José Armando Santas Ciavatta, Juan Ramón Bermejo Higuera^*, Javier Bermejo Higuera, Juan Antonio Sicilia Montalvo, Tomás Sureda Riera, Jesús Pérez Melero

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074566 - 12 January 2026

Abstract As artificial Intelligence (AI) continues to expand exponentially, particularly with the emergence of generative pre-trained transformers (GPT) based on a transformer’s architecture, which has revolutionized data processing and enabled significant improvements in various applications. This document seeks to investigate the security vulnerabilities detection in the source code using a range of large language models (LLM). Our primary objective is to evaluate the effectiveness of Static Application Security Testing (SAST) by applying various techniques such as prompt persona, structure outputs and zero-shot. To the selection of the LLMs (CodeLlama 7B, DeepSeek coder 7B, Gemini 1.5 Flash,… More >

Safwan Al-sayed¹, Xi Wang¹, Yijiang Peng^1,*, Esraa Hyarat², Ahmad Ali AlZubi³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.074068 - 12 January 2026

Abstract In modern construction, Lightweight Aggregate Concrete (LWAC) has been recognized as a vital material of concern because of its unique properties, such as reduced density and improved thermal insulation. Despite the extensive knowledge regarding its macroscopic properties, there is a wide knowledge gap in understanding the influence of microscale parameters like aggregate porosity and volume ratio on the mechanical response of LWAC. This study aims to bridge this knowledge gap, spurred by the need to enhance the predictability and applicability of LWAC in various construction environments. With the help of advanced numerical methods, including the… More >

Changjun Zhou¹, Dongfang Chen¹, Chenyang Shi¹, Taiyong Li^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073662 - 12 January 2026

Abstract With the rapid development of smart manufacturing, intelligent safety monitoring in industrial workshops has become increasingly important. To address the challenges of complex backgrounds, target scale variation, and excessive model parameters in worker violation detection, this study proposes ADCP-YOLO, an enhanced lightweight model based on YOLOv8. Here, “ADCP” represents four key improvements: Alterable Kernel Convolution (AKConv), Dilated-Wise Residual (DWR) module, Channel Reconstruction Global Attention Mechanism (CRGAM), and Powerful-IoU loss. These components collaboratively enhance feature extraction, multi-scale perception, and localization accuracy while effectively reducing model complexity and computational cost. Experimental results show that ADCP-YOLO achieves a More >

Lingyun Xiang^1,*, Nian Li², Yuling Liu³, Jiayong Hu¹

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073347 - 12 January 2026

Abstract The rapid advancement of large language models (LLMs) has driven the pervasive adoption of AI-generated content (AIGC), while also raising concerns about misinformation, academic misconduct, biased or harmful content, and other risks. Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information. This survey reviews recent progress in detection methods, categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals. Passive detection is further divided into surface linguistic feature-based and language model-based methods, whereas active detection encompasses watermarking-based and semantic retrieval-based More >

Nikita Golovkin^1,2, Olesya Nikulenkova³, Vsevolod Pobezhimov¹, Alexander Nesmelov¹, Sergei Chvalun¹, Fedor Sorokin³, Arthur Krupnin^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073161 - 12 January 2026

Abstract This study presents and verifies a hybrid methodology for reliable determination of parameters in structural rheological models (Zener, Burgers, and Maxwell) describing the viscoelastic behavior of polyurethane specimens manufactured using extrusion-based 3D printing. Through comprehensive testing, including cyclic compression at strain rates ranging from 0.12 to 120 mm/min (0%–15% strain) and creep/relaxation experiments (10%–30% strain), the lumped parameters were independently determined using both analytical and numerical solutions of the models’ differential equations, followed by cross-verification in additional experiments. Numerical solutions for creep and relaxation problems were obtained using finite element analysis, with the three-parameter Mooney-Rivlin… More > Graphic Abstract

Yongmei Zhang^*, Tianxin Zhang, Linghua Tian

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073159 - 12 January 2026

Abstract Marine forecasting is critical for navigation safety and disaster prevention. However, traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features. To address the limitations, the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism. The model treats target forecast values as internal variables, and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer. Using a self-attention structure, the model captures correlations between exogenous variables and target sequences, explores intrinsic More >

Rayeesa Mehmood^*, Sergei Koltcov, Anton Surkov, Vera Ignatenko

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072735 - 12 January 2026

Abstract Activation pruning reduces neural network complexity by eliminating low-importance neuron activations, yet identifying the critical pruning threshold—beyond which accuracy rapidly deteriorates—remains computationally expensive and typically requires exhaustive search. We introduce a thermodynamics-inspired framework that treats activation distributions as energy-filtered physical systems and employs the free energy of activations as a principled evaluation metric. Phase-transition–like phenomena in the free-energy profile—such as extrema, inflection points, and curvature changes—yield reliable estimates of the critical pruning threshold, providing a theoretically grounded means of predicting sharp accuracy degradation. To further enhance efficiency, we propose a renormalized free energy technique that More >

José Arturo Ramírez-Fernández¹, Henevith G. Méndez-Figueroa¹, Sebastián Ossandón^2,*, Ricardo Galván-Martínez³, Miguel Ángel Hernández-Pérez³, Ricardo Orozco-Cruz³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072707 - 12 January 2026

Abstract In this study, artificial neural networks (ANNs) were implemented to determine design parameters for an impressed current cathodic protection (ICCP) prototype. An ASTM A36 steel plate was tested in 3.5% NaCl solution, seawater, and NS4 using electrochemical impedance spectroscopy (EIS) to monitor the evolution of the substrate surface, which affects the current required to reach the protection potential (Eprot). Experimental data were collected as training datasets and analyzed using statistical methods, including box plots and correlation matrices. Subsequently, ANNs were applied to predict the current demand at different exposure times, enabling the estimation of electrochemical More >

Tao Zhang^1,*, Hailun Wang¹, Tianyue Wang¹, Tian Tian²

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072696 - 12 January 2026

Abstract The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation, mechanical wear, and airflow disturbances during prolonged operation. These conditions can lead to a series of issues, including mechanical faults, air path malfunctions, and combustion irregularities. Traditional model-based approaches face inherent limitations due to their inability to handle nonlinear problems, natural factors, measurement uncertainties, fault coupling, and implementation challenges. The development of artificial intelligence algorithms has provided an effective solution to these issues, sparking extensive research into data-driven fault diagnosis methodologies. The review mechanism involved searching IEEE Xplore, ScienceDirect,… More >

Tai Liu^1,2, Mao Ye^2,*, Feng Wu³, Chao Zhu², Bo Chen², Guoyan Zhang^1,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072692 - 12 January 2026

Abstract With the continuous advancement of unmanned technology in various application domains, the development and deployment of blind-spot-free panoramic video systems have gained increasing importance. Such systems are particularly critical in battlefield environments, where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles (UGVs). However, conventional video surveillance systems suffer from several limitations, including limited field of view, high processing latency, low reliability, excessive resource consumption, and significant transmission delays. These shortcomings impede the widespread adoption of UGVs in battlefield settings. To overcome these… More >