Search Results (6,187)

With the increasing frequency of digital economic activities on a global scale, internet governance has become a pressing issue. Traditional multilateral approaches to formulating internet governance rules have struggled to address critical challenges such as privacy leakage and low global internet defense capabilities. To tackle these issues, this study integrates SAR data processing and interpretation using AI techniques with the development of governance rules through international agreements and multi-stakeholder mechanisms. This approach aims to strengthen privacy protection and enhance the overall effectiveness of internet governance. This study incorporates differential privacy protection laws and cert-free cryptography algorithms, combined with SAR data analysis powered by AI techniques, to address privacy protection and security challenges in internet governance. SAR data provides a unique layer of spatial and environmental context, which, when analyzed using advanced AI models, offers valuable insights into network patterns and potential vulnerabilities. By applying these techniques, internet governance can more effectively monitor and secure global data flows, ensuring a more robust defense against cyber threats. Experimental results demonstrate that the proposed approach significantly outperforms traditional methods. When processing 20 GB of data, the encryption time was reduced by approximately 1.2 times compared to other methods. Furthermore, satisfaction with the newly developed internet governance rules increased by 13.3%. By integrating SAR data processing and AI, the model enhances the precision and scalability of governance mechanisms, enabling real-time responses to privacy and security concerns. In the context of the Global Digital Compact, this research effectively improves the standards, rules, and practical pathways for internet governance. It not only enhances the security and privacy of global data networks but also promotes economic development, social progress, and national security. The integration of SAR data analysis and AI techniques provides a powerful toolset for addressing the complexities of internet governance in a digitally connected world. Full article

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t-tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries. Full article

Electrocatalytic water splitting has been demonstrated to be a highly efficient and promising technology for green hydrogen production. However, the inefficiency and instability of the cathode hinder its wide application in water electrolysis. Herein, we report a rapid Joule heating method for synthesizing the Ru-Mo oxide catalyst. Comprehensive characterization results confirmed that the as-prepared catalyst featured an internal porous structure with low crystallinity, which weakened the strength of Ru-H bonds through structural and electronic modulation. The enhanced HER performance was attributed to the incorporation of Mo⁴⁺ species, which strengthened Ru-O-Mo interactions. As tested, the optimized catalyst exhibited ultralow overpotentials (25.08 mV and 120.52 mV @ 10 and 100 mA cm⁻², respectively) and excellent stability (100 h @ 100 mA cm⁻²) in a 1 M KOH solution. Meanwhile, the as-prepared catalyst was equipped in an anion exchange membrane (AEM) alkaline water electrolyzer, which could deliver 185 mA cm⁻² at only 2.16 V with 100% Faradaic efficiency. This study provides a feasible strategy for constructing highly efficient low-crystallinity electrocatalysts. Full article

Precise forecasting of rockburst intensity categories is vital to safeguarding operational safety and refining design protocols in deep underground engineering. This study proposes an intelligent forecasting framework through the integration of k-medoids-SMOTE and the BSLO-optimized Random Forest (BSLO-RF) algorithm. A curated dataset encompassing 351 rockburst instances, stratified into four intensity grades, was compiled via systematic literature synthesis. To mitigate data imbalance and outlier interference, z-score normalization and k-medoids-SMOTE oversampling were implemented, with t-SNE visualization confirming improved inter-class distinguishability. Notably, the BSLO algorithm was utilized for hyperparameter tuning of the Random Forest model, thereby strengthening its global search and local refinement capabilities. Comparative analyses revealed that the optimized BSLO-RF framework outperformed conventional machine learning methods (e.g., BSLO-SVM, BSLO-BP), achieving an average prediction accuracy of 89.16% on the balanced dataset—accompanied by a recall of 87.5% and F1-score of 0.88. It exhibited superior performance in predicting extreme grades: 93.3% accuracy for Level I (no rockburst) and 87.9% for Level IV (severe rockburst), exceeding BSLO-SVM (75.8% for Level IV) and BSLO-BP (72.7% for Level IV). Field validation via the Zhongnanshan Tunnel project further corroborated its reliability, yielding an 80% prediction accuracy (four out of five cases correctly classified) and verifying its adaptability to complex geological settings. This research introduces a robust intelligent classification approach for rockburst intensity, offering actionable insights for risk assessment and mitigation in deep mining and tunneling initiatives. Full article

Background and Aim: The therapeutic landscape for ulcerative colitis (UC) is rapidly evolving, with an increasing number of biologic agents available. This systematic review and meta-analysis synthesized randomized controlled trials (RCTs) data on biologic therapies for achieving key endoscopic and histologic endpoints in moderate to severe UC. Methods: A systematic search of MEDLINE, EMBASE, Cochrane Library, Web of Science and grey literature was conducted through November 2024. Separate meta-analyses were performed for induction and maintenance. A random-effects model was used to estimate relative risks (RR), with 95% confidence intervals (CI), and confidence in estimates was evaluated with the GRADE approach (Grading of Recommendation Assessment, Development and Evaluation). Results: We included 40 RCTs (13 therapies, 14,369 patients). Thirty-two trials provided data in induction and twenty-eight in maintenance. During induction, all biologic therapies, except mirikizumab and filgotinib 100 mg, demonstrated superiority over placebo (RR 2.02, 95% CI: 1.76–2.31, I² = 72%) for endoscopic improvement. Upadacitinib showed the highest efficacy (RR 5.53, 95% CI: 3.78–8.09). For mucosal healing, all interventions were superior to placebo (RR 2.95, 95% CI: 2.11–4.13, I² = 61%), except filgotinib 100 mg. Risankizumab showed the highest efficacy (RR 10.25, 95% CI: 2.49–42.11). In maintenance, all therapies showed superiority over placebo for endoscopic improvement. For mucosal healing all therapies were superior to placebo, except risankizumab. Upadacitinib 30 mg showed the highest efficacy (RR 4.01, 95% CI: 1.81–8.87). Conclusions: Biologic and small-molecule therapies demonstrated substantial efficacy in achieving key endpoints. Standardized outcome definitions and further head-to-head RCTs are essential to strengthen confidence in our findings. Full article

Background: Cardiovascular morbidity and mortality are alarmingly high in Bulgaria, partly due to behavioral risk factors such as smoking. Purpose: This study aimed to assess and compare smoking habits, second-hand smoke exposure, and attitudes of Bulgarian and foreign medical students to better understand smoking behavior in this population. Methods: A cross-sectional survey was conducted among 1063 medical students at the Medical University of Plovdiv (60.8% women; 53% Bulgarian). Results: More Bulgarian students were active smokers and ever-smokers than foreign students (24.7% vs. 14% and 29.3% vs. 18.8%, p < 0.001). Bulgarian women smoked nearly as much as Bulgarian men (24.1% vs. 25.6% for active smokers, p > 0.05), whereas foreign women smoked less than foreign men (15.7% vs. 23.7%, p = 0.034). Women more often replaced classic cigarettes with tobacco heating systems (THSs) than men (40.7% vs. 25.3%, p = 0.020). Nearly 85% of the respondents started smoking by the age of 19. Exposure to second-hand smoke among friends, among colleagues, and in the family was associated with a higher risk of being an ever-smoker (ORs ~8.9; 3.4 and 3.7, respectively). About 20% of students were unsure or disagreed that smoking fewer cigarettes, THSs, or e-cigarettes posed health risks. The majority (61.3%) of active smokers acknowledged negative health effects. Conclusions: These findings highlight a concerning smoking prevalence among Bulgarian medical students and emphasize the need to strengthen medical education and health policies with updated tobacco risk information and targeted prevention programs to reduce smoking and improve future physicians’ cessation counseling skills. Smoking likely contributes significantly to Bulgaria’s high cardiovascular morbidity. Full article

Accurate detection of tomato fruits is a critical component in vision-guided robotic harvesting systems, which play an increasingly important role in automated agriculture. However, this task is challenged by variable lighting conditions and background clutter in natural environments. In addition, the arbitrary orientations of fruits reduce the effectiveness of traditional horizontal bounding boxes. To address these challenges, we propose a novel object detection framework named SN-YOLO. First, we introduce the StarNet’ backbone to enhance the extraction of fine-grained features, thereby improving the detection performance in cluttered backgrounds. Second, we design a Color-Prior Spatial-Channel Attention (CPSCA) module that incorporates red-channel priors to strengthen the model’s focus on salient fruit regions. Third, we implement a multi-level attention fusion strategy to promote effective feature integration across different layers, enhancing background suppression and object discrimination. Furthermore, oriented bounding boxes improve localization precision by better aligning with the actual fruit shapes and poses. Experiments conducted on a custom tomato dataset demonstrate that SN-YOLO outperforms the baseline YOLOv8 OBB, achieving a 1.0% improvement in precision and a 0.8% increase in mAP@0.5. These results confirm the robustness and accuracy of the proposed method under complex field conditions. Overall, SN-YOLO provides a practical and efficient solution for fruit detection in automated harvesting systems, contributing to the deployment of computer vision techniques in smart agriculture. Full article

Background/Objectives: Central sensitization (CS) is identified as a cause of pain in various musculoskeletal diseases, and breathing pattern disorders (BPDs) are reported to be correlated with chronic pain. This study aimed to analyze the relationship between CS and BPDs through regression analysis. Methods: A cross-sectional study was designed according to the strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Forty participants with moderate to extreme CS (central sensitization inventory for Koreans; CSI-K ≥ 40) were enrolled, and their respiratory motion (manual assessment of respiratory motion; MARM), respiratory function (self-evaluation of breathing questionnaire; SEBQ), respiratory muscle strength (maximal inspiratory pressure; MIP, maximal expiratory pressure; MEP), pain intensity (numeric pain rating scale; NPRS), pain cognition (Korean version of pain catastrophizing scale; K-PCS), muscle tone and stiffness were measured. Results: Among participants with moderate to extreme CS, 82.5% showed BPDs and 42.5% reported severe pain intensity. Regression analysis revealed significant relationships between respiratory and pain variables. K-PCS demonstrated significant negative relationships with MARM area (β = −0.437, R² = 0.191) and positive relationships with SEBQ (β = 0.528, R² = 0.279). In the subgroup with BPDs, strong regression relationships were found between MARM area and NPRS usual pain (β = −0.486, R² = 0.237) and K-PCS (β = −0.605, R² = 0.366). Multiple regression analysis showed that MARM area and SEBQ together explained 41.2% of variance in pain catastrophizing. The comprehensive muscle stiffness prediction model using CSI-K, K-PCS, and muscle tone showed remarkably high explanatory power (R² = 0.978). Conclusions: In individuals with moderate to extreme CS, respiratory dysfunction was prevalent and significantly predictable through regression models with pain intensity and pain cognition. These quantitative regression relationships between breathing mechanics, pain measures, and muscle properties provide clinical prediction tools and suggest the importance of assessing breathing patterns in CS management. Full article

Background: China has the largest number of patients with type 2 diabetes (T2D) worldwide, and the chronic complications and economic burden associated with T2D are becoming increasingly severe. Developing accurate and widely applicable risk prediction models is of great significance for the early identification of and intervention in high-risk populations. However, current Chinese models still have many shortcomings in terms of methodological design and clinical application. Objective: This study conducts a systematic review and narrative synthesis of existing risk prediction models for type 2 diabetes in China, aiming to identify issues with existing models and provide references with which Chinese scholars can develop higher-quality risk prediction models. Methods: This study followed the PRISMA guidelines to conduct a systematic search of the literature related to T2D risk prediction models in China published in English journals from October 2019 to October 2024. The databases included PubMed, CNKI and Web of Science. Included studies had to meet criteria such as clear modeling objectives, detailed model development and validation processes, and a focus on non-diabetic populations in China. A total of 20 studies were ultimately selected and comprehensively analyzed based on model type, variable selection, validation methods, and performance metrics. Results: The 20 included studies employed various modeling methods, including statistical and machine learning approaches. The AUC values of the models ranged from 0.728 to 0.977, indicating overall good predictive capability. However, only one study conducted external validation, and 45% (9/20) of the studies binned continuous variables, which may have reduced the models’ generalization ability and predictive performance. Additionally, most models did not include key variables such as lifestyle, socioeconomic factors, and cultural background, resulting in limited data representativeness and adaptability. Conclusions: Chinese T2DM risk prediction models remain in the developmental stage, with issues such as insufficient validation, inconsistent variable handling, and incomplete coverage of key influencing factors. Future research should focus on strengthening multicenter external validation, standardizing modeling processes, and incorporating multidimensional social and behavioral variables to enhance the clinical utility and cross-population applicability of these models. Registration ID: CRD420251072143. Full article

This study aims to explore the effectiveness of a problem-solving method, grounded in Pólya’s methodological proposal and complemented by a STEM electronic educational kit, in strengthening the research competences of newly admitted nursing students at a public university in Peru. The research followed a quantitative approach using a quasi-experimental design with pre- and post-test measurements applied to a group of students who addressed real community health issues in their local context. The intervention was structured into four phases: understanding the problem, planning activities, execution, and reviewing the solution. The results showed significant improvements across all phases, particularly in problem analysis, autonomous planning, technological application, and critical thinking. The Wilcoxon test yielded p-values < 0.05 in all evaluated dimensions, allowing the rejection of the null hypothesis and confirming the effectiveness of the intervention. It is concluded that the problem-solving method, when integrated with relevant technological tools, is an effective strategy to promote formative research in vulnerable educational contexts. Moreover, it aligns with the Sustainable Development Goals—specifically SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities)—by fostering inclusive, equitable, and contextually relevant education through socially and technologically meaningful innovation. Full article

Metropolitan areas worldwide are facing growing pressures, such as high population density, environmental degradation, and socio–economic challenges. Urban resilience has become a key focus in addressing these issues. This study explores the development of urban resilience research in metropolitan areas through a systematic review using the PRISMA method of SCOPUS-indexed articles. The review shows a significant annual increase in urban resilience studies, with three main themes clustered into environment, urban planning, and social–human dimensions. Highly cited research emphasizes urban concepts, resilience measurement of urban systems against various shocks, and resilience dimensions. Notably, metropolitan areas in Asia lead in urban resilience-related discussions, particularly in response to frequent and diverse shocks. Most studies apply quantitative methods at the city/metropolitan scale, using multi-dimensional resilience indicators. The literature highlights the distinct characteristics of Asian metropolitan regions compared to others, underlining the need to assess resilience not only in urban cores but also in peri-urban, desakota, and rural settings. These findings stress the importance of formulating policies that promote adaptive, sustainable and local ecosystem management to strengthen urban resilience across different metropolitan landscapes. Full article

[Introduction] Climate change is a serious global challenge that is currently being faced and could intensify in the future. The resulting climate risks will have varying degrees of impact on sustainable agricultural development. To cope with climate change and achieve sustainable agricultural development, there is an urgent need to enhance agricultural resilience. [Methods] This paper employs fixed effects modeling to explore the impacts of climate change on agricultural resilience (production, economy, society, and ecology) using China’s regional data and examines the moderating roles of digital finance and agricultural infrastructure in the relationship between the two. [Results] The findings indicate the following: first, climate change has a negative impact on agricultural resilience, which constrains sustainable agriculture; second, both digital finance and agricultural infrastructure can mitigate the adverse effects of climate change on agricultural resilience; and third, the heterogeneity analysis further reveals that agricultural resilience in grain functional areas and regions with low levels of agricultural industrial integration is more significantly affected by climate change. [Discussion] Climate change threatens sustainable agriculture as the frequency of extreme climate events increases. Assessing the impact of climate change on agricultural resilience is of profound strategic significance for promoting sustainable agriculture, addressing climate risks, and ensuring food security. Policymakers should take adequate measures to strengthen agricultural resilience, including promoting digital finance in agriculture and increasing targeted infrastructure investments for vulnerable areas. Full article

Background: Hamstring strain injuries (HSIs) are among the most common and recurrent injuries in sports involving high-speed running. While eccentric training has demonstrated efficacy in reducing HSI risk, the role of sprint training as a preventive strategy remains underexplored and often misinterpreted as solely a risk factor. Methods: This review aimed to systematically map the available evidence on the role of sprint training in hamstring injury prevention, identifying mechanisms, outcomes, and potential synergies with other strategies. This scoping review was conducted following the Joanna Briggs Institute’s methodology and reported in accordance with PRISMA-ScR guidelines. Seven databases (PubMed, Scopus, Web of Science, Cochrane CENTRAL, SPORTDiscus, CINAHL, and PEDro) were searched up to October 2024. Studies were included if they involved adult athletes and examined the effects of sprint training, ≥80–90% maximal sprint speed (MSS), on hamstring injury prevention, muscle architecture, or functional outcomes. All databases were searched from inception to 15 October 2024, and the screening and data-charting process was completed on 30 April 2025. Results: Twelve studies met the inclusion criteria. Sprint exposure, when combined with eccentric strengthening and biomechanical optimisation, led to injury reductions ranging from 56% to 94%. Eccentric interventions produced fascicle length increases of up to 20% and strength gains of 15–20%. Improvements in sprint technique and neuromuscular control were also reported. Biomechanical risk factors, including pelvic tilt and hip extension deficits, were linked to increased HSI risk. The most common eccentric protocols included Nordic Hamstring Exercises (NHE), Razor Curls, and hip-dominant exercises, typically performed 1–2 times per week for 4 to 8 weeks. Conclusions: High-speed sprint training, when properly programmed and integrated into comprehensive preventive strategies, may enhance tissue resilience and reduce HSI risk. Combining sprint exposure with eccentric strengthening and technical coaching appears to be more effective than isolated interventions alone. Practically, these results support the systematic inclusion of progressive high-intensity sprint exposure in routine hamstring-injury-prevention programmes for field-sport athletes. Full article

The family-centred Early Intervention model based on routines and natural environments has been widely supported by international evidence in recent decades. Within this framework, Family-Centred Practices (FCP) and their development in natural environments have emerged as an evidence-based intervention model of reference, promoting parental empowerment, shared decision-making, and functional intervention through daily routines. However, its effective implementation in real contexts presents multiple challenges, especially from the perspective of families receiving the service. Background/Objectives: This study explores the experiences, meanings, and assessments of Spanish families with children who have disabilities or developmental difficulties in relation to the application of these professional practices. This study is carried out in the Spanish context, since Campus Capacitas (Campus Capacitas—Catholic University of Valencia, Spain) has been implementing, in recent years, the family-centred model as a model of early intervention. Methods: A qualitative, descriptive, and interpretative methodology was used. Data collection was carried out through semi-structured interviews and discussion groups with 30 families from the 17 Spanish autonomous communities. Data analysis was carried out through thematic coding following criteria of qualitative rigour such as triangulation and theoretical saturation. Results: The findings show a significant gap between the theoretical model of family-centred practices and their practical application. Families who have experienced a clinical model criticise the absence of personalised intervention, unidirectional communication, as well as lack of participation in decision-making. In that sense, it is the different specialists of the early intervention team who are responsible for making intervention decisions. Therefore, these families demand more emotional and educational support. On the other hand, other families report positive experiences associated with collaborative, transdisciplinary, and home-based models based on a family-centred model. Conclusions: The results highlight the urgent need to move towards early intervention that strengthens the active role of families, promotes professional co-responsibility, and adapts to real child development environments, in line with international recommendations. Regarding future lines of research, we are committed to the development of longitudinal studies on the sustainable effects of interventions centred on families and on the global development of children and families. To carry out comparative studies between autonomous communities, to assess the influence of regulatory factors and regional resources on the practices implemented, as well as to carry out triangulation studies of the professional practices implemented, incorporating the perspectives of professionals and other intervention agents to enrich the analysis. Full article

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China. Full article