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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 574-588页

作者： Mochocki, Sean A. Reith, Mark G. Borghetti, Brett J. Peterson, Gilbert L. Jasper, John D. Merkle, Laurence D. Air Force Institute of Technology Department of Electrical and Computer Engineering Wright-Patterson AFBOH45433 United States Air Force Institute of Technology Department of Mathematics and Statistics Wright-Patterson AFBOH45433 United States

E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of the PLP variations, researchers risk making invalid comparisons and conclusions when they use different metaheuristics for different PLP problems. To unify this conversation, this article formally proves the NP-completeness of two common PLP variations and their generalizations and uses them to categorize recent research in the PLP field. It then presents an instance of the PLP problem using real-world data and shows how this instance can be cast into two different NP-complete variations. This article then presents three artificial intelligence (AI) strategies, solving one of the PLP variations with back-tracking and branch and bound heuristics and also converting the PLP variation instance to XCSP{}^{3}, an intermediate constraint satisfaction language to be resolved with a general constraint optimization solver. This article solves the other PLP variation instance using a greedy search heuristic. The article finishes by comparing the results of the two different PLP variations. © 2024 The Authors.

关键词： Contrastive Learning

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Analysis of entropy production in Darcy-Forchheimer peristaltic motion of SWCNTs with Newtonian heating and nonlinear buoyancy forces effects

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Multiscale and Multidisciplinary Modeling, Experiments and Design 2025年第6期8卷 1-12页

作者： Farooq, Shahid Fatima, Iqra Imtiaz, Maria Faisal, Rasan Sarbast Department of Mathematics and Statistics Riphah International University I-14 Islamabad44000 Pakistan Department of Humanities and Sciences National University of Medical Sciences Rawalpindi46000 Pakistan Department of Petroleum Engineering College of Engineering Knowledge University Erbil44001 Iraq

This article incorporates the Darcy Frochheimer peristalsis of CNTs-H2O through tapered half width channel. Here Both linear and nonlinear porous media and mixed convection characteristics are used to carry out fluid flow. Heat generation/absorption, frictional dissipation, Newtonian heating, and linear thermal radiation are all taken into account in heat transfer analysis. In the presence of thermal radiation, analysis of irreversibility caused by heat and fluid friction is also considered through entropy. The large wave-length technique simplifies the flow-formulated system of equations. Then this resulting system is managed through built-in numerical procedure in Mathematica. This built-in method is working under shooting and Rung-Kutta Fehlberg formulas. The graphical analysis of temperature, velocity, pressure gradient, rate of effective heat transfer, and entropy production is thus provided by these numerical results. For both linear and nonlinear permeability parameters, the obtained graphical data demonstrate the opposite behavior of the velocity and pressure gradient. A higher volume proportion of CNTs results in an increase in both Bejan number and entropy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Mixed convection

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Simultaneous segmentation and classification of lung CT scans for COVID-19 diagnosis: a deep multi-task learning perspective

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Neural Computing and Applications 2025年第6期37卷 4185-4205页

作者： Kordnoori, Shirin Sabeti, Maliheh Mostafaei, Hamidreza Banihashemi, Saeed Seyed Agha Department of Computer Engineering North Tehran Branch Islamic Azad University Tehran Iran Department of Statistics North Tehran Branch Islamic Azad University Tehran Iran Department of Mathematics North Tehran Branch Islamic Azad University Tehran Iran

In response to the pressing need for intelligent systems to identify COVID-19 cases and detect infected areas in lung CT scan images during the ongoing pandemic, there is a growing demand for integrated solutions that can effectively handle both segmentation and classification tasks simultaneously. Such systems can provide valuable support to clinicians while enhancing diagnostic accuracy by leveraging shared features. This paper introduces a multi-task architecture designed to concurrently process lung CT scan images for both segmentation and classification tasks. The proposed approach initially employs a shared encoder–decoder architecture based on U-Net, augmented by an additional branch dedicated to classification using a perceptron. To address performance disparities between these tasks during preprocessing, a combination of binary preprocessing algorithms is introduced to establish task equilibrium. Furthermore, convolution block attention module is incorporated into the encoder levels to enhance inter-task coherence. Additionally, the application of conditional random field serves for post-processing in the segmentation task. The effectiveness of the proposed structure is evaluated across four datasets, demonstrating its superiority over previous studies and pre-trained models. The results showcase the highest classification accuracy at 98.14 ± 1.00, accompanied by a Dice index of 89.91 ± 2.00 in segmentation, alongside other relevant evaluation metrics. Furthermore, the multi-task framework is successfully applied to U-Net++ and ResUNet architectures, yielding favorable outcomes. Our proposed model provides a robust solution for accurate COVID-19 analysis, offering valuable support to medical professionals in making diagnostic and treatment decisions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： COVID-19

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Mathematical Dynamics of Meningococcal Meningitis: Examining Carrier Diagnosis and Prophylaxis Treatment

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International Journal of Applied and Computational mathematics 2025年第2期11卷 1-29页

作者： Mustapha, Umar Tasiu Muhammad, Aliyu Iliyasu Yusuf, Abdullahi Althobaiti, Nesreen Aliyu, Aliyu Isa Andrawus, James Department of Mathematics Federal University Dutse Jigawa 7156 Nigeria Department of Mathematics Firat University Elazig Turkey Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai 602105 India Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Department of Mathematics and Statistics College of Science Taif University Taif 11099 Saudi Arabia Operational Research Centre in Healthcare Near East University TRNC Nicosia Turkey

Meningococcal meningitis remains a persistent public health challenge with widespread implications. This paper delves into the sometimes-underrated carrier class, comprising approximately 10% of the population, recognized as the primary source of the overall disease transmission. The challenge lies in the complex task of identifying these asymptomatic carriers in real-world scenarios, as they appear healthy, thus diminishing their incentive for treatment. This study underscores the necessity of proactive measures for individuals who have come into contact with a meningitis patient, emphasizing the importance of meningitis testing or CDC-recommended chemoprophylaxis treatment. In this study, we design a dynamic of Meningococcal Meningitis outbreak, incorporating vaccination, undiagnosed carriers and hospitalisation. We compute basic reproduction number using the generalised next-generation matrix approach and the analysis shows that, the model is globally asymptotically stable for both the DFE and Endemic equilibria. Further analysis indicates the possibility of a backward bifurcation for the system when R0<1. The findings of sensitivity analysis suggest that public health campaigns should prioritize increasing awareness about the significance of diagnosis for carriers of meningococcal meningitis and prophylaxis treatment. This measure will have a significant effect and substantially reduce the spread of the disease. Optimal control analysis shows that, the awareness campaign plays a significant role in controlling the spread of the disease in the community. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2025.

关键词： Basic reproduction number Chemoprophylaxis Diagnosed carriers Meningococcal meningitis Undiagnosed carriers

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The mathematics of mathematics: Using mathematics and Data Science to Analyze the Mathematical Sciences Community and Enhance Social Justice

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Matematica 2025年第1期4卷 110-125页

作者： Buckmire, Ron Hibdon, Joseph E. Lewis, Drew Ortega, Omayra Pabón, José L. Roca, Rachel Vindas-Meléndez, Andrés R. Mathematics Department Occidental College Los Angeles CA United States Department of Mathematics Northeastern Illinois University Chicago IL United States Independent Mathematician and Education Researcher Palo Alto CA United States Department of Mathematics & Statistics Sonoma State University Rohnert Park CA United States Mathematical Sciences Department New Jersey Institute of Technology Newark NJ United States Department of Computational Mathematics Science and Engineering Michigan State University East Lansing MI United States Department of Mathematics University of California Berkeley CA United States Department of Mathematics Harvey Mudd College Claremont CA United States

We present and discuss a curated selection of recent literature related to the application of quantitative techniques, tools, and topics from mathematics and data science that have been used to analyze the mathematical sciences community. We engage in this project with a focus on including research that highlights, documents, or quantifies inequality that exists in the mathematical sciences, specifically, and STEM (science, technology, engineering, and mathematics) more broadly. We seek to enhance social justice in the mathematics and data science communities by providing numerous examples of the ways in which the mathematical sciences fails to meet standards of equity, equal opportunity, and inclusion. We introduce the term “mathematics of mathematics” for this project, explicitly building upon the growing, interdisciplinary field known as “Science of Science” to interrogate, investigate, and identify the nature of the mathematical sciences itself. We aim to promote, provide, and posit sources of productive collaborations and we invite interested researchers to contribute to this developing body of work. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data science quantitative justice Diversity Inequities Mathematical sciences community Social justice

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Modeling prevalence of meningitis control strategies through evaluating with available data on meningitis cases reported in Nigeria

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Modeling Earth Systems and Environment 2025年第4期11卷 1-22页

作者： Peter, Olumuyiwa James Oguntolu, Festus Abiodun Nyerere, Nkuba El-Mesady, A. Department of Mathematics Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai 602105 India Department of Mathematical and Computer Sciences University of Medical Sciences Ondo State Ondo City Nigeria Department of Epidemiology and Biostatistics School of Public Health University of Medical Sciences Ondo State Ondo City Nigeria Department of Mathematics Federal University of Technology Minna Niger State Minna Nigeria Department of Mathematics and Statistics Sokoine University of Agriculture Morogoro Tanzania Department of Physics and Engineering Mathematics Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

Meningitis is a major public health concern, especially in developing nations, due to its devastating consequences for human health. Although modeling studies have examined disease transmission dynamics, little attention has been paid to how control strategies affect the behavior of different population groups, including carriers, symptomatic individuals, hospitalized patients, and those in intensive care. This study proposes a computational framework that compares the effectiveness of vaccination of people at risk of the disease versus treating symptomatic infected persons. The basic reproduction number is used to evaluate the equilibrium points. Assess the precision of the proposed model’s illustration to data. We fit the meningitis model using the information at our disposal on meningitis cases reported in Nigeria from the first week of January to the last week of December 2023;this was obtained from the Nigerian Center for Disease Control (NCDC) database. We also performed a sensitivity analysis using a normalized forward sensitivity index to see which parameters had significant effects on the effective reproduction number. The results of both analytical techniques and numerical simulations reveal that recruitment rate, vaccination, progression from carrier to symptomatic stages, and disease-induced death all significantly reduce the incidence and prevalence of meningitis in the community. The study findings could be used to inform decisions about meningitis control initiatives. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Control measures Meningitis Model simulations Reproduction number Stability analysis

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State-Dependent Switching Control for Nonlinear Impulsive Switched Systems With Dwell Time

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IEEE Transactions on Automation Science and engineering 2025年 22卷 12093-12101页

作者： Yang, Dan Li, Xiaodi Zhang, Guofeng Lee, Heung Wing Joseph Shandong Normal University School of Mathematics and Statistics Jinan250014 China Yantai University School of Mathematics and Information Science Yantai264005 China Shandong Normal University Shandong Provincial Engineering Research Center of System Control and Intelligent Technology Jinan250358 China The Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen518057 China

This paper investigates the problem of globally exponential stability (GES) for impulsive switched systems via a hybrid switching strategy. First, a switching law combining state-dependent and time-dependent (dwell time) switching strategies is presented in the framework of impulse action. On the basis of the switching law, some GES criteria are proposed by using multiple Lyapunov function technique. A potential relationship among system parameters, impulse action including impulse amplitude and impulse frequency, and the combined switching law is established, which is of vital importance to achieving GES. In addition, when the impulse action involved in switched system is given in terms of stabilizing impulse, the impulsive controller including impulsive gains and impulsive frequency can be designed with the help of the proposed switching law to guarantee the stability of the addressed system. Finally, a simulation example is demonstrated to verify the validity of the proposed switching law and corresponding results. © 2004-2012 IEEE.

关键词： Switches Switched systems Control systems Stability criteria Symmetric matrices Automation Lyapunov methods Germanium Electronic mail Trajectory

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Improved SRM Classification Using a GA-Optimized Neural Network Ensemble

Improved SRM Classification Using a GA-Optimized Neural Netw...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Dimaggio, Griffin A. Hartfield, Roy J. Carpenter, Mark Department of Aerospace Engineering Auburn University AuburnAL36832 United States Department of Mathematics and Statistics Auburn University AuburnAL36832 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper develops neural network ensembles to perform solid rocket motor classification based on trajectory data. Two classes of solid rocket motors are defined and fly-out data was generated using a 6-DOF code. Three cases were considered, each with varying levels of similarity between the two classes. The individual model architectures are optimized with a genetic-algorithm and comparisons are made with unoptimized ensembles. Ensembles consisting of optimized models achieved a few percent increase in classification accuracy over the best individual model accuracies. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Genetic algorithms

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Improved Sparse Coded Features for Automatic Identification and Discrimination of Exudates and Drusen in Retinal Fundus Images

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SN Computer Science 2025年第1期6卷 1-12页

作者： Kumar, Mukesh Rani, Kumi Department of Mathematics and Statistics University of Houston-Downtown Houston 77002 TX United States Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India

Diabetic Retinopathy is a medical condition in people having diabetes in which damage occurs to the retina and can cause partial or complete vision loss. It is the most common reason for vision loss in the world. Diabetic Retinopathy is characterized by the appearance of retinal lesions such as microaneurysms, exudates, cotton wool spots, and haemorrhages. Age-Related Macular Degeneration (ARMD) is another eye disease which causes gradual loss of vision with age and is characterized by the appearance of drusen. Exudates and Drusen are known as bright lesions as they appear as yellow deposits in the retina. In this paper, we propose an automated method for the classification of bright lesions in retinal fundus images based on improved sparse coded features and Support Vector Machine (SVM). We employ Scale-Invariant Feature Transform (SIFT) to extract interesting regions in the image in the form of key points and extract local low-level features in the neighbourhoods of key-points. After that, we use the sparse coding technique to get high-level sparse coded features that are used for classification using SVM. Experimental results reveal that the proposed approach gives an accuracy, sensitivity, a specificity of 92.90%, 89.60%, 95.40% for exudate classification and 93.80%, 89.60%, 95.80% for drusen classification, with a dictionary of 300 atoms on a dataset of 1158 images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diabetic retinopathy Drusen Exudates Region of interest Retinal fundus images Sparse coding

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ON THE GENERALIZATION ABILITY OF COARSE-GRAINED MOLECULAR DYNAMICS MODELS FOR NONEQUILIBRIUM PROCESSES

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Multiscale Modeling and Simulation 2025年第2期23卷 816-837页

作者： Lyu, Liyao Lei, Huan Department of Computational Mathematics Science \& Engineering Michigan State University East Lansing 48824 MI United States Department of Statistics \& Probability Michigan State University East Lansing 48824 MI United States

One essential goal of constructing coarse-grained molecular dynamics (CGMD) models is to accurately predict nonequilibrium processes beyond the atomistic scale. While a CG model can be constructed by projecting the full dynamics onto a set of resolved variables, the dynamics of the CG variables can recover the full dynamics only when the conditional distribution of the unresolved variables is close to the one associated with the particular projection operator. In particular, the model's applicability to various nonequilibrium processes is generally unwarranted due to the inconsistency in the conditional distribution. Here, we present a data-driven approach for constructing CGMD models that retain certain generalization ability for nonequilibrium processes. Unlike the conventional CG models based on preselected CG variables (e.g., the center of mass), the present CG model seeks a set of auxiliary CG variables similar to the time-lagged independent component analysis to maximize the velocity correlation. This effectively minimizes the entropy contribution of unresolved variables and ensures the distribution under a broad range of nonequilibrium conditions approaches the one under equilibrium. Numerical results of a polymer melt system demonstrate the significance of this broadly overlooked metric for the model's generalization ability, and the effectiveness of the present CG model for predicting the complex viscoelastic responses under various nonequilibrium flows. Copyright © by SIAM.

关键词： coarse graining machine learning molecular dynamics multiscale modeling

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