Menwa Alshammeri^1,2,*, Noshina Tariq³, NZ Jhanji^4,5, Mamoona Humayun⁶, Muhammad Attique Khan⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074627 - 29 January 2026

Abstract Artificial Intelligence (AI) is changing healthcare by helping with diagnosis. However, for doctors to trust AI tools, they need to be both accurate and easy to understand. In this study, we created a new machine learning system for the early detection of Autism Spectrum Disorder (ASD) in children. Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning. For this, we combined several different models, including Random Forest, XGBoost, and Neural Networks, into a single, more powerful framework. We used two different types More >

Xianrui Lyu, Xiaodan Ren^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.074388 - 29 January 2026

Abstract Inverse design of advanced materials represents a pivotal challenge in materials science. Leveraging the latent space of Variational Autoencoders (VAEs) for material optimization has emerged as a significant advancement in the field of material inverse design. However, VAEs are inherently prone to generating blurred images, posing challenges for precise inverse design and microstructure manufacturing. While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent, it simultaneously imposes a substantial burden on target optimization due to an excessively high search space. To address these limitations, this study adopts a Variational… More >

Hussain H. Al-Kayiem^1,*, Safaa M. Ali², Sundus S. Al-Azawiey³, Raed A. Jessam³

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.073303 - 27 January 2026

Abstract Gas Turbines are among the most important energy systems for aviation and thermal-based power generation. The performance of gas turbine intakes with S-shaped diffusers is vulnerable to flow separation, reversal flow, and pressure distortion, mainly in aggressive S-shaped diffusers. Several methods, including vortex generators and energy promoters, have been proposed and investigated both experimentally and numerically. This paper compiles a review of experimental investigations that have been performed and reported to mitigate flow separation and restore system performance. The operational principles, classifications, design geometries, and performance parameters of S-shaped diffusers are presented to facilitate the… More > Graphic Abstract

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 >

Qiuhong Wang¹, Hao Yin^1,*, Qingping Sun^1,2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012656

Abstract Tubular NiTi is a promising candidate of eco-friendly solid-state refrigerant for elastocaloric cooling, but the severe functional degradation of NiTi material during cyclic phase transition (PT) is a key concern in the technology development. Here, plastic deformation of 6.7% is applied on the NiTi tube by overstress training under 1900 MPa for five cycles to improve the cyclic PT stability without losing cooling efficiency. It is found that after 106 compressive cycles under an applied stress of 1000 MPa, the overstress-trained NiTi tube exhibits small residual strain (0.5%), stable adiabatic temperatures drop (T=11K) and improved… 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 >