Volume 3, Issue 9(1), September 2014 International Journal of Multidisciplinary Educational Research
Published by Sucharitha Publications Visakhapatnam – 530 017 Andhra Pradesh – India Email: [email protected] Website: www.ijmer.in
Editorial Board Editor-in-Chief Dr. Victor Babu Koppula Faculty Department of Philosophy Andhra University – Visakhapatnam -530 003 Andhra Pradesh – India
EDITORIAL BOARD MEMBERS
Prof. S.Mahendra Dev Prof. Josef HÖCHTL Vice Chancellor Department of Political Economy Indira Gandhi Institute of Development University of Vienna, Vienna & Research Ex. Member of the Austrian Parliament Mumbai Austria
Prof.Y.C. Simhadri Prof. Alexander Chumakov Vice Chancellor, Patna University Chair of Philosophy Department Former Director Russian Philosophical Society Institute of Constitutional and Parliamentary Moscow, Russia Studies, New Delhi & Formerly Vice Chancellor of Prof. Fidel Gutierrez Vivanco Benaras Hindu University, Andhra University Founder and President Nagarjuna University, Patna University Escuela Virtual de Asesoría Filosófica Lima Peru Prof. (Dr.) Sohan Raj Tater Former Vice Chancellor Prof. Igor Kondrashin Singhania University, Rajasthan The Member of The Russian Philosophical Society Prof.K.Sreerama Murty The Russian Humanist Society and Expert of Department of Economics the UNESCO, Moscow, Russia Andhra University - Visakhapatnam Dr. Zoran Vujisiæ Prof. K.R.Rajani Rector Department of Philosophy St. Gregory Nazianzen Orthodox Institute Andhra University – Visakhapatnam Universidad Rural de Guatemala, GT, U.S.A
Prof. A.B.S.V.Rangarao Swami Maheshwarananda Department of Social Work Founder and President Andhra University – Visakhapatnam Shree Vishwa Deep Gurukul Swami Maheshwarananda Ashram Education Prof.S.Prasanna Sree & Research Center Department of English Rajasthan, India Andhra University – Visakhapatnam Dr. N.V.S.Suryanarayana Prof. P.Sivunnaidu Head Department of History Dept. of Education, A.U. Campus Andhra University – Visakhapatnam Vizianagaram
Prof. P.D.Satya Paul Department of Anthropology Andhra University – Visakhapatnam Dr. Momin Mohamed Naser Dr.E. Ashok Kumar Department of Geography Department of Education Institute of Arab Research and Studies North- Eastern Hill University, Shillong Cairo University, Egypt Dr.K.Chaitanya I Ketut Donder Postdoctoral Research Fellow Depasar State Institute of Hindu Dharma Department of Chemistry Indonesia Nanjing University of Science and Technology Prof. Roger Wiemers People’s Republic of China Professor of Education Lipscomb University, Nashville, USA Dr.Merina Islam Department of Philosophy Prof. G.Veerraju Cachar College, Assam Department of Philosophy Andhra University Dr R Dhanuja Visakhapatnam PSG College of Arts & Science Coimbatore Prof.G.Subhakar Department of Education Dr. Bipasha Sinha Andhra University, Visakhapatnam S. S. Jalan Girls’ College University of Calcutta Dr.B.S.N.Murthy Calcutta Department of Mechanical Engineering GITAM University –Visakhapatnam Dr. K. John Babu Department of Journalism & Mass Comm N.Suryanarayana (Dhanam) Central University of Kashmir, Kashmir Department of Philosophy Andhra University Dr. H.N. Vidya Visakhapatnam Government Arts College Hassan, Karnataka Dr.Ch.Prema Kumar Department of Philosophy Dr.Ton Quang Cuong Andhra University Dean of Faculty of Teacher Education Visakhapatnam University of Education, VNU, Hanoi
Dr.S.V Lakshmana Rao Prof. Chanakya Kumar Coordinator University of Pune AP State Resource Center PUNE Visakhapatnam
© Editor-in-Chief, IJMER Typeset and Printed in India www.ijmer.in
IJMER, Journal of Multidisciplinary Educational Research, concentrates on critical and creative research in multidisciplinary traditions. This journal seeks to promote original research and cultivate a fruitful dialogue between old and new thought. C O N T E N T S Volume 3 Issue 9(1) September 2014 S. Page
No No 1. Issues & Challenges for Functional Consultant in ERP 1 Implementation L.Madhu Kumar,M.kameswararao and P.Viswanadham
2+ 2. CU Selective Turn on Fluorescent Chemosensor Based 12 on Anthracene Ethanol IMINO Conjugate and its Microscopic Studies K. Rameshbabu,K. Venu Gopal and A. Jayaraju and J. Sreeramulu
3. An Application of Generalized Interval Valued Fuzzy 21 Neutrosophic Soft Sets in Decision Making I.R.Sumathi and I.Arockiarani
4. Managing Resistance to Change: With Reference to BPR 41 of Mekelle University Adisu Fanta 5. Effect of Pesticides on Soil Microbes 55 Ashaq Hussain Dar and Mudasir Gani Wani
6. Rushdie’s Shame: The Dynamics of Politics 65 Bhupinder Singh,Manmohan Singh and Jap Preet Kaur Bhangu
7. Jiddukrishnamurti's : Philosophy of Education 79 Chaman Lal Banga
8. Let’s Have an Ear for Problems of Elementary Teachers 93 Mamta Garg
9. A Correlational Study of The Variables of Learning 104 Acquisition Through Mnemonics Techniques, Cognitive Styles and Self Concept Avanish Kumar
10. Employment Determination in Basic and Alloy Industry 119 of Punjab Manjit Sharma 11. Job Satisfaction of Higher Secondary Teachers: A 127 Comparison of Central Government and State Government Schools in Kerala, India Bineesh John
12. Attitude of Secondary Grade Teachers towards 139 Computer Assisted Instruction in Prakasam District of Andhra Pradesh Nalamotu Venkateswarlu
13. The Apex Bank’s Contribution on Socio-Economic 150 Development in Rural Areas in West Bengal S K. Raju Ali and Aminul Islam
14. Resource Use Efficiency in BT. Cotton Cultivation – 167 Data Envelopment Analysis Approach K. Nirmal Ravi Kumar and A. Siva Sankar
15. Production Management Practices of The SSI Units in 184 North Coastal District of Andhra Pradesh, A Study Report Kosaraju Ravi Kumar and G.Chandra Mouli
16. Experimental Study of Cracking and Slip Behavior of 198 Reinforced Concrete Beams Strengthened by GFRP Ayad S. Adi, B. S. Karkare and Mohammad Makki Abbass
17. Comprehensive Security Versus the Traditional Notion 210 of Security Nisar Ahmad Meer
18. Challenges of Semesterisation as Perceived by Teachers 215 of Under Graduate Courses in Calcutta University and Some Suggestions for Smooth Implementation Partha Sarathi Mallik 19. The Effect of Hartmann Number H on Two Ionized 224 a Fluids Through A Horizontal Channel Between Two Parallel Walls S.V.V.Rama Devi 20. Status of Wetland Birds of Madduvalsa Reservoir, 231 Madduvalasa Village, Vangara Mandal, Srikakulam District, Andhra Pradesh, India S.Mukunda Rao
21. China’s Policy on Kashmir 244 Salfie Muzaffar Parray
22. Effect of SAQ Training on Selected Motor Abilities of 264 Sai East Football Players Between 15-18 Years Santosh Toppo and Basant Tirkey
23. Broken Family Structure and Its Impact on Children 270 Suzain Rashid
24. Sanskrit Sahityom Bhoomi Aur Paryavaran 276 Rakesh Patel
25. Kasikandamu- Kalavatyupakyanamu Parsilana 285 P.Srinivasarao
26. An overview on Quality Teaching for Global Challenges 290 M. Soma Raju
Editorial ……..
Provoking fresh thinking is certainly becoming the prime purpose of International Journal of Multidisciplinary Educational Research (IJMER). The new world era we have entered with enormous contradictions is demanding a unique understanding to face challenges. IJMER’s contents are overwhelmingly contributor, distinctive and are creating the right balance for its readers with its varied knowledge. We are happy to inform you that IJMER got the high Impact Factor 2.735, Index Copernicus Value 5.16 and IJMER is listed and indexed in 34 popular indexed organizations in the world. This academic achievement of IJMER is only author’s contribution in the past issues. I hope this journey of IJMER more benefit to future academic world. In the present issue, we have taken up details of multidisciplinary issues discussed in academic circles. There are well written articles covering a wide range of issues that are thought provoking as well as significant in the contemporary research world. My thanks to the Members of the Editorial Board, to the readers, and in particular I sincerely recognize the efforts of the subscribers of articles. The journal thus receives its recognition from the rich contribution of assorted research papers presented by the experienced scholars and the implied commitment is generating the vision envisaged and that is spreading knowledge. I am happy to note that the readers are benefited. My personal thanks to one and all.
(Dr.Victor Babu Koppula) INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY EDUCATIONAL RESEARCH ISSN : 2277-7881; IMPACT FACTOR - 2.735; IC VALUE:5.16 VOLUME 3, ISSUE 9(1), SEPTEMBER 2014
ISSUES & CHALLENGES FOR FUNCTIONAL CONSULTANT IN ERP IMPLEMENTATION
L.MadhuKumar Dr.M.Kameswararao Research Scholar Sr. Functional Consultant Department of Commerce and &Project Lead Management Studies Tech Mahindra Andhra University Visakhapatnam Visakhapatnam
Prof.P.Viswanadham Professor Dept. of Commerce and Management Studies Andhra University, Visakhapatnam
Introduction to ERP:
Enterprise Resource Planning (ERP) is the latest high-end solution that Information Technology has lent to business applications. ERP solutions streamline and integrate operation processes and information flows in an organization to synergize the resources of an organization namely men, material, money and machine through information. An ERP system is a fully integrated business management system covering functional areas of an enterprise like Logistics, Production, Finance, Accounting and Human Resources.
It can also be defined as “an amalgamation of a company's information systems designed to bind more closely a variety of companyfunctions including human resources, inventories and financials while simultaneously linking the company to customers and vendors
Need for ERP:
The recent changes that are being witnessed in the Global Economy, where the entrepreneurs are architecting the growth of their business by leaps and bounds, have demanded for and necessitated an
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effective control on the business functions across various industries and a perfect accounting system that conforms to Global and International standards like for example GAPP, SOX to name a few. This is possible only through an efficient and effective ERP software. The reasons for going with an ERP are summarized below:
Integration Financial Reporting Operational Efficiency Effective Decision Making. Reducing the cost Minimizing the time frame Security Evolution of ERP:
ERP originated as an extension of MRP (material requirements planning; later manufacturing resource planning) and CIM (Computer Integrated Manufacturing). It was introduced by a research and analysis firm named Gartner in 1990. ERP systems now attempt to cover all core functions of an enterprise, regardless of the organization's business or charter. These systems can now be found in non- manufacturing businesses, non-profit organizations and governments.
To be considered an ERP system, a software package must provide the function of at least two systems. For example, a software package that provides both payroll and accounting functions could technically be considered an ERP software package.
MRP vs. ERP:
Manufacturing Management Systems have evolved in stages over the past 30 years from a simple means of calculating material requirements to the automation of an enterprise. Around 1980, frequent changes in sales forecasts, entailing continual readjustments in production, as well as inflexible fixed system parameters, led MRP to
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evolve into a new concept of Material Resource Planning (MRP-3) and finally the generic concept of Enterprise Resource Planning.
Overview of ERP Solutions:
Each and every business has its own unique operative style which again results in transactions that are peculiar to that business or industry. Businesses across the Globe cannot be expected to conform or limit their operations to what is offered by ERP software. On the contrary, an ERP software is expected to provide solutions by way of automation to various issues or challenges that a particular business may bring up. The solutions provided by an ERP is of three types which are as follows:
Standard Solutions:
These are also known as Standard Functionalities. Every ERP will have standard functionalities which are designed keeping in view the basic business requirements or transactions. Creation of a Purchase Order, Sales Invoice, and Purchase Invoice, Maintenance of Fixed Asset Register etc., are some of the best examples of Standardized functionalities.
Workaround Solutions:
Depending on the complexity of the business issues we have other solutions called “Workaround Solutions”. It can be defined as making use of the existing standard functionalities in a best way to resolve some critical issues. For example, a big retail business is offering Free Gift Vouchers to its customers and the business requirement is to track the number and value of vouchers issued, redeemed (utilized) and unutilized, and also the accounting treatment of the same. A solution for this issue is possible by using existing standard functionalities. If the workaround process could not provide
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the solution for the business issue, then the same will be resolved by resorting to Customization.
Customized Solutions:
When there is any special requirement by the users or in case of critical business issues for which solutions are not possible through Standardization and Workaround, then it is necessary to provide customized solutions to the business. Customization involves creation of new forms to input data and writing of new code to achieve the specific requirement of the users and new tables that are designed to hold the data that are input in the forms; thereby creating new functionalities which are not available in the existing ERP package. Consider a situation of a business which operates and is spread across the globe. It has some internal transactions (buying and selling/service) among its affiliates in the group. The business requirement is that the payments and settlements (Due to and Due From) should happen at frequent intervals in a common currency without any extensive manual interventions. This is a scenario where customization is necessary to offer a solution for this business requirement. There can be several such business requirements peculiar to each and every business across the Globe which necessitate customizations.
Overview of ERP Software:
There are many ERP systems that are available in the global market. But the following are the most popular and widely used ERP software: Oracle e-business suite; SAP R-3, JD Edwards (now part of Oracle), PeopleSoft (now part of Oracle), and Microsoft Dynamics to name a few.
Implementation of ERP:
Installation of ERP software is a critical activity which involves a systematic process. The duration of implementation by and large
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depends on several considerations like business size, complexity of transactions, number of modules, the extent of customization etc. The business which is going in for an ERP software must work in tandem with the consultants by providing to them information on existing business practices and what the new requirements are that need to be achieved using the software. This will help in getting the required results after the implementation. It must be noted that sometimes the implementation demands for a lot of time during which the existing business operations and accounting systems should not be get affected. The ultimate impact of ERP implementation would be on the accounting system of the organization. A typical implementation will have the following stages:
Stage-1: Business Analysis Stage-2: Solution Designing Stage-3: Build-DEMO/PROTOTYPE Stage-4: Testing Stage-5: Go-live Stage-6: Post Production Support. ERP systems are modular, so they don't all need to be implemented all at once. It can be divided into various stages, or phase-ins. A small project (e.g., a company of less than 100 staff) may be planned and delivered within 3-9 months; however, a large, multi-site or multi- country implementation may take years. The length of the implementations is closely tied to the amount of customization desired.
Overview on the Role of Consultants:
The ERP implementation team may consist of the following type of consultants:
Functional Consultants:
A functional consultant is one who takes care of all the front- end issues and usually interacts with the client directly in getting the required inputs and acts as an intermediary between client and the
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technical consultant. He must have a fair knowledge on the ERP product and also the business processes apart from good communication skills.
Technical Consultants:
A Technical Consultant is one who owns the back-end issues and gets all the required inputs through the functional consultant. He must have fair knowledge on all Database related topics like SQL, PL/SQL etc. He must also be aware of the process of designing of tables where the data can be stored and forms that appear from the front-end, apart from having good communication skills.
Techno-functional Consultants:
A techno-functional consultant is one who can take up the role of both functional consultant and technical consultant. In simple words, he will have the combined features of the above two types of consultants.
Role of Functional Consultant in ERP Implementation:
As explained earlier, a Functional Consultant is one who directly interacts with the client particularly with functional heads, and plays a key role in each and every stage in the implementation cycle. The role and the responsibilities have been outlined as follows:
Stage1: Business Analysis:
This stage is also known as “As-Is Study”. This is the preliminary stage where the FC will try to extract the information relating to the existing business processes and methods that are being used. The FC will collect the relevant information from the client in the form of documents and personal interactions with the core members on various aspects including the current business methods, accounting system, reporting system etc. Getting proper support form the client in
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the form of availability of users, getting proper information from the users etc. are the major issues that the FC would normally face at this stage. The outcome of this stage largely depends on the skills and the ability to understand the business processes of the FC. The entire information that has been collected should be properly documented and there is also a need to acquire necessary approvals from the key users. The duration of this stage depends on the project size. If the proposed project is a high level implementation, then the normal duration would be around 3-6 months otherwise the same would be 2-3 months.
Stage2: Solution Design:
This phase is well known as “To-Be Study”. The future business process and methods in the proposed ERP will be explained to the end users of the business during this phase. Once the business analysis is completed successfully and acquired the necessary approvals from the client project manager or project leader, then the FC will move to the next phase called “Solution Design”. Here the FC is expected to undertake the “Gap Analysis” which can be defined as the difference between the existing or current business operations and the operations that are expected to take place in the proposed ERP. The solutions can be of two types’ viz., readymade (Standardized) and tailor made (Workaround and Customization). Customization is the last alternative if mapping is not possible through standardization and workaround. The major issue at this stage is that of designing a custom solution for the critical business applications in which both technical and functional consultants will play equal roles. The FC will mainly concentrate on designing the customs components and will discuss the same with the Technical Consultant about the feasibility of customization. The consultant will also explain about the features and functionality of the proposed ERP system through some presentations. The proposed solutions for the business applications must be properly documented and approvals are to be obtained from the proper authority.
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Stage3: Build Demo/Prototype:
After the solution design or to-be phase is over, the solutions that have been designed in the to-be phase need to be demonstrated in this phase. For this purpose, the FC will choose one demo ERP instance where he/she will do the necessary configurations and set ups. The FC is expected to demonstrate certain selected typical or important transactions or activities in the demo instance. The users may come out with some queries or doubts about the functionality of the ERP applications. They may again ask for a better solution if they are not happy with the proposed one. Here the FC should try to explain the accounting impact as well to the heads of the accounting department. The relevant documents have to be prepared and a sign off is necessary which are witness to the fact that they are happy with these proposed solutions.
Stage4: Testing:
It is at this stage the end users are asked to test the various business scenarios in the proposed ERP system to record the result. The FC will prepare the test script template and send the same to the users. The users are supposed to record their test result against the respective business scenarios. The test scrips will become the base for necessary approvals. These documents will be preserved safely and will be used again in future whenever it is necessary. A step by step process is described in the template and the users have to follow the same process to get the required output. This stage is also known as “User Acceptance Testing (UAT). The FC has to extend the necessary support to the users in doing their testing.
Stage5: Go-Live:
Once the UAT is finished successfully, the FC’s real task begins. He has to configure the original instance (known as Production instance) with all the required set ups as per the documents prepared
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in Solution Design stage. It is at this stage the FC will migrate the historical data to the production instance and should ensure that the migrated data will not result in any duplication of entries in the new accounting system in the new ERP system.
Data migration is one of the most important challenging activities for a functional consultant and perhaps it is the last step before the go-live of the implementation and plays a crucial role in determining the success of an ERP implementation. Since many decisions must be made before migration, a significant amount of planning must occur. Therefore, this data migration receives a paramount attention due to time constraints. The following are steps of a data migration strategy that can help in the success of an ERP implementation:
1. Identifying the data to be migrated 2. Determining the timing of data migration 3. Generating the data templates 4. Freezing the tools for data migration 5. Deciding on migration related setups 6. Deciding on data archiving Stage 6: Production Support:
The Consulting Service Provider must also render post production support services to the client after the successful go-live of the ERP implementation. This is intended to help users in getting habituated to using the ERP system. The duration of the production support depends upon several factors viz., the size of the implementation, the type of implementation etc. The type of implementation includes end to end (full cycle) implementation, upgrading the version, financial enhancements, roll out etc. The normal duration for a medium size implementation would be around one year. If it is a high level end to end implementation the same could be for more years also.
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Conclusion:
ERP implementation is considerably more difficult in organizations structured into nearly independent business units, each responsible for their own profit and loss, because they will each have different processes, business rules, data semantics, authorization hierarchies and decision centers. Solutions include requirements coordination negotiated by local change management professionals or, if this is not possible, federated implementation using loosely integrated instances (e.g. linked via Master Data Management) specifically configured and/or customized to meet local needs.
A major challenge before the Functional consultant is that of understanding the business process in a right way, executing the skills to generate the solutions with less customization, designing the relevant functional documents and interaction with the client. The required qualities that are attributable to a Functional Consultant in ERP implementation are fair communication skills, domain knowledge (accounting/HR/marketing/distributions) and finally knowledge on the ERP product.
A disadvantage usually attributed to ERP is that business processes may redesign to fit the standardized ERP modules which again may lead to a loss of competitive advantage. While documented cases exist where this has indeed materialized, other cases show that following thorough process preparation ERP systems can actually increase sustainable competitive advantage.
References:
1. Monk, Ellen and Wagner, Brett."Concepts in Enterprise Resource Planning" 3rd.ed.Course Technology Cengage Learning.Boston, Massachusetts.2009
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2. Brown, C., and I. Vessey, "Managing the Next Wave of Enterprise Systems: Leveraging Lessons from ERP," MIS Quarterly Executive, 2(1), 2003.
3. Turban et al. (2008). Information Technology for Management, Transforming Organizations in the Digital Economy. Massachusetts: John Wiley & Sons, Inc., p. 320.
4. Dehning,B. and T.Stratopoulos, 'Determinants of a Sustainable Competitive Advantage Due to an IT-enabled Strategy,' Journal of Strategic Information Systems, Vol. 12, 2003
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CU2+ SELECTIVE TURN ON FLUORESCENT CHEMOSENSOR BASED ON ANTHRACENE ETHANOL IMINO CONJUGATE AND ITS MICROSCOPIC STUDIES
K. Rameshbabu K. VenuGopal Department of Chemistry Department of Chemistry Sri Krishnadevaraya University Sri Krishnadevaraya University Anantapuramu,Andhra Pradesh Anantapuramu,Andhra Pradesh India India
A. Jayarajuand J. Sreeramulu Department of Chemistry Sri Krishnadevaraya University Anantapuramu,Andhra Pradesh, India
1. Introduction Cu has its prominence like Zn and Fe and third abundant element in human bodies among the essential heavy metal ions. It has primary importance in synthesis of hemoglobin and acts as an essential element in most metallo enzymes with oxidize activity.1-5 Abnormalities in Copper levels can cause oxidative stress and disorders associated with neurodegenerative diseases such as Menke’s disease and Alzheimer’s disease, Wilson’s disease.6,7. Due to biological significance of Cu, several efforts have made to develop accurate detection techniques for Copper especially in low concentration in aqueous solution8-11. In this study, a highly sensitive and selective fluorescent chemosensor based on anthracene iminoconjugates12 for Copper is examined. The studies with copper based receptors continue to intrigue chemists. Fluorescent chemosensors is elucidated on the basis of host guest concept in the solution13-16. Metal stimulated aggregation17system which shows significant time dependent fluorescence and absorption changes. The aggregation was seen in the form of circular ring sized polymers from the images obtained from advanced microscopy techniques. Synthesized
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a series of anthracene derivatives and studied their fluorescence response towards different transition metal18 ions.
2. EXPERIMENTAL SECTION
General Information and Materials: Emission and absorption spectra were measured on fluorescence and absorption spectrometers respectively. All the solvents used were of HPLC grade. All the metal perchlorate salts, viz., NaClO4.H2O, KClO4, Ca(ClO4)2.4H2O,
Mg(ClO4)2.6H2O, Mn(ClO4)2.6H2O, Fe(ClO4)2.xH2O, Co(ClO4)2.6H2O,
Ni(ClO4)2.6H2O, Cu(ClO4)2.6H2O, Hg(ClO4)2.4H2O, Zn(ClO4)2.6H2O,
Cd(ClO4)2.H2O and Pb(ClO4)2.H2O were procured from commercial sources.
Fluorescence and absorption titrations: Fluorescence emission spectra were measured by exciting the samples at 430 nm and the emission spectra were recorded in 440 - 600 nm range. The bulk solutions of C and metal salts were prepared in methanol. Bulk solution concentration of C and the metal ion concentrations were
-5 maintained at 6 10 M. All the measurements were made in 1 cm quartz cell and maintained an effective cuvette concentration of Cas 5 µM in all the titrations. During the titration, the concentration of metal ions was varied accordingly in order to result in requisite mole ratios of these to C by taking a fixed volume of and/ or varying volumes of the solution of the metal ions. The total volume of the solution used for the fluorescence measurements was maintained constant at 3 ml in all the cases by simply adding the requisite volume of methanol as the make-up solvent.
Absorption titrations: Bulk solutions were prepared by similar procedure as given in fluorescence studies, but the C and metal perchlorate salts were made at 6 X 10-4 M concentration. Titrations were performed by varying equivalents of [Mn+] from 0.2 to 6 and fixing the concentration of C at 10 M.
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3. Results and Discussion
3.1.Synthesis of Schiff base ligands: Synthesized Schiff base ligands by a one-step condensation reaction. A solution of anthracene aldehyde in ethanol was added slowly to a solution of corresponding amines. The reaction mixture was heated to reflux for 6 h with constant stirring. The reaction mixture was cooled to room temperature and the precipitate was filtered filtered. The precipitate was washed with ethanol and dried under vacuum to obtain Schiff base ligands in good
yields (Scheme 1). All the synthesized Schiff base ligands (C1-C2) were characterized by NMR spectroscopy, Mass spectrometry and IR.
N N OH
C1 C2
Molecular structure of C1 and C2.
H O N R
C2H5OH R-NH2 Reflux, 6h
R= and NH2 H2N OH Scheme-1
3.2. Fluorometric detection of Cu2+ using schiff base ligands
Solution preparation: The fluorescence titrations were carried out in absolute ethanol solvent. A stock solution of 10-3 M was prepared both for the ligand and for the metal ions. The ligandsconcentration was
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held constant (50 µL) and metal ion concentration was increased to vary the metal to ligand ratio (0 to 20 equivalent). The fluorescence responses of the same set of sample solutions were recorded at different time instants and marked as: T0, recorded just after metal addition; T1, after one hour and so on till saturation.
4 0 1000 (a) 3 5 (b ) 800 3 0 2 5 600 2 0 0
1 5 I/I 400 1 0 5 Intensity Intensity (a.u) 200 0 0 2 4 6 8 1 0 0 M ole Ratio; [Cu 2 + ]/[C ] 400 450 500 550 600 2 Wavelength (nm)
35 100 90 T0 C T0 C C 30 1 2 2 T1 80 T1 75 25 T3 T3 C1 T6 T6 60 20 60 0 0 T12 T12
0 45 15
T24 I/I I/I T24 40 I/I 10 30 20 5 15 0 0 0 0 5 10 15 20 0 5 10 15 20 T0 T1 T3 T6 T12 T24 2+ Mole Ratio; [Cu ]/[C ] [ 2+] 1 Mole Ratio; Cu /[C2] Time (hours) Figure 01: (a) Spectra from fluorescence titration of C ( = 380nm, 5 λex 2+ 2+ µM) with Cu ; (b) Plot of (Io/I) vs. [Cu ] / [C] mole ratio. Plot of relative fluorescence intensity (I/I0) against mole ratio. Histogram showing the number of folds of fluorescence enhancement (Io/I) in the titration of C with different time intervals
The fluorescence properties of ligands C1and C2were studied upon addition of 20.0 equivalents. of Na+, Mg2+, Ca2+, Mn2+, Fe2+, Co2+, Ni2+, Zn2+, Cd2+, Hg2+, Pb2+, and Cu2+ in methanol
solution. The ligand C2showed weak fluorescence emission at 410 nm upon excitation at 380 nm. Interestingly, fluorescence was enhanced in the presence of Cu2+ (Fig. 01). There was no significant change the
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emission behaviour of C2with other metal ions. Next we studied the fluorescence response of ligand. From the bar graph it is clear that
increment in case of C1 (red) C2 (black) which indicates involvement of
one –OH group in C2. 3.3. Absorbance study of Schiff base ligands with Cu2+
Solution preparation: Absorption titrations were carried out at stock solution concentration of 5x10-3M. The concentration was used to get more intensity in 300 to 500nm reason. Time dependent absorptions studies were carried out similar to fluorescence titrations.
The result obtained from absorption clearly indicates significant spectral changes in 350 to 400nm band with respect to time.
2+ C1ligand against Cu absorption titration
0.75 0.75 0.75 C -T0 000Cu C -T1 000Cu C 1-T3 000Cu 1 025Cu 1 025Cu 025Cu 050Cu 050Cu 050Cu 100Cu 100Cu 0.50 100Cu 0.50 250Cu 0.50 250Cu 250Cu 500Cu 500Cu 500Cu 750Cu 750Cu 750Cu 1000Cu 1000Cu 1000Cu 0.25 0.25 0.25 Absorbance Absorbance Absorbance
0.00 0.00 0.00 300 350 400 450 500 300 350 400 450 500 300 350 400 450 500 Wavelength (nm) Wavelength (nm) Wavelength (nm)
0.75 0.75 0.75 C -T12 000Cu C -T24 C -T6 1 025Cu 1 000Cu 1 000C u 050Cu 025Cu 025C u 050Cu 050C u 100Cu 0.50 0.50 100Cu 100C u 250Cu 0.50 250Cu 250C u 500Cu 500Cu 500C u 750Cu 750Cu 750C u 1000Cu 1000Cu 1000Cu 0.25 0.25 0.25 Absorbance Absorbance Absorbance
0.00 0.00 0.00 300 350 400 450 500 300 350 400 450 500 300 350 400 450 500 Wavelength (nm) Wavelength (nm) Wavelength (nm)
2+ Figure 02: Absorption spectra of C1 against Cu recorded at different time intervals T0, T1, T3, T6, T12 and T24 at concentration (5x10-3M) to explore 300 to 500nm region.
The absorption spectrum of C2in ethanol shows a maximum at 330, 350 and 380 nm and the emission spectrum shows an intense peak around
410 nm. Upon the addition of 2 equivalents of Cu(ClO4)2 in ethanol
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solution of C2. Upon an increase in the concentration of Cu2+ in the
medium, the absorbance of C2 at 410 nm continues to increase, with concomitant formation of new bands at around 440 and 380 nm (Figure 02). The presence of isosbestic point at 390 nm indicates the presence of multiple equilibria in the complexation event.
2+ C2ligand against Cu absorption titration
2+ Figure 03: Absorption spectra of C2 against Cu recorded at T0, T1, T3 and T24 at high concentration (5x10-3M) to explore 300 to 500nm region.
4. Microscopy studies
2+ AFM Studies of C2 in presence of Cu Microscopy of C and its Cu2+comple: The compound C adopts spherical shape particles which are well spread all over the mica surface as studied by AFM (Figure 4) at different time intervals T0, T1, T3, T6, T12 and T24 at concentration (5x10-3M). However, in the presence of
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Cu2+, at T0 to T 24 hours these particles aggregate to result in larger ones due to the involvement of Cu2+ which is capable of bringing C together through coordination followed by stack rings statures of such complexes. Majority of these particles bear sizes in the range 100 to 150 nm (Figure 4).
Figure 04: AFM images obtained with C2 in presence of Cu2+.
As shown above are image obtained from atomic force microscopy which clearly indicates formation of nano sized circular polymer.
5. Conclusions:
A new and simple molecular receptor system C linked to anthracene through imine moiety has been synthesized and characterized. C
2+ shows selective sensing of Cu ion by exhibiting a 352 fold enhancement in fluorescence intensity among the thirteen ions, viz., Na+, Mg2+, K+, Ca2+, Mn2+, Fe2+, Co2+, Ni2+, Cu2+, Zn2+, Cd2+, Hg2+ and
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Pb2+, studied in methanol. Moreover, none of these ions was found to compete with Cu2+ for binding to C. The complexation of C by Cu2+ has been shown by microscopy and absorption spectroscopy through exhibiting isobestic behavior.
6. Acknowledgement
The authors gratefully acknowledge the financial support provided by Department of science and Technology and council of scientific and industrial research. They are also indebted to IICT, Hyderabad, for providing the facilities of elemental analysis. They are also thankful to Sri Krishnadevaraya University, Anantapuramu for providing facilities to carry out research work.
References 1. M. C. Lindler and M. H. Azam, Am. J. Clin. Nutr., 1996, 63, 797S. 2. B. Sarkar, Chem. Rev., 1999, 99, 2535. 3. H. Tapiero, D. M. Townsend and K. D Tew, Biomed.Pharmacol., 2003, 57, 386–398. 4. E. Gaggelli, H. Kozlowski, D. Valensin and G. Valensin, Chem. Rev., 2006, 106, 1995- 2044 5. B. Sarkar, Chem. Rev., 1999, 99, 2535; (b) K. J. Barnham, C. L. Masters and A. I. Bush,Nat. Rev. Drug Discovery, 2004, 3, 205. 6. C. L. Masters and A. I. Bush, Nat. Rev. Drug Discovery, 2004, 3, 205. 7. K. J. Barnham, C. L. Mastersband, A. I Bush, Nat. Rev. Drug Discovery, 2004, 3, 205 8. Guo, Z. Q. Chen, W. Q.; Duan, X. M. Org. Lett. 2010, 12, 2202; 9. Gunnlaugsson, J. Leonard, J. p. Murray, N. S. Org Lett. 2004, 6, 1557
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10. Chen, W. T. G. Guo, Chem. Commun. 2009, 1736. 11. Y. Yuming, Z. Qiang, F. Wei and L. Fuyou, Chem. Rev., 2013, 113, 192–270. 12. K. Rameshbabu, K. Venu Gopal, A. Jayaraju, G. Nageswara Reddy and J. Sreeramulu;J. Chem. Pharm. Res., 2014, 6(4):715- 719. 13. S. K. Kim, S. H. Lee, J.Y. Lee, R. A. Bartsch, and J. S. Kim, J. Am. Chem. Soc. 2004, 126, 16499-165506. 14. S. K. Kim, S. H. Kim, H. J. Kim, S. H. Lee, S. W. Lee, J. Ko, R. A. Bartsch and J. S.Kim, Inorg.Chem. 2005, 44, 7866-7875. 15. S. J. Lee, J. H. Jung, J. Seo, I. Yoon, K. M. Park, L. F. Lindoy and S. S. Lee, Org. Lett., 2006, 8, 1641-1643. 16. S. H. Lee, H. J. Kim, Y. o. Lee, J. Vicens and J. S.Kim, TetrahedranLett., 2006, 47, 4373- 4376. 17. Liang Huang,Ju Cheng,KefengXie,c Pinxian Xi, Fengping Hou, Zhengpeng Li, Guoqiang Xie, YanjunShi, nHongyan Liu, DechengBaib and Zhengzhi Zeng; Dalton Trans., 2011, 40, 10815–10817. 18. SooJin Lee, Shim Sung Lee, MyoungSooLah,b Jae-Min Hongc and Jong Hwa Jung, Chem. Commun., 2006, 4539–4541.
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AN APPLICATION OF GENERALIZED INTERVAL VALUED FUZZY NEUTROSOPHIC SOFT SETS IN DECISION MAKING
I.R.Sumathi I.Arockiarani Department of Mathematics Department of Mathematics Nirmala College for Women Nirmala College for Women Coimbatore Coimbatore Tamilnadu, India Tamilnadu, India
1. INTRODUCTION Many practical problems in economics, engineering, environment, social science, medical science etc. cannot be dealt with by classical methods, because classical methods have inherent difficulties. The reason for these difficulties may be due to the inadequacy of the theories of parameterization tools. Molodtsov [14] initiated the concept of soft set theory as a new mathematical tool for dealing with uncertainties. Maji et al. [8,9] presented the concept of fuzzy soft sets and intuitionistic fuzzy soft set. The theory of fuzzy sets, first developed by Zadeh in [21], is perhaps the most appropriate framework for dealing with uncertainties. The concept of intuitionistic fuzzy sets which is a generalization of fuzzy sets was introduced by Atanassov in [3].
The concept of Neutrosophic set which is a mathematical tool for handling problems involving imprecise, indeterminacy and inconsistent data was introduced by F. Smarandache [15, 16].In neutrosophic set, indeterminacy is quantified explicitly and truth- membership, indeterminacy-membership and falsity-membership are independent. This assumption is very important in many applications such as information fusion in which we try to combine the data from different sensors. Pabitra Kumar Maji [12] had combined the Neutrosophic set with soft sets and introduced a new mathematical
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model ‘Neutrosophic soft set’. Yang et al.[18] presented the concept of interval valued fuzzy soft sets by combining the interval valued fuzzy set and soft set models. Jiang.Y et al.[6] introduced interval valued intuitionistic fuzzy soft set which is an interval valued fuzzy extension of the intuitionistic fuzzy soft set theory. In this paper we define generalized interval valued fuzzy neutrosophic soft set and investigate some of its properties. Also we give the solution for decision making problem using generalized interval valued fuzzy neutrosophic soft set.
2. PRELIMINARIES Definition 2.1[1]: A fuzzy neutrosophic set A on the universe of discourse X is defined as
A= where [0, 1] and 〈 , ( ), ( ), ( )〉, ∈ , , : → . A A A xFxIxT 3)()()(0 Definition 2.2[2]: An interval valued fuzzy neutrosophic set (IVFNS in short) on a universe X is an object of the form where TxA A Ix A Fx A x)(),(),(, T (x) = X Int ([0,1]) , I (x) = X Int ([0,1]) and F (x) = X Int A A A ([0,1]) {Int([0,1]) stands for the set of all closed subinterval of [0,1] satisfies the condition x X, supT (x) + supI (x) + supF (x) 3. A A A Definition 2.3[2]: Let U be an initial universe and E be a set of parameters. IVFNS(U) denotes the set of all interval valued fuzzy neutrosophic sets of U. Let AE. A pair (F,A) is an interval valued fuzzy neutrosophic soft set over U, where F is a mapping given by F: A IVFNS(U). Note : Interval valued fuzzy neutrosophic soft set/sets is denoted by IVFNSS/IVFNSSs.
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Definition 2.4[2]: The complement of an INFNSS (F,A) is denoted by
c c c c (F,A) and is defined as (F,A) = (F , A) where F : A IVFNSS(U) is a mapping given by
c c F ,)( Fxe ),( Ix x ,)( T x)( eF )( eF )( eF )( AeandUxallfor c I x 1)( I x)( eF )( eF )( 1[ I 1),( Ix x)]( eF )( eF )(
Definition 2.5[2]: The union of two IVFNSS (F,A) and (G,B) over a universe U is an IVFNSS (H,C) where C = AB eC.
eF )( )( , UxBAeifxT )( , UxABeifxT eG )( eH )( xT )( eF )( eG )( xTxT )),(),([sup( eF )( eG )( ))](),(sup( , UxBAeifxTxT
eF )( )( , UxBAeifxI eH )( xI )( eG )( )( , UxABeifxI eF )( eG )( eF )( eG )( ))](),(sup()),(),([sup( , UxBAeifxIxIxIxI
eF )( )( , UxBAeifxF eH )( xF )( eG )( )( , UxABeifxF eF )( eG )( eF )( eG )( ))](),(inf()),(),([inf( , UxBAeifxFxFxFxF
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Definition 2.6[2]: The intersection of two IVFNSS (F,A) and (G,B) over a universe U is an IVFNSS (H,C) where C = AB eC.
eF )( )( , UxBAeifxT eH )( xT )( eG )( )( , UxABeifxT eF )( eG )( eF )( eG )( ))](),(inf()),(),([inf( , UxBAeifxTxTxTxT
eF )( )( , UxBAeifxI eH )( xI )( eG )( )( , UxABeifxI eF )( eG )( eF )( eG )( ))](),(inf()),(),([inf( , UxBAeifxIxIxIxI
eF )( )( , UxBAeifxF eH )( xF )( eG )( )( , UxABeifxF eF )( eG )( eF )( eG )( ))](),(sup()),(),([sup( , UxBAeifxFxFxFxF
3. GENERALISED INTERVAL VALUED FUZZY NEUTROSOPHIC SOFT SETS
DEFINITION: 3.1
Let U be an initial universe and E be the set of parameters , A E, ∶ → ( ) and let be an interval valued fuzzy set of A , ∶ → .Define a function as [0,1] ∶ → ( ) × ([0,1]) (e)= where ( , ( )) ( ( )( ), ( )( )), ( )( )) is an interval value and is called the ( )(ℎ) = [ ( )(ℎ), ( )(ℎ)] degree of membership of an element h to
, is an interval value and is called ( ) ( )(ℎ) = [ ( )(ℎ), ( )(ℎ)] the degree of indeterminacy of an element h to , ( ) ( )(ℎ) = is an interval value and is called the degree of [ ( )(ℎ), ( )(ℎ)]
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non-membership of an element h to ( ) and ( ) is called degree of possibility of such belongingness. Then is called type1 generalized interval valued fuzzy neutrosophic soft set over the soft universe (U,E).
Here for each parameter, e, indicates not only the degree of ( ) membership, indeterminacy and non-membership of elements of U in ( ), but also the degree of preference of such belongingness which is represented by ( ). DEFINITION 3.2: Let U be an initial universe and E be the set of
parameters , A E, ∶ → ( ) and let be an interval valued fuzzy set of A , ∶ → ([0,1]) where ([0,1]) denotes the set of all closed subintervals of [0,1]. Define a function ∶ → ( ) × as (e)= where ([0,1]) ( , ( )) ( )(ℎ) = ( ( )( ), ( )( )), ( )( )) , , [ ( )(ℎ), ( )(ℎ)] ( )(ℎ) = [ ( )(ℎ), ( )(ℎ)] ( )(ℎ) = are interval values and are called the degree of [ ( )(ℎ), ( )(ℎ)] membership, indeterminacy and non-membership respectively of an element h to ( ) and ( ) = [ ( ), ( )] is an interval value and is called degree of possibility of such belongingness. Then is called type2 generalized interval valued fuzzy neutrosophic soft set over the soft universe (U,E).
It is clear that if ( ) = ( ) holds for each aA, then type2 generalized interval
valued fuzzy neutrosophic soft set will degenerate to the type1 generalized interval valued fuzzy neutrosophic soft set. In this paper, the type 2 generalized interval valued fuzzy neutrosophic soft set is denoted by GIVFNS-set in short.
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EXAMPLE: 3.3
Let U= {h1,h2,h3} be the set of mobile telephones and A={e , e ,e } E a set of parameters. The e (i=1,2,3) stand for the 1 2 3 i parameters “expensive”, “beautiful”, and “multifunctional” respectively. Let be a function given as ∶ → ( ) × ([0,1]) follows: