Search Results (449)

This study examines the relationship between national open educational resource (OER) policies and Sustainable Development Goal 4 (SDG4) outcomes across 187 countries between 2015 and 2024, with particular attention to the moderating role of artificial intelligence (AI) readiness. Despite widespread optimism about digital technologies as catalysts for universal education, systematic evidence linking formal OER policy frameworks to measurable improvements in educational access and completion remains limited. The analysis employs fixed effects and difference-in-differences estimation strategies using an unbalanced panel dataset comprising 435 country-year observations. The research investigates how OER policies associate with primary completion rates and out-of-school rates while testing whether these relationships depend on countries’ technological and institutional capacity for advanced technology deployment. The findings reveal that AI readiness demonstrates consistent positive associations with educational outcomes, with a ten-point increase in the readiness index corresponding to approximately 0.46 percentage point improvements in primary completion rates and 0.31 percentage point reductions in out-of-school rates across fixed effects specifications. The difference-in-differences analysis indicates that OER-adopting countries experienced completion rate increases averaging 0.52 percentage points relative to non-adopting countries in the post-2020 period, though this estimate remains statistically imprecise (p equals 0.440), preventing definitive causal conclusions. Interaction effects between policies and readiness yield consistently positive coefficients across specifications, but these associations similarly fail to achieve conventional significance thresholds given sample size constraints and limited within-country variation. While the directional patterns align with theoretical expectations that policy effectiveness depends on digital capacity, the evidence should be characterized as suggestive rather than conclusive. These findings represent preliminary assessment of policies in early implementation stages. Most frameworks were adopted between 2019 and 2022, providing observation windows of two to five years before data collection ended in 2024. This timeline proves insufficient for educational system transformations to fully materialize in aggregate indicators, as primary education cycles span six to eight years and implementation processes operate gradually through sequential stages of content development, teacher training, and institutional adaptation. The analysis captures policy impacts during formation rather than at equilibrium, establishing baseline patterns that require extended longitudinal observation for definitive evaluation. High-income countries demonstrate interaction coefficients between policies and readiness that approach marginal statistical significance (p less than 0.10), while low-income subsamples show coefficients near zero with wide confidence intervals. These patterns suggest that OER frameworks function as complementary interventions whose effectiveness depends critically on enabling infrastructure including digital connectivity, governance quality, technical workforce capacity, and innovation ecosystems. The results carry important implications for how countries sequence educational technology reforms and how international development organizations design technical assistance programs. The evidence cautions against uniform policy recommendations across diverse contexts, indicating that countries at different stages of digital development require fundamentally different strategies that coordinate policy adoption with foundational capacity building. However, the modest short-term effects and statistical imprecision observed here should not be interpreted as evidence of policy ineffectiveness, but rather as confirmation that immediate transformation is unlikely given implementation complexities and temporal constraints. The study contributes systematic cross-national evidence on aggregate policy associations while highlighting the conditional nature of educational technology effectiveness and establishing the need for continued longitudinal research as policies mature beyond the early implementation phase captured in this analysis. Full article

Environmental regulations serve as a critical determinant of industrial competitiveness in the global market. Recent policy shifts have driven a gradual convergence of rural environmental standards with urban norms, fostering a dynamic landscape of “top-down competition” between urban and rural regulatory frameworks. While the economic consequences of regional regulatory disparities are well-documented, the specific impacts of this regulatory convergence remain insufficiently explored. To address this gap, this study constructs a novel index to measure the convergence of environmental regulations between urban districts and rural counties at the prefecture level. Utilizing an unbalanced panel dataset of 5600 county-level timber processing enterprises, the Heckman two-stage model is employed for empirical analysis. The results demonstrate that the convergence of urban and rural environmental regulations significantly enhances both the export probability and export intensity of county-level firms, with these effects exhibiting persistence and cumulative growth over time. These findings remain robust across a series of validation tests, including instrumental variable estimation, double machine learning, and alternative model specifications. Mechanism analysis reveals that regulatory convergence promotes exports primarily by improving access to green credit and enhancing peer quality within the industry. Furthermore, heterogeneity tests indicate that the positive effects are most pronounced for start-ups and firms in the decline stage, as well as for enterprises located in eastern China, those outside the Yangtze River Economic Belt, and those subject to minimal government intervention. This study provides critical micro-level evidence that helps enterprises navigate the evolving policy landscape and supports the formulation of strategies to boost export trade amidst the integration of environmental regulations. Full article

Institutional quality and globalization are crucial in influencing both the prevalence and quality of sustainable entrepreneurial ecosystems within an economy. This study examines the relationship between Opportunity-Driven Entrepreneurship (ODE); entrepreneurial quality, as measured by the Motivational Index (MI), and institutional quality, assessed through economic freedom and governance, in high- and middle-income countries. It also examines how globalization impacts both ODE and MI in these country groups. Using data from the Global Entrepreneurship Monitor (GEM) and combined indices of economic freedom, governance, and globalization, the study analyzes an unbalanced panel dataset comprising 64 countries from 2004 to 2018. Estimation is performed using the Robust Least Squares (RLS) method. The findings show that economic freedom has a positive and significant effect on both ODE and MI across high- and middle-income countries. In contrast, governance has a significant impact on ODE and MI only in high-income countries. Globalization exerts a negative influence on ODE across both income groups, with the adverse effect being more pronounced in middle-income countries. Conversely, its effect on MI is positive in middle-income countries but shows no significant influence in high-income economies. The study offers valuable insights for economists, policymakers, and scholars interested in the forces that shape ODE. Full article

In an era where economies increasingly rely on knowledge and innovation, sustaining long-term growth depends on understanding how education drives productivity beyond conventional measures. Yet, existing studies on the education–growth nexus remain fragmented, often focusing narrowly on schooling attainment while overlooking the complementary roles of lifelong learning and public investment in human capital. Addressing this critical gap, the present study adopts a multidimensional approach to evaluate how educational attainment, adult learning participation, and government expenditure on education collectively shape economic performance across the 27 European Union (EU) member states. Drawing on an unbalanced Eurostat panel dataset (2013–2022), the study employs a fixed-effects regression model with White cross-section robust standard errors to account for heteroskedasticity and serial correlation. The empirical results reveal that all three educational dimensions exert positive and statistically significant effects on GDP, with government educational expenditure emerging as the most influential driver, followed by adult learning participation, underscoring the transformative role of continuous skill renewal in dynamic labor markets. These findings advance Human Capital Theory by framing education not merely as an individual asset but as an interactive, systemic driver of national productivity and resilience. The study offers actionable insights for policymakers, calling for integrated strategies that align formal education, lifelong learning systems, and sustained public investment to foster inclusive, knowledge-driven, and sustainable economic growth across the EU. Full article

Health insurance fraud (HIF) places a substantial economic burden on global health systems. While supervised machine learning (SML) offers a promising solution for its detection, most approaches are ad hoc and lack a systematic methodological framework that ensures replicability, adaptability, and effectiveness, especially in contexts with severe class imbalance. We developed PDHIF (Phases for Detecting Fraud in Health Insurance), a six-phase systematic methodology that introduces a holistic focus that integrates fraud theory, actors, manifestations, and factors with the complete SML lifecycle. We applied this methodology in a case study using a dataset of 8.5 million claims from a public health insurance system in Peru. We trained and evaluated three SML models (Random Forest, XGBoost, and multilayer perceptron) in two experimental scenarios: one with the original, highly unbalanced dataset and another with a training set balanced via the K-means SMOTE technique. When PDHIF was applied, the results revealed a stark contrast: in the unbalanced scenario, the models were ineffective at detecting fraud (F1 score < 0.521) despite high accuracy (>98%). In the balanced scenario, the performance improved dramatically. The best-performing model, RF, achieved an F1 score of 0.994, a sensitivity of 0.994, and an AUC of 0.994 on the test set, demonstrating a robust ability to distinguish suspicious claims. Full article

With the rapid development of artificial intelligence technology, facial expression recognition (FER) has gained increasingly widespread applications in digital human generation, humanoid robotics, mental health, and human–computer dialogue. Typical FER algorithms based on machine learning have been widely studied over the past few decades, which motivated our survey. In this study, we have surveyed the state of the art in FER across two categories: traditional machine learning-based (ML-based) and deep learning-based (DL-based) approaches. Each category is analyzed based on six subcategories. Then, twelve methods, including four ML-based models and eight DL-based models, are compared to evaluate FER performance across four datasets. The experimental results show that in validation sets, the average accuracy of HOG-SVM is 50.12%, which is the best performance for the four ML-based methods; in contrast, Poster has an average accuracy of 75.98%, which is the best result obtained among the eight DL-based methods. The most difficult expression to recognize is contempt, with recognition accuracies of 10.00% and 40.06% for ML-based and DL-based methods, respectively. The accuracy of the ML-based method for identifying neutral expression is the highest at 35.25%; the DL-based method has the highest accuracy in identifying surprise at 69.56%. From the theoretical analysis and comparative experimental results of existing methods, we can see that FER faces challenges, including inaccurate recognition in complex environments and unbalanced data categories, highlighting several future research directions, especially those involving the latest applications of digital humans and large language models. Full article

Background/Objectives: Innovation capacity has become a strategic pillar for strengthening healthcare systems in the European Union, yet its effects vary considerably across countries with different levels of institutional development and technological readiness. This study examines how national innovation capacity, measured through the Global Innovation Index, influences health expenditure, healthy life expectancy, and childhood obesity across the EU-27. Methods: Using an unbalanced panel dataset for 2011–2024, we applied panel quantile regression to capture heterogeneous effects across the conditional distribution of health outcomes. Four dependent variables were analyzed: government expenditure on health, provider-level healthcare spending, healthy life expectancy at birth, and childhood obesity prevalence. GDP growth and population were included as controls. Diagnostic tests confirmed cross-sectional dependence and heteroskedasticity, supporting the choice of distributionally robust estimators. Results: Higher innovation capacity was positively and significantly associated with government health expenditure and provider-level spending across all quantiles (p < 0.001), with the strongest effects in lower-performing systems. For healthy life expectancy, innovation exhibited declining coefficients across quantiles, indicating diminishing marginal returns in more advanced systems. No stable association was observed for childhood obesity, which remained largely unaffected by national innovation capacity. Conclusions: Innovation contributes to structural improvements in health financing and population health, particularly in countries with lower baseline performance. In high-performing systems, its role shifts toward incremental efficiency gains. The absence of effects on childhood obesity highlights the dominance of socio-behavioral determinants. Findings are associative and call for future causal and sector-specific research. Full article

The safe and stable operation of ultra-high-voltage (UHV) transmission lines is fundamental to ensuring efficient and large-capacity power delivery. Critical fittings, as essential load-bearing components connecting towers, conductors, and insulator strings, are highly susceptible to damage under complex ice–wind conditions, thereby posing significant threats to grid security. To address the prevalent issues of jumper spacer breakage and conductor abrasion observed in field maintenance, a systematic finite element analysis model incorporating bundled conductors, jumper structures, and associated fittings was established. This model enabled comprehensive investigation of the effects of non-uniform ice accretion, wind loading, and ice-shedding impacts on the bearing characteristics of critical fittings. Through high-throughput computational simulations, a large-scale dataset capturing the bearing characteristics of jumper spacers was constructed. Based on this dataset, a damage risk assessment model under complex ice–wind conditions was developed using a multi-layer feedforward deep neural network (MLF-DNN). The results indicated that wind loading had a relatively minor influence on jumper spacers, whereas ice accretion and ice-shedding impacts were the dominant factors leading to damage. In particular, non-uniform ice-shedding readily induced unbalanced forces among sub-conductors, significantly increasing stress levels in jumper spacers and resulting in substantial risk. The proposed risk assessment model demonstrated high predictive accuracy and strong generalization capability, providing effective support for rapid evaluation and early warning of damage to fittings in UHV transmission lines under complex ice–wind environments. Full article

Recently, partial discharge pattern recognition (PDPR) for transmission cables has garnered increasing attention due to the severe power outages, equipment damage, and even major safety incidents resulting from the failure of partial discharge (PD) detection. However, existing PD data samples usually suffer from highly similar features and unbalanced distribution. Determining how to precisely realize the PDPR has become a challenge. In this study, an effective PDPR approach is proposed based on a newly designed deconstructed PD (DecPD) model and a customized loss function for PDPR. Notably, the refined deep learning network captures the discriminative features in both temporal and spatial dimensions through a dual-channel learning architecture. Additionally, an adaptive focal loss function is designed, which introduces a peak factor to establish focusing parameters for PDPR, thereby addressing the class imbalance issues. A comprehensive experimental evaluation using real datasets generated on a physical platform is conducted to verify our proposed method. Compared to other existing methods, our DecPD approach demonstrates superior performance, achieving an overall accuracy of 96.65% in the presence of environment noise. Full article

The rapid industrialization of global livestock production has intensified the threat of viral epidemics, in which the intestinal, respiratory, and reproductive systems are susceptible to viral attacks. Understanding the mechanism of virus–host interactions will facilitate the development of prevention strategies against highly mutable and fast-spreading pathogens. This review examines recent progress in applying machine learning (ML) and deep learning (DL) to the study and control of animal viral diseases. By analyzing existing research, we show how these techniques improve the prediction of host–microbe interactions, support continuous monitoring of animal health, and accelerate the discovery of drug targets and vaccine candidates. Integrating ML and DL frameworks enables more accurate modeling of complex biological processes and offers new tools for data-driven veterinary science. Nevertheless, challenges remain, including unbalanced datasets, the structural and evolutionary complexity of viruses, and the poor cross-species transferability of predictive models. Future work should emphasize algorithmic designs suited to small-sample, multivariate time series data and promote the development of intelligent systems that unite virology, immunology, and epidemiology. The combination of reinforcement learning for optimizing vaccination strategies and unsupervised learning for detecting emerging pathogens may ultimately lead to adaptive, efficient, and precise systems for disease prevention, supporting both animal health and sustainable livestock development. Full article

With the intensification of global warming, surface thermal environment issues have become increasingly prominent, particularly in the ecologically fragile Yellow River Basin (YRB). However, most studies neglect the synergistic effects of underlying surface composition and geomorphological context, limiting the understanding of regional thermal contribution patterns. Based on MODIS-derived land surface temperature and Landsat 8-based land use and Fathom DEM-derived geomorphological datasets, this study constructs an integrated assessment framework combining a dual “quality–quantity” perspective with land use–geomorphology coupling, systematically analyzing the comprehensive thermal contributions of different underlying surfaces. Results show that (1) the YRB features diverse underlying surfaces, transitioning from natural (forest, grassland) to human-dominated (cropland, construction land) land uses, and from high-altitude, large undulating mountains to low-altitude, small undulating plains along the source-to-downstream gradient. (2) The average LST is 17.97 °C, displaying a south–north and east–west gradient. Human disturbance intensity drives thermal responses at the land use level, with natural surfaces contributing to cooling regulation, while artificial and desert surfaces generate heat accumulation. Geomorphology jointly shapes the thermal distribution, with high mountains acting as cold sources and plains/hills as heat sources. (3) Dual “quality–quantity” dimensional evaluation reveals that temperature-based assessments alone overestimate localized extremes (e.g., towns, extremely high mountains) and underestimate broad, moderate surfaces (e.g., drylands, large and medium undulating high mountains). This “area-neglect effect” may lead to biased regional thermal assessments and unbalanced resource allocation. (4) Coupled land use–geomorphology analysis uncovers the multi-scale composite mechanisms of thermal formation and mitigates single-factor assessment biases. Geomorphology defines macro-scale energy exchange, while land use regulates local heat responses. The results provide scientific support for large-scale thermal assessment and refined management. Full article

Fault diagnosis is critical for ensuring the reliability of reciprocating pumps in industrial settings. However, challenges such as strong noise interference and unbalanced conditions of existing methods persist. To address these issues, this paper proposes a novel fusion framework integrating a multiple-branch residual module and a hybrid attention module for reciprocating pump fault diagnosis. The framework introduces a multiple-branch residual module with parallel depth-wise separable convolution, dilated convolution, and direct mapping paths to capture complementary features across different scales. A hybrid attention module is designed to achieve adaptive fusion of channel and spatial attention information while reducing computational overhead through learnable gate mechanisms. Experimental validation on the reciprocating pump dataset demonstrates that the proposed framework outperforms existing methods, achieving an average diagnostic accuracy exceeding 98% even in low signal-to-noise ratio (SNR = −3 dB) environments. This research provides a new perspective for mechanical fault diagnosis, offering significant advantages in diagnostic accuracy, robustness, and industrial applicability. Full article

High-dimensional analytical datasets, such as those generated by inductively coupled plasma–mass spectrometry (ICP-MS), require robust computational frameworks for dimensionality reduction, classification, and model validation. This study presents a comparative evaluation of Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms applied to multivariate chemometric data for food origin authentication. The research employs a workflow that integrates Principal Component Analysis (PCA) for feature extraction, followed by supervised classification using LDA and PLS-DA. Model performance and stability were systematically assessed. The dataset comprised 28 apple samples from four geographical regions and was processed with normalization, scaling, and transformation prior to modeling. Each model was validated via leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, balanced accuracy, detection prevalence, p-value, and Cohen’s Kappa. The results demonstrate that, as a linear projection-based classifier, LDA provides higher robustness and interpretability in small and unbalanced datasets. In contrast, PLS-DA, which is optimized for covariance maximization, exhibits higher apparent sensitivity but lower reproducibility under similar conditions. The study also emphasizes the importance of dimensionality reduction strategies, such as PCA-based variable selection versus latent space extraction in PLS-DA, in controlling overfitting and improving model generalizability. The proposed algorithmic workflow provides a reproducible and statistically sound approach for evaluating discriminant methods in chemometric classification. Full article

The rapid proliferation of fintech has created unprecedented opportunities for enhancing bank credit-risk management and promoting financial sustainability. Using an unbalanced panel dataset of Chinese commercial banks spanning 2013–2023, we construct a bank-specific fintech index through text mining of annual reports combined with an entropy-weighted methodology, and systematically examine the relationship between fintech adoption and credit risk. Our empirical findings reveal that fintech adoption significantly mitigates credit risk, reducing the non-performing loan ratio by an average of 0.9 percentage points. This effect is more pronounced among non-state-owned banks and in regions with less developed service sectors. Mechanism analysis further demonstrates that financial sustainability is a critical transmission mechanism: fintech mitigates credit risk by improving both cost efficiency and asset efficiency, thereby enhancing banks’ economic resilience. Additionally, we find that regional green development is a powerful moderator that significantly amplifies the risk-reducing impact of fintech. These findings offer robust empirical evidence for guiding commercial banks’ digital transformation strategies and informing regulators’ green finance policy formulation. Our results underscore the strategic importance of fintech investment in building more resilient and sustainable banking systems. Full article

Background/Objectives: Computerized diagnostic algorithms could achieve early detection of acute kidney injury (AKI) only with available baseline serum creatinine (SCr). To tackle this weakness, we tried to construct a machine learning model for AKI diagnosis based on point-of-care clinical features regardless of baseline SCr. Methods: Patients with SCr > 1.3 mg/dL were recruited retrospectively from Wan Fang Hospital, Taipei. A Dataset A (n = 2846) was used as the training dataset and a Dataset B (n = 1331) was used as the testing dataset. Point-of-care features, including laboratory data and physical readings, were inputted into machine learning models. The repeated machine learning models randomly used 70% and 30% of Dataset A as training dataset and testing dataset for 1000 rounds, respectively. The single machine learning models used Dataset A as training dataset and Dataset B as testing dataset. A computerized algorithm for AKI diagnosis based on 1.5× increase in SCr and clinician’s AKI diagnosis compared to machine learning models. Results: On an independent, unbalanced test set (n = 1331), our machine learning models achieved AUROC values ranging from 0.67 to 0.74. A pre-existing computerized algorithm performed best (AUROC = 0.94). Crucially, all machine learning models significantly outperformed the routine clinician’s diagnosis (AUROC ~0.74 vs. 0.53, p < 0.05). For context, a pre-existing computerized algorithm, which requires available baseline SCr data, achieved an AUROC of 0.94 on a relevant subset of the data, highlighting the performance benchmark when baseline data is available. Formal statistical comparisons revealed that the top-performing models (e.g., Random Forest, SVM) were often statistically indistinguishable. Model performance was highly dependent on the test scenario, with precision and F1 scores improving markedly on a balanced dataset. Conclusions: In the absence of baseline SCr, machine learning models can diagnose AKI with significantly greater accuracy than routine clinical diagnoses. Our robust statistical analysis suggests that several advanced algorithms achieve a similarly high level of performance. Full article