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A STUDY ON SOME CLASSES OF PANTOGRAPH LANGEVIN FRACTIONAL DIFFERENTIAL PROBLEMS WITH NONLOCAL CONDITIONS

Journal of Mathematical Sciences (United States) 2025年 1-18页

作者： Bouzid, Houari Benali, Abdelkader Salim, Abdelkrim Erhan, İnci M. Department of Mathematics Faculty of Exact Science and Informatics Laboratory of Mathematics and Applications (LMA) Hassiba Benbouali University of Chlef Ouled Fares Chlef 02000 Algeria Faculty of Technology Hassiba Benbouali University of Chlef Chlef 02000 Algeria Laboratory of Mathematics Djillali Liabes University of Sidi Bel-Abbes Sidi Bel-Abbes 22000 Algeria Department of Computer Engineering Aydın Adnan Menderes University Aydın 09010 Turkey

This paper concentrates on the examination of a class of nonlocal initial value problems for Langevin fractional differential equations featuring the ψ-Caputo fractional derivative within Banach spaces. By utilizing Banach’s fixed-point theorem, we establish the uniqueness of the solutions to these problems. The existence of solutions is then confirmed using Schaefer’s and Darbo’s fixed-point theorems. Finally, we provide examples to illustrate our findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Darbo theorem Fractional differential equations (FDE) Measure of noncompactness ψ-fractional integral

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Joint System Latency and Data Freshness Optimization for Cache-enabled Mobile Crowdsensing Networks

arXiv

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arXiv 2025年

作者： Shi, Kexin Fu, Yaru Guo, Yongna Wang, Fu Lee Zhang, Yan School of Science and Technology Hong Kong Metropolitan University Hong Kong Department of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Department of Informatics University of Oslo Norway

Mobile crowdsensing (MCS) networks enable large-scale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge caching has been proposed as a solution for storing recently collected data. Nonetheless, this approach may compromise data freshness. In this paper, we investigate the trade-off between re-using cached task results and re-sensing tasks in cache-enabled MCS networks, aiming to minimize system latency while maintaining information freshness. To this end, we formulate a weighted delay and age of information (AoI) minimization problem, jointly optimizing sensing decisions, user selection, channel selection, task allocation, and caching strategies. The problem is a mixed-integer non-convex programming problem which is intractable. Therefore, we decompose the long-term problem into sequential one-shot sub-problems and design a framework that optimizes system latency, task sensing decision, and caching strategy subproblems. When one task is re-sensing, the one-shot problem simplifies to the system latency minimization problem, which can be solved optimally. The task sensing decision is then made by comparing the system latency and AoI. Additionally, a Bayesian update strategy is developed to manage the cached task results. Building upon this framework, we propose a lightweight and time-efficient algorithm that makes real-time decisions for the long-term optimization problem. Extensive simulation results validate the effectiveness of our approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Integer programming

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Core Temperature Estimation of Lithium-Ion Batteries Using Long Short-Term Memory (LSTM) Network and Kolmogorov-Arnold Network (KAN)

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IEEE Transactions on Transportation Electrification 2025年

作者： Karnehm, Dominic Samanta, Akash Rosenmuller, Christian Neve, Antje Williamson, Sheldon University of Bundeswehr Munich Electrical Engineering and Technical Informatics Department Neubiberg85577 Germany Ontario Tech University Electrical Computer and Software Engineering ONL1G 0C5 Canada Munich80335 Germany

Health-conscious battery management systems (BMS) that rely on surface temperature measurements are insufficient for managing automotive lithium-ion batteries (LIBs). Experimental studies have shown temperature differences of up to 10°C between surface and core of cylindrical LIBs. BMSs that consider only surface temperature overlook critical thermal information. The missing monitoring can delay detecting thermal events within the cell, accelerating battery degradation and increasing the risk of thermal runaway. This paper introduces two deep learning algorithms to address this: Kolmogorov-Arnold Network (KAN) and interconnected Long Short-Term Memory (LSTM) network. Both approaches estimate the core temperature of LIBs without requiring surface temperature feedback to the neural network. Experimental validation revealed a core temperature mean absolute error (MAE) of 0.55 °C with a computational cost of 2.9 ms to 3.2 ms for KAN. The proposed interconnected LSTM reached a MAE of 0.80 °C. The performance of the two core temperature estimation techniques was further evaluated under dynamic loading profile using UDDS drive cycle. The KAN method achieved a MAE of 0.325 °C, demonstrating its adaptability to dynamic operating conditions. The two proposed methods, primarily KAN, are both adaptive and computationally efficient, making them suitable for integrating onboard BMS and cloud-enabled digital-twin-based BMS systems. © 2015 IEEE.

关键词： Long short-term memory

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Holographic Joint Communications and Sensing With Cramér-Rao Bounds

arXiv

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arXiv 2025年

作者： Sheemar, Chandan Kumar Khan, Wali Ullah Alexandropoulos, George Querol, Jorge Chatzinotas, Symeon The SnT Department The University of Luxembourg Luxembourg The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States

Joint Communication and Sensing (JCAS) technology facilitates the seamless integration of communication and sensing functionalities within a unified framework, enhancing spectral efficiency, reducing hardware complexity, and enabling simultaneous data transmission and environmental perception. This paper explores the potential of holographic JCAS systems by leveraging reconfigurable holographic surfaces (RHS) to achieve high-resolution hybrid holographic beamforming while simultaneously sensing the environment. As the holographic transceivers are governed by arbitrary antenna spacing, we first derive exact Cramér-Rao Bounds (CRBs) for azimuth and elevation angles to rigorously characterize the three-dimensional (3D) sensing accuracy. To optimize the system performance, we propose a novel weighted multi-objective problem formulation that aims to simultaneously maximize the communication rate and minimize the CRBs. However, this formulation is highly non-convex due to the inverse dependence of the CRB on the optimization variables, making the solution extremely challenging. To address this, we propose a novel algorithmic framework based on the Majorization-Maximization (MM) principle, employing alternating optimization to efficiently solve the problem. The proposed method relies on the closed-form surrogate functions that majorize the original objective derived herein, enabling tractable optimization. Simulation results are presented to validate the effectiveness of the proposed framework under diverse system configurations, demonstrating its potential for next-generation holographic JCAS systems. © 2025, CC BY.

关键词： Cramer-Rao bounds

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Generative AI-Enabled Quantum Encryption Algorithm for Securing IoT-Based Healthcare Application Using Blockchain

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IEEE Internet of Things Journal 2025年

作者： Prajapat, Sunil Kumar, Pankaj Das, Ashok Kumar Muhammad, Ghulam Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics Dharamshala176215 India International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has transformed numerous domains through the AI of Things (AIoT). Nonetheless, AIoT encounters issues related to energy usage and carbon emissions as mobile technology continues to progress. Generative AI (GAI) possesses significant potential to mitigate carbon emissions associated with AIoT, owing to its higher reasoning and generative powers. Conventional security protocols frequently encounter issues with computational efficiency, latency, and overall security comprehensiveness. Blockchain technology, characterized by its decentralized and immutable properties, is a viable approach for improving electronic healthcare data transmission and node authentication in IoT networks. This research examines secure data transmission and node encryption in IoT systems, with a particular emphasis on data management. Conventional approaches encounter constraints in computational efficiency, latency, and comprehensive security. This study presents a novel protocol that combines GAI and blockchain technology with quantum encryption to enhance authentication and ensure secure data transmission. The algorithm comprises multiple consecutive processes, including the encoding and transmission of node requests, followed by the authentication process utilizing hash functions and digital signatures. The authentication approach utilizes a challenge-response technique, guaranteeing that only nodes with authentic credentials can advance. Thereafter, a dynamic key exchange protocol and quantum encryption method provide secure data delivery. The results indicate the procedure’s effectiveness in ensuring secure and regulated access to patient data, underscoring its significance in medical facilities. The system’s functionalities are augmented by a thorough evaluation employing machine learning. The findings indicate that the system exhibits an accuracy of 99.4%, precision of 99.10%, recall of 98.66%, F1-score of

关键词： Quantum computers

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Modeling the Electricity Generation Processes of a Combined Solar and Small Hydropower Plant

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Energies 2025年第9期18卷 2351-2351页

作者： Dyvak, Mykola Manzhula, Volodymyr Melnyk, Andriy Petryshyn, Nataliia Pan, Tiande Banasik, Arkadiusz Pikiewicz, Piotr Kempa, Wojciech M. Department of Computer Science West Ukrainian National University Ternopil46009 Ukraine Department of Clinical Engineering Academy of Silesia Katowice40-555 Poland Department of Mathematical Methods in Technics and Informatics Silesian University of Technology Gliwice44-100 Poland

This article proposes the concept of an integrated system consisting of two components: a small hydropower plant (SHPP) and a solar power plant (SPP), where the SHPP operates as a pumped-storage hydropower plant, and the SPP supplies energy for lifting water to the upper reservoir. A methodology is proposed for evaluating the joint operation of a solar power plant and a small hydropower plant. The methodology is based on modeling the electricity generation processes of combined solar and small hydropower plants. Additionally, a novel hybrid method is proposed for identifying interval models of small hydropower plants (SHPPs) and solar power plants (SPPs). This method integrates a metaheuristic algorithm for model structure synthesis, inspired by the behavioral model of a bee colony, with gradient-based methods for parameter identification. Using the proposed method, interval models have been developed for both small hydropower plants and the electricity generation of solar power plants. This study confirms the feasibility of using interval models to describe the relationship between electricity generation in a small hydropower plant and factors such as head difference, reactive power, and water level in the reservoir (i.e., available water resources). Furthermore, a mathematical model in the form of a difference equation is used to describe the daily electricity generation of a solar power plant. This model accounts for the characteristics of compressors that pump water from the lower to the upper reservoir. Based on the developed models, an assessment is conducted on the efficiency of the pumped-storage SHPP in ensuring operational stability during peak loads in the power grid and addressing seasonal variations. © 2025 by the authors.

关键词： Hydro energy

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Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning

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computers in Biology and Medicine 2025年 192卷 110150-110150页

作者： Iyer, Balaji Deoghare, Smruti Ranjan, Krish Aronow, Bruce J. Prasath, V.B. Surya Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Electrical Engineering and Computer Science University of Cincinnati OH45221 United States

Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classification, significantly enhances overall performance when executed with precision. In this study, we conduct a systematic literature review of HEp-2 cell segmentation techniques, identifying 28 key papers utilizing traditional image processing, machine learning classifiers, deep convolutional neural networks (CNNs), and generative adversarial network (GAN) frameworks. Building on these insights, we benchmark 17 CNN models without pretraining and 8 CNN models pretrained on ImageNet using both Frozen Encoder and Tunable Encoder strategies on the I3A dataset. Cross-validation (CV) and Benjamini–Hochberg (BH) significance correction were employed to ensure statistical rigor in model comparisons. Domain-Specific Pretraining (DSPT) experiments demonstrated performance improvements, particularly for underrepresented classes, while Data Augmentation strategies (DA-1 and DA-2) revealed distinct impacts across model categories. GAN-based segmentation experiments using the top-performing CNN architectures as generators within a Pix2Pix framework revealed performance degradation due to data limitations and adversarial training instabilities. Nonetheless, GANs displayed class-specific improvements in visual alignment of segmentation masks. Results were evaluated comprehensively across eight performance metrics, including Dice, IOU, Accuracy, Precision, Sensitivity, Specificity, AU-ROC and AU-PR. This work offers a robust benchmarking of state-of-the-art CNN, GAN, and Transformer-based models for HEp-2 cell segmentation, providing valuable insights for future research directions, including ensemble approaches, dynamic patch sampling, and diffusion models. © 2025 The Authors

关键词： Generative adversarial networks

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AI-enabled driver assistance: monitoring head and gaze movements for enhanced safety

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Complex and Intelligent Systems 2025年第7期11卷 1-28页

作者： Mudassar Shah, Sayyed Zengkang, Gan Sun, Zhaoyun Hussain, Tariq Zaman, Khalid Alwabli, Abdullah Jaffar, Amar Y. Ali, Farman School of Civil and Transportation Engineering Shenzhen University Guangdong 518060 China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou 310018 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Guangdong Shenzhen 518000 China Department of Electrical Engineering College of Engineering and Computing in Alqunfudah Umm Al-Qura University Mecca 21955 Saudi Arabia Computer and Network Engineering Department College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science and Engineering School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

This paper introduces a real-time head-pose detection and eye-gaze estimation system for Automatic Driver Assistance Technology (ADAT) aimed at enhancing driver safety by accurately collecting and transmitting data on the driver’s head position and eye gaze to mitigate potential risks. Existing methods are constrained by significant limitations, including reduced accuracy under challenging conditions such as varying head orientations and lighting, higher latency in real-time applications (e.g., Faster-RCNN and TPH-YOLOv5), and computational inefficiency, which hinders their deployment in resource-constrained environments. To address these challenges, we propose a novel framework using the Transformer Detection of Gaze Head - YOLOv7 (TDGH-YOLOv7) object detector. The key contributions of this work include the development of a reference image dataset encompassing diverse vertical and horizontal gaze positions alongside the implementation of an optimized detection system that achieves state-of-the-art performance in terms of accuracy and latency. The proposed system achieves superior precision, with a weighted accuracy of 95.02% and Root Mean Square Errors of 2.23 and 1.68 for vertical and horizontal gaze estimation, respectively, validated on the MPII-Gaze and DG-Unicamp datasets. A comprehensive comparative analysis with existing models, such as CNN, SSD, Faster-RCNN, and YOLOv8, underscores the robustness and efficiency of the proposed approach. Finally, the implications of these findings are discussed, and potential avenues for future research are outlined. © The Author(s) 2025.

关键词： Eye gaze estimation Eye tracking Facial detection Gaze Head pose detection

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Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense

arXiv

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arXiv 2025年

作者： Zhang, Haibo Yao, Zhihua Sakurai, Kouichi Saitoh, Takeshi Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Faculty of Economics and Business Administration The University of Kitakyushu Japan Department of Informatics Faculty of Information Science and Electrical Engineering Kyushu University Japan

In the rapidly evolving field of artificial intelligence, machine learning emerges as a key technology characterized by its vast potential and inherent risks. The stability and reliability of these models are important, as they are frequent targets of security threats. Adversarial attacks, first rigorously defined by Ian Goodfellow et al. in 2013, highlight a critical vulnerability: they can trick machine learning models into making incorrect predictions by applying nearly invisible perturbations to images. Although many studies have focused on constructing sophisticated defensive mechanisms to mitigate such attacks, they often overlook the substantial time and computational costs of training and maintaining these models. Ideally, a defense method should be able to generalize across various, even unseen, adversarial attacks with minimal overhead. Building on our previous work on image-to-image translation-based defenses, this study introduces an improved model that incorporates residual blocks to enhance generalizability. The proposed method requires training only a single model, effectively defends against diverse attack types, and is well-transferable between different target models. Experiments show that our model can restore the classification accuracy from near zero to an average of 72% while maintaining competitive performance compared to state-of-the-art methods. Significantly, our model operates more efficiently, reducing the time needed to process individual images and speeding up the training process to achieve faster convergence. Robustness tests further confirm stable performance under varying attack strengths, demonstrating the model’s practical value in real-world adversarial settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Empowering Remote Healthcare with Federated Learning for Early Diagnosis of Pulmonary Disease

引用

IEEE Internet of Things Journal 2025年

作者： Kumar, Santosh Shvetsov, Alexey V. Alsamhi, Saeed Hamood IIIT Naya Raipur Computer Science and Engineering Department Chhattisgarh India Moscow Polytechnic University Department of Smart Technologies Moscow Russia North-Eastern Federal University Yakutsk677000 Russia Tolyatti State University Tolyatti445020 Russia RUDN University Moscow117198 Russia Korea University College of Informatics Department of Computer Science and Engineering Seoul02841 Korea Republic of IBB University Department of Electronics Engineering Ibb Yemen

Recently, the field of healthcare has experienced remarkable technological advancements. However, a considerable challenge persists in providing state-of-the-art healthcare services to individuals in tribal and remote areas. To solve health issues in remote areas, this paper introduces a federated learning framework for the early diagnosis of multivariate pulmonary diseases based on cough (voice) samples collected from tribal regions. The proposed framework performs model training on connected local devices, including those living in remote or tribal regions with limited connectivity to centralized servers. The proposed framework ensures the early diagnosis of multivariate lung diseases, such as chronic obstructive pulmonary disease (COPD), to obtain accurate prediction using ensemble learning techniques. The framework applies a Convolutional Neural Network (CNN) to extract discriminatory features from generated spectrograms of cough (voice) samples to classify COPD and non-COPD (healthy) using transfer learning techniques. The proposed framework demonstrates the efficacy of our proposed framework in minimizing resource utilization and model complexity, achieving an impressive accuracy of up to 98.62% with reduced communication rounds and latency, thus facilitating the early diagnosis of COPD. The prototype model of the proposed framework offers an alternative way of a fast diagnosis of respiratory diseases based on cough samples collected from tribal people, which is used with the support of pulmonary and tuberculosis experts (doctors and professionals) for statistical analysis of critical cases of COPD for early diagnosis. © 2014 IEEE.

关键词： Lung cancer

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