HyeSun Chang, Sangjun Lee^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082718 - 15 June 2026

Abstract Accurate identification of heart murmurs from auscultation recordings is essential for early cardiovascular screening and diagnosis. While deep learning offers strong potential for automated heart murmur classification, existing models often exhibit overconfident, incorrect predictions and limited generalization due to dataset bias and class imbalance. To address these challenges, this study proposes a two-stage confidence-regulated learning framework that jointly optimizes feature representation and decision reliability. Rather than focusing solely on improving classification performance, this work emphasizes enhancing prediction reliability through confidence-aware decision-making. The proposed framework integrates supervised contrastive learning (SCL) to strengthen the discriminative structure of… More >

Leonidas Theodorakopoulos, Alexandra Theodoropoulou^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082270 - 15 June 2026

Abstract Auditable autonomy is becoming a practical requirement for deploying large language model (LLM) agents in operational workflows where recommendations can trigger consequential actions. Many autonomy claims remain hard to evaluate because studies emphasize task completion or fluent explanations while underreporting tool privileges, verification conditions, rollback feasibility, and trace completeness. This review develops a decision-making–centered framework that treats autonomy as an auditable engineering property. It introduces a three-plane big data foundation: an evidence plane with provenance and freshness constraints; a decision-trace plane that records retrieval identifiers, tool invocations, intermediate checks, and policy evaluations; and an outcomes More >

Federico Minelli^1,*, Diana D’Agostino¹, Vennapusa Jagadeeswara Reddy², Panagiotis Michailidis^3,4

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.074048 - 27 May 2026

Abstract This study investigates the problem of prioritizing rooftop renewable energy (RE) system configurations for a multi-family residential building in Mediterranean climate. The analysis focuses on fixed-tilt photovoltaics (PV), single-axis and dual-axis tracking PV, and small vertical-axis wind turbines (VAWT), each assessed with and without lithium-ion storage. A co-simulation framework is used, coupling EnergyPlus building-HVAC system simulation with PV and wind generation modeling and rule-based battery dispatch to evaluate hourly demand–supply interactions. Three decision criteria are considered for each alternative: total system cost, annual building electric energy demand reduction, and net avoided life-cycle emissions. Stakeholder preferences… More > Graphic Abstract

Linda Naidoo^*, Mariette Coetzee

Journal of Psychology in Africa, Vol.36, No.2, pp. 161-170, 2026, DOI:10.32604/jpa.2026.073305 - 29 April 2026

Abstract This study tested the internal reliability and validity of a 28-item Employee Empowerment scale within a higher education context. A sample of 452 university employees (N = 452) from a South African higher education institution, comprising academics (46%), administrative staff (33%), and professional and managerial staff (21%), participated in the study. The participants were required to complete an employee empowerment questionnaire. The exploratory factor analysis and confirmatory factor analysis identified a three-construct measurement model for employee empowerment: management support, autonomy and decision-making, and access to information and resources. The results revealed that the research instrument More >

Muhammad Mukhtar^1,2, Farizah Yunus¹, Ahmad Shukri Mohd Noor^1,*, Zulfiqar Ali³, Muhammad Junaid^4,*, Mehmood Ahmed⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076246 - 09 April 2026

Abstract The increasing adoption of 5G cellular networks has introduced significant challenges for network operators. The main challenge lies in the management of seamless handoff (HO), which occurs owing to the rapid expansion of equipment, data, and network complexity. To address this challenge, a model named optimal HO management deep learning neural network (OHMDLNN) is proposed. The model is trained on network activity data, and it uses KPIs (key performance indicators) and system-level parameters to make HO decisions. As demonstrated in the article, OHMDLNN is successful in analyzing the effect and interdependence of KPIs from both… More >

Sana Shahab¹, Vladimir Simic^2,*, Ashit Kumar Dutta^3,4, Mohd Anjum^5,*, Dragan Pamucar^6,7,8

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.073945 - 29 January 2026

Abstract Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation… More >

Xue Zhang¹, Jie Chen^2,*, Zhihui Zhang³, Dewei Zhang³, Yuejiao Ming³, Xinde Zhang³

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069487 - 27 December 2025

Abstract The integration of wind power and natural gas for hydrogen production forms a Green and Blue Hydrogen Integrated Energy System (GBH-IES), which is a promising cogeneration approach characterized by multi-energy complementarity, flexible dispatch, and efficient utilization. This system can meet the demands for electricity, heat, and hydrogen while demonstrating significant performance in energy supply, energy conversion, economy, and environment (4E). To evaluate the GBH-IES system effectively, a comprehensive performance evaluation index system was constructed from the 4E dimensions. The fuzzy DEMATEL method was used to quantify the causal relationships between indicators, establishing a scientific input-output… More > Graphic Abstract

Bo Zhu^1,#, Enzhi Dong^1,#, Zhonghua Cheng^1,*, Xianbiao Zhan², Kexin Jiang¹, Rongcai Wang ³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-26, 2026, DOI:10.32604/cmc.2025.069194 - 10 November 2025

Abstract With the increasing complexity of industrial automation, planetary gearboxes play a vital role in large-scale equipment transmission systems, directly impacting operational efficiency and safety. Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment, leading to excessive maintenance costs or potential failure risks. However, existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes. To address these challenges, this study proposes a novel condition-based maintenance framework for planetary gearboxes. A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals, which were then processed using a… More >

Hansam Cho¹, Seokho Moon¹, Sunhyeok Hwang¹, Seoung Bum Kim^1,*, Younghoon Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1941-1967, 2025, DOI:10.32604/cmes.2025.072455 - 26 November 2025

Abstract Accurately determining when and what to remanufacture is essential for maximizing the lifecycle value of industrial equipment. However, existing approaches face three significant limitations: (1) reliance on predefined mathematical models that often fail to capture equipment-specific degradation, (2) offline optimization methods that assume access to future data, and (3) the absence of component-level guidance. To address these challenges, we propose a data-driven framework for component-level decision-making. The framework leverages streaming sensor data to predict the remaining useful life (RUL) without relying on mathematical models, employs an online optimization algorithm suitable for practical settings, and, through More >

Zhe Liu^1,2,3,*, Sijia Zhu⁴, Yulong Huang^1,*, Tapan Senapati^5,6,7, Xiangyu Li⁸, Wulfran Fendzi Mbasso⁹, Himanshu Dhumras¹⁰, Mehdi Hosseinzadeh^11,12,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2157-2188, 2025, DOI:10.32604/cmes.2025.072352 - 26 November 2025

Abstract Uncertainty and ambiguity are pervasive in real-world intelligent systems, necessitating advanced mathematical frameworks for effective modeling and analysis. Fermatean fuzzy sets (FFSs), as a recent extension of classical fuzzy theory, provide enhanced flexibility for representing complex uncertainty. In this paper, we propose a unified parametric divergence operator for FFSs, which comprehensively captures the interplay among membership, non-membership, and hesitation degrees. The proposed operator is rigorously analyzed with respect to key mathematical properties, including non-negativity, non-degeneracy, and symmetry. Notably, several well-known divergence operators, such as Jensen-Shannon divergence, Hellinger distance, and χ²-divergence, are shown to be special cases More >