ORIGINAL_ARTICLE
Chemical salt reactions as algebraic hyperstructures
A salt metathesis reaction is a chemical process involving the exchange of bonds between two reacting chemical species, which results in the creation of products with similar or identical bonding affiliations. Hyperstructure theory is studied from the theoretical point of view and for its applications. In this paper, we provide some examples of hyperstructures associated with salt metathesis reactions, and we observe that these chemical reactions are examples of the phenomena when composition of two elements is a set of elements.
http://ijmc.kashanu.ac.ir/article_92469_1bc5ada7c1440a78980f519e8f59aedf.pdf
2019-07-01T11:23:20
2020-02-21T11:23:20
93
102
10.22052/ijmc.2018.114473.1339
Hyperstructures
semihypergroup
$H_v$-semigroup
salt reaction
Dariush
Heidari
dheidari82@gmail.com
true
1
Faculty of science, Mahallat Institute of Higher Education, Mahallat, Iran
Faculty of science, Mahallat Institute of Higher Education, Mahallat, Iran
Faculty of science, Mahallat Institute of Higher Education, Mahallat, Iran
LEAD_AUTHOR
Davood
Mazaheri
davood.mazaheri@gmail.com
true
2
Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran
AUTHOR
Bijan
Davvaz
davvaz@yazd.ac.ir
true
3
Department of Mathematics, Yazd University, Yazd, Iran
Department of Mathematics, Yazd University, Yazd, Iran
Department of Mathematics, Yazd University, Yazd, Iran
AUTHOR
ORIGINAL_ARTICLE
Solving Multi-objective Optimal Control Problems of chemical processes using Hybrid Evolutionary Algorithm
Evolutionary algorithms have been recognized to be suitable for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. This paper applies an evolutionary optimization scheme, inspired by Multi-objective Invasive Weed Optimization (MOIWO) and Non-dominated Sorting (NS) strategies, to find approximate solutions for multi-objective optimal control problems (MOCPs). The desired control function may be subjected to severe changes over a period of time. In response to deficiency, the process of dispersal has been modified in the MOIWO. This modification will increase the exploration power of the weeds and reduces the search space gradually during the iteration process. The performance of the proposed algorithm is compared with conventional Non-dominated Sorting Genetic Algorithm (NSGA-II) and Non-dominated Sorting Invasive Weed Optimization (NSIWO) algorithm.The results show that the proposed algorithm has better performance than others in terms of computing time, convergence rate and diversity of solutions on the Pareto frontier.
http://ijmc.kashanu.ac.ir/article_92471_07fe84d73accbaed46145bd87d59b7f9.pdf
2019-07-01T11:23:20
2020-02-21T11:23:20
103
126
10.22052/ijmc.2018.137247.1370
Multi-objective optimal control
Pareto optimal frontier
Non-dominated sorting
invasive weed optimization
Fed Batch Reactor
Gholam
Askarirobati
askary2010@gmail.com
true
1
Department of Mathematics, Payame Noor University, P.O.Box 19395-3697, Tehran, Iran
Department of Mathematics, Payame Noor University, P.O.Box 19395-3697, Tehran, Iran
Department of Mathematics, Payame Noor University, P.O.Box 19395-3697, Tehran, Iran
AUTHOR
Akbar
Hashemi Borzabadi
a.hashemi.bor@gmail.com
true
2
Department of Mathematics and Computer Science, Damghan University, Damghan, Iran
Department of Mathematics and Computer Science, Damghan University, Damghan, Iran
Department of Mathematics and Computer Science, Damghan University, Damghan, Iran
LEAD_AUTHOR
Aghileh
Heydari
a_heidari@pnu.ac.ir
true
3
Department of Mathematics, Payame Noor University, Tehran, Iran
Department of Mathematics, Payame Noor University, Tehran, Iran
Department of Mathematics, Payame Noor University, Tehran, Iran
AUTHOR
1. L. Biegler, Solution of dynamic optimization problems by successive quadratic programming and orthogonal collocation, Comput. Chem. Eng. 8 (1984) 243−248.
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2. L. Biegler, An overview of simultaneous strategies for dynamic optimization, Chem. Eng Process: Process Intensif. 46 (11) (2007) 1043−1053.
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7. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms,Wiley 2001.
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9. T. Erickson, A. Mayer andJ. Horn, The niched Pareto genetic algorithm 2 applied to the design of ground water remediation systems, Evolutionary Multi-Criterion Optimization: First International Conference, EMO. (2001) 681−695.
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C. M. Fonseca, Multi-Objective Genetic Algorithms with Application to Control Engineering Problems, Ph.D. Thesis, University of Sheffield. Sheffield, 1995.
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S. M. K. Heris and H. Khaloozadeh, Open- and closed-loop multi-objective optimal strategies for HIV therapy using NSGA-II, IEEE. Trans. Biomed. Eng. 58 (6) (2011) 1678−1685.
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J. Knowles andD. Corne, ThePareto archived evolution strategy: A new baseline algorithm for Pareto multi-objective optimization, Proceedings of the 1999 IEEE Congress on Evolutionary Computation 1999.
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S. Kukkonen and K. Deb, Improved Pruning of Non-Dominated Solutions Based on Crowding Distance for Bi-Objective Optimization Problems, IEEE Congress on Evolutionary Computation, pp. 91−98, 2007.
13
D. Kundu, K. Suresh, S. Ghosh, S. Das and B. K. Panigrahi, Multi-objective optimization with artificial weed colonies, J. Inf. Sci. 181 (2011) 2441−2454.
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D. Leineweber, I. Bauer, H. Bock and J. Schlder, An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization, Part I: Theoretical aspects, Comput. Chem. Eng. 27 (2003) 157−166.
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F. Logist, P. M. Van Erdeghem and J. F. Van Impe, Efficient deterministic multiple objective optimal control of (bio)chemical processes, Chem. Eng. Sci. 64 (2009) 2527−2538.
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F. Logist, B. Houska, M. Diehl and J. Van Impe, Fast Pareto set generation for nonlinear optimal control problems with multiple objectives, Struct. Multidisc. Optim. 42 (2010) 591−603.
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F. Logist, B. Houska, M. Diehl and J. Van Impe, Robust multi-objective optimal control of uncertain (bio)chemical processes, Chem. Eng. Sci. 66 (2011) 4670−4682.
18
F. Logist, S. Sager, C. Kirchesand and J. Van Impe, Efficient multiple objective optimal control of dynamic systems with integer controls, J. Process Control 20 (2010) 810−822.
19
F. Logist, M. Vallerio, B. Houska, M. Diehl and J. Van Impe, Multi-objective optimal control of chemical processes using ACADO toolkit, Comput. Chem. Eng. 37 (2012) 191−199.
20
R. Mehrabian and C. Lucas, A novel numerical optimization algorithm inspired from weed colonization, Ecol. Inform. 1 (4) (2006) 355−366.
21
A. Messac, A. Ismail-Yahaya and C. A. Mattson, The normalized normal constraint method for generating the Pareto frontier, Struct. Multidisc. Optim. 25 (2) (2003) 86−98.
22
K. Miettinen, Nonlinear Multi-Objective Optimization, Kluwer, Boston, 1999.
23
A. H. Nikoofard, H. Hajimirsadeghi, A. Rahimi-Kian and C. Lucas, Multi-objective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets, Appl. Soft. Comput. 12 (2012) 100−112.
24
H. Modares and M. N. Sistani, Solving nonlinear optimal control problems using a hybrid IPSOSQP algorithm, Eng. Appl. Artif. Intel. 24 (2011) 476−484.
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26
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27
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28
N. Patel and N. Padhiyar, Multi-objective dynamic optimization study of fed-batch bio-reactor, Chem. Eng. Res. Des. 119 (2017) 160−170.
29
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36
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38
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39
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40
ORIGINAL_ARTICLE
M-polynomial of some graph operations and Cycle related graphs
In this paper, we obtain M-polynomial of some graph operations and cy- cle related graphs. As an application, we compute M-polynomial of some nanostruc- tures viz., TUC4C8[p; q] nanotube, TUC4C8[p; q] nanotorus, line graph of subdivision graph of TUC4C8[p; q] nanotube and TUC4C8[p; q] nanotorus, V-tetracenic nanotube and V-tetracenic nanotorus. Further, we derive some degree based topological indices from the obtained polynomials.
http://ijmc.kashanu.ac.ir/article_92585_0c8907c78eff491f82a148e57d0c2cb1.pdf
2019-07-01T11:23:20
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127
150
10.22052/ijmc.2019.146761.1388
M-polynomial
Degree-based topological index
line graph
subdivision graph
wheel graph
Bommanahal
Basavanagoud
b.basavanagoud@gmail.com
true
1
KARNATAK UNIVERSITY DHARWAD
KARNATAK UNIVERSITY DHARWAD
KARNATAK UNIVERSITY DHARWAD
LEAD_AUTHOR
Anand
Barangi
apb4maths@gmail.com
true
2
Department of Mathematics
Karnatak University
Dharwad, Karnatak-580003
India.
Department of Mathematics
Karnatak University
Dharwad, Karnatak-580003
India.
Department of Mathematics
Karnatak University
Dharwad, Karnatak-580003
India.
AUTHOR
Praveen
Jakkannavar
jpraveen021@gmail.com
true
3
Department of Mathematics
Karnatak University
Dharwad, Karnataka-580003
India.
Department of Mathematics
Karnatak University
Dharwad, Karnataka-580003
India.
Department of Mathematics
Karnatak University
Dharwad, Karnataka-580003
India.
AUTHOR
ORIGINAL_ARTICLE
Trees with the greatest Wiener and edge-Wiener index
The Wiener index W and the edge-Wiener index W_e of G are defined as the sum of distances between all pairs of vertices in G and the sum of distances between all pairs of edges in G, respectively. In this paper, we identify the four trees, with the first through fourth greatest Wiener and edge-Wiener index among all trees of order n ≥ 10.
http://ijmc.kashanu.ac.ir/article_48335_04cc974385e6e35f7f9e18d45299dac1.pdf
2019-07-01T11:23:20
2020-02-21T11:23:20
151
159
10.22052/ijmc.2017.81498.1279
tree
Wiener index
edge-Wiener index
Graph operation
Ali
Ghalavand
ali.ghalavand.kh@gmail.com
true
1
Department of Pure Mathematics, Faculty of Mathematical Sciences, University of Kashan, Kashan 87317-53153, I R Iran
Department of Pure Mathematics, Faculty of Mathematical Sciences, University of Kashan, Kashan 87317-53153, I R Iran
Department of Pure Mathematics, Faculty of Mathematical Sciences, University of Kashan, Kashan 87317-53153, I R Iran
LEAD_AUTHOR
Y. Alizadeh, A. Iranmanesh, T. Doŝlić, M. Azari, The edge Wiener index of suspensions, bottlenecks, and thorny graphs, Glas. Mat. Ser. III 49 (69) (2014) 1−12.
1
M. Azari, A. Iranmanesh, A. Tehranian, A method for calculating an edge version of the Wiener number of a graph operation, Util. Math. 87 (2012) 151−164.
2
F. Buckley, Mean distance in line graphs, Congr. Numer. 32 (1981) 153−162.
3
A. Chen, X. Xiong, F. Lin, Explicit relation between the Wiener index and the edge-Wiener index of the catacondensed hexagonal systems, Appl. Math. Comput. 273 (2016) 1100−1106.
4
P. Dankelmann, I. Gutman, S. Mukwembi, H. C. Swart, The edge–Wiener index of a graph, Discrete Math. 309 (2009) 3452−3457.
5
Y. Dou, H. Bian, H. Gao, H. Yu, The polyphenyl chains with extremal edge–Wiener indices, MATCH Commun. Math. Comput. Chem. 64 (2010) 757−766.
6
J. Devillers, A.T. Balaban, Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach Science Publishers, 1999.
7
H.-Y. Deng, The trees on vertices with the first to seventeenth greatest Wiener indices are chemical trees, MATCH Commun. Math. Comput. Chem. 57 (2007) 393−402.
8
A. Iranmanesh, M. Azari, Edge–Wiener descriptors in chemical graph theory: a survey, Curr. Org. Chem. 19 (2015) 219−239.
9
A. Iranmanesh, I. Gutman, O. Khormali, A. Mahmiani, The edge versions of Wiener index, MATCH Commun. Math. Comput. Chem. 61 (2009) 663−672.
10
M. Karelson, Molecular Descriptors in QSAR/QSPR, Wiley, New York, 2000.
11
M. Knor, P. Potočnik, R. Škrekovski, Relationship between the edge-Wiener index and the Gutman index of a graph, Discrete Appl. Math. 167 (2014) 197−201.
12
M. H. Khalifeh, H. Yousefi Azari, A. R. Ashrafi, S. G. Wagner, Some new results on distance–based graph invariants, European J. Comb. 30 (2009) 1149−1163.
13
A. Kelenc, S. Klavžar, N. Tratnik, The Edge–Wiener index of benzenoid systems in linear time, MATCH Commun. Math. Comput. Chem. 74 (2015) 521−532.
14
M. Liu, B. Liu, Q. Li, Erratum to: The trees on vertices with the first to seventeenth greatest Wiener indices are chemical trees, MATCH Commun. Math. Comput. Chem. 64 (2010) 743−756.
15
M. J. Nadjafi–Arani, H. Khodashenas, A. R. Ashrafi, Relationship between edge Szeged and edge Wiener indices of graphs, Glas. Mat. Ser. III 47 (67) (2012) 21−29.
16
N. Tratnik, P. Žigert Pleteršek, Relationship between the Hosoya polynomial and the edge-Hosoya polynomial of trees, MATCH Commun. Math. Comput. Chem. 78 (2017) 181−187.
17
H. Wiener, Structural determination of paraffin boiling points, J. Am. Chem. Soc.
18
69 (1947) 17−20.
19
H. Yousefi–Azari, M. H. Khalifeh, A. R. Ashrafi, Calculating the edge Wiener and edge Szeged indices of graphs, J. Comput. Appl. Math. 235 (2011) 4866−4870.
20
ORIGINAL_ARTICLE
Predeiction of ∆H0f(gas) ,∆H0f(Liq) of Amines Derivatives Using chemometrics (A Quantitative Structure-Property Relationship Study)
In this study, multiple linear regression method that is based on property-structure model has been used to predict the standard enthalpies of formation for the gas and liquid phases of the 33 different types of amines. It was indicated that among studied topological and geometric descriptors to predict the ∆H˚f(liquid), descriptors as PSA, H, MaxZL and V have more importance than the other descriptors. Also, the results of experiments on studied amines were compared with the results of multiple linear regression calculations and it was observed that such descriptors as MaxZL MaxPA, DE, J and WW are the best descriptors for predicting the values of ∆H˚f(gas) of this class of amines.
http://ijmc.kashanu.ac.ir/article_92956_d97a8a954630b00348abfeeecc162a60.pdf
2019-07-01T11:23:20
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161
179
10.22052/ijmc.2018.72357.1264
Amines Derivatives
Standard Enthalpy of Formation
Molecular Descriptors
chemometrics
Morteza
Rezaei
mortezarezaei211@gmail.com
true
1
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
AUTHOR
Esmat
Mohammadinasab
esmohammadinasab@gmail.com
true
2
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran
LEAD_AUTHOR
1. M. A. Sharaf, D. L. Illman and B. R. Kowalski, Chemometrics, Chemical Analysis Series, Wiley, New York, 82, 1986.
1
2. D. L. Massart, B. G. M. Vandeginste, S. N. Deming, Y. Michotte and L. Kaufmann, Chemometrics–a Textbook, Elsevier, Amsterdam, 1988.
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3. R. Todeschini and V. Consonni, Handbook of Molecular Descriptors, Methods and Principles in Medicinal Chemistry, Wiley-Vch Verlag Gmbh, 2008.
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4. H. Martens and T. Naes, Multivariate Calibration, New York, Wiley, 1989.
4
5. W. G. Hunter, Statistics and Chemistry and the Linear Calibration Problem, In B. R. Kowalski, Chemometrics: Mathematics and Statistics in Chemistry, Boston: Riedel, 1984.
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6. J. F. Mac Gregor and T. Kourti, Statistical control of multivariate processes, Control Engineering Practice 3 (3) (1995) 403–414.
6
7. S. Ahmadi and E. Habibpour, Application of GA-MLR for QSAR modeling of the arylthioindole class of tubulin polymerization inhibitors as anticancer agents, Anti-Cancer Agents Med. Chem. 17 (4) (2017) 552− 565.
7
8. M. B. Smith and J. March, Advanced Organic Chemistry: Reactions, Mechanisms, and Structure, Wiley-Interscience, New York, 2007.
8
9. G. W. Snedecor and G. W. Cochran, Statistical Methods, Oxford and IBH, New Delhi, 2010.
9
10. I. Gutman and O. E. Polansky, Mathematical Concepts in Organic Chemistry, Springer-Verlag, Berlin, 1986.
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11. M. Randić and S. C. Basak, Multiple regression analysis with optimal molecular descriptors, SAR QSAR Environ. 11 (1) (2000) 1−23.
11
12. J. Sangster, Octanol-water partition coefficients of simple organic compounds, J. Phys. Chem. Ref. Data 18 (3) (1989).
12
13. E. Mohammadinasab, Determination of critical properties of alkanes derivatives using multiple linear regression, Iranian J. Math. Chem. 8 (2) (2017) 199 −220.
13
14. M. Goodarzi and M. Mohammadinasab, Theoretical investigation of relationship between quantum chemical descriptors, Topological indices, energy and electric moments of zig-zag polyhex carbon nanotubes TUHC6 [2p,q] with various circumference [2p] and fixed lengths, Fullerenes Nanotubes Carbon Nanostructures 21 (2013) 102−112.
14
15. H. Wiener, Structural determination of paraffin boiling points, J. Am. Chem. Soc. 69 (1947) 17−20.
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16. D. J. Klein, Z. Mihalic, D. Plavsic and N. Trinajstić, Molecular topological index: a relation with the Wiener index, J. Chem. Inf. Computer. Sci, 32 (4) (1992) 304−305.
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17. G. Cash, S. Klavžar and M. Petkovšek, Three methods for calculation of the hyper-Wiener index of molecular graphs, J. Chem. Inf. Comput. Sci. 42 (2002) 571−576.
17
18. H. Wang, On the extremal Wiener-polarity index of Hückel graphs, Comput. Math. Methods Med. 2016, Article ID 3873597, 6 pages.
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19. J. Baskar Babujee, Topological indices and new graph structures, Appl. Math. Sci. 6 (108) (2012) 5383 – 5401.
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20. M. Randic, Charactrization of molecular branching, J. Am. Chem. 97 (23) (1975) 6609−6615.
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21. A. T. Balaban and T. S. Balaban, New vertex invariant and topological indices of chemical graphs based on information on distance, Math. Chem. 8 (1991) 383−397.
21
22. A. T. Balaban, Topological index based on topological distances in molecular graph, Pure Appl. Chem. 55 (1983) 199−206.
22
23. K. Xu and K. Ch. Das, On Harary index of graphs, Discrete Appl. Math. 159 (2011) 1631−1640.
23
24. P. V. Khadikar, N. V. Deshpande, P. P. Kale, A. Dobrynin, I. Gutman and G. Domotor, The Szeged index and an analogy with the Wiener index, J. Chem. Inf. Comput. Sci. 35 (1995) 547−550.
24
25. Web search engine developed by ChemAxon, software available at http:// www.chemicalize.org.
25
26. J. Devillers and A. T. Balaban, Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach Science, Netherlands, 1999.
26
27. I. Gutman and B. Furtula (eds), Novel Molecular Structure Descriptors-Theory and Applications, University of Kragujevac and Faculty of Science, Kragujevac, 2010.
27
ORIGINAL_ARTICLE
The new high approximation of stiff systems of first order IVPs arising from chemical reactions by k-step L-stable hybrid methods
In this paper, we present a new class of hybrid methods for the numerical solution of first order ordinary differential equations (ODEs). The accuracy and stability properties of the new methods are investigated. In the final section, we apply new hybrid methods to solve two stiff chemical problems such as Roberston problem.
http://ijmc.kashanu.ac.ir/article_92957_eb38dd0a1d348c727b99045c5b58a2b9.pdf
2019-07-01T11:23:20
2020-02-21T11:23:20
181
193
10.22052/ijmc.2018.111016.1335
Stiff initial value problems
Hybrid methods
Roberston problem
A-Stability
L-Stability
Mohammad
Mehdizadeh Khalsaraei
muhammad.mehdizadeh@gmail.com
true
1
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
LEAD_AUTHOR
Ali
Shokri
shokri2090@gmail.com
true
2
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
AUTHOR
Maryam
Molayi
m.molayi.66@gmail.com
true
3
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
Department of Mathematics, Faculty of Sciences, University of Maragheh, Maragheh, Iran
AUTHOR