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Journal articles on the topic 'Computing and Mathematical Sciences'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Wang, Yingxu. "On the Mathematical Theories and Cognitive Foundations of Information." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 3 (July 2015): 42–64. http://dx.doi.org/10.4018/ijcini.2015070103.

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A recent discovery in computer and software sciences is that information in general is a deterministic abstract quantity rather than a probability-based property of the nature. Information is a general form of abstract objects represented by symbolical, mathematical, communication, computing, and cognitive systems. Therefore, information science is one of the contemporary scientific disciplines collectively known as abstract sciences such as system, information, cybernetics, cognition, knowledge, and intelligence sciences. This paper presents the cognitive foundations, mathematical models, and formal properties of information towards an extended theory of information science. From this point of view, information is classified into the categories of classic, computational, and cognitive information in the contexts of communication, computation, and cognition, respectively. Based on the three generations of information theories, a coherent framework of contemporary information is introduced, which reveals the nature of information and the fundamental principles of information science and engineering.

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2

Bhadane, Mr Sandip, Mrs Megha Kishor Kothawade, Dr Mahendra D. Shinde, and Dr Vishal Sulakhe. "Applications of Mathematical in Computer Science." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 705–9. http://dx.doi.org/10.22214/ijraset.2023.48667.

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Abstract: Mathematics (The QUEEN’s mother of all Sciences), is the foundation of Computer Science. Mathematics can be perceived in our garden or park from symmetry of leaves, flowers, fruits etc. and by so many examples of Geometry and symmetry can be seen in nature. Scientists and researchers cannot ideally accomplish their work without the inclusion of mathematics. Mathematics is sociable for analytical skills needed in Computer Disciplines like; Concepts of binary number system, Boolean algebra, Calculus, Discrete mathematics, linear algebra, number theory, and graph theory are the most applicable to the subject of computer science with the accessional emergence of new concepts like machine learning, artificial intelligence, virtual reality and augmented reality make the future of mathematics grow endless. Mathematics has been an important intellectual preoccupation of man for a long time. Computer Science as a formal discipline is about seven decades young. Is the almost spontaneous use of computing? In this article, this paper convey to the frontage the many close connections and parallels between the Mother and daughter sciences. The paper underscores the strong interplay and interactions by looking at some exciting contemporary results from number theory and combinatorial mathematics and algorithms of computer science.

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3

Chiu, Mei-Hung, and Mark Cesa. "Gender Gap in Science." Chemistry International 42, no. 3 (July 1, 2020): 16–21. http://dx.doi.org/10.1515/ci-2020-0306.

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AbstractThere continues to be a persistent gap between women’s and men’s participation, access, rights, pay, and benefits in the natural sciences, mathematics, and computing. The UNESCO Institute of Statistics reports that fewer than 30% of the world’s researchers are women. Many scientists, mathematicians, computing experts, and policy makers are working to reduce this gender gap by way of a wide range of initiatives. The International Science Council (ISC) funded a unique three-year project in 2017-2019 called, “A Global Approach to the Gender Gap in Mathematical, Computing and Natural Sciences: How to measure it, how to reduce it?” that has provided a wide-ranging view of the issues women face in the sciences and how these issues may be overcome.

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4

Chen, Yiling, Arpita Ghosh, Michael Kearns, Tim Roughgarden, and Jennifer Wortman Vaughan. "Mathematical foundations for social computing." Communications of the ACM 59, no. 12 (December 2016): 102–8. http://dx.doi.org/10.1145/2960403.

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5

Anguelov, Roumen, and Svetoslav Markov. "60th Anniversary of the Seminal Paper on Interval Analysis and Computations by T. Sunaga." Biomath Communications 5, no. 2 (February 24, 2019): 137. http://dx.doi.org/10.11145/bmc.2018.12.315.

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Sunaga considered all computational procedures, which had been traditionally defined on real numbers, as being too ideal and proposed to replace them by the procedures on real intervals in order to make everything "more realistic". Sunaga studied many different kinds of numerical procedures including the Taylor-series interval solution of the initial-value problem of ordinary differential equations.The purpose of the present note is to mark the 60-th anniversary of the publication of the seminal work by the Japan mathematician Teruo Sunaga. The paper summarizes the results of his Master Thesis \cite{Sunaga1956}. Sunaga's work sets the foundation of the contemporary interval analysis and reliable computing. This is an interdisciplinary field, combining abstract mathematical theories and practical applications related to computer science, numerical analysis and mathematical modeling in the natural, engineering and social-economic sciences.

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Henderson, Peter B. "Mathematical reasoning in computing education II." ACM Inroads 2, no. 1 (February 25, 2011): 23–24. http://dx.doi.org/10.1145/1929887.1929897.

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7

Holden, A. V. "Nonlinear Science — The Impact of Biology." International Journal of Bifurcation and Chaos 07, no. 09 (September 1997): 2075–104. http://dx.doi.org/10.1142/s0218127497001552.

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Nonlinear science has primarily developed from applications of mathematics to physics. The biological sciences are emerging as the dominant growth points of science and technology, and biological systems are characterized by being information dense, spatially extended, organized in interacting hierarchies, and rich in diversity. These characteristics, linked with an increase in available computing power and accessible memory, may lead to a nonlinear science of complicated interacting systems that will link different types of mathematical objects within a framework of algebraic models of computing systems. Examples, drawn from current work on intracellular, cellular, tissue, organ, and integrative physiology of an individual, are outlined within the theory of synchronous concurrent algorithms. Possible directions in population dynamics and applications to ecosystem management are outlined.

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8

Kumar, Rakesh. "FUTURE FOR SCIENTIFIC COMPUTING USING PYTHON." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 30–41. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.28.

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Computational science (scientific computing or scientific computation) is concerned with constructing mathematical models as well as quantitative analysis techniques and using computers to analyze as well as solve scientific problems. In practical use, it is basically the application of computer simulation as well as other forms of computation from numerical analysis and theoretical computer science to problems in different scientific disciplines. The scientific computing approach is to gain understanding, basically through the analysis of mathematical models implemented on computers. Python is frequently used for highperformance scientific applications and widely used in academia as well as scientific projects because it is easy to write and performs well. Due to its high performance nature, scientific computing in Python often utilizes external libraries like NumPy, SciPy and Matplotlib etc.

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9

Wu, Hualong, Muhammad Kamran Siddiqui, Bo Zhao, Jianhou Gan, and Wei Gao. "Computing the Ediz eccentric connectivity index of discrete dynamic structures." Open Physics 15, no. 1 (June 14, 2017): 354–59. http://dx.doi.org/10.1515/phys-2017-0039.

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AbstractFrom the earlier studies in physical and chemical sciences, it is found that the physico-chemical characteristics of chemical compounds are internally connected with their molecular structures. As a theoretical basis, it provides a new way of thinking by analyzing the molecular structure of the compounds to understand their physical and chemical properties. In our article, we study the physico-chemical properties of certain molecular structures via computing the Ediz eccentric connectivity index from mathematical standpoint. The results we yielded mainly apply to the techniques of distance and degree computation of mathematical derivation, and the conclusions have guiding significance in physical engineering.

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10

Prykarpatsky, A., and A. Plichko. "MYKHAILO KRAWTCHOUK AND COMPUTING DEVICES. ON ETHIC OF INVESTIGATIONS IN HISTORY OF EXACT SCIENCES." Bukovinian Mathematical Journal 9, no. 1 (2021): 264–72. http://dx.doi.org/10.31861/bmj2021.01.22.

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In this note, we focus on some aspects of the use of virtual ethics in the study of the scientiﬁc heritage of the outstanding Ukrainian mathematician Mykhailo Krawtchouk and its involvement in the invention of the ﬁrst electronic computer by Atanasov and Berry. In particular, the biased and clearly propagandistic activity of the Canadian political scientist I. Kachanovsky is analyzed concerning the contrived contribution of Mykhailo Krawtchouk’s mathematical advice to an allegedly substantial solution of the designer G. Atanasov problems of implanting computational algorithms in his designed ﬁrst electronic computing device. We also noted the ill-considered popularization of these false as well as harmful statements in scientiﬁc and popular science Ukrainian literature. Separately, we focused on the openly anti- Ukrainian propaganda activity of I. Kachanovsky, which concerns his clumsy eﬀorts in investi- gating the activities of Ukrainian nationalists during World War II and the last events on the Maidan, and its aggressive dissemination in the press of insinuations, pseudo-historical and pseudo-scientiﬁc anti-Ukrainian insults.

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11

Hu, Guoding. "On the Mathematical Model of Computing Machine." Journal of Computer Science and Technology 3, no. 4 (October 1988): 273–88. http://dx.doi.org/10.1007/bf02943352.

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12

Mainzer, Klaus. "The Digital and the Real Universe. Foundations of Natural Philosophy and Computational Physics." Philosophies 4, no. 1 (January 3, 2019): 3. http://dx.doi.org/10.3390/philosophies4010003.

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In the age of digitization, the world seems to be reducible to a digital computer. However, mathematically, modern quantum field theories do not only depend on discrete, but also continuous concepts. Ancient debates in natural philosophy on atomism versus the continuum are deeply involved in modern research on digital and computational physics. This example underlines that modern physics, in the tradition of Newton’s Principia Mathematica Philosophiae Naturalis, is a further development of natural philosophy with the rigorous methods of mathematics, measuring, and computing. We consider fundamental concepts of natural philosophy with mathematical and computational methods and ask for their ontological and epistemic status. The following article refers to the author’s book, “The Digital and the Real World. Computational Foundations of Mathematics, Science, Technology, and Philosophy.”

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13

Álvarez, Alonso, Narcisa Salazar, and José Tinajero. "Scientific Computing and the Huygens' Principle." KnE Engineering 1, no. 2 (January 30, 2018): 44. http://dx.doi.org/10.18502/keg.v1i2.1485.

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Abstract. Mathematics has been present in the development of society since time immemorial; great figures have dedicated their entire life to analysis and research in various branches of this broad science. Scientific Computing is closely related to the design and construction of mathematical models aimed at solving scientific, social and engineering problems. There are several applications of this discipline, for example for mathematical simulations of differential equations in partial derivatives that describe the propagation of a variety of waves such as sound waves, or heat conduction problems in different media, which can be solved using The Fourier Analysis. Through Graphical Computing the Huygens Principle can be verified. All these models can be implemented through the computer in order to facilitate the complex calculations that must be done to solve problems of this type and depending on the case to condense all this information into a graph "A good graph says more than a thousand words (Chinese Proverb) ".

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14

Annamalai, Chinnaraji. "Computation and Calculus for Combinatorial Geometric Series and Binomial Identities and Expansions." Journal of Engineering and Exact Sciences 8, no. 7 (September 22, 2022): 14648–01. http://dx.doi.org/10.18540/jcecvl8iss7pp14648-01i.

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Nowadays, the growing complexity of mathematical and computational modelling demands the simplicity of mathematical and computational equations for solving today’s scientific problems and challenges. This paper presents combinatorial geometric series, innovative binomial coefficients, combinatorial equations, binomial expansions, calculus with combinatorial geometric series, and innovative binomial theorems. Combinatorics involves integers, factorials, binomial coefficients, discrete mathematics, and theoretical computer science for finding solutions to the problems in computing and engineering science. The combinatorial geometric series with binomial expansions and its theorems refer to the methodological advances which are useful for researchers who are working in computational science. Computational science is a rapidly growing multi-and inter-disciplinary area where science, engineering, computation, mathematics, and collaboration use advance computing capabilities to understand and solve the most complex real-life problems.

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MAINZER, KLAUS. "CELLULAR NEURAL NETWORKS AND VISUAL COMPUTING." International Journal of Bifurcation and Chaos 13, no. 01 (January 2003): 1–6. http://dx.doi.org/10.1142/s0218127403006534.

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Brain-like information processing has become a challenge to modern computer science and chip technology. The CNN (Cellular Neural Network) Universal Chip is the first fully programmable industrial-sized brain-like stored-program dynamic array computer which dates back to an invention of Leon O. Chua and Lin Yang in Berkeley in 1988. Since then, many papers have been written on the mathematical foundations and technical applications of CNN chips. They are already used to model artificial, physical, chemical, as well as living biological systems. CNN is now a new computing paradigm of interdisciplinary interest. In this state of development a textbook is needed in order to recruit new generations of students and researchers from different fields of research. Thus, Chua's and Roska's textbook is a timely and historic publication. On the background of their teaching experience, they have aimed at undergraduate students from engineering, physics, chemistry, as well as biology departments. But, actually, it offers more. It is a brilliant introduction to the foundations and applications of CNN which is distinguished with conceptual and mathematical precision as well as with detailed explanations of applications in visual computing.

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16

Gopal, Тadepalli. "Teaching Mathematics with the Software Engineering Body of Knowledge." Innovative STEM Education 4, no. 1 (June 10, 2022): 8–12. http://dx.doi.org/10.55630/stem.2022.0401.

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It is a fact that employability of the graduates and post-graduates in engineering disciplines does not explicitly require any specific mathematical knowledge. It is unfortunate but true that even though Mathematics is the foundation of computer science it is now being considered as a totally separate subject. The inclusion of topics from Mathematics in the Computing Curricula all over the world has often times been very tough to justify. Mathematical foundations any beyond the first year in a typical Undergraduate Engineering programme are rarely found in the computing curricula today. The author is associated with working on teaching computer science to K-12 schools. Integrating Computer Science and Mathematics has a huge potential but so are the risks. The focus is the IEEE Software Engineering Body of Knowledge [SWEBOK].

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17

Greenberg, Harvey J., and Frederic H. Murphy. "Computing Market Equilibria with Price Regulations Using Mathematical Programming." Operations Research 33, no. 5 (October 1985): 935–54. http://dx.doi.org/10.1287/opre.33.5.935.

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18

Annamalai, Chinnaraji. "Combinatorial and Multinomial Coefficients and its Computing Techniques for Machine Learning and Cybersecurity." Journal of Engineering and Exact Sciences 8, no. 8 (September 29, 2022): 14713–01. http://dx.doi.org/10.18540/jcecvl8iss8pp14713-01i.

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Mathematical and combinatorial techniques with nonnegative integers are used as computing algorithms for the programs development to apply in artificial intelligence and cybersecurity. Methodological advances in combinatorics and mathematics play a vital role in artificial intelligence and machine learning for data analysis and artificial intelligence-based cybersecurity for protection of the computing systems, devices, networks, programs and data from cyber-attacks. In connection with these ideas, this article is prepared for applications in computing science and cybersecurity. This paper presents computing and combinatorial formulae such as theorems on factorials, binomial, and multinomial coefficients and probability and binomial distributions.

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19

Chinnaraji Annamalai. "Analysis and Modelling of Annamalai Computing Geometric Series and Summability." Mathematical Journal of Interdisciplinary Sciences 6, no. 1 (September 1, 2017): 11–15. http://dx.doi.org/10.15415/mjis.2017.61002.

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This paper presents a mathematical model for the formation as well as computation of geometric series in a novel way. Using Annamalai computing methoda simple mathematical model is established for analysis and manipulation of geometric series and summability.This new modelcould be used in the research fields of physics, engineering, biology, economics, computer science, queueing theory, and finance. In this paper, a novel computational model had also been developed such that a∑i=k∞ yi=ayk/1-y and ∑i=0∞ ∑j=i∞ ayj=a/(1-y)2,(0<y<1). This could be very interesting and informative for current students and researchers.

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20

Walker, Henry M. "Beyond the cliche, mathematical fluency, in the computing curriculum." ACM Inroads 6, no. 4 (November 17, 2015): 24–26. http://dx.doi.org/10.1145/2818349.

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21

Patterson, Evan, Owen Lynch, and James Fairbanks. "Categorical Data Structures for Technical Computing." Compositionality 4 (December 28, 2022): 5. http://dx.doi.org/10.32408/compositionality-4-5.

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Many mathematical objects can be represented as functors from finitely-presented categories C to Set. For instance, graphs are functors to Set from the category with two parallel arrows. Such functors are known informally as C-sets. In this paper, we describe and implement an extension of C-sets having data attributes with fixed types, such as graphs with labeled vertices or real-valued edge weights. We call such structures acsets, short for attributed C-sets. Derived from previous work on algebraic databases, acsets are a joint generalization of graphs and data frames. They also encompass more elaborate graph-like objects such as wiring diagrams and Petri nets with rate constants. We develop the mathematical theory of acsets and then describe a generic implementation in the Julia programming language, which uses advanced language features to achieve performance comparable with specialized data structures.

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22

Pap, E. "Pseudo-analysis as a mathematical base for soft computing." Soft Computing - A Fusion of Foundations, Methodologies and Applications 1, no. 2 (June 26, 1997): 61–68. http://dx.doi.org/10.1007/s005000050007.

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23

Hashizume, Makoto. "Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence." Cyborg and Bionic Systems 2021 (January 8, 2021): 1–3. http://dx.doi.org/10.34133/2021/9160478.

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Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of “dynamic living human anatomy.” It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extending from an embryo to post mortem body; (3) the functional axis on physiology or metabolism which is reflected in a variety of medical image modalities; and (4) the pathological axis, extending from a healthy physical condition to a diseased condition. It aims to integrate multiple prediction models such as multiscale prediction model, temporal prediction model, anatomy function prediction model, and anatomy-pathology prediction model. Artificial intelligence has been introduced to accelerate the calculation of statistic mathematical analysis. The future perspective is expected to promote the development of human resources as well as a new MCA-based scientific interdisciplinary field composed of mathematical statistics, information sciences, computing data science, robotics, and biomedical engineering and clinical applications. The MCA-based medicine might be one of the solutions to overcome the difficulties in the current medicine.

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Hashizume, Makoto. "Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence." Cyborg and Bionic Systems 2021 (January 8, 2021): 1–3. http://dx.doi.org/10.34133/2021/9160478.

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Multidisciplinary computational anatomy (MCA) is a new frontier of science that provides a mathematical analysis basis for the comprehensive and useful understanding of “dynamic living human anatomy.” It defines a new mathematical modeling method for the early detection and highly intelligent diagnosis and treatment of incurable or intractable diseases. The MCA is a method of scientific research on innovative areas based on the medical images that are integrated with the information related to: (1) the spatial axis, extending from a cell size to an organ size; (2) the time series axis, extending from an embryo to post mortem body; (3) the functional axis on physiology or metabolism which is reflected in a variety of medical image modalities; and (4) the pathological axis, extending from a healthy physical condition to a diseased condition. It aims to integrate multiple prediction models such as multiscale prediction model, temporal prediction model, anatomy function prediction model, and anatomy-pathology prediction model. Artificial intelligence has been introduced to accelerate the calculation of statistic mathematical analysis. The future perspective is expected to promote the development of human resources as well as a new MCA-based scientific interdisciplinary field composed of mathematical statistics, information sciences, computing data science, robotics, and biomedical engineering and clinical applications. The MCA-based medicine might be one of the solutions to overcome the difficulties in the current medicine.

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25

Leeds, Adam E. "Dreams in Cybernetic Fugue." Historical Studies in the Natural Sciences 46, no. 5 (November 1, 2016): 633–68. http://dx.doi.org/10.1525/hsns.2016.46.5.633.

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This article positions the vogue for cybernetics as a key driver of the transformation of the institutional structures and epistemic order of Soviet technoscience that occurred in the 1950s and 1960s. Inseparable from the rapid growth of Soviet military science, Soviet cybernetics was both the result and medium of surprising recombinations of different forms of scientific and engineering expertise to create novel military technologies. Military computing was the point of entry for cybernetics, while its focal tasks—the bomb, rocketry, and radar—in turn shaped cybernetic understandings. The rapid growth and cyberneticization of these new areas of militarily driven science caused a tectonic transformation of the Stalinist articulation of science, technology, and politics. A crucial moment of these latter shifts, the article further suggests, was the transformation of Soviet economics into a properly mathematical economics. In a series of analogical transfers, mathematicians and engineers derived a radical vision of cybernetic communism from their specific military engineering tasks. Their encounter with reformist economics, mediated by computational utopias, enabled the transfer of advanced mathematical techniques, metaphors, and personnel from military science to the social sciences. This complex process constituted Soviet mathematical economics. Soviet cybernetics’ challenge to the Stalinist order of knowledge and its attendant institutional reconfigurations thus opened up a critical space for political reflection for the Cold War era “scientific-technical intelligentsia” at the heart of the party-state.

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Tag, M. A., and M. E. Mansour. "Automatic computing of the grand potential in finite temperature many-body perturbation theory." International Journal of Modern Physics C 30, no. 11 (November 2019): 1950100. http://dx.doi.org/10.1142/s0129183119501006.

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A new program created in C/C[Formula: see text] language generates automatically the analytic expression of grand potential and prints it in Latex2e format and in textual data. Another code created in Mathematica language can translate the textual data into a mathematical expression and help any interested to evaluate the thermodynamic quantities in analytic or numeric forms.

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LEUCKER, MARTIN, JORGE A. PÉREZ, CAMILO RUEDA, and FRANK D. VALENCIA. "Preface to special issue: ICTAC 2015." Mathematical Structures in Computer Science 29, no. 1 (June 20, 2018): 1–2. http://dx.doi.org/10.1017/s0960129518000130.

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This issue of Mathematical Structures in Computer Science (MSCS) contains a selection of papers presented at the 12th International Colloquium on Theoretical Aspects of Computing (ICTAC 2015), which took place in Cali, Colombia, on October 29–31, 2015.

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Swope, W. C. "Deep computing for the life sciences." IBM Systems Journal 40, no. 2 (2001): 248–62. http://dx.doi.org/10.1147/sj.402.0248.

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Yakovleva, G. N., B. F. Bogatikov, and E. I. Khabarova. "NIKOLAY PROKOFYEVICH FEDORENKO: THE EXACT CALCULATION AND FORESIGHT." Fine Chemical Technologies 12, no. 2 (April 28, 2017): 105–10. http://dx.doi.org/10.32362/2410-6593-2017-12-2-105-110.

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The article is devoted to the 100th anniversary of the birth of Nikolay Prokofyevich Fedorenko, a graduate of M.V. Lomonosov MITHT, a participant of the Great Patriotic War, the head of MITHT department for chemical industry economy (1951-1962), since 1953 to 1958 - the deputy director of MITHT for studies. N.P. Fedorenko is Doctor of Economics, professor, academician of the Academy of Sciences of the USSR, member of the presidium of the Academy of Sciences of the USSR, academician-secretary of the Economy department of the Academy of Sciences of the USSR, one of the main founders and the first director of the Central Economics and Mathematics Institute of the Academy of Sciences of the USSR (1963-1985). N.P. Fedorenko was the most talented organizer of the economic science. He made a large contribution to the chemicalization of the national economy, to the application of modern mathematical methods and computing hardware for economic research, to the planning, management and studying of the theoretical and methodological bases of optimum performance of economy.

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Beynon, Robert J. "Computing in the biological sciences a survey." Bioinformatics 1, no. 1 (1985): 7–9. http://dx.doi.org/10.1093/bioinformatics/1.1.7.

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Cortés, J. C., L. Jódar, and L. Villafuerte. "Random linear-quadratic mathematical models: Computing explicit solutions and applications." Mathematics and Computers in Simulation 79, no. 7 (March 2009): 2076–90. http://dx.doi.org/10.1016/j.matcom.2008.11.008.

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Aman, Bogdan, and Gabriel Ciobanu. "Membrane Computing after 25 Years." Mathematics 10, no. 12 (June 9, 2022): 1992. http://dx.doi.org/10.3390/math10121992.

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SELINGER, PETER. "Special issue on quantum programming languages." Mathematical Structures in Computer Science 16, no. 3 (June 2006): 373–74. http://dx.doi.org/10.1017/s0960129506005226.

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This special issue of Mathematical Structures in Computer Science grew out of the 2nd International Workshop on Quantum Programming Languages (QPL 2004), which was held July 12–13, 2004 in Turku, Finland. The purpose of the workshop was to bring together researchers working on mathematical formalisms and programming languages for quantum computing. It was the second in a series of workshops aimed at addressing a growing interest in logical tools, languages, and semantical methods for analysing quantum computation.

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Bochkov, A. P., A. D. Khomonenko, and A. M. Baranovsky. "MODEL OF FORMATION OF CLUSTERS OF INFORMATIVE NODES OF INTEGRATED AND DISTRIBUTED DATA PROCESSING IN A COMPUTER NETWORK." H&ES Research 13, no. 1 (2021): 44–57. http://dx.doi.org/10.36724/2409-5419-2021-13-1-44-57.

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The spread of network and computational technologies is relevant when solving applied issues in various areas of human activity. One of these issues is the accumulation of data in network and computing structures. The solution of the corresponding problem is made on the basis of integrated and distributed data processing in computer networks, which is characterized by the use of intelligent and cognitive data analysis, as well as distance metrics. The purpose of this study is to develop a mathematical model that takes into account the peculiarities of the computing activity of network node devices to calculate its informative load for the estimated time and the formation of clusters of informative nodes (their spaces) in computer networks. On the basis of data mining, it is proposed to consider deviations in the computing activity of network devices as information content of a network node, the ability to generate big data. In further modeling, the methods of the theories of pattern recognition and simulation were used, as well as the distance metrics toolkit, which allow to formalize heterogeneous data, taking into account the peculiarities of computer networks. An element of novelty of the proposed mathematical model is that it takes into account the peculiarities of the data of the computing activity of network node devices for the estimated time by using the Manhattan metric. A computational example of the formation of clusters of informative network nodes and their spaces is given. In the calculations, the author's sensor for modeling random variables is used, which significantly increases the quality of calculations and their clarity. The practical significance of the proposed mathematical model lies, first of all, in the fact that it becomes possible to track the dynamics of changes in information content in network structures with a large number oencf nodes. This makes it possible to control data processing, in addition, to increase the efficiy of computer networks by taking preventive measures to maintain their technical condition and information security.

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Hoekstra, Alfons G., Simon Portegies Zwart, and Peter V. Coveney. "Multiscale modelling, simulation and computing: from the desktop to the exascale." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 377, no. 2142 (February 18, 2019): 20180355. http://dx.doi.org/10.1098/rsta.2018.0355.

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This short contribution introduces a theme issue dedicated to ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’. It holds a collection of articles presenting cutting-edge research in generic multiscale modelling and multiscale computing, and applications thereof on high-performance computing systems. The special issue starts with a position paper to discuss the paradigm of multiscale computing in the face of the emerging exascale, followed by a review and critical assessment of existing multiscale computing environments. This theme issue provides a state-of-the-art account of generic multiscale computing, as well as exciting examples of applications of such concepts in domains ranging from astrophysics, via material science and fusion, to biomedical sciences. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.

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BERGER, ULRICH, VASCO BRATTKA, VICTOR SELIVANOV, DIETER SPREEN, and HIDEKI TSUIKI. "Preface to the special issue: Computing with infinite data: topological and logical foundations." Mathematical Structures in Computer Science 25, no. 7 (November 13, 2014): 1463–65. http://dx.doi.org/10.1017/s0960129513000364.

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This special issue of Mathematical Structures in Computer Science is composed mainly of papers submitted by participants of the Dagstuhl Seminar on Computing with Infinite Data: Topological and Logical Foundations. The workshop took place in the Schloss Dagstuhl - Leibniz Center for Informatics in the first half of October 2011.

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Kuzmenko, O., H. Yarovenko, and L. Skrynka. "ANALYSIS OF MATHEMATICAL MODELS FOR COUNTERING CYBER FRAUD IN BANKS." Vìsnik Sumsʹkogo deržavnogo unìversitetu 2022, no. 2 (2022): 111–20. http://dx.doi.org/10.21272/1817-9215.2022.2-13.

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The article is devoted to the current topic of analysis of mathematical models for countering cyber fraud in banks. This problem is due to the security risks growth in the banking system, which are formed by fraudsters' cyberattacks and cybercrimes implementation. Therefore, the priority task for cyberbanking security is the application of modern mathematical methods to analyse the sources of cyber attacks, identify threats and losses in the banking services market, identify cyber-attacks and assess the scenario of potential cyber risk, etc. The article analyses the most widespread types of cyber fraud: social engineering, phishing, stalking, farming, DoS attacks, online fraud, potentially unwanted programs, etc. The study also considered a model of cognitive computing and detection of suspicious transactions in banking cyber-physical systems based on quantum computing in BCPS for the post-quantum era. The advantages, disadvantages and results of the model are defined. Predictive modelling is proposed to detect fraud in real-time by analysing incoming bank transactions with payment cards. Within the framework of this method, such models are used for the classification of fraud detection as logistic regression, a decision tree, and a narrower technique - a random forest decision tree. The study also considered using the harmonic search algorithm in neural networks to improve fraud detection in the banking system. It is found that although this model has the advantage of learning ability based on past behaviour, there are difficulties in the long-term processing of many neural networks. The stages of model implementation are also given. In addition, the modelling of credit card fraud detection is based on using two types of models: supervised and unsupervised. Supervised models include logistic regression, K-nearest neighbours, and extreme gradient boosting. The one-class support vector model, restricted Boltzmann model, and generative-competitive network are considered among uncontrolled generative models.

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Çevik, Ahmet, Selçuk Topal, and Florentin Smarandache. "Neutrosophic Logic Based Quantum Computing." Symmetry 10, no. 11 (November 20, 2018): 656. http://dx.doi.org/10.3390/sym10110656.

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We introduce refined concepts for neutrosophic quantum computing such as neutrosophic quantum states and transformation gates, neutrosophic Hadamard matrix, coherent and decoherent superposition states, entanglement and measurement notions based on neutrosophic quantum states. We also give some observations using these principles. We present a number of quantum computational matrix transformations based on neutrosophic logic and clarify quantum mechanical notions relying on neutrosophic states. The paper is intended to extend the work of Smarandache by introducing a mathematical framework for neutrosophic quantum computing and presenting some results.

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G., W., R. Glowinski, and J. L. Lions. "Computing Methods in Applied Sciences and Engineering, VI." Mathematics of Computation 47, no. 175 (July 1986): 384. http://dx.doi.org/10.2307/2008118.

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Zhan, Jin Song, Yong Ning Guo, and Cheng Lian Liu. "A Deadlock Prevention Using Adjacency Matrix on Dining Philosophers Problem." Applied Mechanics and Materials 121-126 (October 2011): 1191–95. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.1191.

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In computer science, the dining philosopher’s problem is an illustrative example of a common computing problem in concurrency. It is a classic multi-process synchronization problem. In this paper, we proposed a mathematical model which it expresses an adjacency matrix to show the deadlock occurs, and how resolve it.

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Concolato, Claude E., and Li M. Chen. "Data Science: A New Paradigm in the Age of Big-Data Science and Analytics." New Mathematics and Natural Computation 13, no. 02 (July 2017): 119–43. http://dx.doi.org/10.1142/s1793005717400038.

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As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

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Singh, Vinai K., and Qin Sheng. "Editorial: Bridging the science and technology by modern mathematical methods and high performance computing." Applications of Mathematics 66, no. 2 (January 29, 2021): 177–81. http://dx.doi.org/10.21136/am.2021.0362-20.

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Mumtaz, Hafiza Bushra, Muhammad Javaid, Hafiz Muhammad Awais, and Ebenezer Bonyah. "Topological Indices of Pent-Heptagonal Nanosheets via M-Polynomials." Journal of Mathematics 2021 (November 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/4863993.

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The combination of mathematical sciences, physical chemistry, and information sciences leads to a modern field known as cheminformatics. It shows a mathematical relationship between a property and structural attributes of different types of chemicals called quantitative-structures’ activity and qualitative-structures’ property relationships that are utilized to forecast the chemical sciences and biological properties, in the field of engineering and technology. Graph theory has originated a significant usage in the field of physical chemistry and mathematics that is famous as chemical graph theory. The computing of topological indices (TIs) is a new topic of chemical graphs that associates many physiochemical characteristics of the fundamental organic compounds. In this paper, we used the M-polynomial-based TIs such as 1st Zagreb, 2nd Zagreb, modified 2nd Zagreb, symmetric division deg, general Randi c ´ , inverse sum, harmonic, and augmented indices to study the chemical structures of pent-heptagonal nanosheets of V C 5 C 7 and H C 5 C 7 . An estimation among the computed TIs with the help of numerical results is also presented.

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Anderson, Ronald E. "Females Surpass Males in Computer Problem Solving: Findings from the Minnesota Computer Literacy Assessment." Journal of Educational Computing Research 3, no. 1 (February 1987): 39–51. http://dx.doi.org/10.2190/wft2-661v-hkpt-84h9.

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Results from the 1979 Minnesota Computer Literacy Assessment conducted by the Minnesota Educational Computing Consortium, show that high school females performed better than males in some specific areas of programming. The areas of female superiority are those such as problem analysis and algorithmic application where the problems are expressed verbally rather than mathematically. While these findings may result from unique features of computer education in Minnesota, the findings may also be a consequence of the fact that the Minnesota assessment instrument was relatively free of mathematical bias. These findings and those of the 1982 National Assessment of Science on female superiority in “science decision making” imply that women are better than men at tasks usually defined as systems analysis rather than program coding.

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Calude, Cristian, and Gheorghe Păun. "Solomon Marcus Contributions to Theoretical Computer Science and Applications." Axioms 10, no. 2 (April 5, 2021): 54. http://dx.doi.org/10.3390/axioms10020054.

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Solomon Marcus (1925–2016) was one of the founders of the Romanian theoretical computer science. His pioneering contributions to automata and formal language theories, mathematical linguistics and natural computing have been widely recognised internationally. In this paper we briefly present his publications in theoretical computer science and related areas, which consist in almost ninety papers. Finally we present a selection of ten Marcus books in these areas.

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Glavelis, Themistoklis, Nikolaos Ploskas, and Nikolaos Samaras. "A computational evaluation of some free mathematical software for scientific computing." Journal of Computational Science 1, no. 3 (August 2010): 150–58. http://dx.doi.org/10.1016/j.jocs.2010.06.002.

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Sekigawa, Hiroshi. "Research on the effect of perturbation on algebraic problems and the methodology to cope with it." Impact 2019, no. 10 (December 30, 2019): 58–60. http://dx.doi.org/10.21820/23987073.2019.10.58.

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The reliance modern society has on computing is easily forgotten on a day-to-day basis. However, most people begin the day by looking at a mobile phone. A device that is connected to the Internet and gets the day started with weather updates, messages you've received via email or social media and the morning news. Then as they head out of the houses into the streets for the morning commute, they receive up to the minute directions, public transport schedules, delays or road traffic interruptions. During their commute people may even listen to their favourite songs, compressed to fit on the device in near perfect quality. All this, and more, is powered on some level by computing and it is hard to now imagine a world without it. However, it is equally hard to imagine what actually goes into making all of this happen. Perhaps, one has heard of the limits of computation, usually dependent on hardware like silicon chips. Less well known though is the mathematical nature of computation, the algorithms and calculations that go into designing the software which powers our economies and modern lifestyle. 'Computation underpins all computer science, a discipline that studies the processes that interact with data which we often refer to as programs,' outlines computation expert Professor Hiroshi Sekigawa, who is based at the Tokyo University of Science. Computation itself is a calculation that is carried out based on an explicitly designed model, like an algorithm. These algorithms make up the programs in our computers and are designed to solve problems. While there are different modes of developing algorithms and computing models, numeric computation is the one that is easily applicable to scientific computing. Fields like engineering, the physical sciences, life sciences, medicine and business all use elements of scientific computing to, among other things make detailed models of the world. 'To allow these computations to run more efficiently, using less memory, the technique of floating-point arithmetic is used,' explains Sekigawa. 'In short, this method uses formulas to represent real numbers resulting in approximations. This is useful for systems containing miniscule or immense numbers and require speedy processing times.' He says the drawback is that error analysis is needed to validate these outputs based on approximations. His team are working to improve on the current situation by exploring different modes of computation and the effects of combing them.

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Geete, Prof Nitin, and Prof Saurabh Verma. "MATLAB APPLICATIONS FOR MATHEMATICS STUDENTS." INTERNATIONAL JOURNAL OF MATHEMATICS AND COMPUTER RESEARCH 11, no. 01 (January 13, 2023): 3138–41. http://dx.doi.org/10.47191/ijmcr/v11i1.05.

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One of the oldest professions in human society is mathematics. It is closely tied to all stages of the development of human society's cognitive system. An essential tool for researching and resolving these real-world issues is mathematical modeling. A mathematical model is a structure that can be created mathematically by using the right tools and making the necessary, simplified assumptions about the underlying laws of a specific object and purpose in the real world. A mathematical model is an abstraction of real-world issues, to put it simply. Computer application technology has greatly influenced many different businesses as a result of computer progress. Among these, MATLAB is a piece of commercial math software that excels in applications for math in science and technology. One of the most widely used fourth-generation programming languages worldwide is MATLAB. It is one of the greatest environments for numerical analysis. Additionally, the most potent and effective language utilized in technical computing is MATLAB. It is designed to address mathematically notated challenges. It is employed in algorithm development and matrix calculations. Additionally, it enables us to run programmers written in any other programming language. It implies that MATLAB can make use of the advantages of other programming languages. It is extensively utilized in robotics, communications, control systems, signal processing, and image processing. This paper first examines MATLAB's benefits and discusses how to utilize it for mathematical operations and modeling.

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Hussain, Zafar, Mobeen Munir, Maqbool Chaudhary, and Shin Kang. "Computing Metric Dimension and Metric Basis of 2D Lattice of Alpha-Boron Nanotubes." Symmetry 10, no. 8 (July 25, 2018): 300. http://dx.doi.org/10.3390/sym10080300.

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Concepts of resolving set and metric basis has enjoyed a lot of success because of multi-purpose applications both in computer and mathematical sciences. For a connected graph G(V,E) a subset W of V(G) is a resolving set for G if every two vertices of G have distinct representations with respect to W. A resolving set of minimum cardinality is called a metric basis for graph G and this minimum cardinality is known as metric dimension of G. Boron nanotubes with different lattice structures, radii and chirality’s have attracted attention due to their transport properties, electronic structure and structural stability. In the present article, we compute the metric dimension and metric basis of 2D lattices of alpha-boron nanotubes.

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Kaburlasos, Vassilis G. "Lattice Computing: A Mathematical Modelling Paradigm for Cyber-Physical System Applications." Mathematics 10, no. 2 (January 16, 2022): 271. http://dx.doi.org/10.3390/math10020271.

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