Yue Cui¹, Ziqing Yang², Hao Gao^1,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.072986 - 28 January 2026

Abstract Background: In the Chinese context, the impact of short video applications on the psychological well-being of older adults is contested. While often examined through a pathological lens of addiction, this perspective may overlook paradoxical, context-dependent positive outcomes. Therefore, the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use. Methods: Data were collected between July and August 2025 via the Credamo online survey platform, yielding 384 valid responses… More >

Jimin Chae¹, Youngbin Lym^2,*, Geiguen Shin^2,3,*

International Journal of Mental Health Promotion, Vol.28, No.1, 2026, DOI:10.32604/ijmhp.2025.071482 - 28 January 2026

Abstract Background: Social connection is widely recognized as a protective determinant of health, yet its direct and indirect effects on mental health remain underexplored. This study examines the relationship between social connection and mental health, focusing on the mediating role of quality of life (QoL) and the moderating effect of regional differences. Methods: We analyzed data from the 2019 Korean Community Health Survey, comprising 229,099 adults. Mental health was assessed through validated measures of depressive symptoms and psychological well-being. Social connection was measured using indicators of interpersonal ties and community participation, and QoL was assessed via self-reported… More >

Sk. A. Shezan^*

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.073418 - 27 December 2025

Abstract Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization. However, the high penetration of intermittent renewable sources often causes frequency deviations, voltage fluctuations, and poor reactive power coordination, posing serious challenges to grid stability. Conventional Interconnection Flow Controllers (IFCs) primarily regulate active power flow and fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks. To overcome these limitations, this study proposes an enhanced Interconnection Flow Controller (e-IFC) that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller (IRFC) within a unified adaptive… More >

Yuang Chen^1,2, Yong Li^1,*, Fang Lin^1,2, Shuhan Lv^1,2, Jiaze Jiang^1,2

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.070990 - 09 December 2025

Abstract Aiming at the problem of potential information noise introduced during the generation of ghost feature maps in GhostNet, this paper proposes a novel lightweight neural network model called ResghostNet. This model constructs the Resghost Module by combining residual connections and Adaptive-SE Blocks, which enhances the quality of generated feature maps through direct propagation of original input information and selection of important channels before cheap operations. Specifically, ResghostNet introduces residual connections on the basis of the Ghost Module to optimize the information flow, and designs a weight self-attention mechanism combined with SE blocks to enhance feature More >

Qing Guo^1,2, Juwei Zhang^1,2,3,*, Bingyi Ren^1,2

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069053 - 10 November 2025

Abstract Traffic sign detection is an important part of autonomous driving, and its recognition accuracy and speed are directly related to road traffic safety. Although convolutional neural networks (CNNs) have made certain breakthroughs in this field, in the face of complex scenes, such as image blur and target occlusion, the traffic sign detection continues to exhibit limited accuracy, accompanied by false positives and missed detections. To address the above problems, a traffic sign detection algorithm, You Only Look Once-based Skip Dynamic Way (YOLO-SDW) based on You Only Look Once version 8 small (YOLOv8s), is proposed. Firstly,… More >

Shuoting Xiao^1,*, Nikita Igorevich Fomin¹, Kirill Anatolyevich Khvostunkov², Chong Liu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 2821-2847, 2025, DOI:10.32604/cmes.2025.071961 - 23 December 2025

Abstract Precast concrete structures have gained popularity due to their advantages. However, the seismic performance of their connection joints remains an area of ongoing research and improvement. Grouted Sleeve Connection (GSC) offers a solution for connecting reinforcements in precast components, but their vulnerability to internal defects, such as construction errors and material variability, can significantly impact performance. This article presents a finite element analysis (FEA) to evaluate the impact of internal grouting defects in GSC on the structural performance of precast reinforced concrete columns. Four finite element models representing GSC with varying degrees of defects were… More > Graphic Abstract

Wang Gu¹, Yuanyuan Song², Zhihu Zhang³, Minggang Zheng^1,*

Energy Engineering, Vol.122, No.12, pp. 4837-4856, 2025, DOI:10.32604/ee.2025.069529 - 27 November 2025

Abstract Driven by the global “dual-carbon” goals, hydrogen fuel cell electric vehicles (FCEVs) are being rapidly promoted as a zero-emission transportation solution. However, their large-scale application is constrained by issues such as inefficient operation, poor information flow between vehicles and stations, and potential safety hazards, which are caused by insufficient intelligence of hydrogen refueling stations. This study aims to address these problems by deeply integrating Cellular Vehicle-to-Everything (C-V2X) technology with hydrogen refueling stations, thereby building a safe, efficient, and low-carbon hydrogen energy application ecosystem to promote the global transition to zero-carbon transportation. Firstly, through literature review… More >

Yifei Wang, Zhao Zhang^*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.33, No.3, pp. 1-1, 2025, DOI:10.32604/icces.2025.011121

Abstract Additive manufacturing (AM) reveals high anisotropy in mechanical properties due to the thermal accumulation induced microstructures. How to reveal the internal connection between the microstructures and the mechanical properties in additive manufacturing is a challenge. There are many methods to predict the mechanical properties based on the microstructural evolutions in additive manufacturing [1–3]. Here we summarized the main methods for the prediction of the mechanical properties in additive manufacturing, including crystal plasticity finite element method (CPFEM), dislocation dynamics (DD), and molecular dynamics (MD). We systematically examine these primary approaches for mechanical property predictions in AM,… More >

Qiji Dai¹, Jikai Li^2,*, Bohui Ning¹, Yutao Xu¹, Chang Liu², Xuan Zhang¹

Energy Engineering, Vol.122, No.10, pp. 4061-4080, 2025, DOI:10.32604/ee.2025.067304 - 30 September 2025

Abstract Aiming at the challenge of complex load balancing coordination for a three-phase four-leg (3P4L) based multi-ended low voltage flexible DC distribution system (M-LVDC) considering unbalanced power compensation, this paper proposes a phase-split power decoupling unbalanced compensation strategy based load balancing strategy for 3P4L based M-LVDC. Firstly, the topology and operation principle of the 3P4L-based M-LVDC system is introduced, and quasi-proportional resonant (QPR) based phase-split power current control for the 3P4L converter is proposed. Secondly, a load-balancing control strategy considering unbalanced compensation for 3P4L-based M-LVDC is presented, in which the control diagrams for each 3P4L-based converter… More >

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 331-366, 2025, DOI:10.32604/cmc.2025.067915 - 29 August 2025

Abstract Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational More >