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Zeitschriftenartikel zum Thema „Mathematical modeling - science“

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Veröffentlicht am 26. Juli 2024

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1

Abdimurotovna, Nigora Kholmirzayeva. „SPECIFIC CHARACTERISTICS OF THE APPLICATION OF MATHEMATICAL MODELING IN SOIL SCIENCE“. European International Journal of Multidisciplinary Research and Management Studies 02, Nr. 09 (01.09.2022): 112–16. http://dx.doi.org/10.55640/eijmrms-02-09-25.

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The article talks about the use of mathematical modeling in the field of soil science. Empirical, semi-empirical, theoretical models are used in soil science, as well as their specific features. An overview, advantages and disadvantages of empirical, semi-empirical and theoretical models are analyzed. Necessary formulas and algorithms for mathematical modeling of heat transfer in the soil are compiled and their features are described.

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Casetti, Emilio. „SPATIAL MATHEMATICAL MODELING AND REGIONAL SCIENCE“. Papers in Regional Science 74, Nr. 1 (14.01.2005): 3–11. http://dx.doi.org/10.1111/j.1435-5597.1995.tb00625.x.

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3

Ash, C. „Mathematical modeling of infectious diseases“. Science 347, Nr. 6227 (12.03.2015): 1213. http://dx.doi.org/10.1126/science.347.6227.1213-j.

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4

Szekely, Julian. „Mathematical Modeling in Materials Science and Engineering“. MRS Bulletin 19, Nr. 1 (Januar 1994): 11–13. http://dx.doi.org/10.1557/s0883769400038793.

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During the past two decades, mathematical modeling has been gaining acceptance as a legitimate part of materials science and engineering. However, as common to all relatively new disciplines, we still lack a realistic perspective regarding the uses, limitations, and even the optimal methodologies of mathematical modeling techniques.The term “mathematical modeling” covers a broad range of activities, including molecular dynamics, other atomistic scale systems, continuum fluid and solid mechanics, deformation processing, systems analysis, input-output models, and lifecycle analyses. The common point is that we use algebraic expressions or differential equations to represent physical systems to varying degrees of approximation and then manipulate these equations, using computers, to obtain graphical output.While it is becoming an accepted fact that some kind of mathematical modeling will be needed to make most research programs complete, there is still considerable ambiguity as to what form this should take and what might be the actual usefulness of such an effort.Among the more seasoned and successful practitioners of this art, clear guidelines have emerged regarding the uses and limitations of the mathematical modeling approach. We seek to illustrate these uses through the successful modeling examples presented by some leading practitioners. Some general principles may be worth repeating as an introduction to this interesting collection of articles.

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Weigend, Michael. „Mathematical Modeling and Programming in Science Education“. Computer Tools in Education, Nr. 2 (28.06.2019): 55–64. http://dx.doi.org/10.32603/2071-2340-2019-2-55-64.

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6

Gelrud, Yakov D., und Lyudmila I. Shestakova. „Fundamentals of mathematical modeling in political science“. Bulletin of the South Ural State University. Ser. Computer Technologies, Automatic Control & Radioelectronics 22, Nr. 1 (Januar 2022): 116–24. http://dx.doi.org/10.14529/ctcr220110.

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Political processes are a complex system of organizational type. In this article, we are considering certain aspects, which stem from the principle of the system approach and are directly related to management of complex organizational systems. At the same time, the main attention is paid to the close connection of the principle of the system approach and the mathematical modeling of the systems of orga¬nizational management. Goals and objectives. The main goal of the article is to consider the ideas of the system approach, which imply the focus of attention on the quality and properties inherent to the system in general. The behavior of separate elements of the system is analyzed only in the context in which these are related to the achievement of the goal and to the effectiveness of the system’s functioning on the whole. Mathematical modeling ensures the fulfillment of the system approach for management organization taking into account the feedback principle. Methods. Mathematical model allows to form a logically harmonious formalized description of the managerial tasks. The following elements of decision-making are distinguished in the formal structure: goals, controllable variables, external variables, uncontrolled parameters, limitations, decision, efficiency criterion. The development of a mathematical model includes the determi¬ning of the interrelations between all the elements of the formal structure of the task and portraying them as mathematical expressions (equations, inequalities, etc.). Results. The article presents a decomposition of the process of developing a managerial solution comprising the following stages: verbal task setting (problem statement), forming of a mathematical model, task solving, solution analysis, model correction (if necessary) and finding a corrected solution, implementation of the final decision made in the management practice. In the end of the article, we consider an example of solving a managerial task in accordance with all the listed stages. Conclusion. The use of mathematical modeling and methods of solving managerial tasks in professional activity of a political figure allows to improve the effectiveness of the decisions made by this person and provides him/her with communication means, thanks to using the professional language of mathematics.

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Il’in, V. P. „Mathematical Modeling and the Philosophy of Science“. Herald of the Russian Academy of Sciences 88, Nr. 1 (Januar 2018): 81–88. http://dx.doi.org/10.1134/s1019331618010021.

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8

Abiad, Fouad. „Mathematical Modeling of the Strategy of the Early Islamic Wars“. International Journal of Social Science Research and Review 3, Nr. 1 (10.03.2020): 1–14. http://dx.doi.org/10.47814/ijssrr.v3i1.29.

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A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in the natural sciences (such as physics, biology, earth science, chemistry) and engineering disciplines (such as computer science, electrical engineering), as well as in the social sciences (such as economics, psychology, sociology, political science).The main activities involved in this procedure are observation followed by mathematical modeling; simulation, analysis, optimization and back to observation, Mathematics has been applied to all sciences; and religious and military sciences are no exception, and mathematics can be used highly to design different war operations and solve battlefield equations to gain relative or absolute superiority over the enemy. We can also see clearly the application of mathematics in the Game Theory of war in abundance. In this applied research, conducted in a library method, the challenges between the army of Amir al-Mu’minin, ʿAlī ibn Abī Ṭālib (as), and the army of Muʿāwiya ibn Abī Sufyān in the Battle of Siffin have been modeled using Game Theory and the strategies of each of these two fronts are compared.

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Dibdin, George H. „Mathematical Modeling of Biofilms“. Advances in Dental Research 11, Nr. 1 (April 1997): 127–32. http://dx.doi.org/10.1177/08959374970110010301.

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A set of mathematical equations constitutes a mathematical model if it aims to represent a real system and is based on some theory of that system's operation. On this definition, mathematical models, some very simple, are everywhere in science. A complex system like a biofilm requires modeling by numerical methods and, because of inevitable uncertainties in its theoretical basis, may not be able to make precise predictions. Nevertheless, such models almost always give new insight into the mechanisms involved, and stimulate further investigation. The way in which diffusion coefficients are measured for use in a model, particularly whether they include effects of reversible reaction, is a key element in the modeling. Reasons are given for separating diffusion from reversible reaction effects and dealing with them in a separate subroutine of the model.

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Pandey, Hemant, und Romi Bala. „Mathematical Approaches to Network Science: Modeling and Analysis“. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, Nr. 1 (30.04.2020): 1668–73. http://dx.doi.org/10.61841/turcomat.v11i1.14629.

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Network science, a multidisciplinary field, employs mathematical approaches to model and analyze complex systems as networks or graphs. This paper provides an overview of the fundamental concepts, mathematical modeling techniques, analysis methods, and applications of network science. It emphasizes the importance of mathematical approaches in understanding the structure and dynamics of networks in various domains, including social, biological, and technological networks. The paper also discusses challenges such as scalability and incorporating dynamics, along with future research directions. Overall, mathematical approaches are essential for advancing network science and unlocking new insights into complex systems.

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CANCÈS, ERIC, und CLAUDE LE BRIS. „MATHEMATICAL MODELING OF POINT DEFECTS IN MATERIALS SCIENCE“. Mathematical Models and Methods in Applied Sciences 23, Nr. 10 (12.07.2013): 1795–859. http://dx.doi.org/10.1142/s0218202513500528.

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We survey some recent mathematical works we have contributed to that are related to the modeling of defects in materials science at different scales. We emphasize the similarities (need of a reference, often periodic system; renormalization procedure; etc.) shared by models arising in different contexts. Our illustrative examples are taken from electronic structure models, atomistic models, homogenization problems. The exposition is pedagogic and deliberately kept elementary. Both theoretical and numerical aspects are addressed.

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Yanik, H. Bahadir, und Yasin Memis. „Making Insulation Decisions Through Mathematical Modeling“. Teaching Children Mathematics 21, Nr. 5 (Dezember 2014): 314–19. http://dx.doi.org/10.5951/teacchilmath.21.5.0314.

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Engaging students in studies about conservation and sustainability can support their understanding of making environmental conscious decisions to conserve Earth. This article aims to contribute these efforts and direct students' attention to how they can use mathematics to make environmental decisions. Contributors to iSTEM: Integrating Science, Technology, and Engineering in Mathematics share ideas and activities that stimulate student interest in the integrated fields of science, technology, engineering, and mathematics (STEM) in K—grade 6 classrooms.

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Prisnyakov, V. F., und L. M. Prisnyakova. „Mathematical modeling of emotions“. Cybernetics and Systems Analysis 30, Nr. 1 (Januar 1994): 142–49. http://dx.doi.org/10.1007/bf02366374.

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14

Meyer, Dan. „Missing the Promise Of Mathematical Modeling“. Mathematics Teacher 108, Nr. 8 (April 2015): 578–83. http://dx.doi.org/10.5951/mathteacher.108.8.0578.

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15

Wiedemann, Kenia, Jie Chao, Benjamin Galluzzo und Eric Simoneau. „Mathematical modeling with R“. ACM Inroads 11, Nr. 1 (13.02.2020): 33–42. http://dx.doi.org/10.1145/3380956.

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16

Beshley, Andriy, Ihor Borachok, Olha Ivanyshyn Yaman, Volodymyr Makarov, Nataliya Mayko, Vyacheslav Ryabichev, Olexander Timokha, Vitalii Vasylyk und Vasyl Vavrychuk. „60th anniversary of birthday of professor Roman Chapko“. Journal of Applied and Numerical Analysis 1, Nr. 1 (25.12.2023): 135–37. http://dx.doi.org/10.30970/ana.2023.1.135.

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On October 26, 2023, the distinguished Ukrainian mathematician Roman Chapko, Doctor of Sciences, Professor in the Department of Computational Mathematics of the Faculty of Applied Mathematics and Informatics at Ivan Franko National University of Lviv, Ukraine, has turned 60. He is renowned in the broad mathematical community in Ukraine and beyond for his significant contributions to numerical analysis, computational mathematics, and mathematical modeling. His decades-long scientific activity has earned him a high reputation and has significantly elevated the standing of Ukrainian mathematics and science as a whole.

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MELNYK, Andriy. „SOFTWARE ARCHITECTURE FOR MATHEMATICAL MODELING BASED ON INTERVAL AND ONTOLOGICAL APPROACH“. Herald of Khmelnytskyi National University. Technical sciences 309, Nr. 3 (26.05.2022): 265–73. http://dx.doi.org/10.31891/2307-5732-2022-309-3-265-273.

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Mathematical knowledge is a term often used to describe various components of mathematical science, such as theorems, lemmas, axioms, proofs, etc.. By the term “knowledge from mathematical modeling” we understand the definition of a huge amount of knowledge, which is formalized both within the framework of a specific mathematical model and in the process of its construction, as well as related procedures of practical use. Compared to other forms of knowledge, such as rules, solution trees, mathematical knowledge is more abstract and more structured. In general, the ontological approach to mathematical modeling refers to the use of ontology, as it is based on the terms of mathematical modeling and descriptions of relationships between the main processes of its course.. The general ontological approach to mathematical modeling from an applied point of view does not allow to manage the processes of building mathematical models, because it can only be used to describe the area of mathematical modeling. From an applied point of view, most basic forms of mathematical knowledge are either embedded in specific software tools, such as models of aggregate operation in simulation software, or must be formally interpreted into a more general mathematical tool, following appropriate syntactic rules. Most of this type of knowledge relates to specific modeling tasks and is clearly implemented with the help of appropriate procedural descriptions, rather than declarative representations, unlike the philosophical vision of mathematical modeling. The paper substantiates the use of the ontological approach as an effective tool for managing the processes of building mathematical models based on interval data and using these models for applied problems. The use of the ontological model made it possible to formalize the process of obtaining, storing and using knowledge obtained in the process of mathematical modeling. The article also presents the features of building a software architecture for mathematical modeling based on interval analysis and an ontological approach. The technology for creating software based on the developed ontological add-on for mathematical modeling using interval data for various objects, as well as various forms of user interface implementation, is described. A number of diagrams illustrating the peculiarities of using the ontological approach based on interval data are presented and the peculiarities of its interpretation in applied fields, in particular, in the tasks of environmental monitoring, are described.

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Wang, Lijie, und Mingyu Shi. „A Study on the Implementation of Teaching Mathematical Modeling Classes Based on Mathematical Learning Objectives“. SHS Web of Conferences 174 (2023): 01002. http://dx.doi.org/10.1051/shsconf/202317401002.

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Mathematical modeling literacy is one of the six core literacies in high school mathematics and occupies an important place in the objectives of the high school mathematics curriculum. “The core element of mathematical modeling literacy is to abstract mathematically from real problems, express them in mathematical language, and construct models to solve them with mathematical methods. The STEAM education concept has received much attention in the education field because of its effective integration of “science, technology, engineering, art and mathematics” in the practice of education and teaching, which not only makes up for the shortage of traditional teaching in knowledge inquiry, but also helps to improve the problems of traditional classrooms. This paper explores the development of mathematical modeling literacy in high school by drawing on the STEAM education concept, and examines the effectiveness of the integration of the two through specific teaching cases.

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Hwang, Seonyoung, und Sunyoung Han. „A Study on Mathematical Modeling Trends in Korea“. Korean Society of Educational Studies in Mathematics - Journal of Educational Research in Mathematics 33, Nr. 3 (31.08.2023): 639–66. http://dx.doi.org/10.29275/jerm.2023.33.3.639.

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Mathematical modeling refers to a core competency and teaching-learning method that is being treated as important worldwide. This study aimed at examining trends of previous studies on mathematical modeling, which were published in Korean journals. This study was conducted with the aim of introducing domestic studies on mathematical modeling to both domestic and foreign researchers. Fifty-four studies from 2013 to 2022 were selected for the current trend study and classified in terms of years, research subjects, and research methods. By year, at least one paper and up to 10 papers were published from 2013 to 2022. As a result of examining the trends of the studies by subject, we revealed that studies targeting teachers were very insufficient. Moreover, the findings show that biased research methods and quantitatively simple analysis methods were mainly used. Last, the relational trend between research topics and implications were diverse depending on the theme such as task, lesson, and teacher education. Specifically, although the studies have provided implications on teacher education steadily, research targeting the topic of teacher education for mathematical modeling have been very limited. For the future study on mathematical modeling, mathematics educators and researchers need to recognize that teacher education is significant in implementing mathematical modeling in school classrooms successively, and to try diverse studies on teacher education for mathematical modeling. This paper will contribute to helping foreign scholars to know the research on mathematical modeling being conducted in Korea, which will ultimately contribute to the literature on mathematical modeling.

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ZVYAGIN, L. S. „DEVELOPMENT OF SYSTEM MODELING AND METHODS OF FORMALIZED REPRESENTATION OF SYSTEMS“. EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 1, Nr. 9 (2020): 40–49. http://dx.doi.org/10.36871/ek.up.pr2020.09.01.005.

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Modern science constantly improves approaches and methods of formalization of certain regularities using the latest achievements in mathematics, computer science and other exact Sciences. The methodological basis for research and modeling of systems is often considered to be the theory and practice of mathematical modeling. In the classical system approach, as a rule, system modeling is based on the use of similarity theories and scientific experiment, as well as mathematical statistics, algorithm theory, and a number of other fundamental classical theories. However, focusing only on mathematical modeling ignores the system nature of objects and their models, and also complicates the formalization of the problem, its translation from the verbal form of description to the formal one. The need to understand the relationships between the elements of the system, its structure and functions, combined with the description of system patterns, led to the development of system modeling, which aims not only to gain knowledge about the system, but also to optimize it.

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Okorokova, Vira. „The problem of virtual modeling of historical processes in modern science“. Bulletin of Luhansk Taras Shevchenko National University, Nr. 6 (337) (2020): 4–13. http://dx.doi.org/10.12958/2227-2844-2020-6(337)-4-13.

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The article is devoted to the study of the problem of virtual modeling of historical events and processes. It is noted that the modeling of social and historical processes began to be developed as a method for studying society only from the 70s-80s of the XX century. At the turn of the XX - XXI century, such an interdisciplinary direction as cliodynamics, devoted to the modeling of historical processes based on mathematical methods, appeared. The emergence of this scientific direction shows that the topic of modeling historical processes is based on the methodology of mathematical modeling. The article draws special attention to the virtualization of modern society as a factor in improving the modeling method. Computer technologies are becoming the main means, which greatly simplifies the modeling technology. Problems in creating a model of historical processes are noted, which is associated with a source study basis, the use of additional technologies, and it is also necessary to take into account the complexity, irreversibility, nonlinearity of the historical process itself. Also, as an example, ABM (agent-based models) are given, as an example of imitation and visualization of objects, phenomena. Among the individual characteristics of virtual modeling, the author identifies the ability to create a plausible imitation of an event, interactivity, information content, the ability to change / correct the intended nature of the process or the result of an event, unlimited time and space. This is the advantage of virtual modeling as a method of modern reconstruction of historical events, especially those that do not have accurate data, are debatable. Moreover, the article points out that this type of modeling has already embraced even those historical sciences that were more problematic in this regard (archeology, paleontology).

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Zinnes, Dina A. „Musings on Mathematical Modeling“. Conflict Management and Peace Science 11, Nr. 2 (Februar 1991): 1–16. http://dx.doi.org/10.1177/073889429101100201.

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23

Siepmann, J., und F. Siepmann. „Mathematical modeling of drug dissolution“. International Journal of Pharmaceutics 453, Nr. 1 (August 2013): 12–24. http://dx.doi.org/10.1016/j.ijpharm.2013.04.044.

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24

Siepmann, J., und F. Siepmann. „Mathematical modeling of drug delivery“. International Journal of Pharmaceutics 364, Nr. 2 (Dezember 2008): 328–43. http://dx.doi.org/10.1016/j.ijpharm.2008.09.004.

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Scherrman, Jean-Michel. „Mathematical Modeling of Pharmacokinetic Data.“ Journal of Pharmaceutical Sciences 84, Nr. 8 (August 1995): 1028. http://dx.doi.org/10.1002/jps.2600840821.

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26

Sudakova, Tatiana. „Mathematical Modeling in Development and Improvement of Methodological Basics of Criminology“. Academic Law Journal 24, Nr. 3 (31.08.2023): 388–95. http://dx.doi.org/10.17150/1819-0928.2023.24(3).388-395.

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The article deals with the application of mathematical methods in criminological research and the applicable significance of these processes, which is, in general, developed scientific problems, requiring reassessment in modern conditions of development of mathematical modeling theory and information technologies, the technologization of science and the state of criminological theory. The scientific approaches announced today on the use of mathematics as the language of social sciences and their basic methodology need to be analyzed. Criminometrics is assessed ambiguously, the integrated industry has not received significant development and testing of findings directly in the criminological context. Certain studies on the measurement of crime dynamics, the degree of interaction of criminologically significant processes represent to a greater extent the application of mathematical modelling methods. Other methods of econometrics in solving private or group scientific problems are not used in practical crime prevention. The measurement of the level of statistically significant relationships having criminological significance is carried out mainly by area specialists in the sphere of econometric and mathematical knowledge application that is logical from the methodology viewpoint, since the system analyst able to apply an econometric approach is the presence of specific training. There are no interdisciplinary studies using the integrative criminological and mathematical potential of these sciences.

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Hirano, Tohru, und Kenji Wakashima. „Mathematical Modeling and Design“. MRS Bulletin 20, Nr. 1 (Januar 1995): 40–42. http://dx.doi.org/10.1557/s0883769400048922.

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For the design of functionally gradient materials (FGMs), necessary material properties, such as thermal-expansion-coefficient and Young's modulus in the specific region, are optimized by controlling the distribution profiles of composition and microstructures, as well as micropores in the materials. For this purpose, our research team employs the inverse design procedure in which both the basic material combination and the optimum profile of the composition and microstructures are determined with respect to the objective structural shape and the thermomechanical boundary conditions. Figure 1 shows the inverse design procedure for FGM, in which the final structure to be developed, as well as the boundary conditions, are specified first. After the fabrication method and an allowable material combination are selected from the FGM database, the estimation rules for the material properties of the intermediate compositions are determined based upon the micro-structure. Then, the temperature distribution and the thermal-stress distribution are calculated with the assumed profiles of the distribution functions for the constituents. Other possible combinations and different profiles are also investigated until the optimum is obtained.

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Chernenko, Varvara. „THE FORMATION OF INFORMATICS COMPETENCY FOR FUTURE COMPUTER SCIENCE TEACHERS IN THE PROCESS OF STUDYING COMPUTER MATHEMATICS“. Physical and Mathematical Education 30, Nr. 4 (13.09.2021): 6–12. http://dx.doi.org/10.31110/2413-1571-2021-030-4-001.

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Relevance and expediency of introduction of a training course of computer mathematics for students of “Secondary Education (Computer Science)” is caused by necessity of use of computer equipment with the corresponding software almost in all areas of human activity; the fact that computer mathematics is one of priority directions of research work both in the field of mathematical sciences, and in the field of computer science. Computer mathematics is a field of applied computer science in which problems of development, implementation and use of information technologies for solving mathematical problems are studied. The purpose of teaching computer mathematics is to study and use computer mathematics systems by students to solve applied problems; to master the conceptual and terminological base of modern computer science as a fundamental science; to master theoretical fundamentals of computer science related to formal systems, knowledge bases and models of their representation, models and algorithms of decision making. Formulation of the problem. The study of computer mathematics by future computer science teachers and the use of modern systems of computer mathematics to solve applied problems, creates their system of professional competencies, in particular, informatics competencies in computer mathematics, informatics and mathematical competencies and skills to use modern information technology to analyze mathematical models of processes and phenomena from a variety of fields of knowledge and human activities. Materials and methods. To achieve this goal, the following research methods were used: analysis of scientific and pedagogical literature on the research topic; analysis of curricula, work programs and manuals on the subject "Computer Mathematics"; empirical methods (questionnaire, conversation, pedagogical observation, modeling). Results. This paper has built the model of building informatics competence within the professional competence of the future computer science teacher at the expense of integration of mathematical and information knowledge on the basis of mathematical modeling in environments of systems of computer mathematics, as these systems are an effective means of realization of inter-subject connections of computer science with other subjects of a natural-mathematical cycle. Conclusions. The study of "Computer Mathematics" courses by future computer science teachers, using computer mathematics systems, contributes to the formation of components of the information competence system in the field of information, mathematical and computer modeling.

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Grachev, M. I., V. G. Burlov und N. G. Gracheva. „MATHEMATICAL MODELING OF ORGANIZATIONAL SYSTEMS“. H&ES Research 14, Nr. 5 (2022): 14–20. http://dx.doi.org/10.36724/2409-5419-2022-14-5-14-20.

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Introduction. Modern rates of development of information systems and web-technologies are increasingly being introduced into the life of all mankind, including organizational systems, which requires the improvement of management and decision-making processes. In modern sources of literature, the consideration of management processes is given on the basis of analysis, but from the side of the synthesis of the mathematical model of management decisions, they are not considered, which allows us to consider the ongoing research as an urgent task. For the head of the organizational system, the solution of the problem of management is the most important task based on the complex interaction of all departments of the managed system. Practical relevance: When a destructive impact occurs in a managed system, a decision-maker should have a management decision model that allows timely management decisions to counter emerging threats. Counteraction is carried out at the expense of the resources available to the decision-maker. The mathematical model of a management decision makes it possible to respond in a timely manner to emerging threats in the system and make appropriate management decisions with a given level of the efficiency indicator of the decision being made. The application of the Kolmogorov differential equations makes it possible to link the finding of a mathematical modeling system with various states of the controlled system. Discussion: The resulting indicator of the effectiveness of the decision makes it possible to link three states of the system when considering the target process, namely, the process of threat formation, the process of threat identification and the process of threat neutralization. By setting the required indicator of the effectiveness of a management decision, it allows the manager to respond in a timely manner to the destructive impact, to use the available resources to ensure the achievement of the management goal.

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Fauzi, Ahmad, Dwi Teguh Rahardjo, Utoro Romadhon und Kunthi Ratna Kawuri. „Using Spreadsheet Modeling in Basic Physics Laboratory Practice for Physics Education Curriculum“. International Journal of Science and Applied Science: Conference Series 2, Nr. 1 (10.12.2017): 8. http://dx.doi.org/10.20961/ijsascs.v2i1.16666.

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<p class="Abstract">Physics is one of a branch of science which uses much of mathematical concept. Usually, the concept of physics is expressed in a mathematical equation; it will make physics easier to be understood. Therefore, the students need to understand about mathematical modelling to help them understand physics. Students who take fundamental physics and physics laboratory course required to understand the concept of feedback that is mathematically expressed in differential equations. However, most of the students have not been taught the concept of differential equations at early semester. Therefore, we are interested in reviewing the use of mathematical modelling with a spreadsheet in the case of feedback that is integrated with laboratory practice. The results of this study indicate that students gave positive perceptions and improve their ability in understanding the concept of feedback that is mathematically expressed in the differential equation.<strong></strong></p>

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Page, Scott E. „Computational and Mathematical Modeling in the Social Sciences“. Public Choice 129, Nr. 3-4 (11.07.2006): 511–14. http://dx.doi.org/10.1007/s11127-006-9040-1.

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Venu, Vaisakh, Sreenath B. und Ramdas E. R. „Various Mathematical Models in Agricultural Engineering“. Current Journal of Applied Science and Technology 42, Nr. 41 (07.11.2023): 13–20. http://dx.doi.org/10.9734/cjast/2023/v42i414263.

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This paper investigates the important role of mathematics in the solution of complicated issues in the field of agricultural engineering and technology. It shows examples of mathematical modelling and analytical techniques that are used in agriculture, such as Crop Growth, Irrigation Management, Soil Moisture Modeling , Environmental management, Pest and Disease Management, Fertilizer Applications, Watershed Management etc.

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Jones, Graham A. „Mathematical Modeling in a Feast of Rabbits“. Mathematics Teacher 86, Nr. 9 (Dezember 1993): 770–73. http://dx.doi.org/10.5951/mt.86.9.0770.

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This article was developed while the author was teaching a course on mathematical modeling in the Middle School Mathematics Program at Illinois State Unjversity. This program was developed under Grant #TEI-865203 from the National Science Foundation; however, the views expressed do not necessarily reflect those of the funding agency.

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Gürbüz, Ramazan, und Muammer Çalık. „INTERTWINING MATHEMATICAL MODELING WITH ENVIRONMENTAL ISSUES“. Problems of Education in the 21st Century 79, Nr. 3 (10.06.2021): 412–24. http://dx.doi.org/10.33225/pec/21.79.412.

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Since contemporary learning theories, strategies and models offer the interdisciplinary approach, educators need new pedagogical alternative ways to attain it in practicum. For this reason, the current research aimed to illustrate how to intertwine mathematical modeling with an environmental issue that recruits waste management (e.g., reuse-recycle-reduce) to live an environmentally friendly lifestyle. Through a case study research, 6 seventh-grade students (3 females and 3 males; aged 13-14) voluntarily participated in the research. The researchers videotaped and analyzed all interactive learning processes to elicit the students’ environmental dialogues. The results indicated that the interdisciplinary mathematical modeling afforded the students to acquire the targeted environmental concepts/issues and somewhat supported their arguments. Since the current research illustrates an alternative pedagogy to integrate science/environmental education into mathematics, it may be used to facilitate dissemination and applicability of the STEM education. Keywords: environmental issues, interdisciplinary approach, mathematical modeling

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Hayward, John. „Mathematical modeling of church growth“. Journal of Mathematical Sociology 23, Nr. 4 (Februar 1999): 255–92. http://dx.doi.org/10.1080/0022250x.1999.9990223.

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Singh, Vijay P., und David A. Woolhiser. „Mathematical Modeling of Watershed Hydrology“. Journal of Hydrologic Engineering 7, Nr. 4 (Juli 2002): 270–92. http://dx.doi.org/10.1061/(asce)1084-0699(2002)7:4(270).

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Sniedovich, Moshe. „The Art and Science of Modeling Decision-Making Under Severe Uncertainty“. Decision Making in Manufacturing and Services 1, Nr. 2 (11.10.2007): 111–36. http://dx.doi.org/10.7494/dmms.2007.1.2.111.

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For obvious reasons, models for decision-making under severe uncertainty are austere. Simply put, there is precious little to work with under these conditions. This fact highlights the great importance of utilizing in such cases the ingredients of the mathematical model to the fullest extent, which in turn brings under the spotlight the art of mathematical modeling. In this discussion we examine some of the subtle considerations that are called for in the mathematical modeling of decision-making under severe uncertainty in general, and worst-case analysis in particular. As a case study we discuss the lessons learnt on this front from the Info-Gap experience.

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Il’in, V. P. „Soviet experience in the management of applied and academic science: In memory of Academician G.I. Marchuk“. Вестник Российской академии наук 93, Nr. 5 (01.05.2023): 479–85. http://dx.doi.org/10.31857/s0869587323050043.

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Ten years ago, on March 24, 2013, Academician G.I. Marchuk died; his creative path includes outstanding results of basic and applied research in the field of computational mathematics and computer modeling, the creation of world-class scientific schools in atmospheric and ocean physics, nuclear reactor calculations, and mathematical immunology, as well as unique experience in managing industrial and mathematical science at the state level. The scientist’s behests, concentrated in his book The Science of Managing Science, indicate ways to reform the Russian Academy of Sciences, designed to ensure scientific and technological progress and sustainable development of society in our country.

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Amonlirdviman, K. „Mathematical Modeling of Planar Cell Polarity to Understand Domineering Nonautonomy“. Science 307, Nr. 5708 (21.01.2005): 423–26. http://dx.doi.org/10.1126/science.1105471.

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POTAPOVA, Nadiia, Ludmila VOLONTYR und Oksana Zelinska. „MATHEMATICAL AND COMPUTER MODELING OF FUNCTIONING LOGISTICS PROCESSES AND SYSTEMS“. Herald of Khmelnytskyi National University. Technical sciences 307, Nr. 2 (02.05.2022): 73–80. http://dx.doi.org/10.31891/2307-5732-2022-307-2-73-80.

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The paper substantiates the need of usage mathematical and computer modelling in data analysis in assessing with the functioning of logistic processes and systems. Based on the analysis of existing approaches, the peculiarities of using an integrated method of mathematical and computer modelling in order to effective set up and implement a process research experiment are revealed. The logistic system is formalized within the supply of material and technical resources by a centralized method of flow distribution and transfer of management influence from the highest level of the hierarchy to the lower levels such as branches or separate units. Formal identification of the logistic system of supply of material and technical resources is based on the separation of the main factors of the system and the limits of their impact. The main stages of modelling the functioning of logistics processes are specified, which is the basis for clarifying the algorithmic features of the computer experiment. It is stipulated that one of the main approaches to the study of these systems is modelling based on the use of methodological principles of queuing theory, which is based on modelling the flow characteristics of processes, orders and the discipline of their service. The procedure for determining the main characteristics of the system is focused on conducting computer simulations by setting up a computer experiment aimed at simulating the behaviour of the system and its evaluation. Probabilistic estimates of the queuing system are obtained under stationary operating conditions, in the established mode, which achieves limiting the impact of the conditions of the initial state. Emphasis is placed on combining simulation modelling as one of the approaches of computer modelling with elements of optimization solutions that can be obtained as an analytical solution to specific applications of logistics of varying complexity, in particular, inventory management.

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PEDYASH, Volodymyr. „MATHEMATICAL MODELING OF FIBER-OPTIC TRANSMISSION SYSTEM WITH INTENSITY MODULATION“. Herald of Khmelnytskyi National University. Technical sciences 317, Nr. 1 (23.02.2023): 162–66. http://dx.doi.org/10.31891/2307-5732-2023-317-1-162-166.

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Fibre optic transmission systems are widespread in the transport network. Optical Transport Hierarchy (OTH) transmission systems are the basis of the transport network. The principle of wavelength multiplexing of channels is used to increase the technical and economic performance. Technically a simpler type of intensity modulation is used to transmit OTU1 and OTU2 OTH frames. Erbium-doped fibre-based optical amplifiers also generate amplified spontaneous emission noise, which degrades the performance of the transmission system. The qualitative characteristics of the optical channel of OTH transmission system with intensity modulation are investigated in this paper. In the simulation process, it is possible to neglect the nonlinear signal distortion in the optical fiber at the nominal signal power of the transponder transmitter. Therefore, in the studied transmission system model, an optical oscillation consisting of two components (signal and noise) arrives at the receiver input. In this case, an electrical signal consisting of three summands (signal-signal, signal-noise and noise-noise) will be generated at the output of the photodiode. A simplified formula for calculating the Q-factor of the optical signal, which affects the error probability in the optical channel, is proposed in most of the specialized literature. Numerical Q-factor values were calculated for OTU1 and OTU2 frames. They were compared with the reference results which were obtained by simulation in Optiwave Optisystem software. The analysis of the obtained data has proved that the simplified formula for the calculation of the Q-factor has a relative error of the order of 39-59%. A more accurate result can be obtained by using an expression that takes into account both components of the noise at the photodiode output.

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Bohza, Tetiana, und Andrii Siasiev. „SOME ASPECTS OF DIFFERENTIAL MATHEMATICAL MODELING OF INDOOR FIRE“. Grail of Science, Nr. 14-15 (10.06.2022): 382–84. http://dx.doi.org/10.36074/grail-of-science.27.05.2022.069.

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Modern scientific methods of forecasting fire hazards (FFH) are based on mathematical modeling, that is mathematical models of fire. The mathematical model of fire describes in the most general form the change of parameters of the state of the environment in the room over time, as well as the change of parameters of the state of the enclosing structures of the room and various elements of technological equipment.

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Erusalimskiy, Iakov M., und Irina A. Shkuray. „On the History of Mathematical Modeling in the Southern Federal University“. UNIVERSITY NEWS. NORTH-CAUCASIAN REGION. NATURAL SCIENCES SERIES, Nr. 1 (29.03.2024): 4–16. http://dx.doi.org/10.18522/1026-2237-2024-1-4-16.

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The article is devoted to the history of mathematical modeling at Rostov State University (RSU), now Southern Federal University (SFU). The authors have been describing it since 1950. As elsewhere in the world, its begin-nings are associated with problems of mechanics. At the Rostov State University, its founders are considered to be I.I. Vorovich and N.N. Moiseev, future academicians of the Russian Academy of Sciences. The flourishing of work on mathematical modeling is associated with the opening of a computer center at the Rostov State University (1958), and then a research institute of mechanics and applied mathematics (1971), where a simulation model of the Sea of Azov was created, for which its developers were awarded the USSR State Prize. The article examines not only the history of mathematical modeling as a scientific direction, but also the history of its formation as an academic subject at the Faculty of Mechanics and Mathematics (now the I.I. Vorovich Institute of Mathematics, Mechanics and Computer Science of the SFedU).

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Zhang, J., und A. K. Datta. „Mathematical modeling of bread baking process“. Journal of Food Engineering 75, Nr. 1 (Juli 2006): 78–89. http://dx.doi.org/10.1016/j.jfoodeng.2005.03.058.

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Goncharova, Anastaciya B., Eugeny P. Kolpak, Madina M. Rasulova und Alina V. Abramova. „Mathematical modeling of cancer treatment“. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes 16, Nr. 4 (2020): 437–46. http://dx.doi.org/10.21638/11701/spbu10.2020.408.

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The paper proposes mathematical models of ovarian neoplasms. The models are based on a mathematical model of interference competition. Two types of cells are involved in the competition for functional space: normal and tumor cells. The mathematical interpretation of the models is the Cauchy problem for a system of ordinary differential equations. The dynamics of tumor growth is determined on the basis of the model. A model for the distribution of conditional patients according to four stages of the disease, a model for assessing survival times for groups of conditional patients, and a chemotherapy model are also proposed.

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Bellomo, N., und F. Brezzi. „Collective dynamics in science and society“. Mathematical Models and Methods in Applied Sciences 31, Nr. 06 (15.06.2021): 1053–58. http://dx.doi.org/10.1142/s0218202521020012.

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This editorial paper presents the articles published in a special issue devoted to active particle methods applied to modeling, qualitative analysis, and simulation of the collective dynamics of large systems of interacting living entities in science and society. The modeling approach refers to the mathematical tools of behavioral swarms theory and to the kinetic theory of active particles. Applications focus on classical problems of swarms theory, on crowd dynamics related to virus contagion problems, and to multiscale problems related to the derivation of models at a large scale from the mathematical description at the microscopic scale. A critical analysis of the overall contents of the issue is proposed, with the aim to provide a forward look to research perspectives.

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Iñiguez, Gerardo, Hang-Hyun Jo und Kimmo Kaski. „Special Issue “Computational Social Science”“. Information 10, Nr. 10 (01.10.2019): 307. http://dx.doi.org/10.3390/info10100307.

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The last centuries have seen a great surge in our understanding and control of “simple” physical,chemical, and biological processes through data analysis and the mathematical modeling of theirunderlying dynamics [...]

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MELNYK, Andriy. „SOFTWARE ARCHITECTURE FOR MATHEMATICAL MODELING BASED ON INTERVAL AND ONTOLOGICAL APPROACH“. Herald of Khmelnytskyi National University. Technical sciences 311, Nr. 4 (August 2022): 141–49. http://dx.doi.org/10.31891/2307-5732-2022-311-4-141-149.

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The paper substantiates the use of the ontological approach as an effective tool for managing the processes of building mathematical models based on interval data and using these models for applied problems. The use of the ontological model made it possible to formalize the process of obtaining, storing and using knowledge obtained in the process of mathematical modeling. The article also presents the features of building a software architecture for mathematical modeling based on interval analysis and an ontological approach. The technology for creating software based on the developed ontological add-on for mathematical modeling using interval data for various objects, as well as various forms of user interface implementation, is described. A number of diagrams illustrating the peculiarities of using the ontological approach based on interval data are presented and the peculiarities of its interpretation in applied fields, in particular, in the tasks of environmental monitoring, are described.

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Brigadnov, I. A., und A. Dorfmann. „Mathematical modeling of magnetorheological fluids“. Continuum Mechanics and Thermodynamics 17, Nr. 1 (April 2005): 29–42. http://dx.doi.org/10.1007/s00161-004-0185-1.

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Rajagopal, K. R., und M. Ružička. „Mathematical modeling of electrorheological materials“. Continuum Mechanics and Thermodynamics 13, Nr. 1 (01.02.2001): 59–78. http://dx.doi.org/10.1007/s001610100034.

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