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Abstract: Ionic liquids, as green solvents have been studied widely now a days due to their appealing properties such as negligible vapour pressure, large liquid range, high thermal stability, high ionic conductivity, large electrochemical window, and ability to solvate compounds of widely varying polarity. Utilizing ionic liquids is one of the goals of green chemistry because they create a cleaner and more sustainable chemistry and are receiving increasing interest as environmental friendly solvents for many synthetic and catalytic processes. Ionic liquids have been investigated for a wide range of synthetic applications; they have fascinated considerable interest for used as non-volatile solvent based electrolytes in the field of organic synthesis, catalysis, electrochemistry, solar cells, fuel cells, etc., as they possess many benefits than volatile organic solvents.
Keywords: Green chemistry, applications, classification, ionic liquids, properties.
References:
1. Anon., Ionic Liquids: Enabling Solvents, Covalent Associates, Woburn, MA, April 2001.
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5. P. Frank, S. Danuta, S. Harald, and G. Ralf, "Bionic liquids: imidazolium-based ionic liquids with antimicrobial activity". Zeitschrift für Naturforschung, 2013, vol. B 68b, pp.1123-1128.
6. J. Stoimenovski, D. R. MacFarlane, K. Bica, R. D. Rogers "Crystalline vs. ionic liquid salt forms of active pharmaceutical ingredients: a position paper", Pharmaceutical Research, 2010, vol.27, pp. 521–526.
7. J.D. Holbrey, M.B. Turner and R.D. Rogers, “Selection of ionic liquids for green chemical applications. in Ionic liquids as green solvents: progress and prospects”, ACS Symposium Series, Amer Chemical Soc, Washington, 2003, vol. 856, pp. 2-12.
8. A. Kumar & D. Sarma, “Recent applications of chloroaluminate ionic liquids in promoting organic reactions, inionic liquids iib: fundamentals, progress, challenges and opportunities, transformations and processe”s, edited by R D Rogers & K R Seddon, ACS Symposium Series, ,American Chemical Society, Washington, DC, 2005, pp. 350.
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14. N.V. Plechkova, K.R. Seddon Chem Soc.Rev 2008, vol.37, pp. 123-150.
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Abstract: Bayesian networks tend to be increasingly used for the management of uncertainty in modelling of the learner. They have been successfully used in many systems, with different objectives. However their use as part of the cognitive process modelling raises a number of problems. On the one hand the underlying semantics of arcs is often not clearly explained, and on the other hand the evolution of the knowledge structure is not taken into account. Our work focuses on the question of the orientation of the arcs, and more generally on the structure of Bayesian network modeling of the learner. We try to show in this work how this question is crucial. In addition, the issue of structural adjustment in the network behavior of the learner sometimes had been suggested, and while different results from cognitive psychology attests to the existence of structural differences by level of expertise. The central hypothesis of our work is that has been a link between the structure of the learner model and level of expertise. We present our probabilistic graphical models of multi- networks to take into account several networks within the same model. The experiments presented in this work are arguments in favor of our hypothesis on the link between the level of expertise of the learner and the structure of Bayesian network.
Keywords: Bayesians Networks, cognitive diagnosis, Learner modeling.
References:
1. [PEARL 88] PEARL, J., Probabilistic Reasoning in Intelligent System, Morgan Kaufmann. 1988.
2. [BECKER & NAIM 99] BECKER, A., NAÏM, P., Les réseaux Bayésiens, modèles graphiques de connaissances, Eyrolles, 1999.
3. [CONATI et al., 02] CONATI, C., GERTNER, A., VANLEHN, K., Using Bayesian networks to manage uncertainty in student modeling, Journal of User Modeling and User-Adapted Interaction, volume 12 (4), pages 371-417, 2002.
4. [MAYO & MITROVIC 01] ] Mayo M., Mitrovic. Optimising, A., ITS behaviour with Bayesian networks and decision theory, in International Journal of Artificial Intelligence in Education, n°12, pages 124-153, 2001.
5. [MISLEVY & GITOMER 1996] Mislevy R., Gitomer D. (1996). The role of probability-based inference in an intelligent tutoring system, User-Modeling and User- Adapted Interaction, n°5, pp 253-282.
6. [ZAPATA-RIVERA&GREER 2002] Zapata-Rivera J.-D., Greer J.(2000) « Inspecting and Visualizing Distributed Bayesian Student Models », Intelligent Tutoring Systems 00, pp. 544-553.
7. [VERMA & PEARL 1991] Geiger D., Heckermann D. (1996). Knowledge representation and inference in similarity networks and Bayesian multinets, Artificial Intelligence, volume 82 (1–2), pp. 45–74.
8. [GEIGER & HECKERMAN 96] GEIGER, D., HECKERMAN, D., Knowledge representation and inference in similarity networks and Bayesian multinets, Artificial Intelligence, volume 82 (1–2), pages 45–74, 1996.
9. [RAGNEMALM, 1995] RAGNEMALM E. (1995). Student Diagnosis in Practice; Bridging the Gap. User Modeling and User-Adapted Interaction (UMUAI) International Journal, 5, Kluwer Academic Publishers, pp. 93-116.
10. [VASSILEVA, 1996] VASSILEVA J. (1996). A Task-centered Approach for User Modelling in a Hypermedia Office Documentation System, User Modeling and User-Adapted Interaction Journal, 6, pp. 185-223
11. [SELF ,1991] SELF J. (1991). Formal Approaches to Student Modeling. Technical Report AI-59. Lancaster University, England.
12. [JAMESON 96] JAMESON, A., Numerical uncertainty management in user and student modeling: an overview of systems and issues, in User- Adapted Interaction, volume 5 (3- 4), n°5, pages 193-251, 1996.
13. [LAUITZEN, 1996] Lauritzen S.L. (1995). The EM-algorithm for graphical association models with missing data, Computational Statistics and Data Analysis, vol 1, pp 191- 201.
14. [M. ANOUAR TADLAOUI et al. 2014] M. Anouar Tadlaoui, M. Khaldi, S. Aammou (2014) TOWARDS A LEARNING MODEL BASED ON BAYESIAN NETWORKS,EDULEARN14 Proceedings, pp. 3185-3193.
15. [CHI et al., 1981] Chi M.T.H., Feltovitch P.J., Glaser R. (1981). Categorization and representation of physics problems by experts and novices, Cognitive Science, vol 5, pp.121-152.
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Abstract: The aim of this work is to study some properties related to fuzzy soft topological spaces particularly fuzzy soft boundary point, fuzzy soft compact space, fuzzy soft open base and fuzzy soft open sub-base.
Keywords: Fuzzy soft set, fuzzy soft topological space, fuzzy soft interior, fuzzy soft closure, fuzzy soft boundary point,fuzzy soft neighborhood, fuzzy soft compact space, fuzzy soft open base, fuzzy soft open subbase, fuzzy soft basic open cover, fuzzy soft sub basic cover fuzzy soft closed base, fuzzy soft closed subbase.
References:
1. Tanay, Bekir, and M. BurcKandemir. "Topological structure of fuzzy soft sets."Computers & Mathematics with Applications 61.10 (2011): 2952-2957.
2. Mahanta, J., and P. K. Das. "Results on fuzzy soft topological spaces." arXiv preprint arXiv:1203.0634 (2012).
3. Atmaca, S. E. R. K. A. N., and I. D. R. I. S. Zorlutuna. "On fuzzy soft topological spaces."Ann. Fuzzy Math. Inform 5.2 (2013): 377-386.
4. Simsekler, Tugbahan, and SaziyeYuksel. "Fuzzy soft topological spaces."Ann. Fuzzy Math.Inf 5.1 (2012): 87-96.
5. Maji, Pabitra Kumar, R. Biswas, and A. R. Roy. "Fuzzy soft sets." Journal of Fuzzy Mathematics 9.3 (2001): 589-602.
6. Maji, P. K., Ranjit Biswas, and AkhilRanjan Roy. "Soft set theory." Computers & Mathematics with Applications 45.4 (2003): 555-562.
7. Brown, Joseph G. "A note on fuzzy sets." Information and control 18.1 (1971): 32-39.
8. Neog, TridivJyoti, Dusmanta Kumar Sut, and Gopal Chandra Hazarika. "Fuzzy Soft Topological Spaces."International Journal of Latest Trends in Mathematics 2.1 (2012).
9. Gain, Pradip Kumar, et al. "On Some Structural Properties of Fuzzy Soft Topological Spaces."
10. Tanay, Bekir, and M. BurçKandemir. "Topological structure of fuzzy soft sets."Computers & Mathematics with Applications 61.10 (2011): 2952-2957.
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