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The Effect of Reduced Co Content in CrMnFeCoNi Alloys: A First Principles Study

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Metals 2025年第4期15卷 362-362页

作者： Johansson, Gustav Kuorak, Johan-Michael Gorbatov, Oleg Etz, Corina Applied Physics Division of Material Science Department of Engineering Sciences and Mathematics Luleå University of Technology Luleå97187 Sweden Faculty of Industrial Engineering Mechanical Engineering and Computer Science University of Iceland Sæmundargötu 2 Reykjavík102 Iceland

This study investigates high-entropy CrMnFeCoNi alloys with reduced Co content using density functional theory. The muffin-tin orbital method and coherent potential approximation successfully predict experimental values for volume, magnetic moment, and elastic constants. Thermodynamic properties, analyzed using the Debye–Gruneisen model, emphasize the need to consider both electronic and magnetic contributions to the free energy. The alloys exhibit anti-Invar behavior, with a significant increase in the linear thermal expansion coefficient with increased temperature. This effect is slightly more pronounced for reduced Co content, leading to a larger lattice parameter and a decrease in elastic constants. However, the changes are small, suggesting that similar mechanical properties can be achieved with lower Co content. © 2025 by the authors.

关键词： Thermal expansion

A Novel Methodology to Predict Flood and Landslide Disasters using Learning Assisted Artificial Intelligence (AI) Logic 3

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A Novel Methodology to Predict Flood and Landslide Disasters...

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Raj, G. Bhupal Patan, Muzeeb Khan Gupta, Ashwani Kumar Lakshmi, C. Gopinath, D. Yadav, Ajay Singh Sr University School of Agriculture Warangal India University of Technology and Applied Sciences College of Engineering and Technology Department of Eee Muscat Oman Chandigarh University Faculty of Business and Commerce Uttar Pradesh Unnao India Saveetha Engineering College Chennai India Department of Computer Science Karnataka Bengaluru India Srm Institute of Science and Technology Department of Mathematics Delhi-NCR Campus U.P Ghaziabad India

ISBN: (纸本)9798331509675

Comprehending the dynamics of flood and landslide prediction in current and adjacent regions is crucial for the formulation and advancement of effective predictive models, conservation, and management techniques. Conventional forecasting techniques, typically dependent on statistical models and empirical data, may prove inadequate because of the intrinsic complexities and variability of environmental elements. The complex interaction of climatic conditions, hydrological processes, geological attributes, and topographical aspects complicates precise forecasting. The constraints of traditional methods highlight the pressing necessity for more advanced and efficient predictive instruments. Artificial intelligence, especially machine learning (ML) and deep learning (DL), presents a robust paradigm that utilizes sophisticated algorithms to examine extensive datasets, discern intricate patterns, and produce more precise and prompt predictions. This proposed ViT-DL model prediction ability can greatly boost catastrophe preparedness, mitigation strategies, and ultimately, preserve lives and reduce economic damages. This study will comprehensively examine contemporary research trends, emphasizing the methodology used, model efficacy, related problems, and potential future research avenues in AI-based flood and landslide prediction. The experimental results of our proposed model produces 96.6% of Accuracy and 96.31% of Precision, comparision shows our model outperforms the overall accuracy to conventions model techniques. © 2025 IEEE.

关键词： Landslides

Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand 10

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Negative Binomial Regression Model for Predicting the Number...

10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Thepsumritporn, Sthaporn Jitpattanakul, Anuchit Mekruksavanich, Sakorn Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

This article examines the use of count data models to predict the number of tourists visiting Thailand's national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models - Poisson (POI), and Negative Binomial (NB) - were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data's overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model's utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data. © 2025 IEEE.

关键词： Logistic regression

Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set 10

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Classification Models and Variable Selection for SME Credit ...

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization

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arXiv 2025年

作者： Zhang, Jiayi Zhang, Yiming Zheng, Yuan Wang, Yuchen You, Jinjiang Xu, Yuchen Jiang, Wenxing Dev, Soumyabrata School of Mathematics and Applied Mathematics University of Nottingham Ningbo China School of Computer Science University of Nottingham Ningbo China Department of Electrical and Computer Engineering University of Washington United States School of Computer Science Carnegie Mellon University United States School of Engineering University of Nottingham Ningbo China School of Computer Science University College Dublin Ireland The ADAPT SFI Research Centre Ireland

Urban traffic optimization is crucial for improving transportation efficiency and alleviating congestion. Traditional algorithms [1] like Dijkstra’s [2] and Floyd’s [3], along with Genetic Algorithms (GA) [4], are widely used for route planning but struggle with large-scale, dynamic traffic networks due to computational complexity and limited ability to capture nonlinear spatial-temporal dependencies [5]. To address these challenges, we propose a hybrid framework combining Kolmogorov-Arnold Networks (KAN) with Graph Convolutional Networks (GCN) [6, 7, 8] for urban traffic flow optimization. KAN’s ability to approximate nonlinear functions, along with GCN’s capacity to model spatial dependencies in graph-structured data, provides a more effective representation of traffic dynamics. We evaluate KAN-GCN on real-world datasets and compare it to traditional models like Multi-Layer Perceptrons (MLP) [9] and decision trees. While MLP-GCN shows higher accuracy, KAN-GCN excels in handling noisy and complex traffic patterns [7]. Results highlight challenges in applying KAN to large-scale networks and suggest potential improvements. Additionally, we explore extending the framework to Transformer models for scalable and dynamic traffic forecasting. Our findings indicate that combining KAN with GCN offers promising potential for intelligent traffic management, with further optimization possible for large-scale *** the spirit of reproducible research, the model code, dataset, and results of the experiments in this paper are available at: https://***/ZhangJiayi24/KAN_GCN_*** Codes 90B20, 68T07, 05C85, 90C90 © 2025, CC BY.

关键词： Decision trees

CLEARING SECTIONS OF LATTICE LIABILITY NETWORKS

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arXiv 2025年

作者： Ghrist, Robert Gould, Julian Lopez, Miguel Riess, Hans Department of Mathematics and Electrical & Systems Engineering University of Pennsylvania Philadelphia PA Department of Mathematics University of Pennsylvania Philadelphia PA Applied Mathematics & Computational Science University of Pennsylvania Philadelphia PA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

Modern financial networks involve complex obligations that transcend simple monetary debts: multiple currencies, prioritized claims, supply chain dependencies, and more. We present a mathematical framework that unifies and extends these scenarios by recasting the classical Eisenberg-Noe model of financial clearing in terms of lattice liability networks. Each node in the network carries a complete lattice of possible states, while edges encode nominal liabilities. Our framework generalizes the scalar-valued clearing vectors of the classical model to lattice-valued clearing sections, preserving the elegant fixed-point structure while dramatically expanding its descriptive power. Our main theorem establishes that such networks possess clearing sections that themselves form a complete lattice under the product order. This structure theorem enables tractable analysis of equilibria in diverse domains, including multi-currency financial systems, decentralized finance with automated market makers, supply chains with resource transformation, and permission networks with complex authorization structures. We further extend our framework to chain-complete lattices for term structure models and multivalued mappings for complex negotiation systems. Our results demonstrate how lattice theory provides a natural language for understanding complex network dynamics across multiple domains, creating a unified mathematical foundation for analyzing systemic risk, resource allocation, and network *** Codes 91G40, 06B23, 91D30, 68Q85 Copyright © 2025, The Authors. All rights reserved.

关键词： Lattice theory

WEIERSTRASS SEMIGROUPS AND AUTOMORPHISM GROUP OF A MAXIMAL FUNCTION FIELD WITH THE THIRD LARGEST POSSIBLE GENUS, q ≡ 0 (mod 3)

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arXiv 2025年

作者： Beelen, Peter Montanucci, Maria Vicino, Lara Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby2800 Denmark Faculty of Science and Engineering Bernoulli Institute University of Groningen Groningen9747AG Netherlands

In this article we complete the work started in [3] and [4], explicitly determining the Weierstrass semigroup at any place and the full automorphism group of a known Fq2-maximal function field Z3 having the third largest genus, for q ≡ 0 (mod 3). The cases q ≡ 2 (mod 3) and q ≡ 1 (mod 3) have been in fact analyzed in [3] and [4], respectively. As in the other two cases, the function field Z3 arises as a Galois subfield of the Hermitian function field, and its uniqueness (with respect to the value of its genus) is a well-known open problem. Knowing the Weierstrass semigroups may provide a key towards solving this problem. Surprisingly enough, Z3 has many different types of Weierstrass semigroups and the set of its Weierstrass places is much richer than its set of Fq2-rational places. We show that a similar exceptional behaviour does not occur in terms of automorphisms, that is, Aut(Z3) is exactly the automorphism group inherited from the Hermitian function field, apart from the case q = *** Codes 14H37, 14H05 Copyright © 2025, The Authors. All rights reserved.

关键词： Algebra

Fractional order modeling of dengue transmission dynamics in Bangladesh

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Partial Differential Equations in applied mathematics 2025年 14卷

作者： Sikder, Arun Kumar Islam, Md Hamidul Department of Computer Science and Engineering Varendra University Rajshahi 6204 Bangladesh Department of Applied Mathematics University of Rajshahi Rajshahi 6205 Bangladesh

Over the past three decades, the epidemiological characteristics of dengue fever in Bangladesh have raised significant public health concerns, with the disease progressively spreading across the country's subtropical and tropical regions since 2004. This study introduces a fractional-order mathematical model that incorporates both human and mosquito populations using Caputo derivatives to account for memory and hereditary effects in disease transmission. Analytical solutions are approximated using the Laplace–Adomian decomposition method, while the Adams–Bashforth-Moulton predictor–corrector (PECE) scheme is applied for numerical solutions. Model parameters are estimated via the maximum likelihood method, using dengue case data from Bangladesh (June 1, 2022–September 30, 2022). Our findings indicate that the fractional-order model effectively captures a range of transmission scenarios, with lower derivative orders correlating with reduced outbreak severity. Moreover, numerical solutions exhibit higher accuracy compared to analytical approximations, particularly for complex nonlinear systems. Additionally, the results suggest that controlling mosquito populations, reducing transmission rates, and improving treatment measures are critical strategies for dengue mitigation. © 2025 The Authors

关键词： Dengue fever Fractional order modeling LADM Mathematical modeling MLE fitting Monte Carlo method PECE method

Teaching An Old Dog New Tricks: Porting Legacy Code to Heterogeneous Compute Architectures With Automated Code Translation

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arXiv 2025年

作者： Nytko, Nicolas Reisner, Andrew Moulton, J. David Olson, Luke N. West, Matthew Department of Computer Science University of Illinois at Urbana-Champaign United States Applied Computer Science Los Alamos National Laboratory New Zealand Applied Mathematics and Plasma Physics Los Alamos National Laboratory New Zealand Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign United States

Legacy codes are in ubiquitous use in scientific simulations;they are well-tested and there is significant time investment in their use. However, one challenge is the adoption of new, sometimes incompatible computing paradigms, such as GPU hardware. In this paper, we explore using automated code translation to enable execution of legacy multigrid solver code on GPUs without significant time investment and while avoiding intrusive changes to the codebase. We developed a thin, reusable translation layer that parses Fortran 2003 at compile time, interfacing with the existing library Loopy (Klöckner 2014) to transpile to C++/GPU code, which is then managed by a custom MPI runtime system that we created. With this low-effort approach, we are able to achieve a payoff of an approximately 2–3× speedup over a full CPU socket, and 6× in multi-node settings. © 2025, CC BY-SA.

关键词： Investments