Journal Vol – 14 No -1, February 2019

Abstract:

Transportation model provides a powerful framework to meet the Business challenges. In highly competitive market the pressure is increasing rapidly to the organizations to determine the better ways to deliver goods to the customers with minimum transportation cost. In this paper we proposed a new algorithm based on Least Cost Method(LCM)for finding Initial Basic Feasible Solution(IBFS) to minimize transportation cost .Our proposed algorithm provides a IBFS which is either optimal or near to the optimal value with minimum steps comparatively better than those obtain by traditional algorithm or method .For the validity of this algorithm we considered a numerical transportation problem and comparative study has been made minimum cost with graphically.

Keywords:

Transportation Cost, Least Cost Method, Supply,Demand, Initial Basic feasible Solution,Optimum solution,

Refference:

I.Ahuja, R.K.(1986). Algorithms for minimax transportation problem. Naval Research Logistics Quarterly.33 (4), 725-739. II.A.Gupta, S.Khanna and M. Puri, (1992), Paradoxical situations in transportation problems, Cahiers du Centre d’Etudes de RechercheOperationnell, 37–49.

III.Charnes, A. and Cooper, W. W. (1961). Management Models and Industrial Applications of Linear Programming, 1, John Wiley & Sons, New York.

IV.Erlander S.B (2010) Cost-Minimizing Choice Behavior in Transportation Planning: A Theoretical. Page 8-10.

V.Goyal, S.K.(1984). Improving VAM for unbalanced transportation problem. Journal of Operational Research Society. 35(12), 1113-1114.

VI.Hadley, G., (1972). Linear Programming, Addition-Wesley Publishing Company, Massachusetts.

VII.Hemaida, R. & Kwak, N. K. (1994). A linear goal programming model for transshipment problems with flexible supply and demand constraints. Journalof Operational Research Society, 45(2), 1994, 215-224.

VIII.Hitchcock, F.L.(1941). The distribution of a product from several sources to numerous localities. Journal of Mathematics & Physics. 20, 224-230.

IX.Kvanli, A. (1980). Financial planning using goal programming. Omega, 8, 207-218.

X.Kwak, N.K. & Schniederjans, M.J.(1979) “A goal programming model for improved transportation problem solutions,” Omega, 12, 367-370.

XI.Lee, S.M., (1972). Goal Programming for Decision Analysis, Auerbach, Philadelphia.

XII.M.A .Hakim, M. A. Hossen, M. Sarif Uddin (2016),A credit policy approach of an inventory model for deteriorating item with price and time dependent demandaccepted for publication inJournal of Mechanics of Continua and Mathematical Sciences, ISSN 0973-8975,Volume -10 No. -2 .

XIII.Tolstoi, A.N. (1939). Methody ustraneniya neratsional’nykh perevozok pri planirovanii [Russian; Methods of removing irrational transportation in planning], Sotsialisticheskii Transport 9, 28-51 [also published a ‘pamphlet’: Methods of Removing Irrational Transportation in the construction of Operations Plans], Transzheldorizdat, Moscow, 1941.

XIV.V K Kapoor ,Operation Research (Problem and solution) ,sultan chand &sons, educational publishers ,new delhi.

XV.Veena Adlakha and Krzysztof Kowalski (2001), A heuristic method for more –for-less in distribution related problems, International Journal of Mathematical Education in Science and Technology, 32 61-71.

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Abstract:

This letter presents a single feed novel miniaturized patch antenna for WiMax applications and small wireless technologies. Antenna is fabricated on FR4 substrate with 1.6mm thickness and copper sheet of 0.035mm. The miniaturization of 82% is achieved by etching a Fork shape slot in ground plane as response is observed at 3.4GHz. Simulated and measured results shows acceptable gain of 3.4 to 3.6dB and efficiency ranging to 82% with 260MHz bandwidth. The proposed antenna is simulated in Computer Simulation Technology 2015. The measurement results demonstrate that the proposed antenna provides acceptable radiation performances with directional radiation patterns at desired frequency.

Keywords:

Miniaturization,Microstrip Patch Antenna (MPA),directivity,gain,bandwidth,Slots,Computer Simulation Technology (CST),

Refference:

I.Aguilar, Suzette M., Mudar A. Al-Joumayly, Matthew J. Burfeindt, Nader Behdad, and Susan C. Hagness. ”Multiband miniaturized patch antennas for a compact, shielded microwave breast imaging array.” IEEE transactions on antennas and propagation 62, no. 3 (2014): 1221-1231.

II.Ali, M. S. M., Rahim, S. K. A., Sabran, M. I., Abedian, M., Eteng, A., Islam, M. T. (2016). Dual band miniaturized microstrip slot antenna for WLAN applications. Microwave and Optical Technology Letters, 58(6), 1358-1362.

III.Amit K. Singh*, Mahesh P.Abegaonkar, and Shiban K. Koul, “Miniaturized Multiband Microstrip Patch Antenna Using Metamaterial Loading for Wireless Application” Progress In Electromagnetics Research C, Vol. 83, 71–82, 2018.

IV.Boukarkar, Abdelheq, Xian Qi Lin, Yuan Jiang, and Yi QiangYu. “Miniaturized single-feed multiband patch antennas.” IEEE Transactions on Antennas and Propagation 65, no. 2 (2017): 850-854.

V.Chen, Richard H., and Yi-Cheng Lin. “Miniaturized design of microstrip-fed slot antennas loaded with C-shaped rings.” IEEE Antennas and Wireless Propagation Letters 10 (2011): 203-206.

VI.Fritz-Andrade, E., Tirado-Mendez, J. A., Jardon-Aguilar, H., & Flores-Leal, R. (2017). Application of complementary split ring resonators for size reduction in patch antenna arrays. Journal of Electromagnetic Waves and Applications, 31(16), 1755-1768.

VII.Gupta, Ashish. “Miniaturized dual‐band metamaterial inspired antenna with modified SRR loading.” International Journal of RF and Microwave Computer‐Aided Engineering (2018): e21283.

VIII.Li, Ziyang, Leilei Liu, Pinyan Li, and Jian Wang. “Miniaturized design of CPW-Fed slot antennas using slits.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

IX.M. M. Bait-Suwailam and H. M. Al-Rizzo, “Size reduction of microstrip patch antennas using slotted Complementary Split-Ring Resonators,” in Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013 International Conference on, 2013, pp. 528-531.

X.Motevasselian, Alireza, and William G. Whittow. “Miniaturization of a Circular Patch Microstrip Antenna Using an Arc Projection.” IEEE Antennas and Wireless Propagation Letters 16 (2017): 517-520.

XI.Saad Hassan Kiani, Khalid Mahmood, Mehre Munir and Alex James Cole, “A Novel Design of Patch Antenna using U-Slotand Defected Ground Structure” International Journal of Advanced Computer Science and Applications(ijacsa),8(3),2017. http://dx.doi.org/10.14569/IJACSA.2017.080303E.

XII.Tirado‐Mendez, J. A., Jardon‐Aguilar, H., Flores‐Leal, R., & Rangel‐Merino, A. (2018). Multiband reduced‐size patch antenna by employing a modified DMS‐spur‐line combo technique. International Journal of RF and Microwave Computer‐Aided Engineering, 28(4), e21232.

XIII.Wang, Qian, Ning Mu, Linli Wang, Jingping Liu, and Ying Wang. “Miniaturization microstrip antenna design based on artificial electromagnetic structure.” In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 1-3. IEEE, 2017.

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Abstract:

This study is a very good approach to find the solution of secure authentication for IOT based smart home environment and its appliances. The study aims to compare the different authentication methods with respect to smart home environment and trying to identify its limitation. After analyzing the existing authentication methods its limitation and core issues then targeted the message authentication for SHE. Presently SHE authentication is based on Exchange of six message authentication techniques in Enhance authentication and key establishment scheme 6LOWPAN (EAKES6Lo) which is advance version of secure authentication and key establishment scheme (SAKES). This authentication method cause much high end to end delay, energy consumption, overall throughput of the system, complexity and poor security approach. By simulation of EAKES6Lo and SAKES scheme found some results, in contrast to these results, there may be another solution to access any SHE lights, fans, refrigerators, air condition, geezer, door lock, microwave oven, television and water pump, HVAC control and security alarms etc remotely with better security, better complexity, minimum energy consumption, better key length, better throughput and minor end to end delay named two step authentication (TSA). The proposed model also helps to monitor accessing system by comparing security codes and its complexity.

Keywords:

Internet of Things(IOT),Smart Home Environment (SHE),Version 6 Low Power Wireless Personal Area Network (6LoWPAN),Enhanced Authentication and Key Establishment Scheme for 6LoWPAN (EAKES6Lo),Secure Authentication and Key Establishment Scheme(SAKES),Two Step Authentication(TSA),

Refference:

I.Atzori, L., Iera, Antonio,Morabito, Giacomo, The internet of things: A survey. Computer networks, 2010. 54(15): p. 2787-2805.

II.Commission, E., The alliance for internet of things innovation (AIOTI). 2016.

III.Costin Badic ̆ a ̆, M.B., Amelia Badic ̆, a ̆, An Overview of Smart Home Environments: Architectures, Technologies and Applications. 2017: p. 8.

IV.Ding, F.S., A.; Tong, E.;Li,J., A smart gateway architecture for improving effeciency of home network application. 2016.

V.Geoff Mulligan , M.y., Patrick Wetterwal, ColinPatrickO’Flyn, MakingsensornetworksIPv6ready. 2008.

VI.Huichen Lin, N.W.B., IoT Privacy and Security Challenges for Smart Home Environments. 2016(4 July 2016).

VII.Internet, ADVANCE AUTHENTICATION TECHNIQUES.

VIII.Kenji, I.M., T.; Toyoda, K.; Sasase, I, Secure parent node selection scheme in route construction to exclude attacking nodes from rpl network. 2015. 4: p.5.

IX.Komninos, N., Phillppou, E. & Pitsillides, A. , Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures 2014.

X.Madakam, S., R. Ramaswamy, and S. Tripathi, Internet of Things (IoT): A Literature Review. IT Applications Group, 2015 3: p. 164-173.

XI.Mangal Sain, Y.J.K., Hoon Jae Lee, Survey on Security in Internet of things: state of the art and challenges 2014.

XII.Md. Alam Hossain, M.B.H., Md. Shafin Uddin, Shariar Md. Imtiaz MD6 Message Digest Algoritham. Reasearch Gate, 2016.16.

XIII.Rescorla, E.M., N., Datagram Transport Layer Security. Internet Engineering task force, 2012.

XIV.Sandeep Kumar Rao, D.M., Dr. Danish Ali Khan, A Survey on Advanced Encryption Standard 2017.

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Abstract:

With the passage of time, the demand of electricity is increasing day by day. The conventional electricity resources are getting depleted because of limited reserves of coal, natural gas and oil. Also most of the electricity resources are not environmental friendly. There was a need to design a mechanism that can be used as an alternative resource for the production of electricity that can be environmental friendly as well as a cheap source of generation. In the last couple of years, it is indicated that energy obtained from the sun can be the best alternate resource for energy. In this research work, the system design approach based on the Maximum Power Point Tracking (MPPT) Controller has been designed. This approach is utilized for extracting maximum available power from PV module through simulation in protius software. This system is quite efficient, effective and has high performances. Buck and boost converter have been utilized for better efficiency.

Keywords:

Electricity,Renewable Energy,Solar Charge Controller, Maximum Power Point Tracking,

Refference:

I.A. Ali, Y. Wang, W. Li and X. He, “Implementation of simple moving voltage average technique with direct control incremental conductance method to optimize the efficiency of DC microgrid,” in Emerging Technologies (ICET), 2015 International Conference on, 2015.

II.A. Argentiero, C. A. Bollino, S. Micheli and C. Zopounidis, “Renewable energy sources policies in a Bayesian DSGE model,” Renewable Energy, vol. 120, pp. 60-68, 2018.

III.A. Naserbegi, M. Aghaie, A. Minuchehr and G. Alahyarizadeh, “A novel exergy optimizationof Bushehr nuclear power plant by Gravitational Search Algorithm (GSA),” Energy, 2018.

IV.A. Soetedjo, A. Lomi and B. J. Puspita, “A Hardware Testbed of Grid-Connected Wind-Solar Power System,” International Journal of Smart Grid and Sustainable Energy Technologies, vol. 1, pp. 52-56, 2018.

V.A. M. Atallah, A. Y. Abdelaziz and R. S. Jumaah, “Implementation of perturb and observe MPPT of PV system with direct control method using buck and buck-boost converters,” Emerging Trends in Electrical, Electronics & Instrumentation Engineering: An international Journal (EEIEJ), vol. 1, pp. 31-44, 2014.

VI.B. Gjorgiev and G. Sansavini, “Electrical power generation under policy constrained water-energy nexus,” Applied Energy, vol. 210, pp. 568-579, 2018.

VII.F. Zhou, Y.-F. Chang, B. Fowler, K. Byun and J. C. Lee, “Stabilization of multiple resistance levels by current-sweep in SiOx-based resistive switching memory,” Applied Physics Letters, vol. 106, p. 063508, 2015.

VIII.J. Ahmed and Z. Salam, “An improved perturb and observe (P&O)maximum power point tracking (MPPT) algorithm for higher efficiency,” Applied Energy, vol. 150, pp. 97-108, 2015.

IX.K. Khanafer and K. Vafai, “A review on the applications of nanofluids in solar energy field,” Renewable Energy, 2018.

X.K. Ishaque, Z. Salamand G. Lauss, “The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions,” Applied Energy, vol. 119, pp. 228-236, 2014.

XI.K. S. Tey and S. Mekhilef, “Modified incremental conductance MPPT algorithm to mitigate inaccurate responses under fast-changing solar irradiation level,” Solar Energy, vol. 101, pp. 333-342, 2014.

XII.L.-L. Li, G.-Q. Lin, M.-L. Tseng, K. Tan and M. K. Lim, “A Maximum Power Point Tracking Method for PV System with Improved Gravitational Search Algorithm,” Applied Soft Computing, 2018.

XIII.M. Peng, Y. Li, Z. Zhao and C. Wang, “System architecture and key technologies for 5G heterogeneous cloud radio access networks,” IEEE network, vol. 29, pp. 6-14, 2015.

XIV.P. Sivakumar, A. A. Kader, Y. Kaliavaradhan and M. Arutchelvi, “Analysis and enhancement of PV efficiency with incremental conductance MPPT technique under non-linear loading conditions,” Renewable Energy, vol. 81, pp. 543-550, 2015.

XV.P. Ghamisi and J. A. Benediktsson, “Feature selection based on hybridization of genetic algorithm and particle swarm optimization,” IEEE Geoscience and Remote Sensing Letters, vol. 12, pp. 309-313, 2015.

XVI.R. Kardooni, S. B. Yusoff, F. B. Kari and L. Moeenizadeh, “Public opinion on renewable energy technologies and climate change in Peninsular Malaysia,” Renewable Energy, vol. 116, pp. 659-668, 2018.

XVII.R. M. Linus and P. Damodharan, “Maximum power point tracking method using a modified perturb and observe algorithm for grid connected wind energy conversion systems,” IET Renewable Power Generation, vol. 9, pp. 682-689, 2015.

XVIII.R. Cheng and Y. Jin, “A social learning particle swarmoptimization algorithm for scalable optimization,” Information Sciences, vol. 291, pp. 43-60, 2015.

XIX.S. Dincer and I. Dincer, “Comparative Evaluation of Possible Desalination Options With Various Nuclear Power Plants,” in Exergetic, Energetic and Environmental Dimensions, Elsevier, 2018, pp. 569-582.

XX.S. Krauter, “Simple and effective methods to match photovoltaic power generation to the grid load profile for a PV based energy system,” Solar Energy, vol. 159, pp. 768-776, 2018.

XXI.S. Carley, “State renewable energy electricity policies: An empirical evaluation of effectiveness,” Energy policy, vol. 37, pp. 3071-3081, 2009.

XXII.S. P. Ayeng’o, T. Schirmer, K.-P. Kairies, H. Axelsen and D. U. Sauer, “Comparison of off-grid power supply systems using lead-acid and lithium-ion batteries,” Solar Energy, vol. 162, pp. 140-152, 2018.

XXIII.S. Kiranyaz, T. Ince and M. Gabbouj, “Particle swarm optimization,” in Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition, Springer, 2014, pp. 45-82.

XXIV.T. Esram and P. L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Transactions on energy conversion, vol. 22, pp. 439-449, 2007.

XXV.V. Salas, E. Olias, A. Barrado and A. Lazaro, “Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems,” Solar energy materials and solar cells, vol. 90, pp. 1555-1578, 2006.

XXVI.Y. Shi and R. C. Eberhart, “Fuzzy adaptive particle swarm optimization,” in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, 2001.

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Abstract:

Social media data made real world like a web of data which is highly categorical in nature. Data having categorical attributes are omnipresent in existing real world. Clustering is an effective approach to deal with categorical data. However, partitional clustering algorithms are prone to fall into local optima for categorical data. A novel approach of ABC K-modes has been proposed to address this issue but acceleration issue of this algorithm was still a challenge for it. In this paper, we address this challenge to reduce the acceleration factor of algorithm and proposing a novel modified ABC K-modes approach which we refer as N-ABC K-modes approach. In our approach, unlike existing ABC K-modes we introduces different attribute matrix for each data sets. In further step, we apply XOR operation to combine the matrix of similar attributes. In last phase, dissimilar data would form a cluster and we apply clustering follow by searching on this cluster. The performance of New ABC K-modes evaluated by a series of tests and experiments over real time streaming social media data like twitter and facebook in comparison with that of other popular algorithms for categorical data.

Keywords:

Big data,Twitter,Clustering,Big data Analysis,Artificial Bee Colony(ABC), Data classification,

Refference:

I.Arthur, D., &Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In N. Bansal, K.Pruhs, & C. Stein (Eds.), Proc. of the eighteenth anual ACMSIAM symposium on discrete algorithms, SODA (pp. 1027–1035).

II.Han J, Kamber M, Pei J. Data mining concepts and techniques. 3rd ed. Waltham: Morgan Kaufmann; 2012.

III.Handl, J., Knowles, J., &Dorigo, M. (2006). Ant-based clustering and topographic mapping. Artificial Life, 12(1), 35–62.

IV.Hruschka, E., Campello, R., & de Castro, L. (2006). Evolving clusters in gene-expression data. Information Sciences, 176(13), 1898–1927.

V.Huang Z. Clustering large data sets with mixed numeric and categorical values. In the first Pacific-Asia Conference on Knowledge Discovery and Data Mining. 1997; pp. 21–34.

VI.Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery. 1998; 2: 283–304.

VII.Ikura Y, Gimple M. Efficient scheduling algorithms for a single batch processing machine. Operations Research Letters. 1986; 5: 61–65.

VIII.Ji J, Pang W, Zheng Y, Wang Z, Ma Z (2015) A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data. PLoS ONE 10(5): e0127125. doi:10.1371/journal.pone.0127125.

IX.Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2004). A local search approximation algorithm for k-meansclustering. Computational Geometry, 28(2–3), 89–112.

X.Kao Y-T, Zahara E, Kao IW. A hybridized approach to data clustering. Expert Systems with Applications.2008; 34: 1754–1762.

XI.Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing.2008; 8: 687–697.XII.Karaboga D, Ozturk C. A novel clustering approach: artificial bee colony (ABC) algorithm. Applied Soft Computing. 2011; 11: 652–657.

XIII.Li, L., Yang, Y., Peng, H., & Wang, X. (2006). An optimization method inspired by chaotic ant behavior. International Journal of Bifurcation and Chaos, 16, 2351–2364.

XIV.Luo C, Pang W, Wang Z (2014) Semi-Supervised clustering on heterogeneous information networks. In: Proceedings of 18th Pacific Asia Conference of Knowledge Discovery and Data Mining (PAKDD’14). Taiwan, pp 548-559.

XV.MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley symposium on mathematical statistics and probability (pp. 281–297).

XVI.Rand WM. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association. 1971; 66: 846–850.

XVII.Shamanth Kumar, Fred Morastatter, Huan Liu, Twitter Data analytics, Springer, Aug 19,2013.

XVIII.Shelokar PS, Jayaraman VK, Kulkarni BD. An ant colony approach for clustering. AnalyticaChimicaActa. 2004; 509: 187–195.

XIX.Teodorović D. Bee Colony Optimization (BCO). In: Lim C, Jain L, Dehuri S, editors. Innovations in Swarm Intelligence. Berlin: Springer-Verlag; 2009. pp. 39–60.

XX.Van der Merwe, D. W., &Engelbrecht, A. P. (2003). Data clustering using particle swarm optimization. In Proceedings of IEEE congress on evolutionary computation (pp. 215–220).

XXI.Wan M, Li L, Xiao J, Wang C, Yang Y. Data clustering using bacterial foraging optimization. Journal of Intelligent Information Systems. 2012; 38: 321–341.

XXII.Wan, M., Li, L., Xiao, J., Yang, Y., Wang, C., &Guo, X. (2010). CAS based clustering algorithm for web users. Nonlinear Dynamics, 61(3), 347–361.

XXIII.Yang Y. An evaluation of statistical approaches to text categorization. Journal of Information Retrieval. 1999; 1: 67–88.

XXIV.Zhang C, Ouyang D, Ning J. An artificial bee colony approach for clustering. Expert Systems with Applications.2010; 37: 4761–4767.

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Abstract:

We study the SIR model for the mathematical modeling of diseases of greater Noakhali. This model describes the spread of infectious diseases in which an individual may move from susceptible to infected and to recover. We discussed the mathematics behind the model and various tools for judging effectiveness in a certain territory. We completed the paper with an example using the infectious diseases, Pneumonia and Dysentery, commonly the children are infected. The current results of this paper are greatly instructive for us to further understand the epidemic spreading and design some fruitful prevention and disposal strategies to fight the epidemics.

Keywords:

SIR Model,Effective removal rate,Basic reproductive ratio,Effective reproductive ratio,

Refference:

I.Civil Surgeon Office Noakhali and Population &Housing Census 2011, Zila Report: Noakhali, Bangladesh Statistical Bureau, Bangladesh.

II.Civil Surgeon Office Lakhshmipur and Population & Housing Census 2011. Zila Report: Lakhshmipur, Bangladesh Statistical Bureau, Bangladesh.

III.Hackborn, Bill. Susceptible, Infected, Recovered: the SIR model of an Epidemic. University of Alberta: Augustana. Fall 2008.

IV.Hartl, Daniel (2007).Principles of Population Genetics.Sinauer Associates. p.45., ISBN978-0-87893-308-2.

V.School of Public Health. Concepts for the Prevention and Control of Microbial Threats. Center for Infectious Diseases and Emergency Readiness. June 2006. University of California Berkeley.

VI.Smith, David, and L. Moore. The SIR Model for Spread of Disease. MathDL. Dec. 2001. MMA. Fall 2008.

VII.T. D. Murray, Mathematical biology, Third edition, Springer-Verlag New York Berlin Heidelberg.

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Abstract:

The present study deals with the applicability and effectiveness of the algorithm of generalized Kudryashov method (GKM), which is one of the most workable methods to constitute the exact traveling wave solutions of non-linear evolution equations (NLEEs) in physical and mathematical science. The recent paper, we enucleated this method for each of the following Couple Boiti-Leon-Pempinelli equations system, DSSH equation and fourth-order nonlinear Ablowitz-Kaup-Newell-Segur (AKNS) water wave dynamical equation. The prominent competence of this method is to naturalize the way of solving systems of NLEEs. Moreover, we can see that when the parameters are ascribed to the particular values, obtain solitary wave solution from the exact travelling wave solution. The obtained new solutions have a wide range of inflictions in the field of physics and other areas of applied science. To perceive the physical phenomena, we have plotted coupled with some 2𝐷 and 3𝐷 graphical patterns of analytic solutions obtained in this study by using computer programming wolfram Mathematica. The worked-out solutions ascertained that the suggested method is effectual, simple and direct and can be exerted to several types of nonlinear systems of partial differential equations.

Keywords:

The generalized Kudryashov method, Couple Boiti-Leon-Pempinelli equations, DSSH equation, fourth-order nonlinear AKNS equation,travelingwave solution,exact solution,

Refference:

.A. Akbulut, M. Kaplan, F. Tascan, “The investigation of exact solutions of nonlinear partial differential equations by using exp (–𝜑𝜉)method”, Optik-Int. J. Light Elect. Optics, Vol.: 132, pp.: 382-387, 2017.

II.A. Ali, A. R. Seadawy, D. Lu, “Computational methods and traveling waves solutions for the fourth-order nonlinear Ablowitz-Kaup-Neweel-Segur water wave dynamical equation via two methods and its application”,Open Phys., Vol.: 16, Issue: 1, pp.: 219-226, 2018.

III.A. Bekir, A. Boz, “Applicationof He’s exp-function method for nonlinear evolution equations”,Comp. Math. Appl., Vol.: 58, Issue: 11-12, pp.: 2286-2293, 2009.

IV.A. Bekir, M. Kaplan, “Exponential rational function method for solving nonlinear equations arising in various physical models”,Chinese J. Phys., Vol.: 54, Issue: 3, pp.: 365-370, 2016.

V.A. Bekir, M. Kaplan, O. Guner, “A novel modified simple equation method and its application to some nonlinear evolution equation system”, 2ndInt. Conf. Analy. Appl. Math., Vol.:1611, Issue: 30, 2014.

VI.A. K. M. K. S. Hossain, M. A. Akbar, “Closed form solutions of two nonlinear equation via the enhanced (𝐺′/𝐺)-expansion method”, Cogent Math., Vol.: 4, ID: 1355958, 2017.

VII.A. M. Wazwaz,”A Sine-Cosine method for handling nonlinear wave equations”,Math. Comp. Modell., Vol.: 40, Issue: 5-6, pp.: 499-508, 2004.

VIII.A. M. Wazwaz,”The simplified Hirota’s method for studying three extended higher-order KdV-type equation”,J. Ocean Eng. Sci., Vol.: 1, Issue: 2, pp.:181-185, 2016.

IX.A. Sonmezoglu, M. Ekici, A. H. Arnous, Q. Zhou, H. Triki, S. P. Moshokoa, M. Z. Ullah, A. Biswas, M. Belic, “Embedded solitons with 𝑥2and𝑥3nonlinear susceptibilities by extended trial equation method”,Optik –Int. J. Light Elec. Optics,Vol.: 154, pp.: 1-9, 2018.

X.D. Kumar, A. R. Seadawy, A. K. Joardar, “Modified Kudryashov method via new exact solutions for some conformable fractional differential equations arising in mathematical biology”,Chinese J. Phys., Vol.: 56, Issue: 1, pp.: 75-85, 2018.

XI.D. U. Zhong, B. O. Tian, X. Y. Xie,J. Chai, X. Y. Wu, “Backlund transformation and soliton solutions in terms of the wronskian for the Khdomtsev-petviashvili-based system in fluid dynamics”,Pramana J. Phys., Vol.: 90, Issue: 45, pp.:1-6, 2018.

XII.E. Fan, “Extended tanh method and its application to nonlinear equations”,Phys. Lett. A, Vol.: 277, Issue: 4-5, pp.: 212-218, 2000.

XIII.E. Fan, J. Zhang, “Application of the Jacobi elliptic function method to special type nonlinear equations”,Phys. Lett. A, Vol.: 305, Issue: 6, pp.: 383-392, 2002.

XIV.E. M. E. Zayed, A. G. A. Nowehy, “Solitons and other exact solutions for a class of nonlinear Schrodinger type equation”,Optik-Int. J. Light Elec. Optics, Vol.: 130, pp.:1295-1311, 2017.

XV.E. M. E. Zayed, K. A. E. Alurrfi, “Homogeneous balance method and itsapplication for finding the exact solution for nonlinear evolution equation”,Ital. J. Pure Appl. Math., Vol.: 33, pp.: 307-318, 2014.

XVI.F. Mahmud, M. Samsuzzoha, M. A. Akbar, “The generalized Kudryashov method to obtain exact traveling wave solutions of thePHI-four equation and the Fishers equation”, Results Phys., Vol.: 7, pp.: 4296-4302, 2017.

XVII.H. Bulut, S. S. Atas, H. M. Baskonus, “Some novel exponential function structures to the Cahn-Allen equation”,Cogent Phys., Vol.: 3, ID: 1240886, 2016.

XVIII.J. L. Zhang, M. L. Wang, Y. M. Wang, Z. D. Fang, “The improved F-expansion method and its applications”,Phys. Letters A, Vol.: 350, Issue:1-2, pp.:103-109, 2006.

XIX.K. A. Gepreel, T. A. Nofal, A. A. Alasmari, “Exact solutions for nonlinear integro-partial differential equations using the generalized Kudryashov method”,J. Egyp. Math. Soci., Vol.: 25, Issue: 4, pp.: 438-444, 2017.

XX.K. Khan, M. A. Akbar, N. H. M. Ali,”The modified simple equation method for exact and solitary wave solutions of nonlinear evolution equation-the GZK-BBM equation and Right-Handed non-commutative Burgers equation”, Int. Schol. Resear. Notices, Vol.: 2013, ID:146704, 2013.

XXI.K. Khan, M. A. Akbar, “The exp(–𝜑𝜉)-expansion method for finding travelling wave solution of Vakhnenko-Parkes equation”, Int. J. Dyna. Syst. Diff. Equ., Vol.: 5, Issue: 1, pp.: 72, 2014.

XXII.K. R. Raslan, T. S. E. Danfal, K. A. Ali, “New exact solutions of coupled generalized regularized long wave equation”,J. Egyp. Math. Soci., Vol.: 25, Issue: 4, pp.: 400-405, 2017.

XXIII.L. Jianbin, K.Yang, “The extended F-expansion method and exact solution of nonlinear PDEs”,Chaos Solit. Frac., Vol.: 22, Issue:1, pp.:111-121, 2004.

XXIV.M. A. Akbar, N. H. M. Ali, “An ansatz for solving nonlinear partial differential equation in mathematical physics”,Springer Plus, Vol.: 5, Issue: 24, pp.:1-13, 2016.

XXV.M. A. Khater, A. R. Seadawy, D.Lu, “Elliptic and solitary wave solutions for the Bogoyavlenskii equations system: Couple Boiti-Leon-Pempinelli equations system and time fractional Cahn-allen equation”,Results Phys., Vol.: 7, pp.: 2325-2333, 2017.

XXVI.M. K. Elboree, “The Jacobi elliptic function method and its application for two component BKP hieracy equations”,Comp. Math. Appl., Vol.: 62, Issue: 12, pp.: 4402-4414, 2011.

XXVII.M. koparan, M. Kaplan, A. Bekir, O. Guner, “A novel generalized Kudryashov method for exact solutions of nonlinear evolution equations”,AIP Conference Proc., Vol.: 1798, Issue: 1, 2017.

XXVIII.M. M. Kabir, A. Khajeh, E. Aghdam, A. Y. Koma, “Modified Kudryashov method for finding exact solitary wave solutions of higher order nonlinear equations”,Math. Meth. Appl. Sci., Vol.: 34, Issue: 2, pp.: 213-219, 2011.

XXIX.M. N. Alam, M. A. Akbar, “Some new exact traveling wave solutions to the Simplified MCH equation and the (1+1)-dimensional combined KdV-mKdV equations”,J. Assoc. Arab Univ. Basic Appl. Sci., Vol.: 17, pp.: 6-13, 2015.

XXX.M. S. Islam, K. Khan, M. A. Akbar, “Application of the improved F-expansion method with Riccati equation to find the exact solution of the nonlinear evolution equation”, J. Egypt. Math. Soci., Vol.: 25, Issue: 1, pp.: 13-18, 2017.

XXXI.N. Ahmed, S. Bibi, U. Khan, S. T. Mohyud-Din, “A new modification in the exponential rational function method for nonlinear fractional differential equation”,Eur. Phys. J. Plus, Vol.: 133, Issue: 45, pp.: 1-11, 2018.

XXXII.N. A. Kudryashov, “One method for finding exact solutions of nonlinear differential equations”,Commun. Nonl. Sci. Num. Simul., Vol.: 17, Issue: 6, pp.: 2248-2253, 2012.

XXXIII.O. Guner, A. Bekir, “Solving nonlinear space-time fractional differential equation via ansatz method”,Comp. Meth. Diff. Equ., Vol.: 6, Issue: 1, pp.: 1-11, 2018.

XXXIV.Q. Feng, B. Zheng, B., “Traveling wave solutions for three nonlinear equations by (𝐺′/𝐺)-expansion method”,WSEAS Trans. Comp., Vol.: 9, Issue: 3, pp.: 225-234, 2010.

XXXV.S. Bibi, S. T. Mohyud-Din, U. Khan, N. Ahmed, “Khater method for non-linear Sharma Tasso-Olever (STO) equation of fractional order”, Results in Phys., Vol.: 7, pp.:4440-4450, 2017.

XXXVI.S. T. Demiray,Y. Pandir, H. Bulut, “Generalized Kudryashov method for time fractional differential equations”,Abst. Appl. Analy., Vol.: 2014, ID: 901540, 2014.

XXXVII.S. T. Mohyud-Din, A. Yildirim, M. M. Hosseini, “Variational iteration method for initial and boundary value problems using He’s polynomials”, Int. J. Diff. Equ., ID 426213, 2010.

XXXVIII.W. Malfliet, “The tanh method: a tool for solving certain classes of nonlinear evolution and wave equations”,J. Comp. Appl. Math., Vol.: 164-165, Issue: 1, pp.: 529-541, 2004.

XXXIX.W. M. Taha, M. S. M. Noorani, “Application of the (𝐺′/𝐺)-expansion method to the generalized Fisher’s equation and modified equal width equation”,J. Assoc. Arab Univ. Basic Appl. Sci., Vol.:15, pp.: 82-89, 2014.

XL.Y. Pandir, Y. Gurefe, E. Misirli, “The extended trial equation method for some Time-Fractional differential equation”,Disc. Dyna. Nature Soci., Vol.: 2013, ID: 491359, 2013

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Abstract:

The La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) is categorized as a mixed ionic-electronic conducting oxide has found significant attention as cathode material in solid oxide fuel cells (SOFCs) operating at intermediate temperatures, 500-850oC. The performance of LSCF electrode is limited by the oxygen ion transport process at the surface, which is the rate determining step of oxygen reduction reaction. To enhance the oxygen surface exchange process of LSCF electrode, a nano-composite electrolyte is introduced at the surface, which substantially improves the electrochemical performance. The electrical conductivity relaxation technique (ECR) has been used to study the oxygen surface exchange kinetics of bare LSCF and coated with a mixture of Ce0.8Sm0.2O2-δ (SDC) and ZrO2.Y2O3 (Yttria-stabilized zirconia -YSZ) nano-powders in three different weight ratios, SDC:YSZ = 0.5:1, 1:1, 1:0.5. The chemical oxygen surface exchange coefficient kchem of surface modified specimens were derived with a one-parameter fitting process. The results show that the oxygen surface exchange kinetics of LSCF is affected by the SDC-YSZ coating and the average kchem values of SDC-YSZ coated LSCF increases by a factor 2 to 8 from 650 to 850 oC, respectively. It has been concluded that the high ionic conductive oxide coating improves the oxygen surface exchange kinetics of underlying LSCF mixed conducting oxide and consequently enhances the performance of electrochemical device such as solid oxide fuel cell.

Keywords:

SOFC,ECR,Nano-composite,Coating,

Refference:

I.A.Samreen, S. Saher, S. Ali, H. Shahzad, A. Qamar, Effect of hetero-structured nano-particulate coating on the oxygen surface exchange properties of La0.6Sr0.4Co0.2Fe0.8O3-δ, Int. Journal of Hydrogen Energy, HE-25296, 2019 (in press).

II.Cheng, F., & Chen, J. (2012). Metal–air batteries: from oxygen reduction electrochemistry to cathode catalysts.Chemical Society Reviews,41(6), 2172-2192.

III.Fekete, M., Hocking, R. K., Chang, S. L., Italiano, C., Patti, A. F., Arena, F., &Spiccia, L. (2013). Highly active screen-printed electrocatalysts for water oxidation based on β-manganese oxide.Energy & Environmental Science,6(7), 2222-2232.

IV.Gorlin, Y., & Jaramillo, T. F. (2010). A bifunctional nonprecious metal catalyst for oxygen reduction and water oxidation.Journal ofthe American Chemical Society,132(39), 13612-13614.

V.Haoran, Y., Lifang, D., Tao, L., & Yong, C. (2014). Hydrothermal synthesis of nanostructured manganese oxide as cathodic catalyst in a microbial fuel cell fed with leachate.The Scientific World Journal,2014.

VI.He, G., Qiao, M., Li, W., Lu, Y., Zhao, T., Zou, R., &Parkin, I. P. (2017). S, N‐Co‐Doped Graphene‐Nickel Cobalt Sulfide Aerogel: Improved Energy Storage and Electrocatalytic Performance.Advanced Science,4(1), 1600214.

VII.Iyer, A., Del-Pilar, J., King’ondu, C. K., Kissel, E., Garces, H. F., Huang, H., &Suib, S. L. (2012). Water oxidation catalysis using amorphous manganese oxides, octahedral molecular sieves (OMS-2), and octahedral layered (OL-1) manganese oxide structures.The Journal of Physical Chemistry C,116(10), 6474-6483.

VIII.Kjaergaard, C. H., Rossmeisl, J., &Nørskov, J. K. (2010). Enzymatic versus inorganic oxygen reduction catalysts: Comparison of the energy levels in a free-energy scheme.Inorganic chemistry,49(8), 3567-3572.

IX.Kundu, S., Nagaiah, T. C., Xia, W., Wang, Y., Dommele, S. V., Bitter, J. H., &Muhler, M. (2009). Electrocatalytic activity and stability of nitrogen-containing carbon nanotubes in the oxygen reduction reaction.The Journal of Physical Chemistry C,113(32), 14302-14310.

X.Liao, L., Zhang, Q., Su, Z., Zhao, Z., Wang, Y., Li, Y., &Cai, X. (2014). Efficient solar water-splitting using a nanocrystallineCoOphotocatalyst.Nature nanotechnology,9(1), 69.

XI.Mukerjee, S., &Srinivasan, S. (1993). Enhanced electrocatalysis of oxygenreduction on platinum alloys in proton exchange membrane fuel cells.Journal of Electroanalytical Chemistry,357(1-2), 201-224.

XII.Shi, X., Iqbal, N., Kunwar, S. S., Wahab, G., Kasat, H. A., &Kannan, A. M. (2018). PtCo@ NCNTs cathode catalyst using ZIF-67 for proton exchange membrane fuel cell.International Journal of Hydrogen Energy,43(6), 3520-3526.

XIII.Shinozaki, K., Zack, J. W., Richards, R. M., Pivovar, B. S., &Kocha, S. S. (2015). Oxygen reduction reaction measurements on platinum electrocatalysts utilizing rotating disk electrode technique I. Impact of impurities, measurement protocols and applied corrections.Journal of The Electrochemical Society,162(10), F1144-F1158.

XIV.S. Saher, S. Naqash, B. A. Boukamp, B. Li, C. Xia, H. J. M. Bouwmeester, Influence of ionic conductivity of the nano-particulate coating phase on oxygen surface exchange of La0.58Sr0.4Co0.8Fe0.2O3-δ , J. Mater. Chem. A, 5 (3),2017, 4991-4999.

XV.Song, E., Shi, C., & Anson, F. C. (1998). Comparison of the behavior of several cobalt porphyrins as electrocatalysts for the reduction of O2 at graphite electrodes.Langmuir,14(15), 4315-4321.

XVI.Su, B., Hatay, I., Trojánek, A., Samec, Z., Khoury, T., Gros, C. P., &Girault, H. H. (2010). Molecular electrocatalysis for oxygen reduction by cobalt porphyrins adsorbed at liquid/liquid interfaces.Journal of the American Chemical Society,132(8), 2655-2662.

XVII.Xia, B. Y., Yan, Y., Li, N., Wu, H. B., Lou, X. W. D., & Wang, X. (2016). A metal–organic framework-derived bifunctional oxygen electrocatalyst.Nature Energy,1(1), 15006.

XVIII.Yang, J., Sun, H., Liang, H., Ji, H., Song, L., Gao, C., &Xu, H. (2016). A highly efficient metal‐free oxygen reduction electrocatalyst assembled from carbon nanotubes and graphene.Advanced Materials,28(23), 4606-4613.

XIX.Zhang, W., Shaikh, A. U., Tsui, E. Y., &Swager, T. M. (2009). Cobalt porphyrin functionalized carbon nanotubes for oxygen reduction.Chemistry of Materials,21(14), 3234-3241.

XX.Zhang, X., Chen, Y., Wang, J., &Zhong, Q. (2016). Nitrogen and fluorine dual‐doped carbon black as an efficient cathode catalyst for oxygen reduction reaction in neutral medium.ChemistrySelect,1(4), 696-702.

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Abstract:

The oxygen reduction reaction (ORR) in proton exchange membrane fuel cell (PEMFC) having sluggish kinetics at cathode side requires highly active and low cost catalyst. Conventionally, platinum (Pt) is considered to be the most feasible and active catalyst for ORR at cathode, however,it’s far expensive to meet the demand for commercialization. Herein novel non platinum group metal (N-PGM) nickel (Ni) nanotubes were prepared by using solvothermal method using transition metal precursor forming Ni ZeoliticImidazolateFramework (Ni/ZIF). Ni nanotubes were obtained after carbonizing Ni/ZIF at 850oC under inert nitrogen atmosphere. The morphology and motif were extensively studied by conducting XRD and SEM. The electro-catalytic performance of Pt/C catalyst, pristine Ni/ZIF and Ni nanotubes were investigated by Linear Sweep Voltammetry (LSV) performed with Rotating Disk Electrode (RDE) in alkaline medium. The Ni/ZIF shows a current density of -1.4 mA/cm2and Ni nanotubes depicts current density of -2.7mA.cm-2 and an over potential of -0.27V Vs Saturated Calomel Electrode (SCE). RDE Results were obtained at 400, 800, 1200 and 1600 rpm in 0.1M KOH solution. The evaluation shows that Ni nanotubes own extraordinary electro-catalytic behavior towards ORR activity and Ni nanotubes has potential to be used for PEMFC application.

Keywords:

Ni ZeoliticImidazolateFramework(Ni/ZIF),Ni nanotubes,Oxygen Reduction Reaction,Linear Sweep Voltammetry,

Refference:

I.Cheng, F., & Chen, J. (2012). Metal–air batteries: from oxygen reduction electrochemistry to cathode catalysts.Chemical Society Reviews,41(6), 2172-2192.

II.Fekete, M., Hocking, R. K., Chang, S. L., Italiano, C., Patti, A. F., Arena, F., &Spiccia, L. (2013). Highly active screen-printed electrocatalysts for water oxidation based on β-manganese oxide.Energy & Environmental Science,6(7), 2222-2232.

III.Gorlin, Y., & Jaramillo, T. F. (2010). A bifunctional nonprecious metal catalyst for oxygen reduction and water oxidation.Journal of the American Chemical Society,132(39), 13612-13614.

IV.Haoran, Y., Lifang, D., Tao, L., & Yong, C. (2014). Hydrothermal synthesis of nanostructured manganese oxide as cathodic catalyst in a microbial fuel cell fed with leachate.The Scientific World Journal,2014.

V.He, G., Qiao, M., Li, W., Lu, Y., Zhao, T., Zou, R., &Parkin, I. P. (2017). S, N‐Co‐Doped Graphene‐Nickel Cobalt Sulfide Aerogel: Improved Energy Storage and Electrocatalytic Performance.Advanced Science,4(1), 1600214.

VI.Iyer, A., Del-Pilar, J., King’ondu, C. K., Kissel, E., Garces, H. F., Huang, H., &Suib, S. L. (2012). Water oxidation catalysis using amorphous manganese oxides, octahedral molecular sieves (OMS-2), and octahedral layered (OL-1) manganese oxide structures.The Journal of Physical Chemistry C,116(10), 6474-6483.

VII.Kjaergaard, C. H., Rossmeisl, J., &Nørskov, J. K. (2010). Enzymatic versus inorganic oxygen reduction catalysts: Comparison of the energy levels in a free-energy scheme.Inorganic chemistry,49(8), 3567-3572.

VIII.Kundu, S., Nagaiah, T. C., Xia, W., Wang, Y., Dommele, S. V., Bitter, J. H., &Muhler, M. (2009). Electrocatalytic activity and stability of nitrogen-containing carbon nanotubes in the oxygen reduction reaction.The Journal of Physical Chemistry C,113(32), 14302-14310.

IX.Liao, L., Zhang, Q., Su, Z., Zhao, Z., Wang, Y., Li, Y., &Cai, X. (2014). Efficient solar water-splitting using a nanocrystallineCoOphotocatalyst.Nature nanotechnology,9(1), 69.

X.Mukerjee, S., &Srinivasan, S. (1993). Enhanced electrocatalysis of oxygen reduction on platinum alloys in proton exchange membrane fuel cells.Journal of Electroanalytical Chemistry,357(1-2), 201-224.

XI.Shi, X., Iqbal, N., Kunwar, S. S., Wahab, G., Kasat, H. A., &Kannan, A. M. (2018). PtCo@ NCNTs cathode catalyst using ZIF-67 for proton exchange membrane fuel cell.International Journal of Hydrogen Energy,43(6), 3520-3526.

XII.Shinozaki, K., Zack, J. W., Richards, R. M., Pivovar, B. S., &Kocha, S. S. (2015). Oxygen reduction reaction measurements on platinum electrocatalystsutilizing rotating disk electrode technique I. Impact of impurities, measurement protocols and applied corrections.Journal of The Electrochemical Society,162(10), F1144-F115

XIII.Su, B., Hatay, I., Trojánek, A., Samec, Z., Khoury, T., Gros, C. P.,&Girault, H. H. (2010). Molecular electrocatalysis for oxygen reduction by cobalt porphyrins adsorbed at liquid/liquid interfaces.Journal of the American Chemical Society,132(8), 2655-2662.

XIV.Song, E., Shi, C., & Anson, F. C. (1998). Comparison of the behavior of several cobalt porphyrins as electrocatalysts for the reduction of O2 at graphite electrodes.Langmuir,14(15), 4315-4321.

XV.Xia, B. Y., Yan, Y., Li, N., Wu, H. B., Lou, X. W. D., & Wang, X. (2016). A metal–organic framework-derived bifunctional oxygen electrocatalyst.Nature Energy,1(1), 15006.

XVI.Yang, J., Sun, H., Liang, H., Ji, H., Song, L., Gao, C., &Xu, H. (2016). A highly efficient metal‐free oxygen reduction electrocatalyst assembled from carbon nanotubes and graphene.Advanced Materials,28(23), 4606-4613.

XVII.Zhang, W., Shaikh, A. U., Tsui, E. Y., &Swager, T. M. (2009). Cobalt porphyrin functionalized carbon nanotubes for oxygen reduction.Chemistry of Materials,21(14), 3234-3241.

XVIII.Zhang, X., Chen, Y., Wang, J., &Zhong, Q. (2016). Nitrogen and fluorine dual‐doped carbon black as an efficient cathode catalyst for oxygen reduction reaction in neutral medium.ChemistrySelect,1(4), 696-702.

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Abstract:

The Porous silica nanoparticles deposited on the glass as well as bare silicon wafer substrate to obtain super hydrophilicity and antireflectivity. The coating is performed by using aerosol impact deposition system using silane, air and helium as precursor gases. The desired coating thickness over the substrate surface is achieved by tuning the RF power, pressures ratio of reaction to deposition chamber and maneuvering of silane flow rate, helium and air mixture. Scanning electron microscopy reveals the particle size of 12.6 nm, whereas, atomic force microscopy (AFM) is deployed to study the coated film surface topology. This indicates outstanding antifogging and super-hydrophilic properties due to surface roughness and nano-porosity. Moreover, the coated surface graded index increases the transmissivity from 90% to 99.2%. Such enhancements are much favorable for the solar PV applications.

Keywords:

Nanoparticles, Antireflectivity,erosol deposition,Solar PV,SEM,AFM,

Refference:

I.Cebeci, F.Ç., Wu, Z., Zhai, L., Cohen, R.E. and Rubner, M.F., 2006. Nanoporosity-driven superhydrophilicity: a means to create multifunctional antifogging coatings. Langmuir, 22(6), pp.2856-2862.

II.Chen, D., 2001. Anti-reflection (AR) coatings made by sol–gel processes: a review. Solar Energy Materials and Solar Cells, 68(3-4), pp.313-336.

III.Deubener, J., Helsch, G., Moiseev, A. and Bornhöft, H., 2009. Glasses for solar energy conversion systems. Journal of the European Ceramic Society, 29(7), pp.1203-1210.

IV.Eshaghi, A., Aghaei, A.A., Zabolian, H., Jannesari, M.O.H.A.M.M.A.D. and Firoozifar, A.L.I.R.E.Z.A., 2013. Transparent superhydrophilic SiO2/TiO2/SiO2 tri-layer nanostructured antifogging thin film. Ceram–Silikaty, 57, pp.210-214.

V.Faustini, M, Nicole, L., Boissi ere, C., Innocenzi, P., Sanchez, C., Grosso, D., 2010. Hydrophobic, antireflective, self-Cleaning, and antifogging sol–gel coatings: an example of multifunctional nano structured materials for photovoltaic cells, Chemistry of Materials 22 (2010) 4406–4413.

VI.Granqvist, C.G., 2007. Transparent conductors as solar energy materials: A panoramic review. Solar energy materials and solar cells, 91(17), pp.1529-1598

VII.Guillemot, F., Brunet-Bruneau, A., Bourgeat-Lami, E., Gacoin, T., Barthel, E. andBoilot, J.P., 2010. Latex-templated silica films: tailoring porosity to get a stable low-refractive index. Chemistry of Materials, 22(9), pp.2822-2828.

VIII.Hassan, A. H., Rahoma, U. A., Elminir, H. K., and Fathy, A. M., 2005, “Effect of airborne dust concentration on the performance of PV modules,” J AstronSoc Egypt, 13, pp. 24-38.

IX.Karasiński, P., Jaglarz, J., Reben, M., Skoczek, E. and Mazur, J., 2011. Porous silica xerogel films as antireflective coatings–Fabrication and characterization. Optical Materials, 13(12), pp.1989-1994.

X.Lee, S., Cho, L. S., Lee, J. H., Kim, D. H., Kim, D. W., Kim, J. Y., Shin, H, Lee, J. k., Jung, H. S., Park, N. G., Kim, K.,, M.J.Ko, K.S.Hong, 2010. Two-step sol–gel method-based TiO2 nanoparticles with uniform morphology and size for efficient photo-energy conversiondevices. Chemistry of Materials, 22, pp.1958–1965.

XI.Li, D., Liu, Z., Wang, Y., Shan, Y. and Huang, F., 2015. Efficiency Enhancement of Cu (In, Ga) Se 2 Solar Cells by Applying SiO 2–PEG/PVP Antireflection Coatings. Journal of Materials Science & Technology, 31(2), pp.229-234.

XII.Li, X. and He, J., 2016. Synthesis of raspberry-like SiO2–TiO2 nanoparticles toward antireflective and self-cleaning coatings. ACS applied materials & interfaces, 5(11), pp.5282-5290.

XIII.Li, Y., Zhang, J., Zhu, S., Dong, H., Jia, F., Wang, Z., Sun, Z., Zhang, L., Li, Y., Li, H. and Xu, W., 2009. Biomimetic surfaces for high‐performance optics. Advanced Materials, 21(46), pp.4731-4734.Yang, Adv. Mater. 21 (2009) 4731.

XIV.Prado, R., Beobide, G., Marcaide, A., Goikoetxea, J. and Aranzabe, A., 2016. Development of multifunctional sol–gel coatings: Anti-reflection coatings with enhanced self-cleaning capacity. Solar Energy Materials and Solar Cells, 94(6), pp.1081-1088.

XV.Prosser, J. H., Brugarolas, T., Lee, S., Nolte, A. J., Lee, D., 2012. Avoiding Cracks in Nanoparticle Films. Nano Lett., 12, pp.5287− 5291.

XVI.Raut, H.K., Ganesh, V.A., Nair, A.S. and Ramakrishna, S., 2011. Anti-reflective coatings: A critical, in-depth review. Energy & Environmental Science, 4(10), pp.3779-3804.

XVII.Singh, K. B., Tirumkudulu, M. S., Cracking in Drying Colloidal Films, 2007. Phys. Rev. Lett., 98, pp.218302.

XVIII.Tanesab, J., Parlevliet, D., Whale, J., Urmee, T. and Pryor, T., 2015. The contribution of dust to performance degradation of PV modulesin a temperate climate zone. Solar Energy, 120, pp.147-157.

XIX.Verma, L.K., Sakhuja, M., Son, J., Danner, A.J., Yang, H., Zeng, H.C. and Bhatia, C.S., 2011. Self-cleaning and antireflective packaging glass for solar modules. Renewable Energy, 36(9), pp.2489-2493.

XX.Xu, G., Jin, P., Tazawa, M. and Yoshimura, K., 2004. Optimization of antireflection coating for VO2-based energy efficient window. Solar Energy Materials andSolar Cells, 83(1), pp.29-37.

XXI.Zhang, X.P., Lan, P.J., Lu, Y.H., Li, J., Xu, H., Zhang, J., Lee, Y., Rhee, J. Y., Choy, K. L., Song, W. J., 2014. Multifunctional antireflection coatings based on novel hollow silica-silica nanocomposites, ACS Appl. Mater. Interfaces, 6, pp.1415–1423.

XXII.Zhang, X.X., Xia, B.B., Ye, H.P., Zhang, Y.L., Xiao, B., Yan, L.H., Lv, H.B. and Jiang, B., 2012. One-step sol–gel preparation of PDMS–silica ORMOSILs as environment-resistant and crack-free thick antireflective coatings. Journal of Materials Chemistry, 22(26), pp.13132-13140.

XXIII.Zhang, L., Qiao, Z.A., Zheng, M., Huo, Q. and Sun, J., 2010. Rapid and substrate-independent layer-by-layer fabrication of antireflection-and antifogging-integrated coatings. Journal of Materials Chemistry, 20(29), pp.6125-6130.

XXIV.Zhang, X., Fujishima, A., Jin, M., Emeline, A.V. and Murakami, T., 2006. Double-layered TiO2− SiO2 nanostructured films with self-cleaning and antireflective properties. The Journal of Physical Chemistry B, 110(50), pp.25142-25148.

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Abstract:

In order to fulfill all demands of the customers, related to energy, the capacity of the Grid is designed in such a way that, it satisfies not only the average power demand, but also the peak power demand. In this paper, Demand Side Management (DSM),programs which play a vital role in near future Smart Grid (SG) has been considered.DSM aims either at reducing or shifting consumption to shape users’ energy consumption profile. In the energy zone, the country is facing severe short fall from the last two decades, and hence effecting not only the economic growth, but also the industrial development. The main goal of DSM is usually to encourage the consumers to use less energy during peak hours or to move the time to use energy in the off-peak hours.

Keywords:

Smart Grid (SG),Demand Side Management (DSM),Peak-to Average Ratio (PAR),

Refference:

I. Fang, Xi, SatyajayantMisra, GuoliangXue, and Dejun Yang. “Smart grid—The new and improved power grid: A survey.” IEEE communications surveys & tutorials 14, no. 4 (2012): 944-980.

II. Hussain, Hafiz Majid, NadeemJavaid, Sohail Iqbal, QadeerUlHasan, Khursheed Aurangzeb, and MusaedAlhussein. “An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid.” Energies 11, no. 1 (2018): 190.

III. Jacquot, Paulin, Olivier Beaude, StéphaneGaubert, and Nadia Oudjane. “Demand side management in the smart grid: an efficiency and fairness tradeoff.” In Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2017 IEEE PES, pp. 1-6.IEEE, 2017.

IV. Kim, Sungwook. “An adaptive smart grid management scheme based on the coopetition game model.” ETRI journal36, no. 1 (2014): 80-88.

V. Latifi, Milad, AzamKhalili, Amir Rastegarnia, SajadZandi, and Wael M. Bazzi. “A distributed algorithm fordemand-side management: Selling back to the grid.” Heliyon 3, no. 11 (2017): e00457.

VI. Liu, Yi, Chau Yuen, Shisheng Huang, NaveedUl Hassan, Xiumin Wang, and ShengliXie. “Peakto-average ratio constrained demand-side management with consumer’s preference in residential smart grid.” IEEE Journal of Selected Topics in Signal Processing 8, no. 6 (2014): 1084-1097.

VII. Longe, O. M., K. Ouahada, H. C. Ferreira, and S. Rimer. “Consumer preference electricity usage plan for demand side management in the smart grid.” SAIEE Africa Research Journal 108, no. 4 (2017): 174-183.

VIII. Longe, Omowunmi Mary, and KhmaiesOuahada. “Mitigating Household Energy Poverty through Energy Expenditure Affordability Algorithm in a Smart Grid.” Energies 11, no. 4 (2018): 947.

IX. Mahmood, Anzar, M. N. Ullah, S. Razzaq, Abdul Basit, U. Mustafa, M. Naeem, and NadeemJavaid. “A new scheme for demand side management in future smart grid networks.”ProcediaComputer Science 32 (2014): 477-484.

X. Safdarian, Amir, Mahmud Fotuhi-Firuzabad, and MattiLehtonen. “Optimal residential load managementin smart grids: A decentralized framework.” IEEE Transactions on Smart Grid 7, no. 4 (2016): 1836-1845.

XI. Touzene, Abderezak, Sultan Al Yahyai, and Amar Oukil. “Smart Grid Resources Optimization Using Service Oriented Middleware.” International Journal of Computer Applications in Technology (2018): 1.

XII. Ullah, M. N., NadeemJavaid, I. Khan, AnzarMahmood, and M. U. Farooq. “Residential energy consumption controlling techniques to enable autonomous demand side management in future smart grid communications.” In Broadband and Wireless Computing, Communication and Applications (BWCCA), 2013 Eighth International Conference on, pp. 545-550. IEEE, 2013

XIII. Wijaya, Tri Kurniawan, Thanasis G. Papaioannou, Xin Liu, and Karl Aberer. “Effective consumption scheduling for demand-side management in the smart grid using non-uniform participation rate.” In Sustainable Internet and ICT for Sustainability (SustainIT), 2013, pp. 1-8. IEEE, 2013.

XIV. Yu, Mengmeng, and Seung Ho Hong. “A Real-Time Demand-Response Algorithm for Smart Grids: A Stackelberg Game Approach.” IEEE Trans. Smart Grid 7, no. 2 (2016): 879-888.

XV. Yu, Yixin, Yanli Liu, and Chao Qin. “Basicideas of the smart grid.” Engineering 1, no. 4 (2016): 405-408.

XVI. Zahoor, Saman, NadeemJavaid, Asif Khan, F. J. Muhammad, M. Zahid, and M. Guizani. “A cloud-fog-based smart grid model for efficient resource utilization.”In 14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC-2018). 2018.

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Abstract:

This article presents the effectiveness of Chip Mental Arithmetic Kit in teaching and learning of topic addition and subtraction. The methods used in the research weredescriptive analysis. The sample consist of two hundred and forty-six(246)standard one students of 3 primary school in total in the state ofSelangor and Perak. Samples were divided into groups and the Chip Mental Arithmetic Kit was distributed to each group.The researcher applied the Chip Mental Arithmetic Kit in the classroom during their teaching and learning process of topic addition and subtraction in primary education. The instrument used by researcher is questionnaire. The questionnaire contained 21 questions which include the figure, the reliability, the endurance of Chip Mental Arithmetic Kit. The student’s answers wereanalyzed. The result showed that Chip Mental Arithmetic Kit had a significant positive impact among the students, and the latter preferred Chip Mental Arithmetic Kit rather than using the traditional method of teaching addition and subtraction in standardone. The findings of the current study encouraged teachers and students to use Chip Mental Arithmetic Kit in their teaching and learning process.

Keywords:

addition and subtraction,Chip Mental Arithmetic Kit,teaching manipulative,

Refference:

I.C.Allen, “An Action Based Research Study on How Using Manipulatives Will Increase Students’ Achievement in Mathematics “, Chicago: MarygroveCollege, 2007.

II.C. J. Ross, “The Effect Of Mathematical Manipulative Materials on Third Grade Students’ Participation, Engagement, And Academic Performance”, Master’s Thesis. University of Central Florida, Orlando, 2008.

III.D. H. Clements, “Concrete Manipulatives, Concrete Ideas”,Contemporary Issues in Early Childhood, Volume:1, Issue: 1, pp: 45 -60, 1999.

IV.D. George, M. Mallery, “SPSS for Windows Step by Step: A Simple Guide and Reference”, 17.0 update (10a ed.) Boston: Pearson, 2010.

V.F. Nisih, “Does the Japanese Abacus Improve Underachieving Children’s Performance inMathematics? ” Proceedings of the British Society for Research into Learning Mathematics, pp: 13-18,2014.

VI.J. C. Nunnally, McGraw-Hill series in psychology. Psychometric theory. New York, NY, US:McGraw-Hill, 1967

VII.J. M. Shaw, “Manipulatives Enhance the Learning of Mathematics”, (C. Y. R. N. Farah, Ed.) Retrieved (May 8, 2017), [Online] Available: http://www. eduplace.com/state/author/shaw.pdf, 2002.

VIII.K. P. Hinzman, “Use of Manipulatives in Mathematics at The Middle School Level and Their Effects on Students’ Grades and Attitudes”(Degree’s Thesis). Salem-Teikyo University, Salem, 1997.

IX.M. Siegel, R. Borasi, and J. Fonzi, “Supporting Students’ Mathematical Inquiries through Reading”, Journal for Research in Mathematics Education,Volume: 29, Issue: 4, pp: 378 –413, 1998.

X.Md. Yunus, A. Suraya and Wan Ali, Wan Zah, “Metacognition and motivation in mathematicalproblem solving “, The International Journal of Learning, Volume: 15, Issue: 3, pp: 121-132, 2008.

XI.R. N. Farah, N. Bahirah, “Chip Mental Arithmetic Kit as A New Teaching Aids in Teaching andLearning 21stCentury”, Journal of Advanced Research in Dynamical and Control Systems, Volume: 9, pp: 348 –352, 2018.

XII.R. N. Farah, N. Bahirah, R. L. Zuraida, “Manipulative Kit Used in Teaching and Learning TopicAddition and Subtraction in 21stCentury”, International Journal of Recent Scientific Research, Volume: 9, pp: 29508 –29513, 2018.

XIII.Y. Liu, “Tangram Race Mathematical Game: Combining Wearable Technology and Traditional Games for Enhancing Mathematics Learning “, Massachusetts: Worcester Polytechnic Institute, 2014

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Abstract:

Nosql database is also known as not only sql database. For real time web application and for large set of distributed data, nosql database gaining popularity to handle big data. Nosql database has enormous function to handle big data. In contrast to this, nosql database also supports auto sharding, auto replication and many other feature making it suitable to be used as storage mechanism. Nosql database is used to store data in an unstructured way, when more attention is paid to Performance and real time access rather than consistency, then nosql databases seems to be more appropriate. However, research on the security of nosql database is very limited. Although, there are many research benefit in nosql database like scalability, faster data access and availability as compare to rdbms. But nosql database has some security issues. The experimental testing on advance nosql injections is performed. The demonstration of advance nosql injection attack against a nosql database is performed with php and JavaScript. It shows the client’s data. Method are discussed to prevent this type of security problems from happening again. This paper also shows how to create a security layer of nosql application to prevent nosql injection. In this paper, we will demonstrate, advance nosql injection attack and propose defense method to secure the nosql database. Hence nosql database programmer be aware of the nosql injection attack mechanism and build a more secure database to store huge data.

Keywords:

Nosql,MongoDB,Injection,Attack,Consistency,Vulnerability,Scalability,

Refference:

I.Abramova, Veronika, and Jorge Bernardino”NoSQL databases: MongoDB vs Cassandra.” Proceedings of the International C* Conference on Computer Science and Software Engineering10 Jul: 14-22, 2013.

II.Ahmed M. Eassa , Hazem M. El-Bakry“NoSQL Racket: A Testing Tool for Detecting NoSQL Injection Attacks in Web Applications”International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, 2017.

III.Aviv Ron,Alexandra Shulman-Peleg,Emanuel Bronshtein “No SQL, No Injection? Examining NoSQL Security Examining NoSQL Security” In proceedings of the 9thworkshop on web 2.0 security and privacy (W2SP) 2015.

IV.BoyuHou,Kai Qian “MongoDBNoSQL injection Analysis and detection” 2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing.

V.BoyuHou,yongshi“ Towards analyzing MongoDBNoSQL security and designing injection defense solution” ieee3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids), 26-28 may 2017.

VI.Chickerur, Satyadhyan, AnoopGoudar, and AnkitaKinnerkar”Comparison of Relational Database with Document-Oriented Database (MongoDB) for Big Data Applications.” 28th International Conference on Advanced Software Engineering & Its Applications (ASEA) 25 Nov. 2015: 41-47.

VII.Changlin He,“Survey on nosql database technology”,journal of applied science and engineering innovation vol. 2 no. 2,2015.

VIII.EbrahimSahafizadeh, Mohammad Ali Nematbakhsh“A Survey on Security Issues in Big Data and NoSQL” ACSIJ Advances in ComputerScience: an International Journal, Vol. 4, Issue 4, No.16 , July 2015 ISSN : 2322-5157.

IX.https://www.mongodb.org

X.Jing Han,haihongE,GuanLe,JianDu,“survey on nosql database”, 2011 IEEE.

XI.Kadebu, Prudence, and Innocent Mapanga, “A Security Requirements Perspective towards a Secured NOSQL Database Environment.” International Conference of Advance Research and Innovation, 2014.

XII.ManovegSaxena,ZakirAli, Vinod Kumar Singh,“NOSQL database –analysis,Techniques and classification” journal of advanced database management &system,volume 1 issue 2,2014.

XIII.Noiumkar, Preecha, and TawatchaiChomsiri,”A Comparison the Level of Security on Top 5 Open Source NoSQL Databases.” The 9th International Conference on Information Technology and Applications (ICITA2014).

XIV.“No SQL Injection in MongoDB” https://zanon.io/posts/nosql-injection-in-mongodb.

XV.Okman, Lior et al, “Security issues in nosql databases.” 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications 16 Nov. 2011:541-547.

XVI.Pokorny, Jaroslav,”NoSQL databases: a step to database scalability in web environment.” International Journal of Web Information Systems9.1 :69-82,2013.

XVII.RoshniBajpayee,Sonalipriya Sinha,Vinod Kumar ,“Big data :A brief investigation on NOSQL database”,International journal of innovations & advancement in computer science,volume 4, issue 1 January 2015.

XVIII.S.Priyadharshini, R. Rajmohan“Analysis on data base security model against nosql injection” 2017 International Journal of Scientific Research in Computer Science, Engineering and Information Technology , Volume 2 , Issue 2 ,2017,ISSN : 2456-3307

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Abstract:

MEMS, macroscopic devices posses characteristic length of less than 1μm and integrate mechanical and electronic components on a single chip. Sensitivity is the major concern in existing MEMS/NEMS devices which are mostly made of elastic cantilever beam. In this work porous MEMS cantilevers are designed using Silicon dioxide, Polysilicon, Silicon nitride & Aluminium. The designed cantilevers are in the micrometer range with optimized dimension as l=120, w=10 and t=1.5 (all are in micrometers). Sensitivity is measured on Silicon dioxide based cantiliver with different type of hole on fixed end as rectangle, circle and ellipse. The ellipse hole gives better result (maximum resultant stress 1767.5 N/m2) in terms of sensitivity of the device. Futher elliptical hole parameters (position, number and dimension) are varied in order to achieve maximum stress and in response maximum deflection of microcantilevers. The optimized design achieved is implemented with two more materials viz. polysilicon and silicon nitride for comparison.

Keywords:

MEMS,Microcantilevers,COMSOL Multiphysics,

Refference:

I.Ansari, M.Z., Cho, C., Kim, J.,Bang, B. Comparison between deflection and vibration characteristics of rectangular and trapezoidal profile microcantilevers. Sensors2009, 9, 2706–2718.

II.Anuj Kumar Goel. Analytical modeling and simulation of microcantilever based MEMS devices. Wulfenia, 2017, vol. 24, No.1, pp.79-91.

III.Anuj Kumar Goel, Kuldip Kumar, Dushyant Gupta. Design and Simulation of Microcantilevrs for Sensing applications. International Journal of applied engineering research, 2016, Vol. 11 No. 1 pp 501-503.

IV.ChivukulaV,Wang M,Ji HF,KhaliqA,FangJ&VarahramyanK. Simulation of SiO based piezoresistive microcantilevers. Sensors andActuatorsA, 2006; vol.125: pp.526-533.

V.Fernando, S., Austin, M., Chaffey, J. Improved cantilever profiles for sensor elements. J. Phys. D: Appl. Phys.2007, 40, 7652–7655.

VI.Madhu Santosh Ku Mutyala, Deepika Bandhanadham, Liu Pan, Vijaya Rohini Pendyala, Hai-Feng Ji. Mechanical and electronic approaches to improve the sensitivity of microcantilever sensors. Acta Mechanica Sinica, 2009, Vol. 25. No. 1. pp 1-12.

VII.Mansour Abtahi, Gholamreza Vossoughi, Ali Meghdari. Full Operational Range Dynamic Modeling of Microcantilever Beams. Journal of Microelectromechanical systems, 2013,Vol. 22. No. 5.

VIII.Mansour Abtahi, GholamrezaVossoughi, Ali Meghdari. Full Operational Range Dynamic Modeling of Microcantilever Beams. Journal of Microelectromechanicalsystems,.2013, Vol. 22. No. 5.

IX.Naeli, K., Brand, O.Cantilever sensor with stress-concentrating piezoresistor design, Sensors, 2005 IEEE, pp. 592–595.

X.RasmussenPA,HansenO&Boisen A. Cantilever surface stress sensors with single crystalline silicon piezoresistors. Applied physics letter, 2005; Vol. 86.

XI.Shahriar Kouravand. esign and modeling of some sensing and actuating mechanisms for MEMS applications, Applied Mathematical Modelling. Elsevier,2011,Vol. 35. pp 5173–5181.

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Abstract:

In the context of Supply Chain Management (SCM), Internet of Things (IoT) and Big Data Analytics (BDA) play a significant role in the evolution and success of a data intensive application and its respective security concerns. The aim of the research is to assess the suitability of IoT and BDA to strengthen and enhance SCM network. There have been independent research on IoT and Big Data in this domain; however no conclusive systematic study has been done to gather knowledge and expertise in analysis of SCM with respect to a combined application of IoT and big data analytics. The research in the literature has been put to documentation and recommendations for practitioners of SCM in industry have been addressed for future scope of IoT and data analysis.

Keywords:

SCM, Internet of Things (IoT),Data Analytics,

Refference:

I.”Achieving supply chain Integration -ResearchGate.” https://www.researchgate.net/publication/235283479_Achieving_supply_chain_Integration_using_RFID_Technology_the_Case_of_Emerging_Intelligent_B_to_B_e-commerce_Processes_in_a_Living_Laboratory. .

II.Analytics: The real-world use of big data in financial services -IBM.” https://www.ibm.com/services/multimedia/Analytics_The_real_world_use_of_big_data_in_Financial_services_Mai_2013.pdf.‖

III.”Analysing the interaction of supply chain synchronization and material‖ https://www.tandfonline.com/doi/abs/10.1080/13675567.2016.1174202.

IV.”A Framework of Sustainable Service Supply Chain Management -MDPI.” 12 Mar. 2017, http://www.mdpi.com/2071-1050/9/3/421/pdf.

V.”Big Data computing and clouds: Trends and future -The CLOUDS Lab.” 27 Aug. 2014, http://www.cloudbus.org/papers/BDC-Trends-JPDC.pdf.

VI.”Big Data -Related Technologies, Challenges and Future Prospects” https://www.springer.com/gp/book/9783319062440.

VII.”Big data analytics for supply chain management: A literature review.” http://fossowambasamuel.com/wp-content/uploads/2016/10/Big-data-analytics-for-supply-chain-management-A-literature-review-and-research-agenda.pdf. .

VIII.”Big data analytics in supply chain-Research Gate.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

IX.”Beyond the hype: Big data concepts, methods, and analytics.” http://psycnet.apa.org/record/2015-06483-002.

X.”Big data analytics in supply chain –Research Gate.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

XI.”Big data analytics in supply chain-Research Gate.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

XII.”Benefits of implementing RFID in Supply Chain Management -RFID” 14 Nov. 2013, http://www.rfidarena.com/2013/11/14/benefits-of-implementing-rfid-in-supply-chain-management.aspx.

XIII.”Big data analytics for supply chain management.” 4 Jul. 2018, https://zapdf.com/big-data-analytics-for-supply-chain-management.html.

XIV.”Big data analytics for supply chain management” http://www.academia.edu/33699458/Big_data_analytics_for_supply_chain_management.

XV.Big data analytics in supply chain.” https://www.researchgate.net/publication/318300929_Big_data_analytics_in_supply_chain_management_A_state-of-the-art_literature_review.

XVI.”Big data and predictive analytics for supply chain and organizational” https://www.sciencedirect.com/science/article/pii/S014829631630491X.

XVII.”Big data analytics in logistics and supply chain management” https://www.emeraldinsight.com/doi/full/10.1108/IJLM-02-2018-0026.

XVIII.”Deloitte’s 2018 global block chain survey.” https://www2.deloitte.com/content/dam/Deloitte/cz/Documents/financial-services/cz-2018-deloitte-global-blockchain-survey.pdf.

XIX.”Data Science, Predictive Analytics, and Big Data in Supply Chain” http://www.logisticsexpert.org/top_articles/2016/2016-Research-JBL Data Science, Predictive Analytics, and Big Data in Supply Chain Managementl.pdf.

XX.”Gross domestic product (GDP) growth rate in India 2022 -Statistic.”https://www.statista.com/statistics/263617/gross-domestic-product-gdp-growth-rate-in-india/.

XXI.”GitHub -car2go/ AnyMaps: Easily switch between Google, Baidu‖ https://github.com/car2go/AnyMaps.

XXII.”How IoT Will Impact The Supply Chain -Forbes.” 9 Jan. 2018, https://www.forbes.com/sites/danielnewman/2018/01/09/how-iot-will-impact-the-supply-chain/. .

XXIII.”How ‘big data’ can make big impact: findings from a systematic review‖ http://ro.uow.edu.au/buspapers/725/. XXIV.https://pdfs.semanticscholar.org/dd31/accf431497cf6a774880280280bf5f48c48e.pdf. XXV.https://www.researchgate.net/publication/261418480_RiskVis_Supply_chain_visualization_with_risk_management_and_real-time_monitoring. XXVI.https://www.researchgate.net/publication/269107422_Big_Data_Analytics_for_Supply_Chain_Management. XXVII.”IoT in Supply Chain Management, How to Leverage IoT Benefits” 28 Feb. 2018, https://www.embitel.com/blog/ecommerce-blog/how-modern-retailers-can-leverage-the-iot-benefits-in-their-supply-chain-management. .XXVIII.”Supply Chain GameChangers —Mega, Nano, and Virtual Trends ” 25 Jun. 2014, https://www.ssrn.com/abstract=2458168.

XXIX.”The 5 Vs of Big Data -Watson Health Perspectives -IBM.” 17 Sep. 2016, https://www.ibm.com/blogs/watson-health/the-5-vs-of-big-data/. .

XXX.”The Internet of Things: Today and Tomorrow -Aruba Networks.” https://www.arubanetworks.com/assets/eo/HPE_Aruba_IoT_Research_Report.pdf.

XXXI.”Understandable Big Data: A survey | Request PDF –Research Gate.” 30 Apr. 2018, https://www.researchgate.net/publication/278160976_Understandable_Big_Data_A_survey.

XXXII.”White Paper: Big Data for Development: Opportunities & Challenges” https://www.unglobalpulse.org/projects/BigDataforDevelopment.

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