Mudasser Mehmood^1,*, Zoahaib Aslam Khan¹, Adil Mehmood², Madiha Zaynab³, Muhammad Atiq ur Rahman⁴, Mohammad Khalid Al-Sadoon⁵, M. Harshini⁶, Ling Shing Wong⁷

Phyton-International Journal of Experimental Botany, Vol.94, No.4, pp. 1111-1135, 2025, DOI:10.32604/phyton.2025.059812 - 30 April 2025

Abstract Wheat (Triticum aestivum L.) is a staple crop critical for global food security, yet its productivity is significantly affected by abiotic stresses such as drought, salinity, and waterlogging, which are exacerbated by climate change. This study evaluated the effects of these stresses on vegetative growth, physiological responses, and yield. Field experiments were conducted using a Randomized Complete Block Design (RCBD) at the Mona Reclamation Experimental Project (MREP), WAPDA, Bhalwal, Sargodha, Punjab Pakistan. Stress treatments included three levels of drought (25%, 50%, and 75% field capacity), salinity (4, 8, and 12 dS/m), and waterlogging (24, 48, and… More >

Ran Ma^1,#, Chunyang Zhang^2,#, Wei Xu^1,*

International Journal of Mental Health Promotion, Vol.27, No.4, pp. 469-483, 2025, DOI:10.32604/ijmhp.2025.060351 - 30 April 2025

Abstract Background: Maintaining optimal quality of life (QoL) is a pivotal for “successful aging”. Understanding how the QoL of the elderly develops and what role psychological factors play in its development will help improve QoL from a psychological perspective. Embedded within the lifespan theory of control, this longitudinal study aimed to (1) map the temporal trajectory of QoL among Chinese older adults, (2) examine differential effects of tripartite negative emotions (stress, anxiety, depression), and (3) test the moderating role of control strategies (goal engagement, goal disengagement, self-protection) in emotion-QoL dynamics. Method: A prospective cohort of 345 community-dwelling… More >

Jasem Almotiri^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2109-2142, 2025, DOI:10.32604/cmc.2025.062923 - 16 April 2025

Abstract The evolving field of Alzheimer’s disease (AD) diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance (MR) images. This study introduces Dynamic GradNet, a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification. Initially, four state-of-the-art convolutional neural network (CNN) architectures, the self-regulated network (RegNet), residual network (ResNet), densely connected convolutional network (DenseNet), and efficient network (EfficientNet), were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation. Among these models, EfficientNet consistently demonstrated superior performance in terms of accuracy, precision, recall, and… More >

Mustafa Lateef Fadhil Jumaili^1,2, Emrullah Sonuç^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2947-2969, 2025, DOI:10.32604/cmc.2025.062719 - 16 April 2025

Abstract Alzheimer’s disease (AD) is a significant challenge in modern healthcare, with early detection and accurate staging remaining critical priorities for effective intervention. While Deep Learning (DL) approaches have shown promise in AD diagnosis, existing methods often struggle with the issues of precision, interpretability, and class imbalance. This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence (XAI) techniques, in particular attention mechanisms, Gradient-Weighted Class Activation Mapping (Grad-CAM), and Local Interpretable Model-Agnostic Explanations (LIME), to improve both model interpretability and feature selection. The study evaluates four different DL architectures (ResMLP, VGG16, Xception, More >

Qingqing Song^1,*, Shaoliang Xia¹, Zhen Wu²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2619-2642, 2025, DOI:10.32604/cmc.2025.062526 - 16 April 2025

Abstract Load time series analysis is critical for resource management and optimization decisions, especially automated analysis techniques. Existing research has insufficiently interpreted the overall characteristics of samples, leading to significant differences in load level detection conclusions for samples with different characteristics (trend, seasonality, cyclicality). Achieving automated, feature-adaptive, and quantifiable analysis methods remains a challenge. This paper proposes a Threshold Recognition-based Load Level Detection Algorithm (TRLLD), which effectively identifies different load level regions in samples of arbitrary size and distribution type based on sample characteristics. By utilizing distribution density uniformity, the algorithm classifies data points and ultimately… More >

Muhammad Saad, Ki-Woong Park^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1881-1900, 2025, DOI:10.32604/cmc.2025.061733 - 16 April 2025

Abstract The proliferation of Internet of Things (IoT) devices introduces substantial security challenges. Currently, privacy constitutes a significant concern for individuals. While maintaining privacy within these systems is an essential characteristic, it often necessitates certain compromises, such as complexity and scalability, thereby complicating management efforts. The principal challenge lies in ensuring confidentiality while simultaneously preserving individuals’ anonymity within the system. To address this, we present our proposed architecture for managing IoT devices using blockchain technology. Our proposed architecture works on and off blockchain and is integrated with dashcams and closed-circuit television (CCTV) security cameras. In this… More >

Meng Wang¹, Onkei Lei^1,2, Frankie U Kei Wong¹, Water Soi Po Wong¹, Walter Heung Chin Hui¹, Gasper Chi Hong Leong¹, Wenze Fang^1,3, Zhaowei Kong^1,*

International Journal of Mental Health Promotion, Vol.27, No.3, pp. 363-378, 2025, DOI:10.32604/ijmhp.2025.060875 - 31 March 2025

Abstract Background: This study evaluated physical fitness and mental health in young and elderly women 3 months after mild COVID-19 infection, and examined the impact of infection and age on long COVID occurrence and trajectory. Methods: There were 213 eligible female volunteers (107 young, 106 elderly) recruited approximately three months after the significant outbreak of COVID-19 in China. Participants completed a fitness test and mental health assessment using the Post-Traumatic Stress Disorder Self-Assessment Scale (PTSD) and the Pittsburgh Sleep Quality Inventory (PSQI). Results: Despite no significant difference in physical fitness, infected young and elderly females experienced poorer… More >

Smita Kapse¹, Ganesh Yenurkar^1,*, Vincent Omollo Nyangaresi^2,3,*, Gunjan Balpande¹, Shravani Kale¹, Manthan Jadhav¹, Sahil Lawankar¹, Vikrant Jaunjale¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 935-976, 2025, DOI:10.32604/cmc.2025.061981 - 26 March 2025

Abstract In today’s fast-paced world, many elderly individuals struggle to adhere to their medication schedules, especially those with memory-related conditions like Alzheimer’s disease, leading to serious health risks, hospitalizations, and increased healthcare costs. Traditional reminder systems often fail due to a lack of personalization and real-time intervention. To address this critical challenge, we introduce MediServe, an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized, secure, and adaptive solution. MediServe features a smart medication box equipped with biometric authentication, such as fingerprint recognition, ensuring authorized access to prescribed medication while… More >

Qinglei Zhang, Bai Hu^*, Jiyun Qin, Jianguo Duan, Ying Zhou

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1257-1273, 2025, DOI:10.32604/cmc.2025.059955 - 26 March 2025

Abstract Grasping is one of the most fundamental operations in modern robotics applications. While deep reinforcement learning (DRL) has demonstrated strong potential in robotics, there is too much emphasis on maximizing the cumulative reward in executing tasks, and the potential safety risks are often ignored. In this paper, an optimization method based on safe reinforcement learning (Safe RL) is proposed to address the robotic grasping problem under safety constraints. Specifically, considering the obstacle avoidance constraints of the system, the grasping problem of the manipulator is modeled as a Constrained Markov Decision Process (CMDP). The Lagrange multiplier… More >

Zachariah Taylor^1,*, Kayla Meyer², Danielle Terrenzio², Ryan Wong³, Sharon Larson⁴, Stephanie Kjelstrom⁴, Natalina Contoreggi⁵, Laurence Belkoff^1,6, Ilia Zeltser^1,6

Canadian Journal of Urology, Vol.32, No.1, pp. 21-27, 2025, DOI:10.32604/cju.2025.064705 - 20 March 2025

Abstract Purpose: While the mental health impact of a prostate cancer diagnosis, including low-risk prostate cancer, is well-documented, the effect of pre-existing anxiety and/or depression on adherence to active surveillance protocols in low-risk prostate cancer patients remains unclear. This study assessed the association between prior anxiety and/or depression and active surveillance adherence in men with low-risk prostate cancer. Methods: We conducted a retrospective, multicenter study involving 426 men diagnosed with low-risk prostate cancer who were recommended active surveillance as the primary management strategy. Active surveillance adherence was defined by completion of both a prostate-specific antigen test… More >