Search Results (354)

This study investigates the relationship between public health expenditures and national climate vulnerability, measured by the Notre Dame Global Adaptation Initiative (ND-GAIN) Index, across 62 developed and developing countries from 2000 to 2023. Motivated by contradictory findings in the prior literature and a lack of large-scale panel econometric evidence, this research aims to determine whether health investments significantly increase climate vulnerability. Using a dynamic generalized method of moments (GMM), the findings show that public health expenditure per capita has a statistically significant positive impact on the ND-GAIN composite index. Findings show that public health expenditure per capita has a statistically significant positive impact on the ND-GAIN composite index—where higher ND-GAIN values indicate lower climate vulnerability and greater adaptive capacity—implying that increased public health spending is associated with reduced national climate vulnerability. In high-income countries, health spending may improve adaptive capacity by leveraging established infrastructure and governance. As a result, policymakers should make funding for public health a top priority in their plans for adapting to climate change. This is because investing in health alone is not enough; they also need to invest in infrastructure, governance, and adaptive capacity, especially in developing countries. Full article

Background/Objectives: Because the numbers of middle-aged and older adults living alone in Korea have substantially increased, which warrants greater attention to their health-related quality of life. Therefore, we aimed to develop a predictive model for the health-related quality of life among community-dwelling middle-aged and older adults living alone. Methods: Using 2022 Korea Health Panel Survey data, 1313 participants with complete EQ-5D component data were analyzed. All candidate predictors were entered into benchmarked models without pre-model feature selection. Preprocessing and 5-fold cross-validated hyperparameter tuning were conducted within the training data. Final performance was evaluated on a held-out test set, and the selected model was interpreted using SHAP. Results: XGBoost had the lowest training cross-validated RMSE and was selected as the final explainable model. On the test set, it showed moderate performance (R² = 0.373, MAE = 0.070, RMSE = 0.096), outperforming the mean baseline model (RMSE = 0.121) but remaining comparable with other top-performing models. Predictions were within absolute errors of 0.05 and 0.10 for 45.6% and 76.4% of participants, respectively. SHAP ranked subjective health, age, walking time, need for care, and monthly household income as the five highest-ranked predictors. Other highly ranked predictors included unmet medical needs, total annual out-of-pocket expenditure, disability, anxiety, and regular exercise. Conclusions: These findings may inform targeted interventions and support strategies, although external validation and longitudinal studies are needed to confirm generalizability and causal relationships. Full article

Sustainability monitoring has mainly focused on measuring where countries stand today, rather than anticipating where they are headed. This study develops an AI-based forecasting framework to predict national sustainability outcomes and identify countries whose actual paths deviate from predictions. Using 749 World Development Indicators across 184 countries and regions from 2003 to 2022, a Temporal Fusion Transformer (TFT) is developed using data from 2003 to 2017 (training and validation) and evaluated on a held-out 2018 to 2022 test period, with calibrated prediction intervals constructed retrospectively over the test period. Assuming that historical development patterns remain informative over the forecast horizon, the model achieves mean absolute errors of 1.10 for the Sustainable Development Goals Index (SDGI, 0 to 100 scale) and 0.008 for the Human Development Index (HDI, 0 to 1 scale), reducing error by at least 19 percent for SDGI and 60 percent for HDI relative to linear trend and XGBoost baselines. Of 184 countries and regions, 115 (62 percent) are classified as on-track for both indices. Among the rest, 35 show positive SDGI deviations, mostly developing nations in Sub-Saharan Africa and South Asia that are exceeding their forecast trajectories, while 23 show negative HDI deviations concentrated among nations affected by conflict and economic disruption. We find this asymmetric pattern is consistent with a decoupling between goal-level and capability-level sustainability, in which policy-driven SDG indicators can advance while foundational human development in health and income stalls. Our model identifies economic indicators as the dominant predictors of HDI (7 of the top 10), while SDGI prediction draws on a more balanced mix of economic, social, environmental, and institutional indicators. We also find that better governance is associated with lower prediction error for both SDGI (p = 0.004) and HDI (p < 0.001), suggesting that countries and regions with stronger institutions follow more predictable sustainability trajectories. Full article

An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuable information to support decision-making. Although existing studies have proposed models for forecasting and portfolio optimization, most rely mainly on traditional techniques and metaheuristic approaches. This work introduces ESIPO (Ensemble Strategies for Investment Portfolio Optimization), a methodology that integrates deep learning and metaheuristic algorithms to perform asset forecasting and investment portfolio optimization. The dataset is obtained from the S&P 500 index, one of the main stock markets. To enhance forecasting accuracy, ESIPO combines five methods from the top-performing models of the international M4 competition: (a) ARIMA (AutoRegressive Integrated Moving Average) and ETS (the statistical exponential-smoothing state-space), which represent classical statistical approaches; (b) FFORMA (Feature-based FORecast Model Averaging) and JAGANATHAN, two ensemble-based methods; (c) CNN (Convolutional Neural Network), which is one of the most common deep learning models. ESIPO improves the forecast performance of the portfolio by applying the TAIPO (Threshold Accepting Investment Portfolio Optimization) metaheuristic to select the best assets and optimize portfolio composition. The results obtained 45% of improvement according to the Sharpe Ratio metric. Full article

Urbanization and climate change intensify urban heat islands and air pollution; therefore, street canyon building planning that accounts for road orientation, shading, thermal environment, and ventilation is crucial. This study uses numerical simulations to investigate how non-uniform wall and road heating affects airflow and pollutant dispersion in street canyons under varying Richardson numbers (Ri) and heating scenarios (windward wall, leeward wall, road surface). The results indicate that large wall–atmosphere temperature differences combined with low incoming wind speed (high Ri) make thermal buoyancy a dominant control on canyon flow and pollutant transport. Heating of the leeward wall and road surface enhances ventilation and pollutant removal (prominently when the Ri ≥ 0.49), whereas heating of the windward wall suppresses dispersion and increases concentrations (prominently when the Ri ≥ 0.12). For a north–south street, diurnal solar heating produces strong micro-environmental contrasts. With easterly winds, morning heating of the windward wall elevates pollutant levels, while afternoon heating of the leeward wall promotes dispersion and lowers concentrations. Specifically, compared with the isothermal condition, the turbulent exchange rate at the top of the street canyon is enhanced to 1.71~6.86 times, while the convective exchange rate is suppressed to 58%~83% in the morning and enhanced to 1.21~1.92 times. These findings suggest that urban planning should limit windward wall temperature rises via shading and greening; thus, single-sided sidewalk and greening layouts on the windward side are recommended. Full article

Docker systems are gaining widespread use due to their consistency, scalability, and ease of application portability, which addresses specific security challenges. Traditional monitoring and intrusion detection systems based on predefined rules often struggle with advanced attack patterns due to a lack of the capability to correlate incoming log messages. This paper proposes a correlation-aware log analysis approach based on a Mixture-of-Experts (MoE) large language models, enabling detection of malicious activity by analyzing both individual log entries and their contextual relationships within sequences of logs. The system processes each log in the context of 50 preceding messages, allowing identification of attack patterns that are not observable from isolated logs. To evaluate the approach, we generated a comprehensive dataset based on OWASP Top 10 attack scenarios, enriched with zero-day attacks such as Log4j and React2Shell, deployed in a distributed Docker Swarm environment. Multiple LLMs were evaluated under identical hardware conditions to ensure fair comparison. Experimental results demonstrate that while most models achieve comparable performance on single-log detection, significant differences emerge in contextual analysis. The proposed MoE-based approach demonstrates superior effectiveness, achieving an F1 score from 0.993 to 0.998 for contextual-log analysis. The contribution of this research is the novel use of MoE LLMs for log analysis, the distinct novel attack log dataset, and the unique framework based on offline technology that conserves hardware resources and data privacy. Full article

As the world faces the challenge of mitigating climate change, energy- and emissions-intensive industrial processes must be addressed urgently worldwide. The cement production industry accounts for over 8% of global greenhouse gas (GHG) emissions from calcination and fuel use. Mexico, a middle-income economy, has rising cement demand for infrastructure and commercial growth. Thus, this study analysed national cement production, the primary emitting manufacturing industry in the country, under a business-as-usual (BAU) and two alternative scenarios, using a top-down approach to model energy consumption and GHG emissions by 2050. These scenarios follow the projection of national cement production, estimated using socio-economic indicators, which are considered the main drivers of cement demand, reaching 97.3 Mt. A qualitative analysis evaluates the strengths, weaknesses, opportunities, and threats (SWOT) of implementing emission-reduction strategies. The analysis showed that the BAU scenario might reach 66.5 Mt CO_2e by 2050, while the most ambitious scenario reduced direct emissions by 80.1% through carbon capture, clinker-to-cement reduction, thermal energy intensity reduction, and the use of municipal solid waste as an alternative fuel. However, incorporating these strategies in Mexico requires a more active role and investment support from key stakeholders. Full article

Nearly 2 million stillbirths occur globally each year. These outcomes are often driven by disparities in healthcare access, especially in low- and middle-income countries, where limited resources and shortages of trained medical personnel further increase preventable risks. Addressing these challenges requires not only strengthening healthcare systems but also enhancing intervention strategies. In this context, the development of decision-support systems becomes essential to dynamically identify at-risk pregnancies and improve fetal outcomes. Therefore, we propose AI-FRS (Artificial Intelligence–Fetal Risk Prediction System), a decision support tool for fetal risk prediction, designed to classify fetal conditions as healthy or at risk, using clinical data from Mexican obstetric patients. AI-FRS is built upon seven distinct machine learning models, systematically evaluated through 127 first-order ensemble combinations using hard voting. To further enhance predictive performance, we assessed 32,752 second-order ensembles, constructed by combining top-performing first-order ensembles across recall, precision, and F1-score metrics. The final selected model, called BSOEM, achieved a robust F1-score of 0.812, providing a more balanced and robust decision-making framework than individual models or simple ensembles. Additionally, we conducted an interpretability analysis to identify the clinical variables with the greatest contribution to model predictions, strengthening the system’s transparency and potential clinical trust. AI-FRS features a user-friendly interface specifically designed to facilitate adoption by healthcare professionals. This provides a fast and clinically applicable AI tool for intrapartum and peripartum risk detection in obstetrics, supporting clinical decision-making and improving fetal health outcomes. Full article

The assignment of hospital rooms to patients is a critical operational decision that has a direct impact on patient safety, infection control, and staff workload. This study introduces HRPM–IRC, an epidemiology-aware hyper-heuristic framework developed to optimize room–patient matching by minimizing the risk of nosocomial infections, reducing travel and specialty mismatch costs, and promoting equitable nurse workload distribution. A mixed-integer linear programming model is formulated to capture infection transmission probabilities, isolation and cohorting requirements, and multi-ward capacity constraints. On top of this model, a bio-inspired hyper-heuristic adaptively selects and refines low-level heuristics, including cohort-first greedy allocation, risk-gradient swaps, and pathogen-aware local MILP refinement, on the basis of contextual epidemiological indicators and reinforcement learning. The framework was validated using a real-world dataset obtained from a tertiary hospital in Lebanon, comprising 142 anonymized patient admissions, 35 rooms, and six nursing teams. Results demonstrate that HRPM–IRC consistently reduces modeled infection risk and workload imbalance by up to forty percent compared to conventional assignment heuristics while maintaining near-real-time decision-making capabilities suitable for dynamic hospital operations. These findings underscore the effectiveness of epidemiology-aware hyper-heuristics in enhancing hospital resilience, improving infection prevention, and supporting fair resource utilization in data-limited healthcare environments typical of Lebanon and other middle-income countries. Full article

Plastic pollution constitutes a critical planetary health challenge, undermining the integrity of Earth systems while generating cascading harms to human health, livelihoods, and social equity particularly in low- and middle-income countries. Conventional top-down regulatory and technological responses have proven insufficient to address the complexity of plastic pollution, often excluding those most affected from decision-making and solution design. This paper examines how democratizing plastic governance through grassroots leadership can advance planetary health by simultaneously protecting ecosystems, improving human well-being, and strengthening socio-ecological resilience. Drawing on empirical evidence from the #RestorationX10000 initiative led by Community Action Against Plastic Waste (CAPws), this paper documents implementation processes and outcomes achieved between 2021 and 2025 across 71 impacted communities in 21 countries spanning Africa, Asia-Pacific, and Latin America. The initiative was designed to empower 10,000 youths and women as community leaders, practitioners, and advocates by equipping them with leadership, technical, and policy engagement skills to drive systemic change in plastic governance and circular economy practice. Using a transdisciplinary, community-based action research approach aligned with planetary health principles, the initiative integrates capacity building, citizen science, circular economy interventions (collection, sorting, repair, reuse, repurposing, and recycling), and policy advocacy. Quantitative and qualitative evidence demonstrates that grassroots-led interventions can simultaneously reduce plastic leakage, create decent green livelihoods, and strengthen environmental governance. We argue that inclusive, community-centered plastic governance is not only an environmental intervention but a planetary health strategy, offering policy-relevant insights for national plastic action plans, extended producer responsibility frameworks, and global negotiations toward a legally binding instrument on plastic pollution. Full article

The spread of antibiotic resistance is contributing to 4.95 million cases of mortality per year, and it is categorised as one of the top three threats to public health in modern society, threatening the ability to treat common infections. Wastewater treatment plants influence the dissemination and acquisition of antibiotic resistance to enteric bacteria due to the abundance of nutrients present in them. This narrative review synthesises published evidence on antibiotic resistance patterns in South African Wastewater treatment plants, with specific emphasis on WHO-listed critical priority enteric pathogens. This review is the first to provide a temporal analysis (2009–2024) of antibiotic resistance trends in South African Wastewater treatment plants before and after the WHO’s 2017 Bacterial Priority Pathogen List (BPPL), revealing a 20–50% increase in resistance to critical antibiotics, such as vancomycin and carbapenems, across Escherichia coli, Klebsiella pneumoniae, Enterococcus spp., Salmonella spp., and Campylobacter spp. Inconsistent monitoring methods, provincial disparities, and limited molecular investigations hinder a comprehensive national assessment. This review fills a critical geographic gap by focusing on South Africa, a low-middle-income country with unique socio-economic and environmental challenges and integrates local data with WHO’s global health priorities. By synthesising 24 studies and employing statistical analysis, it identifies region-specific resistance patterns and proposes a novel framework for enhanced monitoring using metagenomics and predictive modelling, advancing beyond existing African wastewater resistome studies. Full article

Thailand’s 2024 excise tax reform aimed to stimulate the tourism economy through the elimination of import tariffs and the reduction in excise rates on wine. This study evaluates the causal economic and distributional impacts of this policy intervention. The analysis employs a quasi-experimental Doubly Robust Difference-in-Differences (DR-DiD) estimator on a stratified cluster sample to isolate shifts in consumption expenditure, volume, and net ethanol intake. Results indicate a null effect for the general population, which confirms that the price floor remained prohibitive for median earners despite the tax reduction. The top income quintile conversely exhibited a statistically significant “additive premiumization” effect characterized by a surge in wine quantity without the substitution of other beverage categories. This behavioral shift generated a substantial Net Economic Loss driven by the divergence between foregone tax revenue and projected human capital productivity losses. The policy consequently functioned as a regressive fiscal transfer to the elite and created severe allocative inefficiency. These findings suggest that ad valorem tax incentives for luxury goods in emerging markets generate deadweight loss. Future policy strategies should therefore prioritize specific volumetric taxation to align fiscal incentives with public health objectives. Full article

Evaluating national climate policy performance requires frameworks that integrate multiple dimensions while accommodating diverse development pathways. This study develops a Multi-Attribute Utility Theory (MAUT) framework to construct a Climate Policy Performance Index (CPPI) for 187 countries. The index integrates four dimensions—mitigation, adaptation, economic capacity, and governance—using explicit utility functions and policy-aligned weights derived from climate policy priorities. Results reveal substantial cross-national heterogeneity, with CPPI scores ranging from 33.67 (Turkmenistan) to 78.46 (Norway). Nordic countries lead with balanced excellence across dimensions, while alternative high-performance pathways emerge through mitigation leadership (Uruguay and Costa Rica) or governance–economy strength (Singapore). Regional analysis identifies Europe as the top-performing region, whereas Sub-Saharan Africa achieves unexpectedly high rankings despite low emissions owing to weak institutional capacity. The relationship between income and climate performance is non-monotonic: lower-middle-income countries achieve aggregate scores comparable to those of high-income nations, with near-perfect mitigation performance compensating for weaker governance. Sensitivity analysis shows that ranking robustness is comparable across equal, adaptation-focused, and multiplicative weighting schemes, whereas mitigation-focused weights yield substantially different orderings (ρ = 0.47). The CPPI correlates moderately with ND-GAIN (r = 0.40) and weakly and negatively with CO₂ per capita (r = −0.28), indicating that the framework captures distinct aspects of climate policy performance. The proposed methodology advances beyond existing indices by providing axiomatic foundations, transparent utility specifications, and comprehensive sensitivity analysis, offering a theoretically grounded tool for cross-national climate policy evaluation. Full article

Castanea henryi is an important economic tree species in China. Its nutrient-rich nuts play a key role in raising farmers’ income in mountainous areas, promoting forestry industry development, and maintaining ecological balance, thereby providing significant economic and ecological value. To systematically elucidate the genetic characteristics of major C. henryi cultivars in China, this study conducted phenotypic trait measurements on 42 cultivars collected from Taining and Jian’ou in Fujian Province. Combined with whole-genome resequencing technology and using the C. henryi genome as a reference, systematic analyses were carried out. The results indicated that the Jian’ou group (HJO) generally exhibited superior performance in key fruit phenotypic traits compared to the Taining group (HTNC), with greater phenotypic diversity observed within the HJO group. Clustering analysis of phenotypic traits further revealed a cross-geographic convergent clustering pattern among the 42 C. henryi cultivars. Further analysis revealed that the overall genetic diversity of the 42 C. henryi cultivars was relatively low (observed heterozygosity: HJO = 0.0275, HTNC = 0.0194). Notably, parameters such as heterozygosity, minor allele frequency, nucleotide polymorphism, and polymorphic information content were slightly higher in the Jian’ou group compared to the Taining group. Divergent selection signal analysis (Fst top 5%) identified 3129 genomic regions under divergent selection. Genes within these regions showed homology to 1205 Arabidopsis thaliana genes, reflecting adaptive divergence driven by differential historical selection pressures between the two groups. Population genetic structure analysis indicated that the two regional groups exhibit high genetic similarity and low differentiation. This study reveals low genetic diversity and high genetic background homogeneity among C. henryi cultivars, findings that could inform the design of future breeding strategies. Full article

Asia presently houses some of the top and dynamic economies in the world. These economies have also experienced high fintech adoption in their banking sectors. This paper examines the impact of fintech adoption and integration on the efficiency and stability of banks in 9 Asian countries, using panel data from 85 banks spanning 11 years from 2014 to 2024. It first analyzes the impact of fintech on banks across all selected countries and then, on a stratified basis, divides them into three categories: developed economies, large economies, and emerging countries. The paper uses non-performing loan (NPL) and provision for loan losses (PLLs) as proxies for risk, efficiency ratios, and the cost-to-income ratio as efficiency measures, and the stability ratio and Z-score as indicators of stability. To estimate the results, it has applied ordinary least squares and fixed-effect techniques. The study finds that fintech adoption reduces associated bank risk, presents mixed effects on efficiency, and strongly supports bank stability. Moreover, total assets and ROA consistently demonstrate lower risk, higher efficiency, and greater stability. Overall, the results of this study indicate that fintech encourages greater competition, leading banks to lend more aggressively and, consequently, increasing NPLs, PLLs, and overall risk exposure. Based on the findings, this research suggests that policymakers may adopt fintech strategies to maximize the benefits. Full article