University of Isfahan Mathematics and Society 2345-6493 4 1 2025 01 07 Interview with the new president of the american mathematical society Interview with the new president of the american mathematical society 1 6 23783 10.22108/msci.2019.112535.1290 FA Ramezan Zarghami Department of Mathematics and Surveying - Marand Faculty of Engineering - University of Tabriz - Tabriz - Iran Ehsan Heydari Faculty of Mathematics, Yazd University Journal Article 1970 01 01 This article is a translation of an interview conducted with Kenneth Ribet, the President of the American Mathematical Society in the fall of 2016. It includes questions and answers regarding topics such as the role of the American Mathematical Society in mathematics education, public awareness of mathematics, employment opportunities for graduates, and the reasons behind the resignation of the previous president of the society. This article is a translation of an interview conducted with Kenneth Ribet, the President of the American Mathematical Society in the fall of 2016. It includes questions and answers regarding topics such as the role of the American Mathematical Society in mathematics education, public awareness of mathematics, employment opportunities for graduates, and the reasons behind the resignation of the previous president of the society.

https://math-sci.ui.ac.ir/article_23783_cc1513c27c2c17dd4fefc75c8ba8ca08.pdf

University of Isfahan Mathematics and Society 2345-6493 4 1 2019 05 22 Level set method for motion by mean curvature Level set method for motion by mean curvature 7 17 23953 10.22108/msci.2019.113074.1296 FA Mehran Aminian Vali-e-Asr University of Rafsanjan, Rafsanjan Mehran Namjoo Vali-e-Asr University of Rafsanjan, Rafsanjan 0000-0001-5949-6766 Journal Article 2018 09 26 This paper is a translation of the the following paper into Persian: [T. H. Colding and W. P. Minicozzi II, Level Set Method For Motion by Mean Curvature, Notices of the AMS, 63 no. 10 (2016) 1148–1153.] Modeling of a wide class of physical phenomena, such as crystal growth and flame propagation, leads to tracking fronts moving with curvature-dependent speed. When the speed is the curvature this leads to one of the classical degenerate nonlinear second-order differential equations on Euclidean space. One naturally wonders, “What is the regularity of solutions?” A priori solutions are only defined in a weak sense, but it turns out that they are always twice differentiable classical solutions. This result is optimal; their second derivative is continuous only in very rigid situations that have a simple geometric interpretation. The proof weaves together analysis and geometry. Without deeply understanding the underlying geometry, it is impossible to prove fine analytical properties. This paper is a translation of the the following paper into Persian: [T. H. Colding and W. P. Minicozzi II, Level Set Method For Motion by Mean Curvature, Notices of the AMS, 63 no. 10 (2016) 1148–1153.] Modeling of a wide class of physical phenomena, such as crystal growth and flame propagation, leads to tracking fronts moving with curvature-dependent speed. When the speed is the curvature this leads to one of the classical degenerate nonlinear second-order differential equations on Euclidean space. One naturally wonders, “What is the regularity of solutions?” A priori solutions are only defined in a weak sense, but it turns out that they are always twice differentiable classical solutions. This result is optimal; their second derivative is continuous only in very rigid situations that have a simple geometric interpretation. The proof weaves together analysis and geometry. Without deeply understanding the underlying geometry, it is impossible to prove fine analytical properties. https://math-sci.ui.ac.ir/article_23953_e3e6bdaa31e2225b3f23cd23f4a810d4.pdf

University of Isfahan Mathematics and Society 2345-6493 4 1 2019 05 22 Illustrating groups with cayley diagrams Illustrating groups with cayley diagrams 19 28 24073 10.22108/msci.2019.118734.1336 FA Sajjad Mahmood Robati Qazvin, Imam Khomeini International University, Faculty of Basic Sciences, Department of Pure Mathematics 0000-0002-9076-2513 Journal Article 2019 08 14 This article examines the process of constructing Cayley diagrams for some of the structures defined in group theory. This article examines the process of constructing Cayley diagrams for some of the structures defined in group theory.

https://math-sci.ui.ac.ir/article_24073_27eb59728292177f7ca27a5622910563.pdf

University of Isfahan Mathematics and Society 2345-6493 4 1 2019 05 22 Wavelets' applications in signal processing Wavelets' applications in signal processing 29 36 24022 10.22108/msci.2019.118249.1331 FA Hojatollah Saeidi Applied Mathematics, Faculty of Mathematics, Shahrekord University, Iran Zohre Saeidi Electrical Engineering, Faculty of Technology, Shahrekord University, Iran Journal Article 2019 07 18 Wavelets are powerful tools for decomposition, analysis, and processing of digital signals. The wavelet transform represents the time-domain of a signal in terms of wavelet coefficients, converting it into a frequency-time representation. Wavelet coefficients can be utilized as part of a frequency-dependent method to achieve various signal processing effects. Additionally, the inverse wavelet transform converts the obtained wavelet coefficients back into the time-domain representation to obtain a modified signal. In this article, after a brief overview of the Fourier method and wavelet transform, the Haar wavelet and Daubechies wavelet are described. Following that, several signal processing techniques using wavelets, including noise reduction, wavelet denoising, data compression, musical effects, and a Java-based wavelet processor, will be examined. Wavelets are powerful tools for decomposition, analysis, and processing of digital signals. The wavelet transform represents the time-domain of a signal in terms of wavelet coefficients, converting it into a frequency-time representation. Wavelet coefficients can be utilized as part of a frequency-dependent method to achieve various signal processing effects. Additionally, the inverse wavelet transform converts the obtained wavelet coefficients back into the time-domain representation to obtain a modified signal. In this article, after a brief overview of the Fourier method and wavelet transform, the Haar wavelet and Daubechies wavelet are described. Following that, several signal processing techniques using wavelets, including noise reduction, wavelet denoising, data compression, musical effects, and a Java-based wavelet processor, will be examined.

https://math-sci.ui.ac.ir/article_24022_bf3ca2e79c1c5ae6262ec68b7b409572.pdf

University of Isfahan Mathematics and Society 2345-6493 4 1 2019 05 22 Graph theory and sports scheduling Graph theory and sports scheduling 37 47 23955 10.22108/msci.2019.102821.1233 FA Shahram Nasiri Department of Computer Engineering, Faculty of Computer Science, Sirjan University of Technology, Sirjan, Iran 0000-0003-3274-6985 Mehdi Dehghanian Department of Mathematics, Sirjan University of Technology, Sirjan, Iran Mohammad Javad Nassiri Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran Afsaneh Nourmandi Department of Computer Engineering, Faculty of Computer Science, Sirjan University of Technology, Sirjan, Iran Journal Article 2017 06 17 This writing is a translation of the following article: Richard Hoshino and Ken-ichi Kawarabayashi‎, ‎Graph Theory and Sports Scheduling‎, ‎Notices of the AMS‎, 60 (2013) 726-731‎. This writing is a translation of the following article: Richard Hoshino and Ken-ichi Kawarabayashi‎, ‎Graph Theory and Sports Scheduling‎, ‎Notices of the AMS‎, 60 (2013) 726-731‎.

https://math-sci.ui.ac.ir/article_23955_badc3b84a94a7e82af208dc3a8d41230.pdf

University of Isfahan Mathematics and Society 2345-6493 4 1 2019 05 22 Optimal portfolio selection based on various risks Optimal portfolio selection based on various risks 49 67 24107 10.22108/msci.2019.118340.1332 FA Seyed Morteza Amini Tehran, Enghelab Street, University of Tehran, Science Campus, Faculty of Mathematics, Statistics and Computer Science Sajedeh Javadi Tehran, Enghelab Street, University of Tehran, Science Campus, Faculty of Mathematics, Statistics and Computer Science Majid Soleimani-damaneh Tehran, Enghelab Street, University of Tehran, Science Campus, Faculty of Mathematics, Statistics and Computer Science Journal Article 2019 07 22 The allocation of capital among investment opportunities in the stock market, known as optimal portfolio selection, is a focal point in mathematical economics and financial management, and it is of great interest to investors. In recent decades, the formulation of this problem in the form of optimization models and its analysis using mathematical programming has been pursued by numerous researchers. The definition and measurement of risk play a key role in this problem and can lead to various optimal stock portfolios. In this article, considering different criteria for measuring risk, including variance (risk), potential risk, and systematic risk, we explore various models for optimal portfolio selection from both theoretical and numerical perspectives. We delve into two examples of optimization models, namely the Min-Max and Max-Min models. We apply the introduced models to analyze data from the Tehran Stock Exchange market and compare the results. The allocation of capital among investment opportunities in the stock market, known as optimal portfolio selection, is a focal point in mathematical economics and financial management, and it is of great interest to investors. In recent decades, the formulation of this problem in the form of optimization models and its analysis using mathematical programming has been pursued by numerous researchers. The definition and measurement of risk play a key role in this problem and can lead to various optimal stock portfolios. In this article, considering different criteria for measuring risk, including variance (risk), potential risk, and systematic risk, we explore various models for optimal portfolio selection from both theoretical and numerical perspectives. We delve into two examples of optimization models, namely the Min-Max and Max-Min models. We apply the introduced models to analyze data from the Tehran Stock Exchange market and compare the results.

https://math-sci.ui.ac.ir/article_24107_60f1858104dbf5fb8691f73b04182f47.pdf