Xin Li, Shuang Shi^*, Qiyi Liu, Yan Zhang

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2026.079956 - 18 June 2026

Abstract Electric heavy-duty truck battery swapping stations (EHDT-BSS) are emerging as flexible resources for power systems due to their high controllability and significant power capacity. However, the participation of EHDT-BSSs in grid frequency regulation is severely constrained by the limited battery quantity and the high stochasticity of swapping demand, where forecasting errors can affect system reliability. To address these challenges, this paper proposes a two-stage optimization strategy for EHDT-BSSs participating in frequency regulation considering demand uncertainty. First, the basic operation mode of BSS is designed, and a deep learning-based method is utilized to forecast swapping demand. More >

Chengyu Hou^1,2, Wenzao Li², Hanyun Li³, Kui Liu¹, Zhuoning Zhao¹, Hongping Shu^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081575 - 15 June 2026

Abstract The rapid growth of Internet of Things (IoT) and 5G technologies has led to a sharp increase in computing demands from wireless devices, making efficient task offloading a critical challenge. Key issues include reducing application latency, lowering the energy consumption of terminal devices, and improving overall system performance, all of which directly affect user experience. Traditional genetic algorithms (GA), inspired by biological evolution, have been widely used in task offloading, but they often suffer from slow convergence and a tendency to fall into local optima in complex scenarios, limiting their effectiveness. To address these drawbacks,… More >

Sheyun Zhang, Ruichun Gu^*, Chaofeng Li, Zhijian Dong, Hefei Wang

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081065 - 15 June 2026

Abstract Federated learning (FL) enables collaborative model training without sharing raw data. However, in real-world applications, clients often exhibit statistical heterogeneity, missing classes, and long-tailed distributions, which can substantially degrade the generalization performance of conventional parameter aggregation and some personalization approaches. Moreover, distillation or alignment-based methods may suffer from unstable supervision and difficult optimization under highly heterogeneous settings. To this end, this paper proposes a novel method called FKD-RTM (Heterogeneous Federated Knowledge Distillation Based on Residual-Enhanced Tree-to-MLP Knowledge Transfer). The key idea is to decouple local teaching from globally aggregatable student learning: we introduce a Gradient… More >

S. D. Al-Sahafi^*

Chalcogenide Letters, Vol.23, No.5, 2026, DOI:10.32604/cl.2026.082364 - 02 June 2026

Abstract In this study, a lead-free double perovskite solar cell structure based on Cs₂BiAgI₆ was simulated and optimized to enhance photovoltaic performance. The device architecture follows the configuration: ITO/PCBM/C₆₀/Cs₂BiAgI₆/CdTe/Au. The thicknesses of the electron transport layer (ETL), absorber layer, and hole transport layer (HTL) are systematically optimized to evaluate their impact on key performance parameters. The results indicate that optimal performance is achieved with 600 nm thicknesses for all ETLs and HTL, and 2 μm for the Cs₂BiAgl₆ absorber. Under these conditions, the device exhibits a short-circuit current density of 23.23 mA/cm², an open-circuit voltage of 1.08 V, More >

Roshana Mukhtar¹, Chuan-Yu Chang², Muhammad Asif Zahoor Raja^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.079390 - 27 May 2026

Abstract Parkinson’s disease (PD) is a complex neurodegenerative disease associated with the accumulation of α-synuclein, which is linked to the dysfunctional ubiquitin–proteasome system. Fractional calculus has emerged as a powerful tool for modeling complex disease dynamics due to its promising features that inherently capture memory and hereditary effects. This paper presents a fractional-order Proteasome-Fibril interaction model (F-PFIM) for the dynamics of PD, represented by three fractional differential classes, showing concentrations of fibrils (F), proteasomes (P), and proteasome fibril complex (C). The three classes of the F-PFIM collectively make a controlling system that works for the clearance… More > Graphic Abstract

Pengcheng Kang¹, Yuanyuan Tian¹, Ying Liu¹, Qian Xu¹, Yuting Chen¹, Lixin Jia², Shuge Guo², Heng Rong², Taolong Xu^2,*

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.077643 - 18 May 2026

Abstract Accurate assessment of gas explosion risks in urban underground culverts is often hindered by the decoupling of leakage diffusion and explosion mechanics. This study develops a high-fidelity numerical framework by implementing a one-way coupling strategy, where the steady-state methane concentration field simulated in FLUENT is mapped into ANSYS/LS-DYNA as the initial material status. Unlike traditional models assuming uniform gas distribution, this approach captures the realistic impact of complex culvert geometries on explosion precursors. A multi-material coupled model involving the confined space, road surface, and surrounding air was established to investigate shock-wave propagation and structural response. The More >

Yang Liu¹, Yinguo Yang¹, Pingping Xie¹, Qiuyu Lu¹, Yue Chen¹, Zhanpeng Xu^2,*, Zejie Huang²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2025.068436 - 27 April 2026

Abstract With the large-scale integration of wind and solar energy into the power grid, the power system is facing uncertainty challenges in multiple links, such as source, grid, and load. How to efficiently dispatch flexible resources, such as energy storage, has become an urgent problem to be solved. To this end, this paper considers the correlation between new energy stations due to natural conditions, uses Vine-Copula theory to describe the correlation characteristics of the output of multiple new energy stations, and proposes a wind solar new energy output scenario generation method based on Vine-Copula theory; Then,… More >

Yuzhe Gu¹, Wenwu Wang^1,2, Guanggen Zeng^1,2, Xia Hao^2,3, Lili Wu^1,2, Jingquan Zhang^1,2,*

Chalcogenide Letters, Vol.23, No.3, 2026, DOI:10.32604/cl.2026.079159 - 03 April 2026

Abstract Doping the absorber layer is a critical process for enhancing the performance of polycrystalline CdSeTe solar cells. Replacing traditional Cu doping with Group V dopants offers a pathway to fabricate devices with improved efficiency and stability. However, the dopant activation rate in polycrystalline structures remains low, typically only a few percent. While rapid thermal annealing (RTA) has been successfully employed to achieve high activation rates in single-crystal CdTe devices, its application to polycrystalline CdSeTe solar cells has been scarcely reported. In this study, we systematically applied multi-step annealing to investigate the dopant activation of in-situ As-doped More > Graphic Abstract

Chang He^#, An Wang^#, Youbo Wang^#, Qinyun Ma^*, Xiaofeng Chen^*

Oncology Research, Vol.34, No.4, 2026, DOI:10.32604/or.2026.074880 - 23 March 2026

Abstract Objectives: Combined chemotherapy and photothermal therapy (PTT) represents a promising approach for enhancing cancer treatment efficacy. This study aimed to develop arsenic trioxide (ATO) and poly(cyclopentadithiophene-alt-benzothiadiazole) (PCPDTBT)-loaded nanoparticles (ATO/PCPDTBT@NPs) to evaluate their synergistic efficacy in inhibiting lung cancer growth and metastasis. Methods: Nanovesicles were synthesized via a streamlined protocol and subjected to 808 nm NIR irradiation to assess their photothermal conversion capabilities. The therapeutic efficacy was evaluated in vitro using A549 lung carcinoma cells to assess apoptosis, invasion, and migration, and in vivo to monitor tumor volume reduction. Results: The nanoparticles exhibited excellent hemocompatibility and low cytotoxicity while More >

Goodness Adediran¹, Kenny Awuson-David², Yussuf Ahmed^1,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077606 - 12 March 2026

Abstract Amazon Web Services (AWS) CloudTrail auditing service provides detailed records of operational and security events, enabling cloud administrators to monitor user activity and manage compliance. Although signature-based threat detection methods have been enhanced with machine learning and Large Language Models (LLMs), these approaches remain limited in addressing emerging threats. This study evaluates a two-step Retrieval Augmented Generation (RAG) approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance. The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework, AWS Threat Technique Catalogue, and threat reports to overcome limitations of… More >