes through different layers of the network, a forward pass and a backward pass. In the forward pass an input vector is applied to the input nodes of the network and its effect propagates through the network layer by layer. Finally a set of outputs is produced as the actual response of the network. During the forward pass the synaptic weights of the networks are not altered. In the backward pass, on the other hand, the synaptic weights are all adjusted in accordance with an error correction rule. Specifically the actual response of the network is subtracted from a desired response to produce an error signal. This error signal is then propagated backward through the network against the direction of synaptic connection, hence the name error back propagation. The synaptic weights are adjusted to make the actual response of the network move closed to the desired response in a statistical sense. The learning process performed with the algorithm is called back propagation learning. The adaptive learning rate says that the human brain performs the formidable task of sorting a continuous flood of sensory information received from the environment. New memories are stored in such a fashion that existing ones are not forgotten or modified. The human brain remains plastic and stable. IV. Conclusion As the major technique for speaker identification, approach based on MFCC and Neural Network performs well. But as the population increase the performance degrades such as accuracy decreases and computational complexity increases. To improve the performance in the large population fuzzy clustering approach is applied. In this approach it partitions the large population of speakers into very small group and determining the speaker group at the leaf node to which a speaker under test belongs. To this leaf node MFCC and neural network approach is applied.

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Reference [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

R. Togneri and D. Pullella, “An overview of speaker identification: Accuracy and robustness issues,” Circuits and systems Magazine, IEEE, vol. 11, no. 2, pp. 23–61, 2011. D. Reynolds, “Large population speaker identification using clean and telephone speech,” Signal Processing Letters, IEEE, vol. 2, no. 3, pp. 46–48, 1995. V. Apsingekar and P. De Leon, “Speaker model clustering for efficient speaker identification in large population applications,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 17, no. 4, pp. 848–853, 2009. Yakun Hu, Dapeng Wu, and Antonio Nucci,” Fuzzy-Clustering-Based Decision Tree Approach forLarge Population Speaker Identification” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 21, no. 4, pp. 762–774, 2013. Y. Hu, D. Wu, and A. Nucci, “Pitch-based gender identification with two-stage classification,” Security and Communication Networks, 2011. M. Grimaldi and F. Cummins, “Speaker identification using instantaneous frequencies,” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 16, no. 6, pp. 1097–1111, 2008 X. Huang et al., Spoken language processing. Prentice Hall PTR New Jersey, 2001. J. Makhoul, “Linear prediction: A tutorial review,” Proceedings of the IEEE, vol. 63, no. 4, pp. 561–580, 1975. C. Wang, “Prosodic modeling for improved speech recognition and understanding,” Ph.D. dissertation, Massachusetts Institute of Technology, 2001. A. Baraldi and P. Blonda, “A survey of fuzzy clustering algorithms for pattern recognition. i,” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 29, no. 6, pp. 778–785, 1999. Ioannis Katsavounidis, C-C. Jay Kuo, and Zhen Zhang,”A New Initialization Technique for Generalized Lloyd Iteration”IEEE signal Processing Letters,vol. 1,No 10 ,pp.144146,1994 B Milner,X Shao,” Prediction of fundamental frequency and voicing from mel- frequency cepstral coefficients for unconstrained speech reconstruction” ” Audio, Speech, and Language Processing, IEEE Transactions on, vol. 15, no.14, pp. 24-33, 2007 T.Poggio, F.Girosi,”Regularization Algorithm for Learning That Are Equivalent to Multilayer Networks”science magazine on vol. 247,, no 4945, pp. 978-982

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net PARAMETER ANALYSIS OF HYBRID POWER SYSTEM WITH UPFC 1

V. K. BHOLA1, T. SHARMA2, P. SAINI3 Assistant Professor, M.M U. Mullana, Ambala, Haryana, India 2 Assistant Professor, S.B.I.E.T, Kaithal, Haryana, India 3 M. Tech. U.I.E.T. K.U.K, Haryana, India

Abstract: The focus of this paper is on a FACTS device known as Unified Power Flow Controller (UPFC), which can provide simultaneous control of basic power system parameters like voltage, active power flow, reactive power flow, impedance and phase angle. In this paper, simulation model for different multi machine systems are carried out, e.g. a hybrid power system, a power system with two synchronized hydro power stations etc., with UPFC located at the load end of the power system, has been developed. Simulation model has been incorporated into MATLAB based Power System Toolbox (PST) for their voltage stability analysis. The model was analyzed for voltage, active power flow, and reactive power flow and phase angle, with UPFC. The result of the power system with UPFC and the conclusion is given at the end. I. INTRODUCTION In today’s high complex and interconnected power systems, there is a great need to improve power utilization while still maintaining reliability and security. Reducing the effective reactance of lines by series compensation is a direct approach to increase transmission capability. However, a power transfer capability of long transmission line is limited by stability consideration.[2] Oscillation of generator angle or line angle are generally associated with the transmission system disturbances and can occur due to step changes in load, sudden change of generator output, transmission line switching and short circuit. A. Basic types of FACTS Controllers FACTS controller can be classified into four categories: a) Series Controller (e.g. Static Synchronous Series Controller (SSSC)) b) Shunt Controller (e.g. STATCOM, D-STATCOM) c) Combined Series – Series Controller d) Combined Series – Shunt Controller (e.g. Unified Power Flow Converter (UPFC)) FACTS devices are used as power flow controller and the voltage source converter in a line ultimately resulting into oscillation damping [1]. Series controllers are used to control power flow and oscillations damping in a line. Shunt devices are effective to control voltage. UPFC is a series-shunt controller that can control active & reactive power, voltage through a line. UPFC can also be used to analyze transient stability of a single machine system. A Unified Power Flow Controller (UPFC) is a typical FACTS device capable of instantaneous control of three system parameters. Unified Power Flow Controller (UPFC) is able to control both the transmitted real power and the reactive power flows at the sending- and the receiving-end, at the midpoint of the transmission line. A control structure with a predictive control loop and pre control signal for a dc-voltage control is used for better stability and transient performance of UPFC, in comparison with the classical decoupled strategy. Generation of electricity using wind power has received considerable attention worldwide in recent years. In the beginning, the wind energy was used for standalone purposes. But as the power demand is growing day by day, there is a need for connecting this wind power to te grid. A lot of methods are adopted for connecting this power to the grid. Here is also one strategy which can be used for connecting this wind energy to the grid. And after connecting to the grid, the analysis is done [3]. The Unified Power Flow Controller (UPFC), with its unique combination of fast shunt and series compensation, is a powerful device which can control three power system parameters. In planning and designing such devices in power systems, power engineers must consider the impact of device internal limits on its performance. B. Unified power flow controller principle & operation The Unified Power Flow Controller is a sort of multi-function controller which can play an important role in solving various transmission system problems. UPFC is able to control, simultaneously or selectively, all the parameter effecting power flow in the transmission line i. e, voltage, impedance and phase angle [7]. This unique capability is signified by the adjective “UNIFIED” in its name, alternatively it can independently control the real and reactive power flow in the line [4]. The objective of this chapter is to describe the construction, principles and different modes of operation of the UPFC. For clarity a simple single-phase circuit is considered in this chapter to describe the different modes of the UPFC operation.

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C. UPFC Principle The UPFC concept was proposed within the concept of using converter based FACTS technology [6]. It consists of two voltage source inverters connected back-to-back through a common D.C link, as illustrated in Figure 1.

Fig. 1 Schematic diagram of a UPFC system. This arrangement functions as an ideal A.C to A.C power converter in which the real power can freely flow in either direction between the A.C sides of the two inverters. Due to the different functions of the two inverters in the system, inverter 1 is referred to as the exciter and inverter 2 as the booster. The reactive power on the two A.C sides of the inverters can be controlled independently [5].The series inverter (inverter 2) is connected to the transmission line through a booster transformer in a manner similar to the SSSC. The shunt inverter is connected to the system bus through an excitation transformer in the same way as an ASVC. Therefore, the UPFC can be considered as a multi-function controller which is capable of providing the performance of one or two FACTS devices. Because of its structure, the UPFC provides new dimensions of controllability, which have not been achieved with other FACTS controllers. II. SIMULATION MODELS FOR PERFORMANCE ANALYSIS OF UPFC CASE- HYBRID POWER SYSTEMS Using UPFC under Normal Condition The UPFC SIMULINK model has been connected to bus B5 as shown in fig. 3.12. UPFC is made on at t=8sec. and the power flow is increased from 5.87 pu to 6.87 pu. The ratings of the various components used are given in the appendix-F. The active power, reactive power, voltage and phase angle at different buses (B1 B2 B3 B4 B5) with respect to time are shown in fig.3-6. It is observed that UPFC increases the power flow level in all the buses under normal conditions (without faults) as shown in observations. Wind Farm A

A+ B+ C+ ABC-

Bb m1

B Cc C

Zigzag 33MW, 25KV Phase-Shifting Transformer1

m m

A+ B+ C+ ABC-

0.5

Pref

Reg_M1

Vf _

1000 MVA

A B C

13.8kV 1000 MVA

200 MW1

a3 b3 c3

L2_50km2

Zigzag Phase-Shifting Transformer

30MVA, 575V/230KV

b3 c3

230 kV

1000MVA, 13.8KV/230KV Aa

200MW

Trip

C c

Tr2: 800 MVA 230 kV/500 kV

UPFC Bypass

Shunt 500 kV, 100 MVA Series 100 MVA,10% injection

L1_65km double circuit

Tr1: 1000 MVA 230kV/500kV

Power Plant #2 Pnom= 1200 MW Cc

By pass

500 kV

[PQref]

PQref

[Vdqref] aA

L2_50km

B2 C2

B_UPFC

Equivalent 500 kV 15 000 MVA

L3_50km

B3 A B C

Vdqref UPFC

200 MW

V Lines [PQref]

Vpos. seq. B1 B2 B3 B4 B5

VPQ Measurements

Pref (pu)

P B1 B2 B3 B4 B5 (MW) Q B1 B2 B3 B4 B5 (Mv ar)

Phasors

UPFC 1

-0.27

P Lines Q Lines

Active Powers (MW) B1 B2 B3 B4 B5

Vphase

Qref (pu) UPFC Measurements

powergui

Vconv _mag (pu) Vdqref Vconv _phase (deg.)

UPFC

[Vdqref]

Vdqref

Fig: 2 A Hybrid Power System Using UPFC under Normal Condition

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Fig 3 Active Power at Bus B2

Fig. 4 Reactive Power at Bus B2

Fig. 5 Voltage at Bus B2 Fig. 6 Phase Angle at Bus B2 Here as can be seen from the table that the UPFC don’t have much impact on the active power flow of the system but in case of fault, the oscillations produced in the system are much less in magnitude and also the time of clearing the fault is less when the system is provided with UPFC [8]. III. CONCLUSION The unified power flow controller (UPFC) is a versatile controller that helps to control all the parameters (voltage, impedance, and phase angle) in a transmission line which ultimately decides the power flow through the line. The UPFC plays an important role in voltage regulation, phase angle regulation and thus in the power flow regulation in the system. In this paper, simulation model for performance analysis in a 5 bus system with UPFC has been made. The system model is observed with two hydro power generating stations and Power system consisting of a combination of a hydro power generating station & a thermal power generating station is designed for performance analysis of UPFC. The work is extended with the addition of wind power generating farm, with UPFC. The analysis is done on the basis of active power flow, reactive power flow, voltage, and phase angle of the system. From the results and observations shown, following conclusions are obtained. 1) It is observed that with the help of the unified power flow controller, the active power flow in all the buses are improved although UPFC has significant effect on reactive power flow, voltage and phase angle of the system. 2) UPFC plays an important role even when the fault is at the other bus. 3) It is also observed that the time of clearing the fault and also the magnitude of oscillations is less when the system is provided with UPFC. 4) The research work shows that UPFC has no adverse effect on any line connected to the hybrid power system. REFERENCES [1] [2] [3]

J. Barati, A. Saeedian and S. S Mortazavi, “Damping Power System Oscillations Improvement by FACTS Devices”, World Academy of Science, Engineering & Technology, 2010. Seul-kikim, Hwachang Song, Byoung Jan Lee and Sae-Hyuk Kwon, “Enhancement of Interface Flow Limit using Static Series Compensators”, Journal of Electrical Engineering & Technology, vol.-1, no.-3, pp.313-319, 2006. P.Suman Parmod Kumar, N. Vijaysimha and C. B. Saravanan, “Static Synchronous Series Compensator for Series Compensation on EHV Transmission Line”, International Journal of Advanced Research in Electrical and Instrumentation Engineering, vol.-2, Issue 7, July 2013.

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[4] [5] [6]

[7] [8]

Alok kumar Mohanty and Amar Kumar Barik, “Power System Stability Improvement using FACTS Devices”, International Journal of Modern Engineering Research (IJMER), vol.-1, Issue.2, pp.666-672, ISSN: 2249-6645. Laszlo Gyugyi, Colin D. Schauder and Kalyan K.Sen, “Static Synchronous Series Compensator: A Solid State Approach to the Series Compensation of Transmission Lines”, IEEE transactions on power delivery, vol.-12, no.-1, January 1997. L. Jebaraj, C. Christober Asir Rajan, K. Sriram, J. Ramesh and R. Sivasankari, “Static Synchronous Static Series Compensator and Static VAR Compensator Interaction on Voltage Stability Limit Enhancement and Active Power Loss Minimization through Differential Evolution Algorithm”, vol.-8, pp.1121-1133, 25 June, 2013, ISSN:1992-2248. Diego Soto and Tim C. Green, “A Comparison of High-Power Converter Topologies for the Implementation of FACTS Controllers”, IEEE Transactions on Industrial Electronics, vol.-49, no.-5, October, 2002. Sidhartha Panda, “Modelling, Simulation and Optimal Tuning of SSSC-based controller in a multi-machine power system”, World Journal of Modeling and Simulation, vol.-6 (2010), no.-2, pp.110-121, ISSN: 1746-7233.

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International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research)

ISSN (Print): 2279-0020 ISSN (Online): 2279-0039

International Journal of Engineering, Business and Enterprise Applications (IJEBEA) www.iasir.net Quantitative Sciences in measuring the Impact of Communication on Shared Decision Making to Determine Drug Adoption 1

Dipanjan Goswami, 2D. R. Aggarwal, 3Neera Jain, 1 Ranbaxy Research Laboratories, Gurgaon India 2 Institute of Technology Management, Gurgaon, India 3 Management Development Institute, Gurgaon, India Abstract: Forecasting medical decisions are very challenging due to lack of predictability of physicians’ drug acceptance behaviour when multiple choices (in therapeutic segment) exists. The role of communication in medical information transaction among the healthcare provider / stakeholders is either nascent stage or might not have been optimized by pharmaceutical firms in emerging markets like India. We present a tractable model for analyzing the relationship between diverse communication sources and adoption of drug, launched for the first time against other branded drugs in same generic class. The article emphasizes the role of shared decision making in form of drug marketer –physician interaction as well as physician –physician interaction in form of word of mouth. Drawing on recent theoretical explanations, hypotheses were developed and tested using multiple linear regression. Based on data set of 102 physicians treating hypertension, our estimations of the model yield four main implications: (i) marketing communication positively influence physicians’ perception on drug benefit but declines to build up intention for drug adoption (jj) referral to professional guideline as well as participation in medical events ( seminar / symposia) substantially influence drug adoption decision (iii) word of mouth factor will act as barrier to drug adoption as imitation effect is not realized by young physicians or opinion leaders / senior cardiologists. The article shows lack of programmed shared decision making leads to low drug adoption particularly for drugs treating hypertension where treatment risk is low and multiple substitutes are available in market. Keywords: Pharmaceutical Marketing; Shared Decision Making; Communication Impact; Physician behaviour; Drug Adoption Model I.

Introduction

During the last two decades, marketing communication has made dramatic advances in pharmaceutical industry and emerging markets like India has been the most lucrative investment destination for multinational corporations. The pharmaceutical industry in India has contributed significant economic growth in recent past, driven by rising consumption level of medicine and strong demand from export markets. With steep 7-8% annual rate of economic growth in recent years, average spending for healthcare services has significantly gone up (Economic Times, 2011). The manufacturers experience low entry barrier in India, compared to the advanced countries due to conducive production infrastructure, well conformed international standards and easy availability of cheap and skilled manpower (Robert and Tybout, 1997). However the intensity of market competition has given rise to the patients’ clout in alternative brand choices. A typical challenge for pharmaceutical companies is to determine the competitive structure of drug products by not only understanding the consumer / patient’s requirement and predicting how well they avail the prescribed drugs but also by assessing the physician’s perception on a particular drug performance, the prescription pattern and reflecting on how physician’s exposure to the source of drug information can cater to the patients’ need ultimately (Vakratsas and Kolsarici 2008). In view of these, many marketing managers are regularly faced with the challenge about which product features to offer and what price to charge to cope up with consumer demand (Goswami,2014). These decisions need to be reviewed not only on what the customer wants, but also how competitors will react. In academic literature, knowledge on adoption of technology is mostly focused on innovation theories, reflected in empirical studies (Winer 1985; Holak, Lehmann Sultan 1987; Rogers 2003). Many new technologies customized for innovative products fail to gain momentum, while only a few drugs make entry in the market. The spending on advertisement optimization is supported by Generalized Bass Model (GBM) advocated by Bass, Krishnan, and Jain (1994) and firms in monopolistic market are trying to fix price on advertisement expenses. Similarly dual-market diffusion model for a new prescription pharmaceutical (Vakratsas and Kolsarici 2008) distinguishes between an “early” adoption market involving patients with severe health problems, for whom demand is accumulated prior to the pharmaceutical's launch, and a “late” market corresponding to prescriptions for patients with mild problems, which is developed after and potentially triggered by the product's launch. Bass model (2004) not only comprises of innovation effect that comes from adopter’s self perception

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and product utility but also posits imitation effect which stems from interactions between early adopters and potential adopters of a product. However a product can be new in several ways: it can be entirely new for the market or it can be new for the firm but not for the market or it can be new to the segments. Bass defines imitators as the adopters whose timing is influenced by the pressure of their social system which is supported in current research also (Lee 2013). Moreover, Hofstede (2005) advocated that cultural dimension for studying drug diffusion/adoption. Over the years, interpersonal communication theories like interaction- centered theories and relationshipcentered theories allow us to focus on distinct dimensions of the marketers’ relationships with physicians (Webster 1968; Baxter et al. 2008). Both, communication accommodation theory (CAT) by Glies (2008) and Speech Code Theory (SCT) by Philipsen (1992) will be evaluated as these interaction centered theories support the underlying assumption that interpersonal communication is transactional where stakeholders are affected by and affect each other simultaneously. Social Penetration Theory (SPT) and the norm of reciprocity and Communication Privacy Management (CPM) have been used in provider – patient relationship exploration (Bylund et al. 2012). These theories on communication concepts may need to be evaluated for late entrant branded drug launch. Though research on antihypertensive medicine / drug adoption in developed countries has been conducted (Salvia and Macchiarulo et al. 2002) yet extrapolation to developing nations will not be appropriate due to significant disparity in economic and social factors. We examine the applicability of two different (Bass 2004; Hofstede 2005) models by comparing emerging market trends of drug adoption against established / structured markets of developed nations (Salvia 2002; Greving 2006). Therefore, our analysis also sheds light on the mind boggling question — what determines the success for the international firms, as researchers try to figure out why there is a high degree of variation in acceptance of alternative branded drug? (Phelps, 1992, Goswami, 2014). II.

Theoretical background

Factors identification for antihypertensive drug adoption variation This paper is related to three strands of the literature. First, understanding the sources of medical communication influencing drug prescriptions indicated the role of interpersonal communication with colleague, physicians’ characteristics and advertising, marketing drive from pharmaceutical industries (Stolley et al. 1969; Hemminki, 1975; Fretheim et al. 2005). However these studies have used an abstract concept of drug adoption framework that is hard to match data. Second, drug adoption literature has focused on prescription intention of antihypertensive drugs (Salvia et al 2002; Dranove and Huges et al. 2003; Greving 2006; Ronteltap and Trijp et al. 2007) and variations existing in their decision patterns. However these studies have not found the causality for significant variation in drug adoption intentions of practicing physicians. Hence, it is difficult to use reported model to operationalize drug adoption. The gap in traditional procedure to measure drug adoption is identified and addressed for the first time. Physician characteristics and drug diffusion Christensen et al. (1981) advocated that physician related characteristic play significant role in adoption level of drugs differing with changing communities. A study focused on Coleman et al.’s findings suggest that earlier adopters of the particular drug were likely to be young or middle aged physicians, rather than older ones (Peay et al. 1988). In another study, findings showed that cosmopolite physicians with strong interpersonal communication channels were drivers of drug adoption (Roger, 2003). However, physician’s gender difference was reported to be unlikely having meaningful clinical or economic consequences in drug adoption (Duetz et al. 2003). These findings indicate that the true understanding can be developed by examining the physicians’ characteristics including years of medical practice and attitudinal response to source of information. Information derived from knowledge and memory Boerkamp et al. (1996) identify habitual decision-making takes place when a choice is made without consideration of alternatives. Variability in physicians’ prescribing of new drugs thus relates to level of acquired knowledge of physicians (Prosser H, Walley T. et al. 2006). Greving and Denig et al. (2006) used different categories of antihypertensive drugs like CCB (calcium channel blocker), ACE (acetylcholine esterase inhibitors), ARB (angiotensin receptor blocker), BB (beta-blockers), DIU (diuretics) are used as medication for therapy of hypertension management. From these five alternative antihypertensive drug categories, the physician will select medicine that meets expectations like (i) user- friendly dosage schedule (ii) efficacy in reducing morbidity/mortality (iii) efficacy in lowering blood pressure (iv) efficacy in preventing end organ damage and hence inter-physician variation in adoption decision can be found. Hypertension can be managed by changing lifestyle (Gupta et al. 2010). The study supports the argument that choice of antihypertensive would be based on information acquired by learning process and would follow the physicians’ knowledge for perceived drug benefit. Webb and Sheeran (2006) in their meta-analysis explored causal impact on behaviour showing low adoption intention as explained by social cognitive theory (SCT; Bandura, 1986) used in health behaviour model. Self-efficacy, the belief in one’s ability to perform the necessary actions successfully, is an important component of SCT influence physician’s perceived behavioral

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control. Since self efficacy is regarded as a mediator between outcome expectancies and intentions (Health Action Process Approach model Schwarzer, 1992), perceived behavioral control is determined by perceived presence or absence of resources and opportunities and the perceived ability of these to induce or hinder performance. Therefore it will be interesting to explore perceived drug benefit is hindering physicians’ intention to build drug adoption due to following reasons (i) awareness or belief on product can’t boost physician or patient’s confidence unless there is brand loyalty or trialability success information is coded. According to Brown (2005), the measurement of perceived benefits as a variable, was not frequently standardized leading to poor reliability and validity of the measurement. Salient Value Similarity (SVS) theory identified construct ‘Perceived Benefit’ indicate judgments of risk and judgments of benefit for a number of different technologies (Alhakami and Slovic, 1994; Frewer et al., 1998; Gregory and Mendelsohn, 1993). Earlier research concluded that consumers were more concerned about perceived risks than benefits (Bhatnagar and Ghose, 2004b).Hence without receiving trialability report from patient or patient’s active participation indecision making, perceived risk will be associated more with drug adoption. The negative relationship of perceived benefit i.e. perceived risk will be directly influence drug adoption. Commercial information source and drug adoption Prosser et al. (2003, 2006) recommend the role of medical information, provided time and again by pharmaceutical firm to physicians play a crucial role in drug prescription due to the interplay of attitude and information seeking behaviour. The company representatives (detail men / communicator), who visit physicians, create a positive impact on drug acceptance based on their communication skills (Webster 1968). Molloy and Strang et al. (2002) advocated that the better quality of detailing has positive relationship with higher drug adoption level. A few pioneers of marketing research advocate that higher is the organizational drive to lower the product cost, more is the perceived benefit and higher will be the degree of adoption (Dranove and Huges et al. 2003; Ronteltap and Trijp et al. 2007). Literature data is inconclusive to demonstrate the causal relationship between information communications and intention to prescribe branded drug (antihypertensive) in developing nations. One possible reason could be the paucity of empirical work or lack of suitably detailed data or some other constraints. Information from guidance, participation in seminars According to Peay et al. (1984), a substantial number of doctors can be identified, using very stringent criteria, as consistently ‘professionally-oriented’ or ‘commercially-oriented’ in their information source preferences. Some researchers found that the commercial source of information (detailing by firm’s representatives) outweighed professional information source (like updating with national guidelines or participating in educational program arranged by professional societies) in ARB antihypertensive drug adoption by Dutch physicians (Greving and Denig et al. 2006). On the contrary, French physicians preferred primary information sources like consultation of the leading prescription practice guidelines, regular reading of several medical journals and accessing electronic resources rather than simply relying on detail men’s information source (Paraponaris and Verger et al. 2004). It is evident from contemporary studies in developed nations that commercial and professional source of medical information for drug adoption, needs to be integrated. Information from externally available treatment opinion Early research outlines that medical practitioners, maintaining contacts with socially integrated physicians, introduced a newer drug into their practices more often in comparison to isolated partners and presented a lively interpersonal process of drug diffusion (Coleman et al. 1959). The colleague physician was the most frequently mentioned information source unlike the representatives’ communication of information from pharmaceutical industries (Boerkamp et al. 1996). There was clear effect of referrals to an internist or cardiologist on ARB drug treatment and most physicians indicated that they usually continued prescriptions initiated by a hospital physician (Greving and Denig et al. 2006). The perception for ARB drugs as effective medication in lowering blood pressure promotes ARB drug adoption rate. Therefore influence of treatment opinion can be deemed as drivers of drug adoption, which needs to be revalidated in Indian context. Word of mouth communication or social interaction in medical community Social interaction in form of word of mouth communication to physicians is applied in predicting drug adoption. Multi-product growth models were first examined in the marketing literature by Peterson and Mahajan (1978) under the assumption of simultaneous (synchronic) launches. They classify co-existing products in the marketplace into four categories: independent, complementary, contingent, and substitute products. Only substitute products generate competition, which is modeled through the introduction of within-brand and crossbrand word of mouth effects related to brand-specific residual markets. Physicians may adopt a drug more than once and each prescription thus dispensed may be classified either as an innovative or an imitative action (avoiding classification of agents). In this sense, the first prescription and repeated purchases / prescriptions may be described through the same model structure. The positive relationship between word of mouth communication between physician - physician and physician-patient can be measured for building intention for drug acceptance (Coleman et al. 1959; Paraponaris et al. 2004; Greving et al. 2006). From an understanding of

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such relationships, pharmaceutical project managers could stear firms to fix production choice for antihypertensive drugs. III. Hypotheses, explanatory variables Conceptual framework and research hypotheses The physicians’ preference for source of potential information can be derived from four primary sources :- (a) drug benefit perceived from basis of physician’s knowledge or memory (b) commercial information promoted by organizations (c) exposure to guideline, seminar, symposia (d) information from word of mouth (Goswami, 2014). These factors will affect drug adoption is assumed to vary with years of practicing experience that physicians have. On the basis of our literature review, a conceptual model is presented in figure 1. This model depicts certain hypothesized relationship among the constructs of internal and external source of information. Figure 1. Theoretical Framework

Since physicians’ attitude in form of four constructs will act as determinant to antihypertensive drug adoption, the attitude models have found wider application in explaining consumer adoption and diffusion of information system (Rogers 2003). Roger’s adoption model indicates characteristic of innovation depends on the information it carries to the potential adoptee. Degree of adoption will be determined by characteristic of adoptee also (Ronteltap et al. 2007) and communication dynamics has been identified in table 1. Table1. Hypotheses formation rationale comprising of elements for each factors Hypotheses

Element constructs involving all variables based on research objectives

References

Reliance on knowledge and memory for Drug benefit (RKMD)

RKMD1

Patient friendly dosage system / schedule is positively related to drug adoption

RKMD2

Cost of medicine is negatively related to adoption

RKMD3

Efficacy in lowering blood pressure is positively related to drug adoption Efficacy in reducing morbidity and controlling end organ diseases is positively related to drug adoption

RKMD4

Boerkamp et al.1996;Ronteltap et al. 2007

Exposure to commercial information (CINFO) CINFO1 H2

Exposure to promotional material from organization is positively related to drug adoption

CINFO2

Acceptance of detailmen’s visit is positively related to drug adoption

CINFO3

Advertisement and reading organizational communication on medicine is positively related to drug adoption

CINFO4

Organizational drive for pharmacovigilence, monitoring side effects is positively related to drug adoption Participation in events/guidelines reference (PEGR)

PEGR1

Reading national drug compendium, guidelines is positively related to drug adoption

PEGR2

Participation in seminar or symposia is positively related to drug adoption

Prosser et al. 2003, 2006; Dranove and Huges et al. 2003; Ronteltap and Trijp, et al. 2007

Peay et al. 1984;Paraponaris and Verger et al. 2004; Greving

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and Denig et al. 2006

PEGR3 Exposure to educational programs, health campaign is positively related to drug adoption Information from word of mouth (IWOM) IWOM1 H4

Colleague physicians' prescription, is positively related to drug adoption

IWOM2

Hospital physicians' prescription, is positively related to drug adoption

IWOM3

Hospital physicians' prescription of new drug, is positively related to adoption

IWOM4

Patient's own experience with antihypertensive use is positively related to drug adoption

Experience of physician is positively related to H1, H2, H3 and negatively related to H4 for drug adoption (ADOPT)

Coleman et al. 1959 ; Paraponaris and Verger et al. 2004; Greving and Denig et al. 2006 Christensen et al. 1981; Peay et al. 1988; Roger 2003

Based on conceptual framework the research methodology for the current study is designed.

IV. Method and research design Data and sample An alternative model where several competing model comes into interplay is proposed to be studied using a convenience sampling method. It is a non-probability sampling technique where participants / physicians are selected because of their convenient accessibility and proximity to the researcher. Since physicians are easily available in hospitals, clinic or dispensaries with prior appointment survey can be conducted with ease and in such type of sampling the purpose of research can also be served to a great extent. Prior to administering the survey with physicians, a one-day focus group interview was conducted with specialist physicians / cardiologist, general practitioners and other specialists who made their important contributions to both the theory and methods and also made helpful suggestions for designing and administering the survey. The group helped us select terminology to use in the survey. The questionnaire items pilot tested for clarity and face validity among five physicians, not related to the study population were revised accordingly. The survey was completed in Delhi NCR region using 71 competed dataset, out of 102 participants. Operationalization and measurement We initially selected randomly physicians / specialist name and email-id from hospital lists and sent the questionnaire requesting their feedback unconditionally. Telephone reminders and one follow-up mailing were made to non-respondents to encourage a high response rate but were not successful as doctors were skeptical to share views through internet / electronic media. Out of eighty doctors interviewed, seventy one physicians’ views (around ninety percent response rate) are deemed acceptable due to completeness and free from extremity bias. The adoption determinants are aggregated together using additive algebraic property and their individual role is explained by computing descriptive statistic principle using psychometric scale (1-6 Likert measures). The frequency for usage of information that can affect the adoption decision of antihypertensive drugs is also presented by ordinal data in form of a 1-4 Likert scale. The other type of questionnaire reflects Guttman scaling with dichotomous responses that focus on physician’s characteristic feature. The adoption of antihypertensive drugs follows an important property of Guttman’s model that a physician’s entire set of responses to all items can be predicted from their cumulative score because the model is deemed deterministic. V. Data analysis The determinants of adoption were studied by linking physician related characteristics and views to their exposure to source of information. All constructs were assessed for reliability, validity, and unidimensionality and hypothesized relationships among the validated constructs were assessed via multiple regression analysis. The four hypotheses as tabulated (Table1) are composed of 24 items. All descriptive and inferential statistical analyses were performed using SPSS package, version 13. Exploratory factor analysis using Principal Component Analysis (PCA) was conducted to check the potential factors that can account for maximum variance of presented model. The loaded factors are used for multiple regression analysis to predict model variations in drug adoption. It evolves from the traditional decomposition method. The general additive decomposition model will include following variables: physician characteristic being explored in terms of years of experience, where 0-3 years experienced are taken as reference against 3-9 years (medium experienced) and 10 years or more (highly experienced) practicing physicians. The four independent and two control variables have been operationalised and summated at each level. Assuming linear relationship between drug adoption by doctors (ADOPT), the dependant variable (DV) and their determinants, H1 hypothesis i.e. reliability on memory and knowledge for drug benefit (RKMD), H2 hypothesis i.e. exposure to commercial information (CINFO), H 3 hypothesis i.e. participation in events, guidelines reference (PEGR) and H4 i.e. information from word of mouth (IWOM) along with dummy variables D1 (medium experienced=1; rest=0) and D2 (high experienced=1; rest=0) form regression model as specified below -

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ADOPT = b0 + b1 RKMD + b2 CINFO + b3 PEGR +b4 IWOM+ b5 D1 + b6 D 2 + ia where b0 is the intercept; b1, b2 b3 and b4 connotes the slope or estimated coefficients of respective determinants i.e. perceived drug benefit based on knowledge and memory per unit, exposure to commercial information per unit, participation in events per unit and information from word of mouth per unit. The fresh doctors in the experience category of 1-3 years are taken as reference category and has not been directly included in the regression equation. The coefficient b5 is the difference in predicted adoption for 3-9 years experienced physicians as compared to fresher with 1-3 years experience and coefficient b6 is the difference in predicted adoption for 10 years experienced physicians as compared to fresher with 1-3 years experience .The intercept, i.e. the respective constant term b1, b2 b3 and b4 captures explanatory variables and some other adoption variables like physicians’ characteristics that are not included in the above model. ia, represent residual variation or the stochastic error as it depicts random effect of assumed model. The regression model assumes that the slope coefficient of the explanatory variables is identical for all determinants. The reliability (convergent validity) of the items for each construct was computed using Cronbach’s alpha (Hair et al. 1988). VI. Results and discussion Model Estimation and comparison The purpose of this paper is to investigate the relationship between physicians’ adoption of drug with their attitudinal dimension for using medical source of information. Some of these variables are intrinsically difficult to measure and in other cases we are limited by the availability of data. The cumulative scores on opinions for five categories of antihypertensive drugs are used to measure the drug adoption level. A multivariate model is developed using below mentioned variables and are presented in tabular form with predicted direction. The model with adoption determinant is evaluated at 95% level of confidence. The testing hypotheses were preceded by univariable analyses and data reduction procedure was employed to reduce the number of variables. For the purpose of data reduction, we used the factor analysis using PCA extraction. The extracted factors were rotated to identify variables that load onto single factor. Varimax rotation an orthogonal rotation criterion that maximizes the variance of the squared elements in the columns of a factor matrix is used (Table 2). Table 2. Factor analysis using Varimax rotation- Loading of measures Elements

Rotated Component Matrix

Component

Extraction Method: Principal Component Analysis

1.a

Reading Promotional material

0.81

1.b

Accepting detailman visit

0.70

1.c

Reading advertisement

0.63

1.d

Side effects

0.87

2.a

Participation Education Program

0.75

2.b

Participation Seminar and Symposia

0.75

2.c

Updating with National Guideline

0.71

3.a

User-friendly dosage preference

0.50

3.b

Efficacy-morbidity reduced

0.81

3.c

Efficacy-blood pressure reduced

0.80

0.45 0.5

The loaded factor suggests that there is acceptable degree of communality (with good correlation, 0.50-0.87). With few exceptions in single model setups, all the indicators exhibit moderate loadings of above 0.50 across the measurement models. Communality is the total amount of variance an original variable shares with all other variables is included in the analysis (Hair et al., 1988). The factor analysis result shows multidimensionality of scales as 3 factors are identified (Eigen value > 1), explaining 65.5% of total scale variance which can be fitted to a model in order to examine statistical significance. The selected variables should have sufficient inter-correlations which are usually confirmed by using measures of sampling adequacy i.e. statistical significance of Bartlett’s test of sphericity is desired. Individual KMO (Ondiagonal of Anti Image correlation Matrix) should be > =0.50 and variables with diagonal anti-image correlations of less that 0.5 is dropped from the analysis as they lack sufficient correlation with other variables (Hair et al., 1988). Since we have removed the responses with extremity bias, with 71 sample-set, result is reliable.

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Descriptive Statistics The descriptive statistics for source of information measured on Likert scale of 1-6, depict that physician rely most on habitual factors like memory, knowledge and professional guidelines as well as participation in seminar and symposia for accepting antihypertensive medicines. The majority of medical practitioners are least influenced by colleague or patient’s choice, while prescribing antihypertensive drugs with highest dispersion as compared to other variables. The information from word of mouth are positively skewed as compared to other independent variables which are little unpredictable. However negligible kurtosis also confirms that all variables more or less are distributed normally. Further, the adoption level measured on 10 point scale, show that antihypertensive are prescribed frequently and physicians have acquaintance with all its categories (Table 3). Table 3. Descriptive statistics of selected variables that fits regression model Variables (N=71)

Minimum

Maximum

Mean

Std. Deviation

Skewness

Kurtosis

RKMD

5.1

0.9

-0.7

-0.5

CINFO

4.6

1.2

-0.8

0.0

PEGR

5.0

1.0

-0.8

-0.4

IWOM

2.9

1.3

0.1

-1.2

ADOPT

6.9

1.0

0.2

-0.5

Regression analysis is conducted using all three measures of adoption determinants taken together as independent variables (IV) and the strength of the models is reported in Table 4. The F-statistics (3.209) indicates model’s statistical usefulness, are significant at 5% (*) level for all variables. The value and significance of F-statistics, which measures the joint significance of all independent and control variables has also improved for demographic variables like physicians’ years of their practice years. Therefore for a given set of adoption determinants, predicted adoption is 0.515 units higher for mid experienced and 0.332 units higher for highly experienced physicians compared to fresher or less experienced doctors (Table 4). The three attitudes for using source of information for drug adoption based on years of physician experience correlate moderately with the dependent variable (0.445). Table 4: Model summary - Multiple regression analysis Variables

Unstandardized Coefficients

Standardized Coefficients

Sig.*

Diagnostic Tests

Dependent Variable ADOPT

Std. Error

INTERCEPT Independent Variable

7.152

0.782

RKMD

-0.395

0.151

CINFO

-0.026

PEGR

0.333

Beta

CI^ 9.146

0.000

1.0

-0.349

-2.610

0.011

2.1

0.120

-0.031

-0.221

0.826

3.9

0.130

0.334

2.558

0.013

12.4 14.9

Control variables Dummy-mid exp (D1)

0.515

0.322

0.219

1.602

0.114

Dummy- high exp (D2)

0.332

0.305

0.160

1.089

0.280

19.2

F- Statistics

R Square

Std. Error of the Estimate

Sig.*

DW^^

3.209

0.445

0.198

0.939

0.012

1.722

CI^ =Condition Index; DW^^=Durbin Watson

*Sig. = p value < 0.05

The fixed effect model that best explains the variance will therefore be represented as: ADOPT= 7.152 - 0.395 RKMD -0.026 CINFO + 0.333 PEGR + 0.515 D1 + 0.332 D2 The result depicts that intercept for RKMD and CINFO are -0.395 and -0.026 respectively, which indicates there is a negative correlation with physicians’ drug adoption and both the factors, reliance on memory or knowledge and exposure to commercial information when years of experience are regressed as dummy variables. Relative importance shows that participation in events like seminar, symposia or guideline referral are linked with mid experienced physicians, having the higher impact on the DV (drug adoption) compared to highly experienced physicians. This is also supported by their corresponding t value. Though the model is useful to predict the variations in drug adoption; but the proportion of the variation in adoption explained by regression relationship

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is poor (R square =0.198).Poor regression might suggest non-linearity, therefore multicollinearity was tested. A condition index greater than 15 indicates a possible collinearity problem. There might be small inter-correlations among independent variables (CI < 13%) in our regressed data. DW in the present data is close to 2 mean reflects negligible autocorrelation. As a consequence, multicollinearity is not a problem in our analysis. Imitation effect and Cultural Effect Bass model allows a time domain control, expanding or reducing sales over time under a fixed market potential. This useful re-allocation tool depends on market-mix policies and strategic interventions as supported by imitation model of Enkel and Mezger (2013). The result of present study provides empirical evidence that information communication plays significant role in constructing drug adoption model using theoretical concepts of Bass model of adoption by imitators. H 1, H2, H3 are useful for model constructs and H4 is rejected. The three attitudes for using source of information (excepting H 4 = word of mouth information source) based on years of physician experience correlate moderately with the dependent variable (R=0.445). The findings guide us to predict that the 19.8% of the variation in the overall adoption for the antihypertensive drugs could be explained by these three attitudes and provides opportunity to drug alternative brand switches. Relative importance shows that source of information in form of reliability on memory and knowledge for drug benefit (RKMD) and participation in events, guidelines reference (PEGR) have the highest impact on drug adoption. This is also supported by their corresponding significance value. The dummy variables signifies that with time, adoption of antihypertensive drugs prescription is predicted to be increased for mid experienced physicians (3-9 years) and gets decreased with higher experienced physicians (greater than 10 years). The findings also resonate with prior research that experience of physicians controls drug adoption behaviour even analyzing at global level performance in healthcare community. Higher the experience of physicians (ten years and above), more will be reliance on memory and habits for antihypertensive drug prescription in 99% of the cases (Pearson correlation). Contrary to the findings commonly reported in literature, the present study found that from three years practicing experience and greater the effect of commercial information will significantly impact negatively to the intention for drug adoption. The lower the physicians’ experience (less than 10 years), higher will be referral to drug compendium or more will be the intention for participating in seminar or symposia (with 95% confidence interval). External influence on treatment using word of mouth source of information is insignificant with respect to control variable like years of their practicing experience. The reliability of measurement scale is further supported since the ten element scale on use of sources of information (RKMD, CINFO and PEGR determinants) to predict drug adoption showed acceptable internal consistency. Moreover, this study, by investigating the context of Indian healthcare providers, contributes to the research on emerging economies. Exploring the individual identity based on the social network (Hofstede , 2001), physicians live and work within a cultural environment of healthcare in which certain values, norms, attitudes. External influence on treatment using word of mouth source of information is insignificant with respect to control variable like years of their practicing experience. The young physicians probably are not influenced by social contagion on established therapeutic segment, unlike senior physician/ cardiologists (Bhatia and Wang ,2011), who are social multiplier acting as opinion leaders. In our current study, all reflective constructs, exhibit composite reliability values of 0.79, providing support for reliability of the construct measures. VII Conclusion Research contribution and limitation Our study contributes to the literature in several ways. This investigation is very similar to empirical study to measure drug adoption in developed nation (Greving and Petitti et al. 2006) though findings are different. Even social/cultural effect as observed by western world (Bhatia and Wang 2011) is yet to be established in medical community of developing nation like India. The study is also valuable for its pioneering presence in India, one of the fast growing developing nations of Asia. The empirical study provides the knowledge to multinational firms planning to cross geographical barriers tapping unleashed potential in emerging markets. The poor scope of adoption of late entrants can be due to multiple factors. However, the main limitation, in our view, is that we do not observe actual physician interactions or patient referrals between physicians. Secondly, study involves physicians of less number and sample size needs to be increased as per the data derived from the research. An important limitation of this study is untapped potential to optimize healthcare supply chain taking into consideration end-user of service/patients such that healthcare chain becomes responsive (Shah, 2004). “Medical gatekeeping” is the process by which healthcare providers allocate resources to patients based on experience and knowledge (Elizabeth 2013) but attempt to measure drug adoption is sparse to the best of knowledge of authors. A much richer understanding of patient-physician partnership in treatment adoption is required as shown in our earlier research (Goswami, 2014). The rising competition on generic drug manufacture in low risk segment like hypertension has given multiple options to prescribers leading to lower drug adoption. Also hospital institutions are not able to measure the demand analytics while late-entrant drugs are getting adopted and patients are and continuous supply to patient chain probably is getting impaired.

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Physicians' responses to medical information sources and the extent to which they rely on clinical experience in prescribing drugs can create business knowledge to develop a decision support system as (Akura 2012). Such application in current setting will strengthen our study findings. A comparative study with generic prescription and physicians’ role (Rodríguez-Calvillo J A., 2011) needs to be explored. The article shows the convincing role of shared decision making where information on disease treatment communicated from drug marketers to doctors influence medical prescribing behaviour of late-entrant drugs. Finally shared decision making between doctors in form of word of mouth communication plays a critical role for drug adoption and underlying variations due to availability of substitutes/ branded late-entrants in same price range. The opportunity is immense and further study is recommended in larger subject pool. Declaration The authors declare that this research article does not reflect opinion/policy of any organization or regulatory institutions but being sole opinion of authors based on contemporary research on society. For any errors authors are responsible and report no conflict of interest. Acknowledgements The author thanks the hospital staffs and physicians for their valuable time during various phases of investigation. The authors acknowledge constructive feedback from Professor G. C. Saha at the Centre for Business Analytics and Intelligence, Arthur Lok Jack Graduate School of Business, University of West Indies. References [1] [2] [3] [4]

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