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One-dimensional monotone nonautonomous dynamical systems

science China mathematics 2024年第2期67卷 281-314页

作者： David Cheban Faculty of Mathematics and Computer Science Laboratory of Fundamental and Applied MathematicsMoldova State UniversityChisinau 2009Moldova

This work is devoted to the study of the dynamics of one-dimensional monotone nonautonomous(cocycle) dynamical systems. A description of the structures of their invariant sets, omega limit sets,Bohr/Levitan almost periodic and almost automorphic motions, global attractors, and pinched and minimalsets is given. An application of our general results is given to scalar differential and difference equations.

关键词： one-dimensional dynamics pinched and minimal sets global attractors monotone nonautonomous cocycle almost periodic solutions scalar differential/difference equations

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An infrastructure software perspective toward computation offloading between executable specifications and foundation models

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science China(Information sciences) 2025年第4期68卷 380-382页

作者： Dezhi RAN Mengzhou WU Yuan CAO Assaf MARRON David HAREL Tao XIE Key Laboratory of High Confidence Software Technologies (PKU) Ministry of Education School of Computer SciencePeking University School of Electronics Engineering and Computer Science Peking University Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Foundation models(FMs) [1] have revolutionized software development and become the core components of large software systems. This paradigm shift, however, demands fundamental re-imagining of software engineering theories and methodologies [2]. Instead of replacing existing software modules implemented by symbolic logic, incorporating FMs' capabilities to build software systems requires entirely new modules that leverage the unique capabilities of ***, while FMs excel at handling uncertainty, recognizing patterns, and processing unstructured data, we need new engineering theories that support the paradigm shift from explicitly programming and maintaining user-defined symbolic logic to creating rich, expressive requirements that FMs can accurately perceive and implement.

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Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning

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science China(Information sciences) 2025年第3期68卷 66-79页

作者： Sichao FU Qinmu PENG Yange HE Baokun DU Bin ZOU Xiao-Yuan JING Xinge YOU School of Electronic Information and Communications Huazhong University of Science and Technology Platform Operation and Marketing Center JD Retail Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University School of Computer Science Wuhan University

In recent years, unsupervised multiplex graph representation learning(UMGRL) has received increasing research interest, which aims to learn discriminative node features from the multiplex graphs supervised by data without the guidance of labels. Although these designed UMGRL methods have obtained great success in various graph-related tasks, most existing UMGRL models still have the following issues: highly depending on complex self-supervised strategies(i.e., data augmentation,pretext tasks, and negative pairs sampling), restricted receptive fields, and only aggregating low-frequency information between nodes. In this paper, we propose a simple unsupervised multiplex graph diffusion network(UMGDN) with the aid of multi-level canonical correlation analysis to solve the above issues. Specifically, we first decouple the feature transform and propagation processes of the graph convolution layer to further improve the generalization of the learnable parameters. And then, we propose adaptive diffusion propagation to capture long-range dependency relationships between nodes, not the local neighborhood interactions. Finally, a multi-level canonical correlation analysis loss on both the feature transform and propagation processes is proposed to maximize the correlation of the same node features from multiple graphs for guiding model optimization. Compared to the existing UMGRL models, our proposed UMGDN does not need to introduce any data augmentation, negative pairs sampling techniques, complex pretext tasks, and also adaptively aggregates the optimal frequency information between nodes to generate more robust node embeddings. Extensive experiments on four popular datasets and two graph-related tasks demonstrate the effectiveness of the proposed method.

关键词： unsupervised multiplex graph representation learning graph neural networks node classification node clustering

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Throughput Prediction Using LS-SVM For 4g-LTE Network and Beyond

Throughput Prediction Using LS-SVM For 4g-LTE Network and Be...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Hamdani, Mostefa Sahnoune, Ahmed Merzougui, Mokhtar Douara, Abdelmalek Khatemi, Fouad University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Mathematics and Computer Science Algeria University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Science and Technology Algeria

ISBN: (纸本)9798331527396

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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LSTM and Linearized Segmented Model for Short-Term Power Output Forecasting of Wind Turbines 10

LSTM and Linearized Segmented Model for Short-Term Power Out...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hontinfinde, Regis Donald Houenou, Amavi G. Hounkpe Pazou, Mahugnon G. Azehoun Houessou, Faith Y. P. J. Didavi, K.B. Audace Agbomahena, MacAire B. Laboratory of Science Engineering and Mathematics Abomey Benin Laboratory of Electronics Telecommunication and Applied Computer Science Lokossa Benin

ISBN: (纸本)9798350384369

In this paper, an indirect model for predicting wind turbine output power is proposed, based on a LSTM and a mathematical model for wind power estimation. The LSTM model is used to forecast future changes in wind speed and direction, and the linearized segmented model to estimate wind turbine output power based on forecast wind speeds. To train the LSTM model, NASA meteorological data were used for a period from January 1,2015 to December 31, 2022 for a coastal site in Benin. The results obtained showed that the forecasts made by the LSTM model are fairly close to the actual values. Values of 0.36 m/s and 8.1° for the Mean Square Error (RMSE) and 0.92 and 0.71 for the Coefficient of Determination (R2) were obtained for wind speed and direction forecasts respectively. The predicted speeds were then used to estimate the power output of the NE-200S wind turbine. © 2024 IEEE.

关键词： Wind forecasting

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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

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Exponential Decay of Laminated Beam with Nonlinear Time-varying Delay and Microtemperature Effect

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Acta Mathematicae Applicatae Sinica 2025年第1期41卷 270-285页

作者： Imene LARIBI Ali KRELIFA Djamel OUCHENANE Fares YAZID Salah BOULAARAS Salah ZITOUNI Department of Mathematics Djilali Bounaama University of Khemis MilianaKhemis MilianaAlgeria Computer Science and Mathematics Laboratory Amar Telidji UniversityLaghouat 03000Algeria Laboratory of Pure and Applied Mathematics Amar Telidji UniversityLaghouat 03000Algeria Department of Mathematics College of ScienceQassim UniversityBuraydah 51452Saudi Arabia Department of Mathematics and Informatics University Mohamed-Cherif MessaadiaSouk AhrasAlgeria

This research paper addresses a topic of interest to many researchers and engineers due to its effective applications in various industrial *** focuses on the thermoelastic laminated beam model with nonlinear structural damping,nonlinear time-varying delay,and microtemperature *** primary goal is to establish the stability of the *** achieve this,and under suitable hypotheses,we demonstrate energy decay and construct a Lyapunov functional that leads to our results.

关键词： structural damping energy decay laminated beam microtemperature time-varying delay partial differential equations nonlinear equations

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Faithful geometric measures for genuine tripartite entanglement

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Physical Review A 2024年第1期110卷 L010402-L010402页

作者： Xiaozhen Ge Lijun Liu Yong Wang Yu Xiang Guofeng Zhang Li Li Shuming Cheng Department of Control Science and Engineering Department of Applied Mathematics The Department of Mathematics and Computer Science State Key Laboratory for Mesoscopic Physics School of Physics Frontiers Science Center for Nano-optoelectronics and Collaborative Innovation Center of Quantum Matter Shanghai Institute of Intelligent Science and Technology Institute for Advanced Study

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation Ei|jkα≤Ej|ikα+Ek|ijα holds for all subadditive bipartite entanglement measure E, all permutations under parties i,j,k, all α∈[0,1], and all pure tripartite states. Then, we rigorously prove that the nonobtuse triangle area, enclosed by side Eα with 0<α≤1/2, is a measure for genuine tripartite entanglement. Finally, it is significantly strengthened for qubits that given a set of subadditive and nonsubadditive measures, some state is always found to violate the triangle relation for any α>1, and the triangle area is not a measure for any α>1/2. Our results pave the way to study discrete and continuous multipartite entanglement within a unified framework.

关键词： Entanglement measures Quantum correlations in quantum information Quantum correlations, foundations & formalism Resource theories

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Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

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Clean Energy 2024年第3期8卷 95-108页

作者： Khaled Ferkous Mawloud Guermoui Abderahmane Bellaour Tayeb boulmaiz Nadjem Bailek LMTESE Laboratory of Materials Technology of Energy Systems and EnvironmentUniversitéde Ghardaia47000Algeria Renewable Energy Applied Research Unit(URAER) Ghardaia47000Algeria Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer ScienceFaculty of Sciences and TechnologyAhmed Draia University of AdrarAdrar 01000Algeria

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power *** study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour *** proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting *** evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all *** proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction ***,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior *** study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy

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Innovative Federated Learning Approach to Secure Internet of Medical Things 1st

Innovative Federated Learning Approach to Secure Internet of...

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1st International Conference on Electrical Systems and Smart Technologies, ICESST 2024

作者： Misbah, Anass Sebbar, Anass Hafidi, Imad National School of Applied Sciences Computer Science and Mathematics Laboratory Khouribga Morocco International University of Rabat ESIN TICLab Rabat Morocco

ISBN: (纸本)9783031867040

The Internet of Medical Things (IoMT) has emerged as a groundbreaking paradigm in healthcare, connecting billions of devices to enable enhanced automation, real-time communication, and comprehensive data collection. However, the rapid expansion of IoMT devices also presents significant security challenges that must be addressed through ongoing research and innovative approaches. In this study, we focus on improving attack classification in IoMT networks by leveraging federated learning, with a particular emphasis on the Random Forest (RF) model. Utilizing the open CICIoMT2024 dataset, which comprises 19 distinct attack classes and 45 features, we deploy the RF model across 10 simulated edge devices. The federated RF model demonstrated exceptional performance, achieving an accuracy of 0.9922, precision of 0.9938, recall of 0.9922, and an F1 score of 0.9909. This innovative approach significantly enhances security at the edge, particularly for devices with limited computational resources, underscoring the potential of federated learning to bolster IoMT security in increasingly complex healthcare environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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