Thi-Xuyen Bui^1,2, Yu-Sheng Lu¹, Yu-Sheng Liao¹, Te-Hua Fang^1,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074862 - 10 February 2026

Abstract The mechanical performance of exceedingly soft materials such as Ag is significantly influenced by various working conditions. Therefore, this study systematically investigates the effects of crack geometry, substrate crystal orientation, and indenter shape on crack propagation. The mechanical response of Ag is analyzed using the quasi-continuum (QC) method. A pre-crack with a predefined depth and angle was introduced to initiate fracture behavior. The results show that when the pre-crack height is 50 Å, the crack propagates rapidly as the imprint depth increases from 0 to 7 Å, grows steadily up to 15 Å, and then… More >

Argho Dey¹, Yunfei Yin^1,2,*, Zheng Yuan¹, Zhiwen Zeng¹, Xianjian Bao³, Md Minhazul Islam¹

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074122 - 10 February 2026

Abstract Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations. Traditional Bird’s Eye View (BEV) methods on semantic scene segmentation, which leverage multimodal sensor fusion, often struggle with noisy data and demand high-performance GPUs, leading to sensor misalignment and performance degradation. This paper introduces an Enhanced Channel Attention BEV (ECABEV), a novel approach designed to address the challenges under insufficient GPU memory conditions. ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism, which utilizes global average and max pooling to effectively filter out noise while… More >

Tongcheng Geng^1,#, Zhiyuan Xu^2,#, Yubin Qu^3,*, W. Eric Wong⁴

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074081 - 10 February 2026

Abstract Large language models (LLMs) have revolutionized AI applications across diverse domains. However, their widespread deployment has introduced critical security vulnerabilities, particularly prompt injection attacks that manipulate model behavior through malicious instructions. Following Kitchenham’s guidelines, this systematic review synthesizes 128 peer-reviewed studies from 2022 to 2025 to provide a unified understanding of this rapidly evolving threat landscape. Our findings reveal a swift progression from simple direct injections to sophisticated multimodal attacks, achieving over 90% success rates against unprotected systems. In response, defense mechanisms show varying effectiveness: input preprocessing achieves 60%–80% detection rates and advanced architectural defenses More >

Bo Zhu^#, Enzhi Dong^#, Zhonghua Cheng^*, Kexin Jiang, Chiming Guo, Shuai Yue

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074009 - 10 February 2026

Abstract Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with More >

Xiaoyu Li^1,2, Jie Zhang³, Wen Shi^1,2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074005 - 10 February 2026

Abstract The emergence of large language models (LLMs) has brought about revolutionary social value. However, concerns have arisen regarding the generation of deceptive content by LLMs and their potential for misuse. Consequently, a crucial research question arises: How can we differentiate between AI-generated and human-authored text? Existing detectors face some challenges, such as operating as black boxes, relying on supervised training, and being vulnerable to manipulation and misinformation. To tackle these challenges, we propose an innovative unsupervised white-box detection method that utilizes a “dual-driven verification mechanism” to achieve high-performance detection, even in the presence of obfuscated… More >

Zeyuan Chen, Yafei Xie, Jinkun Li, Song Wang, Yingqiang Ding^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073846 - 10 February 2026

Abstract Stereo matching is a pivotal task in computer vision, enabling precise depth estimation from stereo image pairs, yet it encounters challenges in regions with reflections, repetitive textures, or fine structures. In this paper, we propose a Semantic-Guided Parallax Attention Stereo Matching Network (SGPASMnet) that can be trained in unsupervised manner, building upon the Parallax Attention Stereo Matching Network (PASMnet). Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets, facilitating robust training across diverse scene-specific datasets and enhancing generalization. SGPASMnet incorporates two novel components: a Cross-Scale Feature Interaction… More >

Ping Fang, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073272 - 10 February 2026

Abstract Defect detection in printed circuit boards (PCB) remains challenging due to the difficulty of identifying small-scale defects, the inefficiency of conventional approaches, and the interference from complex backgrounds. To address these issues, this paper proposes SIM-Net, an enhanced detection framework derived from YOLOv11. The model integrates SPDConv to preserve fine-grained features for small object detection, introduces a novel convolutional partial attention module (C2PAM) to suppress redundant background information and highlight salient regions, and employs a multi-scale fusion network (MFN) with a multi-grain contextual module (MGCT) to strengthen contextual representation and accelerate inference. Experimental evaluations demonstrate More >

Su Zhang¹, Song Deng^1,*, Qingsheng Liu²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072777 - 10 February 2026

Abstract Accurate and reliable power system data are fundamental for critical operations such as grid monitoring, fault diagnosis, and load forecasting, underpinned by increasing intelligentization and digitalization. However, data loss and anomalies frequently compromise data integrity in practical settings, significantly impacting system operational efficiency and security. Most existing data recovery methods require complete datasets for training, leading to substantial data and computational demands and limited generalization. To address these limitations, this study proposes a missing data imputation model based on an improved Generative Adversarial Network (BAC-GAN). Within the BAC-GAN framework, the generator utilizes Bidirectional Long Short-Term… More >

Jing Li¹, Bo Ning^1,*, Bo Li², Xuemei Jin¹, Dezhi Qiu¹, Fenlan Ou¹

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.074335 - 06 February 2026

Abstract As a controllable power generation method requiring no energy storage, Ocean Thermal Energy Conversion (OTEC) technology demonstrates characteristics of abundant reserves, low pollution, and round-the-clock stable operation. The free-standing cold-water pipe (CWP) in the system withstands various complex loads during operation, posing potential failure risks. To reveal the deformation and stress mechanisms of OTEC CWPs, this study first analyzes wave particle velocity and acceleration to determine wave loads at different water depths. Based on the Euler-Bernoulli beam model, a quasi-static load calculation model for OTEC CWPs was established. The governing equations were discretized using the… More >

Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >