Wenfeng Zheng¹, Guangyu Xu², Siyu Lu³, Junmin Lyu⁴, Feng Bao^5,*, Lirong Yin^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075741 - 29 January 2026

Abstract Graph Neural Networks (GNNs), as a deep learning framework specifically designed for graph-structured data, have achieved deep representation learning of graph data through message passing mechanisms and have become a core technology in the field of graph analysis. However, current reviews on GNN models are mainly focused on smaller domains, and there is a lack of systematic reviews on the classification and applications of GNN models. This review systematically synthesizes the three canonical branches of GNN, Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sampling Aggregation Network (GraphSAGE), and analyzes their integration pathways More >

Monalisa Jena¹, Noman Khan^2,*, Mi Young Lee^3,*, Seungmin Rho³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.075119 - 29 January 2026

Abstract Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises. When such risks go undetected, consequences can escalate to self-harm, long-term disability, reduced productivity, and significant societal and economic burden. Despite recent advances, detecting risk from online text remains challenging due to heterogeneous language, evolving semantics, and the sequential emergence of new datasets. Effective solutions must encode clinically meaningful cues, reason about causal relations, and adapt to new domains without forgetting prior knowledge. To address these challenges, this paper presents a Continual Neuro-Symbolic Graph… More >

Moein Tosan^1,*, Vahid Nourani^2,3, Ozgur Kisi^4,5,6, 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 >

Fadhil M. Oleiwi¹, Jaber O. Dahloos², Amer Resen Kalash³, Hasanain A. Abdul Wahhab³, Miqdam T. Chaichan^1,4,*

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073313 - 27 January 2026

Abstract In the present study, researchers examined a solar off-grid-connected photovoltaic system for a family house in the city of Baghdad. The design was created with the help of the “How to Design PV Program” and the “Renewable Energy Investment Calculator (REICAL)” software (Version 1.1). In Iraq, the national grid provides around 71% of the overall electricity demand, though this drops to nearly 50% during extremely hot and cold months, where the supply alternates between four hours on and four hours off. During the off periods, power is generated by local generators at high costs. To… More >

Urszula Częścik^1,2,#, Martyna Gryglas³, Arkadiusz Szterk⁴, Sylwia Flis^3,#,*

Oncology Research, Vol.34, No.2, 2026, DOI:10.32604/or.2025.067449 - 19 January 2026

Abstract Colorectal cancer (CRC) is the third most common malignancy worldwide and the second leading cause of cancer-related deaths, accounting for approximately 10% of all cancer cases. By 2050, CRC incidence is expected to rise substantially, driven by population aging and greater exposure to risk factors in developing countries. Despite advances in medicine and pharmacy, the effectiveness of available treatments remains limited, underscoring the urgent need for innovative therapeutic strategies. This review summarizes and critically evaluates currently available CRC therapies and explores new emerging directions. Particular attention is given to the role of immunotherapy, targeted therapies,… More >

Abdul Rahaman Wahab Sait^1,*, Yazeed Alkhurayyif²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072481 - 12 January 2026

Abstract Malware poses a significant threat to the Internet of Things (IoT). It enables unauthorized access to devices in the IoT environment. The lack of unique architectural standards causes challenges in developing robust malware detection (MD) models. The existing models demand substantial computational resources. This study intends to build a lightweight MD model to detect anomalies in IoT networks. The authors develop a transformation technique, converting the malware binaries into images. MobileNet V2 is fine-tuned using improved grey wolf optimization (IGWO) to extract crucial features of malicious and benign samples. The ResNeXt model is combined with… More >

Minghui Li¹, Hongbo Li^1,*, Jiaqi Zhu², Xupeng Zhang¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072406 - 12 January 2026

Abstract To address the challenge of real-time detection of unauthorized drone intrusions in complex low-altitude urban environments such as parks and airports, this paper proposes an enhanced MBS-YOLO (Multi-Branch Small Target Detection YOLO) model for anti-drone object detection, based on the YOLOv8 architecture. To overcome the limitations of existing methods in detecting small objects within complex backgrounds, we designed a C2f-Pu module with excellent feature extraction capability and a more compact parameter set, aiming to reduce the model’s computational complexity. To improve multi-scale feature fusion, we construct a Multi-Branch Feature Pyramid Network (MB-FPN) that employs a… More >

Qian Yu^1,2, Gui Zhang^2,*, Ying Wang¹, Xin Wu², Jiangshu Xiao², Wenbing Kuang¹, Juan Zhang²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072172 - 12 January 2026

Abstract Detecting small forest fire targets in unmanned aerial vehicle (UAV) images is difficult, as flames typically cover only a very limited portion of the visual scene. This study proposes Context-guided Compact Lightweight Network (CCLNet), an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources. CCLNet employs a three-stage network architecture. Its key components include three modules. C3F-Convolutional Gated Linear Unit (C3F-CGLU) performs selective local feature extraction while preserving fine-grained high-frequency flame details. Context-Guided Feature Fusion Module (CGFM) replaces plain concatenation with triplet-attention interactions to… More >

Leyu Zheng¹, Mingming Xiao^1,*, Yi Ren², Ke Li¹, Chang Sun¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072036 - 12 January 2026

Abstract In a wide range of engineering applications, complex constrained multi-objective optimization problems (CMOPs) present significant challenges, as the complexity of constraints often hampers algorithmic convergence and reduces population diversity. To address these challenges, we propose a novel algorithm named Constraint Intensity-Driven Evolutionary Multitasking (CIDEMT), which employs a two-stage, tri-task framework to dynamically integrates problem structure and knowledge transfer. In the first stage, three cooperative tasks are designed to explore the Constrained Pareto Front (CPF), the Unconstrained Pareto Front (UPF), and the ε-relaxed constraint boundary, respectively. A CPF-UPF relationship classifier is employed to construct a problem-type-aware… More >

Tao Geng¹, Shuaibing Li^1,*, Yunyun Yun¹, Yongqiang Kang¹, Hongwei Li², Junmin Zhu²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071644 - 12 January 2026

Abstract In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial… More >