Hafsa Sidaq¹, Lei Wang¹, Sghaier Guizani^2,*, Hussain Haider³, Ateeq Ur Rehman^4,*, Habib Hamam^5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071254 - 12 January 2026

Abstract Numerous sectors, such as education, the IT sector, and corporate organizations, transitioned to virtual meetings after the COVID-19 crisis. Organizations now seek to assess participants’ fatigue levels in online meetings to remain competitive. Instructors cannot effectively monitor every individual in a virtual environment, which raises significant concerns about participant fatigue. Our proposed system monitors fatigue, identifying attentive and drowsy individuals throughout the online session. We leverage Dlib’s pre-trained facial landmark detector and focus on the eye landmarks only, offering a more detailed analysis for predicting eye opening and closing of the eyes, rather than focusing… More >

Heping Qi, Wenyao Sun^*, Yi Zhao, Xiaoyi Qian, Xingyu Jiang

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069603 - 27 December 2025

Abstract Virtual power plant (VPP) integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions, promote the consumption of renewable energy, and improve economic efficiency. In this paper, aiming at the uncertainty of distributed wind power and photovoltaic output, considering the coupling relationship between power, carbon trading, and green card market, the optimal operation model and bidding scheme of VPP in spot market, carbon trading market, and green card market are established. On this basis, through the Shapley value and independent risk contribution theory in cooperative game theory, the quantitative… More > Graphic Abstract

Muhammad Adil¹, Nadeem Javaid^1,*, Imran Ahmed², Abrar Ahmed³, Nabil Alrajeh^4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071215 - 10 November 2025

Abstract Heart disease remains a leading cause of mortality worldwide, emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention. However, existing Deep Learning (DL) approaches often face several limitations, including inefficient feature extraction, class imbalance, suboptimal classification performance, and limited interpretability, which collectively hinder their deployment in clinical settings. To address these challenges, we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture. The preprocessing stage involves label encoding and feature scaling. To address the issue of… More >

Yağmur Yılan, Ahad Beykent^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-16, 2026, DOI:10.32604/cmc.2025.068440 - 10 November 2025

Abstract Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies, hedge risk and plan generation schedules. By leveraging advanced data analytics and machine learning methods, accurate and reliable price forecasts can be achieved. This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting (XGBoost). We benchmark XGBoost against four alternatives—Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Random Forest (RF), and Gradient Boosting (GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul (EXIST). All models were trained on an identical chronological 80/20 train–test split, with hyperparameters More >

Yang Dou¹, Hao Qin¹, Yuzhi Zhang^1,2, Ning Wang¹, Haiqing Liu^3,4, Wanli Yang^1,2,4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.12, pp. 3091-3122, 2025, DOI:10.32604/fdmp.2025.070082 - 31 December 2025

Abstract In recent years, tuned liquid dampers (TLDs) have emerged as a focal point of research due to their remarkable potential for structural vibration mitigation. Yet, progress in this field remains constrained by an incomplete understanding of the fundamental mechanisms governing sloshing-induced loads in liquid-filled containers. Aqueducts present a distinctive case, as the capacity of their contained water to function effectively as a TLD remains uncertain. To address this gap, the present study investigates the generation mechanisms of sloshing loads under non-resonant cases through a two-dimensional (2D) computational fluid dynamics (CFD) model developed in ANSYS Fluent.… More >

Shengzhong Zhao¹, Daiyan Chen¹, Han Zhang^1,2,*, Junhao Yu¹, Lin Xu¹, Zhaoyi Zhuang¹, Fei Wang^1,*

Frontiers in Heat and Mass Transfer, Vol.23, No.6, pp. 1907-1932, 2025, DOI:10.32604/fhmt.2025.071910 - 31 December 2025

Abstract A sealed portal could significantly alter the flame shape and smoke flow characteristics in inclined tunnel fires. In inclined tunnels, two typical sealing conditions could be defined, namely the upper portal sealed and the lower portal sealed. In this study, the effects of tunnel slope on flame shape, flame length, along with smoke mass flow rate and induced velocity at the tunnel portal, are numerically investigated. The results show that, in all scenarios, flames initially rise vertically but tilt toward the sealed portal during the quasi-steady stage, with the largest tilt angle observed in tunnels… More >

Ling Li^1,#, Ninghui Xu^1,#, Wenjing Wang^2,*, Jianfen Ying^1,*

Journal of Psychology in Africa, Vol.35, No.6, pp. 833-842, 2025, DOI:10.32604/jpa.2025.071557 - 30 December 2025

Abstract This study explores the role of teachers’ professional identity (TPI) on employee learning engagement (LE), with mediation by basic needs satisfaction (BNS). Participants were 255 Chinese pre-service teachers (191 females = 74.9%, 16 freshmen = 6.2%, 135 sophomores = 52.9%, 35 juniors = 12.5%, 72 seniors = 28.2%). They completed surveys on the “QuestionStar” online survey platform and 12 of the teachers completed interviews for sharing their personal insights. The results of Structural Equation Modeling (SEM) indicated that teachers’ professional identity significantly predicted both learning engagement and basic needs satisfaction, with basic needs satisfaction partially More >

Muhammad Saad Farooqui¹, Aizaz Ahmad Khattak², Bakri Hossain Awaji³, Nazik Alturki⁴, Noha Alnazzawi⁵, Muhammad Hanif^6,*, Muhammad Shahbaz Khan²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4395-4439, 2025, DOI:10.32604/cmes.2025.072023 - 23 December 2025

Abstract Quality of Service (QoS) assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service (DDoS), spoofing, and botnet intrusions. This paper presents AutoSHARC, a feedback-driven, explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier. AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones, followed by post-hoc SHAP-guided retraining to refine feature importance, forming a feedback loop where only the most impactful attributes are More >

Yang Dou¹, Hao Qin¹, Yuzhi Zhang^1,2, Ning Wang¹, Haiqing Liu^3,4, Wanli Yang^1,2,4,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.11, pp. 2673-2700, 2025, DOI:10.32604/fdmp.2025.069719 - 01 December 2025

Abstract In recent years, tuned liquid dampers (TLDs) have attracted significant research interest; however, overall progress has been limited due to insufficient understanding of the mechanisms governing sloshing-induced loads. In particular, it remains unclear whether the water in aqueducts—common water-diversion structures in many countries—can serve as an effective TLD. This study investigates the generation mechanisms of sloshing loads during the first-order transverse resonance of water in a U-shaped aqueduct using a two-dimensional (2D) numerical model. The results reveal that, at the equilibrium position, the free surface difference between the left and right walls, the horizontal force… More >

Faiza Benabdallah¹, Kaouther Ghachem¹, Walid Hassen², Haythem Baya², Hind Albalawi³, Lioua Kolsi^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1839-1861, 2025, DOI:10.32604/cmes.2025.071678 - 26 November 2025

Abstract Current developments in magnetohydrodynamic (MHD) convection and nanofluid engineering technology have have greatly enhanced heat transfer performance in process systems, particularly through the use of carbon nanotube (CNT)–based fluids that offer exceptional thermal conductivity. Despite extensive research on MHD natural convection in enclosures, the combined effects of complex obstacle geometries, magnetic fields, and CNT nanofluids in three-dimensional configurations remain insufficiently explored. This research investigates MHD natural convection of carbon nanotube (CNT)-water nanofluid within a three-dimensional cavity. The study considers an inclined cross-shaped hot obstacle, a configuration not extensively explored in previous works. The work aims… More >