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  • Open Access

    ARTICLE

    Hybrid AI-IoT Framework with Digital Twin Integration for Predictive Urban Infrastructure Management in Smart Cities

    Abdullah Alourani1, Mehtab Alam2,*, Ashraf Ali3, Ihtiram Raza Khan4, Chandra Kanta Samal2

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-32, 2026, DOI:10.32604/cmc.2025.070161 - 10 November 2025

    Abstract The evolution of cities into digitally managed environments requires computational systems that can operate in real time while supporting predictive and adaptive infrastructure management. Earlier approaches have often advanced one dimension—such as Internet of Things (IoT)-based data acquisition, Artificial Intelligence (AI)-driven analytics, or digital twin visualization—without fully integrating these strands into a single operational loop. As a result, many existing solutions encounter bottlenecks in responsiveness, interoperability, and scalability, while also leaving concerns about data privacy unresolved. This research introduces a hybrid AI–IoT–Digital Twin framework that combines continuous sensing, distributed intelligence, and simulation-based decision support. The… More >

  • Open Access

    ARTICLE

    DPIL-Traj: Differential Privacy Trajectory Generation Framework with Imitation Learning

    Huaxiong Liao1,2, Xiangxuan Zhong2, Xueqi Chen2, Yirui Huang3, Yuwei Lin2, Jing Zhang2,*, Bruce Gu4

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069208 - 10 November 2025

    Abstract The generation of synthetic trajectories has become essential in various fields for analyzing complex movement patterns. However, the use of real-world trajectory data poses significant privacy risks, such as location re-identification and correlation attacks. To address these challenges, privacy-preserving trajectory generation methods are critical for applications relying on sensitive location data. This paper introduces DPIL-Traj, an advanced framework designed to generate synthetic trajectories while achieving a superior balance between data utility and privacy preservation. Firstly, the framework incorporates Differential Privacy Clustering, which anonymizes trajectory data by applying differential privacy techniques that add noise, ensuring the… More >

  • Open Access

    ARTICLE

    LLM-KE: An Ontology-Aware LLM Methodology for Military Domain Knowledge Extraction

    Yu Tao1, Ruopeng Yang1,2, Yongqi Wen1,*, Yihao Zhong1, Kaige Jiao1, Xiaolei Gu1,2

    CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-17, 2026, DOI:10.32604/cmc.2025.068670 - 10 November 2025

    Abstract Since Google introduced the concept of Knowledge Graphs (KGs) in 2012, their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition, extraction, representation, modeling, fusion, computation, and storage. Within this framework, knowledge extraction, as the core component, directly determines KG quality. In military domains, traditional manual curation models face efficiency constraints due to data fragmentation, complex knowledge architectures, and confidentiality protocols. Meanwhile, crowdsourced ontology construction approaches from general domains prove non-transferable, while human-crafted ontologies struggle with generalization deficiencies. To address these challenges, this study proposes an Ontology-Aware LLM Methodology for Military Domain More >

  • Open Access

    PROCEEDINGS

    Study on Friction Behavior of Soft Material Based on Predictive Modeling and Interfacial Tribometry

    Huixin Wei, Baopeng Liao*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012367

    Abstract Friction behavior at soft-hard material interfaces plays a pivotal role in applications spanning biomedical devices, robotics, and tactile systems. While theoretical frameworks and experimental characterization methods have advanced, it is still difficult to unravel interfacial mechanisms. In this study, a theoretical model is firstly developed to predict static-to-sliding transitions by analyzing geometric evolution and stick-slip dynamics at soft material interfaces. The model quantitatively determines the threshold force for slip initiation, offering predictive insights into The sliding behavior of the interface. Second, an innovative tribometry platform is introduced, combining synchronized optical visualization, mechanical loading, and automated More >

  • Open Access

    PROCEEDINGS

    Three-Dimensional Failure Mechanics Theory and Digital Applications

    Pengfei Cui*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.010832

    Abstract With the continuous advancement of aerospace equipment, in addition to performance, function, and reliability requirements, durability is playing an increasingly crucial role. For instance, the objective of China's new - generation space transportation system is to achieve a reliability of 0.9999 or higher for manned flights, and a single rocket is expected to be capable of up to 100 flights. In high - temperature load - bearing structures, nickel - based alloys are extensively used because of their outstanding strength, fatigue resistance, and creep properties. In advanced aerospace engines, their mass fraction can reach as… More >

  • Open Access

    ARTICLE

    Vortex-Induced Vibration Prediction in Floating Structures via Unstructured CFD and Attention-Based Convolutional Modeling

    Yan Li1,2,*, Yibin Wu1,2, Bo Zhang1,2

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 2905-2925, 2025, DOI:10.32604/fdmp.2025.072979 - 31 December 2025

    Abstract Traditional Computational Fluid Dynamics (CFD) simulations are computationally expensive when applied to complex fluid–structure interaction problems and often struggle to capture the essential flow features governing vortex-induced vibrations (VIV) of floating structures. To overcome these limitations, this study develops a hybrid framework that integrates high-fidelity CFD modeling with deep learning techniques to enhance the accuracy and efficiency of VIV response prediction. First, an unstructured finite-volume fluid–structure coupling model is established to generate high-resolution flow field data and extract multi-component time-series feature tensors. These tensors serve as inputs to a Squeeze-and-Excitation Convolutional Neural Network (SE-CNN), which… More >

  • Open Access

    ARTICLE

    Numerical Simulation of Microscopic Seepage Mechanisms in Gas Reservoir Storage Systems

    Yulong Zhao1, Yang Luo1,*, Yuming Luo2, Yulai Pang2, Ruihan Zhang1, Zihan Zhao3

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 3073-3090, 2025, DOI:10.32604/fdmp.2025.070685 - 31 December 2025

    Abstract The development of underground gas storage (UGS) systems is vital for maintaining stability between energy supply and demand. This study explores the dynamic response mechanisms of carbonate reservoirs subjected to intense injection–production cycling during UGS operations. By integrating three-dimensional digital core technology with a coupled poro-mechanical model, we simulate the pore-scale behavior of a representative Huangcaoxia UGS carbonate core. The results demonstrate that fluid–solid coupling effects markedly amplify permeability reduction, far exceeding the influence of porosity variations alone. More significantly, gas production leads to a pronounced decline in permeability driven by rising effective stress, arising More >

  • Open Access

    ARTICLE

    Numerical Investigation of Load Generation in U-Shaped Aqueducts under Lateral Excitation: Part II—Non-Resonant Sloshing

    Yang Dou1, Hao Qin1, Yuzhi Zhang1,2, Ning Wang1, Haiqing Liu3,4, Wanli Yang1,2,4,*

    FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 3091-3122, 2025, DOI:10.32604/fdmp.2025.070082 - 31 December 2025

    Abstract In recent years, tuned liquid dampers (TLDs) have emerged as a focal point of research due to their remarkable potential for structural vibration mitigation. Yet, progress in this field remains constrained by an incomplete understanding of the fundamental mechanisms governing sloshing-induced loads in liquid-filled containers. Aqueducts present a distinctive case, as the capacity of their contained water to function effectively as a TLD remains uncertain. To address this gap, the present study investigates the generation mechanisms of sloshing loads under non-resonant cases through a two-dimensional (2D) computational fluid dynamics (CFD) model developed in ANSYS Fluent.… More >

  • Open Access

    ARTICLE

    Enhancing Well-Being through Psychological Resilience and Social Capital: An Empirical Study of Female Entrepreneurs in the Long-Term Care Industry

    Chia-Hui Hou*

    International Journal of Mental Health Promotion, Vol.27, No.12, pp. 2007-2022, 2025, DOI:10.32604/ijmhp.2025.073748 - 31 December 2025

    Abstract Objectives: With the rapid aging of populations worldwide, the long-term care (LTC) industry has become a critical arena for both social welfare and entrepreneurial development, particularly among women who play a leading role in caregiving enterprises. However, female LTC entrepreneurs often face emotional strain and limited social resources that affect their professional well-being. This study investigates the effects of psychological resilience and social capital on the well-being of female entrepreneurs in the long-term care (LTC) industry and examines the mediating role of entrepreneurial competence. Methods: A mixed-methods design was employed. Quantitative data were collected from 73… More >

  • Open Access

    REVIEW

    Understanding Adolescent Social Media Use: A Narrative Review of Motivations, Risk Factors, and Mental Health Implications

    Kyung-Hyun Suh1,*, Sung-Jin Chung1, Goo-Churl Jeong1, Kunho Lee1, Ji-Hyun Ryu2

    International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1829-1845, 2025, DOI:10.32604/ijmhp.2025.071879 - 31 December 2025

    Abstract Background: Adolescents increasingly engage with social media for connection, self-expression, and identity exploration. This growing digital engagement has raised concerns about its potential risks and mental health implications. Methods: This narrative review examines literature on adolescent social media use by exploring underlying motivations, risk and protective factors across personal, environmental, and digital domains, with a focus on mental health outcomes. Results: Individual vulnerabilities—such as low self-esteem, impulsivity, and poor sleep—interact with contextual factors like peer pressure and family conflict to elevate risks. Digital environments shaped by algorithmic feeds, feedback mechanisms, and curated content promote social comparison and More >

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