Jingshuai Pang^1,2, Songcen Wang¹, Hongyin Chen^1,2,*, Xiaoqiang Jia¹, Yi Guo¹, Ling Cheng¹, Xinhe Zhang¹, Jianfeng Li¹

Energy Engineering, Vol.123, No.1, 2026, DOI:10.32604/ee.2025.070256 - 27 December 2025

Abstract To achieve the goals of sustainable development of the energy system and the construction of a low-carbon society, this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response. Firstly, a dual dimensional DR model is constructed based on the characteristics of load elasticity. The alternative DR enables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources, while the price DR relies on time-of-use electricity price signals to guide load spatiotemporal migration; Secondly, the LCT mechanism is introduced to achieve optimal… More >

S. Younus, N. Amin^*, A. Ali, K. Mahmood

Chalcogenide Letters, Vol.22, No.10, pp. 905-915, 2025, DOI:10.15251/CL.2025.2210.905

Abstract Wastewater generated by the textile industry contains high levels of various pollutants. Advanced conventional methods, such as chemical and electrical treatments, are effective in addressing these contaminants. However, the significant operational and capital costs associated with these conventional systems limit their accessibility for industrial stakeholders. In contrast, more economically viable methods tend to be less efficient. This study aims to identify a suitable approach for integrating photocatalytic degradation (PCD) with a low-cost method to enhance the cost-effectiveness of wastewater treatment processes in the textile sector. The study utilized silver copper sulfide (AgCuS) nanocomposites as a… More >

Edward Danso Ansong^*, Simon Bonsu Osei^*, Raphael Adjetey Adjei

Journal of Cyber Security, Vol.7, pp. 533-564, 2025, DOI:10.32604/jcs.2025.061821 - 11 December 2025

Abstract The surge in identity fraud, driven by the rapid adoption of mobile money, internet banking, and e-services during the COVID-19 pandemic, underscores the need for robust cybersecurity solutions. Zero-Knowledge Proofs (ZKPs) enable secure identity verification by allowing individuals to prove possession of a National ID card without revealing sensitive information. This study implements a ZKP-based identity verification system using Camenisch-Lysyanskaya (CL) signatures, reducing reliance on complex trusted setup ceremonies. While a trusted issuer is still required, as assumed in this work, our approach eliminates the need for broader system-wide trusted parameters. We evaluate the system’s More >

Zhiyong Zhu¹, Jinhao Li¹, Bo Hu^1,*, Hong Chen²

Journal of Psychology in Africa, Vol.35, No.5, pp. 587-597, 2025, DOI:10.32604/jpa.2025.066359 - 24 October 2025

Abstract Grounded in the Job Demands-Resources (JD-R) model, this study investigates the relationship between technostress and professional identity among 313 online international language teachers (82.11% female; 77.64% aged 24 and above; 63.87% with postgraduate education). It further examines the mediating role of growth mindset and the moderating effect of technical support. The results indicate that higher levels of technostress are associated with lower levels of professional identity. Growth mindset partially mediates this relationship: elevated technostress not only directly weakens teachers’ professional identity but also indirectly reduces it by undermining their growth mindset. Moreover, technical support significantly More >

Young-Vin Kim¹, Hyunkyun Ahn^2,*

International Journal of Mental Health Promotion, Vol.27, No.9, pp. 1301-1334, 2025, DOI:10.32604/ijmhp.2025.068251 - 30 September 2025

Abstract Objectives: Recently, the global esports industry has experienced remarkable growth, leading to an expansion in the scale and influence of professional player communities. However, despite this outward growth, systems to protect players’ mental health remain inadequate. Comprehensive analysis of structural risk factors, including performance pressure, public evaluation, and career instability, remains insufficient. This study, aimed to explore stressors encountered by esports athletes, coping strategies, and the role of social support systems in safeguarding mental health. Using the transactional model of stress and coping, the job demands–resources model, and social support theory, the study adopts an… More >

Mingoo Kwon, Kyeongmin Kim, Minseok Song^*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 519-537, 2025, DOI:10.32604/cmc.2025.066754 - 29 August 2025

Abstract Efficient edge caching is essential for maximizing utility in video streaming systems, especially under constraints such as limited storage capacity and dynamically fluctuating content popularity. Utility, defined as the benefit obtained per unit of cache bandwidth usage, degrades when static or greedy caching strategies fail to adapt to changing demand patterns. To address this, we propose a deep reinforcement learning (DRL)-based caching framework built upon the proximal policy optimization (PPO) algorithm. Our approach formulates edge caching as a sequential decision-making problem and introduces a reward model that balances cache hit performance and utility by prioritizing More >

Huaihu Wang¹, Wen Chen², Jin Yang¹, Rui Su¹, Jiale Li³, Liao Yuan³, Zhaobin Du^3,*, Yujie Meng³

Energy Engineering, Vol.122, No.9, pp. 3867-3890, 2025, DOI:10.32604/ee.2025.068062 - 26 August 2025

Abstract Traditional demand response (DR) programs for energy-intensive industries (EIIs) primarily rely on electricity price signals and often overlook carbon emission factors, limiting their effectiveness in supporting low-carbon transitions. To address this challenge, this paper proposes an electricity–carbon integrated DR strategy based on a bi-level collaborative optimization framework that coordinates the interaction between the grid and EIIs. At the upper level, the grid operator minimizes generation and curtailment costs by optimizing unit commitment while determining real-time electricity prices and dynamic carbon emission factors. At the lower level, EIIs respond to these dual signals by minimizing their… More >

Cuiping Li¹, Ziyun Zong¹, Xingxu Zhu¹, Zheng Fang², Caiqi Jia³, Wenbo Si⁴, Gangui Yan¹, Junhui Li^1,*

Energy Engineering, Vol.122, No.9, pp. 3459-3485, 2025, DOI:10.32604/ee.2025.066629 - 26 August 2025

Abstract In response to the issue of determining the appropriate capacity when hybrid energy storage systems (HESS) collaborate with thermal power units (TPU) in the system’s secondary frequency regulation, a configuration method for HESS based on the analysis of frequency regulation demand analysis is proposed. And a corresponding simulation platform is developed. Firstly, a frequency modulation demand method for reducing the frequency modulation losses of TPU is proposed. Secondly, taking into comprehensive consideration that flywheel energy storage features rapid power response and battery energy storage has the characteristic of high energy density, a coordinated control strategy… More > Graphic Abstract

Cuiping Li¹, Jiacheng Sun¹, Qiang Li², Qi Guo², Junhui Li^1,*, Shuo Yu², Jingbo Wang², Wenze Li²

Energy Engineering, Vol.122, No.8, pp. 3055-3077, 2025, DOI:10.32604/ee.2025.064440 - 24 July 2025

Abstract The penetration rate of new wind and photovoltaic energy in the power system has increased significantly, and the dramatic fluctuation of the net load of the grid has led to a severe lack of flexibility in the regional grid. This paper proposes a hierarchical optimal dispatch strategy for a high proportion of new energy power systems that considers the balanced response of grid flexibility. Firstly, various flexibility resource regulation capabilities on the source-load side are analyzed, and then flexibility demand and flexibility response are matched, and flexibility demand response assessment is proposed; then, a hierarchical… More >

Tianwen Zhao¹, Guoqing Chen^2,3, Cong Pang⁴, Piyapatr Busababodhin^3,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2883-2917, 2025, DOI:10.32604/cmes.2025.066442 - 30 June 2025

Abstract Existing power forecasting models struggle to simultaneously handle high-dimensional, noisy load data while capturing long-term dependencies. This critical limitation necessitates an integrated approach combining dimensionality reduction, temporal modeling, and robust prediction, especially for multi-day forecasting. A novel hybrid model, SLHS-TCN-XGBoost, is proposed for power demand forecasting, leveraging SLHS (dimensionality reduction), TCN (temporal feature learning), and XGBoost (ensemble prediction). Applied to the three-year electricity load dataset of Seoul, South Korea, the model’s MAE, RMSE, and MAPE reached 112.08, 148.39, and 2%, respectively, which are significantly reduced in MAE, RMSE, and MAPE by 87.37%, 87.35%, and 87.43%… More >