Search Results (282)

Oil-price volatility represents a major challenge for hydrocarbon-dependent economies pursuing renewable-energy transition. In GCC countries, fluctuations in global oil markets may influence renewable-energy deployment through their effects on fiscal revenues, investment conditions, and long-term energy planning. While previous studies have largely examined the direct effects of oil prices, renewable energy, and financial development separately, limited evidence exists on whether financial development can mitigate the adverse implications of oil-market uncertainty for renewable-energy transition in GCC economies. Using annual data for six GCC countries over the period 1990–2024, this study investigates the links among oil-price volatility, financial development, and renewable-energy transition within a second-generation panel econometric framework that accounts for cross-sectional dependence and heterogeneity. The analysis employs Pesaran cross-sectional dependence tests, CIPS unit-root tests, Westerlund cointegration, common correlated effects mean group (CCE-MG), augmented mean group (AMG), and error-correction modeling. The results support the existence of a stable long-run relationship among the variables. Oil-price volatility is negatively associated with renewable-energy consumption, with a long-run coefficient of approximately −0.21. Financial development exhibits a positive association with renewable-energy transition, while the interaction between oil-price volatility and financial development remains positive and statistically significant. This finding suggests that stronger financial systems may partially reduce the adverse effects of oil-market instability. The short-run estimates also support the presence of a stable adjustment process toward long-run equilibrium. Robustness checks based on alternative financial-development proxies, lagged regressors, Driscoll–Kraay estimations, leave-one-out country analysis, and alternative volatility measures confirm the stability of the main findings. The findings suggest that financial development may strengthen the resilience of renewable-energy transition strategies in GCC economies exposed to volatile energy-market conditions. Full article

Background: Dengue fever has emerged as a major public health concern in Bangladesh, with increasing incidence and geographic spread of outbreaks in recent years. This study aimed to investigate the lagged and non-linear associations between climatic factors and dengue incidence across all eight administrative divisions of Bangladesh from 2014 to 2025. Materials and Methods: An ecological time-series design was employed using monthly dengue case data (n = 741,338) and meteorological variables. A generalized additive model (GAM) with a negative binomial distribution was applied to account for overdispersion and capture complex relationships. Descriptive analysis was conducted to assess spatial heterogeneity, and choropleth maps were constructed to visualize the spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis was performed to identify significant lagged associations between climatic variables and dengue incidence. Results: Descriptive analysis showed substantial spatial heterogeneity, with the highest incidence observed in Dhaka (6.53 per 100,000) and the lowest in Sylhet (0.21 per 100,000). Choropleth maps illustrated distinct spatial distribution and regional variation in dengue burden across the country. Cross-correlation analysis identified significant lagged associations for temperature and rainfall (lag 1–3 months), humidity (lag 1–2 months), and wind speed (lag 2–3 months). The final GAM explained 88.6% of the deviance in dengue incidence (AIC = 7404.15; dispersion = 0.767). The approximate significance of smooth terms revealed that temperature at a lag of 1 month (p < 0.001, edf = 12.28), rainfall at a lag of 3 months (p < 0.001, edf = 2.85), and wind speed at a lag of 2 months (p < 0.001, edf = 2.25) were highly significant non-linear predictors of dengue transmission. Relative humidity was not significantly associated with dengue incidence. Non-linear effects revealed peak dengue risk at temperatures between 25 and 30 °C and moderate rainfall (~10 mm), particularly during monsoon months (June–October). A strong autoregressive effect indicated that prior dengue incidence significantly influenced current transmission. Conclusions: Overall, dengue transmission in Bangladesh is driven by complex, lagged, and non-linear interactions between climatic variables, seasonality, and regional factors. These findings provide critical evidence for climate-based early warning systems, enhance outbreak prediction, and inform evidence-based vector control strategies. Full article

This study assessed the long-term energy self-sufficiency and operational dynamics of a full-scale wastewater treatment plant over the period 2015–2023, with particular emphasis on biogas-driven energy recovery and time-dependent process interactions. The relationship between biogas production and electricity and heat generation was evaluated alongside the influence of different sludge streams on system performance using cross-correlation analysis. The results demonstrated a high level of energy recovery, with biogas-derived electricity covering, on average, 60% of the plant’s demand and reaching a maximum of 74% annually. A very strong correlation was observed between annual biogas production and electricity generation (r = 0.94), confirming the direct energetic coupling of both processes. Monthly analyses further indicated strong consistency between biogas yield and both electricity and heat production (r = 0.55–0.91 and r = 0.86, respectively). Cross-correlation analysis identified Thickened Waste Activated Sludge and Primary Sludge as important process drivers, with statistically significant delayed effects at 10–20 days. In contrast, recirculation-related streams exhibited negligible influence on system dynamics. Statistical analysis revealed that most heavy metals, including Cd, Cr, Ni, and Hg, exhibited high variability (Coefficient Variability > 40%), which can directly impact the stability of methane production. These results indicate that wastewater treatment plants’ energy performance is governed by delayed process responses linked to sludge residence time, highlighting the need for predictive models incorporating at least two weeks of historical operational data. In addition, physicochemical analysis of sewage sludge confirmed generally stable nutrient content, despite variability in biological parameters and heavy metal concentrations. Overall, the study demonstrates that integrating long-term operational datasets with time-lag analysis provides valuable insights for optimizing energy recovery and supporting circular economy strategies in wastewater treatment plants. Full article

In passive-source seismic exploration, even after seismic instruments complete unified start-up acquisition and hardware synchronization, long-duration continuous records may still contain small residual timing errors, which in turn broaden cross-correlation peaks and degrade event-location results. To address this problem, this study proposes a wavefield-domain residual timing refinement method. The method uses stable noise windows and controlled artificial events in continuous records as constraints, and performs data-window preprocessing, reference cross-correlation function construction, pairwise residual lag estimation, confidence-weighted multi-station joint fusion, and smoothing-constrained fitting of a continuous correction curve to achieve a posterior refinement of residual timing errors after hardware synchronization. Fractional-delay interpolation is then used for waveform correction. Validation using a 60 min continuous record from a local six-station array shows that the proposed method can serve as an effective supplement to hardware synchronization, suppress residual timing errors, and improve the temporal consistency, waveform stackability, and interpretation reliability of passive-source seismic exploration data. Full article

Droughts are complex extreme phenomena that severely impact regional development and water availability. Although the influence of interannual and decadal macroclimatic patterns, such as the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), on precipitation alteration is widely recognized, current water management systems lack multivariate predictive approaches to anticipate their phases with sufficient operational lead time. This study developed a predictive framework to project ENSO and PDO phases, establishing an optimal temporal window to forecast drought-triggering conditions. Using monthly historical records, teleconnections were evaluated through cross-correlation and Granger causality. Subsequently, Vector Autoregression (VAR) models and machine learning algorithms (Random Forest) were implemented to project anomalies and classify climatic phases. The Granger causality test demonstrated that ENSO variations statistically precede PDO phase shifts, establishing an optimal forecasting window of three to four months. The VAR model exhibited robust joint explanatory capacity for a continuous four-month projection, while the Random Forest algorithm achieved a predictive accuracy of 52.2% specifically for categorical phase classification at a three-month lead time. It is concluded that this lagged interaction allows for reliable mathematical anticipation, providing an essential analytical framework for exploring regional hydrological dynamics and supporting local preventive water management. Full article

During the critical life transition of higher education, academic performance and mental health are two key factors that influence college students’ personal growth and future career development. Notably, there is ongoing debate regarding whether a bidirectional relationship exists between academic performance and mental health, and existing research lacks longitudinal evidence from students in China’s elite universities. As key indicators of mental health, the dynamic relationship between depression, subjective well-being, and academic performance warrants further investigation. This study utilizes data from two waves of the Beijing College Students Panel Survey, a large-scale longitudinal study of university students in Beijing that was launched in 2009. Using a sample of 874 students from five elite universities in Beijing, China, the study employed a cross-lagged model to examine the longitudinal bidirectional relationship between depression, subjective well-being and academic performance. The results show that academic performance is negatively correlated with depression and positively correlated with subjective well-being. Cross-lagged analysis further indicates that prior academic performance can predict subsequent depression (β = −0.066, p < 0.1) and subjective well-being (β = 0.082, p < 0.05), but there is insufficient evidence for the reverse predictive relationship (p > 0.1). These findings suggest that, for students in China’s elite universities, academic performance is a significant antecedent of subsequent mental health status. The conclusions emphasize the importance of enhancing academic support in elite universities to promote mental health and provide empirical evidence for constructing a collaborative support system that integrates academic and psychological aspects, as well as health communication strategies for behavioral change. Full article

Air and water pollution pose critical threats to public health and environmental stability, particularly in rapidly urbanizing developing nations. This study investigates synergistic interactions between air and water pollutants across 14 cities in Hunan Province, China (2020–2024), using multiparametric statistical approaches. The results show that the coefficient of variation (CV) of particulate matter (PM) with diameters less than 2.5 μm (PM_2.5, CV = 46.9%) and turbidity (TU, CV = 47.4%) showed the highest variability among the air and water quality parameters, respectively. Annual trends revealed significant increases in ozone (O₃) alongside decreases in carbon monoxide (CO) and nitrogen dioxide (NO₂) concentrations. Concurrently, freshwater systems exhibited rising electrical conductivity (EC), water temperature (WT), and pH, paired with declining levels of ammonia nitrogen (NH₃-N), total phosphorus (TP), and turbidity (TU). Principal component analysis (PCA) and Spearman correlation analyses showed significant positive correlations between PM and nitrogen species (TN, NH₃-N), but negative correlations with TP, suggesting potential cross-media pollution interactions. Cross-correlation analysis revealed significant time-lagged relationships (1–5 months) between atmospheric pollutants and aquatic nutrients, suggesting that atmospheric deposition may serve as a contributing pathway for cross-media contamination. The study not only provides empirical evidence for integrated pollution control strategies in urbanizing watersheds, but also offers a transferable framework for addressing similar air–water quality interactions on a global scale. Full article

Accurate short-term traction load forecasting is crucial for optimizing railway operations. However, the strong fluctuations in high-speed railway loads and the general neglect of the physical relationships between adjacent substations in existing studies pose significant challenges to reliable short-term forecasting. To address these issues, this paper proposes a Lag-Adaptive Gradient Aware Network (LAGA-Net). Unlike isolated forecasting methods, LAGA-Net explicitly combines the physical information of train motion with deep learning methods to achieve collaborative load forecasting between adjacent traction substations (TSs). Specifically, it first calculates the cross-correlation coefficients of the load curves of adjacent TSs to quantify the train lag process and achieve load time-series alignment, effectively utilizing the historical load of upstream substations as prior information for load forecasting at this station. Based on this, a dual-stream gradient sensing encoder is proposed to capture the load amplitude and high-frequency pulses of the two TSs, improving the prediction accuracy of the model in highly volatile scenarios. Finally, an adaptive cross-attention mechanism based on Gaussian masks is designed to achieve spatiotemporal coupling and collaborative forecasting of the loads of two adjacent TSs using the aligned load representation information. Extensive experiments on real adjacent traction substation datasets demonstrate that LAGA-Net significantly outperforms existing state-of-the-art benchmark methods in terms of multi-step prediction and peak prediction accuracy, and exhibits strong robustness to operational uncertainties. Full article

Sustainable regional development requires understanding how entrepreneurship and unemployment co-evolve. This study investigates this relationship across Türkiye’s 26 Nomenclature of Territorial Units for Statistics 2 regions over the 2007–2024 period, testing the Schumpeter (pull) and Refugee (push) effects with controls for regional economic and financial determinants. Using the Dynamic Common Correlated Effects estimator, which accounts for cross-sectional dependence and slope heterogeneity across regions, the analysis provides evidence supporting both effects, while revealing that neither effect emerges instantaneously. The Schumpeter effect operates with an approximately one-year lag, reflecting the time new ventures require to complete organizational formation and generate net labor demand, with a creative destruction dynamic appearing from the second year onward. The Refugee effect materializes within one to two years, as unemployed individuals exhaust formal job search alternatives before turning to necessity entrepreneurship. Critically, the findings identify banking sector intermediation efficiency, rather than aggregate credit volume, as a more consistent financial channel for sustainable labor market outcomes, and document a pattern consistent with jobless growth, in which regional output expansion has not systematically translated into unemployment reduction. These results call for employment- and entrepreneurship-linked policy instruments that are timed to the lag structure of both effects and targeted at transforming necessity-driven activities into sustainable, high-value-added structures, rather than merely incentivizing firm entry. Aligning regional financial intermediation with employment creation can foster long-term socio-economic sustainability and promote sustainable regional development. Full article

Auditory neurofeedback training (NFT) based on brain–computer interfaces (BCIs) has recently entered the precision motor domain as a task-embedded neural state regulation paradigm. Compared to traditional standalone NFT approaches (e.g., relaxation or attention training designed to enhance general cognitive abilities), task-embedded paradigms integrate feedback directly into the motor task execution process. However, this design inevitably creates a dual-task scenario, and the effects of such a scenario on neural activity and behavioral performance have received limited systematic investigation in the existing literature. This study designed and implemented a closed-loop BCI system employing five-level heartbeat sound feedback and used this system as a research platform to examine the immediate neural mechanism changes and potential dual-task interference effects induced by single-session auditory NFT in moderately skilled shooters. The system maps real-time EEG features onto graded auditory signals varying in playback rate and volume intensity, incorporating a dynamic threshold adjustment mechanism. Twenty-two moderately skilled shooters completed three within-subject conditions (no-sound baseline, SMR enhancement, and theta suppression) in a single session with 32-channel EEG and behavioral data recorded simultaneously. Analyses employed whole-brain cluster-based permutation tests, cross-frequency coupling analysis, and functional connectivity analysis. Cluster-based permutation tests revealed that theta feedback induced a significant frontal 4–7 Hz suppression cluster (cluster p = 0.004), whereas SMR feedback did not produce significant 12–15 Hz enhancement at the group level. Theta feedback elicited cross-frequency spillover as follows: sensorimotor SMR power decreased significantly in theta responders (d = −0.69), with frontal theta and sensorimotor SMR changes positively correlated (r = 0.67, p < 0.001). Functional connectivity analysis using debiased weighted phase lag index (dwPLI) further demonstrated significant theta-band network reorganization (cluster p = 0.034). At the neural level, clear modulation effects were observed, but shooting ring values did not improve significantly under feedback conditions, and aiming time was significantly prolonged—a behavioral pattern consistent with potential dual-task interference from task-embedded auditory feedback. Single-session auditory NFT can act on the prefrontal cognitive control network and induce cross-frequency network reorganization, but the feedback channel itself constitutes a parallel task that may limit the short-term transfer of induced neural states to behavioral performance. This study examined the neural mechanisms of task-embedded auditory NFT and reported the dual-task costs that have been less characterized in prior “task + feedback” research, providing design considerations and preliminary mechanistic evidence for future development of auditory NFT in precision motor skill training. Full article

Despite legislative advancements, social and reputational norms continue to govern domestic conflict’s institutional visibility. Using an explanatory sequential mixed-methods design in the United Arab Emirates, covering the period 2019–2025, this study analyzes how the transition across two successive domestic violence statutes is associated with women’s institutional trajectories. Quantitatively, 412 first-instance case files were analyzed using non-parametric tests and a CHAID decision tree. Qualitatively, interviews with women (n = 28) and institutional actors (n = 23) explain how “status flipping” occurs through counter-complaints and moral character narratives. Findings indicate that norms-based moral regulation and structural constraints (e.g., financial dependency and custody leverage) are strong correlates of escalation from case closure to formal prosecution. The CHAID model identifies structural constraints as the principal splitter in trajectory separation. Post-2024 patterns suggest an institutional lag, where implementation routines evolve more slowly than formal law. The paper contributes a model of reputation-mediated escalation and proposes procedural safeguarding to curb retaliatory cross-filing and make patterned coercive control legally legible. By situating women’s legal interactions within an interactional pathway of norms, constraints, and institutional translation, the study clarifies why “protection” can paradoxically morph into complex procedural outcomes in legally transitioning contexts. Full article

Accurate prediction of superheated steam temperature (SST) is critical for the safe and efficient operation of large-scale thermal power units, particularly under large load variations and high thermal inertia. This study proposes an iTransformer-based SST prediction framework (iTransformer-SST) to address limitations of conventional proportional–integral–derivative (PID) control and existing data-driven models in capturing multivariable coupling, time-delay effects, and physical consistency. Using the A-side subsystem of a 1000 MW thermal power unit, 19-dimensional process data were collected continuously over two months with a sampling interval of 2.4 s. After data preprocessing, time-lagged cross-correlation (TLCC) analysis combined with expert knowledge was employed for feature screening, resulting in ten highly relevant input variables. To enhance predictive robustness, the baseline iTransformer was augmented with a Local Temporal Convolution (LTC) module for local disturbance modeling and a physics-guided regularization term to enforce delayed monotonicity and smoothness constraints. In 240 min rolling forecasts of the final-stage superheater outlet temperature, the proposed model achieved a mean squared error (MSE) of 0.0887, a mean absolute error (MAE) of 0.2312, and a coefficient of determination ($R^2$) of 0.9650, significantly outperforming long short-term memory (LSTM), Informer, and the baseline iTransformer. The combined LTC and physics-guided design reduced MSE by 13.5%, demonstrating strong potential for feedforward-assisted SST control in industrial thermal power applications. Full article

Background: Mpox vaccination is an important prevention strategy for men who have sex with men (MSM), yet responses to vaccination may not be adequately captured by a simple willing-versus-unwilling framing. We examined correlates of vaccination willingness and heterogeneity within the delayed/refused responses subgroup in Changsha, China. Methods: A cross-sectional survey was conducted using respondent-driven sampling (RDS). Vaccination willingness was classified as immediate willingness versus delayed/refused responses. Analyses included cluster-robust logistic regression, RDS-weighted regression, generalized estimating equations, and a recruiter-linked network-lag model. Among respondents with delayed/refused responses, concern profiles were explored using unsupervised clustering of standardized concern items. Results: Among 405 recruited MSM without a self-reported mpox infection history, immediate willingness and delayed/refused responses were nearly equally distributed, indicating that lack of immediate willingness was common. Across primary models, ever use of pre-exposure prophylaxis (PrEP) and higher mpox-related information exposure frequency were the most consistent correlates of immediate willingness versus delayed/refused responses, whereas basic sociodemographic variables showed little evidence of independent association. Within the delayed/refused group, three partially overlapping patterns emerged: broadly elevated cross-domain concern, low-concern delay with few strongly endorsed barriers, and more selective safety- and burden-related concerns. These findings suggest that a lack of immediate willingness may arise through different psychosocial pathways rather than a single common mechanism. Conclusions: Mpox vaccination willingness among MSM in Changsha appeared to be shaped more by prevention-related behaviors and psychosocial factors than by basic sociodemographic profiles alone. Vaccination strategies may benefit from cross-topic sexual-health communication, integrated prevention efforts, and subgroup-sensitive approaches to delayed or refused willingness. Full article

The Fenwei Plain (FWP) is a typical basin-type region in northern China characterized by complex PM_2.5 and O₃ composite pollution. Based on hourly monitoring data from 11 cities (2015–2024), this study integrated the Mann–Kendall test, standard deviation ellipse (SDE), spatio-temporal cross-correlation function (STCCF), and spatio-temporal geographically weighted regression (GTWR) to systematically analyze decadal pollution patterns and coupling mechanisms. Results revealed a profound transition from particulate-dominated to photochemical-regime pollution: PM_2.5 concentrations decreased significantly by 32%, whereas MDA8 O₃ rose by 47%. Spring emerged as the critical compound pollution window, accounting for 66.7% of simultaneous exceedances. Spatially, both pollutants maintained a consistent Northeast–Southwest orientation, with PM_2.5 hotspots concentrated along the Weihe River Valley. Cluster analysis categorized the 11 cities into O₃-dominant, compound-high-risk, and PM_2.5-dominant clusters. Furthermore, a dominant positive synergy was observed on an annual scale, although a localized “see-saw” effect occurred at a 150–200 km distance with a 3-year lag. The GTWR model demonstrated high robustness (R²: 0.75–0.86), underscoring the influence of localized driving forces. These findings provide a scientific basis for synergistic governance and precision air quality management in northern basin-type regions. Full article

This study investigates the determinants of GDP per capita across 26 European Union member states over the period of 2006–2024, with a particular focus on entrepreneurship, institutional quality, and macroeconomic factors. Given the presence of long-run income differences across EU countries, the analysis explicitly accounts for structural heterogeneity in economic development and institutional capacity. To ensure robust estimation in the presence of cross-sectional dependence and slope heterogeneity, the study employs advanced panel econometric techniques, including tests for cross-sectional dependence, unit roots, and cointegration. Long-run relationships and short-run dynamics are estimated using the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, complemented by robustness checks based on the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Method of Moments Quantile Regression (MMQR) is applied to capture heterogeneity across different points of the income distribution, thereby reflecting long-run income disparities among EU member states. The empirical results confirm the existence of a stable long-run equilibrium relationship among the variables. The baseline CS-ARDL estimates indicate that institutional quality, entrepreneurial activity, trade openness, and government expenditure exert positive and statistically significant effects on GDP per capita, while financial development exhibits a negative effect and foreign direct investment remains insignificant. In the short run, entrepreneurship and trade openness contribute positively to GDP per capita, whereas government expenditure and credit expansion generate contractionary effects. The robustness analysis using AMG and CCEMG estimators largely supports these findings, as the direction of the coefficients remains consistent across alternative specifications, although some variation in statistical significance is observed due to differences in the treatment of cross-sectional dependence and unobserved common factors. The MMQR results further reveal substantial heterogeneity across the income distribution, indicating that the effects of key determinants vary depending on countries’ long-run income levels. In particular, trade openness and institutional quality exert stronger positive effects in lower-income quantiles, while the adverse effects of excessive financial development are more pronounced in higher-income quantiles. Overall, the findings underscore the importance of promoting productive entrepreneurship, strengthening institutional frameworks, facilitating trade integration, and ensuring efficient financial intermediation to enhance GDP per capita within the European Union. The results also highlight the need for differentiated policy approaches that explicitly account for long-run income heterogeneity, structural differences, and varying institutional capacities across EU member states. Full article