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  • Open Access

    ARTICLE

    Shape Memory Properties of Short-Glass Fiber Reinforced Epoxy Composite Programmed below Glass Transition Temperature

    Kartikey Shahi, Velmurugan Ramachandran*, Ranjith Mohan, Boomurugan Ramachandran

    Journal of Polymer Materials, Vol.42, No.2, pp. 477-496, 2025, DOI:10.32604/jpm.2025.062481 - 14 July 2025

    Abstract A Shape Memory Polymer Composite (SMPC) is developed by reinforcing an epoxy-based polymer with randomly oriented short glass fibers. Diverging from previous research, which primarily focused on the hot programming of short glass fiber-based SMPCs, this work explores the potential for programming below the glass transition temperature (Tg) for epoxy-based SMPCs. To mitigate the inherent brittleness of the SMPC during deformation, a linear polymer is incorporated, and a temperature between room temperature and Tg is chosen as the deformation temperature to study the shape memory properties. The findings demonstrate an enhancement in shape fixity and More >

  • Open Access

    REVIEW

    Ethical Implications of AI-Driven Ethical Hacking: A Systematic Review and Governance Framework

    Hossana Maghiri Sufficient*, Abdulazeez Murtala Mohammed, Bashir Danjuma

    Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025

    Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >

  • Open Access

    REVIEW

    Contemporary Management of Failing Modified Fontan after the Total Cavopulmonary Connection

    Honghao Fu#, Zhangwei Wang#, Shoujun Li*

    Congenital Heart Disease, Vol.20, No.3, pp. 287-303, 2025, DOI:10.32604/chd.2025.067619 - 11 July 2025

    Abstract Congenital heart disease (CHD) stands as the most common cardiovascular disorder among children, exerting a profound impact on the growth, development, and quality of life of the affected pediatric population. The modified Fontan procedure, the total cavopulmonary connection (TCPC), has become a pivotal palliative or definitive surgical method for treating complex CHD cases, including single ventricle and tricuspid valve atresia. Through staged surgical processes, this technique directly diverts vena cava blood into the pulmonary artery, thus improving the patient’s oxygenation status. Despite the initial success of the Fontan circulation in providing a means for survival More > Graphic Abstract

    Contemporary Management of Failing Modified Fontan after the Total Cavopulmonary Connection

  • Open Access

    ARTICLE

    Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization

    Chenxi Lyu1, Chen Dong1, Qiancheng Xiong1, Yuzhong Chen1, Qian Weng1,*, Zhenyi Chen2

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3371-3391, 2025, DOI:10.32604/cmc.2025.065153 - 03 July 2025

    Abstract The rapid advancement of Industry 4.0 has revolutionized manufacturing, shifting production from centralized control to decentralized, intelligent systems. Smart factories are now expected to achieve high adaptability and resource efficiency, particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands. To address the challenges of dynamic task allocation, uncertainty, and real-time decision-making, this paper proposes Pathfinder, a deep reinforcement learning-based scheduling framework. Pathfinder models scheduling data through three key matrices: execution time (the time required for a job to complete), completion time (the actual time at which a job is finished),… More >

  • Open Access

    REVIEW

    Research Trends and Networks in Self-Explaining Autonomous Systems: A Bibliometric Study

    Oscar Peña-Cáceres1,2,*, Elvis Garay-Silupu3, Darwin Aguilar-Chuquizuta4, Henry Silva-Marchan4

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2151-2188, 2025, DOI:10.32604/cmc.2025.065149 - 03 July 2025

    Abstract Self-Explaining Autonomous Systems (SEAS) have emerged as a strategic frontier within Artificial Intelligence (AI), responding to growing demands for transparency and interpretability in autonomous decision-making. This study presents a comprehensive bibliometric analysis of SEAS research published between 2020 and February 2025, drawing upon 1380 documents indexed in Scopus. The analysis applies co-citation mapping, keyword co-occurrence, and author collaboration networks using VOSviewer, MASHA, and Python to examine scientific production, intellectual structure, and global collaboration patterns. The results indicate a sustained annual growth rate of 41.38%, with an h-index of 57 and an average of 21.97 citations… More >

  • Open Access

    ARTICLE

    Addressing Modern Cybersecurity Challenges: A Hybrid Machine Learning and Deep Learning Approach for Network Intrusion Detection

    Khadija Bouzaachane1,*, El Mahdi El Guarmah2, Abdullah M. Alnajim3, Sheroz Khan4

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025

    Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >

  • Open Access

    ARTICLE

    Multi-Agent Reinforcement Learning for Moving Target Defense Temporal Decision-Making Approach Based on Stackelberg-FlipIt Games

    Rongbo Sun, Jinlong Fei*, Yuefei Zhu, Zhongyu Guo

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3765-3786, 2025, DOI:10.32604/cmc.2025.064849 - 03 July 2025

    Abstract Moving Target Defense (MTD) necessitates scientifically effective decision-making methodologies for defensive technology implementation. While most MTD decision studies focus on accurately identifying optimal strategies, the issue of optimal defense timing remains underexplored. Current default approaches—periodic or overly frequent MTD triggers—lead to suboptimal trade-offs among system security, performance, and cost. The timing of MTD strategy activation critically impacts both defensive efficacy and operational overhead, yet existing frameworks inadequately address this temporal dimension. To bridge this gap, this paper proposes a Stackelberg-FlipIt game model that formalizes asymmetric cyber conflicts as alternating control over attack surfaces, thereby capturing More >

  • Open Access

    ARTICLE

    Design and Application of a New Distributed Dynamic Spatio-Temporal Privacy Preserving Mechanisms

    Jiacheng Xiong1, Xingshu Chen1,2,3,*, Xiao Lan2,3, Liangguo Chen1,2

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2273-2303, 2025, DOI:10.32604/cmc.2025.063984 - 03 July 2025

    Abstract In the era of big data, the growing number of real-time data streams often contains a lot of sensitive privacy information. Releasing or sharing this data directly without processing will lead to serious privacy information leakage. This poses a great challenge to conventional privacy protection mechanisms (CPPM). The existing data partitioning methods ignore the number of data replications and information exchanges, resulting in complex distance calculations and inefficient indexing for high-dimensional data. Therefore, CPPM often fails to meet the stringent requirements of efficiency and reliability, especially in dynamic spatiotemporal environments. Addressing this concern, we proposed… More >

  • Open Access

    REVIEW

    Generative Artificial Intelligence (GAI) in Breast Cancer Diagnosis and Treatment: A Systematic Review

    Xiao Jian Tan1,2,3,*, Wai Loon Cheor2, Ee Meng Cheng4,5, Chee Chin Lim3,4, Khairul Shakir Ab Rahman6

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2015-2060, 2025, DOI:10.32604/cmc.2025.063407 - 03 July 2025

    Abstract This study systematically reviews the applications of generative artificial intelligence (GAI) in breast cancer research, focusing on its role in diagnosis and therapeutic development. While GAI has gained significant attention across various domains, its utility in breast cancer research has yet to be comprehensively reviewed. This study aims to fill that gap by synthesizing existing research into a unified document. A comprehensive search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in the retrieval of 3827 articles, of which 31 were deemed eligible for analysis. The included studies were… More >

  • Open Access

    ARTICLE

    Enhancing Healthcare Data Privacy in Cloud IoT Networks Using Anomaly Detection and Optimization with Explainable AI (ExAI)

    Jitendra Kumar Samriya1, Virendra Singh2, Gourav Bathla3, Meena Malik4, Varsha Arya5,6, Wadee Alhalabi7, Brij B. Gupta8,9,10,11,*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3893-3910, 2025, DOI:10.32604/cmc.2025.063242 - 03 July 2025

    Abstract The integration of the Internet of Things (IoT) into healthcare systems improves patient care, boosts operational efficiency, and contributes to cost-effective healthcare delivery. However, overcoming several associated challenges, such as data security, interoperability, and ethical concerns, is crucial to realizing the full potential of IoT in healthcare. Real-time anomaly detection plays a key role in protecting patient data and maintaining device integrity amidst the additional security risks posed by interconnected systems. In this context, this paper presents a novel method for healthcare data privacy analysis. The technique is based on the identification of anomalies in… More >

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