Abdullahi Abubakar Imam^1,*, Sahalu Balarabe Junaidu², Hussaini Mamman³, Ganesh Kumar³, Abdullateef Oluwagbemiga Balogun³, Sunder Ali Khowaja⁴, Shuib Basri³, Luiz Fernando Capretz⁵, Asmah Husaini⁶, Hanif Abdul Rahman⁶, Usman Ali¹, Fatoumatta Conteh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.076187 - 30 March 2026

Abstract Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on… More >

Kyoungmin Roh¹, Seungmin Lee², Seong-je Cho^2,*, Youngsup Hwang³, Dongjae Kim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074936 - 30 March 2026

Abstract Many machine learning–based Android malware detection often suffers from concept drift, where models trained on historical data fail to generalize to evolving threats. This paper proposes SCAN (Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection), a hybrid intelligent framework designed to mitigate concept drift without retraining. SCAN integrates Gaussian Mixture Models (GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster. A key challenge in clustering-based malware detection is cluster-wise class imbalance, where clusters contain disproportionate distributions of benign and malicious samples. SCAN addresses this issue through adaptive thresholding, which dynamically… More >

Fen Liu^1,2, Cheng Qu^1,3,*, Hongliang Yuan³, Xinpeng Xiang³, Yingbo Chen³, Yuxuan Cai³, Yue Wang^4,*

Phyton-International Journal of Experimental Botany, Vol.95, No.3, 2026, DOI:10.32604/phyton.2026.077734 - 31 March 2026

Abstract To explore the physiological mechanism of rice seeds’ tolerance to cold damage stress, the hybrid rice variety H-You 518 and the conventional rice variety Zhongjiazao 17 were used as experimental materials. A low-temperature cold damage treatment (4°C) and a normal germination treatment (25°C) were used to measure seed vitality, antioxidant enzyme activity, soluble sugar content, soluble protein content, α-amylase activity, endopeptidase, and related gene expression levels. The results showed that low temperature inhibited seed vitality and germination ability, and after restoring normal growth conditions, the activities of POD and CAT, as well as the content… More >

Polina A. Tretyakova^*, Alexey P. Belkin, Alexander A. Rumyantsev, Anna A. Menshikova

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.075393 - 27 March 2026

Abstract Limited adoption of solar energy in the Northwestern region of Russia is associated with insufficient data on annual solar radiation indicators and on the potential of solar collectors for water heating. The study aims to evaluate the potential of solar water heating for domestic use in Northwestern Russia, using Tyumen city as the case. In this region, the number of cloudy days ranges from 5% to 50%, with cloud cover increasing in winter. New data on the total solar radiation, availability duration, and cloud cover have been collected. Solar irradiance could reach 900 MJ/m² during summer… More >

Xincheng Han¹, Hongyan Ma^1,2,3,*, Shuo Meng¹, Chengzhi Ren¹

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072906 - 27 March 2026

Abstract Lithium-ion (Li-ion) batteries stand as the dominant energy storage solution, despite their widespread adoption, precisely determining the state of charge (SOC) continues to pose significant difficulties, with direct implications for battery safety, operational reliability, and overall performance. Current SOC estimation techniques often demonstrate limited accuracy, particularly when confronted with complex operational scenarios and wide temperature variations, where their generalization capacity and dynamic adaptation prove insufficient. To address these shortcomings, this work presents a PSO-TCN-Transformer network model for SOC estimation. This research uses the Particle Swarm Optimization (PSO) method to automatically configure the architectural parameters of… More >

Xueting Cheng¹, Rui Xu^2,*, Liming Bo¹, Cheng Liu², Huiping Zheng¹, Zhichong Cao²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072350 - 27 March 2026

Abstract To address the issue of transient low-voltage instability in AC-DC hybrid power systems following large disturbances, conventional voltage assessment and control strategies typically adopt a sequential “assess-then-act” paradigm, which struggles to simultaneously meet the requirements for both high accuracy and rapid response. This paper proposes a transient voltage assessment and control method based on a hybrid neural network incorporated with an improved snow ablation optimization (ISAO) algorithm. The core innovation of the proposed method lies in constructing an intelligent “physics-informed and neural network-integrated” framework, which achieves the integration of stability assessment and control strategy generation.… More >

Fengchao Chen^*, Aoqi Mei, Zheng Liu, Ruhao Wu, Qiwei Li

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2026.072188 - 27 March 2026

Abstract With the high proportion of new energy access, the traditional fault self-healing mechanism of the distribution network is challenged. Aiming at the demand for fast recovery of new distribution network faults, this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm. Firstly, the graph theory adjacency matrix is used to characterize the topology of the distribution network, and the dynamic positioning of new energy nodes is realized. Secondly, based on the output model and load characteristic model of wind, photovoltaic, and energy storage,… More >

Yibin Guo¹, Lingxiao Ye^1,*, Xiang Wang¹, Di Wu¹, Zirong Wang¹, Hao Wang²

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072163 - 27 March 2026

Abstract Accurate forecasting of crude oil futures prices is crucial for understanding global energy market dynamics and formulating effective macroeconomic and energy strategies. However, the strong nonlinearity and multi-scale temporal characteristics of crude oil prices pose significant challenges to traditional forecasting methods. To address these issues, this study proposes a hybrid CEEMDAN–HOA–Transformer–GRU model that integrates decomposition, complexity analysis, adaptive modeling, and intelligent optimization. Specifically, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose the original series into multi-scale components, after which entropy-based complexity analysis quantitatively evaluates each component. A differentiated modeling strategy… More > Graphic Abstract

Michael Juma Ayuma^1,*, Shem Mbandu Angolo^1,*, Philemon Nthenge Kasyoka²

Journal on Internet of Things, Vol.8, pp. 31-65, 2026, DOI:10.32604/jiot.2026.074919 - 16 March 2026

Abstract Encryption is essential for safeguarding sensitive data by transforming it into a secret code, which can only be decrypted by authorized parties. This ensures privacy and protects data from unauthorized access. While various encryption algorithms exist, relying on a single method may not provide sufficient security, particularly in the context of token transmission. Common threats such as brute force attacks, man-in-the-middle (MITM) attacks, token modification, and replay attacks are prevalent in adversarial attempts to breach the security of tokens during transmission. When these vulnerabilities are not addressed, they can compromise token integrity and the security… More >

Azhar G. Hamad¹, Nasser Firouzi^2,*, Yousef S. Al Rjoub³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075840 - 12 March 2026

Abstract The accurate mechanical analysis of thick-walled pressure vessel structures composed of advanced materials, such as hyperelastic and functionally graded materials (FGMs), is critical for ensuring their safety and optimizing their design. However, conventional numerical methods can face challenges with the non-linearities inherent in hyperelasticity and the complex spatial variations in FGMs. This paper presents a novel hybrid numerical approach combining Physics-Informed Neural Networks (PINNs) with Finite Element Method (FEM) derived data for the robust analysis of thick-walled, axisymmetric, heterogeneous, hyperelastic pressure vessels with elliptical geometries. A PINN framework incorporating neo-Hookean constitutive relations is developed in… More >