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Multi-directional multiscroll memristive chaotic systems with diverse multistability and application to high-speed rail image encryption

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Nonlinear Dynamics 2025年 1-17页

作者： Huang, Wang-Peng Lai, Qiang Zhao, Xiao-Wen Iu, Herbert Ho-Ching School of Tian You East China Jiaotong University Nanchang China School of Electrical and Automation Engineering East China Jiaotong University Nanchang China School of Mathematics Hefei University of Technology Hefei China School of Electrical Electronic and Computing Engineering University of Western Australia Crawley Australia

Memristors can be applied to the generation of chaotic systems for enhancing the complexity of chaotic signals and assembling potential applicable dynamic behaviors, thereby increasing concerns have been focused on the research of memristive chaotic system (MCS). This paper constructs multi-directional multiscroll memristive chaotic systems (MMCSs) by introducing newly designed locally active in the same dimension. The number and expansion direction of scrolls can be controlled by the internal variables and memristors. For changing the parameters and initial values, the multiscroll attractors break into tunable ratios of periodic and chaotic attractors, yielding diverse multistability. The dynamic behaviors (including multiscroll attractors, multistability, amplitude control), circuit realization and NIST tests are studied for giving a comprehensive observation of the proposed MMCSs. A secure and fast encryption scheme which can be used to protect high-speed rail (HSR) images with important technical information is designed based on the proposed MMCSs for verifying their application potential.

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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Simulation of Emulsion Evolution in Shear Flow Field Under The Control Of Bidirectional Pulsed Electric Field

SSRN

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SSRN 2025年

作者： Wang, Heping Wu, Yi Zhang, Xiaohang Key Laboratory of Engineering Mathematics and Advanced Computing of Nanchang Institute of Technology School of Sciences Nanchang Institute of Technology Nanchang330099 China

This article established a lattice Boltzmann model coupled with electric and shear field. It explored the impact of the combined action of bidirectional pulsed electric field(BPEF) and shear field on the demulsification process. Simulation results reveal that: Under the action of pure shear field, increasing the shear effect will lead oil droplets to move around the flow field center, and forming a stable state eventually. However, after applying bidirectional pulsed electric field on both sides of the flow field, the electric field force breaks this stable state and promotes further aggregation of oil droplets. Analysis of streamline diagrams indicates that: Bidirectional pulsed electric field periodically changes the direction of the electric field force. It leads to induce an unbalanced velocity distribution in the flow field. And this phenomenon can accelerate or decelerate the aggregation of oil droplets. Quantitative analysis results also show the synergistic effect of bidirectional pulsed electric field and shear field on emulsion. It accelerates the aggregation speed of oil droplets and enhances the degree of aggregation. These simulation results are significant important for understand the synergistic demulsification effects of electric and shear fields. © 2025, The Authors. All rights reserved.

关键词： Demulsification

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Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation

arXiv

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arXiv 2025年

作者： Islam, Rofiqul Chowdhury, Nihad Karim Kabir, Muhammad Ashad Department of Computer Science and Engineering University of Chittagong Chattogram4331 Bangladesh School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

This research presents a robust approach to classifying COVID-19 cough sounds using cutting-edge machine learning techniques. Leveraging deep neural decision trees and deep neural decision forests, our methodology demonstrates consistent performance across diverse cough sound datasets. We begin with a comprehensive extraction of features to capture a wide range of audio features from individuals, whether COVID-19 positive or negative. To determine the most important features, we use recursive feature elimination along with cross-validation. Bayesian optimization fine-tunes hyper-parameters of deep neural decision tree and deep neural decision forest models. Additionally, we integrate the synthetic minority over-sampling technique during training to ensure a balanced representation of positive and negative data. Model performance refinement is achieved through threshold optimization, maximizing the ROC-AUC score. Our approach undergoes a comprehensive evaluation in five datasets: Cambridge (asymptomatic and symptomatic), Coswara, COUGHVID, Virufy, and the combined Virufy with the NoCoCoDa dataset. Consistently outperforming state-of-the-art methods, our proposed approach yields notable AUC scores of 0.97, 0.98, 0.92, 0.93, 0.99, and 0.99, alongside remarkable precision scores of 1, 1, 0.72, 0.93, 1, and 1 across the respective datasets. Merging all datasets into a combined dataset, our method, using a deep neural decision forest classifier, achieves an accuracy of 0.97, AUC of 0.97, precision of 0.95, recall of 0.96, F1-score of 0.96, and specificity score of 0.97. Also, our study includes a comprehensive cross-datasets analysis, revealing demographic and geographic differences in the cough sounds associated with COVID-19. These differences highlight the challenges in transferring learned features across diverse datasets and underscore the potential benefits of dataset integration, improving generalizability and enhancing COVID-19 detection from audio signals. © 2025

关键词： COVID-19

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THICK SUBCATEGORIES OF DERIVED CATEGORIES OF GENTLE ALGEBRAS

arXiv

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arXiv 2025年

作者： Page, Callum T. School of Engineering Computing and Mathematics University of Plymouth Drake Circus PlymouthPL4 8AA United Kingdom

We study thick subcategories of derived categories of gentle algebras. Any thick subcategory of a derived category of a gentle algebra is generated by a set of string objects or a set of band objects. We show the thick subcategories generated by string objects are in bijection with sets of non-crossing paths on the geometric model of the derived *** Codes 16G20, 18G80 Copyright © 2025, The Authors. All rights reserved.

关键词： Algebra

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QoS-Based Beamforming and Compression Design for Cooperative Cellular Networks via Lagrangian Duality

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IEEE Transactions on Signal Processing 2025年

作者： Fan, Xilai Liu, Ya-Feng Liu, Liang Chang, Tsung-Hui Chinese Academy of Sciences State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong The Chinese University of HongKong School of Science and Engineering Shenzhen Shenzhen China Shenzhen Research Institute of Big Data China

This paper considers the quality-of-service (QoS)-based joint beamforming and compression design problem in the downlink cooperative cellular network, where multiple relay-like base stations (BSs), connected to the central processor via ratelimited fronthaul links, cooperatively transmit messages to the users. The problem of interest is formulated as the minimization of the total transmit power of the BSs, subject to all users' signal-to-interference-plus-noise ratio (SINR) constraints and all BSs' fronthaul rate constraints. In this paper, we first show that there is no duality gap between the considered joint optimization problem and its Lagrangian dual by showing the tightness of its semidefinite relaxation (SDR). Then, we propose an efficient algorithm based on the above duality result for solving the considered problem. The proposed algorithm judiciously exploits the special structure of an enhanced Karush-Kuhn-Tucker (KKT) conditions of the considered problem and approaches the solution that satisfies the enhanced KKT conditions via two fixed point iterations. Two key features of the proposed algorithm are: (1) it is able to detect whether the considered problem is feasible or not and find its globally optimal solution when it is feasible;(2) it is highly efficient because both of the fixed point iterations in the proposed algorithm are linearly convergent and function evaluations in the fixed point iterations are computationally cheap. Numerical results show the global optimality and efficiency of the proposed algorithm. © 1991-2012 IEEE.

关键词： Lagrange multipliers

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Preserving medical information from doctor's prescription ensuring relation among the terminology

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Computers in Biology and Medicine 2025年 188卷 109812-109812页

作者： Datta, Apurba Hasan, Md. Mehedi Mahmud, Niaz Ferdosi, Bilkis Jamal Islam, Rafiqul Rahman, Ziaur Mehedi, Sk. Tanzir Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh School of Computing Mathematics & Engineering Charles Sturt University Albury2640 Australia School of Computer Science Queensland University of Technology Brisbane4000 Australia

Healthcare is a fundamental human right, yet accessing proper healthcare remains a significant challenge. Many patients still rely on physical documents, requiring them to carry all relevant medical records during consultations. Existing methods for extracting data from medical prescriptions have primarily focused on recognizing medication names using manual image annotation or binarization techniques. These approaches often fail to capture detailed prescription information, struggle with multilingual text, and lack the ability to structure medicine-related data comprehensively. To address these limitations, we propose an advanced Electronic Health Record (EHR) system that provides a secure and accessible digital platform for storing and managing patients’ medical histories. Our study implements the best Region of Interest (ROI) detection model using the You Only Look Once (YOLO) framework, achieving 99.6% detection accuracy. The extracted ROI is processed using the best Optical Character Recognition (OCR) technique to digitize prescription data, which is then organized into a structured format within a central database. Furthermore, the system integrates a spell correction algorithm with a 96% accuracy rate to rectify misspelled medication names. Beyond text extraction, the system links corrected medication names with dosages, instructions, and manufacturer information, combining generic and brand details to ensure precise and comprehensive healthcare data management. This integrated solution enhances medication data organization, facilitates better healthcare delivery, and improves patient outcomes by streamlining prescription handling and ensuring accurate medication administration. Our EHR system bridges the gap between physical and digital records, advancing an efficient and reliable healthcare ecosystem. © 2025 The Authors

关键词： Electronic health record

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An Attention-Guided Deep Learning Approach for Classifying 39 Skin Lesion Types

arXiv

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arXiv 2025年

作者： Hanum, Sauda Adiv Dey, Ashim Kabir, Muhammad Ashad Department of Computer Science and Engineering Chittagong University of Engineering and Technology Raozan Chittagong4349 Bangladesh School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW2795 Australia

The skin, as the largest organ of the human body, is vulnerable to a diverse array of conditions collectively known as skin lesions, which encompass various dermatoses. Diagnosing these lesions presents significant challenges for medical practitioners due to the subtle visual differences that are often imperceptible to the naked eye. While not all skin lesions are life-threatening, certain types can act as early indicators of severe diseases, including skin cancers, underscoring the critical need for timely and accurate diagnostic methods. Deep learning algorithms have demonstrated remarkable potential in facilitating the early detection and prognosis of skin lesions. This study advances the field by curating a comprehensive and diverse dataset comprising 39 categories of skin lesions, synthesized from five publicly available datasets. Using this dataset, the performance of five state-of-the-art deep learning models – MobileNetV2, Xception, InceptionV3, EfficientNetB1, and Vision Transformer – is rigorously evaluated. To enhance the accuracy and robustness of these models, attention mechanisms such as the Efficient Channel Attention (ECA) and the Convolutional Block Attention Module (CBAM) are incorporated into their architectures. Comprehensive evaluation across multiple performance metrics reveals that the Vision Transformer model integrated with CBAM outperforms others, achieving an accuracy of 93.46%, precision of 94%, recall of 93%, F1-score of 93%, and specificity of 93.67%. These results underscore the significant potential of the proposed system in supporting medical professionals with accurate and efficient prognostic tools for diagnosing a broad spectrum of skin lesions. The dataset and code used in this study can be found at https://***/akabircs/Skin-Lesions-Classification. © 2025, CC BY-NC-ND.

关键词： Deep learning

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism 24

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Thomas Lane Cassell Tom Deakin Aksel Alpay Vincent Heuveline Gonzalo Brito Gadeschi School of Computer Science University of Bristol Bristol UK Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany GPU Architecture NVIDIA Munich Germany

ISBN: (纸本)9798350355543

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N2) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling.

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