Search Results (27,740)

The aim of this work was to assess the developments in the Greek collective bargaining system and the wage policy after the period of the Memoranda of Understanding with the lenders. Moreover, it discusses the critical role of collective bargaining (CB) in the Greek economy and society and its contributions to forging a new balance between capital and labor in the post-memoranda era. Finally, it provides a number of proposals that could improve the state of play in the field. Firstly, a comprehensive approach to the current debate on the key issues of collective bargaining was portrayed. Secondly, the main developments in the Greek case of collective bargaining and the wage policy were recorded. Thirdly, an effort to interpret the pertinent developments that could lead to the diffusion of a decent work status in the domestic labor market was made. Finally, this work examined whether the current situation of collective bargaining threatens Greek employees’ living and working conditions, which were regarded as being at stake during the memoranda period. Full article

Aflatoxins (AFs), harmful secondary metabolites produced by the genus Aspergillus, particularly Aspergillus flavus and Aspergillus parasiticus, are one of the best-known potent mycotoxins, posing a significant risk to public health. The primary type, especially aflatoxin B₁ (AFB₁), is a potent carcinogen associated with liver cancer, immunosuppression, and other health problems. Environmental factors such as high temperatures, humidity, and inadequate storage conditions promote the formation of aflatoxin in staple foods such as maize, peanuts, and rice. Immunocompromised individuals, including those with HIV/AIDS, hepatitis, cancer, or diabetes, are at increased risk due to their reduced detoxification capacity and weakened immune defenses. Chronic exposure to AF in these populations exacerbates liver damage, infection rates, and disease progression, particularly in developing countries and moderate-income populations where food safety regulations are inadequate and reliance on contaminated staple foods is widespread. Biomarkers such as aflatoxin-albumin complexes, urinary aflatoxin M₁, and aflatoxin (AF) DNA adducts provide valuable insights but remain underutilized in resource-limited settings. Despite the globally recognized health risk posed by AF, research focused on monitoring human exposure remains limited, particularly among immunocompromised individuals. This dynamic emphasizes the need for targeted studies and interventions to address the particular risks faced by immunocompromised individuals. This review provides an up-to-date overview of AF exposure in immunocompromised populations, including individuals with cancer, hepatitis, diabetes, malnutrition, pregnant women, and the elderly. It also highlights exposure pathways, biomarkers, and biomonitoring strategies, while emphasizing the need for targeted interventions, advanced diagnostics, and policy frameworks to mitigate health risks in these vulnerable groups. Addressing these gaps is crucial to reducing the health burden and developing public health strategies in high-risk regions. Full article

In the digital economy era, artificial intelligence, big data, and 5G are widely applied across various industries. The deep integration of digitalization and traditional sectors has been facilitated by this trend, which has injected new momentum into industrial development. In this context, this paper employs panel data from 29 Chinese provinces that span the years 2017 to 2023. This paper transcends the constraints of current research by integrating the digital economy with the export of new energy vehicles. Furthermore, this paper provides a regional analysis of this impact, thereby contributing to the existing literature. The following are the conclusions: (1) The export of new energy vehicles is substantially stimulated by the development of the digital economy. (2) Exports are indirectly facilitated by the digital economy, which promotes technological innovation and financial services. (3) The digital economy shows a significantly greater impact on the export of new energy vehicles in the eastern and inland areas than in other regions. Based on these discoveries, the paper suggests four critical policy recommendations: expanded openness, technological innovation, intelligent digital marketing, and government support. The objective is to foster the sustainable growth of China’s new energy vehicle export trade. This paper offers theoretical support for the sustainability of Chinese enterprises’ competitiveness in the international market. It also provides policymakers and industry stakeholders with practical advice. Full article

This study examines the extent to which smart cities are expressions of nationally homogeneous development trends by way of an analysis of their structural characteristics from a multivariate viewpoint. Drawing on data from the International Institute for Management Development IMD Smart City Index 2024, we find a sample of 102 cities across the world clustering along six key dimensions of smartness: mobility, environment, government, economy, people, and living. The aim is to examine if cities within a country have similar profiles and, if so, to what degree such similarity translates to other macro-level institutional, political, and cultural conditions. Our results verify a tight correspondence between city profiles and national contexts, implying that macro-level governance arrangements, policy coordination, and institutional capacity are pivotal in influencing local smart city development. Planned centralised countries possess more uniform city characteristics, while decentralised nations possess more variant urban policies. This study contributes to international debate regarding smart cities by empirically identifying national directions of urban innovation. It offers pragmatic inputs for policymakers that aim to align local efforts with overall sustainable development agendas. Moreover, this study introduces a novel application of Linear Discriminant Analysis (LDA) to classify smart city profiles based on national models. While the analysis yields high classification accuracy, it is important to note that the sample is skewed toward cities from the Global North, potentially limiting the generalisability of the results. Full article

Large language models (LLMs) now match or exceed human performance on many open-ended language tasks, yet they continue to produce fluent but incorrect statements, which is a failure mode widely referred to as hallucination. In low-stakes settings this may be tolerable; in regulated or safety-critical domains such as financial services, compliance review, and client decision support, it is not. Motivated by these realities, we develop an integrated mitigation framework that layers complementary controls rather than relying on any single technique. The framework combines structured prompt design, retrieval-augmented generation (RAG) with verifiable evidence sources, and targeted fine-tuning aligned with domain truth constraints. Our interest in this problem is practical. Individual mitigation techniques have matured quickly, yet teams deploying LLMs in production routinely report difficulty stitching them together in a coherent, maintainable pipeline. Decisions about when to ground a response in retrieved data, when to escalate uncertainty, how to capture provenance, and how to evaluate fidelity are often made ad hoc. Drawing on experience from financial technology implementations, where even rare hallucinations can carry material cost, regulatory exposure, or loss of customer trust, we aim to provide clearer guidance in the form of an easy-to-follow tutorial. This paper makes four contributions. First, we introduce a three-layer reference architecture that organizes mitigation activities across input governance, evidence-grounded generation, and post-response verification. Second, we describe a lightweight supervisory agent that manages uncertainty signals and triggers escalation (to humans, alternate models, or constrained workflows) when confidence falls below policy thresholds. Third, we analyze common but under-addressed security surfaces relevant to hallucination mitigation, including prompt injection, retrieval poisoning, and policy evasion attacks. Finally, we outline an implementation playbook for production deployment, including evaluation metrics, operational trade-offs, and lessons learned from early financial-services pilots. Full article

This study examines the feminization of poverty in Indonesia, focusing on the distinct vulnerabilities faced by female-headed households. Utilizing data from the 2023 National Socio-Economic Survey (SUSENAS) involving 291,231 households, this study applies a logistic regression model to investigate gender-specific determinants of household poverty. This research finds that education, digital literacy, financial inclusion, and the employment sector are significant factors influencing poverty status, with female-headed households facing disproportionately higher risks. These gaps are mainly attributed to systemic barriers in financial access, digital literacy gaps, and limited labor market opportunities for women. This study emphasizes the importance of implementing gender-responsive policy measures, including targeted education, enhanced digital literacy training, and inclusive financial programs. By presenting empirical evidence from Indonesia, this study contributes to the discourse on gender and poverty, offering actionable insights for the development of inclusive poverty alleviation strategies. Full article

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda. Full article

The development of new digital infrastructure enables the construction of intelligent agricultural production systems, enhances agricultural sustainability, and supports the national “dual-carbon” goals. Based on a theoretical analysis and using panel data for 31 Chinese provinces during 2011–2023, this study constructs a two-way fixed-effects model to empirically test the impact of new digital infrastructure on agricultural carbon emissions, and provides insights for differentiating provincial heterogeneity, as well as impact mechanism. The main findings are as follows: (1) New digital infrastructure is negatively correlated with agricultural carbon emissions, and this conclusion holds after a series of robustness and endogeneity tests. (2) Heterogeneity analysis reveals that, by geographic location, new digital infrastructure has a significant carbon reduction effect in eastern provinces but increases emissions in central provinces. By the digital development level, this study highlights the dual importance of digital infrastructure and financial supports. Contrary to those provinces leading in digital infrastructure development, there is a positive correlation in lagging areas. By policy support level, the significant carbon reduction effect is only observed in provinces with advanced digital-inclusive finance support. For green development policy support, it significantly reduces agricultural carbon emissions in pioneer regions but increases emissions in follower regions. (3) Mechanism tests indicate that new digital infrastructure promotes agricultural carbon reduction mainly through scale-economy effects and energy efficiency upgrading effects. Policy implications to improve agricultural digital infrastructure development and accelerate carbon emission reductions are finally suggested. Full article

This study examines how air transport infrastructure and trade flows influence economic growth across ASEAN countries, with a focus on identifying the threshold levels at which these factors begin to enhance growth. Despite increasing investment in regional logistics and connectivity, policymakers often lack evidence-based thresholds to guide infrastructure and trade policy for long-term development. Addressing this gap, this study applies a Dynamic Panel Threshold Model to uncover the tipping points at which improvements in air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) translate into stronger economic growth. By employing System-GMM and First-Difference GMM estimations, the analysis captures the threshold effects of air cargo volume (lnCargo) and air transport infrastructure quality (lnQAir) on economic growth over varying regimes. The results reveal significant single-threshold effects for both lnCargo and lnQAir, indicating that their contributions to economic growth become substantial after surpassing specific critical levels. When air cargo volume exceeds approximately 267,067 tons per year (lnCargo > 5.5875), its positive effect on economic growth strengthens, particularly when accompanied by high-quality infrastructure. Similarly, air transport infrastructure quality exhibits a significantly stronger impact on economic growth once it exceeds the critical threshold of lnQAir = 1.5476 (≈4.7001 index points). These findings emphasize the complementarity between trade flows and infrastructure, aligning with endogenous growth theory, which suggests that infrastructure investments yield increasing returns when integrated with trade expansion. Policy implications suggest that ASEAN economies should adopt demand-driven infrastructure development aligned with trade dynamics, prioritizing regional connectivity, logistics efficiency, and investment attraction to sustain long-term economic growth. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Small and Medium Enterprises (SMEs) are vital to economic growth, innovation, and job creation across Sub-Saharan Africa (SSA). Women entrepreneurs are key contributors to this sector, yet they face persistent barriers to accessing finance, which constrain their business growth and broader economic participation. This study investigates the role of financial institutions in closing the financing gap for women-owned SMEs and assesses the effectiveness of various financing mechanisms, including traditional banking, micro-finance, fintech innovations, and government-backed credit schemes. Adopting a quantitative approach, this study utilises structured surveys with women SME owners across multiple SSA countries. Supplementary secondary data from sources such as the World Bank and national financial statistics provide additional context. Econometric modelling and Structural Equation Modelling (SEM) are employed to identify key factors influencing loan accessibility, such as collateral requirements, interest rates, financial literacy, and the regulatory environment. Findings reveal that high collateral demands and interest rates remain major obstacles, particularly for smaller or informal women-led enterprises. Financial literacy emerges as a critical enabler of access to credit. While fintech solutions and digital lending platforms show promise in improving access, issues around infrastructure, regulation, and trust persist. Government-backed schemes also contribute positively but are hindered by implementation inefficiencies. This study offers practical recommendations, including the need for harmonised regional credit reporting systems, gender-responsive policy frameworks, and targeted financial education. Strengthening digital infrastructure and regulatory support across SSA is essential to build inclusive, sustainable financial ecosystems that empower women entrepreneurs and drive regional development. Full article

In 2024, significant increases in surface temperature were recorded in Batam City and Bintan Regency, marking the highest levels observed in regional climate monitoring. The rapid conversion of vegetated land into residential and industrial areas has been identified as a major contributor to the acceleration of local climate warming. Climatological analysis also revealed extreme temperature fluctuations, underscoring the urgent need to understand spatial patterns of temperature distribution in response to climate change and weather variability. This research uses a Cellular Automata–Artificial Neural Network (CA−ANN) approach to model spatial and temporal changes in land surface temperature across the Riau Islands. To overcome the limitations of single-model predictions in a geographically diverse and unevenly developed region, Landsat satellite imagery from 2014, 2019, and 2024 was analyzed. Surface temperature data were extracted using the Brightness Temperature Transformation method. The CA−ANN model, implemented via the MOLUSCE platform in QGIS, incorporated additional environmental variables, such as rainfall distribution, vegetation density, and drought indices, to simulate future climate scenarios. Model validation yielded a Kappa accuracy coefficient of 0.72 for the 2029 projection, demonstrating reliable performance in capturing complex climate–environment interactions. The projection results indicate a continued upward trend in surface temperatures, emphasizing the urgent need for effective mitigation strategies. The findings highlight the essential role of remote sensing and spatial modeling in climate monitoring and policy formulation, especially for small island regions susceptible to microclimatic changes. Despite the strengths of the CA−ANN modeling framework, several inherent limitations constrain its application, particularly in the complex and heterogeneous context of tropical island environments. Notably, the accuracy of model predictions can be limited by the spatial resolution of satellite imagery and the quality of auxiliary environmental data, which may not fully capture fine-scale microclimatic variations. Full article

Background: Tuberculosis (TB) remains a major global health challenge, including in Thailand, where both asymptomatic and symptomatic cases sustain transmission. The disease burden increases treatment complexity and mortality, requiring integrated care and coordinated policies. Methods: We developed a deterministic compartmental model to examine the transmission dynamics of TB in Thailand, incorporating both latent and active stages of infection, as well as vaccination coverage. The model was calibrated using national TB incidence data, and sensitivity analysis revealed that the TB transmission rate was the most influential parameter affecting the basic reproduction number (R₀). We evaluated the impact of several intervention strategies, including increased treatment coverage for latent and active TB infections and improved vaccination rates. Results: Our analysis indicates that among the single interventions, scaling up effective treatment for latent TB infections produced the greatest reduction in asymptomatic and symptomatic cases, while enhanced treatment for active TB cases was second most effective for reducing both asymptomatic and symptomatic cases. Importantly, our results indicate that combining multiple interventions yields significantly greater reductions in overall TB incidence than any single approach alone. Our findings suggest that a modest investment in integrated TB control can substantially reduce TB transmission and disease burden in Thailand. However, complete eradication of TB would require a comprehensive and sustained investment to achieve near-universal coverage of both preventive and curative strategies. Conclusions: TB remains a significant public health threat in Thailand. Targeted interventions and integrated strategies are key to reducing disease burden and improving treatment outcomes. Full article

In Thailand, companies are facing increasing pressure from investors, consumers, customers, and regulators to integrate sustainability into business policies and practices. Achieving corporate sustainable development requires incorporating environmental attitudes and work environments into employee behaviors. This study examines how perceived sustainability policies (PSP) influence pro-environmental behaviors (PEB) in the workplace. A total of 589 respondents from four Thai companies in diverse sectors—rubber, consumer products, B2B industrials, and garments—participated in the study. Grounded in the Focus Theory of Normative Conduct, the research extends individual-level psychological frameworks by incorporating the mediating roles of organizational-level descriptive norms—green shared vision (GSV) and green work climate (GWC)—and the moderating role of individual green value (IGV). Structural equation modeling was conducted using AMOS. The findings supported both a direct effect of PSP on PEB and a sequential mediation pathway through GSV and GWC, while the individual mediation roles of GSV and GWC were not significant. These results reflected strong institutional and in-group collectivist culture of Thailand. Moreover, IGV was found to have a significantly negative moderating effect, suggesting that employees with high IGV may rely less on formal perceived sustainability policies in shaping their pro-environmental behaviors. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article