Subjects -> MATHEMATICS (Total: 1013 journals)
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Showing 1 - 82 of 82 Journals sorted alphabetically
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 12)
Advances in Applied Mathematics and Mechanics     Full-text available via subscription   (Followers: 7)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 15)
AKCE International Journal of Graphs and Combinatorics     Open Access  
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 10)
American Journal of Applied Sciences     Open Access   (Followers: 22)
American Journal of Modeling and Optimization     Open Access   (Followers: 2)
Annals of Actuarial Science     Full-text available via subscription   (Followers: 2)
Applied Mathematical Modelling     Full-text available via subscription   (Followers: 23)
Applied Mathematics and Computation     Hybrid Journal   (Followers: 31)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 4)
Applied Mathematics and Nonlinear Sciences     Open Access   (Followers: 1)
Applied Mathematics and Physics     Open Access   (Followers: 3)
Biometrical Letters     Open Access  
British Actuarial Journal     Full-text available via subscription   (Followers: 2)
Bulletin of Mathematical Sciences and Applications     Open Access  
Communication in Biomathematical Sciences     Open Access   (Followers: 2)
Communications in Applied and Industrial Mathematics     Open Access   (Followers: 1)
Communications on Applied Mathematics and Computation     Hybrid Journal   (Followers: 1)
Differential Geometry and its Applications     Full-text available via subscription   (Followers: 4)
Discrete and Continuous Models and Applied Computational Science     Open Access  
Discrete Applied Mathematics     Hybrid Journal   (Followers: 10)
Doğuş Üniversitesi Dergisi     Open Access  
e-Journal of Analysis and Applied Mathematics     Open Access  
Engineering Mathematics Letters     Open Access   (Followers: 1)
European Actuarial Journal     Hybrid Journal  
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 2)
Frontiers in Applied Mathematics and Statistics     Open Access   (Followers: 1)
Fundamental Journal of Mathematics and Applications     Open Access  
International Journal of Advances in Applied Mathematics and Modeling     Open Access   (Followers: 1)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 3)
International Journal of Computer Mathematics : Computer Systems Theory     Hybrid Journal  
International Journal of Data Mining, Modelling and Management     Hybrid Journal   (Followers: 10)
International Journal of Engineering Mathematics     Open Access   (Followers: 4)
International Journal of Fuzzy Systems     Hybrid Journal  
International Journal of Swarm Intelligence     Hybrid Journal   (Followers: 2)
International Journal of Theoretical and Mathematical Physics     Open Access   (Followers: 13)
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems     Hybrid Journal   (Followers: 3)
Journal of Advanced Mathematics and Applications     Full-text available via subscription   (Followers: 1)
Journal of Advances in Mathematics and Computer Science     Open Access  
Journal of Applied & Computational Mathematics     Open Access  
Journal of Applied Intelligent System     Open Access  
Journal of Applied Mathematics & Bioinformatics     Open Access   (Followers: 6)
Journal of Applied Mathematics and Physics     Open Access   (Followers: 9)
Journal of Computational Geometry     Open Access   (Followers: 3)
Journal of Innovative Applied Mathematics and Computational Sciences     Open Access   (Followers: 11)
Journal of Mathematical Sciences and Applications     Open Access   (Followers: 2)
Journal of Mathematics and Music: Mathematical and Computational Approaches to Music Theory, Analysis, Composition and Performance     Hybrid Journal   (Followers: 12)
Journal of Mathematics and Statistics Studies     Open Access  
Journal of Physical Mathematics     Open Access   (Followers: 2)
Journal of Symbolic Logic     Hybrid Journal   (Followers: 2)
Letters in Biomathematics     Open Access   (Followers: 1)
Mathematical and Computational Applications     Open Access   (Followers: 3)
Mathematical Models and Computer Simulations     Hybrid Journal   (Followers: 3)
Mathematics and Computers in Simulation     Hybrid Journal   (Followers: 3)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Moscow University Computational Mathematics and Cybernetics     Hybrid Journal  
Multiscale Modeling and Simulation     Hybrid Journal   (Followers: 2)
Pacific Journal of Mathematics for Industry     Open Access  
Partial Differential Equations in Applied Mathematics     Open Access   (Followers: 2)
Ratio Mathematica     Open Access  
Results in Applied Mathematics     Open Access   (Followers: 1)
Scandinavian Actuarial Journal     Hybrid Journal   (Followers: 2)
SIAM Journal on Applied Dynamical Systems     Hybrid Journal   (Followers: 3)
SIAM Journal on Applied Mathematics     Hybrid Journal   (Followers: 11)
SIAM Journal on Computing     Hybrid Journal   (Followers: 11)
SIAM Journal on Control and Optimization     Hybrid Journal   (Followers: 18)
SIAM Journal on Discrete Mathematics     Hybrid Journal   (Followers: 8)
SIAM Journal on Financial Mathematics     Hybrid Journal   (Followers: 3)
SIAM Journal on Imaging Sciences     Hybrid Journal   (Followers: 7)
SIAM Journal on Mathematical Analysis     Hybrid Journal   (Followers: 4)
SIAM Journal on Matrix Analysis and Applications     Hybrid Journal   (Followers: 3)
SIAM Journal on Numerical Analysis     Hybrid Journal   (Followers: 7)
SIAM Journal on Optimization     Hybrid Journal   (Followers: 12)
SIAM Journal on Scientific Computing     Hybrid Journal   (Followers: 16)
SIAM Review     Hybrid Journal   (Followers: 9)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 2)
Swarm Intelligence     Hybrid Journal   (Followers: 3)
Theory of Probability and its Applications     Hybrid Journal   (Followers: 2)
Uniform Distribution Theory     Open Access  
Universal Journal of Applied Mathematics     Open Access   (Followers: 1)
Universal Journal of Computational Mathematics     Open Access   (Followers: 3)
Similar Journals
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Frontiers in Applied Mathematics and Statistics
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2297-4687
Published by Frontiers Media Homepage  [96 journals]
  • Impacts of the COVID-19 pandemic on operational losses

    • Authors: Fabio Augusto Scalet Medina, Herbert Kimura
      Abstract: The main objective of the present study was to determine whether the COVID-19 pandemic impacted the frequency and severity of financial institutions' operational losses. We selected four types of operational-risk events and, applying linear regression, concluded that the pandemic impacted the severity of operational losses. In terms of the frequency of operational losses, we observed no statistically significant difference between pre- and postpandemic losses; however, regarding the severity of losses, we observed an increase in the postpandemic period for the aggregate data, and when analyzing the types of operational-risk events individually, we observed that the frequency of some events increased and others decreased after the pandemic, necessitating a detailed investigation into the reasons for the increase or decrease in the severity of losses.
      PubDate: 2022-08-15T00:00:00Z
  • A nested multiscale model to study paratuberculosis in ruminants

    • Authors: Rendani Netshikweta, Winston Garira
      Abstract: In this study, we present a nested multiscale model that integrates the within-host scale and the between-host scale disease dynamics for Paratuberculosis in ruminants (e.g., cattle, goats, and sheep), with the aim of ascertaining the influence of initial infective inoculum dose on its dynamics. Ruminant paratuberculosis is often characterized as an environmentally-transmitted disease and it is caused by bacteria called Mycobacterium avium subspecies paratuberculosis that can survive in the physical environment for a considerable period of time. In the context of nested multiscale models developed at host level, a key feature is that the within-host scale and the between-host scale disease dynamics influence each other in a reciprocal way, with the between-host scale influencing the within-host scale through initial infective inoculum dose which susceptible ruminants may consume from the environment. The numerical results of the nested multiscale model presented in this study demonstrate that once the minimum infectious dose is consumed, then the infection at the within-host scale is sustained more by pathogen replication than by super-infection. From these results we conclude that super-infection might have an insignificant effect on the dynamics of PTB in ruminants. However, at this stage we cannot precisely conclude if super-infection does not effect on the dynamics of the disease. This would be investigated further using an embedded multiscale model, which is more appropriate in giving us conclusive results. We further demonstrate the need to use nested multiscale models over single-scale modeling approach by estimating a key parameter for pathogen replication that cannot be estimated using single-scale models.
      PubDate: 2022-08-15T00:00:00Z
  • Dynamics of heterogeneous population due to spatially distributed
           parameters and an ideal free pair

    • Authors: Ishrat Zahan, Md. Kamrujjaman, Md. Abdul Alim, Muhammad Mohebujjaman, Taufiquar Khan
      Abstract: Population movements are necessary to survive the individuals in many cases and depend on available resources, good habitat, global warming, climate change, supporting the environment, and many other issues. This study explores the spatiotemporal effect on the dynamics of the reaction-diffusion model for two interacting populations in a heterogeneous habitat. Both species are assumed to compete for different fundamental resources, and the diffusion strategies of both organisms follow the resource-based diffusion toward a positive distribution function for a large variety of growth functions. Depending on the values of spatially distributed interspecific competition coefficients, the study is conducted for two cases: weak competition and strong competition, which do not perform earlier in the existing literature. The stability of global attractors is studied for different conditions of resource function and carrying capacity. We investigated that in the case of weak competition, coexistence is attainable, while strong competition leads to competitive exclusion. This is an emphasis on how resource-based diffusion in the niche impacts selection. When natural resources are in sharing, either competition or predator-prey interaction leads to competitive exclusion or coexistence of competing species. However, we concentrate on the situation in which the ideal free pair is achieved without imposing any other additional conditions on the model's parameters. The effectiveness of the model is accomplished by numerical computation for both one and two space dimension cases, which is very important for biological consideration.
      PubDate: 2022-08-09T00:00:00Z
  • A kernel mixing strategy for use in adaptive Markov chain Monte Carlo and
           stochastic optimization contexts

    • Authors: Graham West, Zachariah Sinkala, John Wallin
      Abstract: Performing Markov chain Monte Carlo parameter estimation on complex mathematical models can quickly lead to endless searching through highly multimodal parameter spaces. For computationally complex models, one rarely has prior knowledge of the optimal proposal distribution. In such cases, the Markov chain can become trapped near a suboptimal mode, lowering the computational efficiency of the method. With these challenges in mind, we present a novel MCMC kernel which incorporates both mixing and adaptation. The method is flexible and robust enough to handle parameter spaces that are highly multimodal. Other advantages include not having to locate a near-optimal mode with a different method beforehand, as well as requiring minimal computational and storage overhead from standard Metropolis. Additionally, it can be applied in any stochastic optimization context which uses a Gaussian kernel. We provide results from several benchmark problems, comparing the kernel's performance in both optimization and MCMC cases. For the former, we incorporate the kernel into a simulated annealing method and real-coded genetic algorithm. For the latter, we incorporate it into the standard Metropolis and adaptive Metropolis methods.
      PubDate: 2022-08-08T00:00:00Z
  • Estimation of Some Epidemiological Parameters With the COVID-19 Data of

    • Authors: Solym M. Manou-Abi, Yousri Slaoui, Julien Balicchi
      Abstract: We study in this article some statistical methods to fit some epidemiological parameters. We first consider a fit of the probability distribution which underlines the serial interval distribution of the COVID-19 on a given set of data collected on the viral shedding in patients with laboratory-confirmed. The best-fit model of the non negative serial interval distribution is given by a mixture of two Gamma distributions with different shapes and rates. Thus, we propose a modified version of the generation time function of the package R0. Second, we estimate the time-varying reproduction number in Mayotte. Using a justified mathematical learning model, we estimate the transmission parameters range values during the outbreak together with a sensitivity analysis. Finally, using some regression and forecasting methods, we give some learning models of the hospitalized, intensive care, and death cases over a given period. We end with a discussion and the limit of this study together with some forthcoming theoretical developments.
      PubDate: 2022-08-05T00:00:00Z
  • Corrigendum: A scalar poincaré map for anti-phase bursting in coupled
           inhibitory neurons with synaptic depression

    • Authors: Mark Olenik, Conor Houghton
      PubDate: 2022-08-02T00:00:00Z
  • Editorial: Interfaces and mixing – non-equilibrium dynamics and
           conservation laws at continuous and kinetic scales

    • Authors: Snezhana I. Abarzhi
      PubDate: 2022-07-29T00:00:00Z
  • A survey of statistical methods for inequalities in access to
           healthcare—Kermanshah in West

    • Authors: Sohyla Reshadat, Alireza Zangeneh, Arash Ziapour, Naser Farahmandmoghadam, Fatemeh Khosravi Shadmani, Raziyeh Teimouri, Shirin Zardui Golanbari, Samira Rostami
      Abstract: Background:Access to medical care is one of the major issues affecting human health. This study aims to investigate inequality in access to medical care in the townships in Kermanshah, Iran.MethodsMethodology approach includes a descriptive-analytic study followed by determining the degree of development of the townships calculated in terms of access to medical care through the hierarchical cluster analysis and the combined model of human development index. Additionally, the mean center and standard distance tests are handled in a geographic information system software to identify the deployment pattern of the status of access to medical care indexes.ResultsAs for the ratio of physicians, nursing staff, paramedical staff, administrative staff of health care, dentists, pharmacists, hospitals, general and specialized clinics, radiology, rehabilitation centers and laboratories to a population of 10,000, the results of analyzing the findings were indicative of unequal distribution of facilities at the level of townships. This is based on The results of comparing the mean centers of population and health facilities showed that the centers of both data categories were located in Kermanshah. The two standard distances (i.e., population and health facilities) demonstrated that the health facilities witnessed more dispersion in the northwestern regions than the concentration of population in the central and southeastern regions of the province.ConclusionsThe results indicated that the indexes of development of facilities and healthcare resources were not distributed equitably and with a balance between the townships of the Kermanshah Province.
      PubDate: 2022-07-27T00:00:00Z
  • Editorial: Statistical Data Science - Theory and Applications in Analyzing
           Omics Data

    • Authors: Li Xing, Xuekui Zhang, Liangliang Wang
      PubDate: 2022-07-18T00:00:00Z
  • A New Tobit Ridge-Type Estimator of the Censored Regression Model With
           Multicollinearity Problem

    • Authors: Issam Dawoud, Mohamed R. Abonazel, Fuad A. Awwad, Elsayed Tag Eldin
      Abstract: In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem. To reduce this problem's effects, the Tobit ridge and the Tobit Liu estimators are proposed. Therefore, this study proposes a new kind of the Tobit estimation called the Tobit new ridge-type (TNRT) estimator. Also, the TNRT estimator was theoretically compared with the Tobit maximum likelihood, the Tobit ridge, and the Tobit Liu estimators via the mean squared error criterion. Moreover, we performed a Monte Carlo simulation to study the performance of the TNRT estimator compared with the previously defined estimators. Also, we used the Mroz dataset to confirm the theoretical and the simulation study results.
      PubDate: 2022-07-15T00:00:00Z
  • Self-Retrospect Periphery Gate Model and Its Applications

    • Authors: Xiuhua Cai, Hongxing Cao, Xiaoyi Fang, Jingli Sun, Chen Cheng, Wenjie Fan, Ying Yu
      Abstract: By not only relying on the initial state but also relying on states before, the principle of a self-retrospect dynamic system has been developed to represent the changes in a system since 1991. Afterward, the periphery theory was established, which studies the boundary of a system. We try to integrate the principle of the self-retrospect system and periphery theory in this study. Thus, a self-retrospect periphery gate model, a new expression of temporal-spatial concept, has been derived to investigate the change of a system and forecast it in physics. Firstly, for the equation with a time difference term that controls the motion of the system, a difference-integral equation can be derived by introducing a retrospect function and applying the inner product, partial integral, and mean value theorem. The principle of constructing and solving the difference-integral equation of the system is referred to as the principle of self-retrospect dynamic systems, and the corresponding mathematical model is called the self-retrospect model. The principle of system self-retrospect has been applied to modeling, calculating, and forecasting in many fields such as meteorology, oceanography, hydrology, market, agriculture, transportation, energy, and so on. Secondly, the periphery is defined as an intermediary that can protect the system and exchange with the environment. It is a part of the system and is adjacent to the environment. It has been applied in many fields since the periphery theory was put forward, such as physics, meteorology, water resources, economy, as well as sports. Thirdly, the concept of periphery gate is embedded into the self-retrospect equation, the self-retrospect gate model has been proposed, and the physical implication of the model is mentioned. The mathematical derivation of the model and its physical explanation are the main points of the study. The applications of the model in physics and meteorology are discussed, for example, the relationship between heavy snowfall and airflow passage in Beijing was studied using synoptic meteorology in detail.
      PubDate: 2022-07-15T00:00:00Z
  • An Ultraweak Variational Method for Parameterized Linear
           Differential-Algebraic Equations

    • Authors: Emil Beurer, Moritz Feuerle, Niklas Reich, Karsten Urban
      Abstract: We investigate an ultraweak variational formulation for (parameterized) linear differential-algebraic equations with respect to the time variable which yields an optimally stable system. This is used within a Petrov-Galerkin method to derive a certified detailed discretization which provides an approximate solution in an ultraweak setting as well as for model reduction with respect to time in the spirit of the Reduced Basis Method. A computable sharp error bound is derived. Numerical experiments are presented that show that this method yields a significant reduction and can be combined with well-known system theoretic methods such as Balanced Truncation to reduce the size of the DAE.
      PubDate: 2022-07-14T00:00:00Z
  • Convergence Analysis and Approximate Optimal Temporal Step Sizes for Some
           Finite Difference Methods Discretising Fisher's Equation

    • Authors: Koffi Messan Agbavon, Appanah Rao Appadu, Bilge Inan, Herve Michel Tenkam
      Abstract: In this study, we obtain a numerical solution for Fisher's equation using a numerical experiment with three different cases. The three cases correspond to different coefficients for the reaction term. We use three numerical methods namely; Forward-Time Central Space (FTCS) scheme, a Nonstandard Finite Difference (NSFD) scheme, and the Explicit Exponential Finite Difference (EEFD) scheme. We first study the properties of the schemes such as positivity, boundedness, and stability and obtain convergence estimates. We then obtain values of L1 and L∞ errors in order to obtain an estimate of the optimal time step size at a given value of spatial step size. We determine if the optimal time step size is influenced by the choice of the numerical methods or the coefficient of reaction term used. Finally, we compute the rate of convergence in time using L1 and L∞ errors for all three methods for the three cases.
      PubDate: 2022-07-13T00:00:00Z
  • The Shock of COVID-19 Pandemic on the Moroccan Exchange Rate Dirham/Euro

    • Authors: Mohammed Bouasabah
      PubDate: 2022-07-08T00:00:00Z
  • ExaTN: Scalable GPU-Accelerated High-Performance Processing of General
           Tensor Networks at Exascale

    • Authors: Dmitry I. Lyakh, Thien Nguyen, Daniel Claudino, Eugene Dumitrescu, Alexander J. McCaskey
      Abstract: We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.
      PubDate: 2022-07-06T00:00:00Z
  • Early Warning Signals of Financial Crises Using Persistent Homology and
           Critical Slowing Down: Evidence From Different Correlation Tests

    • Authors: Mohd Sabri Ismail, Mohd Salmi Md Noorani, Munira Ismail, Fatimah Abdul Razak
      Abstract: In this study, a new market representation from persistence homology, known as the L1-norm time series, is used and applied independently with three critical slowing down indicators [autocorrelation function at lag 1, variance, and mean for power spectrum (MPS)] to examine two historical financial crises (Dotcom crash and Lehman Brothers bankruptcy) in the US market. The captured signal is the rising trend in the indicator time series, which can be determined by Kendall's tau correlation test. Furthermore, we examined Pearson's and Spearman's rho correlation tests as potential substitutes for Kendall's tau correlation. After that, we determined a correlation threshold and predicted the whole available date. The point of comparison between these correlation tests is to determine which test is significant and consistent in classifying the rising trend. The results of such a comparison will suggest the best test that can classify the observed rising trend and detect early warning signals (EWSs) of impending financial crises. Our outcome shows that the L1-norm time series is more likely to increase before the two financial crises. Kendall's tau, Pearson's, and Spearman's rho correlation tests consistently indicate a significant rising trend in the MPS time series before the two financial crises. Based on the two evaluation scores (the probability of successful anticipation and probability of erroneous anticipation), by using the L1-norm time series with MPS, our result in the whole prediction demonstrated that Spearman's rho correlation (46.15 and 53.85%) obtains the best score as compared to Kendall's tau (42.31 and 57.69%) and Pearson's (40 and 60%) correlations. Therefore, by using Spearman's rho correlation test, L1-norm time series with MPS is shown to be a better way to detect EWSs of US financial crises.
      PubDate: 2022-06-30T00:00:00Z
  • Modified Quantile Regression for Modeling the Low Birth Weight

    • Authors: Ferra Yanuar, Hazmira Yozza, Aidinil Zetra
      Abstract: This study aims to identify the best model of low birth weight by applying and comparing several methods based on the quantile regression method's modification. The birth weight data is violated with linear model assumptions; thus, quantile approaches are used. The quantile regression is adjusted by combining it with the Bayesian approach since the Bayesian method can produce the best model in small size samples. Three kinds of the modified quantile regression methods considered here are the Bayesian quantile regression, the Bayesian Lasso quantile regression, and the Bayesian Adaptive Lasso quantile regression. This article implements the skewed Laplace distribution as the likelihood function in Bayesian analysis. The cross-sectional study collected the primary data of 150 birth weights in West Sumatera, Indonesia. This study indicated that Bayesian Adaptive Lasso quantile regression performed well compared to the other two methods based on a smaller absolute bias and a shorter Bayesian credible interval based on the simulation study. This study also found that the best model of birth weight is significantly affected by maternal education, the number of pregnancy problems, and parity.
      PubDate: 2022-06-28T00:00:00Z
  • On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models

    • Authors: Nora Schenk, Roland Potthast, Anne Rojahn
      Abstract: Nonlinear data assimilation methods like particle filters aim to improve the numerical weather prediction (NWP) in non-Gaussian setting. In this manuscript, two recent versions of particle filters, namely the Localized Adaptive Particle Filter (LAPF) and the Localized Mixture Coefficient Particle Filter (LMCPF) are studied in comparison with the Ensemble Kalman Filter when applied to the popular Lorenz 1963 and 1996 models. As these particle filters showed mixed results in the global NWP system at the German meteorological service (DWD), the goal of this work is to show that the LMCPF is able to outperform the LETKF within an experimental design reflecting a standard NWP setup and standard NWP scores. We focus on the root-mean-square-error (RMSE) of truth minus background, respectively, analysis ensemble mean to measure the filter performance. To simulate a standard NWP setup, the methods are studied in the realistic situation where the numerical model is different from the true model or the nature run, respectively. In this study, an improved version of the LMCPF with exact Gaussian mixture particle weights instead of approximate weights is derived and used for the comparison to the Localized Ensemble Transform Kalman Filter (LETKF). The advantages of the LMCPF with exact weights are discovered and the two versions are compared. As in complex NWP systems the individual steps of data assimilation methods are overlaid by a multitude of other processes, the ingredients of the LMCPF are illustrated in a single assimilation step with respect to the three-dimensional Lorenz 1963 model.
      PubDate: 2022-06-28T00:00:00Z
  • Agent-Based Modeling of COVID-19 Transmission in Philippine Classrooms

    • Authors: Rojhun O. Macalinao, Jcob C. Malaguit, Destiny S. Lutero
      Abstract: Onsite classes in the Philippines have been prohibited since March 2020 due to the SARS-CoV-2 which causes the COVID-19. This forced millions of learners to adapt with new modes of instruction that may not be optimal for their learning. In this study, we implemented an agent-based model in Netlogo that followed common classroom layouts to assess the effects of human interactions to virus transmission. Results show that the highest value of cumulative proportion of infected individuals inside the classroom (CPI) is achieved when the total allowable seating capacity in the classroom is increased from 25 to 50%. Also, varying transmission rates between 5 and 20% does not pose any significant effect on CPI. Furthermore, in three of the four seating arrangements, allowing in-class mobility and class rotations can pose significant increases in CPI averaging from 40 to 70%. Results also showed that factors including maximum number of students and number of initially infected individuals, significantly affect the likelihood of infection apart from the seating arrangement itself. To minimize the risk of transmission inside the classroom setup considered, it is vital to control these factors by adhering to mitigation efforts such as increased testing and symptoms checking, limiting the maximum number of students, and redefining breaks and class rotations.
      PubDate: 2022-06-27T00:00:00Z
  • Rank–Polyserial Correlation: A Quest for a “Missing”
           Coefficient of Correlation

    • Authors: Jari Metsämuuronen
      Abstract: In the typology of coefficients of correlation, we seem to miss such estimators of correlation as rank–polyserial (RRPS) and rank–polychoric (RRPC) coefficients of correlation. This article discusses a set of options as RRP, including both RRPS and RRPC. A new coefficient JTgX based on Jonckheere–Terpstra test statistic is derived, and it is shown to carry the essence of RRP. Such traditional estimators of correlation as Goodman–Kruskal gamma (G) and Somers delta (D) and dimension-corrected gamma (G2) and delta (D2) are shown to have a strict connection to JTgX, and, hence, they also fulfil the criteria for being relevant options to be taken as RRP. These estimators with a directional nature suit ordinal-scaled variables as well as an ordinal- vs. interval-scaled variable. The behaviour of the estimators of RRP is studied within the measurement modelling settings by using the point-polyserial, coefficient eta, polyserial correlation, and polychoric correlation coefficients as benchmarks. The statistical properties, differences, and limitations of the coefficients are discussed.
      PubDate: 2022-06-27T00:00:00Z
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Heriot-Watt University
Edinburgh, EH14 4AS, UK
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