Journal of Computational Mathematics and Data Science最新文献

{"title":"Capturing patterns and radical changes in long-distance mobility by Flickr data","authors":"Anton Galich","doi":"10.1016/j.jcmds.2025.100122","DOIUrl":"10.1016/j.jcmds.2025.100122","url":null,"abstract":"<div><div>In contrast to daily travel behaviour, long-distance mobility constitutes a poorly understood area in transport research. Only few national household travel surveys include sections on long-distance travel and these usually focus on the trip to the destination without gathering information about mobility behaviour at the destination. Other sources of data on mobility are either restricted to the national level such as cell phone data or to specific modes of transport such as international flight statistics or floating car data. In addition, the outbreak of the COVID-19 pandemic in 2020 has illustrated how difficult it is to grasp abrupt changes in mobility behaviour.</div><div>Against this background this paper investigates the potential of Flickr data for capturing patterns and radical changes in long-distance mobility. Flickr is a social media online platform allowing its users to upload photos and to comment on their own and other users’ photos. It is mainly used for sharing holiday and travel experiences. The results show that Flickr constitutes a viable source of data for capturing patterns and radical changes in long-distance mobility. The distribution of the travel distances, the travel destinations as well as reduction of the mileage of all holiday trips in 2020 in comparison to 2019 due to the pandemic calculated on the basis of the Flickr data is very similar to the same indicators determined on the basis of a national household travel survey, official passenger flight statistics, and other official transportation statistics.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100122"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An in-depth analysis of the IRPSM-Padé algorithm for solving three-dimensional fluid flow problems","authors":"Abdullah Dawar ,&nbsp;Hamid Khan ,&nbsp;Muhammad Ullah","doi":"10.1016/j.jcmds.2025.100123","DOIUrl":"10.1016/j.jcmds.2025.100123","url":null,"abstract":"<div><div>In this article, a comparative analysis of the IRPSM-Padé and DTM-Padé methods has been conducted by solving the fluid flow problem over a bi-directional extending sheet. The fluid flow is expressed by the partial differential equations (PDEs) which are then converted to ordinary differential equations (ODEs) by mean of similarity variables. Both the IRPSM-Padé and DTM-Padé methods are tested at [3,3] and [6,6] Padé approximants. Tables and Figures are used to examine the outcomes and show the consistency and accuracy of both approaches. The outcomes of IRPSM-Padé [3,3] and [6,6] with the same order of approximations closely match the outcomes of DTM-Padé [3,3] and [6,6] using Padé approximants. The significant degree of agreement between the two methods indicates that IRPSM-Padé and DTM-Padé handle the fluid flow problem in a comparable manner. The findings of the IRPSM-Padé and DTM-Padé methods show a strong degree of agreement, indicating the accuracy and dependability of the more recent technique (IRPSM-Padé). The obtained CPU time shows that the DTM consistently perform better that IRPSM in terms of computational efficiency. The total CPU time for IRPSM is nearly three-times greater than that of DTM, indicating that IRPSM demands more computational effort. The recorded times accurately reflect the computational efficiency of IRPSM and DTM because the Padé approximation simply improves the results rationalization and has no influence on CPU time. The residual errors analysis demonstrates that the IRPSM-Padé technique produces exceptionally precise approximations, with errors decreasing as the Padé order increases. Furthermore, the numerical assessment demonstrates that higher Padé orders improve the accuracy and stability of the IRPSM-Padé.</div></div><div><h3>Computational Implementation:</h3><div>Mathematica 14.1 was used to carry out numerical simulations, the DTM-Padé method, and the IRPSM-Padé method. Mathematica’s integrated symbolic and numerical solvers, including the ND Solve function for numerical validation, were used to solve the governing equations. Additionally, plots, such as mesh visualizations and absolute error graphs, were created using Mathematica’s built-in plotting capabilities without the usage of third-party programs.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"16 ","pages":"Article 100123"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On exact line search method for a polynomial matrix equation","authors":"Chacha Stephen Chacha","doi":"10.1016/j.jcmds.2025.100120","DOIUrl":"10.1016/j.jcmds.2025.100120","url":null,"abstract":"<div><div>In this work, we investigate the elementwise minimal non-negative (EMN) solution of the matrix polynomial equation using an exact line search (ELS) technique to enhance the convergence of the Newton method. Nonnegative solutions to matrix equations are essential in engineering, optimization, signal processing, and data mining, driving advancements and improving efficiency in these fields. While recent advancements in solving matrix equations with nonnegative constraints have emphasized iterative methods, optimization strategies, and theoretical developments, efficiently finding the EMN solution remains a significant challenge. The proposed method integrates the Newton method with an exact line search (ELS) strategy to accelerate convergence and improve solution accuracy. Numerical experiments demonstrate that this approach requires fewer iterations to reach the EMN solution compared to the standard Newton method. Moreover, the method shows improved stability, particularly when dealing with ill-conditioned input matrices and very small tolerance errors.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A neural network approach for solving the Monge–Ampère equation with transport boundary condition","authors":"Roel Hacking ,&nbsp;Lisa Kusch ,&nbsp;Koondanibha Mitra ,&nbsp;Martijn Anthonissen ,&nbsp;Wilbert IJzerman","doi":"10.1016/j.jcmds.2025.100119","DOIUrl":"10.1016/j.jcmds.2025.100119","url":null,"abstract":"<div><div>This paper introduces a novel neural network-based approach to solving the Monge–Ampère equation with the transport boundary condition, specifically targeted towards optical design applications. We leverage multilayer perceptron networks to learn approximate solutions by minimizing a loss function that encompasses the equation’s residual, boundary conditions, and convexity constraints. Our main results demonstrate the efficacy of this method, optimized using L-BFGS, through a series of test cases encompassing symmetric and asymmetric circle-to-circle, square-to-circle, and circle-to-flower reflector mapping problems. Comparative analysis with a conventional least-squares finite-difference solver reveals the competitive, and often superior, performance of our neural network approach on the test cases examined here. A comprehensive hyperparameter study further illuminates the impact of factors such as sampling density, network architecture, and optimization algorithm. While promising, further investigation is needed to verify the method’s robustness for more complicated problems and to ensure consistent convergence. Nonetheless, the simplicity and adaptability of this neural network-based approach position it as a compelling alternative to specialized partial differential equation solvers.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven market value prediction for European football players","authors":"Abdullah Tamim ,&nbsp;Md. Wadud Jahan ,&nbsp;Md. Rashid Shahriar Chowdhury ,&nbsp;Ahammad Hossain ,&nbsp;Md. Mizanur Rahman ,&nbsp;A.H.M. Rahmatullah Imon","doi":"10.1016/j.jcmds.2025.100118","DOIUrl":"10.1016/j.jcmds.2025.100118","url":null,"abstract":"<div><div>Football is globally recognized as the most widely practiced and watched sport. Precise player value is crucial for clubs seeking to maximize their player acquisition strategy and overall success in football. Conventional player valuation methodologies are mainly dependent on expert judgments and subjective assessments, missing the objectivity and precision provided by data-driven approaches. This study seeks to close this disparity by utilizing machine learning techniques to predict the market valuations of football players. The analysis is conducted using an extensive dataset sourced from the FIFA 22 video game, which was obtained via sofifa.com. The collection includes more than 16,000 players. The Machine Learning (ML) techniques used in this study are Multiple Linear Regression (MLR), Ridge Regression (RR), Support Vector Regression (SVR), and Random Forest Regression (RFR). The machine learning algorithms undergo training using 80% of the samples and are subsequently tested using the remaining 20% of the samples. We evaluate each algorithm’s performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) value. Numerical results show that the RFR model demonstrates superior performance by achieving the lowest MAE, MSE, RMSE, and the highest R² value across all samples. The RFR effectively captures non-linear interactions and reliably prevents overfitting. This research finding will enhance the existing knowledge in sports economics by demonstrating how ML can be used to anticipate the market prices of football players with better accuracy. This will provide football teams with valuable insights to make more strategic decisions.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved descent hybrid gradient-based projection algorithm for nonlinear equations and signal recovery problems","authors":"M. Koorapetse,&nbsp;P. Kaelo,&nbsp;T. Diphofu,&nbsp;S. Lekoko,&nbsp;T. Yane,&nbsp;B. Modise,&nbsp;C.R. Sam","doi":"10.1016/j.jcmds.2025.100117","DOIUrl":"10.1016/j.jcmds.2025.100117","url":null,"abstract":"<div><div>Derivative-free projection methods for solving nonlinear monotone equations have recently gained favor with researchers. Based on a hybrid conjugate gradient algorithm and the projection techniques, in this work, we present a descent derivative-free projection method for finding solutions to large-scale nonlinear monotone equations. The proposed method satisfies the descent condition and, under some suitable assumptions, its global convergence is established. The presented method’s efficacy is demonstrated through numerical experiments. Results show that, compared to other methods with similar structure, the method performs better. The method is further applied to an application in signal recovery, and it is proving to be efficient.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Universal approximation property of ODENet and ResNet with a single activation function","authors":"Masato Kimura ,&nbsp;Kazunori Matsui ,&nbsp;Yosuke Mizuno","doi":"10.1016/j.jcmds.2025.100116","DOIUrl":"10.1016/j.jcmds.2025.100116","url":null,"abstract":"<div><div>We study a universal approximation property of ODENet and ResNet. The ODENet is a map from an initial value to the final value of an ODE system in a finite interval. It is considered a mathematical model of a ResNet-type deep learning system. We consider dynamical systems with vector fields given by a single composition of the activation function and an affine mapping, which is the most common choice of the ODENet or ResNet vector field in actual machine learning systems. We demonstrate that both ODENets and ResNets with the restricted vector field of a single composition of the activation function and an affine mapping can uniformly approximate ODENets within the broader class that utilize a general vector field.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crafting a Player Impact Metric through analysis of football match event data","authors":"Mohamed Elsharkawi,&nbsp;Raja Hashim Ali,&nbsp;Talha Ali Khan","doi":"10.1016/j.jcmds.2025.100115","DOIUrl":"10.1016/j.jcmds.2025.100115","url":null,"abstract":"<div><div>The evaluation of football players remains a challenging task due to the limitations of existing rating models as well as the diverse nature of in-game actions and their varying impact on outcome of the matches, which often emphasize offensive actions while overlooking key defensive and strategic contributions. While some player impact metrics exist for football, their effectiveness, complete in-depth analysis, and relationship with match outcomes (win, loss, draw) has not been studied well. In this study, we have developed a Player Impact Metric (PIM) that provides a more comprehensive and data-driven assessment of player performance by incorporating match event data, Expected Goals (xG), Expected Threat (xT), and defensive contributions. The PIM framework assigns weighted scores to player actions using ordinal logistic regression based on their influence on match outcomes. The model evaluates player contributions using event-level data, integrating both offensive and defensive actions. The dataset is sourced from WhoScored, with structured data processing in PostgreSQL and analytical modeling techniques applied to derive impact scores. The PIM was tested against WhoScored Ratings, revealing notable differences in player rankings, particularly for defensive players. Our findings show that PIM provides a more balanced assessment, capturing critical non-scoring contributions that traditional rating systems tend to undervalue. We have introduced PIM as an advanced evaluation metric for football analytics, offering a data-driven, context-aware, and holistic approach to player performance assessment in this study. We show that the PIM can serve as a valuable tool for coaches, analysts, and scouts, enabling more accurate talent identification and match analysis.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"15 ","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring singularities in data with the graph Laplacian: An explicit approach","authors":"Martin Andersson,&nbsp;Benny Avelin","doi":"10.1016/j.jcmds.2025.100113","DOIUrl":"10.1016/j.jcmds.2025.100113","url":null,"abstract":"<div><div>We develop theory and methods that use the graph Laplacian to analyze the geometry of the underlying manifolds of datasets. Our theory provides theoretical guarantees and explicit bounds on the functional forms of the graph Laplacian when it acts on functions defined close to singularities of the underlying manifold. We use these explicit bounds to develop tests for singularities and propose methods that can be used to estimate geometric properties of singularities in the datasets.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"14 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penalizing Low-Rank Matrix Factorization: From theoretical connections to practical applications","authors":"Nicoletta Del Buono ,&nbsp;Flavia Esposito ,&nbsp;Laura Selicato","doi":"10.1016/j.jcmds.2025.100111","DOIUrl":"10.1016/j.jcmds.2025.100111","url":null,"abstract":"<div><div>Low-rank (LR) factorization techniques aim to represent data in a low-dimensional space by identifying fundamental sources. Standard LR approaches often require additional constraints to account for real-world complexity, resulting in penalized low-rank matrix factorizations. These techniques incorporate penalties or regularization terms to improve robustness and adaptability to practical constraints, bridging theoretical research with real-world applications.</div><div>This paper explores a nonnegative constrained low-rank decomposition technique, namely, Nonnegative Matrix Factorization (NMF), and its constrained variants as powerful tools for analyzing nonnegative data. We cover theoretical foundations and practical implementations, review algorithms for standard NMF, and address challenges in setting hyperparameters for penalized variants. We emphasize applications in omics data analysis with a model that incorporates biological constraints to extract meaningful insights, and highlight applications in environmental data analysis.</div></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"14 ","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}