Md. Ehsanul Haque¹, Md. Nurul Absur², Fahmid Al Farid³, Md Kamrul Siam⁴, Jia Uddin^5,*, Hezerul Abdul Karim^3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071771 - 12 January 2026

Abstract Manual inspection of onba earing casting defects is not realistic and unreliable, particularly in the case of some micro-level anomalies which lead to major defects on a large scale. To address these challenges, we propose BearFusionNet, an attention-based deep learning architecture with multi-stream, which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19. This hybrid design, figuratively beaming from one layer to another, extracts the enormity of representations on different scales, backed by a pre-preprocessing pipeline that brings defect saliency to the fore through contrast adjustment, denoising, and edge… More >

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 >

Chia-Hui Hou^*

International Journal of Mental Health Promotion, Vol.27, No.12, pp. 2007-2022, 2025, DOI:10.32604/ijmhp.2025.073748 - 31 December 2025

Abstract Objectives: With the rapid aging of populations worldwide, the long-term care (LTC) industry has become a critical arena for both social welfare and entrepreneurial development, particularly among women who play a leading role in caregiving enterprises. However, female LTC entrepreneurs often face emotional strain and limited social resources that affect their professional well-being. This study investigates the effects of psychological resilience and social capital on the well-being of female entrepreneurs in the long-term care (LTC) industry and examines the mediating role of entrepreneurial competence. Methods: A mixed-methods design was employed. Quantitative data were collected from 73… More >

Victor De Araujo^1,2,3,*, Pedro Jardim^3,4, Poliana Pessôa³, Juliano Vasconcelos^2,5, Matheus Souza⁶, José Garcia⁷, Jozef Švajlenka⁸, André Christoforo^3,1

Journal of Renewable Materials, Vol.13, No.12, pp. 2479-2524, 2025, DOI:10.32604/jrm.2025.02025-0127 - 23 December 2025

Abstract Finger-joint lumber is a sustainable building product commercialized as a structural solution for beams, pillars and other thin flat load-bearing elements. This study aims to study finger-joint lumber and its industry to promote this engineered wood product. The first research stage assessed the collection of publications on finger-joint lumber available globally, in which a structured protocol was developed to prospect studies based on two complementary methodologies: PRISMA 2020 using Scopus and Web of Science databases, and Snowball using both forward and backward models to complete with additional literature. The second research stage assessed finger-joint lumber… More >

Asrar Mahboob¹, Muhammad Rashad¹, Ghulam Abbas¹, Zohaib Mushtaq², Tehseen Mazhar^3,*, Ateeq Ur Rehman^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3805-3823, 2025, DOI:10.32604/cmc.2025.067615 - 23 September 2025

Abstract This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three factors, Quantum-resistant feature engineering using the UNSW-NB15 dataset adapted for solar infrastructure anomalies. An enhanced Light Gradient Boosting Machine (LightGBM) classifier with blockchain-validated decision thresholds, and A cryptographic proof-of-threat (PoT) consensus mechanism for cyber attack verification. The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision, recall and F1-score, outperforming conventional intrusion detection systems (IDSs) by… More >

Alehegn Atalay Birlie^*

Journal of Renewable Materials, Vol.13, No.9, pp. 1783-1802, 2025, DOI:10.32604/jrm.2025.02025-0039 - 22 September 2025

Abstract This study explores vegan leather, an eco-friendly substitute for conventional animal-derived leather. Using materials like polyurethane, pineapple leaves, cork, and recycled plastics, vegan leather aims to transform the fashion industry and consumer products while addressing environmental concerns. Despite its advantages, challenges related to availability and durability persist. The booming market for vegan leather is expected to reach billions of dollars, reflecting a broader societal shift towards sustainable and cruelty-free alternatives. The review traces the historical development of vegan leather from its origins in Germany to modern innovations like Mylo and Piñatex. By comparing these materials More >

Mohammed Naif Alatawi^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 695-727, 2025, DOI:10.32604/cmc.2025.066606 - 29 August 2025

Abstract Adaptive robust secure framework plays a vital role in implementing intelligent automation and decentralized decision making of Industry 5.0. Latency, privacy risks and the complexity of industrial networks have been preventing attempts at traditional cloud-based learning systems. We demonstrate that, to overcome these challenges, for instance, the EdgeGuard-IoT framework, a 6G edge intelligence framework enhancing cybersecurity and operational resilience of the smart grid, is needed on the edge to integrate Secure Federated Learning (SFL) and Adaptive Anomaly Detection (AAD). With ultra-reliable low latency communication (URLLC) of 6G, artificial intelligence-based network orchestration, and massive machine type… More >

Gumashvili Megi^1,2, Yiping Mu¹, Kulbo Nora Bakabbey³, Addo Prince Clement^2,3,4,*, Kiti Kanokon¹, Menezes Dalila Batista de Sousa de¹, Baidoo Bernard Ekow¹

Journal of Psychology in Africa, Vol.35, No.3, pp. 287-298, 2025, DOI:10.32604/jpa.2025.067165 - 31 July 2025

Abstract This study investigated the influence of human capital development and technological, strategic, cognitive, and environmental factors on Industry 4.0 readiness, as well as cultural factors acting as a mediator. Respondents were 478 employees from across eight regions in Russia. Survey data were collected on employee technological readiness, human capital development, strategic planning, cognitive perceptions, and environmental and cultural factors influencing the adoption of Industry 4.0 technologies, with cultural factors mediating. These findings from the structure equation analysis show that technological factors and human capital development are the strongest predictors of readiness, suggesting that robust digital More >

Enas Hassan¹, Wafaa Abou-Elseoud^1,2, Samar El-Mekkawi³, Mohammad Hassan^1,2,*

Journal of Renewable Materials, Vol.13, No.6, pp. 1169-1187, 2025, DOI:10.32604/jrm.2025.02024-0030 - 23 June 2025

Abstract Saccharification of lignocellulosic wastes is the bottleneck of different bio-based chemical industries. Using enzymes for saccharification of lignocellulosic materials has several advantages over using chemicals. In the current work, the application of the Maximyze^® enzyme system, which is industrially used in papermaking, was investigated in the saccharification of paper sludge and fiber dust wastes from the tissue paper industry. For optimizing the saccharification process, the effects of the consistency %, enzyme loading, and incubation time were studied and optimized using the Response Surface Methodology. The effect of these factors on the weight loss of paper sludge… More > Graphic Abstract

Mudasir Dilawar^*, Muhammad Shahbaz

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5091-5114, 2025, DOI:10.32604/cmc.2025.064090 - 19 May 2025

Abstract In the era of Industry 4.0, condition monitoring has emerged as an effective solution for process industries to optimize their operational efficiency. Condition monitoring helps minimize unplanned downtime, extending equipment lifespan, reducing maintenance costs, and improving production quality and safety. This research focuses on utilizing Bayesian search-based machine learning and deep learning approaches for the condition monitoring of industrial equipment. The study aims to enhance predictive maintenance for industrial equipment by forecasting vibration values based on domain-specific feature engineering. Early prediction of vibration enables proactive interventions to minimize downtime and extend the lifespan of critical… More >