Jiajia Liu^1,*, Junyi Lin², Wenxiang Dong², Xuan Zhao², Jianhua Liu², Huiru Li³

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.071397 - 09 December 2025

Abstract Single Image Super-Resolution (SISR) seeks to reconstruct high-resolution (HR) images from low-resolution (LR) inputs, thereby enhancing visual fidelity and the perception of fine details. While Transformer-based models—such as SwinIR, Restormer, and HAT—have recently achieved impressive results in super-resolution tasks by capturing global contextual information, these methods often suffer from substantial computational and memory overhead, which limits their deployment on resource-constrained edge devices. To address these challenges, we propose a novel lightweight super-resolution network, termed Binary Attention-Guided Information Distillation (BAID), which integrates frequency-aware modeling with a binary attention mechanism to significantly reduce computational complexity and parameter… More >

Ran Wei^*, Hui Shu

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069562 - 10 November 2025

Abstract Transformer-based models have significantly advanced binary code similarity detection (BCSD) by leveraging their semantic encoding capabilities for efficient function matching across diverse compilation settings. Although adversarial examples can strategically undermine the accuracy of BCSD models and protect critical code, existing techniques predominantly depend on inserting artificial instructions, which incur high computational costs and offer limited diversity of perturbations. To address these limitations, we propose AIMA, a novel gradient-guided assembly instruction relocation method. Our method decouples the detection model into tokenization, embedding, and encoding layers to enable efficient gradient computation. Since token IDs of instructions are… More >

I. Sajjad^a, U. Parveen¹, H. Al-Ghamdi^b,*, M. Yaseen^a, S. Saleem^a, Nasarullah^a

Chalcogenide Letters, Vol.22, No.9, pp. 829-845, 2025, DOI:10.15251/CL.2025.229.829

Abstract Herein, we employed modified Becke-Johson (mBJ) potential based first principles method to investigate the structural, optoelectronic, and magnetic properties of pure SrS and Ni doped Sr_1-xNi_xS alloys at varying doping concentrations. Formation enthalpy analysis predicts thermodynamical stability of resultant alloys. Geometry optimization was performed in order to optimize the super cells to obtain ground state energy state. After confirming their stability, we investigated their magnetic, electronic, and optical attributes. Pure SrS exhibits an indirect band gap of 3.53 eV (which is in good agreement with experiments), while nickel doping in SrS results in lowering the bandgap… More >

M. Danish^a, Z. A. Sandhu^b, S. Sajid^a, S. R. Shafqat^a, R. Abbas^c, M. Shahid^d, N. Amjed^e, A. Abo Elnasr^f, H. T. Ali^f, M. A. Raza^b,*

Chalcogenide Letters, Vol.22, No.10, pp. 863-870, 2025, DOI:10.15251/CL.2025.2210.863

Abstract The increase in energy crisis and environmental concerns are now considering as major hurdle in way to sustainability and clean energy solution. Metal sulfides have been investigated for the fabrication of energy conversion ad storage devices to overcome the effect of energy demand. In this concern, a microemulsion mediated hydrothermal method was employed for the successful synthesis of pure Co₄S₃ and Ce₂S₃-Co₄S₃ binary nanocomposite materials. This study was investigated for Supercapacitor application using cyclic voltammetry and electrochemical impedance spectroscopy. The scanning electron microscope analysis of composite material showed compact smooth morphology with strong interparticle interactions. The More >

Sam Rahimzadeh Holagh¹, Bugao Xu^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2383-2399, 2025, DOI:10.32604/cmes.2025.072259 - 26 November 2025

Abstract Cardiovascular diseases (CVD) remain a leading cause of mortality worldwide, highlighting the need for precise risk assessment tools to support clinical decision-making. This study introduces a meta-learning model for predicting mortality risk in patients with CVD, classifying them into high-risk and low-risk groups. Data were collected from 868 patients at Tabriz Heart Hospital (THH) in Iran, along with two open-access datasets—the Cleveland Heart Disease (CHD) and Faisalabad Institute of Cardiology (FIC) datasets. Data preprocessing involved class balancing via the Synthetic Minority Over-Sampling Technique (SMOTE). Each dataset was then split into training and test sets, and… More >

Kui Lin^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.4, pp. 1-1, 2025, DOI:10.32604/icces.2025.011838

Abstract The phase separation of confined polymeric mixtures plays a critical role in the design of advanced materials and nanoscale devices. Over the past decades, extensive studies have highlighted the interplay of wetting dynamics, hydrodynamics, and interfacial forces in governing phase separation under confinement. In this work [1], we employ molecular dynamics simulations to investigate the dynamics of a binary mixture confined by van der Waals (vdW) walls, revealing a novel phenomenon termed Perpendicular Separation of Two Phases (PSTP). In the initial stage, water molecules residing in the central region rapidly diffuse and condense symmetrically along… More >

Tao Chen¹, Xiaoxi Mi^2,*, Shibo Zhou^3,*, Shijun Tong¹, Yunxuan Zhou¹, Yulin Zhang¹, Yuan Yuan⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4571-4586, 2025, DOI:10.32604/cmc.2025.070649 - 23 October 2025

Abstract Magnesium (Mg) alloys face a critical challenge in balancing performance optimization and unintended density increases caused by high-density secondary phases. To address this, machine learning was employed to predict the density and volume of Mg-containing binary phases, aiming to guide lightweight alloy design. Using 211 experimentally observed data points, five machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Bayesian Ridge (Bayes)—were trained and tested. Quantitative results showed that RF achieved exceptional performance in volume prediction, with a testing coefficient of determination (R²) exceeding 0.96 and More > Graphic Abstract

Shengjia Chang, Baojiang Cui^*, Shaocong Feng

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4345-4374, 2025, DOI:10.32604/cmc.2025.070195 - 23 October 2025

Abstract Binary Code Similarity Detection (BCSD) is vital for vulnerability discovery, malware detection, and software security, especially when source code is unavailable. Yet, it faces challenges from semantic loss, recompilation variations, and obfuscation. Recent advances in artificial intelligence—particularly natural language processing (NLP), graph representation learning (GRL), and large language models (LLMs)—have markedly improved accuracy, enabling better recognition of code variants and deeper semantic understanding. This paper presents a comprehensive review of 82 studies published between 1975 and 2025, systematically tracing the historical evolution of BCSD and analyzing the progressive incorporation of artificial intelligence (AI) techniques. Particular… More >

Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko

Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025

Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

Puneet Sharma¹, Dhirendra Prasad Yadav¹, Bhisham Sharma^2,*, Surbhi B. Khan^3,4,*, Ahlam Almusharraf ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2179-2193, 2025, DOI:10.32604/cmc.2025.063190 - 29 August 2025

Abstract The swift expansion of cloud computing has heightened the demand for energy-efficient and high-performance resource allocation solutions across extensive systems. This research presents an innovative hybrid framework that combines a Quantum Tensor-based Deep Neural Network (QT-DNN) with Binary Bird Swarm Optimization (BBSO) to enhance resource allocation while preserving Quality of Service (QoS). In contrast to conventional approaches, the QT-DNN accurately predicts task-resource mappings using tensor-based task representation, significantly minimizing computing overhead. The BBSO allocates resources dynamically, optimizing energy efficiency and task distribution. Experimental results from extensive simulations indicate the efficacy of the suggested strategy; the… More >