Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (936)
  • Open Access

    REVIEW

    Mental Health and Well-Being of Doctoral Students: A Systematic Review

    Yuxin Guo1,2, Xinqiao Liu3,*

    International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.074063 - 28 January 2026

    Abstract Background: Mental health concerns among doctoral students have become increasingly prominent, with consistently low levels of well-being making this issue a critical focus in higher education research. This study aims to synthesize existing evidence on the mental health and well-being of doctoral students and to identify key factors and intervention strategies reported in the literature. Methods: A systematic review was conducted to examine the determinants and interventions related to doctoral students’ mental health and well-being. Relevant studies were comprehensively searched in Web of Science, PubMed, Scopus, and EBSCO, with the final search conducted on September 19,… More >

  • Open Access

    ARTICLE

    A Robust Vision-Based Framework for Traffic Sign and Light Detection in Automated Driving Systems

    Mohammed Al-Mahbashi1,2,*, Ali Ahmed3, Abdolraheem Khader4,*, Shakeel Ahmad3, Mohamed A. Damos5, Ahmed Abdu6

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075909 - 29 January 2026

    Abstract Reliable detection of traffic signs and lights (TSLs) at long range and under varying illumination is essential for improving the perception and safety of autonomous driving systems (ADS). Traditional object detection models often exhibit significant performance degradation in real-world environments characterized by high dynamic range and complex lighting conditions. To overcome these limitations, this research presents FED-YOLOv10s, an improved and lightweight object detection framework based on You Only look Once v10 (YOLOv10). The proposed model integrates a C2f-Faster block derived from FasterNet to reduce parameters and floating-point operations, an Efficient Multiscale Attention (EMA) mechanism to More >

  • Open Access

    REVIEW

    The Transparency Revolution in Geohazard Science: A Systematic Review and Research Roadmap for Explainable Artificial Intelligence

    Moein Tosan1,*, Vahid Nourani2,3, Ozgur Kisi4,5,6, Yongqiang Zhang7, Sameh A. Kantoush8, Mekonnen Gebremichael9, Ruhollah Taghizadeh-Mehrjardi10, Jinhui Jeanne Huang11

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074768 - 29 January 2026

    Abstract The integration of machine learning (ML) into geohazard assessment has successfully instigated a paradigm shift, leading to the production of models that possess a level of predictive accuracy previously considered unattainable. However, the black-box nature of these systems presents a significant barrier, hindering their operational adoption, regulatory approval, and full scientific validation. This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence (XAI) as applied to geohazard science (GeoXAI), a domain that aims to resolve the long-standing trade-off between model performance and interpretability. A rigorous synthesis of 87 foundational… More >

  • Open Access

    ARTICLE

    Context Patch Fusion with Class Token Enhancement for Weakly Supervised Semantic Segmentation

    Yiyang Fu1, Hui Li2,*, Wangyu Wu3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074467 - 29 January 2026

    Abstract Weakly Supervised Semantic Segmentation (WSSS), which relies only on image-level labels, has attracted significant attention for its cost-effectiveness and scalability. Existing methods mainly enhance inter-class distinctions and employ data augmentation to mitigate semantic ambiguity and reduce spurious activations. However, they often neglect the complex contextual dependencies among image patches, resulting in incomplete local representations and limited segmentation accuracy. To address these issues, we propose the Context Patch Fusion with Class Token Enhancement (CPF-CTE) framework, which exploits contextual relations among patches to enrich feature representations and improve segmentation. At its core, the Contextual-Fusion Bidirectional Long Short-Term More >

  • Open Access

    ARTICLE

    A Fractional-Order Study for Bicomplex Haemorrhagic Infection in Several Populations Conditions

    Muhammad Farman1,2,3,*, Muhammad Hashir Zubair4, Hua Li4, Kottakkaran Sooppy Nisar5,6, Mohamad Hafez7,8

    CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074160 - 29 January 2026

    Abstract Lassa Fever (LF) is a viral hemorrhagic illness transmitted via rodents and is endemic in West Africa, causing thousands of deaths annually. This study develops a dynamic model of Lassa virus transmission, capturing the progression of the disease through susceptible, exposed, infected, and recovered populations. The focus is on simulating this model using the fractional Caputo derivative, allowing both qualitative and quantitative analyses of boundedness, positivity, and solution uniqueness. Fixed-point theory and Lipschitz conditions are employed to confirm the existence and uniqueness of solutions, while Lyapunov functions establish the global stability of both disease-free and… More >

  • Open Access

    ARTICLE

    Predictive Maintenance Strategy for Photovoltaic Power Systems: Collaborative Optimization of Transformer-Based Lifetime Prediction and Opposition-Based Learning HHO Algorithm

    Wei Chen, Yang Wu*, Tingting Pei, Jie Lin, Guojing Yuan

    Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070905 - 27 January 2026

    Abstract In view of the insufficient utilization of condition-monitoring information and the improper scheduling often observed in conventional maintenance strategies for photovoltaic (PV) modules, this study proposes a predictive maintenance (PdM) strategy based on Remaining Useful Life (RUL) estimation. First, a RUL prediction model is established using the Transformer architecture, which enables the effective processing of sequential degradation data. By employing the historical degradation data of PV modules, the proposed model provides accurate forecasts of the remaining useful life, thereby supplying essential inputs for maintenance decision-making. Subsequently, the RUL information obtained from the prediction process is… More >

  • Open Access

    REVIEW

    A Systematic Review of Frameworks for the Detection and Prevention of Card-Not-Present (CNP) Fraud

    Kwabena Owusu-Mensah*, Edward Danso Ansong , Kofi Sarpong Adu-Manu, Winfred Yaokumah

    Journal of Cyber Security, Vol.8, pp. 33-92, 2026, DOI:10.32604/jcs.2026.074265 - 20 January 2026

    Abstract The rapid growth of digital payment systems and remote financial services has led to a significant increase in Card-Not-Present (CNP) fraud, which is now the primary source of card-related losses worldwide. Traditional rule-based fraud detection methods are becoming insufficient due to several challenges, including data imbalance, concept drift, privacy concerns, and limited interpretability. In response to these issues, a systematic review of twenty-four CNP fraud detection frameworks developed between 2014 and 2025 was conducted. This review aimed to identify the technologies, strategies, and design considerations necessary for adaptive solutions that align with evolving regulatory standards.… More >

  • Open Access

    REVIEW

    The Frontier of Melanoma Treatment: Defeating Immunotherapy Resistance—A Systematic Review

    Kamila Mozga1, Olga Synowiecka1, Igor Rydzyk1, Anna Marek1, Ewelina Wieczorek1, Alicja Petniak2,*, Paulina Gil-Kulik2

    Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.070505 - 19 January 2026

    Abstract Objectives: Immunotherapy based on immune checkpoint blockade (ICB) has become a key treatment for melanoma. However, the increasing number of cases of melanoma resistant to immunotherapy highlights the need to develop methods to overcome this resistance. This study aims to collect the most recent information on melanoma immunotherapy, discuss potential strategies to overcome resistance to immunotherapy, and identify areas that require further analysis. Methods: To achieve this goal, scientific publications from 2021–2024 available in PubMed and Google Scholar databases were analyzed. The databases were searched using the following terms: “melanoma”, “immunotherapy”, “Immune Checkpoint Blockade”, and More >

  • Open Access

    REVIEW

    Unveiling the Anticancer Potential of Urolithin A in Colorectal Cancer: A Systematic Review

    Mariana Francisco1, Fernando Mendes1,2,3,4,5,*, Diana Martins1,2,3,4, Joana Liberal1,2

    Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.070276 - 19 January 2026

    Abstract Objectives: Colorectal cancer (CRC) is a major global health burden, and Urolithin A (Uro-A) has emerged as a promising anticancer agent. This systematic review aims to synthesize current in vitro evidence on the anticancer effects of Uro-A in CRC, highlighting effective concentration ranges, exposure times, relevant outcomes, and underlying molecular mechanisms. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in PubMed, Scopus, and Web of Science using the following strategy: (colorectal cancer) AND (urolithin a) OR (3,8-dihydroxy-6H-dibenzo(b,d)pyran-6-one). Eligibility criteria were defined by the PICO framework: (P) in vitro CRC cell models; (I) Uro-A alone or… More > Graphic Abstract

    Unveiling the Anticancer Potential of Urolithin A in Colorectal Cancer: A Systematic Review

  • Open Access

    ARTICLE

    Enhancing Anomaly Detection with Causal Reasoning and Semantic Guidance

    Weishan Gao1,2, Ye Wang1,2, Xiaoyin Wang1,2, Xiaochuan Jing1,2,*

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073850 - 12 January 2026

    Abstract In the field of intelligent surveillance, weakly supervised video anomaly detection (WSVAD) has garnered widespread attention as a key technology that identifies anomalous events using only video-level labels. Although multiple instance learning (MIL) has dominated the WSVAD for a long time, its reliance solely on video-level labels without semantic grounding hinders a fine-grained understanding of visually similar yet semantically distinct events. In addition, insufficient temporal modeling obscures causal relationships between events, making anomaly decisions reactive rather than reasoning-based. To overcome the limitations above, this paper proposes an adaptive knowledge-based guidance method that integrates external structured… More >

Displaying 1-10 on page 1 of 936. Per Page