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  • Open Access

    ARTICLE

    A Dual-Stream Framework for Landslide Segmentation with Cross-Attention Enhancement and Gated Multimodal Fusion

    Md Minhazul Islam1,2, Yunfei Yin1,2,*, Md Tanvir Islam1,2, Zheng Yuan1,2, Argho Dey1,2

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072550 - 12 January 2026

    Abstract Automatic segmentation of landslides from remote sensing imagery is challenging because traditional machine learning and early CNN-based models often fail to generalize across heterogeneous landscapes, where segmentation maps contain sparse and fragmented landslide regions under diverse geographical conditions. To address these issues, we propose a lightweight dual-stream siamese deep learning framework that integrates optical and topographical data fusion with an adaptive decoder, guided multimodal fusion, and deep supervision. The framework is built upon the synergistic combination of cross-attention, gated fusion, and sub-pixel upsampling within a unified dual-stream architecture specifically optimized for landslide segmentation, enabling efficient… More >

  • Open Access

    ARTICLE

    Research on the Classification of Digital Cultural Texts Based on ASSC-TextRCNN Algorithm

    Zixuan Guo1, Houbin Wang2, Sameer Kumar1,*, Yuanfang Chen3

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072064 - 12 January 2026

    Abstract With the rapid development of digital culture, a large number of cultural texts are presented in the form of digital and network. These texts have significant characteristics such as sparsity, real-time and non-standard expression, which bring serious challenges to traditional classification methods. In order to cope with the above problems, this paper proposes a new ASSC (ALBERT, SVD, Self-Attention and Cross-Entropy)-TextRCNN digital cultural text classification model. Based on the framework of TextRCNN, the Albert pre-training language model is introduced to improve the depth and accuracy of semantic embedding. Combined with the dual attention mechanism, the… More >

  • Open Access

    REVIEW

    Toward Robust Deepfake Defense: A Review of Deepfake Detection and Prevention Techniques in Images

    Ahmed Abdel-Wahab1, Mohammad Alkhatib2,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-34, 2026, DOI:10.32604/cmc.2025.070010 - 09 December 2025

    Abstract Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks (GANs). Deepfake technology has many useful uses in education and entertainment, but it also raises a lot of ethical, social, and security issues, such as identity theft, the dissemination of false information, and privacy violations. This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes, with a particular focus on image-based Deepfakes. There are three main types of detection methods: classical, machine learning (ML) and deep learning (DL)-based, and hybrid methods. There are… More >

  • 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

    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

    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 >

  • Open Access

    ARTICLE

    Understanding Young Adults’ Social Media Anxiety: Mediating Role of Upward Social Comparison and the Moderating Role of Psychological Resilience

    Jinqian Li1, Jianhong Wu2,*

    International Journal of Mental Health Promotion, Vol.27, No.12, pp. 1883-1896, 2025, DOI:10.32604/ijmhp.2025.071306 - 31 December 2025

    Abstract Background: Platform algorithms driving content presentation are profoundly shaping the experience of younger users. While prior research has examined anxiety stemming from young adults’ social media usage, the link between upward social comparison and anxiety remains unclear. This study aims to investigate the mediating role of upward social comparison in this relationship and determine the moderating role of psychological resilience. Methods: A cross-sectional survey was conducted among 562 young Chinese adults aged 18–35 (53% female). Data were collected via an online questionnaire employing validated measurement instruments, including scales for social media usage patterns, upward comparator behaviour… More >

  • Open Access

    ARTICLE

    Digital mental health: Integrating psychotherapeutic innovations and technology—A Nigerian perspective

    A. O. Onwudiwe, C. I. Onyemaechi*, S. C. Achebe, P. O. Philip, O. A. Ugwu

    Journal of Psychology in Africa, Vol.35, No.6, pp. 843-851, 2025, DOI:10.32604/jpa.2025.069734 - 30 December 2025

    Abstract Despite high burden of mental disorders in Nigeria, access to care remains critically limited, with stigma, inadequate infrastructure, and economic constraints posing significant barriers. Integration of mental health and technology offers unprecedented opportunities to bridge this treatment gap. This paper explores the potential of digital mental health interventions like mobile applications and teletherapy, as viable solutions through which mental health services could be expanded. Leveraging Nigeria’s growing digital ecosystem and mobile phone penetration, these innovations can provide scalable, cost-effective, and culturally relevant interventions, particularly in underserved areas. However, challenges such as digital literacy gaps, socio-cultural More >

  • Open Access

    ARTICLE

    Leaders’ artificial intelligence symbolization behavior and enterprise digital transformation: Mediation by employees’ attitude towards digital transformation, and moderation of learning orientation

    Yungui Guo*, Xuan Fan

    Journal of Psychology in Africa, Vol.35, No.6, pp. 791-796, 2025, DOI:10.32604/jpa.2025.067238 - 30 December 2025

    Abstract This study examined the moderating role of employees’ learning orientation on the relationship between leaders’ artificial intelligence symbolization behavior (LAISB), employees’ attitude towards digital transformation (ATDT), and enterprise digital transformation. The sample consisted of 261 employees from five enterprises in China (female = 34.5%; primary industry includes the internet and transportation; mean age = 42.51 years, SD = 8.63 years; bachelor’s degree or above = 72.8%). The results of structural equation modeling and simple slope test indicated that LAISB predicted higher enterprise digital transformation, with ATDT partial mediation. Furthermore, employees’ learning orientation weakened the relationship More >

  • Open Access

    ARTICLE

    PPG Based Digital Biomarker for Diabetes Detection with Multiset Spatiotemporal Feature Fusion and XAI

    Mubashir Ali1,2, Jingzhen Li1, Zedong Nie1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4153-4177, 2025, DOI:10.32604/cmes.2025.073048 - 23 December 2025

    Abstract Diabetes imposes a substantial burden on global healthcare systems. Worldwide, nearly half of individuals with diabetes remain undiagnosed, while conventional diagnostic techniques are often invasive, painful, and expensive. In this study, we propose a noninvasive approach for diabetes detection using photoplethysmography (PPG), which is widely integrated into modern wearable devices. First, we derived velocity plethysmography (VPG) and acceleration plethysmography (APG) signals from PPG to construct multi-channel waveform representations. Second, we introduced a novel multiset spatiotemporal feature fusion framework that integrates hand-crafted temporal, statistical, and nonlinear features with recursive feature elimination and deep feature extraction using… More >

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