Search Results (108)

Background: Population ageing and the gradual implementation of delayed retirement policies have drawn increasing attention to the lives of older adults after retirement age. Although previous studies have examined the relationship between retirement and health outcomes, limited evidence is available on whether continued workforce participation after retirement age affects healthcare utilization. Methods: Using nationally representative data from the 2018 China Health and Retirement Longitudinal Study (CHARLS), we employ negative binomial regression as the baseline model and use an instrumental variable two-stage least squares (IV-2SLS) to address endogeneity. We further conduct heterogeneity and mechanism analyses. Results: The findings reveal that workforce participation after retirement age significantly reduces healthcare utilization: post-retirement workers have 42.1% fewer outpatient visits and 49.2% fewer inpatient admissions than their fully retired counterparts. Mechanism analyses indicate that the negative effect operates primarily through tighter time constraints that crowd out care-seeking time and income fluctuations that alter health investment behaviors. Heterogeneity analyses further show that the reduction in outpatient utilization is more pronounced among males and highly educated individuals, whereas the reduction in inpatient utilization is stronger for females and those with good self-rated health. Conclusions: Workforce participation after retirement age may hinder healthcare utilization among older adults. These findings reveal an unintended consequence of delayed retirement policies and call for flexible, targeted arrangements to balance labor participation and healthcare access for older workers. Full article

Background/Objectives: Obstructive sleep apnea (OSA) and related sleep disorders affect a substantial proportion of hospitalized patients, with an estimated 48% pooled prevalence of undiagnosed OSA in cardiac inpatients and up to 80% of moderate-to-severe community OSA cases carrying no formal diagnosis at the time of hospital admission. In parallel, frailty—a state of heightened physiological vulnerability arising from cumulative multi-system biological decline—is present in 40–80% of inpatients and shares deep, bidirectional neurobiological pathways with sleep-disordered breathing through circadian dysregulation, intermittent hypoxia, hypothalamic–pituitary–adrenal axis activation, and chronic low-grade inflammation. Despite this convergence, no prior study has integrated validated, administratively computable frailty phenotyping with a machine learning framework specifically designed to predict inpatient sleep disorder diagnosis—and OSA in particular—at the point of hospital admission. The present study addresses this gap by developing an admission-time, explainable machine learning framework for the prediction of inpatient sleep disorder diagnoses (ICD-10 G47.x, encompassing OSA G47.3, insomnia G47.0, hypersomnia, and circadian rhythm disorders) and of insomnia specifically (ICD-10 G47.00). Methods: We developed and evaluated a suite of five binary classification models—XGBoost, Random Forest, LightGBM, CatBoost, and Decision Tree—using 9682 balanced hospitalization episodes from the MIMIC-IV (version 2.2) database. The predictor set comprised 23 admission-time structured features across three domains: (i) frailty and comorbidity burden, including the Hospital Frailty Risk Score (HFRS) derived from ICD-10 codes, the Elixhauser comorbidity index, prior admission history, and six binary disease flags (obesity, hypertension, type 2 diabetes, heart failure, COPD, and depression/anxiety); (ii) physiological and laboratory biomarkers from the first 24 h of care, including minimum SpO₂, heart rate variability, hemoglobin, creatinine, albumin, and arterial blood gas parameters; and (iii) sociodemographic and administrative variables encompassing age, sex, ethnicity, insurance type, and admission acuity. Model performance was assessed through five-fold stratified cross-validation and bootstrap confidence intervals (n = 1000 iterations), with predictor importance quantified using SHapley Additive exPlanations (SHAP). Results: XGBoost achieved the strongest aggregate performance across all evaluation metrics, attaining an area under the receiver operating characteristic curve (AUC) of 0.871 (95% CI: 0.856–0.887), accuracy of 79.6%, F1-score of 0.820, and sensitivity of 94.9%, correctly identifying 903 of 952 true positive cases in the held-out test set; all gradient boosting frameworks substantially outperformed the Decision Tree baseline (AUC 0.836). SHAP analysis identified the HFRS and Elixhauser index as the two dominant predictors, followed by depression/anxiety, obesity, hypertension, and minimum SpO₂—a hierarchy that recapitulates the canonical clinical phenotype of obstructive sleep apnea in frail inpatients rather than that of primary insomnia, indicating that the model is preferentially capturing the OSA–frailty axis within the broader G47.x outcome. The predicted probability outputs were well-calibrated across all risk deciles. Conclusions: Frailty-derived features, in combination with admission-time clinical and physiological data, can predict inpatient sleep disorder diagnoses—predominantly OSA—with high sensitivity and well-calibrated risk estimates. The deployable, interpretable nature of the XGBoost model makes it directly suitable for integration into clinical decision support systems, offering a screening tool that requires no dedicated instrumentation beyond routine admission data. By flagging high-risk patients at the moment of admission, the framework provides a concrete mechanism for accelerating referral for definitive diagnostic confirmation (overnight oximetry, polysomnography) and earlier initiation of CPAP and related therapies, with direct implications for reducing the persistent diagnostic gap, perioperative risk, and preventable adverse outcomes in frail hospitalized populations. Full article

Tidal turbine wake predictions in regional-scale hydrodynamic models typically account for rotor thrust but neglect the drag of support structures. This study introduces a method for representing turbine support structures as permeable drag volumes within TELEMAC-3D and evaluates their influence on wake characteristics. The method is demonstrated for the 1 MW DeepGen-IV turbine deployed at the Fall of Warness test site at the European Marine Energy Centre, Scotland. The tripod foundation, tower, and nacelle are each implemented as momentum source terms alongside an actuator disc rotor in a regional-scale model with mesh resolution down to 1.5 m with 24 sigma layers and output at 60 s intervals (1 s at instrument locations), validated against seabed-mounted ADCP measurements. Including the support structures improves the agreement with measured wake profiles by 6–18% in root-mean-square error at 3.7 rotor diameters downstream and extends the hub-height 5% velocity deficit distance by an average of three rotor diameters (~54 m), with substantial variability across tidal conditions. The tripod and tower drag also extend the velocity deficit into the lower water column, a feature absent from the rotor-only formulation, with potential relevance to near-bed processes such as bed shear stress and sediment transport which are not examined in the present study. The implementation is in principle extendable to other support concepts and multi-device studies, and the results indicate that support structure drag should be considered in regional wake models where wake persistence and downstream interactions are important. Full article

Background: Many SCOR performance measures rely on internal operational data, which limits empirical work using public information. Methods: This study evaluates a small set of publicly auditable, SCOR-linked ratios (SCOR_E) in a panel of 12 publicly traded firms across four sectors from 2000 to 2022. Using firm- and year-fixed-effects panel models, the paper examines whether these candidate proxies show pre-specified directional associations within firms and whether the same ratios are associated with operating margin in parallel models. Instrumental-variable (IV) specifications are reported only as sensitivity analyses, and nearly all are weak by the paper’s reported first-stage diagnostics. Results: Accordingly, most findings are interpreted as associative rather than causal. After false-discovery-rate adjustment and weak-instrument-robust inference, only four firm–proxy pairs meet the paper’s detection criterion; all remaining estimates are treated as non-robust. Conclusions: The contribution is therefore narrow: this is a constrained exploratory screening exercise showing which candidate mappings survive the paper’s inferential filters in this sample and which do not. The results do not establish a validated cross-industry scorecard, a scalable benchmarking framework, or a basis for policy claims. Full article

In the context of China’s pursuit of high-quality economic development, enhancing agricultural productivity is crucial for ensuring food security and promoting common prosperity. This paper constructs a systematic IV-LP-ACF-SAR econometric framework to analyze agricultural Total Factor Productivity (TFP) growth using panel data from 31 Chinese provinces spanning 2014 to 2023 (n = 341 observations). The framework employs the instrumental variable (IV)-based Levinsohn–Petrin (LP) proxy variable method under the Ackerberg–Caves–Frazer (ACF) system to estimate a Translog production function while addressing endogeneity using multiple spatial weight matrices. TFP growth is decomposed into technical change (TC), technical efficiency (EC), and scale efficiency (SC). A Spatial Autoregressive (SAR) model with Dynamic Common Correlated Effects (DCCE) explores spatial spillover effects and regional heterogeneity. Results show that China’s agricultural TFP remained largely stagnant from 2014 to 2023 with an average annual growth rate of −0.18%, where technical efficiency decline (−0.33% annually) was the main constraint. Technical change remained neutral, while scale efficiency contributed positively (+0.15% annually). Mechanization showed the highest output elasticity (0.99), while fertilizers, pesticides, and labor exhibited negative marginal returns. Spatial analysis revealed significant negative scale efficiency spillovers with regional patterns of “scale synergy in the Northeast/Northwest” and “efficiency synergy in East/North China.” These findings suggest that productivity policy should shift toward a dual-driver model combining efficiency enhancement and optimal scaling, with differentiated regional policies and inter-provincial coordination mechanisms necessary to mitigate negative spillovers and enhance sustainable agricultural growth quality. Full article

Developing new quality productive forces represents a core strategy for steering China’s path to modernization and shaping new competitive advantages for the nation. As a leading technology in the new round of technological revolution and industrial transformation, artificial intelligence (AI) serves as a key engine for fostering new quality productive forces. Utilizing panel data from China’s A-share listed manufacturing firms (2012–2024), this study employs the penetration rate of industrial robots to proxy for AI development levels and the entropy method to measure new quality productive forces. From the perspective of supply chain resilience, ordinary least squares (OLS) and instrumental variable (IV) methods are employed to examine the impact of AI on enterprise new quality productive forces and its underlying mechanisms. The findings indicate that AI significantly enhances corporate new quality productive forces, a conclusion that remains robust after addressing potential endogeneity and conducting robustness checks. Mediation analysis reveals that AI reinforces corporate supply chain resilience by improving supply chain efficiency and strengthening supply chain discourse power, which in turn drives the enhancement of corporate new quality productive forces. Heterogeneity analysis indicates that the impact of AI on corporate new quality productive forces is heterogeneous, with particularly pronounced effects observed in firms with higher innovation levels, state-owned enterprises, and firms located in western China. This study contributes new evidence from a supply chain resilience perspective to understand the micro-level pathways through which AI empowers new quality productive forces, and offers targeted policy and managerial recommendations to foster the sustainable development of the manufacturing sector. Full article

Background: Although elevated triglyceride (TG) levels are consistently associated with hypertension in observational studies, whether TGs have a causal effect on hypertension remains uncertain, and evidence in East Asian populations is limited. Methods: We analyzed 2159 Korean adults (20–86 years) whose individual-level genetic and phenotypic data were obtained from a cross-sectional health check cohort. Candidate TG-associated genetic variants were identified using genome-wide association analysis and evaluated as instrumental variables (IVs). An individual-level, two-stage IV Mendelian randomization (MR) framework was applied to assess the potential effect of TGs on hypertension, alongside conventional observational analyses using logistic regression. Results: Three candidate TG-associated single-nucleotide polymorphisms (SNPs)—rs78115082 (TRPC7), rs117867615 (TTLL1), and rs34463296 (LINC03019)—were identified and combined to construct a weighted genetic risk score (GRS). Although all the instruments met the conventional strength criteria (F statistics > 10), they explained only a modest proportion of the variance in TG levels (partial R², 0.008–0.020). Observational analyses showed a strong positive association between TG levels and hypertension (crude odds ratio [OR] = 2.12; 95% confidence interval [CI]: 1.76–2.54; adjusted OR = 1.43; 95% CI: 1.16–1.75). In contrast, MR estimates based on individual SNPs and the GRS were directionally positive but statistically nonsignificant, with wide CIs crossing the null, indicating limited precision. Conclusions: In this Korean cohort, observational analyses demonstrated a robust association between TG levels and hypertension, whereas individual-level MR provided inconclusive genetic evidence for a causal effect under the available instruments. The difference between the observational and genetic estimates is compatible with the finding that TG levels reflect broader cardiometabolic dysregulation rather than acting as an isolated causal determinant of hypertension. These findings underscore the need for larger studies with stronger, externally derived instruments to refine the causal inference in East Asian populations. Full article

Cassava mosaic disease (CMD) is a major constraint to cassava production in sub-Saharan Africa, particularly in Burkina Faso, where it poses a serious threat to rural food security. This study examined the impact of adopting innovative cassava mosaic disease management practices on cassava yields in the Guiriko and Nando regions of Burkina Faso. To address potential biases arising from differences in characteristics between adopters and non-adopters, an econometric approach based on the instrumental variables (IV) method within a counterfactual framework was employed to estimate the local average treatment effect (LATE). The data were drawn from a survey conducted in September 2023 among 511 cassava producers. The results indicate that the adoption of innovative cassava mosaic disease management practices had a positive and statistically significant effect on agricultural yields. Productivity gains were estimated at 29% in the Guiriko region and 41% in the Nando region, highlighting spatial heterogeneity in impacts. These findings suggest that promoting the diffusion of such practices can substantially improve cassava productivity and reduce the vulnerability of rural households. In addition, the analysis showed that socioeconomic and technical factors, including farmers’ age, membership in cassava producer organizations, household income levels, and the use of chemical fertilizers, also influence productivity outcomes. Overall, the study underscores the importance of strengthening agricultural extension services, supporting producer organizations, and promoting appropriate technologies to maximize the benefits of cassava mosaic disease management practices for food security and rural development. Full article

This paper constructs a two-way fixed effects model using data from 4623 Chinese A-share listed enterprises from 2011 to 2022, confirming that firm digital transformation can enhance access to sustainable trade credit. Specifically, for every 1% increase in the standard deviation of digital transformation, the trade credit obtained by enterprises increases by 2.14% in relation to their average value. We employed instrumental variable (IV) and propensity score matching (PSM) methods, utilizing the Broadband China pilot policy as a quasi-natural experiment to conduct a multi-period propensity score matching-difference in differences (PSM-DID) analysis to address potential issues of reverse causality and sample selection bias. Mechanism analysis indicates that the diversification of supplier structures, R&D innovation, and market share facilitated by digitalization are three main channels. This effect is particularly significant in state-owned enterprises, mature enterprises, and those with higher social trust. Finally, the study also found that the spillover effects of digital transformation encourage client enterprises to allocate credit resources to downstream firms, thereby promoting the sustainable development of supply chain finance. Furthermore, the digital transformation primarily alleviates short-term credit challenges for enterprises and reduces their reliance on bank credit. Full article

A highly sensitive and fast procedure for the determination of trace germanium is presented. The carbon nanotubes/spherical glassy carbon electrode (CNTs/SGCE) has been applied for adsorptive stripping voltammetric determination of trace concentrations of Ge(IV) in solution, preceded by complexation with chloranilic acid. Carbon nanomaterials were used for the first time in the voltammetric determination of Ge(IV). The experimental variables such as supporting electrolyte concentration, chloranilic acid concentration, modification of the CNTs/SGCE by forming a bismuth film, and the potential and time for Ge(IV)-chloranilic acid adsorption, as well as instrumental variables on the germanium signal response, were tested. Under optimized conditions, the peak current was found to be proportional to the concentration of Ge(IV) over the range of 0.9 to 30 nmol L⁻¹ with R = 0.998. The detection limit, estimated from three times the standard deviation at low Ge(IV) concentration, was about 0.3 nmol L⁻¹. Possible interferences were evaluated. Finally, the proposed method was successfully applied for the determination of the total amount of germanium in drinking and river water samples. Full article

This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance, drawing upon the multi-methodological framework of combining econometrics with state-of-the-art machine learning approaches. Employing Instrumental Variable (IV) Panel data regressions, viz., 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz., Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz., Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model-Based, and Random Forest—were applied to uncover unknown structures and predict the behavior of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), social (S), and governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labor, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing us to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust. Full article

Background/Objectives: The relationship between household tobacco expenditure and child health has attracted considerable attention from both academic and policy communities, as tobacco expenditure can influence children’s health, nutrition, and overall well-being in multiple ways, particularly in rural and low-income settings. This study examines the causal impact of household tobacco expenditure on child health outcomes in a transitional economy. Methods: Using nationally representative microdata from the most recent Household Living Standards Survey, the authors employ Ordinary Least Squares (OLS), Random Effects (RE), and Instrumental Variable (IV) estimations to identify the effects of tobacco spending on children’s healthcare utilization and health status. Results: The results consistently show that higher household tobacco expenditure significantly increases the likelihood of hospitalization among Vietnamese children, with the effects being most pronounced for those under six years of age. Moreover, the authors uncover substantial heterogeneity across gender, maternal age at childbirth, and regional contexts, highlighting persistent socioeconomic inequalities in health outcomes. Conclusions: This study provides compelling evidence of the adverse effects of household tobacco expenditure on children’s health in Vietnam. Theoretically, the study contributes to the literature on the economics of health and intra-household resource allocation by providing micro-level causal evidence from a transitional setting. From a policy perspective, the findings underscore the need for targeted fiscal and public health interventions to mitigate tobacco-related welfare losses and to promote equitable access to healthcare among vulnerable populations. Full article

The integration of the digital economy and higher education is a core driver of sustainable development, yet the mechanisms and heterogeneous effects of this interaction remain underexplored. Using balanced panel data from 30 Chinese provinces over 2011–2020, this study empirically investigates the impact of the digital economy on higher education development (scale, structure, quality) and its transmission channels. The digital economy development index (DEDI) is constructed via the entropy-weighted method, and a comprehensive empirical strategy is adopted, including baseline regression, instrumental variable (IV) estimation, difference-in-differences (DID), mediation analysis, and regional heterogeneity tests. The results reveal three key findings: (1) The digital economy exerts a significantly positive causal effect on higher education scale and structure optimization, with robustness confirmed by multiple tests. (2) It has no direct impact on higher education quality, but indirectly promotes quality through regional income levels, while institutional quality partially mediates the effect on structure. (3) Significant regional heterogeneity exists: the impact is strongest in eastern provinces, moderate in central provinces, and insignificant in western provinces, constrained by weak digital infrastructure. This study enriches the theoretical framework of digital economy–education interaction and provides actionable policy implications for promoting sustainable, balanced higher education development: strengthening digital infrastructure in underdeveloped regions, aligning educational structure with digital industrial demand, linking digital economic growth to educational investment, and implementing region-specific policies. These findings contribute to advancing the synergy between digital transformation and high-quality higher education, supporting long-term sustainable economic and social development. Full article

Background/Objectives: Psoriasis and age-related macular degeneration (AMD) may share immune-related pathophysiologic characteristics. However, few studies have investigated the relationship between psoriasis and AMD. We assessed the possible causal link between psoriasis and AMD in European populations. Methods: Single-nucleotide polymorphisms associated with psoriasis exposure were employed as instrumental variables (IVs) based on genome-wide significance (p < 5.0 ×${10}^{-8}$) in the FinnGen genome-wide association study (GWAS). The GWAS data for AMD were obtained from 11 studies performed by the International AMD Genomics Consortium. We performed a two-sample Mendelian randomisation (MR) study to estimate causal effects using the inverse-variance weighted, weighted median, and MR-Egger methods, as well as the MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) test. Results: We observed significant causal associations of psoriasis with AMD. Using the weighted median method, the odds ratio (OR) was 1.09 (95% CI = [1.03–1.16] and p = 0.005), and using the MR-PRESSO test, the OR was 1.04 (95% CI = [1.00–1.09] and p = 0.043). Conclusions: A potential causal association between psoriasis and AMD underscores the need to investigate inflammation as a risk factor for AMD. Full article

Driven by innovations in digital technologies, the digital economy is reshaping societal production and consumption patterns, exerting systematic effects on the energy system through the digital transformation of both the supply and demand sides of energy. Based on empirical analysis using provincial panel data from China between 2011 and 2022, this study demonstrates that the development of the digital economy significantly suppresses the scale of energy consumption while simultaneously improving energy utilization efficiency. After applying the instrumental variable method (with the interaction term of fixed-line telephones and information technology service revenue as the IV) and conducting multiple robustness checks (including lagged explanatory variables, variable substitution, sample trimming, and additional control variables), the core conclusion remains statistically significant. Mechanism tests reveal that the collaborative effects of green technological innovation, the upgrading of industrial structure, and digital inclusive finance form the key transmission path. Finally, heterogeneity analysis shows that the impact of the digital economy on energy consumption and energy efficiency is particularly pronounced in western regions, demonstrating significant regional heterogeneity. Full article