Search Results (98)

Colonias are historically underserved communities along the Texas–Mexico border facing infrastructure deficiencies, socioeconomic vulnerability, and environmental hazards. This study examines parcel-level development conditions across 66,160 properties in Cameron, Hidalgo, Starr, and Willacy Counties, Texas. Property appraisal records, colonia boundaries and 2014 classifications, and geographic information system analyses were integrated to evaluate property values, platting, public service district coverage, floodplain exposure, and reconstructed building patterns from 1990 to 2025. These indicators represent physical and economic conditions associated with resilience rather than a comprehensive measure of community resilience. Results reveal variation among counties and 2014 baseline classification categories. Communities classified as Green exhibited higher property values, greater district coverage, and more established development, whereas those classified as Red showed lower values and greater developmental disadvantages. Reconstructed building inventories more than doubled in Hidalgo and Cameron Counties and increased in Starr County, although survivorship bias and missing year-built data limit interpretation of growth rates. Many colonia properties remain within mapped floodplains, particularly in Hidalgo County. Because the analysis is descriptive, these patterns cannot be causally attributed to specific policies. This study provides a replicable parcel-level framework for evaluating development disparities and floodplain exposure to inform equitable infrastructure investment and planning in underserved border communities. Full article

Climate change and increasing socio-environmental pressures are intensifying the occurrence of compound and cascading hazards, highlighting the need for systematic approaches to reconstruct and analyze multi-hazard interactions from historical evidence. However, historical inventories are typically event-based, unevenly documented, and lack information on non-events, limiting their direct use for probabilistic interpretation. This study presents a reproducible evidence-mining framework that transforms a historical multi-hazard inventory into a hierarchical process–hazard–location dataset while explicitly distinguishing documented event-tree relationships from exploratory within-catalog associations. Applied to a historical multi-hazard inventory of Tenerife (Canary Islands), the framework reconstructs documented parent–child hazard relationships, identifies recurrent compound motifs, and formalizes association screening through transparent statistical procedures, including contingency-table analysis, uncertainty intervals, effect-size estimates, exact significance testing with false-discovery-rate control, and Bayesian sensitivity analysis. A quantitative assessment of documentation density reveals a strong increase in recorded evidence through time, demonstrating that temporal variations in the inventory primarily reflect changes in documentation rather than changes in hazard occurrence. Consequently, all derived association metrics and screening thresholds are interpreted as hypothesis-generating evidence rather than predictive or causal estimates. The proposed framework provides a transparent and reproducible basis for extracting structured knowledge from historical multi-hazard inventories, supporting research prioritization and future probabilistic developments while avoiding overinterpretation of heterogeneous historical records. Full article

Secondary inorganic aerosols (SIAs) are critical components of regional air pollution, yet uncertainties in bottom–up emission inventories lead to biases in the simulation of nitrate (PNO₃⁻) and ammonium (PNH₄⁺). This study utilizes a joint NO_x−NH₃ inversion inventory constrained by satellite observations to investigate the response characteristics of SIAs to precursor reductions in the Beijing–Tianjin–Hebei (BTH) region during July 2020. Results indicate that the a priori emission inventory significantly underestimated NH₃ emissions in the BTH region, with a posteriori emission in cities such as Shijiazhuang and Handan increasing to 2–3 times the a priori levels, while NO_x emissions were slightly underestimated. Sensitivity analysis conducted via Comprehensive Air Quality Model with extensions (CAMx)—Decoupled Direct Method (DDM) reveals that the sensitivities of PNO₃⁻ and PNH₄⁺ to precursor variations are higher in south–central BTH; specifically, the sensitivity of PNO₃⁻ concentration to NO_x emission abatement under the a posteriori inventory rises substantially compared with a priori estimates, with relative growth rates spanning 33% to 308% across the study domain. Scenario simulations demonstrate that synergistic NO_x and NH₃ control is the most effective strategy, reducing PNO₃⁻ and PNH₄⁺ concentrations by 2.27 ug/m³ and 0.77 ug/m³, respectively. Full article

Background/Objectives: Despite growing evidence on the importance of compassionate care, it receives little attention in geriatrics literature. The aim is to study the variation and key components of team compassionate care and its relation to individual healthcare provider (HCP) emotional exhaustion in acute geriatric units (AGUs). Methods: A cross-sectional survey study, from February to April 2025, with a convenience sample of HCPs in five Belgian AGUs (70% response rate). Validated questionnaires were used: The Sinclair Compassion Questionnaire (SCQ), Emotional Exhaustion (EE) subscale of Maslach Burnout Inventory and Ethical Decision-Making Climate Questionnaire (EDMCQ). Results: In total, 118 HCPs participated: 11% team leaders, 28% paramedics, 61% nursing professionals. Mean AGU SCQ scores ranged from 3.62 to 4.28 on a scale from one (lowest) to five (highest). Multivariate linear regression models showed significant differences in team compassion scores across AGUs (estimate 0.084, p = 0.003) and increased with higher ethical climate scores (estimate per point on the EDMCQ 0.035, p < 0.001). Two EDMCQ domains (open interprofessional reflection and mutual respect) were associated with team compassionate care beyond the effect of AGUs, whereas demographics and self-reported emotional exhaustion were not (R² = 0.283). Emotional exhaustion was significantly associated with professional role (estimate −3.004, p = 0.011), but not with AGUs (estimate 0.768, p = 0.269), ethical climate (estimate per point on the EDMCQ −0.248, p = 0.117) and team-based compassion scores (R² = 0.115). Nursing professionals were significantly at higher risk for emotional exhaustion compared to paramedics (estimate 4.497, p = 0.037). Conclusions: The level of team compassionate care differed across AGUs and was correlated to the perceived ethical climate of the workplace, and not to individual HCP demographic variables or emotional exhaustion. Mutual respect and open interprofessional reflection may be specific areas for future research in improving high-quality compassionate care. Full article

Traditional wooden granaries in rural Türkiye are disappearing at an accelerating rate due to agricultural abandonment, rural depopulation, and the absence of systematic documentation and conservation frameworks. In the Sındırgı district of Balıkesir, one of the richest concentrations of vernacular granary architecture in the Marmara Region, these structures remain largely unprotected and unstudied within a sustainable design framework, constituting an urgent conservation challenge. This study aims to assess the current preservation status of Sındırgı granaries, classify their typological diversity, and evaluate their sustainability performance against a defined set of ecological design criteria. A mixed methods approach was employed, combining a systematic literature review with extensive fieldwork across 33 neighborhoods. In total, 1411 granaries were identified and grouped into five typologies: evli, Simav, kabak, sandık, and üstü örtülü sandık. These typologies were systematically compared to five parameters: spatial distribution across neighborhoods, plan and section geometry, construction system and structural elements, material selection and condition, and preservation status. This comparison revealed that typological variation is not incidental but directly reflects differences in land ownership, agricultural production capacity, topography, and distance from the district center. Representative examples from each typology were documented through onsite measurements, photogrammetry, technical drawings, and interviews with local craftsmen. The sustainability performance of the granaries was then assessed across seven ecological design criteria: spatial organization, building form design, structural element design, material use and conservation, design with nature, urban design area planning, and nature interaction. The findings demonstrate that the long-term durability of these structures depends on an interrelated system of climate-responsive design decisions rather than any single factor. The study concludes by proposing a holistic conservation model comprising typology-based inventory, roof water moisture-focused intervention, periodic monitoring, and transmission of vernacular building knowledge, a framework applicable to comparable rural granary heritage across the region. Full article

Background: Traditional refills of intrathecal infusion pumps rely on manual palpation and the use of external templates, a method that can be challenging in patients with anatomical variations or a high body mass index. Ultrasound guidance has emerged as a precision-based alternative. This study aimed to evaluate the impact of the ultrasound-guided technique versus the conventional template-based technique on patient satisfaction. Methods: A quasi-experimental before-and-after study was conducted on a cohort of 45 chronic pain patients. Immediate satisfaction with procedure duration (IPP-SQ), overall treatment efficacy (CRES-4), and pain interference via the Brief Pain Inventory (BPI) were assessed. Results: The use of ultrasound was associated with significantly higher satisfaction regarding procedure duration, with a mean score of 5.00 (95% CI: 4.35–5.65) compared to 3.22 (95% CI: 2.70–3.75) with the traditional method (p < 0.001). Overall satisfaction (CRES-4) also improved significantly (12.4 vs. 11.3; p = 0.001). Regarding patient-reported outcome measures (PROMs), the mean pain intensity in the subsequent week was lower following the ultrasound technique (mean difference −0.48; p = 0.040). Technically, no first-attempt failures were recorded under ultrasound guidance in this sample, compared to a 20% re-attempt rate observed with the manual method. Conclusions: The transition from the traditional method to ultrasound-guided refill optimizes technical precision and substantially enhances the patient experience. By reducing pain and increasing satisfaction, ultrasound guidance proves to be a valuable resource for improving procedural precision, representing an advancement toward a more personalized medicine approach. Full article

Backpack mobile mapping systems (MMS) equipped with LiDAR and RGB cameras, as well as an optional GNSS/INS direct georeferencing unit, are increasingly utilized in forest inventory applications. In general, LiDAR point clouds provide detailed structural information, whereas imagery offers visual specifics of surface features. However, cameras typically operate at lower acquisition rates compared to LiDAR. In proximal mapping, another challenge is the inconsistent reception of GNSS signals beneath forest canopies. Additionally, georeferencing accuracy may differ between LiDAR and imagery due to biases in the system calibration parameters and variations in post-processing approaches. To address these challenges, this study introduces a Backpack MMS that uses cameras configured at elevated frame rates to enhance image overlap. Concurrently, this study presents an algorithmic approach to addressing georeferencing issues by integrating imagery and LiDAR data, thereby enhancing system calibration and improving platform trajectory. The method is based on the hypothesis that forest environments are rich with geometrically well-defined features, such as tree trunks and ground patches. By identifying conjugate primitives in point clouds from both imagery and LiDAR, the procedure optimizes feature models while simultaneously minimizing calibration biases and/or trajectory errors. The proposed approach is validated using multiple field datasets collected in diverse forest environments. Quantitative results show that the procedure reduces image–LiDAR feature misalignment across all datasets from up to 1.1 m in the planimetric direction and 2 m in the vertical direction to within 5 cm in both. The feature fitting accuracy also improves from 2.9 cm to 0.85 cm for LiDAR point clouds and from 10 cm to 0.9 cm for image-based point clouds. However, the results indicate that despite increased data availability, imagery alone remains less reliable than LiDAR for extracting structural information. Nevertheless, the proposed image–LiDAR alignment strategy represents a crucial step toward developing a comprehensive tree inventory. Full article

Anxiety is a common psychological challenge among athletes, particularly in response to intense training sessions. This randomized crossover study investigated the immediate effects of a single session of focused attention meditation on anxiety, autonomic responses, and performance during high-intensity intermittent training (HIIT) in twenty-six university-level speed skaters. Participants completed three pre-exercise interventions (focused attention meditation, controlled breathing, and random thinking) on separate occasions in a randomized order. Following each intervention, participants performed a leg cycling-based HIIT protocol consisting of 20 s of maximal effort work followed by 10 s of passive rest, repeated for 8 sets using a cycling ergometer. State anxiety was assessed using the State–Trait Anxiety Inventory, and mood disturbance was evaluated using the Profile of Mood States. Autonomic and physiological responses were assessed via heart rate variability (coefficient of variation), oxygen uptake, and power output, measured before and after the intervention and the HIIT bout. Focused attention meditation significantly reduced state anxiety compared with random thinking (ΔSTAI: −5.0 [6.0] vs. −1.0 [4.3]; p < 0.05, effect size = 0.527), whereas controlled breathing primarily influenced heart rate variability (CV: 0.10 [0.11] vs. 0.07 [0.03]; p = 0.041, effect size = 0.736). No significant differences were observed among conditions in mean power output or fatigue index during HIIT. These findings suggest that single-session focused attention meditation may serve as a practical pre-exercise strategy for an immediate reduction in state anxiety, without compromising subsequent high-intensity exercise performance. Full article

China’s rapid urbanization has precipitated severe atmospheric pollution, drawing sustained scientific and policy attention. Although nationwide implementations of emission control measures have achieved measurable reductions in ambient NO₂ concentrations, fundamental uncertainties persist in quantifying anthropogenic NO_x emission and their interannual variability. In this study, NO_x emissions over China are inferred using the Regional Air Pollutant Assimilation System (RAPAS) combined with ground-based hourly NO₂ observations, and a detailed analysis of the spatiotemporal variation patterns of NO_x emissions is also provided. Nationally, most sites display declining NO₂ concentrations during 2014–2021, with steeper reduction trends in winter, particularly in pollution hotspots. The RAPAS-optimized NO_x emission estimates demonstrate superior performance relative to prior inventories, with site-averaged biases, root mean square errors, and correlation coefficients improved substantially across all geographic regions in China. The trajectories of changes in NO_x emissions exhibit marked regional disparities: South and Northeast China experienced more than 8.0% emission growth during 2014–2017, while NO_x emissions in northwest and southwest China increased by 35% and 26%, significantly higher than those in East China. The reductions accelerated significantly post 2018, particularly in central and eastern regions (more than −20%). The interannual variation in NO_x emissions in the five national urban agglomerations shows a similar trend of first rising and then decreasing. The NO_x emissions of Anhui, Yunnan, Shanxi, Gansu and Xinjiang provinces increased significantly from 2014 to 2017, while the emissions of Shandong and Zhejiang decreased at a relatively high rate (more than 80 Gg per year). These findings are helpful to provide a more comprehensive understanding of current NO_x pollution and provide scientific basis for policymakers to propose effective strategies. Full article

Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R² = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions. Full article

This research communication aims to present three mathematical methods for analyzing inventory management in dynamic supply chains, starting from the basic definition in differential equations for inventory levels, which relates with production and demand rates. Initially, the study adopts a systemic perspective to examine the role of energy within a production–inventory system. Subsequently, the analysis focuses on inventory dynamics under parameters expressed in complex variables, with the aim of quantifying fluctuations in a generic production system and demonstrating the influence of inventory variation rates on system behavior. Finally, the investigation addresses the impact of variable capacity on production system inventories, drawing on analogies with corresponding physical systems to support the analysis. Full article

Traditional landslide susceptibility assessment (LSA) methods typically adopt a global modeling strategy, which struggles to account for the pronounced spatial heterogeneity arising from variations in topography, geology, and vegetation conditions within a region. Furthermore, model predictive performance is often undermined by feature redundancy. To address these limitations, this study focuses on the landslide disaster early-warning demonstration area in Honghe Prefecture, Yunnan Province. It proposes an ensemble learning model termed heterogeneity feature optimized stacking (HF-stacking), which integrates spatial heterogeneity partitioning (SHP) with feature selection to improve the scientific rigor of LSA. This method initially establishes an LSA system comprising 15 static landslide conditioning factors (LCFs) and two dynamic factors representing the average annual deformation rates derived from interferometric synthetic aperture radar (InSAR) technology. Based on landslide inventory data, an SHP method combining t-distributed stochastic neighbor embedding (t-SNE) and iterative self-organizing (ISO) clustering was developed to divide the study area into subregions. Within each subregion, a tailored feature selection strategy was applied to determine the optimal feature subset. The final LSA was performed using the stacking ensemble learning approach. The results show that the HF-stacking model achieved the best overall performance, with an average AUC of 95.90% across subregions, 4.23% higher than the traditional stacking model. Other evaluation metrics also demonstrated comprehensive improvements. This study confirms that constructing an SHP framework and implementing feature selection strategies can effectively reduce the impact of spatial heterogeneity and feature redundancy, thereby significantly enhancing the predictive performance of LSA models. The proposed method contributes to improving the reliability of regional landslide risk assessments. Full article

Background: Induced after-death communication (IADC) therapy is an emerging approach for addressing grief-related distress, particularly in individuals experiencing complicated grief (CG). Developed from eye movement desensitization and reprocessing (EMDR), IADC therapy aims to change the meanings with which loss is read and to transform acute grief into integrated grief. While spontaneous after-death communications (ADCs) have been widely reported across different cultures, IADC therapy provides a structured procedure for inducing a state of mind in which such experiences can spontaneously arise and develop. Methods: This study employed a prospective observational cohort design with a retrospective analysis, comparing the effectiveness of IADC therapy (experimental group, n = 42) to standard grief interventions combining talk therapy and EMDR (control group, n = 43). Participants completed standardized measures, including the Inventory of Complicated Grief (ICG) and the IADC Grief Questionnaire (IADC-GQ), at pre-treatment (T1), post-treatment (T2), and six-month follow-up (T3). Additional analyses explored the role of spirituality, religious affiliation, and therapist characteristics in grief processing. Results: IADC therapy led to a significantly greater reduction in grief intensity (ICG scores) immediately after the intervention and at the six-month follow-up compared to the control group. The experimental group also showed a more pronounced decrease in distress symptoms (CS scores) and higher ratings of therapeutic satisfaction. Furthermore, participants in the experimental group exhibited a significantly greater increase in their continuing bond (CB) scores, suggesting a more adaptive connection with the deceased. Additional analyses examined therapist characteristics, treatment-related factors, and the nature of ADC experiences, which are further explored in the discussion. Conclusions: These findings highlight the clinical utility of IADC therapy as a brief and cost-effective grief intervention, offering comparable or superior outcomes to traditional grief therapies. The results suggest that recognizing and integrating spontaneous ADC experiences into grief therapy may provide a valuable therapeutic pathway. Future research should further explore the long-term effects, cultural variations, and therapist characteristics to optimize the integration of IADC therapy into mainstream clinical practice. Full article

Coastal lagoons are dynamic and highly productive systems that offer a remarkable number of ecological services and benefits for humans. However, our understanding of them is still far from adequate. The Mar Menor lagoon is an ecosystem subject to anthropogenic pressures that have worsened in recent years. These pressures include coastal works, such as dredging and sand dumping, as well as changes in agricultural regimes that have induced a process of eutrophication that set off alarms after the eutrophic crisis that occurred in 2016. Benthic organisms, and in particular mollusks, are very sensitive to environmental variations, often serving as indicators of these changes. This work analyzes the malacofauna of the Mar Menor from 1981 to 2019 in the context of the environmental changes that have occurred in it during these years. Eighty-six species have been recorded throughout our study period, and species richness, abundances, local assemblage structures, along with changes in the main environmental parameters of the water column (salinity, temperature, and chlorophyll a concentration) have been used to explain the composition of the communities of the main lagoon habitats and to detect their spatial and temporal variations. With the information provided, the complete inventory of mollusks reported in the lagoon has been updated to 126 species. The results indicate that, during these almost 40 years, the total number of species has remained relatively constant, but with a high percentage of occasional and very rare species, along with a high rate of change from one species to another over time, accompanied by variations in the abundance and dominance of some species compared to others depending on the environmental conditions and pressures that the lagoon has undergone. The high spatial and temporal heterogeneity detected is determined by the restricted connectivity with the open sea, the diversity of environments and habitats, and the changes in environmental conditions due to human actions. Full article

Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs’ current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r², bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation’s linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r²s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology. Full article