Mohamad Khairi Ishak¹, Samir Ait Lhadj Lamin^2,3, Mohammad Shokouhifar^4,*, Aseel Smerat⁵, Kamal M. Othman⁶, Abdulfattah Noorwali⁶, Esam Y.O. Zafar⁶

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.078029 - 23 July 2026

Abstract Radio Frequency Identification (RFID) has emerged as an effective remote technology for real-time monitoring and management of medical assets in hospitals. Most existing RFID Network Planning (RNP) methods are primarily based on either heuristic or metaheuristic approaches. While heuristic approaches are computationally efficient and converge rapidly, they often suffer from premature convergence and suboptimal network configurations. Conversely, metaheuristic algorithms provide stronger global search capabilities and improved solution quality, but they typically require higher computational effort and may still exhibit stagnation in local optima when applied to complex hospital layouts. To overcome these limitations while utilizing… More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Anitya Kumar Gupta^*, Pankaj Vaidya, Anurag Rana

Journal on Artificial Intelligence, Vol.8, pp. 377-402, 2026, DOI:10.32604/jai.2026.074171 - 22 July 2026

Abstract Sentiment analysis aims at determining the stance or point of view of a topic, author, or speaker about a certain topic, document, or event. The given paper proposes a hybrid quantum-classical model of sentiment classification, which is known as Complex-Valued Quantum-Enhanced Recurrent Neural Network (CQRNN). The model combines Quantum Long Short-Term Memory (QLSTM) and Quantum Gated Recurrent Units (QGRU) with the use of Variational Quantum Circuits (VQCs) and complex-valued embeddings designed at the level of semantic information, both in the real and imaginary domains. Experiments on benchmark sentiment datasets show that CQRNN can be fine-tuned More >

Yueyang Ji¹, Yaohui Peng¹, Haoran Ji^1,*, Xinran Na¹, Yuxuan Chen¹, Wei Li², Shengbin Chen²

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.074599 - 12 July 2026

Abstract With the increasing penetration of distributed generations and continuous growth of loads, traditional rural distribution networks face severe challenges in both hosting capacity and reliability. Addressing these issues requires planning approaches that strike a balance between economic efficiency in infrastructure development and resilience in operation. Considering the dynamic growth of distributed generations and rural loads over the planning horizon, this paper presents a multi-stage expansion planning approach that coordinates flexible interconnection devices (FIDs) with substation and line construction to improve both economic performance and system reliability. The proposed method account for the time-varying growth of… More >

Ruosong Hou^1,2,*, Jiakun An¹, Zihao Zhao¹, Wei Guo¹, Hua Shao²

Energy Engineering, Vol.123, No.8, 2026, DOI:10.32604/ee.2025.069068 - 12 July 2026

Abstract This article evaluates the connectivity with energy sharing in low-voltage distribution areas. Indicators like wind-solar complementing effectiveness, source-load energy sharing possibility, or transformer capacity interconnection measurements are part of the assessment index framework for interconnection capacity that is established after an analysis of the features of linked scenarios. Radial and inflexible, conventional distribution systems can’t handle bidirectional power flow, fluctuating demand, or grid disruptions. Using real-world examples, we can see that the suggested strategy improves power supply efficiency across zones and increases the usage of distributed energy resources, proving the method’s validity. With the help… More >

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

Abstract This article has no abstract. More >

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