Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 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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Mathematical and Computational Applications
Number of Followers: 3  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1300-686X - ISSN (Online) 2297-8747
Published by MDPI Homepage  [84 journals]
  • MCA, Vol. 27, Pages 54: Learning Motion Primitives Automata for Autonomous
           Driving Applications

    • Authors: Matheus V. A. Pedrosa, Tristan Schneider, Kathrin Flaßkamp
      First page: 54
      Abstract: Motion planning methods often rely on libraries of primitives. The selection of primitives is then crucial for assuring feasible solutions and good performance within the motion planner. In the literature, the library is usually designed by either learning from demonstration, relying entirely on data, or by model-based approaches, with the advantage of exploiting the dynamical system’s property, e.g., symmetries. In this work, we propose a method combining data with a dynamical model to optimally select primitives. The library is designed based on primitives with highest occurrences within the data set, while Lie group symmetries from a model are analysed in the available data to allow for structure-exploiting primitives. We illustrate our technique in an autonomous driving application. Primitives are identified based on data from human driving, with the freedom to build libraries of different sizes as a parameter of choice. We also compare the extracted library with a custom selection of primitives regarding the performance of obtained solutions for a street layout based on a real-world scenario.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-21
      DOI: 10.3390/mca27040054
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 55: The Generalized Odd Linear Exponential Family of
           Distributions with Applications to Reliability Theory

    • Authors: Farrukh Jamal, Laba Handique, Abdul Hadi N. Ahmed, Sadaf Khan, Shakaiba Shafiq, Waleed Marzouk
      First page: 55
      Abstract: A new family of continuous distributions called the generalized odd linear exponential family is proposed. The probability density and cumulative distribution function are expressed as infinite linear mixtures of exponentiated-F distribution. Important statistical properties such as quantile function, moment generating function, distribution of order statistics, moments, mean deviations, asymptotes and the stress–strength model of the proposed family are investigated. The maximum likelihood estimation of the parameters is presented. Simulation is carried out for two of the mentioned sub-models to check the asymptotic behavior of the maximum likelihood estimates. Two real-life data sets are used to establish the credibility of the proposed model. This is achieved by conducting data fitting of two of its sub-models and then comparing the results with suitable competitive lifetime models to generate conclusive evidence.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-23
      DOI: 10.3390/mca27040055
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 56: Double-Diffusive Convection in Bidispersive Porous
           Medium with Coriolis Effect

    • Authors: Chirnam Ramchandraiah, Naikoti Kishan, Gundlapally Shiva Kumar Reddy, Kiran Kumar Paidipati, Christophe Chesneau
      First page: 56
      Abstract: In this paper, the thermal instability of rotating convection in a bidispersive porous layer is analyzed. The linear stability analysis is employed to examine the stability of the system. The neutral curves for different values of the physical parameters are shown graphically. The critical Rayleigh number is evaluated for appropriate values of the other governing parameters. Among the obtained results, we find: the Taylor number has a stabilizing effect on the onset of convection; the Soret number does not show any effect on oscillatory convection, as the oscillatory Rayleigh number is independent of the Soret number; there exists a threshold, Rc* ∈ (0.45, 0.46), for the solute Rayleigh number, such that, if RC > Rc*, then the convection arises via an oscillatory mode; and the oscillatory convection sets in and as soon as the value of the Soret number reaches a critical value, (∈(0.6, 0.7)), and the convection arises via stationary convection.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-30
      DOI: 10.3390/mca27040056
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 57: Resolving Boundary Layers with Harmonic Extension
           Finite Elements

    • Authors: Harri Hakula
      First page: 57
      Abstract: In recent years, the standard numerical methods for partial differential equations have been extended with variants that address the issue of domain discretisation in complicated domains. Sometimes similar requirements are induced by local parameter-dependent features of the solutions, for instance, boundary or internal layers. The adaptive reference elements are one way with which harmonic extension elements, an extension of the p-version of the finite element method, can be implemented. In combination with simple replacement rule-based mesh generation, the performance of the method is shown to be equivalent to that of the standard p-version in problems where the boundary layers dominate the solution. The performance over a parameter range is demonstrated in an application of computational asymptotic analysis, where known estimates are recovered via computational means only.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-08
      DOI: 10.3390/mca27040057
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 58: Numerical Study of the Effect of a Heated Cylinder
           on Natural Convection in a Square Cavity in the Presence of a Magnetic

    • Authors: Muhammad Sajjad Hossain, Muhammad Fayz-Al-Asad, Muhammad Saiful Islam Mallik, Mehmet Yavuz, Md. Abdul Alim, Kazi Md. Khairul Basher
      First page: 58
      Abstract: The present research was developed to find out the effect of heated cylinder configurations in accordance with the magnetic field on the natural convective flow within a square cavity. In the cavity, four types of configurations—left bottom heated cylinder (LBC), right bottom heated cylinder (RBC), left top heated cylinder (LTC) and right top heated cylinder (RTC)—were considered in the investigation. The current mathematical problem was formulated using the non-linear governing equations and then solved by engaging the process of Galerkin weighted residuals based on the finite element scheme (FES). The investigation of the present problem was conducted using numerous parameters: the Rayleigh number (Ra = 103–105), the Hartmann number (Ha = 0–200) at Pr = 0.71 on the flow field, thermal pattern and the variation of heat inside the enclosure. The clarifications of the numerical result were exhibited in the form of streamlines, isotherms, velocity profiles and temperature profiles, local and mean Nusselt number, along with heated cylinder configurations. From the obtained outcomes, it was observed that the rate of heat transport, as well as the local Nusselt number, decreased for the LBC and LTC configurations, but increased for the RBC and RTC configurations with the increase of the Hartmann number within the square cavity. In addition, the mean Nusselt number for the LBC, RBC, LTC and RTC configurations increased when the Hartmann number was absent, but decreased when the Hartmann number increased in the cavity. The computational results were verified in relation to a published work and were found to be in good agreement.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-11
      DOI: 10.3390/mca27040058
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 59: On the Convergence of the Damped Additive Schwarz
           Methods and the Subdomain Coloring

    • Authors: Lori Badea
      First page: 59
      Abstract: In this paper, we consider that the subdomains of the domain decomposition are colored such that the subdomains with the same color do not intersect and introduce and analyze the convergence of a damped additive Schwarz method related to such a subdomain coloring for the resolution of variational inequalities and equations. In this damped method, a single damping value is associated with all the subdomains having the same color. We first make this analysis both for variational inequalities and, as a special case, for equations in an abstract framework. By introducing an assumption on the decomposition of the convex set of the variational inequality, we theoretically analyze in a reflexive Banach space the convergence of the damped additive Schwarz method. The introduced assumption contains a constant C0, and we explicitly write the expression of the convergence rates, depending on the number of colors and the constant C0, and find the values of the damping constants which minimize them. For problems in the finite element spaces, we write the constant C0 as a function of the overlap parameter of the domain decomposition and the number of colors of the subdomains. We show that, for a fixed overlap parameter, the convergence rate, as a function of the number of subdomains has an upper limit which depends only on the number of the colors of the subdomains. Obviously, this limit is independent of the total number of subdomains. Numerical results are in agreement with the theoretical ones. They have been performed for an elasto-plastic problem to verify the theoretical predictions concerning the choice of the damping parameter, the dependence of the convergence on the overlap parameter and on the number of subdomains.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-13
      DOI: 10.3390/mca27040059
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 60: Prony Method for Two-Generator Sparse Expansion

    • Authors: Abdulmtalb Hussen, Wenjie He
      First page: 60
      Abstract: In data analysis and signal processing, the recovery of structured functions from the given sampling values is a fundamental problem. Many methods generalized from the Prony method have been developed to solve this problem; however, the current research mainly deals with the functions represented in sparse expansions using a single generating function. In this paper, we generalize the Prony method to solve the sparse expansion problem for two generating functions, so that more types of functions can be recovered by Prony-type methods. The two-generator sparse expansion problem has some special properties. For example, the two sets of frequencies need to be separated from the zeros of the Prony polynomial. We propose a two-stage least-square detection method to solve this problem effectively.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-15
      DOI: 10.3390/mca27040060
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 61: A Bivariate Beta from Gamma Ratios for Determining
           a Potential Variance Change Point: Inspired from a Process Control

    • Authors: Schalk W. Human, Andriette Bekker, Johannes T. Ferreira, Philip Albert Mijburgh
      First page: 61
      Abstract: Within statistical process control (SPC), normality is often assumed as the underlying probabilistic generator where the process variance is assumed equal for all rational subgroups. The parameters of the underlying process are usually assumed to be known—if this is not the case, some challenges arise in the estimation of unknown parameters in the SPC environment especially in the case of few observations. This paper proposes a bivariate beta type distribution to guide the user in the detection of a permanent upward or downward step shift in the process’ variance that does not directly rely on parameter estimates, and as such presents itself as an attractive and intuitive approach for not only potentially identifying the magnitude of the shift, but also the position in time where this shift is most likely to occur. Certain statistical properties of this distribution are derived and simulation illustrates the theoretical results. In particular, some insights are gained by comparing the newly proposed model’s performance with an existing approach. A multivariate extension is described, and useful relationships between the derived model and other bivariate beta distributions are also included.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-16
      DOI: 10.3390/mca27040061
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 62: The Binomial–Natural Discrete Lindley
           Distribution: Properties and Application to Count Data

    • Authors: Shakaiba Shafiq, Sadaf Khan, Waleed Marzouk, Jiju Gillariose, Farrukh Jamal
      First page: 62
      Abstract: In this paper, a new discrete distribution called Binomial–Natural Discrete Lindley distribution is proposed by compounding the binomial and natural discrete Lindley distributions. Some properties of the distribution are discussed including the moment-generating function, moments and hazard rate function. Estimation of the distribution’s parameter is studied by methods of moments, proportions and maximum likelihood. A simulation study is performed to compare the performance of the different estimates in terms of bias and mean square error. SO2 data applications are also presented to see that the new distribution is useful in modeling data.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-20
      DOI: 10.3390/mca27040062
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 63: On the Use of High-Order Shape Functions in the
           SAFE Method and Their Performance in Wave Propagation Problems

    • Authors: Elyas Mirzaee Kakhki, Jalil Rezaeepazhand, Fabian Duvigneau, Lotfollah Pahlavan, Resam Makvandi, Daniel Juhre, Majid Moavenian, Sascha Eisenträger
      First page: 63
      Abstract: In this research, high-order shape functions commonly used in different finite element implementations are investigated with a special focus on their applicability in the semi-analytical finite element (SAFE) method being applied to wave propagation problems. Hierarchical shape functions (p-version of the finite element method), Lagrange polynomials defined over non-equidistant nodes (spectral element method), and non-uniform rational B-splines (isogeometric analysis) are implemented in an in-house SAFE code, along with different refinement strategies such as h-, p-, and k-refinement. Since the numerical analysis of wave propagation is computationally quite challenging, high-order shape functions and local mesh refinement techniques are required to increase the accuracy of the solution, while at the same time decreasing the computational costs. The obtained results reveal that employing a suitable high-order basis in combination with one of the mentioned mesh refinement techniques has a notable effect on the performance of the SAFE method. This point becomes especially beneficial when dealing with applications in the areas of structural health monitoring or material property identification, where a model problem has to be solved repeatedly.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-25
      DOI: 10.3390/mca27040063
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 64: Using the Theory of Functional Connections to
           Solve Boundary Value Geodesic Problems

    • Authors: Daniele Mortari
      First page: 64
      Abstract: This study provides a least-squares-based numerical approach to estimate the boundary value geodesic trajectory and associated parametric velocity on curved surfaces. The approach is based on the Theory of Functional Connections, an analytical framework to perform functional interpolation. Numerical examples are provided for a set of two-dimensional quadrics, including ellipsoid, elliptic hyperboloid, elliptic paraboloid, hyperbolic paraboloid, torus, one-sheeted hyperboloid, Moëbius strips, as well as on a generic surface. The estimated geodesic solutions for the tested surfaces are obtained with residuals at the machine-error level. In principle, the proposed approach can be applied to solve boundary value problems in more complex scenarios, such as on Riemannian manifolds.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-27
      DOI: 10.3390/mca27040064
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 65: Modeling the Adaptive Immune Response in an HBV
           Infection Model with Virus to Cell Transmission in Both Liver with CTL
           Immune Response and the Extrahepatic Tissue

    • Authors: Fatima Ezzahra Fikri, Karam Allali
      First page: 65
      Abstract: The objective of this paper is to investigate a mathematical model describing the infection of hepatitis B virus (HBV) in intrahepatic and extrahepatic tissues. Additionally, the model includes the effect of the cytotoxic T cell (CTL) immunity, which is described by a linear activation rate by infected cells. The positivity and boundedness of solutions for non-negative initial data are proven, which is consistent with the biological studies. The local stability of the equilibrium is established. In addition to this, the global stability of the disease-free equilibrium and the endemic equilibrium is fulfilled by using appropriate Lyapanov functions. Finally, numerical simulations are performed to support our theoretical findings. It has been revealed that the fractional-order derivatives have no influence on the stability but only on the speed of convergence toward the equilibria.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-07-28
      DOI: 10.3390/mca27040065
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 66: Analytical Solutions of Microplastic Particles
           Dispersion Using a Lotka–Volterra Predator–Prey Model with
           Time-Varying Intraspecies Coefficients

    • Authors: Lindomar Soares Dos Santos, José Renato Alcarás, Lucas Murilo Da Costa, Mateus Mendonça Ramos Simões, Alexandre Souto Martinez
      First page: 66
      Abstract: Discarded plastic is subjected to weather effects from different ecosystems and becomes microplastic particles. Due to their small size, they have spread across the planet. Their presence in living organisms can have several harmful consequences, such as altering the interaction between prey and predator. Huang et al. successfully modeled this system presenting numerical results of ecological relevance. Here, we have rewritten their equations and solved a set of them analytically, confirming that microplastic particles accumulate faster in predators than in prey and calculating the time values from which it happens. Using these analytical solutions, we have retrieved the Lotka–Volterra predator–prey model with time-varying intraspecific coefficients, allowing us to interpret ecological quantities referring to microplastics dispersion. After validating our equations, we solved analytically particular situations of ecological interest, characterized by extreme effects on predatory performance, and proposed a second-order differential equation as a possible next step to address this model. Our results open space for further refinement in the study of predator–prey models under the effects of microplastic particles, either exploring the second-order equation that we propose or modify the Huang et al. model to reduce the number of parameters, embedding in the time-varying intraspecies coefficients all the adverse effects caused by microplastic particles.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-08-03
      DOI: 10.3390/mca27040066
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 67: An Efficient Numerical Scheme Based on Radial
           Basis Functions and a Hybrid Quasi-Newton Method for a Nonlinear Shape
           Optimization Problem

    • Authors: Youness El Yazidi, Abdellatif Ellabib
      First page: 67
      Abstract: The purpose of this work is to construct a robust numerical scheme for a class of nonlinear free boundary identification problems. First, a shape optimization problem is constructed based on a least square functional. Schauder’s fixed point theorem is manipulated to show the existence solution for the state solution. The existence of an optimal solution of the optimization problem is proved. The proposed numerical scheme is based on the Radial Basis Functions method as a discretization approach, the minimization process is a hybrid Differential Evolution heuristic method and the quasi-Newton method. At the end we establish some numerical examples to show the validity of the theoretical results and robustness of the proposed scheme.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-08-04
      DOI: 10.3390/mca27040067
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 68: Odd Exponential-Logarithmic Family of
           Distributions: Features and Modeling

    • Authors: Christophe Chesneau, Lishamol Tomy, Meenu Jose, Kuttappan Vallikkattil Jayamol
      First page: 68
      Abstract: This paper introduces a general family of continuous distributions, based on the exponential-logarithmic distribution and the odd transformation. It is called the “odd exponential logarithmic family”. We intend to create novel distributions with desired qualities for practical applications, using the unique properties of the exponential-logarithmic distribution as an initial inspiration. Thus, we present some special members of this family that stand out for the versatile shape properties of their corresponding functions. Then, a comprehensive mathematical treatment of the family is provided, including some asymptotic properties, the determination of the quantile function, a useful sum expression of the probability density function, tractable series expressions for the moments, moment generating function, Rényi entropy and Shannon entropy, as well as results on order statistics and stochastic ordering. We estimate the model parameters quite efficiently by the method of maximum likelihood, with discussions on the observed information matrix and a complete simulation study. As a major interest, the odd exponential logarithmic models reveal how to successfully accommodate various kinds of data. This aspect is demonstrated by using three practical data sets, showing that an odd exponential logarithmic model outperforms two strong competitors in terms of data fitting.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-08-08
      DOI: 10.3390/mca27040068
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 69: On Some Numerical Methods for Solving Large
           Differential Nonsymmetric Stein Matrix Equations

    • Authors: Lakhlifa Sadek, El Mostafa Sadek, Hamad Talibi Alaoui
      First page: 69
      Abstract: In this paper, we propose a new numerical method based on the extended block Arnoldi algorithm for solving large-scale differential nonsymmetric Stein matrix equations with low-rank right-hand sides. This algorithm is based on projecting the initial problem on the extended block Krylov subspace to obtain a low-dimensional differential Stein matrix equation. The obtained reduced-order problem is solved by the backward differentiation formula (BDF) method or the Rosenbrock (Ros) method, the obtained solution is used to build the low-rank approximate solution of the original problem. We give some theoretical results and report some numerical experiments.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-08-12
      DOI: 10.3390/mca27040069
      Issue No: Vol. 27, No. 4 (2022)
  • MCA, Vol. 27, Pages 33: Coupled Neural–Glial Dynamics and the Role
           of Astrocytes in Alzheimer’s Disease

    • Authors: Swadesh Pal, Roderick Melnik
      First page: 33
      Abstract: Neurodegenerative diseases such as Alzheimer’s (AD) are associated with the propagation and aggregation of toxic proteins. In the case of AD, it was Alzheimer himself who showed the importance of both amyloid beta (Aβ) plaques and tau protein neurofibrillary tangles (NFTs) in what he called the “disease of forgetfulness”. The amyloid beta forms extracellular aggregates and plaques, whereas tau proteins are intracellular proteins that stabilize axons by cross-linking microtubules that can form largely messy tangles. On the other hand, astrocytes and microglial cells constantly clear these plaques and NFTs from the brain. Astrocytes transport nutrients from the blood to neurons. Activated astrocytes produce monocyte chemoattractant protein-1 (MCP-1), which attracts anti-inflammatory macrophages and clears Aβ. At the same time, the microglia cells are poorly phagocytic for Aβ compared to proinflammatory and anti-inflammatory macrophages. In addition to such distinctive neuropathological features of AD as amyloid beta and tau proteins, neuroinflammation has to be brought into the picture as well. Taking advantage of a coupled mathematical modelling framework, we formulate a network model, accounting for the coupling between neurons and astroglia and integrating all three main neuropathological features with the brain connectome data. We provide details on the coupled dynamics involving cytokines, astrocytes, and microglia. Further, we apply the tumour necrosis factor alpha (TNF-α) inhibitor and anti-Aβ drug and analyze their influence on the brain cells, suggesting conditions under which the drug can prevent cell damage. The important role of astrocytes and TNF-α inhibitors in AD pathophysiology is emphasized, along with potentially promising pathways for developing new AD therapies.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-21
      DOI: 10.3390/mca27030033
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 34: Reduced Order Modeling Using Advection-Aware

    • Authors: Sourav Dutta, Peter Rivera-Casillas, Brent Styles, Matthew W. Farthing
      First page: 34
      Abstract: Physical systems governed by advection-dominated partial differential equations (PDEs) are found in applications ranging from engineering design to weather forecasting. They are known to pose severe challenges to both projection-based and non-intrusive reduced order modeling, especially when linear subspace approximations are used. In this work, we develop an advection-aware (AA) autoencoder network that can address some of these limitations by learning efficient, physics-informed, nonlinear embeddings of the high-fidelity system snapshots. A fully non-intrusive reduced order model is developed by mapping the high-fidelity snapshots to a latent space defined by an AA autoencoder, followed by learning the latent space dynamics using a long-short-term memory (LSTM) network. This framework is also extended to parametric problems by explicitly incorporating parameter information into both the high-fidelity snapshots and the encoded latent space. Numerical results obtained with parametric linear and nonlinear advection problems indicate that the proposed framework can reproduce the dominant flow features even for unseen parameter values.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-21
      DOI: 10.3390/mca27030034
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 35: Can DtN and GenEO Coarse Spaces Be Sufficiently
           Robust for Heterogeneous Helmholtz Problems'

    • Authors: Niall Bootland, Victorita Dolean
      First page: 35
      Abstract: Numerical solutions of heterogeneous Helmholtz problems present various computational challenges, with descriptive theory remaining out of reach for many popular approaches. Robustness and scalability are key for practical and reliable solvers in large-scale applications, especially for large wave number problems. In this work, we explore the use of a GenEO-type coarse space to build a two-level additive Schwarz method applicable to highly indefinite Helmholtz problems. Through a range of numerical tests on a 2D model problem, discretised by finite elements on pollution-free meshes, we observe robust convergence, iteration counts that do not increase with the wave number, and good scalability of our approach. We further provide results showing a favourable comparison with the DtN coarse space. Our numerical study shows promise that our solver methodology can be effective for challenging heterogeneous applications.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-21
      DOI: 10.3390/mca27030035
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 36: Challenges in Kinetic-Kinematic Driven
           Musculoskeletal Subject-Specific Infant Modeling

    • Authors: Yeram Lim, Tamara Chambers, Christine Walck, Safeer Siddicky, Erin Mannen, Victor Huayamave
      First page: 36
      Abstract: Musculoskeletal computational models provide a non-invasive approach to investigate human movement biomechanics. These models could be particularly useful for pediatric applications where in vivo and in vitro biomechanical parameters are difficult or impossible to examine using physical experiments alone. The objective was to develop a novel musculoskeletal subject-specific infant model to investigate hip joint biomechanics during cyclic leg movements. Experimental motion-capture marker data of a supine-lying 2-month-old infant were placed on a generic GAIT 2392 OpenSim model. After scaling the model using body segment anthropometric measurements and joint center locations, inverse kinematics and dynamics were used to estimate hip ranges of motion and moments. For the left hip, a maximum moment of 0.975 Nm and a minimum joint moment of 0.031 Nm were estimated at 34.6° and 65.5° of flexion, respectively. For the right hip, a maximum moment of 0.906 Nm and a minimum joint moment of 0.265 Nm were estimated at 23.4° and 66.5° of flexion, respectively. Results showed agreement with reported values from the literature. Further model refinements and validations are needed to develop and establish a normative infant dataset, which will be particularly important when investigating the movement of infants with pathologies such as developmental dysplasia of the hip. This research represents the first step in the longitudinal development of a model that will critically contribute to our understanding of infant growth and development during the first year of life.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-22
      DOI: 10.3390/mca27030036
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 37: Multivariable Panel Data Cluster Analysis of
           Meteorological Stations in Thailand for ENSO Phenomenon

    • Authors: Porntip Dechpichai, Nuttawadee Jinapang, Pariyakorn Yamphli, Sakulrat Polamnuay, Sittisak Injan, Usa Humphries
      First page: 37
      Abstract: The purpose of this research is to study the spatial and temporal groupings of 124 meteorological stations in Thailand under ENSO. The multivariate climate variables are rainfall, relative humidity, temperature, max temperature, min temperature, solar downwelling, and horizontal wind from the conformal cubic atmospheric model (CCAM) in years of El Niño (1987, 2004, and 2015) and La Niña (1999, 2000, and 2011). Euclidean distance timed and spaced with average linkage for clustering and silhouette width for cluster validation were employed. Five spatial clusters (SCs) and three temporal clusters (TCs) in each SC with different average precipitation were compared by El Niño and La Niña. The pattern of SCs and TCs was similar for both events except in the case when severe El Niño occurred. This method could be applied using variables forecasted in the future to be used for planning and managing crop cultivation with the climate change in each area.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-24
      DOI: 10.3390/mca27030037
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 38: Interval-Based Computation of the Uncertainty in
           the Mechanical Properties and the Failure Analysis of Unidirectional
           Composite Materials

    • Authors: Dimitris G. Sotiropoulos, Konstantinos Tserpes
      First page: 38
      Abstract: An interval-based method is presented to evaluate the uncertainty in the computed mechanical properties and the failure assessment of composite unidirectional (UD) laminates. The method was applied to two composite laminates: a carbon/epoxy and a glass/epoxy. The mechanical properties of the UD lamina were derived using simplified micromechanical equations. An uncertainty level of ±5% was assumed for the input properties of the constituents. The global minimum and maximum values of the properties were computed using an interval branch-and-bound algorithm. Interval arithmetic operations were used to evaluate the uncertainty in the Hashin-type failure criteria in a closed form. Using the closed-form uncertainties of intervals and sets of stresses obtained by finite element analysis, the uncertainty in the failure assessment was quantified for the two composite laminates. For the assumed uncertainty level of ±5%, the computed uncertainty for the mechanical properties ranges from 6.64% to 10.63% for the carbon/epoxy material and from 6.72% to 12.28% for the glass/epoxy material. For evaluating the uncertainty effect on the efficiency of failure criteria, a probability of failure function, which employs interval boundaries, was defined and proved capable of evaluating the whole spectrum of stresses.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-29
      DOI: 10.3390/mca27030038
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 39: A Trust Region Reduced Basis Pascoletti-Serafini
           Algorithm for Multi-Objective PDE-Constrained Parameter Optimization

    • Authors: Stefan Banholzer, Luca Mechelli, Stefan Volkwein
      First page: 39
      Abstract: In the present paper non-convex multi-objective parameter optimization problems are considered which are governed by elliptic parametrized partial differential equations (PDEs). To solve these problems numerically the Pascoletti-Serafini scalarization is applied and the obtained scalar optimization problems are solved by an augmented Lagrangian method. However, due to the PDE constraints, the numerical solution is very expensive so that a model reduction is utilized by using the reduced basis (RB) method. The quality of the RB approximation is ensured by a trust-region strategy which does not require any offline procedure, in which the RB functions are computed in a greedy algorithm. Moreover, convergence of the proposed method is guaranteed and different techniques to prevent the excessive growth of the number of basis functions are explored. Numerical examples illustrate the efficiency of the proposed solution technique.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-03
      DOI: 10.3390/mca27030039
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 40: Enhancing Quasi-Newton Acceleration for
           Fluid-Structure Interaction

    • Authors: Kyle Davis, Miriam Schulte, Benjamin Uekermann
      First page: 40
      Abstract: We propose two enhancements of quasi-Newton methods used to accelerate coupling iterations for partitioned fluid-structure interaction. Quasi-Newton methods have been established as flexible, yet robust, efficient and accurate coupling methods of multi-physics simulations in general. The coupling library preCICE provides several variants, the so-called IQN-ILS method being the most commonly used. It uses input and output differences of the coupled solvers collected in previous iterations and time steps to approximate Newton iterations. To make quasi-Newton methods both applicable for parallel coupling (where these differences contain data from different physical fields) and to provide a robust approach for re-using information, a combination of information filtering and scaling for the different physical fields is typically required. This leads to good convergence, but increases the cost per iteration. We propose two new approaches—pre-scaling weight monitoring and a new, so-called QR3 filter, to substantially improve runtime while not affecting convergence quality. We evaluate these for a variety of fluid-structure interaction examples. Results show that we achieve drastic speedups for the pure quasi-Newton update steps. In the future, we intend to apply the methods also to volume-coupled scenarios, where these gains can be decisive for the feasibility of the coupling approach.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-06
      DOI: 10.3390/mca27030040
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 41: Microwave Characterization and Modelling of
           PA6/GNPs Composites

    • Authors: Erika Pittella, Emanuele Piuzzi, Pietro Russo, Francesco Fabbrocino
      First page: 41
      Abstract: The interest in composite materials has increased in the last decades since they have the advantages of combining intrinsic properties of each component and offer better performance with respect to the base constituents. In particular, these kinds of materials can have different electrical characteristics by varying the filling percentage and, therefore, they can be used in diverse applications. Thus, a detailed study of the microwave response of these composite systems is of great practical importance. In fact, the dielectric constant and loss tangent are key factors in the design of microwave components. In this frame, the outstanding properties of graphene-like fillers may be exploited to develop new very interesting materials to study and characterize. In this paper, microwave characterization of compounds, based on nylon 6 containing different percentages of graphene nanoplatelets, is carried out taking the neat matrix sample processed under the same conditions as benchmark. The measurements were carried out using two microwave systems, operating at two different frequency bands, appropriate to characterize solid and compact material samples. The achieved results, in line with limited data from the literature and from material data sheets, highlight the possibility to use the present polymers as an excellent electromagnetic interference shielding, as confirmed by full wave electromagnetic numerical simulations that were conducted with a numerical electromagnetic software.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-11
      DOI: 10.3390/mca27030041
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 42: Finding the Conjectured Sequence of Largest Small
           n-Polygons by Numerical Optimization

    • Authors: János D. Pintér, Frank J. Kampas, Ignacio Castillo
      First page: 42
      Abstract: LSP(n), the largest small polygon with n vertices, is a polygon with a unit diameter that has a maximal of area A(n). It is known that for all odd values n≥3, LSP(n) is a regular n-polygon; however, this statement is not valid even for values of n. Finding the polygon LSP(n) and A(n) for even values n≥6 has been a long-standing challenge. In this work, we developed high-precision numerical solution estimates of A(n) for even values n≥4, using the Mathematica model development environment and the IPOPT local nonlinear optimization solver engine. First, we present a revised (tightened) LSP model that greatly assists in the efficient numerical solution of the model-class considered. This is followed by results for an illustrative sequence of even values of n, up to n≤1000. Most of the earlier research addressed special cases up to n≤20, while others obtained numerical optimization results for a range of values from 6≤n≤100. The results obtained were used to provide regression model-based estimates of the optimal area sequence {A(n)}, for even values n of interest, thereby essentially solving the LSP model-class numerically, with demonstrably high precision.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-16
      DOI: 10.3390/mca27030042
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 43: Applications of the Sine Modified Lindley
           Distribution to Biomedical Data

    • Authors: Lishamol Tomy, Veena G, Christophe Chesneau
      First page: 43
      Abstract: In this paper, the applicability of the sine modified Lindley distribution, recently introduced in the statistical literature, is highlighted via the goodness-of-fit approach on biological data. In particular, it is shown to be beneficial in estimating and modeling the life periods of growth hormone guinea pigs given tubercle bacilli, growth hormone treatment for children, and the size of tumors in cancer patients. We anticipate that our model will be effective in modeling the survival times of diseases related to cancer. The R codes for the figures, as well as information on how the data are processed, are provided.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-16
      DOI: 10.3390/mca27030043
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 44: Small Area Estimation of Zone-Level Malnutrition
           among Children under Five in Ethiopia

    • Authors: Kindie Fentahun Muchie, Anthony Kibira Wanjoya, Samuel Musili Mwalili
      First page: 44
      Abstract: Child undernutrition is one of the 10 most significant public health problems worldwide. There is a rapidly growing demand to produce reliable estimates at the micro administrative level with small sample sizes. In this research, the authors employed small area estimation techniques to estimate the prevalence of malnutrition at the zonal level among children under five in Ethiopia. The small area estimation concept was sought for by linking the most recent possible survey data and census data in Ethiopia. The results show that there is spatial variation of stunting, wasting and being underweight across the zone level, showing different locations facing different challenges or different extents.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-22
      DOI: 10.3390/mca27030044
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 45: Solution of a Complex Nonlinear Fractional
           Biochemical Reaction Model

    • Authors: Fatima Rabah, Marwan Abukhaled, Suheil A. Khuri
      First page: 45
      Abstract: This paper discusses a complex nonlinear fractional model of enzyme inhibitor reaction where reaction memory is taken into account. Analytical expressions of the concentrations of enzyme, substrate, inhibitor, product, and other complex intermediate species are derived using Laplace decomposition and differential transformation methods. Since different rate constants, large initial concentrations, and large time domains are unavoidable in biochemical reactions, different dynamics will result; hence, the convergence of the approximate concentrations may be lost. In this case, the proposed analytical methods will be coupled with Padé approximation. The validity and accuracy of the derived analytical solutions will be established by direct comparison with numerical simulations.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-26
      DOI: 10.3390/mca27030045
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 46: Magneto Mixed Convection of Williamson Nanofluid
           Flow through a Double Stratified Porous Medium in Attendance of Activation

    • Authors: B. M. Tamilzharasan, S. Karthikeyan, Mohammed K. A. Kaabar, Mehmet Yavuz, Fatma Özköse
      First page: 46
      Abstract: This article aims to develop a mathematical simulation of the steady mixed convective Darcy–Forchheimer flow of Williamson nanofluid over a linear stretchable surface. In addition, the effects of Cattaneo–Christov heat and mass flux, Brownian motion, activation energy, and thermophoresis are also studied. The novel aspect of this study is that it incorporates thermal radiation to investigate the physical effects of thermal and solutal stratification on mixed convection flow and heat transfer. First, the profiles of velocity and energy equations were transformed toward the ordinary differential equation using the appropriate similarity transformation. Then, the system of equations was modified by first-order ODEs in MATLAB and solved using the bvp4c approach. Graphs and tables imply the impact of physical parameters on concentration, temperature, velocity, skin friction coefficient, mass, and heat transfer rate. The outcomes show that the nanofluid temperature and concentration are reduced with the more significant thermal and mass stratification parameters estimation.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-26
      DOI: 10.3390/mca27030046
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 47: Integrated Finite Strip Computation for Modelling
           and Frequency Analysis of Hybrid Laminated FRP Structures

    • Authors: Hamidreza Naderian, Moe M. S. Cheung, Elena Dragomirescu, Abdolmajid Mohammadian
      First page: 47
      Abstract: This paper proposes an efficient numerical technique for simulating hybrid fiber-reinforced polymer (FRP) bridge systems. An integrated finite strip method (IFSM) is proposed to evaluate the free vibration performance of cable-stayed FRP bridges. The structural performance of the ultra-long span cable-stayed bridge (ULSCSB) is totally different than steel and concrete bridge structures due to the complexity of the mechanical behavior of the FRP deck. Herein, the anisotropic nature of the FRP laminated deck is considered in the analysis by introducing so-called laminate spline strips in the integrated finite strip solution. The structural interactions between all the components of the bridge can be handled using the proposed method. Column strips and cable strips are introduced and used to model the towers and cables, respectively. In addition, a straightforward scheme for modeling boundary conditions is developed. A case study is presented through which the accuracy and efficiency of the IFSM in modeling such structures, as well as in performing natural frequency analysis of long-span cable-stayed FRP bridges, are evaluated. The finite strip results are verified against the finite element analysis, and a significant enhancement in efficiency in terms of reduction in computational cost is demonstrated with the same level of accuracy.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-05-27
      DOI: 10.3390/mca27030047
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 48: A Bounded Archiver for Hausdorff Approximations of
           the Pareto Front for Multi-Objective Evolutionary Algorithms

    • Authors: Carlos Ignacio Hernández Castellanos, Oliver Schütze
      First page: 48
      Abstract: Multi-objective evolutionary algorithms (MOEAs) have been successfully applied for the numerical treatment of multi-objective optimization problems (MOP) during the last three decades. One important task within MOEAs is the archiving (or selection) of the computed candidate solutions, since one can expect that an MOP has infinitely many solutions. We present and analyze in this work ArchiveUpdateHD, which is a bounded archiver that aims for Hausdorff approximations of the Pareto front. We show that the sequence of archives generated by ArchiveUpdateHD yields under certain (mild) assumptions with a probability of one after finitely many steps a Δ+-approximation of the Pareto front, where the value Δ+ is computed by the archiver within the run of the algorithm without any prior knowledge of the Pareto front. The knowledge of this value is of great importance for the decision maker, since it is a measure for the “completeness” of the Pareto front approximation. Numerical results on several well-known academic test problems as well as the usage of ArchiveUpdateHD as an external archiver within three state-of-the-art MOEAs indicate the benefit of the novel strategy.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-01
      DOI: 10.3390/mca27030048
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 49: An Efficient Orthogonal Polynomial Method for
           Auxetic Structure Analysis with Epistemic Uncertainties

    • Authors: Shengwen Yin, Haogang Qin, Qiang Gao
      First page: 49
      Abstract: Traditional approaches used for analyzing the mechanical properties of auxetic structures are commonly based on deterministic techniques, where the effects of uncertainties are neglected. However, uncertainty is widely presented in auxetic structures, which may affect their mechanical properties greatly. The evidence theory has a strong ability to deal with uncertainties; thus, it is introduced for the modelling of epistemic uncertainties in auxetic structures. For the response analysis of a typical double-V negative Poisson’s ratio (NPR) structure with epistemic uncertainty, a new sequence-sampling-based arbitrary orthogonal polynomial (SS-AOP) expansion is proposed by introducing arbitrary orthogonal polynomial theory and the sequential sampling strategy. In SS-AOP, a sampling technique is developed to calculate the coefficient of AOP expansion. In particular, the candidate points for sampling are generated using the Gauss points associated with the optimal Gauss weight function for each evidence variable, and the sequential-sampling technique is introduced to select the sampling points from candidate points. By using the SS-AOP, the number of sampling points needed for establishing AOP expansion can be effectively reduced; thus, the efficiency of the AOP expansion method can be improved without sacrificing accuracy. The proposed SS-AOP is thoroughly investigated through comparison to the Gaussian quadrature-based AOP method, the Latin-hypercube-sampling-based AOP (LHS-AOP) method and the optimal Latin-hypercube-sampling-based AOP (OLHS-AOP) method.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-02
      DOI: 10.3390/mca27030049
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 50: Morlet Cross-Wavelet Analysis of Climatic State
           Variables Expressed as a Function of Latitude, Longitude, and Time: New
           Light on Extreme Events

    • Authors: Jean-Louis Pinault
      First page: 50
      Abstract: This study aims to advance our knowledge in the genesis of extreme climatic events with the dual aim of improving forecasting methods while clarifying the role played by anthropogenic warming. Wavelet analysis is used to highlight the role of coherent Sea Surface Temperature (SST) anomalies produced from short-period oceanic Rossby waves resonantly forced, with two case studies: a Marine Heatwave (MHW) that occurred in the northwestern Pacific with a strong climatic impact in Japan, and an extreme flood event that occurred in Germany. Ocean–atmosphere interactions are evidenced by decomposing state variables into period bands within the cross-wavelet power spectra, namely SST, Sea Surface Height (SSH), and the zonal and meridional modulated geostrophic currents as well as precipitation height, i.e., the thickness of the layer of water produced during a day, with regard to subtropical cyclones. The bands are chosen according to the different harmonic modes of the oceanic Rossby waves. In each period band, the joint analysis of the amplitude and the phase of the state variables allow the estimation of the regionalized intensity of anomalies versus their time lag in relation to the date of occurrence of the extreme event. Regarding MHWs in the northwestern Pacific, it is shown how a warm SST anomaly associated with the northward component of the wind resulting from the low-pression system induces an SST response to latent and sensible heat transfer where the latitudinal SST gradient is steep. The SST anomaly is then shifted to the north as the phase becomes homogenized. As for subtropical cyclones, extreme events are the culmination of exceptional circumstances, some of which are foreseeable due to their relatively long maturation time. This is particularly the case of ocean–atmosphere interactions leading to the homogenization of the phase of SST anomalies that can potentially contribute to the supply of low-pressure systems. The same goes for the coalescence of distinct low-pressure systems during cyclogenesis. Some avenues are developed with the aim of better understanding how anthropogenic warming can modify certain key mechanisms in the evolution of those dynamic systems leading to extreme events.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-04
      DOI: 10.3390/mca27030050
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 51: A Note on Gerber–Shiu Function with Delayed
           Claim Reporting under Constant Force of Interest

    • Authors: Kokou Essiomle, Franck Adekambi
      First page: 51
      Abstract: In this paper, we analyze the Gerber–Shiu discounted penalty function for a constant interest rate in delayed claim reporting times. Using the Poisson claim arrival scenario, we derive the differential equation of the Laplace transform of the generalized Gerber–Shiu function and show that the differential equation can be transformed to a Volterra equation of the second kind with a degenerated kernel. In the case of an exponential claim distribution, a closed-expression for the Gerber–Shiu function is obtained via sequence expansion. This result allows us to calculate the absolute (relative) ruin probability. Additionally, we discuss a method of solving the Volterra equation numerically and provide an illustration of the ruin’s probability to support the finding.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-20
      DOI: 10.3390/mca27030051
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 52: Exploration of Darcy–Forchheimer Flows of
           Non-Newtonian Casson and Williamson Conveying Tiny Particles Experiencing
           Binary Chemical Reaction and Thermal Radiation: Comparative Analysis

    • Authors: Sheniyappan Eswaramoorthi, S. Thamaraiselvi, Karuppusamy Loganathan
      First page: 52
      Abstract: This discussion intends to scrutinize the Darcy–Forchheimer flow of Casson–Williamson nanofluid in a stretching surface with non-linear thermal radiation, suction and heat consumption. In addition, this investigation assimilates the influence of the Brownian motion, thermophoresis, activation energy and binary chemical reaction effects. Catteneo–Christov heat-mass flux theory is used to frame the energy and nanoparticle concentration equations. The suitable transformation is used to remodel the governing PDE model into an ODE model. The remodeled flow problems are numerically solved via the BVP4C scheme. The effects of various material characteristics on nanofluid velocity, nanofluid temperature and nanofluid concentration, as well as connected engineering aspects such as drag force, heat, and mass transfer gradients, are also calculated and displayed through tables, charts and figures. It is noticed that the nanofluid velocity upsurges when improving the quantity of Richardson number, and it downfalls for larger magnitudes of magnetic field and porosity parameters. The nanofluid temperature grows when enhancing the radiation parameter and Eckert number. The nanoparticle concentration upgrades for larger values of activation energy parameter while it slumps against the reaction rate parameter. The surface shear stress for the Williamson nanofluid is greater than the Casson nanofluid. There are more heat transfer gradient losses the greater the heat generation/absorption parameter and Eckert number. In addition, the local Sherwood number grows when strengthening the Forchheimer number and fitted rate parameter.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-20
      DOI: 10.3390/mca27030052
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 53: Dissolution-Driven Convection in a Porous Medium
           due to Vertical Axis of Rotation and Magnetic Field

    • Authors: Gundlapally Shiva Kumar Reddy, Nilam Venkata Koteswararao, Ragoju Ravi, Kiran Kumar Paidipati, Christophe Chesneau
      First page: 53
      Abstract: This article aims to study the effect of the vertical rotation and magnetic field on the dissolution-driven convection in a saturated porous layer with a first-order chemical reaction. The system’s physical parameters depend on the Vadasz number, the Hartmann number, the Taylor number, and the Damkohler number. We analyze them in an in-depth manner. On the other hand, based on an artificial neural network (ANN) technique, the Levenberg–Marquardt backpropagation algorithm is adopted to predict the distribution of the critical Rayleigh number and for the linear stability analysis. The simulated critical Rayleigh numbers obtained by the numerical study and the predicted critical Rayleigh numbers by the ANN are compared and are in good agreement. The system becomes more stable by increasing the Damkohler and Taylor numbers.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-06-20
      DOI: 10.3390/mca27030053
      Issue No: Vol. 27, No. 3 (2022)
  • MCA, Vol. 27, Pages 18: Equity Warrants Pricing Formula for Uncertain
           Financial Market

    • Authors: Foad Shokrollahi
      First page: 18
      Abstract: In this paper, inside the system of uncertainty theory, the valuation of equity warrants is explored. Different from the strategies of probability theory, the valuation problem of equity warrants is unraveled by utilizing the strategy of uncertain calculus. Based on the suspicion that the firm price follows an uncertain differential equation, a valuation formula of equity warrants is proposed for an uncertain stock model.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-22
      DOI: 10.3390/mca27020018
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 19: Meshless Computational Strategy for Higher Order
           Strain Gradient Plate Models

    • Authors: Francesco Fabbrocino, Serena Saitta, Riccardo Vescovini, Nicholas Fantuzzi, Raimondo Luciano
      First page: 19
      Abstract: The present research focuses on the use of a meshless method for the solution of nanoplates by considering strain gradient thin plate theory. Unlike the most common finite element method, meshless methods do not rely on a domain decomposition. In the present approach approximating functions at collocation nodes are obtained by using radial basis functions which depend on shape parameters. The selection of such parameters can strongly influences the accuracy of the numerical technique. Therefore the authors are presenting some numerical benchmarks which involve the solution of nanoplates by employing an optimization approach for the evaluation of the undetermined shape parameters. Stability is discussed as well as numerical reliability against solutions taken for the existing literature.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-22
      DOI: 10.3390/mca27020019
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 20: On Appearance of Fast or Late Self-Synchronization
           between Non-Ideal Sources Mounted on a Rectangular Plate Due to Time Delay

    • Authors: Armand Anthelme Nanha Djanan, Steffen Marburg, Blaise Roméo Nana Nbendjo
      First page: 20
      Abstract: The present paper aims to present the effects of late switching on (time delay) between two or three DC electrical machines characterized by limited power supplies on their fast or late self-synchronization when mounted on a rectangular plate with simply supported edges. The DC electrical machines are considered here as non-ideal oscillators, rotating in the same direction and acting as an external excitation on a specific surface of the plate. The stability analysis of the whole studied system (with two machines) around the obtained fixed point is done through analytical and numerical approaches by using the generalized Lyapunov and Routh-Hurwitz criterion. The existence conditions of the fixed point and the stability conditions are derived and presented. Great attention is put on the incidence of such study on the vibrations amplitude of the plate, which are considerably reduced in some cases. It appears that the time delay induces a rapid or late synchronization observed between the DC sources. This has been observed by numerically exploring the dynamics of the system for various possibilities that could occur. Moreover, in the modelling of the system, the positions on the plate occupied by DC electrical machines are taken into account by using the Heaviside function. It is shown that, in the case of three DC electrical machines, these positions influence the time to obtain a synchronous state between the DC electrical machines.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-24
      DOI: 10.3390/mca27020020
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 21: Attention Measurement of an Autism Spectrum
           Disorder User Using EEG Signals: A Case Study

    • Authors: José Jaime Esqueda-Elizondo, Reyes Juárez-Ramírez, Oscar Roberto López-Bonilla, Enrique Efrén García-Guerrero, Gilberto Manuel Galindo-Aldana, Laura Jiménez-Beristáin, Alejandra Serrano-Trujillo, Esteban Tlelo-Cuautle, Everardo Inzunza-González
      First page: 21
      Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-02
      DOI: 10.3390/mca27020021
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 22: Stochastic Neural Networks for Automatic Cell
           Tracking in Microscopy Image Sequences of Bacterial Colonies

    • Authors: Sorena Sarmadi, James J. Winkle, Razan N. Alnahhas, Matthew R. Bennett, Krešimir Josić, Andreas Mang, Robert Azencott
      First page: 22
      Abstract: Our work targets automated analysis to quantify the growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate the proposed automatic cell tracking algorithm using (i) recordings of simulated cell colonies that closely mimic the growth dynamics of E. coli in microfluidic traps and (ii) real data. On a batch of 1100 simulated image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences (i.e., real data) of E. coli colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-02
      DOI: 10.3390/mca27020022
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 23: Variable Decomposition for Large-Scale Constrained
           Optimization Problems Using a Grouping Genetic Algorithm

    • Authors: Guadalupe Carmona-Arroyo, Marcela Quiroz-Castellanos, Efrén Mezura-Montes
      First page: 23
      Abstract: Several real optimization problems are very difficult, and their optimal solutions cannot be found with a traditional method. Moreover, for some of these problems, the large number of decision variables is a major contributing factor to their complexity; they are known as Large-Scale Optimization Problems, and various strategies have been proposed to deal with them. One of the most popular tools is called Cooperative Co-Evolution, which works through a decomposition of the decision variables into smaller subproblems or variables subgroups, which are optimized separately and cooperate to finally create a complete solution of the original problem. This kind of decomposition can be handled as a combinatorial optimization problem where we want to group variables that interact with each other. In this work, we propose a Grouping Genetic Algorithm to optimize the variable decomposition by reducing their interaction. Although the Cooperative Co-Evolution approach is widely used to deal with unconstrained optimization problems, there are few works related to constrained problems. Therefore, our experiments were performed on a test benchmark of 18 constrained functions under 100, 500, and 1000 variables. The results obtained indicate that a Grouping Genetic Algorithm is an appropriate tool to optimize the variable decomposition for Large-Scale Constrained Optimization Problems, outperforming the decomposition obtained by a state-of-the-art genetic algorithm.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-03
      DOI: 10.3390/mca27020023
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 24: Evaluation of Machine Learning Algorithms for
           Early Diagnosis of Deep Venous Thrombosis

    • Authors: Eduardo Enrique Contreras-Luján, Enrique Efrén García-Guerrero, Oscar Roberto López-Bonilla, Esteban Tlelo-Cuautle, Didier López-Mancilla, Everardo Inzunza-González
      First page: 24
      Abstract: Deep venous thrombosis (DVT) is a disease that must be diagnosed quickly, as it can trigger the death of patients. Nowadays, one can find different ways to determine it, including clinical scoring, D-dimer, ultrasonography, etc. Recently, scientists have focused efforts on using machine learning (ML) and neural networks for disease diagnosis, progressively increasing the accuracy and efficacy. Patients with suspected DVT have no apparent symptoms. Using pattern recognition techniques, aiding good timely diagnosis, as well as well-trained ML models help to make good decisions and validation. The aim of this paper is to propose several ML models for a more efficient and reliable DVT diagnosis through its implementation on an edge device for the development of instruments that are smart, portable, reliable, and cost-effective. The dataset was obtained from a state-of-the-art article. It is divided into 85% for training and cross-validation and 15% for testing. The input data in this study are the Wells criteria, the patient’s age, and the patient’s gender. The output data correspond to the patient’s diagnosis. This study includes the evaluation of several classifiers such as Decision Trees (DT), Extra Trees (ET), K-Nearest Neighbor (KNN), Multi-Layer Perceptron Neural Network (MLP-NN), Random Forest (RF), and Support Vector Machine (SVM). Finally, the implementation of these ML models on a high-performance embedded system is proposed to develop an intelligent system for early DVT diagnosis. It is reliable, portable, open source, and low cost. The performance of different ML algorithms was evaluated, where KNN achieved the highest accuracy of 90.4% and specificity of 80.66% implemented on personal computer (PC) and Raspberry Pi 4 (RPi4). The accuracy of all trained models on PC and Raspberry Pi 4 is greater than 85%, while the area under the curve (AUC) values are between 0.81 and 0.86. In conclusion, as compared to traditional methods, the best ML classifiers are effective at predicting DVT in an early and efficient manner.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-04
      DOI: 10.3390/mca27020024
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 25: Soret & Dufour and Triple Stratification
           Effect on MHD Flow with Velocity Slip towards a Stretching Cylinder

    • Authors: Kandasamy Jagan, Sivanandam Sivasankaran
      First page: 25
      Abstract: The phenomenon of convective flow with heat and mass transfer has been studied extensively due to its applications in various fields. The effects of nonlinear thermal radiation (NLTR), slip, thermal-diffusion (Soret) and diffusion-thermo (Dufour) on magenoto-hydrodynamic (MHD) flow towards a stretching cylinder in the presence of triple stratification (TSF) are investigated in this paper. The governing equations are transformed into an ODE by suitable transformations. The homotopy analysis method (HAM) is used to solve the ODE. The revamping of fluid flow, and heat transfer due to the presence of the Soret and Dufour effect, concentration slip and concentration stratification are analyzed. The temperature and local Sherwood number increases as the Dufour number rises, whereas the local Nusselt number decreases. While elevating the Soret number, the Sherwood number diminishes, whereas the concentration profile rises. The thermal boundary layer thickness enhances when thermal radiation increases. The rate of solute transport reduces while the concentration slip increases.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-09
      DOI: 10.3390/mca27020025
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 26: Image Segmentation with a Priori Conditions:
           Applications to Medical and Geophysical Imaging

    • Authors: Guzel Khayretdinova, Christian Gout, Théophile Chaumont-Frelet, Sergei Kuksenko
      First page: 26
      Abstract: In this paper, we propose a method for semi-supervised image segmentation based on geometric active contours. The main novelty of the proposed method is the initialization of the segmentation process, which is performed with a polynomial approximation of a user defined initialization (for instance, a set of points or a curve to be interpolated). This work is related to many potential applications: the geometric conditions can be useful to improve the quality the segmentation process in medicine and geophysics when it is required (weak contrast of the image, missing parts in the image, non-continuous contour…). We compare our method to other segmentation algorithms, and we give experimental results related to several medical and geophysical applications.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-11
      DOI: 10.3390/mca27020026
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 27: Theoretical and Computational Results of a
           Memory-Type Swelling Porous-Elastic System

    • Authors: Adel M. Al-Mahdi, Mohammad M. Al-Gharabli, Mohamed Alahyane
      First page: 27
      Abstract: In this paper, we consider a memory-type swelling porous-elastic system. First, we use the multiplier method to prove explicit and general decay results to solutions of the system with sufficient regularities. These decay results are established under a very general assumption on the relaxation function and for suitable given data. We also perform several numerical tests to illustrate our theoretical decay results. Our results generalize and improve many earlier results in the literature.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-11
      DOI: 10.3390/mca27020027
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 28: In Vivo Validation of a Cardiovascular Simulation
           Model in Pigs

    • Authors: Moriz A. Habigt, Jonas Gesenhues, Maike Stemmler, Marc Hein, Rolf Rossaint, Mare Mechelinck
      First page: 28
      Abstract: Many computer simulation models of the cardiovascular system, of varying complexity and objectives, have been proposed in physiological science. Every model needs to be parameterized and evaluated individually. We conducted a porcine animal model to parameterize and evaluate a computer simulation model, recently proposed by our group. The results of an animal model, on thirteen healthy pigs, were used to generate consistent parameterization data for the full heart computer simulation model. To evaluate the simulation model, differences between the resulting simulation output and original animal data were analysed. The input parameters of the animal model, used to individualize the computer simulation, showed high interindividual variability (range of coefficient of variation: 10.1–84.5%), which was well-reflected by the resulting haemodynamic output parameters of the simulation (range of coefficient of variation: 12.6–45.7%). The overall bias between the animal and simulation model was low (mean: −3.24%, range: from −26.5 to 20.1%). The simulation model used in this study was able to adapt to the high physiological variability in the animal model. Possible reasons for the remaining differences between the animal and simulation model might be a static measurement error, unconsidered inaccuracies within the model, or unconsidered physiological interactions.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-18
      DOI: 10.3390/mca27020028
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 29: Applications of ANFIS-Type Methods in Simulation
           of Systems in Marine Environments

    • Authors: Aakanksha Jain, Iman Bahreini Toussi, Abdolmajid Mohammadian, Hossein Bonakdari, Majid Sartaj
      First page: 29
      Abstract: ANFIS-type algorithms have been used in various modeling and simulation problems. With the help of algorithms with more accuracy and adaptability, it is possible to obtain better real-life emulating models. A critical environmental problem is the discharge of saline industrial effluents in the form of buoyant jets into water bodies. Given the potentially harmful effects of the discharge effluents from desalination plants on the marine environment and the coastal ecosystem, minimizing such an effect is crucial. Hence, it is important to design the outfall system properly to reduce these impacts. To the best of the authors’ knowledge, a study that formulates the effluent discharge to find an optimum numerical model under the conditions considered here using AI methods has not been completed before. In this study, submerged discharges, specifically, negatively buoyant jets are modeled. The objective of this study is to compare various artificial intelligence algorithms along with multivariate regression models to find the best fit model emulating effluent discharge and determine the model with less computational time. This is achieved by training and testing the Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS-Genetic Algorithm (GA), ANFIS-Particle Swarm Optimization (PSO) and ANFIS-Firefly Algorithm (FFA) models with input parameters, which are obtained by using the realizable k-ε turbulence model, and simulated parameters, which are obtained after modeling the turbulent jet using the OpenFOAM simulation platform. A comparison of the realizable k-ε turbulence model outputs and AI algorithms’ outputs is conducted in this study. Statistical parameters such as least error, coefficient of determination (R2), Mean Absolute Error (MAE), and Average Absolute Deviation (AED) are measured to evaluate the performance of the models. In this work, it is found that ANFIS-PSO performs better compared to the other four models and the multivariate regression model. It is shown that this model provides better R2, MAE, and AED, however, the non-hybrid ANFIS model provides reasonably acceptable results with lower computational costs. The results of the study demonstrate an error of 6.908% as the best-case scenario in the AI models.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-03-21
      DOI: 10.3390/mca27020029
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 30: Nadarajah–Haghighi Lomax Distribution
           and Its Applications

    • Authors: Vasili B. V. Nagarjuna, Rudravaram Vishnu Vardhan, Christophe Chesneau
      First page: 30
      Abstract: Over the years, several researchers have worked to model phenomena in which the distribution of data presents more or less heavy tails. With this aim, several generalizations or extensions of the Lomax distribution have been proposed. In this paper, an attempt is made to create a hybrid distribution mixing the functionalities of the Nadarajah–Haghighi and Lomax distributions, namely the Nadarajah–Haghighi Lomax (NHLx) distribution. It can also be thought of as an extension of the exponential Lomax distribution. The NHLx distribution has the features of having four parameters, a lower bounded support, and very flexible distributional functions, including a decreasing or unimodal probability density function and an increasing, decreasing, or upside-down bathtub hazard rate function. In addition, it benefits from the treatable statistical properties of moments and quantiles. The statistical applicability of the NHLx model is highlighted, with simulations carried out. Four real data sets are also used to illustrate the practical applications. In particular, results are compared with Lomax-based models of importance, such as the Lomax, Weibull Lomax, and exponential Lomax models, and it is observed that the NHLx model fits better.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-01
      DOI: 10.3390/mca27020030
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 31: Benchmarking Regridding Libraries Used in Earth
           System Modelling

    • Authors: Sophie Valcke, Andrea Piacentini, Gabriel Jonville
      First page: 31
      Abstract: Components of Earth system models (ESMs) usually use different numerical grids because of the different environments they represent. Therefore, a coupling field sent by a source model has to be regridded to be used by a target model. The regridding has to be accurate and, in some cases, conservative, in order to ensure the consistency of the coupled model. Here, we present work done to benchmark the quality of four regridding libraries currently used in ESMs, i.e., SCRIP, YAC, ESMF and XIOS. We evaluated five regridding algorithms with four different analytical functions for different combinations of six grids used in real ocean or atmosphere models. Four analytical functions were used to define the coupling fields to be regridded. This benchmark calculated some of the metrics proposed by the CANGA project, including the mean, maximum, RMS misfit, and global conservation. The results show that, besides a few very specific cases that present anomalous values, the regridding functionality in YAC, ESMF and XIOS can be considered of high quality and do not present the specific problems observed for the conservative SCRIP remapping. The evaluation of the computing performance of those libraries is not included in the current work but is planned to be performed in the coming months. This exercise shows that benchmarking can be a great opportunity to favour interactions between users and developers of regridding libraries.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-01
      DOI: 10.3390/mca27020031
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 32: On the Prediction of Evaporation in Arid Climate
           Using Machine Learning Model

    • Authors: Mansura Jasmine, Abdolmajid Mohammadian, Hossein Bonakdari
      First page: 32
      Abstract: Evaporation calculations are important for the proper management of hydrological resources, such as reservoirs, lakes, and rivers. Data-driven approaches, such as adaptive neuro fuzzy inference, are getting popular in many hydrological fields. This paper investigates the effective implementation of artificial intelligence on the prediction of evaporation for agricultural area. In particular, it presents the adaptive neuro fuzzy inference system (ANFIS) and hybridization of ANFIS with three optimizers, which include the genetic algorithm (GA), firefly algorithm (FFA), and particle swarm optimizer (PSO). Six different measured weather variables are taken for the proposed modelling approach, including the maximum, minimum, and average air temperature, sunshine hours, wind speed, and relative humidity of a given location. Models are separately calibrated with a total of 86 data points over an eight-year period, from 2010 to 2017, at the specified station, located in Arizona, United States of America. Farming lands and humid climates are the reason for choosing this location. Ten statistical indices are calculated to find the best fit model. Comparisons shows that ANFIS and ANFIS–PSO are slightly better than ANFIS–FFA and ANFIS–GA. Though the hybrid ANFIS–PSO (R2= 0.99, VAF = 98.85, RMSE = 9.73, SI = 0.05) is very close to the ANFIS (R2 = 0.99, VAF = 99.04, RMSE = 8.92, SI = 0.05) model, preference can be given to ANFIS, due to its simplicity and easy operation.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-04-05
      DOI: 10.3390/mca27020032
      Issue No: Vol. 27, No. 2 (2022)
  • MCA, Vol. 27, Pages 4: Taming Hyperchaos with Exact Spectral Derivative
           Discretization Finite Difference Discretization of a Conformable
           Fractional Derivative Financial System with Market Confidence and Ethics

    • Authors: Dominic P. Clemence-Mkhope, Gregory A. Gibson
      First page: 4
      Abstract: Four discrete models, using the exact spectral derivative discretization finite difference (ESDDFD) method, are proposed for a chaotic five-dimensional, conformable fractional derivative financial system incorporating ethics and market confidence. Since the system considered was recently studied using the conformable Euler finite difference (CEFD) method and found to be hyperchaotic, and the CEFD method was recently shown to be valid only at fractional index α=1, the source of the hyperchaos is in question. Through numerical experiments, illustration is presented that the hyperchaos previously detected is, in part, an artifact of the CEFD method, as it is absent from the ESDDFD models.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-10
      DOI: 10.3390/mca27010004
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 5: Analysis and Detection of Erosion in Wind Turbine

    • Authors: Josué Enríquez Zárate, María de los Ángeles Gómez López, Javier Alberto Carmona Troyo, Leonardo Trujillo
      First page: 5
      Abstract: This paper studies erosion at the tip of wind turbine blades by considering aerodynamic analysis, modal analysis and predictive machine learning modeling. Erosion can be caused by several factors and can affect different parts of the blade, reducing its dynamic performance and useful life. The ability to detect and quantify erosion on a blade is an important predictive maintenance task for wind turbines that can have broad repercussions in terms of avoiding serious damage, improving power efficiency and reducing downtimes. This study considers both sides of the leading edge of the blade (top and bottom), evaluating the mechanical imbalance caused by the material loss that induces variations of the power coefficient resulting in a loss in efficiency. The QBlade software is used in our analysis and load calculations are preformed by using blade element momentum theory. Numerical results show the performance of a blade based on the relationship between mechanical damage and aerodynamic behavior, which are then validated on a physical model. Moreover, two machine learning (ML) problems are posed to automatically detect the location of erosion (top of the edge, bottom or both) and to determine erosion levels (from 8% to 18%) present in the blade. The first problem is solved using classification models, while the second is solved using ML regression, achieving accurate results. ML pipelines are automatically designed by using an AutoML system with little human intervention, achieving highly accurate results. This work makes several contributions by developing ML models to both detect the presence and location of erosion on a blade, estimating its level and applying AutoML for the first time in this domain.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-13
      DOI: 10.3390/mca27010005
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 6: AutoML for Feature Selection and Model Tuning
           Applied to Fault Severity Diagnosis in Spur Gearboxes

    • Authors: Mariela Cerrada, Leonardo Trujillo, Daniel E. Hernández, Horacio A. Correa Zevallos, Jean Carlo Macancela, Diego Cabrera, René Vinicio Sánchez
      First page: 6
      Abstract: Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to different types of decision tree methods, with ensembles of XGBoost models preferred by H2O while TPOT generated different types of stacked models. The models produced by both systems achieved very high, and practically equivalent, performances on all problems. Both AutoML systems converged to pipelines that focus on very similar subsets of features across all problems, indicating that several problems in this domain can be solved by a rather small set of 10 common features, with accuracy up to 90%. This latter result is important in the research of useful feature selection for gearbox fault diagnosis.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-13
      DOI: 10.3390/mca27010006
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 7: Approximating the Steady-State Temperature of 3D
           Electronic Systems with Convolutional Neural Networks

    • Authors: Monika Stipsitz, Hèlios Sanchis-Alepuz
      First page: 7
      Abstract: Thermal simulations are an important part of the design process in many engineering disciplines. In simulation-based design approaches, a considerable amount of time is spent by repeated simulations. An alternative, fast simulation tool would be a welcome addition to any automatized and simulation-based optimisation workflow. In this work, we present a proof-of-concept study of the application of convolutional neural networks to accelerate thermal simulations. We focus on the thermal aspect of electronic systems. The goal of such a tool is to provide accurate approximations of a full solution, in order to quickly select promising designs for more detailed investigations. Based on a training set of randomly generated circuits with corresponding finite element solutions, the full 3D steady-state temperature field is estimated using a fully convolutional neural network. A custom network architecture is proposed which captures the long-range correlations present in heat conduction problems. We test the network on a separate dataset and find that the mean relative error is around 2% and the typical evaluation time is 35 ms per sample (2 ms for evaluation, 33 ms for data transfer). The benefit of this neural-network-based approach is that, once training is completed, the network can be applied to any system within the design space spanned by the randomized training dataset (which includes different components, material properties, different positioning of components on a PCB, etc.).
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-14
      DOI: 10.3390/mca27010007
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 8: Fractional Modeling of Viscous Fluid over a
           Moveable Inclined Plate Subject to Exponential Heating with Singular and
           Non-Singular Kernels

    • Authors: Aziz Ur Rehman, Muhammad Bilal Riaz, Wajeeha Rehman, Jan Awrejcewicz, Dumitru Baleanu
      First page: 8
      Abstract: In this paper, a new approach to investigating the unsteady natural convection flow of viscous fluid over a moveable inclined plate with exponential heating is carried out. The mathematical modeling is based on fractional treatment of the governing equation subject to the temperature, velocity and concentration field. Innovative definitions of time fractional operators with singular and non-singular kernels have been working on the developed constitutive mass, energy and momentum equations. The fractionalized analytical solutions based on special functions are obtained by using Laplace transform method to tackle the non-dimensional partial differential equations for velocity, mass and energy. Our results propose that by increasing the value of the Schimdth number and Prandtl number the concentration and temperature profiles decreased, respectively. The presence of a Prandtl number increases the thermal conductivity and reflects the control of thickness of momentum. The experimental results for flow features are shown in graphs over a limited period of time for various parameters. Furthermore, some special cases for the movement of the plate are also studied and results are demonstrated graphically via Mathcad-15 software.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-19
      DOI: 10.3390/mca27010008
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 9: Numerical Study on Mixed Convection Flow and Energy
           Transfer in an Inclined Channel Cavity: Effect of Baffle Size

    • Authors: Sivanandam Sivasankaran, Kandasamy Janagi
      First page: 9
      Abstract: The objective of the current numerical study is to explore the combined natural and forced convection and energy transport in a channel with an open cavity. An adiabatic baffle of finite length is attached to the top wall. The sinusoidal heating is implemented on the lower horizontal wall of the open cavity. The other areas of the channel cavity are treated as adiabatic. The governing equations are solved by the control volume technique for various values of relevant factors. The drag force, bulk temperature and average Nusselt number are computed. It is recognised that recirculating eddies beside the baffle become weak or disappear upon increasing the inclination angle of the channel/cavity. The average thermal energy transportation reduces steadily until the Ri = 1 and then it rises for all inclination angles and lengths of the baffle.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-23
      DOI: 10.3390/mca27010009
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 10: Numerical and Theoretical Stability Study of a
           Viscoelastic Plate Equation with Nonlinear Frictional Damping Term and a
           Logarithmic Source Term

    • Authors: Mohammad M. Al-Gharabli, Adel M. Almahdi, Maher Noor, Johnson D. Audu
      First page: 10
      Abstract: This paper is designed to explore the asymptotic behaviour of a two dimensional visco-elastic plate equation with a logarithmic nonlinearity under the influence of nonlinear frictional damping. Assuming that relaxation function g satisfies g′(t)≤−ξ(t)G(g(t)), we establish an explicit general decay rates without imposing a restrictive growth assumption on the damping term. This general condition allows us to recover the exponential and polynomial rates. Our results improve and extend some existing results in the literature. We preform some numerical experiments to illustrate our theoretical results.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-28
      DOI: 10.3390/mca27010010
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 11: Impact of Infective Immigrants on COVID-19

    • Authors: Stéphane Yanick Tchoumi, Herieth Rwezaura, Mamadou Lamine Diagne, Gilberto González-Parra, Jean Tchuenche
      First page: 11
      Abstract: The COVID-19 epidemic is an unprecedented and major social and economic challenge worldwide due to the various restrictions. Inflow of infective immigrants have not been given prominence in several mathematical and epidemiological models. To investigate the impact of imported infection on the number of deaths, cumulative infected and cumulative asymptomatic, we formulate a mathematical model with infective immigrants and considering vaccination. The basic reproduction number of the special case of the model without immigration of infective people is derived. We varied two key factors that affect the transmission of COVID-19, namely the immigration and vaccination rates. In addition, we considered two different SARS-CoV-2 transmissibilities in order to account for new more contagious variants such as Omicron. Numerical simulations using initial conditions approximating the situation in the US when the vaccination program was starting show that increasing the vaccination rate significantly improves the outcomes regarding the number of deaths, cumulative infected and cumulative asymptomatic. Other factors are the natural recovery rates of infected and asymptomatic individuals, the waning rate of the vaccine and the vaccination rate. When the immigration rate is increased significantly, the number of deaths, cumulative infected and cumulative asymptomatic increase. Consequently, accounting for the level of inflow of infective immigrants may help health policy/decision-makers to formulate policies for public health prevention programs, especially with respect to the implementation of the stringent preventive lock down measure.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-01-29
      DOI: 10.3390/mca27010011
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 12: The Unit Teissier Distribution and Its

    • Authors: Anuresha Krishna, Radhakumari Maya, Christophe Chesneau, Muhammed Rasheed Irshad
      First page: 12
      Abstract: A bounded form of the Teissier distribution, namely the unit Teissier distribution, is introduced. It is subjected to a thorough examination of its important properties, including shape analysis of the main functions, analytical expression for moments based on upper incomplete gamma function, incomplete moments, probability-weighted moments, and quantile function. The uncertainty measures Shannon entropy and extropy are also performed. The maximum likelihood estimation, least square estimation, weighted least square estimation, and Bayesian estimation methods are used to estimate the parameters of the model, and their respective performances are assessed via a simulation study. Finally, the competency of the proposed model is illustrated by using two data sets from diverse fields.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-01
      DOI: 10.3390/mca27010012
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 13: Acknowledgment to Reviewers of MCA in 2021

    • Authors: MCA Editorial Office MCA Editorial Office
      First page: 13
      Abstract: Rigorous peer-reviews are the basis of high-quality academic publishing [...]
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-07
      DOI: 10.3390/mca27010013
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 14: Arbitrarily Accurate Analytical Approximations for
           the Error Function

    • Authors: Roy M. Howard
      First page: 14
      Abstract: A spline-based integral approximation is utilized to define a sequence of approximations to the error function that converge at a significantly faster manner than the default Taylor series. The real case is considered and the approximations can be improved by utilizing the approximation erf(x)≈1 for x >xo and with xo optimally chosen. Two generalizations are possible; the first is based on demarcating the integration interval into m equally spaced subintervals. The second, is based on utilizing a larger fixed subinterval, with a known integral, and a smaller subinterval whose integral is to be approximated. Both generalizations lead to significantly improved accuracy. Furthermore, the initial approximations, and those arising from the first generalization, can be utilized as inputs to a custom dynamic system to establish approximations with better convergence properties. Indicative results include those of a fourth-order approximation, based on four subintervals, which leads to a relative error bound of 1.43 × 10−7 over the interval [0, ∞]. The corresponding sixteenth-order approximation achieves a relative error bound of 2.01 × 10−19. Various approximations that achieve the set relative error bounds of 10−4, 10−6, 10−10, and 10−16, over [0, ∞], are specified. Applications include, first, the definition of functions that are upper and lower bounds, of arbitrary accuracy, for the error function. Second, new series for the error function. Third, new sequences of approximations for exp(−x2) that have significantly higher convergence properties than a Taylor series approximation. Fourth, the definition of a complementary demarcation function eC(x) that satisfies the constraint eC2(x)+erf2(x)=1. Fifth, arbitrarily accurate approximations for the power and harmonic distortion for a sinusoidal signal subject to an error function nonlinearity. Sixth, approximate expressions for the linear filtering of a step signal that is modeled by the error function.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-09
      DOI: 10.3390/mca27010014
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 15: Multi-Physics Inverse Homogenization for the
           Design of Innovative Cellular Materials: Application to Thermo-Elastic

    • Authors: Matteo Gavazzoni, Nicola Ferro, Simona Perotto, Stefano Foletti
      First page: 15
      Abstract: We present a new algorithm to design lightweight cellular materials with required properties in a multi-physics context. In particular, we focus on a thermo-elastic setting by promoting the design of unit cells characterized both by an isotropic and an anisotropic behavior with respect to mechanical and thermal requirements. The proposed procedure generalizes the microSIMPATY algorithm to a thermo-elastic framework by preserving all the good properties of the reference design methodology. The resulting layouts exhibit non-standard topologies and are characterized by very sharp contours, thus limiting the post-processing before manufacturing. The new cellular materials are compared with the state-of-art in engineering practice in terms of thermo-elastic properties, thus highlighting the good performance of the new layouts which, in some cases, outperform the consolidated choices.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-15
      DOI: 10.3390/mca27010015
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 16: The Minimum Lindley Lomax Distribution: Properties
           and Applications

    • Authors: Sadaf Khan, Gholamhossein G. Hamedani, Hesham Mohamed Reyad, Farrukh Jamal, Shakaiba Shafiq, Soha Othman
      First page: 16
      Abstract: By fusing the Lindley and Lomax distributions, we present a unique three-parameter continuous model titled the minimum Lindley Lomax distribution. The quantile function, ordinary and incomplete moments, moment generating function, Lorenz and Bonferroni curves, order statistics, Rényi entropy, stress strength model, and stochastic sequencing are all carefully examined as basic statistical aspects of the new distribution. The characterizations of the new model are investigated. The proposed distribution’s parameters were evaluated using the maximum likelihood procedures. The stability of the parameter estimations is explored using a Monte Carlo simulation. Two applications are used to objectively assess the new model’s extensibility.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-18
      DOI: 10.3390/mca27010016
      Issue No: Vol. 27, No. 1 (2022)
  • MCA, Vol. 27, Pages 17: On a Modified Weighted Exponential Distribution
           with Applications

    • Authors: Christophe Chesneau, Vijay Kumar, Mukti Khetan, Mohd Arshad
      First page: 17
      Abstract: Practitioners in all applied domains value simple and adaptable lifetime distributions. They make it possible to create statistical models that are relatively easy to manage. A novel simple lifetime distribution with two parameters is proposed in this article. It is based on a parametric mixture of the exponential and weighted exponential distributions, with a mixture weight depending on a parameter of the involved distribution; no extra parameter is added in this mixture operation. It can also be viewed as a special generalized mixture of two exponential distributions. This decision is based on sound mathematical and physical reasoning; the weight modification allows us to combine some joint properties of the exponential and weighted exponential distribution, which are known as complementary in several modeling aspects. As a result, the proposed distribution may have a decreasing or unimodal probability density function and possess the demanded increasing hazard rate property. Other properties are studied, such as the moments, Bonferroni and Lorenz curves, Rényi entropy, stress-strength reliability, and mean residual life function. Subsequently, a part is devoted to the associated model, which demonstrates how it can be used in a real-world statistical scenario involving data. In this regard, we demonstrate how the estimated model performs well using five different estimation methods and simulated data. The analysis of two data sets demonstrates these excellent results. The new model is compared to the weighted exponential, Weibull, gamma, and generalized exponential models for performance. The obtained comparison results are overwhelmingly in favor of the proposed model according to some standard criteria.
      Citation: Mathematical and Computational Applications
      PubDate: 2022-02-21
      DOI: 10.3390/mca27010017
      Issue No: Vol. 27, No. 1 (2022)
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