Huajie Zhang^1,2, Xiaopeng Li^1,2, Hanlin Xiao^3,*, Lifeng Xing³, Wenyue Zhou^1,2

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2026.086505 - 12 July 2026

Abstract This article has no abstract. More >

Chenying Yi¹, Yangjun Zhou^1,2, Wei Zhang¹, Like Gao¹, Hongwen Wu³, Yuanchao Zhou^4,*, Ke Zhou¹, Weixiang Huang¹, Juntao Pan⁵, Shan Li¹, Bin Feng⁵

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.073805 - 12 July 2026

Abstract Ensuring reliability in distribution networks is essential under increasing operational and economic constraints. Traditional planning models rely on power flow calculations, leading to high computational costs and poor scalability. This study proposes a quantitative decomposition framework that establishes a direct linkage among reliability improvement measures, reliability parameters, and reliability indices, enabling fast and analytical reliability evaluation without power flow analysis. A bi-objective optimization model is developed to minimize both reliability indices (SAIDI) and investment costs, solved using Pareto-based multi-objective PSO combined with the TOPSIS method. Case studies on a 519-node distribution network demonstrate that the More >

Menghua Li^1,2,3, Bin Ou^1,2,3,4, Jiahao Li^1,2,3, Sitong Jin^1,2,3, Yanming Zhang^1,2,3, Shuyan Fu^1,2,3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082514 - 30 June 2026

Abstract Accurate prediction of seepage discharge in earth-rock dams remains challenging due to the strong non-stationary and nonlinear characteristics, limited robustness of individual models, and poor interpretability of black-box approaches. To address these issues, this paper proposes an interpretable hybrid model that integrates Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM) networks, and Support Vector Machine (SVM). The model first decomposes the seepage discharge sequence and relevant lagged features using VMD. The LSTM network then captures temporal dependencies of the decomposed components, while the SVM performs regression on the original sequences and features. An adaptive fusion… More >

Daxun Luo^#, Bixiao Wang^#, Haifeng Song, Chaoyue Ji, Weiguo Hu, Bo Xiao, Boxing Su, Yubao Liu^*, Jianxing Li^*

Canadian Journal of Urology, Vol.33, No.3, pp. 623-634, 2026, DOI:10.32604/cju.2026.076790 - 29 June 2026

Abstract Objectives: Urinary stone composition critically influences treatment selection and recurrence prevention, yet current intraoperative assessment remains imprecise. This study aims to achieve intraoperative prediction of stone composition by applying a deep convolutional neural network (CNN) to routinely captured endoscopic images. Methods: We retrospectively studied endoscopic images from stone-breaking surgeries in Beijing Tsinghua Changgung Hospital during 2022-12–2024-12. Images were captured before and after laser lithotripsy. Based on postoperative infrared spectroscopy, stones were divided into five categories. In total, 1780 images (1167 from RIRS, 613 from PCNL) were included and split into training and testing sets at… More >

Xiaolan Li^1,2,*, Jinyu Shen^1,2, Jinhuang Liang^1,2, Yanting Wang^1,2

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076521 - 18 June 2026

Abstract The intrinsic volatility and stochasticity of large-scale wind power generation pose significant challenges to grid stability. To address the limitations of conventional models in capturing strong non-stationarity, this study proposes a novel Multi-Stage Adaptive Forecasting Network (MSAF-Net). The framework features a hierarchical signal refinement strategy coupled with an intelligently optimized hybrid predictor. Initially, input redundancy is minimized via Pearson Correlation Coefficient (PCC) analysis to isolate significant meteorological variables. A two-phase decomposition-reconstruction mechanism is then implemented: the wind power series is first decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). To optimize the… More >

Xiaolan Li^1,2, Yanting Wang^1,2,*

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.076417 - 18 June 2026

Abstract To address the operational challenges of power systems with high renewable penetration, this research targets the non-stationarity and stochasticity of wind power. A novel hybrid framework for probabilistic forecasting and risk assessment is proposed. Initially, Empirical Mode Decomposition (EMD) adaptively decomposes the raw power signal into multi-scale Intrinsic Mode Functions (IMFs) and a residual trend, effectively segregating temporal features and reducing complexity. These components are then fused with historical data to form a comprehensive input. The core predictor is a Bidirectional Gated Recurrent Unit (BiGRU) network enhanced with a Temporal Attention (TA) mechanism. The BiGRU… More >

Jiguang Wu¹, Qing Zhi^2,*, Jin Guan², Ruopeng Zhang², Lixia Wu², Shuhui Zhang², Caifeng Wen^3,4

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2025.073914 - 18 June 2026

Abstract This paper proposes a hybrid energy storage control method that coordinates the minimum output of the wind–storage system and the SOC self-recovery capability, applied to stand-alone energy storage stations. Under the premise of meeting the wind power smoothing requirements, model predictive control (MPC) is employed to rapidly regulate the SOC and output of the energy storage system during the smoothing process, thereby enhancing its sustained and stable operation capability, and decomposing the original wind power into a direct grid-connected component and a hybrid energy storage smoothing component. Subsequently, the Northern Goshawk Algorithm-Improved Complete Ensemble Empirical… More >

Hongbin Wang^1,2, Liusong Li^1,2, Di Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079330 - 15 June 2026

Abstract Multimodal sentiment analysis aims to fuse emotional information from data across different modalities to predict human emotional states. Although existing multimodal sentiment analysis methods have made significant progress, the heterogeneity between modalities still leads to an imbalance in feature space distribution, thereby hindering the effective learning and fusion of multimodal representations. In addition, the presence of emotion-irrelevant information in auxiliary modalities is another major factor contributing to differences in feature space distributions. To address this issue, we propose a Hierarchical Contrastive Representation Learning framework with Multimodal Feature Decoupling (HCRL-MFD). To reduce emotion-irrelevant information and optimize… More >

Xiaoshan Wang¹, Qiang Cui¹, Lei Pu^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.5, 2026, DOI:10.32604/fdmp.2026.081184 - 27 May 2026

Abstract The passability (transport behavior) of while-drilling lost circulation materials (LCMs) through directional tools is strongly influenced by material composition and particle characteristics. In this study, a coupled computational fluid dynamics–discrete element method (CFD–DEM) model is developed to systematically evaluate the effects of particle size distribution, concentration, morphology, and fiber inclusion on LCM transport behavior. Visualization experiments conducted using a transparent screen section demonstrate good agreement with the simulated pressure-drop evolution, supporting the validity of the model. The results reveal that increasing particle size from 1.2–1.6 mm to above 2.8 mm shifts the system from a More > Graphic Abstract

Marco Roccetti^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.082258 - 27 May 2026

Abstract Geospatial health risk signals, characterized by associations with high magnitude statistical significance, may frequently originate from circumscribed observational data streams. When these signals are fueled by massive N-size datasets, the large dimensional scale of the sample can induce a misleading interpretation of local evidence as a statistically significant risk inflation. The objective of this study is to verify whether such health risk configurations constitute geospatial structural artifacts: namely, stochastic distortions generated by the spatial information of local health repositories that, despite their massive scale, may remain fundamentally distant from broader contextual realities. To this aim,… More >