Trung-Hieu Tran^*

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 869-886, 2025, DOI:10.32604/sdhm.2025.064902 - 30 June 2025

Abstract The design principles for conventional reinforced concrete structures have gradually transitioned to seismic-resistant design since the 1970s. However, until recently, the implementation of strength capacity and ductility design has not been rigorously enforced in many developing countries that are prone to seismic risks. Numerous studies have evaluated the effectiveness of joint behavior based on both ductile and non-ductile designs under cyclic loading. Previous research has demonstrated that enhancing joint regions with Ultra-High Performance Steel Fiber Reinforced Concrete (UHPSFRC) significantly improves the seismic resistance of structural components. This paper presents a detailed analysis of the considerable… More >

Weiyang Xie^1,2, Cheng Chang^1,2, Ziqin Lai^1,2,*, Sha Liu^1,2, Han Xiao^1,2

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.6, pp. 1417-1438, 2025, DOI:10.32604/fdmp.2025.060956 - 30 June 2025

Abstract In the context of post-stimulation shale gas wells, the terms “shut-in” and “flowback” refer to two critical phases that occur after hydraulic fracturing (fracking) has been completed. These stages play a crucial role in determining both the well’s initial production performance and its long-term hydrocarbon recovery. By establishing a comprehensive big data analysis platform, the flowback dynamics of over 1000 shale gas wells were analyzed in this work, leading to the development of an index system for evaluating flowback production capacity. Additionally, a shut-in chart was created for wells with different types of post-stimulation fracture More >

Yuan-Yu Tsai^1,*, Alfrindo Lin¹, Wen-Ting Jao¹, Yi-Hui Chen^2,*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 681-697, 2025, DOI:10.32604/cmc.2025.064370 - 09 June 2025

Abstract With the rapid expansion of multimedia data, protecting digital information has become increasingly critical. Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction. Audio, as a vital medium in communication, entertainment, and information sharing, demands the same level of security as images. However, embedding data in encrypted audio poses unique challenges due to the trade-offs between security, data integrity, and embedding capacity. This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves… More >

Hanan Hardan^1,*, Osama A. Khashan^2,*, Mohammad Alshinwan¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1681-1710, 2025, DOI:10.32604/cmc.2025.061659 - 09 June 2025

Abstract This study introduces an Edge-Based Data Hiding and Extraction Algorithm (EBDHEA) to address the problem of data embedding in images while preserving robust security and high image quality. The algorithm produces three classes of pixels from the pixels in the cover image: edges found by the Canny edge detection method, pixels arising from the expansion of neighboring edge pixels, and pixels that are neither edges nor components of the neighboring edge pixels. The number of Least Significant Bits (LSBs) that are used to hide data depends on these classifications. Furthermore, the lossless compression method, Huffman… More >

Liang Tang¹, Hongwei Wang¹, Xinyuan Zhu¹, Jiying Liu^2,*, Kaiyue Li^2,*

Energy Engineering, Vol.122, No.6, pp. 2257-2289, 2025, DOI:10.32604/ee.2025.063918 - 29 May 2025

Abstract The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment, hindering the efficient utilization of renewable energy and the low-carbon development of energy systems. To enhance the consumption capacity of green power, the green power system consumption optimization scheduling model (GPS-COSM) is proposed, which comprehensively integrates green power system, electric boiler, combined heat and power unit, thermal energy storage, and electrical energy storage. The optimization objectives are to minimize operating cost, minimize carbon emission, and maximize the consumption of wind and solar curtailment. The multi-objective particle swarm… More >

Zeyang Zhou^1,*, Zachary James Ryan¹, Utkarsh Sharma², Tran Tien Anh³, Shashi Mehrotra⁴, Angelo Greco⁵, Jason West⁶, Mukesh Prasad^1,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4795-4813, 2025, DOI:10.32604/cmc.2025.060291 - 19 May 2025

Abstract Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced… More >

Yuyao Li¹, Mingmin He¹, Mingjie Cai¹, Shiqian Xu^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.21, No.4, pp. 799-817, 2025, DOI:10.32604/fdmp.2025.062862 - 06 May 2025

Abstract In ultra-deep and large well sections, high collapse stresses and diminished annular return velocity present significant challenges to wellbore cleaning. With increasing depth, rising temperature and pressure constrain the regulation of displacement and drilling fluid rheology, impairing the fluid’s capacity to transport cuttings effectively. A precise understanding of cuttings settlement behavior and terminal velocity is therefore essential for optimizing their removal. This study accounts for variations in wellbore temperature and pressure, incorporates non-spherical cuttings and wellbore diameter parameters, and develops accordingly a simplified model to predict terminal settlement velocity. The cuttings carrying ratio is introduced… More > Graphic Abstract

Youming Song^*, Yuxin Li, Yin Wang, Yuan Xie, Gang Qiao, Jingyi Chen

Journal of Psychology in Africa, Vol.35, No.1, pp. 51-59, 2025, DOI:10.32604/jpa.2025.065781 - 30 April 2025

Abstract Although numerous findings show that people experience both positive and negative experiences with regards to solitude, the relationship between solitude capacity and emotional experience remains unclear. The current study investigated the extent to which emotion regulation may play a suppressive role in the relationship between solitude capacity and emotional experience. Questionnaires on solitude capacity, emotion regulation, and emotional experience were completed by a sample of Chinese college students (n = 844; 432 females; Mean_age = 19.79 years, SD = 1.43 years). The results of the indirect effect test showed that cognitive reappraisal suppresses the prediction of solitude More >

Zhen-Hong Wang^1,2,*, Hai-Long Wu³

Journal of Psychology in Africa, Vol.35, No.1, pp. 69-73, 2025, DOI:10.32604/jpa.2025.065769 - 30 April 2025

Abstract We examined the mediating effect of burnout and subjective well-being on the relationship between work-family conflict and learning capacity among college teachers. Using a cross-sectional study design, 422 Chinese college teachers (females = 57.3%, mean years of service = 35.56, SD = 6.38) completed the Work-Family Conflict Questionnaire (WFCQ), the Burnout Scale (BS), the Subjective Well-Being Scale (SWBS), and the Teacher Learning Capacity Evaluation Scale (TLCES). The results indicated that work-family conflict had a direct connection with learning capacity. Moreover, work-family conflict had an indirect association with learning capacity through the sequential mediating roles of More >

Cong Zhang^1,2, Chutong Zhang², Lei Shen¹, Renwei Guo², Wan Chen¹, Hui Huang², Jie Ji^2,*

Energy Engineering, Vol.122, No.5, pp. 2077-2097, 2025, DOI:10.32604/ee.2025.064175 - 25 April 2025

Abstract This paper presents a solution for energy storage system capacity configuration and renewable energy integration in smart grids using a multi-disciplinary optimization method. The solution involves a hybrid prediction framework based on an improved grey regression neural network (IGRNN), which combines grey prediction, an improved BP neural network, and multiple linear regression with a dynamic weight allocation mechanism to enhance prediction accuracy. Additionally, an improved cuckoo search (ICS) algorithm is designed to empower the neural network model, incorporating a gamma distribution disturbance factor and adaptive inertia weight to balance global exploration and local exploitation, achieving… More >