Search Results (6,899)

Background/Objectives: With the global rise in chronic diseases among older adults, rehabilitation services have become essential, particularly for those with cerebrovascular and central nervous system (CNS) disorders, which lead to significant long-term disabilities. To determine the impact of designated rehabilitation medical institutions on the readmission rates of older patients with CNS disorders who receive surgical interventions. Methods: This was a population-based cohort study. Data was obtained from the National Health Insurance Service database (2002–2019). Fifteen designated institutions participated in the pilot project for convalescent rehabilitation. We analyzed the data of 1019 patients before and after the implementation of the designated rehabilitation institution. The study sample included (1) patients admitted to 15 designated institutions participating in the pilot project for convalescent rehabilitation and (2) patients diagnosed with conditions classified under the rehabilitation patient group, Rehabilitation Impairment Category 1 to 7. The intervention was the pilot project for designated rehabilitation institutions, launched in October 2017. The primary outcome of interest was the readmission rate of older patients with CNS disorders who received surgical interventions. Interrupted time series analysis with segmented regression was used to assess changes in the 30-day readmission rates. Results: Post-intervention, an 8% reduction in 30-day readmission rates (estimate, 0.9225; 95% confidence interval: 0.9129–0.9322, p < 0.0001) was observed. Subgroup analysis showed a significant decline in readmission rates across various patient groups, including those with disabilities, high Charlson Comorbidity Index scores, and extended hospital stays. The regions outside Seoul (capital city), particularly Gyeonggi/Incheon (areas around Seoul) and other areas (i.e., rural), also showed a significant decrease in readmission trends after the intervention. Conclusions: Designated rehabilitation medical institutions led to a significant reduction in readmission rates of older patients with CNS disorders, suggesting that these institutions effectively support recovery and reduce the burden of readmission for patients with severe conditions and those residing in non-capital cities. Full article

A series of catalytic oxides (Fe₂O₃, CuO, ZnO, and Cu₂O) were investigated as prospective additives shaping the thermal features of a model solid rocket propellant (SRP) formulation utilising ammonium nitrate as the oxidising agent. An extensive investigation of the thermal behaviour (DSC and ignition/explosion temperature studies) of the model and catalyst-bearing SRP formulations was conducted, providing insights into both the thermodynamics and mechanism of combustion of these systems. XRD analysis of post-combustion residues was used to validate the mechanistic claims, as well as to provide information about the behaviour of copper oxides in the SRP system. In addition, the linear combustion velocity was experimentally determined, and the power output was estimated from density, linear combustion velocity and DSC data, in order to assess the potential motor performance of the tested formulations. The obtained results show that the utilisation of metal oxides significantly improves the combustion performance of ammonium nitrate-based SRP formulations relative to the unmodified ammonium nitrate-based propellants. Full article

In the current context of global warming, quantifying carbon fluxes between biosphere and atmosphere and identifying ecosystems as carbon sources or sinks is essential. The goal of this study is to quantify net carbon fluxes for the main forest types in peninsular Spain and characterize them as carbon sources or sinks. A hybrid methodology is proposed. First, net primary production (NPP) is obtained through machine learning using site properties, time metrics of meteorological series, and forest inventory data as inputs. The most accurate NPP estimates (R² ≥ 0.8 and relative RMSE ≤ 30%) were obtained by kernel ridge regression and gaussian process regression using latitude, elevation, time metrics of air temperature, precipitation and incoming solar radiation, and growing stock volume as inputs. Secondly, net ecosystem production (NEP) is obtained by subtracting heterotrophic respiration simulated by Biome-BGC from the previous NPP. All considered forest types presented small and mostly positive NPP and NEP values (greater for deciduous than for evergreen forests), thus generally acting as carbon sinks during the 2004–2018 period. Full article

China has implemented a series of clean air policies, resulting in improved air quality since 2013. However, there remains a paucity of national prospective evidence regarding the relationship between fine particulate matter (PM_2.5) and kidney disease (KD) incidence in China, as well as the potential mediating effects of lipid profiles in this association. This study aimed to assess the association of decreased PM_2.5 concentration and KD incidence in China from 2013 to 2020. Utilizing data from the China Health and Retirement Longitudinal Study (CHARLS), we included 15,368 participants who were free of KD in 2013 and followed up until 2020. For each participant, we calculated the 3-year and 2-year average PM_2.5 concentrations. The Cox proportional hazards model was employed to estimate the association between PM_2.5 exposure and KD incidence. Mediation analyses were conducted using eight lipid indices, and subgroup analyses were performed. The annual average PM_2.5 concentration for CHARLS participants reduced from 61.72 μg/m³ in 2013 to 32.75 μg/m³ in 2020. A reduction of 5 μg/m³ in 3-year and 2-year average PM_2.5 concentrations was associated with 14.3% (hazard ratio [HR]: 0.857, 95% confidence interval [CI]: 0.841, 0.873) and 14.4% (HR: 0.856, 95% CI: 0.840, 0.873) reductions in KD incidence in the fully adjusted models. The TyG-BMI and TyG-WHtR indices exhibited small mediating effects of 7.36% (95% CI: 2.35%, 12.38%) and 4.48% (95% CI: 0.51%, 8.45%) on the relationship of PM_2.5–KD, while other indicators did not demonstrate significant mediation. The findings of this study suggest that reductions in PM_2.5 concentration were associated with a decreased incidence of KD during the period from 2013 to 2020. The implementation of clean air policies since 2013 may have contributed to the decrease in chronic diseases like KD. Full article

Current mainstream remote sensing approaches to forest age estimation frequently neglect interspecific differences in functional traits, which may limit the accurate representation of species-specific tree growth strategies. This study develops and validates a technical framework that incorporates multi-source remote sensing and tree species functional trait heterogeneity to systematically improve the accuracy of plantation age mapping. We constructed a processing chain—“multi-source feature fusion–species identification–heterogeneity modeling”—for a typical karst plantation landscape in southeastern Yunnan. Using the Google Earth Engine (GEE) platform, we integrated Sentinel-1/2 and Landsat time-series data, implemented a Gradient Boosting Decision Tree (GBDT) algorithm for species classification, and built age estimation models that incorporate species identity as a proxy for the growth strategy heterogeneity delineated by the Plant Economic Spectrum (PES) theory. Key results indicate: (1) Species classification reached an overall accuracy of 89.34% under spatial block cross-validation, establishing a reliable basis for subsequent modeling. (2) The operational model incorporating species information achieved an R² (coefficient of determination) of 0.84 (RMSE (Root Mean Square Error) = 6.52 years) on the test set, demonstrating a substantial improvement over the baseline model that ignored species heterogeneity (R² = 0.62). This demonstrates that species identity serves as an effective proxy for capturing the growth strategy heterogeneity described by the Plant Economic Spectrum (PES) theory, which is both distinguishable and valuable for modeling within the remote sensing feature space. (3) Error propagation analysis demonstrated strong robustness to classification uncertainties (γ = 0.23). (4) Plantation structure in the region was predominantly young-aged, with forests aged 0–20 years covering over 70% of the area. Despite inherent uncertainties in ground-reference age data, the integrated framework exhibited clear relative superiority, improving R² from 0.62 to 0.84. Both error propagation analysis (γ = 0.23) and Monte Carlo simulations affirmed the robustness of the tandem workflow and the stability of the findings, providing a reliable methodology for improved-accuracy plantation carbon sink quantification. Full article

Karst aquifers provide critical water resources in the Mediterranean region, yet climate change threatens their sustainability. This study integrates stable isotope analysis (δ²H, δ¹⁸O), hydrochemistry, and hydrological time series to characterize precipitation–groundwater dynamics in the Mt. Učka karst system (Croatia). Precipitation samples collected across an altitudinal gradient of approximately 1400 m and groundwater from three major groundwater sources were analyzed over a 2.5-year period. Precipitation exhibits pronounced isotopic variability with d-excess values indicating mixed Atlantic–Mediterranean moisture sources. Groundwater is primarily recharged by precipitation from the cold part of the hydrological year. It exhibits substantial attenuation of isotopic signals, which indicates extensive mixing processes but prevents quantitative estimation of mean residence time. Groundwater is predominantly recharged from elevations above 900 m a.s.l., with one spring showing evidence of higher-elevation recharge. Analysis confirms the system’s dual porosity: a rapid, conduit-dominated response indicates high vulnerability to surface contamination, while a sustained, matrix-dominated response provides greater buffering capacity. These findings highlight the vulnerability of karst systems to projected reductions in autumn precipitation, the critical recharge season, and demonstrate the necessity of multi-tracer approaches for comprehensive aquifer characterization. Full article

The strong heterogeneity of karst aquifers limits the applicability of traditional pumping test parameter inversion methods, and karst conduits are the key factor causing this heterogeneity. To reveal how karst conduits influence the inversion of hydrogeological parameters, this study established a series of s numerical models in FEFLOW, based on the Lianhuashan mining area in Jingmen. These models was used to systematically analyze the effects of conduit characteristics (hydraulic conductivity, diameter, length, burial depth) and pumping test conditions (pumping rate and distance from the well) on the flow field, drawdown behavior, and parameter inversion results. Results indicate that the well-conduit distance R is the most critical factor: inversion errors exceeded 60% when R < 25 m; the larger the deviation between the conduit permeability coefficient (Kp) and the aquifer permeability coefficient, the larger the inversion error; the conduit length (L) and diameter (D) determine the catchment area and the cross-sectional area for flow, respectively, and are positively correlated with the inversion error; the conduit burial depth (Z) and the pumping rate (Q) affect the lag in vertical recharge and the magnitude of the drawdown, respectively, and have a small impact on the inversion error. The findings provide a theoretical basis for improving parameter estimation and well-field design in karst terrains. Full article

Motion capture experiments can be conducted more easily if marker-based motion (marker-based MoCap) can be captured using an asynchronous multicamera system (Async MCS). However, camera calibration is essential for marker-based MoCap, and a wand calibration method that utilizes timestamp functions has been proposed for Async MCS. However, in practice, many cameras do not provide accurate timestamp functions, limiting the applicability of existing methods in such environments. A wand calibration method for Async MCS that does not rely on timestamp functions is proposed to evaluate the accuracy of estimated camera parameters. In conventional methods, the time offset in image acquisition is obtained from timestamp information, and synchronous coordinates are estimated by interpolating time-series digitized coordinates of wand markers. In this study, the time offset is treated as an optimization variable, which enables camera parameter estimation without using timestamp functions. Consequently, the three-dimensional reconstruction errors of fixed points obtained using the proposed method are significantly smaller (1.445 ± 0.833 and 1.746 ± 0.908 mm) compared to estimations that ignore time offsets. These findings demonstrate that the proposed method enables more accurate camera calibration. Full article

The quest for environmental sustainability continues to gain prominence, but the environmental goods trade-environmental sustainability nexus has not received adequate research attention. Therefore, this study evaluates the impact of environmental goods trade on environmental performance. Environmental goods (EGs) are defined as products designed to support environmental protection and climate-change mitigation and are identified using the IMF environmental goods classification based on the WTO–OECD list, ensuring cross-country comparability. Using second-generation panel time series methods and the Augmented Anderson–Hsiao (AAH) estimation technique with a sample of 47 Asian countries over the period 1994–2021, this study provides interesting findings and insightful policy implications. First, the findings confirm the EKC Hypothesis in all the models. Second, the results support the pollution halo hypothesis because trade openness has a significant negative impact on the ecological footprint in all the models. This implies that trade openness reduces environmental degradation. Also, the result revealed that an increase in ecological goods reduces ecological footprint in production, consumption, and distribution, as well as imports and exports, based on ecological footprint in Asia. Therefore, we conclude that environmental goods trade enhances environmental sustainability. Full article

The Sındırgı earthquake sequence, with moment magnitudes of 6.1 on 10 August and 27 October 2025, respectively, occurred within the Simav Fault Zone in western Türkiye, rupturing nearby but structurally distinct fault segments. In this study, we combine Sentinel-1 InSAR time-series measurements with seismological data, geomorphic observations, and post-event field surveys to examine how deformation evolved between and after these events. InSAR results indicate coseismic line-of-sight displacements of 6–7 cm, followed by post-seismic deformation that persisted for months at 8–10 mm/yr. This behavior signifies that deformation continued well beyond the initial rupture. The estimated displacement does not align with a single fault plane. Instead, it corresponds to a network of early-mapped and previously unrecognized fault segments. Seismicity patterns and stress tensor inversions show that activity migrated spatially after 10 August and that the faulting mechanism altered before the second earthquake. When synthesized, observations indicate stress transfer within a modular, segmented fault system, thought to have been influenced by regional structural complexity. Field investigations after the October earthquake reported new surface cracks and fault traces, providing evidence of shallow deformation. The collected results indicate that post-seismic stress redistribution played a leading role in modulating the 2025 Sındırgı earthquake sequence. Full article

This study applies spectral analysis and singular spectrum analysis (SSA) to mean annual runoff of the Dniester River for 1950–2024 to identify dominant periodic components governing the hydrological regime of this transboundary basin shared by Ukraine and Moldova. The novelty lies in a basin-specific integration in the first systematic application of a combined spectral–SSA framework to the Dniester River, enabling consistent characterization of runoff variability and assessment of large-scale natural drivers. Time series from three gauging stations are analysed to develop data-driven runoff models and medium-term forecasts. Four stable groups of periodic variability are identified, with characteristic timescales of approximately 30, 11, 3–5.8, and 2 years, corresponding to major atmospheric–oceanic oscillations (AMO, NAO, PDO, ENSO, QBO) and the 11-year solar cycle. Cross-spectral and coherence analyses reveal a statistically significant relationship between solar activity and river discharge, with an estimated lag of about 2 years. SSA reconstructions explain more than 80% of discharge variance, indicating high model reliability. Forecast comparisons show that spectral methods tend to amplify long-term trends, CNN–LSTM models produce conservative trajectories, while a hybrid ensemble approach provides the most balanced and physically interpretable projections. Ensemble forecasts indicate reduced runoff during 2025–2028, followed by recovery in 2029–2034, supporting long-term water-resources planning and climate adaptation. Full article

In the preliminary design stage of wind farms, the theoretical energy output must be adjusted by multiple reduction factors to estimate the actual grid-connected power. As renewable energy becomes increasingly integrated into electricity markets, the conventional approach using static, averaged reduction coefficients for annual yield estimation can no longer meet the market’s demand for high-resolution power time series. Addressing this gap, the novelty of this paper lies in shifting the focus from total annual estimation to hourly-level dynamic allocation. This paper proposes a time-series reduction coefficient evaluation method based on the time-varying entropy weight method (TV-EWM). Under the assumption that the total annual reduction quantity adheres to standard design specifications, this method utilizes long-term wind measurement data, integrates unique ultra-high-altitude wind resource characteristics, and constructs a scenario-based indicator system. By quantifying the coupling relationships between key meteorological variables and incorporating a dynamic weighting mechanism, the proposed approach achieves hourly refined reduction estimation for theoretical power output. Comparative analysis was conducted against the traditional static average reduction method. Results indicate that, compared to the traditional average reduction method, the TV-EWM approach significantly enhances the model’s ability to capture seasonal variability, increasing the coefficient of determination (R²) by 4.19% to 0.7061. Furthermore, it demonstrates higher stability in error control, reducing the Normalized Root Mean Square Error (NRMSE) by 4.51% to 15.45%. The TV-EWM more accurately captures the temporal evolution and coupling effects between meteorological elements and curtailed generation under various reduction scenarios, retains full-load operational features, and enhances physical interpretability and time responsiveness, providing a new analytical framework for market-oriented power generation assessment. Full article

The remote monitoring and quantification of industrial gas emissions, such as sulfur dioxide (SO₂), are critical for environmental protection. This research demonstrates an integrated methodology for estimating SO₂ emission rates (kg/s) from an industrial chimney using passive Fourier transform infrared (FTIR) spectroscopy combined with atmospheric dispersion modeling. Infrared spectra were acquired at a stand-off distance of 570 m within the 7–14 $\mathsf{\mu }$m spectral range at a resolution of 4 cm⁻¹. Path-integrated SO₂ concentrations were determined through cross-sectional scanning of the gas plume. To translate these optical measurements into an emission rate, the atmospheric dispersion of the plume was modeled using the Pasquill–Briggs approach, incorporating source parameters and meteorological data. Over two experimental series, the calculated average SO₂ emission rates were 15 kg/s and 22 kg/s. While passive FTIR spectroscopy has long been applied to remote gas detection, this work demonstrates a consolidated framework for retrieving industrial emission rates from stand-off, line-integrated measurements under real industrial conditions. The proposed approach fills a niche between local in-stack measurements and large-scale remote sensing systems, which contributes to the development of flexible ways to monitor industrial emissions. Full article

High-purity germanium (HPGe) gamma-ray detectors are core instruments in nuclear physics and astrophysics experiments, where long-term stability and reliable extraction of decay parameters are essential. However, the standard exponential decay analyses of the detector time-series data are often affected by the strong correlations between the fitted parameters and the sensitivity to detector-related fluctuations and outliers. In this study, we present a robust analysis framework for HPGe detector decay data based on pairwise ratios and the Steiner’s most frequent value (MFV) statistic. By forming point-to-point ratios of background-subtracted net counts, the dependence on the absolute detector response is eliminated, removing the amplitude–lifetime correlation that is inherent to conventional regression. The resulting pairwise lifetime estimates exhibit heavy-tailed behavior, which is efficiently summarized using the MFV, a robust estimator designed for such distributions. For the case study, a long and stable dataset from an HPGe detector was used. This data was gathered during a low-temperature nuclear physics experiment focused on observing the 216 keV gamma-ray line in ⁹⁷Ru. Using measurements spanning approximately 10 half-lives, we obtain a mean lifetime of $\tau =4.0959\pm 0.{0007}_{\mathrm{stat}}\pm 0.{0110}_{\mathrm{syst}}\mathrm{d}$, corresponding to a half-life of $T_{1/2}=2.8391\pm 0.{0005}_{\mathrm{stat}}\pm 0.{0076}_{\mathrm{syst}}\mathrm{d}$. These results demonstrate that the pairwise–MFV approach provides a robust and reproducible tool for analyzing long-duration HPGe detector data in nuclear physics and nuclear astrophysics experiments, particularly for precision decay measurements, detector-stability studies, and low-background monitoring. Full article

Urban parks are central to public health and equity, yet less is known about how park travel distance, park “attractor” types, and time-of-day visitation rhythms co-evolved through and after the COVID-19 pandemic. Using anonymized smartphone mobility traces for public parks in Las Vegas, USA (2018–2022), we construct weekly origin–destination flows between census block groups (CBGs) and parks and link origins to socio-economic indicators. We first estimate visitor-weighted mean travel distance with a segmented time-series model that allows pandemic-related breakpoints. Results show that average park-trip distance (≈8.4 km pre-pandemic), including a substantial share of long-distance trips (≈52% of visits), contracted sharply at the onset of COVID-19, and that both travel radii and seasonal excursion peaks only partially rebounded by 2022. Next, cross-sectional OLS/WLS models (R² ≈ 0.08–0.14) indicate persistent socio-spatial disparities: CBGs with higher educational attainment and larger shares of Black and Hispanic residents are consistently associated with shorter park-trip distances, suggesting constrained recreational mobility for socially disadvantaged groups. We then identify a stable two-type park typology—local versus regional attractors—using clustering on origin diversity and long-distance share (silhouette ≈ 0.46–0.52); this typology is strongly related to visitation volume and temporal usage profiles. Finally, mixed-effects models of evening and late-night visit shares show that regional attractors sustain higher nighttime activity than local parks, even as citywide evening/late-night visitation dipped during the mid-pandemic period and only partly recovered thereafter. Overall, our findings reveal a durable post-pandemic re-scaling of park use toward more proximate, CBG-embedded patterns layered on enduring inequities in access to distant, destination-oriented parks. These insights offer actionable evidence for equitable park planning, targeted investment in high-need areas, and time-sensitive management strategies that account for daytime versus nighttime use. Full article