Search Results (490)

Background: With China’s rapidly aging population, frailty has become a growing concern among older adults. Physical activity (PA) is known to mitigate frailty-related decline, yet few studies have examined these associations longitudinally. Methods: Using five waves (2011–2020) of CHARLS data, we analyzed Chinese adults aged 60+ to assess the association between frailty—measured by a frailty index (FI)—and PA across various types (light, moderate, vigorous, total, and leisure). A generalized linear mixed-effects model was used, adjusting for demographic, socioeconomic, and health-related factors. Results: All PA types were significantly associated with lower odds of concurrent frailty, including light (OR = 0.37), moderate (OR = 0.37), vigorous (OR = 0.40), total (OR = 0.23), and leisure PA (OR = 0.56). Lagged PA also predicted reduced frailty risk over time, except for light PA. Conclusion: Regular PA is linked to a lower risk of frailty among older Chinese adults. These findings underscore the importance of sustained PA as a strategy to promote healthy aging and inform public health interventions for this population. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article

The measurement of national well-being has become central to both academic and policy debates, particularly within the framework of sustainable development. In this context, this study investigates the relationship between macroeconomic conditions and subjective well-being in Portugal. Using annual data from 2004 to 2022, we explore the effects of GDP per capita, unemployment, and inflation on the Global Well-Being Index (GWBI). Employing ordinary least squares (OLS) regression, the results indicate a significant positive relationship between GDP per capita and subjective well-being, while inflation is negatively associated. Contrary to expectations, the unemployment rate showed a positive and significant association with the GWBI. This counterintuitive result may reflect institutional buffering effects, such as social safety nets, strong family structures, or lagged responses in perceptions of well-being. Similar patterns were observed in other southern European countries with strong informal social support systems. These findings contribute to a deeper understanding of how economic indicators relate to perceived well-being, particularly in the context of a southern European country. The study offers relevant insights for public policy, including the alignment of macroeconomic management with the Sustainable Development Goals (SDGs), especially SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth). Full article

Increasing temperatures associated with climate change will lead to (periodic) temperature-induced reductions in food intake in human and other mammal populations. Human adults, however, are both tolerant and resilient to periodic nutritional deficits, and the associated health effects should be limited. Intermittent nutritional deficits may also cause growth restriction in developing foetuses and young children, which potentially affects their food intake in later life. Therefore, temperature-induced hypophagia can be hypothesised to manifest as later compensatory responses with multiple concomitant (or extended) lags of varying temporal dimensions. We examined the relationship between calorie intake and ambient outdoor temperatures for a time series covering past decades (FAO data for 1961–2013) in 80 countries to determine if humans alter their food intake in response to elevated temperatures. We included eleven different temporal “windows of exposure” of varying lag. These windows considered current and recent exposure, just as lagged effects allowed for a consideration of past effects on mothers, their children, and childhood exposure. It was hypothesised that one of these could provide a basis for predicting future changes in human calorie intake in response to climate change. Our analyses showed no apparent association with temperatures in ten of the eleven hypotheses/models. The remaining hypothesis suggests that current calorie intake is linked to decadal mean temperatures with a lag of approximately three decades, pointing to an impact on mothers and their (developing) children. The impact of an increase in mean temperature varies with temperature amplitudes, and negative impacts are only found in countries with low temperature amplitudes (warmer countries), albeit the impact on calorie intake caused by a 2–3 °C change in temperatures or temperature amplitudes is generally modest. However, in considering calorie intake, we only address quantities of food (with unspecified quality), which insufficiently reflect the full range of nutritional challenges associated with increasing temperatures. Understanding climate-driven changes in human food intake requires global interdisciplinary collaboration across public health, environmental science, and policy. Full article

This study investigates the health impacts of air pollution in the Greater Athens Area (GAA), Greece, by estimating the Relative Risk (RR) of hospital admissions (HA) for cardiovascular (CVD) and respiratory diseases (RD) from 2018 to 2020. The analysis focuses on daily exceedances of key air pollutants—PM₁₀, O₃, and NO₂—based on the “Fair” threshold and above, as defined by the European Union Air Quality Index (EU AQI). Data from ten monitoring stations operated by the Ministry of Environment and Energy were spatially matched with six hospitals across the GAA. A Distributed Lag Non-linear Model (DLNM) was employed to capture both the delayed and non-linear exposure–response (ER) relationships between pollutant exceedances and daily HA. Additionally, the synergistic effects of pollutant interactions were assessed to provide a more comprehensive understanding of cumulative health risks. The combined exposure term showed a peak RR of 1.49 (95% CI: 0.79–2.78), indicating a notable amplification of risk when multiple pollutants exceed thresholds simultaneously. The study utilizes R for data processing and statistical modeling. Findings aim to inform public health strategies by identifying critical exposure thresholds and time-lagged effects, ultimately supporting targeted interventions in urban environments experiencing air quality challenges. Full article

This study examines the added value of territorial cooperation within the LEADER approach, a key pillar of the EU’s rural development policy. Both interterritorial and transnational cooperation projects empower Local Action Groups (LAGs) to tackle common challenges through innovative and community-driven strategies. Drawing on over 3000 projects since 1994, LEADER cooperation has proven its ability to deliver tangible results—such as joint publications, pilot projects, and shared digital platforms—alongside intangible benefits like knowledge exchange, improved governance, and stronger social capital. By facilitating experiential learning and inter-organizational collaboration, cooperation enables stakeholders to work across territorial boundaries and build networks that respond to both national and transnational development issues. The interaction among diverse actors often fosters innovative responses to local and regional problems. Using a mixed-methods approach, including case studies of Italian LAGs, this research analyses the dynamics, challenges, and impacts of cooperation, with a focus on learning processes, capacity building, and long-term sustainability. Therefore, this study focuses not only on project outcomes but also on the processes and learning dynamics that generate added value through cooperation. The findings highlight how territorial cooperation promotes inclusivity, fosters cross-border dialogue, and supports the development of context-specific solutions, ultimately enhancing rural resilience and innovation. In conclusion, LEADER cooperation contributes to a more effective, participatory, and sustainable model of rural development, offering valuable insights for the broader EU cohesion policy. Full article

This systematic review examines state-of-the-art artificial intelligence applications in energy systems, assessing their performance, real-world deployments and transformative potential. Guided by PRISMA 2020, we searched Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar for English-language studies published between January 2015 and January 2025 that reported novel AI uses in energy, empirical results, or significant theoretical advances and passed peer review. After title–abstract screening and full-text assessment, it was determined that 129 of 3000 records met the inclusion criteria. The methodological quality, reproducibility and real-world validation were appraised, and the findings were synthesised narratively around four critical themes: reinforcement learning (35 studies), multi-agent systems (28), planning under uncertainty (25), and AI for resilience (22), with a further 19 studies covering other areas. Notable outcomes include DeepMind-based reinforcement learning cutting data centre cooling energy by 40%, multi-agent control boosting virtual power plant revenue by 28%, AI-enhanced planning slashing the computation time by 87% without sacrificing solution quality, battery management AI raising efficiency by 30%, and machine learning accelerating hydrogen catalyst discovery 200,000-fold. Across domains, AI consistently outperformed traditional techniques. The review is limited by its English-only scope, potential under-representation of proprietary industrial work, and the inevitable lag between rapid AI advances and peer-reviewed publication. Overall, the evidence positions AI as a pivotal enabler of cleaner, more reliable, and efficient energy systems, though progress will depend on data quality, computational resources, legacy system integration, equity considerations, and interdisciplinary collaboration. No formal review protocol was registered because this study is a comprehensive state-of-the-art assessment rather than a clinical intervention analysis. Full article

As global urbanization accelerates, communities have emerged as key areas where social conflicts and public safety risks clash. Traditional crime prevention models experience difficulties handling dynamic crime hotspots due to data lags and poor spatiotemporal resolution. Therefore, this study proposes a hybrid model combining Informer and Spatiotemporal Graph Convolutional Network (ST-GCN) to achieve precise crime prediction at the community level. By employing a community topology and incorporating historical crime, weather, and holiday data, ST-GCN captures spatiotemporal crime trends, while Informer identifies temporal dependencies. Moreover, the model leverages a fully connected layer to map features to predicted latitudes. The experimental results from 320,000 crime records from 22 police districts in Chicago, IL, USA, from 2015 to 2020 show that our model outperforms traditional and deep learning models in predicting assaults, robberies, property damage, and thefts. Specifically, the mean average error (MAE) is 0.73 for assaults, 1.36 for theft, 1.03 for robbery, and 1.05 for criminal damage. In addition, anomalous event fluctuations are effectively captured. The results indicate that our model furthers data-driven public safety governance through spatiotemporal dependency integration and long-sequence modeling, facilitating dynamic crime hotspot prediction and resource allocation optimization. Future research should integrate multisource socioeconomic data to further enhance model adaptability and cross-regional generalization capabilities. Full article

Family structure has long been regarded as an important determinant of household saving, yet the empirical evidence for developing economies remains limited. Using the 2018–2022 panels of the China Family Panel Studies (CFPS), a nationwide survey that follows 16,519 households across three waves, the present study investigates how family size, the elderly share, and the child share jointly shape saving behavior. A household fixed effects framework is employed to control for time-invariant heterogeneity, followed by a sequential endogeneity strategy: external-shock instruments are tested and rejected, lagged two-stage least squares implement internal instruments, and a dynamic System-GMM model is estimated to capture saving persistence. Robustness checks include province-by-year fixed effects, inverse probability weighting for attrition, balanced-panel replication, alternative variable definitions, lag structures, and sample filters. Family size raises the saving rate by 4.6 percentage points in the preferred dynamic specification (p < 0.01). The elderly ratio remains insignificant throughout, whereas the child ratio exerts a negative but model-sensitive association. A three-path mediation analysis indicates that approximately 26 percent of the total family size effect operates through scale economy savings on quasi-fixed expenses, 19 percent is offset by resource dilution pressure, and less than 1 percent flows through a precautionary saving channel linked to income volatility. These findings extend the resource dilution literature by quantifying the relative strength of competing mechanisms in a middle-income context and showing that cost-sharing economies dominate child-related dilution for most households. Policy discussion highlights the importance of public childcare subsidies and targeted credit access for rural parents, whose saving capacity is the most constrained by additional children. The study also demonstrates that fixed effects estimates of family structure can be upward-biased unless dynamic saving behavior and internal instruments are considered. Full article

Background/Objectives: Pediatric asthma is a leading cause of emergency department visits, and air pollution is a known primary environmental trigger. Although worldwide air pollutants have been associated with asthma exacerbations, limited data have been reported in the Eastern Province of Saudi Arabia. This study aimed to investigate the relationship between air pollution and pediatric asthma admissions among children aged 2 to 14 years old at King Fahd Hospital of the University Hospital (KFHU). Methods: This is a retrospective cohort study, over 366 days, including 1750 pediatric asthma-related ER visits and daily concentrations of air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃) and meteorological factors (temperature and humidity). Various statistical models, such as Poisson regression and ARIMA, were applied to determine the association between pollutants levels and hospital ER visits. The data were visit-based in nature, and it was not possible to follow up with repeat visits or for admission status for individual patients. Results: Elevated levels of PM_2.5, NO₂, and CO were significantly associated with more pediatric asthma ER visits, mainly on the same day and with short lags. PM_2.5 displayed the strongest association, consistent with its deeper pulmonary penetration and greater toxicity. Also, PM₁₀ levels were inversely associated with ER visits, possibly due to particle size and deposition location differences. Significantly correlated with increased ER visits are lower ambient temperature and higher humidity. Conclusions: This study offers strong evidence on the relationship between air pollution and pediatric asthma events, in turn highlighting the vital importance of air quality regulation, public health policies, and clinical vigilance for environmental exposures. Full article

Air quality (AQ) modeling is at the forefront of estimating pollution levels in areas where the spatial representativity is low. Large metropolitan areas in Asia such as Beijing face significant pollution issues due to rapid industrialization and urbanization. AQ nowcasting, especially in dense urban centers like Beijing, is crucial for public health and safety. One of the most popular and accurate modeling methodologies relies on black-box models that fail to explain the phenomena in an interpretable way. This study investigates the performance and interpretability of Explainable AI (XAI) applied with the eXtreme Gradient Boosting (XGBoost) algorithm employing the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model-Agnostic Explanations (LIME) for PM_2.5 nowcasting. Using a SHAP-based technique for dimensionality reduction, we identified the features responsible for 95% of the target variance, allowing us to perform an effective feature selection with minimal impact on accuracy. In addition, the findings show that SHAP and LIME supported orthogonal insights: SHAP provided a view of the model performance at a high level, identifying interaction effects that are often overlooked using gain-based metrics such as feature importance; while LIME presented an enhanced overlook by justifying its local explanation, providing low-bias estimates of the environmental data values that affect predictions. Our evaluation set included 12 monitoring stations using temporal split methods with or without lagged-feature engineering approaches. Moreover, the evaluation showed that models retained a substantial degree of predictive power (R² > 0.93) even in a reduced complexity size. The findings provide evidence for deploying interpretable and performant AQ modeling tools where policy interventions cannot solely depend on predictive analytics tools. Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes. Full article

Dairy farm profitability in the European Union has become increasingly volatile following market deregulation, complicating farm operations and undermining food security amid geopolitical tensions. To address the need for a streamlined analytical tool, this study develops a simulation model of milk production profitability tailored to small, open economies, using Poland as a case study. The model defines a profitability coefficient as the ratio of sector-level milk revenues to feed costs and decomposes it into three dynamic components: production efficiency (milk yield per feed unit), the price spread between milk and feed, and the net effect of policy interventions on revenues and costs. Exogenous variables (milk prices, feed prices, and policy support indices) are projected under baseline, optimistic, and pessimistic scenarios, while endogenous variables (profitability, herd size, and yield) evolve recursively based on estimated lags reflecting biological and economic responses. Simulation results for 2023–2027 indicate that profitability trajectories hinge primarily on price spreads, with policy measures playing a stabilizing but secondary role. Optimistic scenarios yield significant increases in profitability, whereas pessimistic assumptions lead to significant declines. These findings highlight the need to balance key market drivers—such as the relationship between milk prices and feed costs—with appropriately designed support instruments for milk producers. The model provides policymakers with a tool to adjust interventions so that support instruments are effective but do not lead to excessive reliance on public assistance. Full article

Microcystis aeruginosa is a cyanobacterium frequently associated with toxic blooms in eutrophic freshwater systems. Certain strains produce microcystins (MCs), a group of hepatotoxins with significant ecological and public health implications. In this study, we examined the quantitative response of a temperate native M. aeruginosa strain to combinations of temperature (26, 30, and 36 °C), light intensity (30, 50, and 70 µmol photons·m⁻²·s⁻¹), and N:P ratio (10, 100, 150), using a full-factorial experimental design. Growth parameters (µ, lag phase duration, and maximum cell density), chlorophyll-a production, and MC-LR synthesis were modeled using Gompertz, linear, and dynamic approaches. High temperature and irradiance increased the specific growth rate but decreased final biomass, while elevated N:P ratios shortened the lag phase. MC-LR production peaked under low temperature, low irradiance, and low N:P ratio. Although MC-LR synthesis did not correlate positively with growth rate, and the environmental conditions maximizing growth differed from those enhancing toxin production, a population-level coupling between both processes was observed using the Long model. These findings suggest that MC-LR synthesis in M. aeruginosa is not merely a metabolic by-product of growth, but a context-dependent trait with potential adaptive significance. Full article

Urban parks are essential public resources that contribute significantly to residents’ well-being. However, disparities in the spatial distribution and social benefits of urban parks remain a pressing issue. This study focuses on the central urban area of Shanghai, a representative high-density megacity, and its findings hold significant reference value for similar cities, systematically evaluating urban park services from the perspectives of accessibility, spatial equity, and social equity. Leveraging multi-source big data and enhanced analytical methods, this study examines disparities and spatial mismatches in park services. By incorporating dynamic data, such as actual visitor attendance and residents’ travel preferences, and improving analytical models, such as an enhanced Gaussian two-step floating catchment area method and spatial lag regression models, this research significantly improves the accuracy and reliability of its findings. Key findings include (1) significant variations in accessibility exist across different types of parks, with regional and city parks offering better accessibility compared to pocket parks and community parks. (2) Park resources are unevenly distributed, with neighborhoods within the inner ring exhibiting relatively low overall accessibility. (3) A spatial mismatch is observed between park accessibility and housing prices, highlighting equity concerns. The dual spatial-social imbalance phenomenon reveals the prevalent contradiction in rapidly urbanizing areas where public service provision lags behind land development. Based on these results, this study proposes targeted recommendations for optimizing urban park layouts, including increasing the supply of small parks in inner-ring areas, enhancing the multifunctionality of parks, and strengthening policy support for disadvantaged communities. These findings contribute new theoretical insights into urban park equity and fine-grained governance while offering valuable references for urban planning and policymaking. Full article

While air pollution is known as a risk factor for acute myocardial infarction (AMI) incidence, its impact on AMI subtypes—ST-elevation (STEMI) and non-ST-elevation myocardial infarction (NSTEMI)—remains incompletely understood. This study analyzed 149,632 AMI hospital admissions (70,730 STEMI and 69,594 NSTEM) in Beijing, China, from 2013 to 2019 using a time-stratified case-crossover design to evaluate the association between daily concentrations of six air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) and daily hospital admissions for total AMI, STEMI, and NSTEMI. Elevated levels of PM_2.5, PM₁₀, SO₂, NO₂, and CO were significantly associated with increased admission risk for total AMI, STEMI, and NSTEMI, with the strongest lag effects observed at lag0 for STEMI and at lag1 for NSTEMI. Subgroup analyses showed enhanced effects of PM_2.5, SO₂, and NO₂ for total AMI and SO₂ for NSTEMI among individuals with asthma. Additionally, a stronger effect of PM₁₀ on STEMI was observed among individuals with stroke. These findings demonstrate that air pollutants differentially impact AMI subtypes through distinct temporal patterns and population vulnerabilities, underscoring the necessity of incorporating AMI subtype classification and individual susceptibility factors in environmental health risk assessments and related public health policies. Full article