Search Results (20,180)

Electric vehicles are key to sustainable mobility, but their limited range remains a major obstacle to widespread adoption. Extending driving distance requires optimizing energy use across subsystems. This study combines bibliometric mapping (2017–2024, Scopus) with a focused qualitative review to structure recent research. Results highlight a strong emphasis on energy efficiency, with China leading due to its market size, industrial base, and supportive policies. Major research directions tied to range extension include energy storage, motion control, thermal regulation, cooperative driving, and grid interaction. Among these, hybrid energy storage systems and motor control stand out for their measurable impact and industrial relevance, while thermal management, regenerative braking, and systemic approaches (V2V and V2G) remain underexplored. Beyond mapping contributions, the study identifies ongoing gaps and calls for integrated strategies that combine electrical, thermal, and mechanical aspects. As EV adoption accelerates and battery demand increases, the findings emphasize the need for battery-aware, multi-objective energy management strategies. This synthesis provides a vital framework to guide future research and support the development of robust, integrated, and industry-ready solutions for optimizing EV energy use and extending driving range. Full article

Background: Bullying victimization is a common phenomenon that can affect middle school students’ malevolent creativity. However, the underlying mechanisms between the two remain unclear. This study integrates the social hostility model and the Conservation of Resources theory to further explore the relationship between bullying victimization and malevolent creativity, the mediating roles of trait anger and social mindfulness, and the moderating role of emotion regulation, thereby advancing the research and filling the relevant gaps. Method: Using validated Chinese versions of the Olweus Bullying Scale, Trait Anger Scale, Social Mindfulness Self-Report Scale, malevolent Creativity Behavior Scale, and Emotion Regulation Questionnaire, N = 860 students were surveyed in a cross-sectional design. Results: The results showed that bullying victimization was positively related to malevolent creativity (total effect size β = 0.44), with a direct effect of size β = 0.17 and significant indirect effects via social mindfulness (β = 0.05; 11%), trait anger (β = 0.18; 41%), and the sequential path (β= 0.04; 9%). Emotion regulation moderated the links of social mindfulness and trait anger with malevolent creativity, such that higher emotion regulation strengthened the negative association for social mindfulness and weakened the positive association for trait anger. Implications: These findings suggest that school-based programs targeting emotion regulation and social mindfulness, alongside anger management components, may help mitigate the harmful impact of bullying on malevolent creativity. Full article

Eosinophilic esophagitis (EoE) is a chronic, immune-mediated disease of the esophagus within the type 2 inflammatory spectrum, characterized by progressive tissue remodeling driven by uncontrolled inflammation. Its incidence and prevalence are rising sharply, likely reflecting environmental triggers acting on genetic and epigenetic susceptibility. Therapeutic options have expanded rapidly, with recent approvals of new topical steroidal formulations together with biologic compounds. Proton pump inhibitors (PPIs), older swallowed topical corticosteroid (STC), and dietary interventions remain in use but are limited by suboptimal adherence and treatment discontinuation. This has driven a shift toward advanced orally dispersible STCs formulations—most notably budesonide orally dispersible tablets (BOT), budesonide oral suspension (BOS), and fluticasone orally dispersible tablets (FOT). BOT, the most extensively studied, achieves high rates of histologic and clinical remission, with favorable safety and superior adherence compared to earlier STCs formulations. This comprehensive overview focuses on following key research findings and novelty aspects of new treatments: (a) optimized esophageal targeting through orally dispersible or viscous formulations of STC, enhancing mucosal contact time and improving drug delivery to affected tissues compared to older formulations; (b) robust evidence for both induction and maintenance rates of remission, with data extending up to nearly 2 years and showing stable efficacy across clinical, histologic, and endoscopic endpoints; (c) effectiveness in STC-refractory patients, with BOT showing benefit even in those previously unresponsive to older STC formulations. This review synthesizes evidence of steroid therapy in EoE, from pharmacological aspects to clinical efficacy from randomized trials and emerging real-world studies, highlighting its impact on EoE management and outlining future therapeutic directions. Full article

The rational delineation of open-pit mining areas constitutes the core foundation for achieving safe, efficient, economical, and sustainable mining operations. The quality of this decision-making directly impacts the economic benefits experienced throughout the mine’s entire lifecycle and the efficiency of resource recovery. Traditional open-pit mining area delineation relies on an experience-driven manual process that is inefficient and incapable of real-time dynamic data optimization. Thus, there is an urgent need to establish an intelligent decision-making model integrating multi-source data and multi-objective optimization. To this end, this study proposes an intelligent mining area division algorithm. First, a geological complexity quantification model is constructed, incorporating innovative adaptive discretisation resolution technology to achieve precise quantification of coal seam distribution. Second, based on the quantified stripping-to-mining ratio within grids, a block-growing algorithm generates block grids, ensuring uniformity of the stripping-to-mining ratio within each block. Subsequently, a matrix of primary directional variations in the stripping-to-mining ratio is constructed to determine the principal orientation for merging blocks into mining areas. Finally, intelligent open-pit mining area delineation is achieved by comprehensively considering factors such as the principal direction of mining areas, geological conditions, boundary shapes, and economic scale. Practical validation was conducted using the Shitoumei No. 1 Open-Pit Coal Mine in Xinjiang as a case study in engineering. Engineering practice demonstrates that adopting this methodology transforms mining area delineation from an experience-driven to a data-driven approach, significantly enhancing delineation efficiency. Manual simulation of a single scheme previously required approximately 15 days. Applying the methodology proposed herein reduces this to just 0.5 days per scheme, representing a 96% increase in efficiency. Design costs were reduced by approximately CNY 190,000 per iteration. Crucially, the intelligently recommended scheme matched the original design, validating the algorithm’s reliability. This research provides crucial support for theoretical and technological innovation in intelligent open-pit coal mining design, offering technical underpinnings for the sustainable development of open-pit coal mines. Full article

Under global warming, drought frequency and its severity have risen notably, posing considerable challenges to vegetation growth. Central Asia (CA), recognized as the largest non-zonal arid zone globally, features dryland ecosystems that are particularly vulnerable to drought stress. This research examines how plant life in CA reacts to prolonged dry spells by analyzing multiple datasets, including drought indices and satellite-derived NDVI measurements, spanning four decades (1982–2022). This study also delves into the compound impact of drought, revealing how its influence on vegetation unfolds through both cumulative stress and delayed ecological responses. Based on the research results, the vegetation coverage in CA exhibited a notable rising tendency from 1982 to 1998. Specifically, it increased at a rate of 4 × 10⁻³ per year (p < 0.05). On the other hand, the direction of this trend shifted to a downward one during the period from 1999 to 2022. During this latter phase, the vegetation coverage decreased at a rate of −4 × 10⁻³ per year (p > 0.05). Vegetation changes in the study area underwent a fundamental reversal around 1998, shifting from widespread greening during 1982–1998 to persistent browning during 1999–2022. Specifically, 98.6% of the region underwent pronounced summer drought stress, which triggered a substantial rise in vegetation browning. The vegetation response to the accumulated and lagged effects of drought varied across seasons, with summer exhibiting the strongest sensitivity, followed by spring and autumn. The lagged effect of drought predominantly influences the vegetation during the growing season and spring, affecting 59.44% and 79.27% of CA, respectively. In contrast, the accumulated effect of drought is more prominent in summer and autumn, affecting 54.92% and 56.52% of CA. These insights offer valuable guidance for ecological restoration initiatives and sustainable management of dryland ecosystems. Full article

Anxiety and depression are mental disorders with significant global impact, and are especially prominent in women during times of hormonal fluctuations. The microbiota–gut–brain axis (MGB axis) has emerged as a crucial pathway in the pathogenesis of these disorders, as it directly influences the production of neurotransmitters such as serotonin (5-HT), gamma-aminobutyric acid (GABA) and dopamine (DA). In addition, they have shown estrogenic effects through enzymes such as β-glucuronidase, which modulate hormone metabolism and consequently mood. A comprehensive search of recent preclinical studies has found that probiotic intake in female rats led to significant improvements in anxiety- and depression-related behaviors. Similarly, clinical trials in certain populations, particularly women with hormonal imbalances during menopause or premenstrual syndrome, have shown promising results. However, there are still significant problems, such as the individual variability of responses and the need for controlled long-term studies. The development of specific probiotics for hormonal modulation and the implementation of personalized approaches integrating omics and neuroimaging technologies to optimize therapeutic interventions in the field of mental health are promising. Accordingly, a comprehensive search was conducted in scientific databases such as PubMed, ScienceDirect, Scopus and Web of Science. Preclinical studies investigating the estrogenic effects of different probiotic strains in animal models and in controlled clinical trials during chronic treatment were selected, excluding those studies that did not provide access to full text. Full article

River–lake systems are essential for sustaining ecosystems and human livelihoods. However, the complexity and variability of large river–lake systems, coupled with characteristic differences in water bodies across regions, have made quantifying their extent and changes inherently challenging. This study implements a robust water extraction method based on the multidimensional X-means clustering algorithm. This method leverages the advantages of Sentinel-2 imagery for water detection. Utilizing the X-means algorithm, it generates a new seasonal surface water area (SWA) product for the mid-lower reaches of the Yangtze River (MLRYR). The implemented method achieved an overall accuracy of 97.98%, a producer’s accuracy of 98.02%, a user’s accuracy of 96.01%, a Matthews correlation coefficient of 0.954, and a Kappa coefficient of 0.954. Analysis of water body dynamics reveals that over the past six years, the overall trend of SWA in the MLRYR has remained stable. However, within a broad range including multiple sub-basins, a decline in SWA has been observed on an inter-annual scale. Among the large lakes and reservoirs in the MLRYR, the water areas of Poyang Lake, Dongting Lake and Shijiu Lake all showed a marked decline. Among all water bodies with a significant increase in area, the Danjiangkou Reservoir is the largest. Further correlation analysis indicates that SWA exhibited the strongest correlations with precipitation and drought index in most sub-basins. In sub-basins where large lakes and reservoirs exist, the presence of river networks played a buffering role by regulating and storing water, thereby reducing the direct influence of climatic factors on lake and reservoir water extent. These findings highlight the complex interplay of climatic and hydrological factors. By integrating satellite imagery and Earth observation, this study advances understanding of MLRYR surface water dynamics, providing a robust framework for monitoring in other regions. It offers critical insights into drought impacts and informs effective water resource management and conservation strategies. Full article

Windborne debris generated during tornadoes and hurricanes plays a critical role in building damage. This damage occurs either through direct impact on structural and nonstructural components or indirectly by increasing internal pressure when debris penetrates openings (e.g., windows and doors) or creates new ones. These breaches can significantly raise internal pressure, even at lower wind speeds compared to debris-free conditions. Current provisions in ASCE 7, the nationally adopted standard for wind load calculations in the United States, account for factors such as building geometry, location, and exposure category. However, they do not consider the effects of windborne debris on internal pressure coefficients. This study proposes an enhancement to ASCE 7 by incorporating debris effects through the use of a more conservative enclosure classification. Real-world damage observations from three tornado-impacted residential buildings are presented, followed by a failure mechanism analysis, supporting analytical fragility data, and numerical simulations of debris effects on building damage. The findings suggest that treating buildings as Partially Enclosed under ASCE 7 can more accurately reflect debris-induced internal pressures and improve building resilience under extreme wind events. Full article

Ultra-short-term wind power exhibits significant minute-level fluctuation characteristics, leading to substantial instantaneous prediction errors. To mitigate the impact of instantaneous wind power prediction errors, the following steps are taken: First, the correlation between instantaneous prediction errors and meteorological factors is determined, and strongly associated variables are selected as model inputs. Next, the particle swarm optimization algorithm is employed to optimize the initial weights and threshold parameters of the spatiotemporal backpropagation neural network prediction model to enhance its performance. Subsequently, based on the nonlinear relationship between wind speed/direction data and instantaneous prediction errors, a wind speed matrix gradient correction method and a deep learning correction method with physical constraints on prediction errors are constructed to address errors caused by declining model generalization under strong disturbances. To validate the effectiveness of the proposed prediction algorithm integrating parameter optimization and the error correction method, it is compared with typical convolutional neural networks, long short-term memory networks, and backpropagation neural algorithms. The results demonstrate that compared to other wind power prediction strategies, this method reduces the mean absolute percentage error, root mean square error, and mean absolute error by 48.49%, 45.51%, and 50.8%, respectively. These results confirm that combining error correction strategies with prediction model parameter optimization effectively enhances the ability to reduce instantaneous wind power prediction errors, providing a practical technical solution for optimizing ultra-short-term wind power prediction accuracy and offering valuable insights for ensuring the stability of wind power grid integration. Full article

Transmission error (TE) is an important parameter in gear dynamics that has a direct impact on the vibration and noise of gears. Under quasi-static conditions, gear elastic deformation and assembly errors amplify with increasing load, potentially contributing to noise and vibration. This paper presents a novel method for measuring the quasi-static transmission error (QSTE) of spur gears under quasi-static conditions. In particular, the study investigates the relationship between quasi-static transmission error, elastic deformation transmission error, and gear tangential acceleration. Gear elastic deformation transmission error was calculated from experimental data obtained with single-point, symmetrical dual-point, and orthogonal four-point configurations of tangential acceleration sensors. The orthogonal four-point sensor configuration greatly improves measurement accuracy when compared to theoretical values derived from material mechanics calculations. A dedicated on-machine acquisition system for spur gear tangential acceleration was constructed. Tangential acceleration tests were conducted across varying loads and rotational speeds. The acquired data underwent filtering and integration processing in order to obtain gear elastic deformation and quasi-static transmission error. The feasibility of the acceleration approach for measuring both gear elastic deformation and quasi-static transmission error is confirmed by a comparative analysis of the acceleration method results with transmission errors obtained via material mechanics calculations and magnetic grating detection. Full article

This study examines the effect of environmental innovation on emission performance and the moderating role of board gender diversity among firms in the Middle East and North Africa (MENA) region. Using a panel dataset of 2319 firm-year observations from 13 countries between 2013 and 2024, the analysis applies fixed-effects regression and robustness checks using the Generalized Method of Moments (GMM). The findings show that environmental innovation significantly improves emissions performance, confirming its strategic role in achieving sustainability goals. Board gender diversity has a positive direct impact on emissions outcomes, suggesting that diverse boards enhance sustainability-oriented governance. However, the interaction term has a negative and significant effect, indicating that gender diversity, while beneficial overall, can weaken the link between environmental innovation and emission performance, possibly because of complex decision-making processes. This study contributes theoretically by extending the Resource-Based View and Porter Hypothesis to an under-researched context, while emphasizing the need for governance mechanisms that leverage diversity without slowing innovation implementation. Future research should incorporate qualitative insights and examine other governance factors to deepen our understanding of how board composition influences sustainability strategies. Full article

Gacha games, particularly those using Free-to-Play (F2P) models, have become increasingly popular yet controversial due to their addictive mechanics, often likened to gambling. This study investigates the inherent addictive mechanisms of Gacha games, focusing on Genshin Impact, a leading title in the genre. We analyze the interplay between reward frequency, game attractiveness, and player addiction using the Game Refinement theory and the Motion in Mind framework. Our analysis identifies a critical threshold at approximately 55 pulls per rare item ($N\approx 55$), with a corresponding gravity-in-mind value of 7.4. Beyond this point, the system exhibits gambling-like dynamics, as indicated by Game Refinement and Motion in Mind metrics. This threshold was measured using empirical gacha data collected from Genshin Impact players and analyzed through theoretical models. While not claiming direct causal evidence of player behavior change, the results highlight a measurable boundary where structural design risks fostering addiction-like compulsion. The study contributes theoretical insights with ethical implications for game design, by identifying critical thresholds in reward frequency and game dynamics that mark the shift toward gambling-like reinforcement. The methodologies, including quantitative analysis and empirical data, ensure robust results contributing to responsible digital entertainment discourse. Full article

Objectives: The aim of this study was to review the importance of peritoneal fluid steroid hormone concentrations to understand the mechanism of hormonal medical treatment of endometriosis-associated pain. Design: The study included a PubMed search and a pilot trial in 8 adolescents. Results: Oral contraceptives (OCs) were designed to inhibit ovulation in all women, and doses are much higher than the mean ovulation-inhibiting dose. Therefore, in most women, half a dose and in some women, even less is sufficient to inhibit ovulation. The inhibition of ovarian function and ovulation decreases estrogen and progesterone concentrations in plasma and peritoneal fluid. Surprisingly, the effect on peritoneal fluid steroid hormone concentrations has not been considered to explain the impact on endometriosis-associated pain. The lowering of the high estrogen concentrations in peritoneal fluid is sufficient to explain the pain decrease in superficial and ovarian endometriosis. A direct progesterone effect is unlikely, given the high progesterone concentrations in the peritoneal fluid of ovulatory women. In 8 adolescents, half an OC dose resulted in an apparently similar pain relief as a full dose (personal observation). Conclusions: The decrease in ovarian and superficial pelvic endometriosis-associated pain with OCs can be explained by lowering the intra-ovarian and the high estrogen concentrations in peritoneal fluid after ovulation. A direct progesterone effect is unlikely. Since OCs are severely overdosed in most women, half a dose is sufficient in most with fewer side effects, permitting individualization of therapy in women not requiring contraception. Understanding peritoneal fluid also explains that hormone replacement therapy is not contraindicated in most women with a history of endometriosis. Since the mechanisms of medical therapy of endometriosis-associated pain and the prevention of progression might be different, the growth of lesions must be monitored during treatment. Full article

Impact investing and sustainable finance are crucial in addressing social and environmental issues while developing a more resilient, equitable, and sustainable world. The purpose of this article is to analyze, synthesize, and evaluate the existing literature on the impact investing and sustainable finance research domain. Using PRISMA protocol, data was extracted from the Web of Science and Scopus databases, resulting in the compilation of 498 documents. Researchers use Biblioshiny and VOSviewer to analyze the bibliographic meta data. The findings show that the number of publications in this field has increased significantly over the last five years. In terms of journal productivity, Sustainability is the most prominent source, followed by Resources Policy and Journal of Cleaner Production. The results indicate that China published 189 articles, securing the first position, followed by India with 82 articles and the UK with 72 articles. Thematic map analysis underscores the significance of impact investing in renewable energy for sustainable economic growth. In addition, four research themes have emerged from the co-occurrence of keywords analysis. These themes are “sustainable finance for sustainable economic development”; “the rise of ESG investing in the changing world”; “corporate governance and CSR in enhancing firm performance”; and “mobilizing sustainable finance to tackle climate changes”. Furthermore, the research gives a complete summary of current research trends, future research directions and policy recommendations to assist academic researchers, investors, policymakers, business organizations and financial institutions in better understanding the impact investment and sustainable finance. Full article

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and conditions. This study applies explainable machine learning to enhance insights into predicting direct filtration operations at the Ålesund WTP in Norway. Three baseline models (Multiple Linear Regression, Support Vector Regression, and K-Nearest Neighbour (KNN)) and three ensemble models (Random Forest (RF), Extra Trees (ET), and XGBoost) were optimised using the GridSearchCV algorithm and implemented on seven filter units to predict their filtered water turbidity. The results indicate that ML models can reliably predict filtered water turbidity in WTPs, with Extra Trees models achieving the highest predictive performance (R² = 0.92). ET, RF, and KNN ranked as the three top-performing models using Alternative Technique for Order of Preference by Similarity to Ideal Solution (A-TOPSIS) ranking for the suite of algorithms used. The feature importance analysis ranked the filter runtime, flow rate, and bed level. SHAP interpretation of the best model provided actionable insights, revealing how operational adjustments during the ripening stage can help mitigate filter breakthroughs. These findings offer valuable guidance for plant operators and highlight the benefits of explainable machine learning in water quality management. Full article