Bo Su¹, Mwansa Chambalile¹, Shihao He¹, Wan Sun², Enyuan Zhang¹, Tong Guo³, Jianming Hao⁴, Md. Mahbub Alam^5,*

FDMP-Fluid Dynamics & Materials Processing, Vol.22, No.1, 2026, DOI:10.32604/fdmp.2026.076681 - 06 February 2026

Abstract Predicting the precise impacts of climate change on extreme winds remains challenging, yet strong storms are widely expected to occur more frequently in a warming climate. Wind barriers are commonly used on bridges to reduce aerodynamic loads on trains through blocking effects. This study develops a novel wind barrier based on Tesla valves, which not only blocks incoming flow but also dissipates mechanical energy through fluid collision. To demonstrate this energy-dissipation capability, a Tesla plate is placed in a circular duct to examine its influence on pressure drop. Experimental tests and numerical simulations comparing a… More >

Sajid Ali¹, Qazi Mazhar Ul Haq^1,2,*, Ala Saleh Alluhaidan^3,*, Muhammad Shahid Anwar⁴, Sadique Ahmad⁵, Leila Jamel³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2026.074395 - 29 January 2026

Abstract Spam emails remain one of the most persistent threats to digital communication, necessitating effective detection solutions that safeguard both individuals and organisations. We propose a spam email classification framework that uses Bidirectional Encoder Representations from Transformers (BERT) for contextual feature extraction and a multiple-window Convolutional Neural Network (CNN) for classification. To identify semantic nuances in email content, BERT embeddings are used, and CNN filters extract discriminative n-gram patterns at various levels of detail, enabling accurate spam identification. The proposed model outperformed Word2Vec-based baselines on a sample of 5728 labelled emails, achieving an accuracy of 98.69%, More >

Weiqi Mao^1,2,3, Enbo Yu^1,*, Guoji Xu³, Xiaozhen Li³

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.1, 2026, DOI:10.32604/cmes.2025.069733 - 29 January 2026

Abstract Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration. This study presents a novel machine learning model that integrates clustering, deep learning, and transfer learning to mitigate accuracy degradation in 24-h forecasting. Initially, an optimized DB-SCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm clusters wind fields based on wind direction, probability density, and spectral features, enhancing physical interpretability and reducing training complexity. Subsequently, a ResNet (Residual Network) extracts multi-scale patterns from decomposed wind signals, while transfer learning adapts the backbone network across clusters, cutting training time by over… More >

Haokai Xie¹, Yi Lu¹, Xiaojun Ni¹, Yilei Gu¹, Sihao Fu^2,*, Wenyao Ye³, Zheren Zhang², Zheng Xu²

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.070934 - 27 January 2026

Abstract With the rapid development of large-scale offshore wind farms, efficient and reliable power transmission systems are urgently needed. Hybrid high-voltage direct current (HVDC) configurations combining a diode rectifier unit (DRU) and a modular multilevel converter (MMC) have emerged as a promising solution, offering advantages in cost-effectiveness and control capability. However, the uncontrollable nature of the DRU poses significant challenges for system stability under offshore AC fault conditions, particularly due to its inability to provide fault current or voltage support. This paper investigates the offshore AC fault characteristics and fault ride-through (FRT) strategy of a hybrid… More >

Hongsheng Su, Zhensheng Teng^*, Zihan Zhou

Energy Engineering, Vol.123, No.2, 2026, DOI:10.32604/ee.2025.069495 - 27 January 2026

Abstract Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy, replacement-based maintenance practices that deviate from actual operational conditions, and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization, this study proposes a Time-Based Incomplete Maintenance (TBIM) strategy incorporating reliability constraints through stochastic differential equations (SDE). By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions, a high-precision SDE degradation model is constructed, achieving 16% residual reduction compared to… More >

Junyan Tian¹, Qingyuan Zhang¹, Lili Wu^1,2,*, Xia Hao^1,2, Guanggen Zeng¹, Wenwu Wang¹, Jingquan Zhang^1,2

Chalcogenide Letters, Vol.23, No.1, 2026, DOI:10.32604/cl.2026.076362 - 26 January 2026

Abstract The incorporation of the Se element in CdTe solar cells is critical, while the low bandgap CdSe_xTe_1−x, formed by the interdiffusion of CdTe and CdSe during device preparation, can promote the carrier lifetime. Different window layers formed by CdSe w/o MZO or CdS have different Se distributions. This paper systematically evaluates the influence of four types of window layers (CdSe, CdS/CdSe, MZO/CdSe and MZO/CdS/CdSe) on the performance of CdTe solar cells, and focuses on the correlation between the window layers and the Se distribution characteristic, carrier recombination mechanism, and device efficiency. The results show that CdSe… More >

Lifu He¹, Zhongchu Huang¹, Haidong Shao^2,*, Zhangbo Hu¹, Yuting Wang¹, Jie Mei¹, Xiaofei Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073227 - 12 January 2026

Abstract Deep learning-based wind turbine blade fault diagnosis has been widely applied due to its advantages in end-to-end feature extraction. However, several challenges remain. First, signal noise collected during blade operation masks fault features, severely impairing the fault diagnosis performance of deep learning models. Second, current blade fault diagnosis often relies on single-sensor data, resulting in limited monitoring dimensions and ability to comprehensively capture complex fault states. To address these issues, a multi-sensor fusion-based wind turbine blade fault diagnosis method is proposed. Specifically, a CNN-Transformer Coupled Feature Learning Architecture is constructed to enhance the ability to More >

Tiankui Sun^*, Shuyi Zhuang, Yongling Lu, Wenqiang Xie, Ning Guo, Sudi Xu

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070213 - 27 December 2025

Abstract To ensure an uninterrupted power supply, mobile power sources (MPS) are widely deployed in power grids during emergencies. Comprising mobile emergency generators (MEGs) and mobile energy storage systems (MESS), MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies, offering advantages such as flexibility and high resilience through electricity delivery via transportation networks. This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator (DWIG) intended for MEG applications, employing an improved particle swarm optimization (PSO) algorithm based on a back-propagation neural network (BPNN). A… More >

Mengxuan Shi¹, Lintao Li², Dejun Shao¹, Xiaojie Pan¹, Xingyu Shi^2,*, Yuxun Wang²

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.069915 - 27 December 2025

Abstract In wind power transmission via modular multilevel converter based high voltage direct current (MMC-HVDC) systems, under traditional control strategies, MMC-HVDC cannot provide inertia support to the receiving-end grid (REG) during disturbances. Moreover, due to the frequency decoupling between the two ends of the MMC-HVDC, the sending-end wind farm (SEWF) cannot obtain the frequency variation information of the REG to provide inertia response. Therefore, this paper proposes a novel coordinated source-network-storage inertia control strategy based on wind power transmission via MMC-HVDC system. First, the grid-side MMC station (GS-MMC) maps the frequency variations of the REG to… More >

Siwen Xu¹, Hanning Chen², Rui Ni¹, Maowei He², Zhaodi Ge³, Xiaodan Liang^2,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-25, 2026, DOI:10.32604/cmc.2025.071398 - 09 December 2025

Abstract Selective Laser Melting (SLM), an advanced metal additive manufacturing technology, offers high precision and personalized customization advantages. However, selecting reasonable SLM parameters is challenging due to complex relationships. This study proposes a method for identifying the optimal process window by combining the simulation model with an optimization algorithm. JAYA is guided by the principle of preferential behavior towards best solutions and avoidance of worst ones, but it is prone to premature convergence thus leading to insufficient global search. To overcome limitations, this research proposes a Differential Evolution-framed JAYA algorithm (DEJAYA). DEJAYA incorporates four key enhancements More >