Results for 'Mathematical Modeling'

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  1. Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored (...)
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  2.  34
    Mathematical Modeling in the Social Sciences.Paul Humphreys - 2003 - In Stephen P. Turner & Paul A. Roth (eds.), The Blackwell Guide to the Philosophy of the Social Sciences. Oxford, UK: Blackwell. pp. 166–184.
    This chapter contains sections titled: Why Use Mathematical Models? Theory‐based Models Data‐based Modeling Computational Approaches Conclusions Notes.
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  3.  47
    Mathematical modeling in wound healing, bone regeneration and tissue engineering.Richard C. Schugart - 2010 - Acta Biotheoretica 58 (4):355-367.
    The processes of wound healing and bone regeneration and problems in tissue engineering have been an active area for mathematical modeling in the last decade. Here we review a selection of recent models which aim at deriving strategies for improved healing. In wound healing, the models have particularly focused on the inflammatory response in order to improve the healing of chronic wound. For bone regeneration, the mathematical models have been applied to design optimal and new treatment strategies (...)
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  4.  43
    Mathematical Modeling of Multiattack Behavior Discrimination in the WSN Based on Incidence Matrix.Yu Shuai-Jing & Wang Peng-Fei - 2022 - Complexity 2022:1-8.
    The current WSN is vulnerable to a variety of malicious attacks, resulting in the decline of its comprehensive performance. Multihop routing involves a number of safety and privacy issues. Problems such as snooping, sinkhole, manipulation, cloning, wormhole, spoofing, and so on affect the integrity, availability, and confidentiality of the WSNs. Therefore, this paper mainly studies the mathematical modeling of WSN multiattack behavior discrimination based on the incidence matrix. The WSN node model is used to collect relevant data and (...)
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  5. Mathematical Modeling and the Nature of Problem Solving.C. W. Castillo-Garsow - 2014 - Constructivist Foundations 9 (3):373-375.
    Open peer commentary on the article “Examining the Role of Re-Presentation in Mathematical Problem Solving: An Application of Ernst von Glasersfeld’s Conceptual Analysis” by Victor V. Cifarelli & Volkan Sevim. Upshot: Problem solving is an enormous field of study, where so-called “problems” can end up having very little in common. One of the least studied categories of problems is open-ended mathematical modeling research. Cifarelli and Sevim’s framework - although not developed for this purpose - may be a (...)
     
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  6.  34
    Mathematical modeling of intrusive growth of fusiform initials in relation to radial growth and expanding cambial circumference in pinus sylvestris L.D. Karczewska, J. Karczewski, W. Włoch, J. Jura-Morawiec, P. Kojs, M. Iqbal & J. Krawczyszyn - 2009 - Acta Biotheoretica 57 (3):331-348.
    This study on the cambium of Pinus sylvestris L. examines the intrusive growth of fusiform cambial initials and its possible contribution to the tangential and radial expansions of the cambial cylinder. The location and extent of intrusive growth of the fusiform initials were determined by microscopic observations and by mathematical modeling. In order to meet the required circumferential expansion of the cambial cylinder, the fusiform initials grow in groups by means of a symplastic rather than intrusive growth, leaving (...)
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  7. Mathematical modeling.In Jae Myung & Mark A. Pitt - 2002 - In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.
     
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  8.  49
    Mathematical modeling of the effects of 'capability' and 'intent' on the stability of a competitive international system.Alvin M. Saperstein - 1994 - Synthese 100 (3):359 - 378.
    In international relations theory, there is a long history of Richardson-like modeling of the evolution of military capability. Usually, such models are deterministic and predictive and do not allow for the representation of the transition from competitive peace to shooting war. More recently, models have been developed which attempt to represent the evolution of relationship between nations. The relationship between nations, varying from friendship to hostility, is taken to be synonymous with the intent of nations towards each other, varying (...)
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  9. Mathematical Modeling of Biological and Social Evolutionary Macrotrends.Leonid Grinin, Alexander V. Markov & Andrey V. Korotayev - 2014 - In History & Mathematics: Trends and Cycles. Volgograd,Russia: Uchitel Publishing House. pp. 9-48.
    In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...)
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  10.  34
    Mathematical Modeling of Metabolism and Hemodynamics.J. Francoise, C. Menuel, M. Lahutte & J. -N. Vallée - 2012 - Acta Biotheoretica 60 (1-2):99-107.
    We provide a mathematical study of a model of energy metabolism and hemodynamics of glioma allowing a better understanding of metabolic modifications leading to anaplastic transformation from low grade glioma.This mathematical analysis allows ultimately to unveil the solution to a viability problem which seems quite pertinent for applications to medecine.
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  11.  31
    Mathematical Modeling of Substrates Fluxes and Tumor Growth in the Brain.Angélique Perrillat-Mercerot, Nicolas Bourmeyster, Carole Guillevin, Alain Miranville & Rémy Guillevin - 2019 - Acta Biotheoretica 67 (2):149-175.
    The aim of this article is to show how a tumor can modify energy substrates fluxes in the brain to support its own growth. To address this question we use a modeling approach to explain brain nutrient kinetics. In particular we set up a system of 17 equations for oxygen, lactate, glucose concentrations and cells number in the brain. We prove the existence and uniqueness of nonnegative solutions and give bounds on the solutions. We also provide numerical simulations.
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  12.  22
    Mathematical modeling of the interaction between terrorism and counter‐terrorism and its policy implications.Alvin M. Saperstein - 2008 - Complexity 14 (1):45-49.
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  13.  27
    Mathematical Modeling of Respiratory System Mechanics in the Newborn Lamb.Virginie Le Rolle, Nathalie Samson, Jean-Paul Praud & Alfredo I. Hernández - 2013 - Acta Biotheoretica 61 (1):91-107.
    In this paper, a mathematical model of the respiratory mechanics is used to reproduce experimental signal waveforms acquired from three newborn lambs. As the main challenge is to determine specific lamb parameters, a sensitivity analysis has been realized to find the most influent parameters, which are identified using an evolutionary algorithm. Results show a close match between experimental and simulated pressure and flow waveforms obtained during spontaneous ventilation and pleural pressure variations acquired during the application of positive pressure, since (...)
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  14.  16
    Mathematical Modeling and Dynamic Analysis of Complex Biological Systems.Alain Vande Wouwer, Philippe Bogaerts, Jan Van Impe & Alejandro Vargas - 2019 - Complexity 2019:1-2.
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  15. Theoretical neuroscience: computational and mathematical modeling of neural systems.Peter Dayan & L. Abbott - 2001 - Philosophical Psychology 15 (4):563-577.
  16.  24
    I. mathematical modeling of election predictions: Final reply to professor Aubert.Herbert A. Simon - 1983 - Inquiry: An Interdisciplinary Journal of Philosophy 26 (2):231 – 232.
    Professor Aubert's ?three?stage rocket? (Inquiry, Vol. 26 [1983], No. 1) has reached periodic orbit. His comments on my earlier reply to his critique of my election predictions paper simply repeat arguments I have already refuted. In this note, I limit myself largely to pointing out Professor Aubert's misconceptions of what my position actually is. I find no reasons for revising the views stated in my original election predictions paper, nor any reasons for thinking that paper violated norms of scientific method (...)
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  17. Economic and mathematical modeling of integration influence of information and communication technologies on the development of e-commerce of industrial enterprises.Igor Kryvovyazyuk, Igor Britchenko, Liubov Kovalska, Iryna Oleksandrenko, Liudmyla Pavliuk & Olena Zavadska - 2023 - Journal of Theoretical and Applied Information Technology 101 (11):3801-3815.
    This research aims at establishing the impact of information and communication technologies (ICT) on e-commerce development of industrial enterprises by means of economic and mathematical modelling. The goal was achieved using the following methods: theoretical generalization, analysis and synthesis (to critically analyse the scientific approaches of scientists regarding the expediency of using mathematical models in the context of enterprises’ e-commerce development), target, comparison and grouping (to reveal innovative methodological approach to assessing ICT impact on e-commerce development of industrial (...)
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  18.  13
    Ontology of Mathematical Modeling Based on Interval Data.Mykola Dyvak, Andriy Melnyk, Artur Rot, Marcin Hernes & Andriy Pukas - 2022 - Complexity 2022:1-19.
    An ontological approach as a tool for managing the processes of constructing mathematical models based on interval data and further use of these models for solving applied problems is proposed in this article. Mathematical models built using interval data analysis are quite effective in many applications, as they have “guaranteed” predictive properties, which are determined by the accuracy of experimental data. However, the application of mathematical modeling methods is complicated by the lack of software tools for (...)
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  19. The Dynamics of Explanation: Mathematical Modeling and Scientific Understanding.Ruth Berger - 1997 - Dissertation, Indiana University
    This dissertation challenges two prevalent views on the topic of scientific explanation: that science explains by revealing causal mechanisms, and that science explains by unifying our knowledge of the world. ;My methodological strategy is to compare our best current philosophical accounts of scientific explanation with evidence from contemporary scientific research. In particular, I focus on evidence from dynamical explanations, that is, explanations which appeal to nonlinear dynamical modeling for their force. Nonlinear dynamical modeling is a type of (...) modeling which is used by scientists in many different disciplines, including population biology, ecology, physics, and psychology. ;Chapter 1 argues that dynamical explanations are a philosophically important class of explanations, based on their prevalence and their close relationship with the semantic view of theories. ;Chapters 2 and 3 conclude that extant causal and unification accounts encounter serious difficulties when applied to dynamical explanations. Chapter 2 argues that causal relevance is neither a necessary nor a sufficient condition for explanatory relevance. Chapter 3 clarifies and evaluates different claims about the unifying power of science. Here, I argue that dynamical explanations provide some support for the thesis that unification is constitutive of explanation. However, dynamical explanations also indicate that some of the intuitions embodied in extant unification accounts are untenable. ;Chapter 4 concludes by advancing diagnostic criticisms of existing causal and unification accounts. I explore the relationships between dynamical explanations and three conceptions of scientific explanation, as well as the relationships between different types of inference and scientific understanding. I suggest that philosophers look to cognitive science and the semantic view of theories for aid in constructing a viable positive account of explanation. ;The technical appendix introduces several key definitions and concepts of dynamical systems theory. (shrink)
     
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  20.  33
    Methodological Problems of Mathematical Modeling in Natural Science.I. A. Akchurin, M. F. Vedenov & Iu V. Sachkov - 1966 - Russian Studies in Philosophy 5 (2):23-34.
    The constantly accelerating progress of contemporary natural science is indissolubly associated with the development and use of mathematics and with the processes of mathematical modeling of the phenomena of nature. The essence of this diverse and highly fertile interaction of mathematics and natural science and the dialectics of this interaction can only be disclosed through analysis of the nature of theoretical notions in general. Today, above all in the ranks of materialistically minded researchers, it is generally accepted that (...)
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  21.  5
    The limits of mathematical modeling in the social sciences: the significance of Gödel's incompleteness phenomenon.Francisco Antônio Doria (ed.) - 2017 - New Jersey: World Scientific.
    Current mathematical models are notoriously unreliable in describing the time evolution of unexpected social phenomena, from financial crashes to revolution. Can such events be forecast? Can we compute probabilities about them? Can we model them? This book investigates and attempts to answer these questions through GOdel's two incompleteness theorems, and in doing so demonstrates how influential GOdel is in modern logical and mathematical thinking. Many mathematical models are applied to economics and social theory, while GOdel's theorems are (...)
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  22.  31
    The Power of Mathematical Modeling in Developmental Biology.Diego Rasskin-Gutman - 2007 - Biological Theory 2 (1):108-111.
  23.  33
    A New Mathematical Modeling Method for Four-Stage Helicopter Main Gearbox and Dynamic Response Optimization.Yuan Chen, Rupeng Zhu, Guanghu Jin, Yeping Xiong, Jie Gao & Meijun Liao - 2019 - Complexity 2019:1-13.
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  24.  19
    On incest and mathematical modeling.C. J. Lumsden & E. O. Wilson - 1984 - Behavioral and Brain Sciences 7 (4):742.
  25.  19
    Complex systems, trade-offs and mathematical modeling: a response to Sober and Orzack.Jay Odenbaugh - 2003 - Philosophy of Science 70 (5):1496-1507.
    Ecologist Richard Levins argues population biologists must trade-off the generality, realism, and precision of their models since biological systems are complex and our limitations are severe. Steven Orzack and Elliott Sober argue that there are cases where these model properties cannot be varied independently of one another. If this is correct, then Levins's thesis that there is a necessary trade-off between generality, precision, and realism in mathematical models in biology is false. I argue that Orzack and Sober's arguments fail (...)
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  26.  9
    On improving the efficiency of mathematical modeling of the problem of stability of construction.Chistyakov A. V. - 2020 - Artificial Intelligence Scientific Journal 25 (3):27-36.
    Algorithmic software for mathematical modeling of structural stability is considered, which is reduced to solving a partial generalized eigenvalues problem of sparse matrices, with automatic parallelization of calculations on modern parallel computers with graphics processors. Peculiarities of realization of parallel algorithms for different structures of sparse matrices are presented. The times of solving the problem of stability of composite materialsusing a three-dimensional model of "finite size fibers" on computers of different architectures are given. In mathematical modeling (...)
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    Optimization of management processes in the electric power industry based on mathematical modeling.Nikolay Dmitrievich Dmitriev, Dmitriy Grigoryevich Rodionov & Sergey Alekseevich Zhiltsov - 2021 - Kant 38 (1):17-23.
    The paper deals with the problems in the electric power industry, do not allow us to ensure a sufficient level of competitiveness of the industry and each individual subject of the electric power industry, the threatens national security, in particular, the economic security of the state, and creates barriers to maintaining the sustainable development of territories. It is proposed to use the economic and mathematical modeling allowed us to determine the high importance of human resources in the final (...)
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    The Power of Mathematical Modeling in Developmental Biology: Biological Physics of the Developing Embryo Gabor Forgacs and Stuart A. Newman Cambridge: Cambridge University Press, 2005 (337 pp; $ 64 hbk; ISBN 0-521-78337-2). [REVIEW]Diego Rasskin-Gutman - 2007 - Biological Theory 2 (1):108-111.
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    Seven Layers of Computation: Methodological Analysis and Mathematical Modeling.Mark Burgin & Rao Mikkililineni - 2022 - Filozofia i Nauka 10:11-32.
    We live in an information society where the usage, creation, distribution, manipulation, and integration of information is a significant activity. Computations allow us to process information from various sources in various forms and use the derived knowledge in improving efficiency and resilience in our interactions with each other and with our environment. The general theory of information tells us that information to knowledge is as energy is to matter. Energy has the potential to create or modify material structures and information (...)
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  30.  4
    Seven Layers of Computation: Methodological Analysis and Mathematical Modeling.Mark Burgin & Rao Mikkililineni - 2022 - Filozofia i Nauka. Studia Filozoficzne I Interdyscyplinarne 10:11-32.
    We live in an information society where the usage, creation, distribution, manipulation, and integration of information is a significant activity. Computations allow us to process information from various sources in various forms and use the derived knowledge in improving efficiency and resilience in our interactions with each other and with our environment. The general theory of information tells us that information to knowledge is as energy is to matter. Energy has the potential to create or modify material structures and information (...)
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  31.  37
    Preventing HIV Transmission via HIV Exposure Laws: Applying Logic and Mathematical Modeling to Compare Statutory Approaches to Penalizing Undisclosed Exposure to HIV.Carol L. Galletly & Steven D. Pinkerton - 2008 - Journal of Law, Medicine and Ethics 36 (3):577-584.
    Twenty-four U.S. states have enacted HIV exposure laws that prohibit HIV-positive persons from engaging in sexual activities with partners to whom they have not disclosed their HIV status. There is little standardization among existing HIV exposure laws, which vary substantially with respect to the sexual activities that are prohibited without prior serostatus disclosure. Logical analysis and mathematical modeling were used to explore the HIV prevention effectiveness of two types of HIV exposure laws: “strict” laws that require HIV-positive persons (...)
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  32.  31
    Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling.Xiaoyang Liu, Chao Liu & Xiaoping Zeng - 2017 - Complexity:1-7.
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  33.  29
    Short‐term information processing, long‐term responses: Insights by mathematical modeling of signal transduction.Annette Schneider, Ursula Klingmüller & Marcel Schilling - 2012 - Bioessays 34 (7):542-550.
  34.  31
    Seeking diversity in mathematics education: Mathematical modeling in the practice of biologists and mathematicians.Erick Smith, Shawn Haarer & Jere Confrey - 1997 - Science & Education 6 (5):441-472.
  35. On Similarities between Biological and Social Evolutionary Mechanisms: Mathematical Modeling.Leonid Grinin - 2013 - Cliodynamics: The Journal of Theoretical and Mathematical History 4:185-228.
    In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. This is more or less identical with the working of the collective learning (...)
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  36. The Nature of the Structures of Applied Mathematics and the Metatheoretical Justification for the Mathematical Modeling.Catalin Barboianu - 2015 - Romanian Journal of Analytic Philosophy 9 (2):1-32.
    The classical (set-theoretic) concept of structure has become essential for every contemporary account of a scientific theory, but also for the metatheoretical accounts dealing with the adequacy of such theories and their methods. In the latter category of accounts, and in particular, the structural metamodels designed for the applicability of mathematics have struggled over the last decade to justify the use of mathematical models in sciences beyond their 'indispensability' in terms of either method or concepts/entities. In this paper, I (...)
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    Congenital and Blood Transfusion Transmission of Chagas Disease: A Framework Using Mathematical Modeling.Edneide Ramalho, Jones Albuquerque, Cláudio Cristino, Virginia Lorena, Jordi Gómez I. Prat, Clara Prats & Daniel López - 2018 - Complexity 2018:1-10.
    Chagas disease or American trypanosomiasis is an important health problem in Latin America. Due to the mobility of Latin American population around the world, countries without vector presence started to report disease cases. We developed a deterministic compartmental model in order to gain insights into the disease dynamics in a scenario without vector presence, considering congenital transmission and transmission by blood transfusion. The model was used to evaluate the epidemiological effect of control measures. It was applied to demographic data from (...)
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  38.  34
    Flow cytometric analysis of the cell cycle: Mathematical modeling and biological interpretation.José Pierrez & Xavier Ronot - 1992 - Acta Biotheoretica 40 (2-3):131-137.
    Estimation of the repartition of asynchronous cells in the cell cycle can be explained by two hypotheses:– - the cells are supposed to be distributed into three groups: cells with a 2c DNA content (G0/1 phase), cells with a 4c DNA content (G2+M phase) and cells with a DNA content ranging from 2c to 4c (S phase); – - there is a linear relationship between the amount of fluorescence emitted by the fluorescent probe which reveals the DNA and the DNA (...)
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    Preventing HIV Transmission via HIV Exposure Laws: Applying Logic and Mathematical Modeling to Compare Statutory Approaches to Penalizing Undisclosed Exposure to HIV.Carol L. Galletly & Steven D. Pinkerton - 2008 - Journal of Law, Medicine and Ethics 36 (3):577-584.
    Twenty-four U.S. states have enacted HIV exposure laws that prohibit HIV-positive persons from engaging in sexual activities with partners to whom they have not disclosed their HIV-status. From a public health perspective, HIV serostatus exposure laws can be viewed as structural interventions that seek to limit the spread of HIV by acting at the policy level. A central premise of these laws is that informed partners are more likely to protect themselves by declining sex, by substituting less risky activities for (...)
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  40.  25
    Gestalt theory, formal models and mathematical modeling.Abraham S. Luchins & Edith H. Luchins - 1993 - Behavioral and Brain Sciences 16 (2):355-356.
  41.  71
    Modeling without Mathematics.Martin Thomson-Jones - 2012 - Philosophy of Science 79 (5):761-772.
    Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to (...)
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  42.  7
    Review of Martin H. Krieger: Constitutions of Matter: Mathematically Modeling the Most Everyday of Physical Phenomena[REVIEW]Martin H. Krieger & Steven French - 1998 - British Journal for the Philosophy of Science 49 (2):355-358.
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  43.  8
    Mathematics as an Empirical Phenomenon, Subject to Modeling.Reuben Hersh - 2017 - Journal of the Indian Council of Philosophical Research 34 (2):331-342.
    Among the universal attributes of homo sapiens, several have become established as special fields of study—language, art and music, religion, and political economy. But mathematics, another universal attribute of our species, is still modeled separately by logicians, historians, neuroscientists, and others. Could it be integrated into “mathematics studies,” a coherent, many-faceted branch of empirical science? Could philosophers facilitate such a unification? Some philosophers of mathematics identify themselves with “positions” on the nature of mathematics. Those “positions” could more productively serve as (...)
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  44. Agent-based modeling: the right mathematics for the social sciences?Paul L. Borrill & Leigh Tesfatsion - 2011 - In J. B. Davis & D. W. Hands (eds.), Elgar Companion to Recent Economic Methodology. Edward Elgar Publishers. pp. 228.
    This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research. The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the (...)
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  45. Mathematics as an Empirical Phenomenon, Subject to Modeling.Reuben Hersh - 2016 - In Emiliano Ippoliti, Fabio Sterpetti & Thomas Nickles (eds.), Models and Inferences in Science. Cham: Springer.
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  46. On the epistemological analysis of modeling and computational error in the mathematical sciences.Nicolas Fillion & Robert M. Corless - 2014 - Synthese 191 (7):1451-1467.
    Interest in the computational aspects of modeling has been steadily growing in philosophy of science. This paper aims to advance the discussion by articulating the way in which modeling and computational errors are related and by explaining the significance of error management strategies for the rational reconstruction of scientific practice. To this end, we first characterize the role and nature of modeling error in relation to a recipe for model construction known as Euler’s recipe. We then describe (...)
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  47.  31
    Feynman Diagrams: Modeling between Physics and Mathematics.Michael Stöltzner - 2018 - Perspectives on Science 26 (4):482-500.
    Since its inception in the late 1920s and 30s, the main problem of quantum electrodynamics had been that any interaction or scattering event involved processes of a higher order that arose from vacuum polarization, the creation and subsequent annihilation of particle-antiparticle pairs, and the mutual interactions of all those short-lived entities.1 These processes posed two kinds of conceptual problems. First, they were not detectable individually, but had a measurable effect on the energy of the overall process. Even in simple quantum (...)
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  48.  12
    Inseparable Bedfellows: Imagination and Mathematics in Economic Modeling.Fiora Salis & Mary Leng - 2023 - Philosophy of the Social Sciences 53 (4):255-280.
    In this paper we explore the hypothesis that constrained uses of imagination are crucial to economic modeling. We propose a theoretical framework to develop this thesis through a number of specific hypotheses that we test and refine through six new, representative case studies. Our ultimate goal is to develop a philosophical account that is practice oriented and informed by empirical evidence. To do this, we deploy an abductive reasoning strategy. We start from a robust set of hypotheses and leave (...)
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  49.  13
    Components of Mathematics Anxiety: Factor Modeling of the MARS30-Brief.Belinda Pletzer, Guilherme Wood, Thomas Scherndl, Hubert H. Kerschbaum & Hans-Christoph Nuerk - 2016 - Frontiers in Psychology 7.
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  50.  9
    The Challenge of Modeling the Acquisition of Mathematical Concepts.Alberto Testolin - 2020 - Frontiers in Human Neuroscience 14.
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