Peter Kipngeno Langat^*, Michael Kimwele, Dennis Kaburu

Journal of Cyber Security, Vol.8, pp. 541-558, 2026, DOI:10.32604/jcs.2026.083592 - 21 August 2026

Abstract Zero-day attacks pose a significant threat to computer systems and networks as they exploit weaknesses that have not been recognized by security professionals or software creators and for which there are no existing protective measures. This study introduced an innovative method for identifying Zero-day attacks through a Recurrent neural network model. To effectively mitigate these risks, not only is continuous monitoring essential, but also the implementation of machine learning. The model was trained on network traffic data and leveraged on the ability of Recurrent Neural Networks (RNNs) to learn complex patterns and identify anomalies that… More >

Pei Xie¹, Xiaoying Yang^1,*, Bo Li¹, Zhijie Pei¹, Fenghai Yang²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083100 - 23 July 2026

Abstract To address the critical requirements for collaborative delivery of multiple tasks within each order in personalized mass customization, this paper develops a dynamic parallel machine scheduling model that accounts for stochastic machine failures and order priorities, thereby more accurately reflecting the uncertainties and complexities of real-world production environments. A dual-objective optimization framework is adopted to minimize both the makespan (maximum task completion time) and the variance of task completion times, aiming to improve the coordination and reliability of intra-order task delivery. An adaptive weighted reward function is designed to balance overall scheduling efficiency with consistency… More >

Riya Sharma^1,*, Ashima Singh¹, Anju Bala¹, Mukesh Singh²

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082720 - 23 July 2026

Abstract State of health (SoH) prediction of lithium-ion batteries is a critical yet challenging task due to the complex, highly non-linear, and time-dependent nature of degradation processes under diverse operating conditions. Variability in usage patterns, environmental factors, and electrochemical dynamics further limits the robustness and generalisation capability of conventional estimation models. This study proposes a hybrid deep learning framework that combines Gated Recurrent Units (GRUs) with Kolmogorov–Arnold Networks (KANs) to address these challenges. GRUs are employed to effectively capture temporal dependencies in sequential battery data, while KANs enhance the model’s ability to learn complex non-linear functional… More >

Shidi Wen^1,2,#, Lu Wang^1,2,#, Ying Jiang³, Zhiyang Zhang^4,*, Yuejuan Cheng^4,*

Oncology Research, Vol.34, No.8, 2026, DOI:10.32604/or.2026.080462 - 16 July 2026

Abstract Background: Hidradenocarcinoma is a rare and highly aggressive malignancy with limited therapeutic options. This report describes the clinical course and treatment response of a patient with recurrent metastatic hidradenocarcinoma treated with sequential immunochemotherapy combined with anti-angiogenic therapy, with the aim of providing further insight into potential treatment strategies for this rare malignancy. Case Description: A 60-year-old male initially presented in 2020 with scrotal erythema and was diagnosed with hidradenocarcinoma after surgery. Despite surgical treatment, he developed recurrent disease with diffuse metastases. First-line chemoimmunotherapy (sintilimab, cisplatin, 5-fluorouracil; six cycles) achieved a progression-free survival (PFS) of 6 months. Following… More >

Xing Wen¹, Huan Chen¹, Ning Wang^1,*, Yu Song¹, Zhuqiao Qiao², Bin Zhang¹

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.073567 - 12 July 2026

Abstract High voltage direct current (HVDC) systems play a pivotal role in long-distance, high-capacity, and cross-regional power transmission. However, their complex structure, wide-ranging impact of faults, and stringent safety requirements pose significant challenges to operational stability. Conventional model-based and data-driven methods for tasks such as text classification, fault diagnosis, and operation and maintenance support suffer from limited scalability and interpretability. Recent advances in large language model (LLM) provide new opportunities to address these issues. This paper provides a systematic review of LLM applications in HVDC systems. Firstly, it introduces the core architecture and training mechanisms of… 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 >

Tao Wang¹, Ze Feng^1,*, Jinghao Ma², Shenhui Chen², Jihui Zhang², Tong Wang²

Energy Engineering, Vol.123, No.7, 2026, DOI:10.32604/ee.2025.074054 - 18 June 2026

Abstract This paper develops an MPPT control strategy utilizing the particle swarm optimization (PSO) algorithm to enhance the tracking accuracy of photovoltaic arrays under complex operating conditions and to mitigate the transient effects on energy storage batteries during grid-connected and off-grid transitions. Initially, the operational principle of the three-phase voltage source PWM converter and the bidirectional DC/DC converter within solar power generation and energy storage systems is carefully examined, leading to the establishment of the appropriate mathematical model. Secondly, a voltage and current double closed-loop control structure utilizing feedforward decoupling is devised to meet the cooperative… More >

Sajid Ullah Khan^*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082458 - 15 June 2026

Abstract Autonomous underwater vehicles (AUVs) play a crucial role in oceanographic research, monitoring the environment, and exploring resources in the ocean. Nevertheless, the operational efficiency of these devices is frequently constrained by the limited battery capacity and the requirement for charging while connected to a power source. Wireless power transfer (WPT) offers a non-contact alternative to conventional wet-mate electrical connectors, with inductive coupling receiving particular attention because of its relatively high efficiency, safety, and suitability for underwater charging over short transfer gaps. However, it is limited by the transfer distance, coil misalignment, coupler design constraints, and… More >

Weina Xu¹, Yi Xia¹, Qing Shen¹, Ling Ai¹, Yingye Lu^2,*

BIOCELL, Vol.50, No.6, 2026, DOI:10.32604/biocell.2026.076001 - 09 June 2026

Abstract Background: Dysfunction of decidual macrophages (dMφ) mediated by high mobility group box 1 (HMGB1) is related to unexplained recurrent spontaneous abortion (URSA), but its upstream regulatory mechanism remains unclear. The research explores whether miR-216a-5p regulates the toll-like receptor 4/nuclear factor-κB/nucleotide-binding oligomerization domain-like receptor protein 3 (TLR4/NF-κB/NLRP3) signaling axis by targeting HMGB1, thereby affecting the M1 polarization and pyroptosis of dMφ in URSA. Methods: The URSA mouse model was established, and primary dMφ was isolated and cultured. HMGB1 and miR-216a-5p were overexpressed or knocked down. Their expressions were examined. Their targeting relationship was verified through a bioinformatics… More > Graphic Abstract

Megabi Adane Yizengaw^1,*, Alehegn Atalay Birlie¹, Tamerat Tesfaye², Rajan Katrikan¹, Eldana Bizuneh Cheklie¹, Zelalem Girma¹

Journal of Renewable Materials, Vol.14, No.5, 2026, DOI:10.32604/jrm.2025.02025-0175 - 28 May 2026

Abstract Grape by-product of the wine industry, rich in polyphenols, tannins, lignin, and natural waxes, the chemical constituents grape skins 45%–55%, seeds 25%–35%, and stems or stalks 25%–35% weight of grape provide intrinsic cross-linking, mechanical reinforcement, antioxidant activity, and water resistance, closely replicating the effects of conventional vegetable tanning without using toxic chemicals. This review comprehensively examines current eco-friendly extraction methods to isolate bioactive compounds, as well as fiber modification techniques to improve polymer compatibility. Composite fabrication involves blending processed grape waste fibers with bio-based polymers and renewable plasticizers to produce materials exhibiting competitive tensile strength,… More >