BIOCELL, Vol.50, Suppl.1, pp. 1-56, 2026

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Ahmed M. Abdel-Khalek¹, Salaheldin Farah Attallah Bakhiet^2,*, Habab A. Osman³, Intisar Abunagma Mohamed Saad⁴, David Lester⁵

Journal of Psychology in Africa, Vol.36, No.3, pp. 417-423, 2026, DOI:10.32604/jpa.2026.071085 - 30 June 2026

Abstract Research on the relationship between self-esteem and the Big-Five personality traits has focused on Western populations. However, it is crucial to explore this relationship in non-Western contexts to better understand cultural variations in personality and self-perception. This study examined the associations between the Big-Five personality traits and self-esteem in a sample of 583 Sudanese university students (299 men, 284 women). The Big-Five traits were measured using the Arabic Big-Five Personality Inventory (ABFPI), and self-esteem was assessed with the Rosenberg Self-Esteem Scale. Both instruments have demonstrated good psychometric properties in previous studies. Results indicated that all… More >

Chengyao Xue^1,2, Leting Wang², Jihao Yang², Hao Zhang², Xinhang Duan², Yizhou Dong², Yiwen Yan², Yu Zhang³, Jinghui Shi⁴, Wenliang Song^1,2,*

Journal of Polymer Materials, Vol.43, No.2, 2026, DOI:10.32604/jpm.2026.082848 - 30 June 2026

Abstract Food packaging films play a crucial role in maintaining food quality and safeguarding human health, making the development of advanced packaging materials an important research priority. Conventional petroleum-based plastic films suffer from poor degradability and may pose environmental and potential health concerns, while many currently available preservative films still exhibit limited freshness-retention performance. Therefore, the development of environmentally friendly, non-toxic, biodegradable, and efficient food-packaging materials is of great significance. In this study, coaxial electrospinning was employed to fabricate a core–shell nanofiber film by encapsulating resveratrol within gelatin/zein (GA/ZN) fibers, aiming to enhance the preservation performance… More >

Shahidul Islam¹, Md. Abdul Jalil^2,*, Marija Kodric³, Zorica Erakovic⁴, Md. Byzed Hasan⁵

Journal of Polymer Materials, Vol.43, No.2, 2026, DOI:10.32604/jpm.2026.075107 - 30 June 2026

Abstract This systematic review critically evaluates the mechanical performance, durability, processing routes, and industrial applicability of sisal fiber reinforced polymer (FRP) composites in relation to their readiness for wider engineering and industrial implementation. The review analyzes and summarizes science articles published 2020, 2025 to identify the performance trends, technical limitations, and techno, economic constraints influencing the application of these composites. A PRISMA, based approach was implemented, which included systematic searches of Scopus, Web of Science, PubMed, and Google Scholar by using pre, set keywords, inclusion criteria, and clear screening procedures, to ensure reproducibility and quality control.… More > Graphic Abstract

Salman Khan^*, Mai Alzamel^*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.084282 - 30 June 2026

Abstract Traditional threat detection has proven ineffective in large-scale, moving data in the era of ever-more complex adversarial techniques and interconnected systems. The challenge becomes even more complex when high-volume, unstructured data continuously streams from social media platforms, requiring them to process the data efficiently and intelligently to provide timely security insights. Considering the big data security, the present study presents a scalable deep-learning-based system for real-time cyber threat detection, which has been developed and validated especially for distributed big data processing environments. A hybrid embedding approach that combines Word2Vec and Iterated Dilated Convolutional Neural Networks… More > Graphic Abstract

Chenghao Kou¹, Zunyi Duan^2,*, Shengjie Wang¹, Jun Ma¹, Zhongwei Yang¹, Xudong Tang¹, Rongchun Hu²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083699 - 30 June 2026

Abstract Piezoelectric actuators are widely used in precision positioning systems. However, their inherent nonlinear behaviors, particularly hysteresis and output saturation, degrade modeling accuracy and limit control performance. Existing studies have generally used either black-box models or traditional physical models. The former typically lack physical interpretability, while the latter can exhibit limited accuracy when the actuator response includes coupled nonlinear effects. To address this issue, this paper proposes a hybrid physics-informed neural network (Hybrid-PINN) framework. An equivalent attenuation model, with a calibrated attenuation coefficient, is first established to describe output saturation and provide a nominal physical reference.… More >

Yuyan Ning^1,2, Haiyun Huang¹, Legend Zhang³, Wei Wei⁴, Hao Quan⁵, Bo Yang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.083345 - 30 June 2026

Abstract Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling. Despite their popularity, these models face several challenges, including computational complexity, noise blurring, and information loss. This paper proposes an enhanced convolutional attention network (ECANet) for liver segmentation. ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections. Both the encoder and decoder are constructed using enhanced convolutional Transformer (ECT) blocks, where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement, and a Token-wise multi-layer perceptron More >

Chi Zhong¹, Bo Ye¹, Xiao Wang², Yang Liu^1,*, Linmin Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.082583 - 30 June 2026

Abstract With the sharp increase in the heat flux of high-power electronic devices, efficient thermal management has become critically important. Boiling heat transfer in parallel small channels, which utilizes latent heat efficiently, has emerged as a key enabling technology for next-generation cooling solutions. However, parallel channel systems are extremely susceptible to flow instabilities, resulting in severely uneven distributions of flow rate and heat transfer among the channels. This unevenness often leads to local overheating, which in turn restricts the system’s reliability and limits its practical application. In this paper, a three-dimensional transient numerical simulation method was… More >

Kottakkaran Sooppy Nisar^1,*, Muhammad Farman^2,3,4, Ali Hasan³, Mohammed Altaf Ahmed⁵, Mohammad Tabish⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080973 - 30 June 2026

Abstract A global health concern, neurodegenerative disorders like Parkinson’s and Alzheimer’s impact both mental and physical functioning. The complex interplay among immunological response, protein accumulation, and brain health necessitates sophisticated mathematical modeling. This study introduces a fractional-order mathematical model using the Mittag-Leffler derivative to describe the dynamics of neurodegeneration, incorporating key biological factors such as functioning and infected neurons, extracellular alpha-synuclein, microglia, and T-cells. A fundamental assumption of the model is that neuronal deterioration is influenced by memory effects, where past states impact current disease progression, making fractional-order calculus more suitable than traditional integer-order models. The… More >

Jaewan Lee¹, Seonghwan Park², Junghwan Kook^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.3, 2026, DOI:10.32604/cmes.2026.080930 - 30 June 2026

Abstract In bearing fault diagnosis for Prognostics and Health Management (PHM), the overall performance of data-driven models is strongly influenced by the coupled effects of preprocessing, model configuration, and decision fusion. However, these components are often optimized independently, resulting in fragmented workflows that limit global optimality, reproducibility, and computational efficiency of the model. This study presents a computationally unified three-stage sequential optimization framework that systematically coordinates the preprocessing selection, model hyperparameter optimization, and decision-level fusion within a consistent surrogate-based optimization architecture. In the first stage, candidate preprocessing schemes reflecting physical fault mechanisms—outer race, inner race, rolling… More > Graphic Abstract