Mohammed Majid Abed^1,2,*, Murat Aydin¹, Esma Yiğider¹, Melek Ekinci³, Ertan Yildirim³

Phyton-International Journal of Experimental Botany, Vol.94, No.12, pp. 3845-3860, 2025, DOI:10.32604/phyton.2025.073163 - 29 December 2025

Abstract Plants are continuously exposed to abiotic and biotic stresses that threaten their growth, reproduction, and survival. Adaptation to these stresses requires complex regulatory networks that coordinate physiological, molecular, and ecological responses. However, such adaptation often incurs significant costs, including reduced growth, yield penalties, and altered ecological interactions. This review systematically synthesizes recent advances published between 2018 and 2025, following PRISMA criteria, on plant responses to abiotic and biotic stressors, with an emphasis on the trade-offs between adaptation and productivity. It also highlights major discrepancies in the literature and discusses strategies for enhancing plant stress tolerance More >

Victor De Araujo^1,2,3,*, Pedro Jardim^3,4, Poliana Pessôa³, Juliano Vasconcelos^2,5, Matheus Souza⁶, José Garcia⁷, Jozef Švajlenka⁸, André Christoforo^3,1

Journal of Renewable Materials, Vol.13, No.12, pp. 2479-2524, 2025, DOI:10.32604/jrm.2025.02025-0127 - 23 December 2025

Abstract Finger-joint lumber is a sustainable building product commercialized as a structural solution for beams, pillars and other thin flat load-bearing elements. This study aims to study finger-joint lumber and its industry to promote this engineered wood product. The first research stage assessed the collection of publications on finger-joint lumber available globally, in which a structured protocol was developed to prospect studies based on two complementary methodologies: PRISMA 2020 using Scopus and Web of Science databases, and Snowball using both forward and backward models to complete with additional literature. The second research stage assessed finger-joint lumber… More >

Sunawar Khan¹, Tehseen Mazhar^1,2,*, Naila Sammar Naz¹, Fahed Ahmed¹, Tariq Shahzad³, Atif Ali⁴, Muhammad Adnan Khan^5,*, Habib Hamam^6,7,8,9

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2347-2401, 2025, DOI:10.32604/cmc.2025.066932 - 23 September 2025

Abstract Feature selection (FS) plays a crucial role in medical imaging by reducing dimensionality, improving computational efficiency, and enhancing diagnostic accuracy. Traditional FS techniques, including filter, wrapper, and embedded methods, have been widely used but often struggle with high-dimensional and heterogeneous medical imaging data. Deep learning-based FS methods, particularly Convolutional Neural Networks (CNNs) and autoencoders, have demonstrated superior performance but lack interpretability. Hybrid approaches that combine classical and deep learning techniques have emerged as a promising solution, offering improved accuracy and explainability. Furthermore, integrating multi-modal imaging data (e.g., Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Positron… More >

Jungpil Shin^1,*, Wahidur Rahman², Tanvir Ahmed², Bakhtiar Mazrur², Md. Mohsin Mia², Romana Idress Ekfa², Md. Sajib Rana², Pankoo Kim^3,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4105-4153, 2025, DOI:10.32604/cmc.2025.066910 - 30 July 2025

Abstract Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data. With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has become essential for deriving actionable insights across various sectors. This study presents a systematic literature review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based approaches, and recent advancements in deep learning techniques. The review follows a structured protocol comprising three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were More >

Abdelhadi Zineddine^1,#, Yousra Belfaik^2,#, Abdeslam Rehaimi¹, Yassine Sadqi^3,*, Said Safi¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4019-4054, 2025, DOI:10.32604/cmc.2025.066139 - 30 July 2025

Abstract With the proliferation of online services and applications, adopting Single Sign-On (SSO) mechanisms has become increasingly prevalent. SSO enables users to authenticate once and gain access to multiple services, eliminating the need to provide their credentials repeatedly. However, this convenience raises concerns about user security and privacy. The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats, identify gaps in existing systems, and explore effective mitigation solutions. This need motivated the first systematic literature review (SLR) of SSO security and privacy, conducted in… More >

Zhimei Cai^1,2,*, Faridah Mydin Kutty^1,*, Muhammad Syawal Amran¹

International Journal of Mental Health Promotion, Vol.27, No.6, pp. 753-769, 2025, DOI:10.32604/ijmhp.2025.064983 - 30 June 2025

Abstract Background: considering the significant issue of learning burnout among university students, it is essential to investigate the connection between mindfulness and learning burnout. This systematic review and meta-analysis sought to thoroughly examine the direct and indirect relationships between mindfulness and learning burnout. Methods: a comprehensive literature search was conducted in Scopus, Google Scholar, and Web of Science databases until 07 July 2024. A comprehensive literature review analysis of 19 articles was included, which identified three dimensions of learning burnout: emotions, behaviors, and outcomes, determined the indirect and direct relationships between mindfulness and learning burnout, and… More >

David Njuguna^*, John Ndia

Journal of Cyber Security, Vol.7, pp. 1-20, 2025, DOI:10.32604/jcs.2025.059398 - 18 February 2025

Abstract A quick response code is a barcode that allows users to instantly access information via a digital device. Quick response codes store data as pixels in a square-shaped grid. QR codes are prone to cyber-attacks. This assault exploits human vulnerabilities, as users can scarcely discern what is concealed in the quick response code prior to usage. The aim of the study was to investigate Quick Response code attack types and the detection techniques. To achieve the objective, 50 relevant studies published between the year 2010 and 2024 were identified. The articles were obtained from the… More >

Rakesh Kumar^1,*, Ajay Singh², Ahmed Subahi Ahmed Kassar³, Mohammed Ismail Humaida³, Sudhanshu Joshi⁴, Manu Sharma⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1289-1349, 2025, DOI:10.32604/cmes.2025.059152 - 27 January 2025

Abstract This review aims to analyze the development and impact of Artificial Intelligence (AI) in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives. It is extensively devoted to AI technology deployment relevant to disease management, healthcare delivery, epidemiology, and policy-making. However, its AI is culturally sensitive and ethically grounded in Islam. Based on the PRISMA framework, an SLR evaluated primary academic literature, cases, and practices of Saudi Arabia’s AI implementation in the public healthcare sector. Instead, it categorizes prior research based on how AI can work, the issues it poses, and… More >

Moein Tosan¹, Mohammad Reza Gharib^2,*, Nasrin Fathollahzadeh Attar³, Ali Maroosi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1109-1154, 2025, DOI:10.32604/cmes.2025.058595 - 27 January 2025

Abstract Accurate estimation of evapotranspiration (ET) is crucial for efficient water resource management, particularly in the face of climate change and increasing water scarcity. This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers, selected according to PRISMA guidelines, to evaluate the performance of Hybrid Artificial Neural Networks (HANNs) in ET estimation. The findings demonstrate that HANNs, particularly those combining Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs), are highly effective in capturing the complex nonlinear relationships and temporal dependencies characteristic of hydrological processes. These… More >

Hassan Saeed¹, Imran Shafi¹, Jamil Ahmad², Adnan Ahmed Khan³, Tahir Khurshaid^4,*, Imran Ashraf^5,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 139-172, 2025, DOI:10.32604/cmc.2024.057587 - 03 January 2025

Abstract Software-related security aspects are a growing and legitimate concern, especially with 5G data available just at our palms. To conduct research in this field, periodic comparative analysis is needed with the new techniques coming up rapidly. The purpose of this study is to review the recent developments in the field of security integration in the software development lifecycle (SDLC) by analyzing the articles published in the last two decades and to propose a way forward. This review follows Kitchenham’s review protocol. The review has been divided into three main stages including planning, execution, and analysis.… More >