Search Results (32,695)

Mechanical draft cooling towers are among the most critical water-consuming equipment in industries such as thermal power and petrochemicals. Strengthening their water usage performance is therefore crucial for alleviating China’s water resource pressure. To this end, this study employs the makeup water rate indicator to analyze the core factors influencing water-use efficiency in mechanical draft cooling towers, utilizing Spearman’s rank correlation coefficient analysis and partial least squares regression (PLSR) methods. The results reveal that ambient temperature and inlet pressure exhibit significant negative correlations with the makeup water rate, while blowdown pressure and concentration multiple show significant positive correlations. Gray correlation analysis indicates that blowdown pressure (correlation degree: 0.923) and concentration multiple (correlation degree: 0.897) are the key driving factors. The PLSR-based prediction model for the makeup water rate demonstrates a strong goodness of fit, with explanatory power exceeding 80%. This research provides a modeling foundation for optimizing the operational control of mechanical draft cooling towers, thereby promoting sustainable management of industrial water use. Full article

This review examined the latest advances in the modeling, analysis, and control of AC/DC power systems based on artificial intelligence (AI) in which renewable energy sources play a significant role. Integrating variable and intermittent renewable energy sources (such as sunlight and wind power) poses a major challenge in maintaining system stability, reliability, and optimal system performance. Traditional modeling and control methods are increasingly inadequate to capture the complex, nonlinear, and dynamic behavior of modern hybrid AC/DC systems. Specialized AI techniques, such as machine learning (ML) and deep learning (DL), and hybrid models, have become important tools to meet these challenges. This article presents a comprehensive overview of AI-based methodologies for system identification, fault diagnosis, predictive control, and real-time optimization. Particular attention is paid to the role of AI in increasing grid resilience, implementing adaptive control strategies, and supporting decision-making under uncertainty. The review also highlights key breakthroughs in AI algorithms, including federated learning, and physics-based neural networks, which offer scalable and interpretable solutions. Furthermore, the article examines current limitations and open research problems related to data quality, computational requirements, and model generalizability. Case studies of smart grids and comparative scenarios demonstrate the practical effectiveness of AI-based approaches in real-world energy system applications. Finally, it proposes future directions to narrow the gap between AI research and industrial application in next-generation smart grids. Full article

The global energy transition, characterized by the proliferation of intermittent renewables and the evolution of electricity markets, has positioned virtual power plants (VPPs) as crucial aggregators of distributed energy resources. However, their participation in competitive markets is fraught with multifaceted uncertainties stemming from price volatility, renewable generation intermittency, and unpredictable prosumer behavior, which necessitate sophisticated, risk-averse bidding strategies to ensure financial viability. This review provides a comprehensive analysis of the state-of-the-art in risk-averse bidding for VPPs. It first establishes a resource-centric taxonomy, categorizing VPPs into four primary archetypes: DER-driven, demand response-oriented, electric vehicle-integrated, and multi-energy systems. The paper then delivers a comparative assessment of different optimization techniques—from stochastic programming with conditional value-at-risk and robust optimization to emerging paradigms such as distributionally robust optimization, game theory, and artificial intelligence. It critically evaluates their application contexts and effectiveness in mitigating specific risks across diverse market types. Finally, the review synthesizes these insights to identify persistent challenges—including computational bottlenecks, data privacy, and a lack of standardization—and outlines a forward-looking research agenda. This agenda emphasizes the development of hybrid AI–physical models, interoperability standards, multi-domain risk modeling, and collaborative VPP ecosystems to advance the field towards a resilient and decarbonized energy future. Full article

With the goal of achieving carbon neutrality, promoting the clean and low-carbon transformation of energy assets, as exemplified by existing thermal power units, has emerged as a pivotal challenge in addressing climate change and achieving sustainable development. Arrangements and technologies such as the electricity–carbon–certificate multi-market, microgrids with direct green power connections, and carbon capture and storage (CCS) retrofitting provide favorable conditions for facing the aforementioned challenge. Based on an analysis of how liquid-storage CCS retrofitting affects the flexibility of thermal power units, this manuscript proposes a bi-level optimization model and solution method for capacity allocation for grid-connected microgrids, while considering CCS retrofits under multi-markets. This approach overcomes two key deficiencies in the existing research: first, neglecting the relationship between electricity–carbon coupling characteristics and unit flexibility and its potential impacts, and second, the significant deviation of scenarios constructed from real policy and market environments, which limits its ability to provide timely and relevant references. A case study in southern China demonstrates that first, multi-market implementation significantly boosts microgrids’ investment in and absolute consumption of renewable energy. However, its effect on reducing carbon emissions is limited, and renewable power curtailment may surge, potentially deviating from the original intent of carbon neutrality policies. In this case study, renewable energy installed capacity and consumption rose by 17.09% and 22.64%, respectively, while net carbon emissions decreased by only 3.32%, and curtailed power nearly doubled. Second, introducing liquid-storage CCS, which decouples the CO₂ absorption and desorption processes, into the capacity allocation significantly enhances microgrid flexibility, markedly reduces the risk of overcapacity in renewable energy units, and enhances investment efficiency. In this case study, following CCS retrofits, renewable energy unit installed capacity decreased by 24%, while consumption dropped by only 7.28%, utilization hours increased by 22%, and the curtailment declined by 78.05%. Third, although CCS retrofitting can significantly reduce microgrid carbon emissions, factors such as current carbon prices, technological efficiency, and economic characteristics hinder large-scale adoption. In this case study, under multi-markets, CCS retrofitting reduced net carbon emissions by 86.16%, but the annualized total cost rose by 3.68%. Finally, based on the aforementioned findings, this manuscript discusses implications for microgrid development decision making, CCS industrialization, and market mechanisms from the perspectives of research directions, policy formulation, and practical work. Full article

This study investigates an integrated solar-driven single-effect H₂O–LiBr absorption chiller powered by low-grade thermal energy. A detailed thermodynamic model, comprising a solar collector, a thermal storage tank, and an absorption cycle, was developed using the Engineering Equation Solver (EES) software V10.561. A comprehensive parametric analysis and multi-objective optimization were then conducted to enhance both the energy and exergy performance of the system. The Response Surface Methodology (RSM), based on the Box–Behnken Design, was employed to develop regression models validated through analysis of variance (ANOVA). The generator temperature (78–86 °C), evaporator temperature (2.5–6.5 °C), and absorber/condenser temperature (30–40 °C) were selected as key variables. According to the results, the single-objective analyses revealed maximum values of COP = 0.8065, cooling capacity = 20.72 kW, and exergy efficiency = 39.29%. Subsequently, the multi-objective RSM optimization produced a balanced global optimum with COP = 0.797, cooling capacity = 20.68 kW, and exergy efficiency = 36.93%, achieved under optimal operating conditions of 78 °C generator temperature, 6.5 °C evaporator temperature, and 30 °C absorber/condenser temperature. The obtained results confirm the significance of the proposed low-grade solar absorption chiller, demonstrating comparable or superior performance to recent studies (e.g., COP ≈ 0.75–0.80 and ≈ 35–37%). This agreement validates the RSM-based optimization approach and confirms the system’s suitability for sustainable cooling applications in low-temperature solar environments. Full article

The transition of Türkiye’s energy market toward net-zero emissions by 2053 requires modeling approaches capable of capturing complex interactions and long-term uncertainties. In this study, a long-term agent-based modeling (ABM) framework was developed, integrating econometric demand forecasting with a seasonal autoregressive integrated moving average (SARIMA) model and machine learning (ML)-based day-ahead market (DAM) price prediction. Of the ML models tested, CatBoost achieved the highest accuracy, outperforming XGBoost and Random Forest, and supported investment analysis through net present value (NPV) calculations. The framework represents major market actors—including generation units, investors, and the market operator—while also incorporating the impact of Türkiye’s first nuclear power plant (NPP) under construction and the potential introduction of a carbon emissions trading scheme (ETS). All model components were validated against historical data, confirming robust forecasting and market replication performance. Hourly simulations were conducted until 2053 under alternative policy and demand scenarios. The results show that renewable generation expands steadily, led by onshore wind and solar photovoltaic (PV), while nuclear capacity, ETS implementation, and demand assumptions significantly reshape prices, generation mix, and carbon emissions. The nuclear plant lowers market prices, whereas an ETS substantially raises them, with both policies contributing to emission reductions. These scenario results were connected to actionable policy recommendations, outlining how renewable expansion, ETS design, nuclear development, and energy efficiency measures can jointly support Türkiye’s 2053 net-zero target. The proposed framework provides an ex-ante decision-support framework for policymakers, investors, and market participants, with future extensions that can include other energy markets, storage integration, and enriched scenario design. Full article

This study systematically examined the effects of Tai Chi exercise on bone health in menopausal women, with subgroup analyses of potential moderators. A systematic search was conducted across nine databases (PubMed, Web of Science, Cochrane Library, EBSCO-Medline, EBSCO-Sportdiscus, Embase, CNKI, VIP and Wanfang Data) on June 1 and updated on 14 September 2025 to identify controlled trials evaluating perimenopausal or postmenopausal women. A three-level meta-analysis was performed to pool effect estimates, reported as standardized mean differences (SMDs), with heterogeneity further explored through subgroup analyses. Across 16 studies involving 1091 participants aged 49–64 years, Tai Chi interventions led to significant improvements in bone health. Training protocols ranged from 6 to 104 weeks, with sessions lasting 30 to 90 min. Bone mineral density (BMD) improved significantly at the femoral neck (SMD = 0.50), greater trochanter (SMD = 0.61), and lumbar spine L2–L4 (SMD = 0.81), with stronger effects observed in perimenopausal women. Bone mineral content (BMC) also increased significantly in menopausal women (SMD = 1.63, I² = 91.46%), although heterogeneity was substantial, and no significant differences were detected in subgroup moderators. In contrast, no significant effects were found for bone mineral metabolism (p = 0.38) or bone turnover markers (p = 0.25). According to GRADE assessments, the certainty of evidence ranged from low to moderate across these outcomes. In conclusion, while Tai Chi has been shown to improve BMD and BMC in menopausal women, the relatively high heterogeneity observed for BMC necessitates cautious interpretation of these particular outcomes. In contrast, no statistically significant effects were observed on bone mineral metabolism (BMM) and bone turnover markers (BTMs). Notably, given the significant differences observed between perimenopausal and postmenopausal women, future well-designed studies that stratify participants by menopausal status and possess adequate statistical power are needed to further explore the potential differential effects of Tai Chi on bone health. Full article

Scientific assessment of sustainable development potential (SDP) and analysis of spatial heterogeneity mechanisms of traditional villages are crucial for promoting the synergy between cultural heritage conservation and rural revitalization strategies. With an emphasis on traditional villages in the Cantonese region, this study develops a thorough evaluation methodology that combines spatial analysis and multi-criteria decision-making. It aims to (1) systematically reveal the spatial differentiation characteristics of sustainable development potential; (2) develop and validate a combined weighting method that effectively integrates both subjective and objective weights; and (3) identify key driving factors and their interaction mechanisms influencing the formation of this potential. To achieve these objectives, the research sequentially conducted the following steps: First, an evaluation indicator system encompassing socioeconomic, cultural, ecological, and infrastructural dimensions was developed. Second, the Analytic Hierarchy Process and the Entropy Weight Method were employed to calculate subjective and objective weights, respectively, followed by integration of these weights using a combined weighting model. Subsequently, the potential assessment results were incorporated into a Geographic Information System, and spatial autocorrelation analysis was applied to identify agglomeration patterns. Finally, the Geographical Detector model was utilized to quantitatively analyze the explanatory power of various influencing factors and their interactions on the spatial heterogeneity of potential. The main findings are as follows: First, the sustainable development potential of traditional Cantonese villages exhibits a significant “core–periphery” spatial structure, forming a high-potential corridor in the Zhongshan–Jiangmen–Foshan border area, while peripheral areas generally display “low–low” agglomeration characteristics. Second, the combined weighting model effectively reconciled 81.0% of case discrepancies, significantly improving assessment consistency (Kappa coefficient above 0.85). Third, we identified economic income (q = 0.661) and ecological baseline (q = 0.616) were identified as key driving factors. Interaction detection revealed that the interaction between economic income and transportation accessibility had the strongest explanatory power (q = 0.742), followed by the synergistic effect between ecological baseline and architectural heritage (q = 0.716), highlighting the characteristic of multi-factor synergistic driving. The quantitative and spatially explicit evaluation framework established in this study not only provides methodological innovation for research on the sustainable development of traditional villages but also offers a scientific basis for formulating regionally differentiated revitalization strategies. The research findings hold significant theoretical and practical importance for achieving a positive interaction between the conservation and development of traditional villages. Full article

This study examined lateral wedge insoles and altered foot progression angles on medial tibiofemoral loading and long-term cartilage failure risk. Fifteen healthy male participants walked under four conditions: neutral gait, lateral wedge insoles, toe-in and toe-out gait. Three-dimensional kinematics were captured using an eight-camera system, and ground reaction forces were measured via a piezoelectric force plate. Musculoskeletal simulation analysis quantified tibiofemoral compressive forces, cartilage stresses, strains, and whole-body metabolic power. Probabilistic modelling was employed to estimate the probability of cartilage failure. Comparisons across the four gait conditions employed linear mixed-effects models with repeated measures. Peak compressive forces, stresses and strains were significantly larger in the neutral (force = 2.68 BW, stress = 2.61 MPa & strain = 0.22), compared to toe-in (force = 2.51 BW, stress 2.47 MPa & strain = 0.21) and toe-out (force = 2.43 BW, stress 2.40 MPa & strain = 0.21) conditions. Medial tibiofemoral cartilage failure probability was also significantly larger in the neutral condition (14.04%) compared to toe-in (10.66%) and toe-out (7.89%) conditions. Whole-body metabolic power was also significantly greater in the toe-out (9.74 W/kg) condition compared to neutral (9.32 W/kg) and lateral insoles (9.36 W/kg). The findings suggest that toe-in or toe-out walking may reduce medial tibiofemoral osteoarthritis risk. However, the greater metabolic demand of toe-out walking may limit its long-term feasibility and practicality as a preventive intervention. Full article

The maintenance of power distribution lines is critically challenged by vegetation encroachment, posing significant risks to the reliability and safety of power utilities. Traditional manual inspection methods are resource-intensive and lack the precision required for effective and proactive maintenance. This paper presents an automated, accurate, and efficient approach to vegetation management near power lines by leveraging advancements in LiDAR as a remote sensing technology and deep learning algorithms. The RandLA-Net model is employed for semantic segmentation of large-scale point clouds to accurately identify vegetation, poles, and power lines. A comprehensive sensitivity analysis is conducted to optimize the model’s hyperparameters, enhancing segmentation accuracy. Post-processing techniques, including clustering and rule-based thresholding, are applied to refine the semantic segmentation results. Proximity detection is applied using spatial queries based on a KDTree structure to assess potential risks of vegetation near power lines. Furthermore, a digital twin of the power distribution network and surrounding trees is developed by integrating 3D object registration and surface generation, enriching it with semantic attributes and incorporating it into City Information Modeling (CIM) systems. This framework demonstrates the potential of remote sensing data integration for efficient environmental monitoring in urban infrastructure. The results of the case study on the Toronto-3D dataset demonstrate the computational efficiency and accuracy of the proposed method, presenting a promising solution for power utilities in proactive vegetation management and infrastructure planning. The optimized full 9-class model achieved an overall accuracy of 96.90% and IoU scores of 97.05% for vegetation, 88.09% for power lines, and 82.33% for poles, supporting comprehensive digital twin creation. An auxiliary 4-class model further improved targeted performance, with IoUs of 99.55% for vegetation, 88.79% for poles, and 87.18% for power lines. Full article

Early pest detection in edible crops demands sensing solutions that can run at the edge under tight power, budget, and maintenance constraints. This review synthesizes peer-reviewed work (2015–2025) on three modality families—vision/AI, spectroscopy/imaging spectroscopy, and indirect sensors—restricted to edible crops and studies reporting some implementation or testing (n = 178; IEEE Xplore and Scopus). Each article was scored with a modality-aware performance–cost–implementability (PCI) rubric using category-specific weights, and the inter-reviewer reliability was quantified with weighted Cohen’s $\kappa$. We translated the evidence into compact decision maps for common deployment profiles (low-power rapid rollout; high-accuracy cost-flexible; and block-scale scouting). Across the corpus, vision/AI and well-engineered sensor systems more often reached deployment-leaning PCI (≥3.5: 32.0% and 33.3%, respectively) than spectroscopy (18.2%); the median PCI was 3.20 (AI), 3.17 (sensors), and 2.60 (spectroscopy). A Pareto analysis highlighted detector/attention models near $(P,C,I)\approx (4,5,4)$; sensor nodes spanning balanced $(4,4,4)$ and ultra-lean $(2,5,4)$ trade-offs; and the spectroscopy split between the early-warning strength $(5,4,3)$ and portability $(4,3,4)$. The inter-rater agreement was substantial for sensors and spectroscopy (pooled quadratic $\kappa$ = 0.73–0.83; up to 0.93 by dimension) and modest for imaging/AI (PA vs. Author 2: ${\kappa }_{\mathrm{quadratic}}=0.30$–$0.44$), supporting rubric stability with adjacency-dominated disagreements. The decision maps operationalize these findings, helping practitioners select a fit-for-purpose modality and encouraging a minimum PCI metadata set to enable reproducible, deployment-oriented comparisons. Full article

The increasing integration of power-electronic devices, such as voltage source converter-based high-voltage direct current (VSC-HVDC) systems and inverter-interfaced renewable energy sources (RESs), has rendered conventional short-circuit current (SCC) calculation methods inadequate. This paper proposes a novel analytical model that explicitly incorporates the current-limiting control dynamics of voltage source converters to accurately determine SCCs. The key contribution is a simplified yet accurate formulation that captures the transient behavior during faults, offering a more realistic assessment compared to traditional quasi-steady-state approaches. The proposed model was rigorously validated through electromagnetic transient (EMT) simulations and large-scale case studies. The results demonstrate that the method reduces the SCC calculation error to below 4%. Furthermore, when applied to the real-world provincial power grids of ZJ and JS, all computations converged within 10 iterations, confirming its robust numerical stability. These findings offer valuable insights for protection coordination studies and verify the model’s effectiveness as a reliable tool for planning future power systems with high power-electronics penetration. Full article

This article presents a methodological framework for evaluating hydrodynamic loading on seabed structures in the eastern Mediterranean, originally motivated by the design requirements of special protective structures for a planned high-voltage subsea interconnection between Crete and the Greek mainland. The associated study highlighted the need for a comprehensive evaluation of hydrodynamic loading on seabed structures in the South-East Mediterranean. A methodology is presented for determining representative design kinematics near the seabed, accounting for large-scale oceanic circulation, local wind-induced currents, wind-generated surface waves, and tsunami effects. The method integrates long-term metocean datasets, spectral wave modelling, and reliability-based combinations of critical processes, with adjustments for anticipated climate change impacts. The approach is demonstrated through two case studies involving an electrode protective cage and a submarine electricity transmission cable, both representative of components in subsea power connections. The analysis provides design values of velocities, accelerations, and hydrodynamic forces, with typical checks against sliding, uplift, and vibration. Results highlight the depth-dependent magnitude interplay between ocean circulation and wave-induced particle motions, as well as the importance of biofouling and marine growth. The findings aim to support the safe and sustainable design of offshore energy infrastructure in the eastern Mediterranean and similar marine environments. Full article

Under operating conditions, metallic particle contaminants inside Gas-Insulated Switchgears (GIS) represent a major threat that can initiate partial discharges (PD) and lead to insulation failure. To investigate the discharge patterns under combined AC and switching impulse voltages, this paper presents an experimental study conducted in SF6 gas on wire-shaped, spherical, and Mixed Metal Particles. By synchronously analyzing PD time-domain waveforms, Phase-Resolved Partial Discharge (PRPD) patterns, and high-speed motion camera recordings, the correlation between particle motion behavior and discharge signals was systematically examined. The results indicate that wire particles exhibit a significant discharge initiation delay under the combined voltage; however, intense, discrete discharges with large magnitudes occur during their vertical jumping phase. In contrast, spherical particles can be activated within the first power frequency cycle without delay, but the subsequent discharge magnitudes are limited. The characteristics of hybrid particles lie between these two types, demonstrating a staged evolution described as “spherical particles lead initiation, wire particles dominate discharge.” Furthermore, under the sustained AC voltage, hybrid particles trigger a more dispersed and violent discharge process. These findings reveal the complex motion-discharge mechanism of Mixed Metal Particles, providing critical insights for fault mechanism analysis and insulation protection related to particle contamination in practical GIS equipment. Full article

Elite capture, a power structure problem involving rent-seeking, hinders sustainable water resources management. Governments play crucial roles in instilling public legitimacy in water governance, a common-pool resource that benefits from cooperative solutions such as pilot competitions, co-monitoring, and inter-agency coordination. A study of South-to-North Water Diversion Projects in China showed how, when governments outsource small projects to local sub-contractors, a method named moderate supervision (ruo jiandu) can enable effective oversight, which is superior to a bidding model with strict supervision (qiang jiandu). The concept of moderate supervision was initiated in 2023, before which most small projects had been left in a risky state with no supervision (ling jiandu). Analysis of a case in Shandong Yellow River Water Diversion Irrigation Area involved semi-structured in-depth interviews. Findings revealed that an elite-government-villagers tripartite spiral was composed of 3 dimensions reshaping a positive elite culture: first, a whitelist of qualified local contractors; second, co-monitoring of multiple stakeholders with influence exerted by a three-tier mobilization system; third, inter-agency coordination innovatively enabling smooth functioning between policy entrepreneurs of formal institutions and local social governance of informal ones. Policy implications to underscore real-world applicability are provided. Full article