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A class of elliptic system in non reflexive Orlicz-Sobolev spaces

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Annali dell'Universita di Ferrara 2025年第1期71卷 1-23页

作者： El-Houari, Hamza University Moulay Ismail Faculty of Sciences and Techniques Research Laboratory “Applied Mathematics Computer Science and Systems” Errachidia Morocco

This paper aims to show that there exists a weak solution to the following quasilinear system driven by the M-Laplacian (Formula presented.) where Ω is a bounded open subset in RN and (-Δm) is the M-Laplacian operat... 详细信息

This paper aims to show that there exists a weak solution to the following quasilinear system driven by the M-Laplacian (Formula presented.) where Ω is a bounded open subset in R^N and (-Δ_m) is the M-Laplacian operator. Here we consider the non-reflexive case taking into account the Orlicz and Orlicz-Sobolev Space. The non-reflexive case occurs when the N-function M¯ does not verify the Δ₂-condition. We consider an approximated quasilinear elliptic problem driven by the M_ϵ-Laplacian and using the Mountain Pass Theorem to obtain the existence of a nontrivial and nonnegative solution for the above system in reflexive case. By tending ϵ→0 we get the solution in the non-reflexive case. © The Author(s) under exclusive license to Università degli Studi di Ferrara 2024.

关键词： Elliptic system M-Laplacian operator Orlicz-Sobolev spaces Variational problem

Online Menu-Based Scheduling of Electric Vehicle Charging

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IEEE Transactions on Smart Grid 2025年第3期16卷 2478-2491页

作者： Mathioudaki, Angeliki Tsaousoglou, Georgios Varvarigos, Emmanouel Fotakis, Dimitris National Technical University of Athens School of Electrical and Computer Engineering Zografou15772 Greece Technical University of Denmark Department of Applied Mathematics and Computer Science Section for Dynamical Systems Kongens Lyngby2800 Denmark Archimedes Research Unit Athena Research Center Marousi15125 Greece

This paper addresses the problem of efficiently scheduling of EV charging requests in a single Charging Station (CS), also taking into consideration the inability of EV users to express their preferences in closed-form utility functions. We consider a paradigm where a menu of possible charging intervals, each associated with an urgency price, is generated online. By letting the EV users pick their most preferable menu option, the proposed algorithm commits on each EV’s charging completion time upon its arrival, achieves a near optimal total weighted charging completion time, and prevents the users from strategically misreporting their preferences, while offering a practical and implementable solution to the problem of EVs - charging station interaction. Our experimental evaluation demonstrates that our algorithmic approach performs nearly optimal in a variety of practically relevant scenarios. © 2010-2012 IEEE.

关键词： Charging stations

Thermal-Aware Communication

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IEEE Transactions on Information Theory 2025年第6期71卷 4145-4155页

作者： Chee, Yeow Meng Etzion, Tuvi Immink, Kees A. Schouhamer Nguyen, Tuan Thanh Vu, Van Khu Weber, Jos H. Yaakobi, Eitan Singapore University of Technology and Design Singapore Technion — Israel Institute of Technology Department of Computer Science Haifa3200003 Israel National University of Singapore Department of Industrial Systems Engineering and Management Singapore Turing Machines Inc Rotterdam3016 DK Netherlands Singapore University of Technology and Design Science Mathematics and Technology Cluster 487372 Singapore Delft University of Technology Department of Applied Mathematics Delft2628 CD Netherlands

Temperature control is of utmost importance in transmission systems. In this paper, a binary channel model is considered in which the transmission of a one causes a temperature increase while communicating a zero causes a temperature drop. By putting constraints on the input sequences, it is guaranteed that the channel temperature will not exceed a certain pre-determined maximum. In the asymptotic regime, the capacity of such a channel is studied. For the non-asymptotic regime, fixed-length codes are presented, with the property that codewords can be freely cascaded without violating the temperature constraint. Optimization of the code size is investigated and codewords are enumerated using generating functions. © 1963-2012 IEEE.

关键词： Channel capacity

The Impact of Predictive Maintenance on the Performance of Industrial Enterprises

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SN computer science 2025年第1期6卷 1-34页

作者： Er-Ratby, Mohamed Kobi, Abdessamad Sadraoui, Youssef Kadiri, Moulay Saddik LaSTI Laboratory of Science and Engineering Techniques National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco LARIS Angevin Laboratory for Systems Engineering Research Polytech Angers University of Angers Angers France LIPIM Laboratory of Process Engineering Computer Science and Mathematics National School of Applied Sciences of Khouribga Sultan Moulay Slimane University Khouribga Morocco

Optimizing maintenance and enhancing the performance of businesses are significant concerns in the modern industrial world. Predictive maintenance is emerging as an innovative approach to address these challenges, allowing companies to shift from corrective maintenance to preventive maintenance. Predictive maintenance relies on the use of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to collect and analyse real-time data from equipment. Through this predictive analysis, it becomes possible to identify early warning signals of failures, enabling the anticipation of potential issues and the proactive planning of maintenance interventions. In this study, we will thoroughly examine the impact of predictive maintenance on the performance of businesses. We will explore the benefits and opportunities it offers in terms of reducing downtime, optimizing maintenance costs, and enhancing productivity. We will also investigate the various technologies and methods used in the implementation of predictive maintenance, along with potential challenges and best practices for successful adoption. This research focuses on studying the application of predictive maintenance within a company using data science and machine learning methods. Predictive maintenance represents an innovative approach aimed at anticipating equipment failures by leveraging real-time collected data. Through the analysis of this data using sophisticated algorithms, it becomes possible to identify early signals of potential problems and implement preventive maintenance actions before breakdowns occur. This approach not only reduces unexpected downtime but also optimizes maintenance operations by avoiding unnecessary interventions and maximizing resource utilization. The ultimate goal is to improve equipment availability, optimize operational performance, and maximize the overall yield of the company. © The Author(s), under exclusive licence to S

关键词： Artificial intelligence (AI) and machine learning (ML) Data science Optimizing maintenance Performance indicators Predictive maintenance Production

Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations

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arXiv 2025年

作者： Prusik, Michal Frej, Bartosz Przewozniczek, Michal W. Faculty of Pure and Applied Mathematics Wroclaw University of Science and Technology Wroclaw Poland Departament of Systems and Computer Networks Wroclaw University of Science and Technology Wroclaw Poland

Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL is important for practice. In this work, we analytically estimate the size of a population sufficient for obtaining a perfect decomposition in case of concatenations of certain unitation-based functions. The experimental study confirms the accuracy of the proposed estimate. Finally, using the proposed estimate, we identify those problem types that may be considered hard for SLL-using optimizers. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

Reading the Readers Mind through Eye Tracking: Can AI Generated Texts Match Human Authors? 25

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Reading the Readers Mind through Eye Tracking: Can AI Genera...

Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Chaudhary Muhammad Aqdus Ilyas Sifat-E Noor Ashkan Tashk Bart Cooreman Sofie Beier Per Bækgaard Department of Applied Mathematics and Computer Science Section for Cognitive Systems Technical University of Denmark Kgs. Lyngby Denmark Technical University of Denmark Kgs. Lyngby Denmark Department of Applied Mathematics and Computer Science Section for Cognitive Systems Denmark Technical University Kgs. Lyngby Denmark Centre for Visibility Design Royal Danish Academy Copenhagen Denmark Royal Danish Academy Copenhagen Denmark

Optimal covering of rectangular grid graphs with tours of constrained length

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arXiv 2025年

作者： Bereg, Sergey Capitán, Jesús Bañez, José Miguel Díaz López, José Manuel Higes Cutiño, Miguel Angel Pérez Canales, Vanesa Sánchez Ventura, Inmaculada Department of Computer Science University of Texas Dallas United States Multi-Robot & Control Systems Group University of Seville Spain Department of Applied Mathematics University of Seville Spain Department of Applied Mathematics University of Seville Spain Virtualmechanics S.L Seville Spain

Given a rectangular grid graph with a special vertex at a corner called base station, we study the problem of covering the vertices of the entire graph with tours that start and end at the base station and whose lengths do not exceed a given threshold, while minimizing a quality measure. We consider two objective functions: minimizing the number of tours and minimizing the sum of their lengths. We present an algorithm that computes the optimal solution for both objectives in linear time with respect to the grid size. © 2025, CC BY-NC-SA.

关键词： Combinatorial optimization

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

Identifying Critical Tokens for Accurate Predictions in Transformer-based Medical Imaging Models

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arXiv 2025年

作者： Kang, Solha Vankerschaver, Joris Ozbulak, Utku Center for Biosystems and Biotech Data Science Ghent University Global Campus Korea Republic of Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Electronics and Information Systems Ghent University Belgium

With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, similar to CNNs, unveiling the decision-making process of transformer-based models remains a challenge. In this work, we take a step towards demystifying the decision-making process of transformer-based medical imaging models and propose "Token Insight", a novel method that identifies the critical tokens that contribute to the prediction made by the model. Our method relies on the principled approach of token discarding native to transformer-based models, requires no additional module, and can be applied to any transformer model. Using the proposed approach, we quantify the importance of each token based on its contribution to the prediction and enable a more nuanced understanding of the model’s decisions. Our experimental results which are showcased on the problem of colonic polyp identification using both supervised and self-supervised pretrained vision transformers indicate that Token Insight contributes to a more transparent and interpretable transformer-based medical imaging model, fostering trust and facilitating broader adoption in clinical settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning