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Results in Engineering 2025年 26卷

作者： Farman, Muhammad Nisar, Kottakkaran Sooppy Jamil, Khadija Akgül, Ali Sambas, Aceng Bayram, Mustafa Habib, Mustafa Hassani, Murad Khan Faculty of Arts and Science Department of Mathematics Near East University Nicosia99138 Cyprus Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu 22200 Malaysia Research Center of Applied Mathematics Khazar University Baku1096 Azerbaijan Department of Mathematics College of Science and Humanities in Alkharj Prince Sattam Bin Abdulaziz University Alkharj11942 Saudi Arabia Institute of Mathematics Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapi Istanbul34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia/Mersin 10 PC: 99138 Turkey Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tamansari Gobras Tasikmalaya46196 Indonesia Department of Mathematics University of Engineering and Technology Lahore Pakistan Ghazni University Department of Mathematics Afghanistan Applied Science Research Center Applied Science Private University Amman11937 Jordan

This study presents a cancer dynamics model incorporating a fractal-fractional operator with a Mittag-Leffler kernel to capture complex interactions among cancer cells, tumor suppressor cells, immune cells, and oncolytic viruses. The model aims to enhance understanding of tumor-immune dynamics and improve treatment strategies. The existence and uniqueness of the solution are established using fixed point theory under the Lipschitz condition. Lyapunov stability of the system is also analyzed in the context of the fractal-fractional operator. To address chaotic behavior in cancer progression, chaos and Proportional-Integral-Derivative (PID) control techniques are implemented. These control methods effectively stabilize the system and regulate treatment administration. Numerical simulations illustrate the influence of fractional-order derivatives on tumor suppression and immune response, confirming the model's effectiveness in reflecting real-world cancer dynamics. © 2025 The Author(s)

关键词： Fractals

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Hierarchical quantum embedding by machine learning for large molecular assemblies

arXiv

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arXiv 2025年

作者： Bensberg, Moritz Eckhoff, Marco Husistein, Raphael T. Teynor, Matthew S. Sora, Valentina Bro-Jørgensen, William Thomasen, F. Emil Krogh, Anders Lindorff-Larsen, Kresten Solomon, Gemma C. Weymuth, Thomas Reiher, Markus ETH Zurich Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 2 Zurich8093 Switzerland University of Copenhagen Department of Chemistry and Nano-Science Center Universitetsparken 5 Copenhagen ØDK-2100 Denmark University of Copenhagen Niels Bohr Institute NNF Quantum Computing Programme Blegdamsvej 17 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Computer Science Universitetsparken 1 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Biology Linderstrøm-Lang Centre for Protein Science Ole Maaløes Vej 5 Copenhagen NDK-2200 Denmark University of Copenhagen Center for Health Data Science Department of Public Health Øster Farimagsgade 5 Copenhagen ØDK-1353 Denmark

We present a quantum-in-quantum embedding strategy coupled to machine learning potentials to improve on the accuracy of quantum-classical hybrid models for the description of large molecules. In such hybrid models, relevant structural regions (such as those around reaction centers or pockets for binding of host molecules) can be described by a quantum model that is then embedded into a classical molecular-mechanics environment. However, this quantum region may become so large that only approximate electronic structure models are applicable. To then restore accuracy in the quantum description, we here introduce the concept of quantum cores within the quantum region that are amenable to accurate electronic structure models due to their limited size. Huzinaga-type projection-based embedding, for example, can deliver accurate electronic energies obtained with advanced electronic structure methods. The resulting total electronic energies are then fed into a transfer learning approach that efficiently exploits the higher-accuracy data to improve on a machine learning potential obtained for the original quantum-classical hybrid approach. We explore the potential of this approach in the context of a well-studied protein-ligand complex for which we calculate the free energy of binding using alchemical free energy and non-equilibrium switching simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecules

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A comprehensive approach to mitigate cyberattacks in power systems using an AI-based detection algorithm and the unit commitment corrective action

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Electric Power Systems Research 2025年 247卷

作者： Yuanrong, Jin Alahmari, Saad Sulaiman, Riza Hassan, Ali Hassan Mohammed, Jamal Alwakeel, Ahmed M. Rasheed, Saim Sichuan Vocational College of Information Technology Lizhou District Sichuan Guangyuan China Department of Computer Science Applied College Northern Border University Arar Saudi Arabia Universiti Kebangsaan Malaysia Bangi43600 Malaysia College of Computer and Cyber Sciences University of Prince Mugrin Madinah41499 Saudi Arabia IT Department University of Prince Mugrin Madinah41499 Saudi Arabia Faculty of Computers & Information Technology University of Tabuk Tabuk71491 Saudi Arabia Research Center University of Tabuk Tabuk71491 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University PO Box 80221 Jeddah21589 Saudi Arabia

In smart power systems, cyber-attacks like False Data Injection (FDI) attacks present a significant threat to grid stability and optimal performance operation. This paper proposes an AI-driven approach to detecting anomalous behavior in the system and mitigating cyberattacks. Specifically, it addresses some common attacks known as Load Redistribution (LR) attacks. The proposed detection methodology leverages a deep learning model based on Variational AutoEncoders (VAE) and utilizes anomaly detection to identify cyberattacks and evaluate their severities. A corrective action using Security Constraint Unit Commitment (SCUC) is introduced to counteract worst-case scenarios. Finally, the effectiveness of the proposed approach is demonstrated through a case study on the IEEE 118-bus system. © 2025

关键词： Deep learning

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka 1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha 34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt 19385 Jordan Bangladesh University of Professionals (BUP) Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Artificial Intelligence Cyber attack Cyber defense Cyber Security IP Address SOC Threat Intelligence

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EHands: Quantum Protocol for Polynomial Computation on Real-Valued Encoded States

arXiv

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arXiv 2025年

作者： Balewski, Jan Amankwah, Mercy G. Bethel, E. Wes Perciano, Talita Van Beeumen, Roel National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mathematics Applied Mathematics and Statistics Case Western Reserve University ClevelandOH44106 United States Computer Science Department San Francisco State University San FranciscoCA94132 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

The novel constructive EHands protocol defines a universal set of quantum operations for multivariable polynomial transformations on quantum processors by introducing four basic subcircuits—multiplication, addition, negation, and parity flip—and by using expectation-value encoding (EVEN) to represent real numbers in quantum states. These elementary arithmetic operations can be systematically composed to compute degree-d polynomials, Pd(x), on a QPU. The resulting quantum circuit structure closely mirrors the stepwise evaluation of polynomials on a classical calculator, providing an intuitive and efficient approach to polynomial computation on quantum hardware. By enabling direct, predictable polynomial and nonlinear data transformations on a QPU, our method reduces dependence on classical post-processing in hybrid quantum-classical algorithms, enabling advancements in many quantum algorithms. The EHands quantum circuits are compact enough to deliver meaningful and accurate results on today’s noisy quantum processors. We present a detailed implementation of P4(x) and report experimental results for polynomial approximations of common functions, obtained using IBM’s Heron-class quantum processors and an ideal Qiskit simulator. Copyright © 2025, The Authors. All rights reserved.

关键词： Polynomial approximation

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Evaluation of carbon based interconnects for digital signalling in printed flexible electronics on sustainable substrates

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年

作者： Wang, Zixin Saleh, Mahdi Alwattar, Aula Alfredsson, Maria Horne, Robert J. Ang, Chee S. Condell, Joan Flowers, Emily Matcham, Faith Robinson, Tony Rooksby, John Saha, Sian Rose, Louise Batchelor, John C. Casson, Alexander J. Henry Royce Institute for Advanced Materials Manchester United Kingdom The University of Manchester School of Engineering Department of Electrical and Electronic Engineering Manchester United Kingdom The University of Manchester School of Natural Sciences Department of Chemistry Manchester United Kingdom University of Kent School of Chemistry and Forensic Science Canterbury United Kingdom University of Kent School of Engineering Canterbury United Kingdom University of Kent School of Computing Canterbury United Kingdom Ulster University School of Computing Engineering and Intelligent Systems Derry United Kingdom King’s College London Division of Digital Health and Applied Technology Assessment London United Kingdom University of Sussex School of Psychology Falmer United Kingdom Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom

Printed electronics using flexible substrates are an emerging area, allowing next-generation electronics to conform and flex with different surfaces, from human skin to clothing. In the hybrid integration, or sea-of-rigids, approach, conventional microchips are mounted onto (generally) plastic substrates such as polyethylene naphthalate (PEN), with (typically) printed silver tracks for interconnections between components. An ongoing research direction is to replace plastic substrates with biodegradable substrates and to replace silver tracks with non-heavy metal-based tracks. While the substrates and tracks form only part of an overall system, replacing them is a step towards increased sustainability and helps to meet net-zero goals for printed electronic systems. Previously, several papers have investigated printed carbon tracks for low-frequency analog sensing applications. This paper explores the feasibility of using printed carbon tracks on biodegradable substrates for high-frequency applications such as digital signaling over a Serial-Peripheral Interface (SPI). We investigate the printability, thermal stability, and electrical conductivity of carbon ink screen-printed onto six commercially available sustainable and flexible substrates. Our results demonstrate that multi-layer screen printing substantially reduced the electrical resistance of carbon tracks, enabling SPI communication at frequencies up to 16 MHz with three layers of carbon ink. A Natureflex™ substrate provided the best balance of printability, thermal stability, and electrical performance. Substrates such as greaseproof paper and ClearFilm PU showed potential for flexible electronics, but require further optimization. This study provides valuable insights into selecting and optimizing biodegradable substrates for high-frequency digital systems, supporting the move towards more sustainable printed electronics. © 2022 IEEE.

关键词： Variable frequency oscillators

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ICGM-FRAX: Iterative Cross Graph Matching for Hip Fracture Risk Assessment using Dual-energy X-ray Absorptiometry Images

arXiv

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arXiv 2025年

作者： Zhao, Chen Shaik, Anjum Keyak, Joyce H. Lane, Nancy E. Deng, Jeffrey D. Su, Kuan-Jui Sha, Qiuying Shen, Hui Deng, Hong-Wen Zhou, Weihua Department of Computer Science Kennesaw State University 680 Arntson Dr MariettaGA30060 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States Department of Radiological Sciences Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering University of California IrvineCA92697 United States Department of Internal Medicine Division of Rheumatology UC Davis Health SacramentoCA95817 United States Geisel School of Medicine Dartmouth College HanoverNH03755 United States Division of Biomedical Informatics and Genomics Tulane Center of Biomedical Informatics and Genomics Deming Department of Medicine Tulane University New OrleansLA70112 United States Department of Mathematical Sciences Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Institute of Computing and Cybersystems Health Research Institute Michigan Technological University HoughtonMI49931 United States Department of Applied Computing Michigan Technological University 1400 Townsend Dr HoughtonMI49931 United States

Hip fractures represent a major health concern, particularly among the elderly, often leading decreased mobility and increased mortality. Early and accurate detection of at risk individuals is crucial for effective intervention. In this study, we propose Iterative Cross Graph Matching for Hip Fracture Risk Assessment (ICGM-FRAX), a novel approach for predicting hip fractures using Dual-energy X-ray Absorptiometry (DXA) images. ICGM-FRAX involves iteratively comparing a test (subject) graph with multiple template graphs representing the characteristics of hip fracture subjects to assess the similarity and accurately to predict hip fracture risk. These graphs are obtained as follows. The DXA images are separated into multiple regions of interest (RoIs), such as the femoral head, shaft, and lesser trochanter. Radiomic features are then calculated for each RoI, with the central coordinates used as nodes in a graph. The connectivity between nodes is established according to the Euclidean distance between these coordinates. This process transforms each DXA image into a graph, where each node represents a RoI, and edges derived by the centroids of RoIs capture the spatial relationships between them. If the test graph closely matches a set of template graphs representing subjects with incident hip fractures, it is classified as indicating high hip fracture risk. We evaluated our method using 547 subjects from the UK Biobank dataset, and experimental results show that ICGM-FRAX achieved a sensitivity of 0.9869, demonstrating high accuracy in predicting hip fractures. © 2025, CC BY.

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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts

Applied AI Letters

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applied AI Letters 2025年第2期6卷

作者： Ramakrishnan, Akshay Bhuvaneswari Madavan, Mukunth Manikandan, R. Gandomi, Amir H. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD United States Mukunth Madhavan AI Security Engineer Banglore India School of Computing SASTRA Deemed University Thanjavur India Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Department of Computer Science Khazar University Baku Azerbaijan

The study suggests using a hybrid convolutional neural networks-support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. Manual processing of fundus images for the automated identification of ocular disorders is laborious, error-prone, and time-consuming. This necessitates computer-assisted technologies that can automatically identify different ocular illnesses from fundus images. The interpretation of the photos also plays a massive role in the diagnosis. Automating the diagnosing procedure reduces human mistakes and helps with early cataract detection. The oneDNN library available in the oneAPI Environment provided by Intel has been used to optimize all transfer learning models for better performance. The suggested approach is verified through a range of metrics in experiments using the openly accessible Ocular Disease Intelligent Recognition dataset. The MobileNet Model outperformed other transfer learning techniques with an accuracy of 0.9836. © 2025 The Author(s). applied AI Letters published by John Wiley & Sons Ltd.

关键词： cataract computer-assisted fundus hybrid ocular oneAPI oneDNN transfer learning

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SOLUTION OF POWER-SYSTEM PROBLEMS THROUGH THE USE OF THE OBJECT-ORIENTED PARADIGM

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1994年第3期16卷 157-165页

作者： DILLON, TS CHANG, E Expert and Intelligent Systems Laboratory Applied Computing Research Institute Australia Department of Computer Science and Computer Engineering La Trobe University Bundoora Vic 3083 Australia

The paper presents a description of the state of the art of the application of object-oriented systems to power system problems. It presents a brief resume of the object-oriented concepts. It examines the application in three areas, namely: traditional software applications, database systems and knowledge-based systems. It also looks at all three stages of the use of object-oriented models in these three areas. The three stages are the conceptual model, the software structure model and the implementation model. It provides a set of conclusions as to future areas of applications of the object-oriented paradigm to power systems applications.

关键词： SOFTWARE ENGINEERING DATABASES KNOWLEDGE-BASED SYSTEMS AND OBJECT-ORIENTED SYSTEMS

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Dynamic merging of frontiers for accelerating the evaluation of betweenness centrality

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ACM Journal of Experimental Algorithmics 2018年第pp1–19期23卷 1.4-1.4页

作者： Vella, Flavio Bernaschi, Massimo Carbone, Giancarlo Computer Science Department Sapienza University of Rome Italy National Research Council of Italy Institute for Applied Computing Italy

Betweenness Centrality (BC) is a widely used metric of the relevance of a node in a network. The fastestknown algorithm for the evaluation of BC on unweighted graphs builds a tree representing information about the shortest paths for each vertex to calculate its contribution to the BC score. Actually, for specific vertices, the shortest-path trees of neighboring nodes could be leveraged to reduce the computational burden, but existing BC algorithms do not exploit that information and carry out redundant *** propose a new algorithm, called dynamic merging of frontiers, which makes use of such information to derive the BC score of degree-2 vertices by re-using the results of the sub-trees of the *** implemented our idea in parallel fashion exploiting Graphics Processing Units. Compared to state-of-the-art implementations, our approach achieves a linear improvement in the number of degree-2 vertices and an average improvement of 4× over a variety of real-world graphs. © 2018 ACM.

关键词： Merging

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