Lingling Yu^1,2, Yanxia Chen^3,*, Jinming Li², André O. Werneck⁴, Fabian Herold⁵, Alyx Taylor⁶, Benjamin Tari⁷, Liye Zou²

International Journal of Mental Health Promotion, Vol.27, No.2, pp. 97-110, 2025, DOI:10.32604/ijmhp.2025.061237 - 03 March 2025

Abstract Background: Taking actions to maintain a healthy lifestyle, including regular engagement in physical activity (PA) and reducing sedentary behavior (SB), may protect against the development of internalizing problems among healthy youth. However, it remains unclear whether such associations exist among youth with chronic pain who often report symptoms of depression and anxiety. To this end, we aimed to investigate the associations between independent and combined PA and/or SB patterns with indicators of internalizing problems in this vulnerable population. Methods: Data used in this cross-sectional study were retrieved from the U.S. National Survey of Children’s Health… More >

Ramadan Abdul-Rashid¹, Mohd Amiruddin Abd Rahman^1,*, Kar Tim Chan¹, Arun Kumar Sangaiah^2,3,4

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2585-2616, 2025, DOI:10.32604/cmes.2025.060548 - 03 March 2025

Abstract This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms. The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments. The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency. A novel closed-form expression is also derived for a generalized asymptotic error variance steady state. Steady and convergence analyses are then presented for the synchronization, with frequency adaptations done using least More >

Rabindra Nath Mondal¹, Giulio Lorenzini^2,*, Sidhartha Bhowmick¹, Sreedham Chandra Adhikari³

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 249-278, 2025, DOI:10.32604/fhmt.2024.057990 - 26 February 2025

Abstract The present study investigates the non-isothermal flow and energy distribution through a loosely bent rectangular duct using a spectral-based numerical approach over a wide range of the Dean number . Unlike previous research, this work offers novel insights by conducting a grid-point-specific velocity analysis and identifying new bifurcation structures. The study reveals how centrifugal and buoyancy forces interact to produce steady, periodic, and chaotic flow regimes significantly influencing heat transfer performance. The Newton-Raphson method is employed to explore four asymmetric steady branches, with vortex solutions ranging from 2- to 12 vortices. Unsteady flow characteristics are… More >

Mariam Habouria¹, Sahbi Ouertani^1,*, Noura Ben Mansour², Soufien Azzouz¹, Mohamed Taher Elaieb³

Frontiers in Heat and Mass Transfer, Vol.23, No.1, pp. 345-360, 2025, DOI:10.32604/fhmt.2024.057387 - 26 February 2025

Abstract The aim of this paper was to characterize through experiment the moisture and temperature kinetic behavior of Eucalyptus gomphocephala wood samples using microwave heating (MWH) in two scenarios: intermittently and continuously. The mechanical properties and surface appearance of the heated samples were also investigated. Continuous and intermittent microwave drying kinetic experiments were conducted at a frequency of 2.45 GHz using a microwave laboratory oven at 300, 500, and 1000 watts. Drying rate curves indicated three distinct phases of MWH. Increasing the microwave power with a shorter drying time led to rapid increases in internal temperature and… More >

Ayman Noor^*

Computer Systems Science and Engineering, Vol.49, pp. 259-286, 2025, DOI:10.32604/csse.2025.060387 - 21 February 2025

Abstract Precisely forecasting the performance of Deep Learning (DL) models, particularly in critical areas such as Uniform Resource Locator (URL)-based threat detection, aids in improving systems developed for difficult tasks. In cybersecurity, recognizing harmful URLs is vital to lowering risks associated with phishing, malware, and other online-based attacks. Since it directly affects the model’s capacity to differentiate between benign and harmful URLs, finding the optimum mix of hyperparameters in DL models is a significant difficulty. Two commonly used architectures for sequential and spatial data processing, Long Short-Term Memory (LSTM)/Gated Recurrent Unit (GRU) and Convolutional Neural Network… More >

Joon-Min Gil¹, Hyunsu Jeong¹, Jihun Kang^2,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2199-2213, 2025, DOI:10.32604/cmc.2025.061182 - 17 February 2025

Abstract In a cloud environment, graphics processing units (GPUs) are the primary devices used for high-performance computation. They exploit flexible resource utilization, a key advantage of cloud environments. Multiple users share GPUs, which serve as coprocessors of central processing units (CPUs) and are activated only if tasks demand GPU computation. In a container environment, where resources can be shared among multiple users, GPU utilization can be increased by minimizing idle time because the tasks of many users run on a single GPU. However, unlike CPUs and memory, GPUs cannot logically multiplex their resources. Additionally, GPU memory… More >

Yahia Said^1,2,*, Yahya Alassaf³, Refka Ghodhbani⁴, Taoufik Saidani⁴, Olfa Ben Rhaiem⁵

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3005-3018, 2025, DOI:10.32604/cmc.2025.060928 - 17 February 2025

Abstract Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic… More >

Amjad Khan¹, Rahim Khan^1,*, Fahad Alturise^2,*, Tamim Alkhalifah³

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3161-3178, 2025, DOI:10.32604/cmc.2025.060372 - 17 February 2025

Abstract The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the… More >

Ruoxin Li^1,*, Shaoxiong Wu¹, Fengping Deng¹, Zhongli Tian¹, Hua Cai¹, Xiang Li¹, Xu Xu¹, Qi Liu^2,3

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2969-2984, 2025, DOI:10.32604/cmc.2025.060230 - 17 February 2025

Abstract Studies to enhance the management of electrical energy have gained considerable momentum in recent years. The question of how much energy will be needed in households is a pressing issue as it allows the management plan of the available resources at the power grids and consumer levels. A non-intrusive inference process can be adopted to predict the amount of energy required by appliances. In this study, an inference process of appliance consumption based on temporal and environmental factors used as a soft sensor is proposed. First, a study of the correlation between the electrical and… More >

Dalal AL-Alimi¹, Mohammed A. A. Al-qaness^2,3,*, Robertas Damaševičius^4,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3539-3561, 2025, DOI:10.32604/cmc.2025.059869 - 17 February 2025

Abstract Accurate forecasting of oil production is essential for optimizing resource management and minimizing operational risks in the energy sector. Traditional time-series forecasting techniques, despite their widespread application, often encounter difficulties in handling the complexities of oil production data, which is characterized by non-linear patterns, skewed distributions, and the presence of outliers. To overcome these limitations, deep learning methods have emerged as more robust alternatives. However, while deep neural networks offer improved accuracy, they demand substantial amounts of data for effective training. Conversely, shallow networks with fewer layers lack the capacity to model complex data distributions… More >