Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Haochen Bao¹, Lingyun Yuan^1,2,*, Tianyu Xie^1,2, Han Chen¹, Hui Dai¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-30, 2026, DOI:10.32604/cmc.2025.069240 - 10 November 2025

Abstract In the age of big data, ensuring data privacy while enabling efficient encrypted data retrieval has become a critical challenge. Traditional searchable encryption schemes face difficulties in handling complex semantic queries. Additionally, they typically rely on honest but curious cloud servers, which introduces the risk of repudiation. Furthermore, the combined operations of search and verification increase system load, thereby reducing performance. Traditional verification mechanisms, which rely on complex hash constructions, suffer from low verification efficiency. To address these challenges, this paper proposes a blockchain-based contextual semantic-aware ciphertext retrieval scheme with efficient verification. Building on existing… More >

Mostafa Mohamed Ahmed Mohamed Alsaedy^1,*, Atef Zaki Ghalwash¹, Aliaa Abd Elhalim Yousif², Safaa Magdy Azzam¹

Journal of Cyber Security, Vol.7, pp. 653-674, 2025, DOI:10.32604/jcs.2025.073547 - 24 December 2025

Abstract Mobile financial applications and payment systems face significant security challenges from reverse engineering attacks. Attackers can decompile Android Package Kit (APK) files, modify permissions, and repackage applications with malicious capabilities. This work introduces E-AAPIV (Enhanced Android Apps Permissions Integrity Verifier), an advanced framework that uses Merkle Tree technology for real-time manifest integrity verification. The proposed system constructs cryptographic Merkle Tree from AndroidManifest.xml permission structures. It establishes secure client-server connections using Elliptic Curve Diffie-Hellman Protocol (ECDH-P384) key exchange. Root hashes are encrypted with Advanced Encryption Standard-256-Galois/Counter Mode (AES-256-GCM), integrated with hardware-backed Android Keystore for enhanced security. More >

Edward Danso Ansong^*, Simon Bonsu Osei^*, Raphael Adjetey Adjei

Journal of Cyber Security, Vol.7, pp. 533-564, 2025, DOI:10.32604/jcs.2025.061821 - 11 December 2025

Abstract The surge in identity fraud, driven by the rapid adoption of mobile money, internet banking, and e-services during the COVID-19 pandemic, underscores the need for robust cybersecurity solutions. Zero-Knowledge Proofs (ZKPs) enable secure identity verification by allowing individuals to prove possession of a National ID card without revealing sensitive information. This study implements a ZKP-based identity verification system using Camenisch-Lysyanskaya (CL) signatures, reducing reliance on complex trusted setup ceremonies. While a trusted issuer is still required, as assumed in this work, our approach eliminates the need for broader system-wide trusted parameters. We evaluate the system’s More >

Leonid Plotnikov^1,*, Danil Davydov^1,2, Dmitry Krasilnikov^1,2, Alexander Ryzhkov²

Energy Engineering, Vol.122, No.11, pp. 4621-4633, 2025, DOI:10.32604/ee.2025.070713 - 27 October 2025

Abstract Improving the specific, technical, economic, and environmental characteristics of piston engines (ICE) operating on alternative gaseous fuels is a pressing task for the energy and mechanical engineering industries. The aim of the study was to optimize the parameters of the ICE working cycle after replacing the base fuel (propane-butane blend) with syngas from wood sawdust to improve its technical and economic performance based on mathematical modeling. The modeling results were verified through experimental studies (differences for key parameters did not exceed 4.0%). The object of the study was an electric generator based on a single-cylinder… More >

Wei Lin, Yi Sun^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5117-5134, 2025, DOI:10.32604/cmc.2025.067968 - 23 October 2025

Abstract To enable efficient sharing of unbounded streaming data, this paper introduces blockchain technology into traditional cloud data, proposing a hybrid on-chain/off-chain storage model. We design a real-time verifiable data structure that is more suitable for streaming data to achieve efficient real-time verifiability for streaming data. Based on the notch gate hash function and vector commitment, an adaptive notch gate hash tree structure is constructed, and an efficient real-time verifiable data structure for on-chain and off-chain stream data is proposed. The structure binds dynamic root nodes sequentially to ordered leaf nodes in its child nodes. Only… More >

Sheng Bi^1,2,*, Jiayan Wang¹, Dong Su¹, Hui Lu¹, Yu Zhang¹

Energy Engineering, Vol.122, No.10, pp. 4135-4151, 2025, DOI:10.32604/ee.2025.066735 - 30 September 2025

Abstract By comparing price plans offered by several retail energy firms, end users with smart meters and controllers may optimize their energy use cost portfolios, due to the growth of deregulated retail power markets. To help smart grid end-users decrease power payment and usage unhappiness, this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection. An enhanced state-based Markov decision process (MDP) without transition probabilities simulates the decision issue. A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue. Several adjustments to the sampling and data… More >

Emad Sami Jaha^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3645-3678, 2025, DOI:10.32604/cmes.2025.068681 - 30 September 2025

Abstract The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification. Among many feasible techniques for ear biometric recognition, convolutional neural network (CNN) models have recently offered high-performance and reliable systems. However, their performance can still be further improved using the capabilities of soft biometrics, a research question yet to be investigated. This research aims to augment the traditional CNN-based ear recognition performance by adding increased discriminatory ear soft biometric traits. It proposes a novel framework of augmented ear identification/verification using a group of discriminative categorical soft biometrics and deriving… More > Graphic Abstract

Junying Wei¹, Guangxian Yin², Jihao Zhang², Wenwen Chang², Chenrui Zhang², Zhengyi Li¹, Long Chang¹, Minghan Peng^3,*

Frontiers in Heat and Mass Transfer, Vol.23, No.4, pp. 1185-1201, 2025, DOI:10.32604/fhmt.2025.067244 - 29 August 2025

Abstract The scroll expander, as the core component of the micro-compressed air energy storage and power generation system, directly affects the output efficiency of the system. Meanwhile, the scroll profile plays a central role in determining the output performance of the scroll expander. In this study, in order to investigate the output characteristics of a variable cross-section scroll expander, numerical simulation and experimental studies were conducted by using Computational Fluid Dynamics (CFD) methods and dynamic mesh techniques. The impact of critical parameters on the output performance of the scroll expander was analyzed through the utilization of… More >

Taimoor Hassan¹, Ibrar Hussain^1,*, Hafiz Mahfooz Ul Haque², Hamid Turab Mirza³, Muhammad Nadeem Ali⁴, Byung-Seo Kim^4,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2071-2094, 2025, DOI:10.32604/cmc.2025.068533 - 29 August 2025

Abstract Smart learning environments have been considered as vital sources and essential needs in modern digital education systems. With the rapid proliferation of smart and assistive technologies, smart learning processes have become quite convenient, comfortable, and financially affordable. This shift has led to the emergence of pervasive computing environments, where user’s intelligent behavior is supported by smart gadgets; however, it is becoming more challenging due to inconsistent behavior of Artificial intelligence (AI) assistive technologies in terms of networking issues, slow user responses to technologies and limited computational resources. This paper presents a context-aware predictive reasoning based… More >