Search Results (863)

This paper develops a Business Resilience Index (BRI) that measures county-level resilience to natural disasters at a county-quarter frequency for the United States over 2014–2024. The index integrates high-frequency labor market outcomes from the Quarterly Census of Employment and Wages with flood insurance policy information from FEMA, disaster damages from the NOAA Storm Events Database, and social and health determinants from County Health Rankings. Starting from a broad candidate set, we apply an interpretable feature-screening pipeline to retain 79 variables and then use principal component analysis to extract four orthogonal structural dimensions of resilience: market scale, socioeconomic resilience, urban density risk, and industrial economy profile. We construct a domain-weighted strategic index and benchmark it against data-driven and equal-weight alternatives, showing that county rankings are highly stable across weighting schemes. To evaluate whether the BRI aligns with recovery behavior under acute shocks, we implement a matched difference-in-differences event study around two major flood episodes—Texas in 2015Q2 and North Carolina in 2018Q3. Conditional on exposure intensity and matched comparability, higher pre-event BRI counties exhibit earlier stabilization and a stronger post-event employment path relative to lower BRI counties, with differences in magnitude and timing across cases. Overall, the BRI provides an interpretable, high-frequency baseline for identifying capacity constraints that may slow recovery and for supporting preparedness targeting and post-disaster monitoring. Full article

Purpose: To evaluate the 1-year behavior of accommodation and optical quality one year after the implantation of phakic intraocular lenses, specifically the implantable collamer lens (ICL), in myopic patients, comparing outcomes between low- and high-myopia groups. Methods: This comparative longitudinal study included 38 eyes of 38 patients who underwent ICL implantation for myopia correction. Patients were divided into two groups based on preoperative manifest sphere: low myopia (−2.50 D to −6.25 D) and high myopia (>−6.25 D to −12.50 D). The amplitude of accommodation (AA), subjective accommodative response (AR), optical quality parameters including the modulation transfer function (MTF) cut-off, objective scatter index (OSI) and Strehl ratio (SR), and objective accommodative response with a double-pass system (HD Analyzer, Visiometrics) were assessed preoperatively, 1 month, and 1 year postoperatively. Results: Both groups achieved postoperative refractive outcomes close to emmetropia, with high efficacy and safety indices. A statistically significant decrease in the amplitude of accommodation was observed at 1 month and remained stable at 1 year in both groups; however, this change was not clinically meaningful. The optical quality parameters (MTF cut-off, OSI, and Strehl ratio) and objective accommodative response with the HD Analyzer showed no clinically relevant changes over time, with no significant intergroup differences detected (p-value > 0.05). Conclusions: An initial reduction in accommodative amplitude was observed after ICL implantation without recovery over time; however, it was not clinically relevant, as it fell within the test–retest variability in the minus lens technique. Other accommodative parameters and optical quality remained stable at 1 year in both low and high myopia. Full article

This study examines how the COVID-19 period is associated with changes in digital payment usage, rather than simply whether adoption increased, in Saudi Arabia using monthly data from January 2019 to July 2025. An Interrupted Time Series (ITS) approach is employed to assess both the immediate and long-term effects associated with the pandemic on a digital payment Intensity (DPI) index constructed from national point-of-sale (POS) transaction data to capture aggregate electronic payment usage relative to cash withdrawals. The results show that the onset of the COVID-19 period is associated with a sharp and statistically significant one-time increase of approximately 7 to 13% in digital payment intensity, followed by stabilization at a higher level rather than sustained acceleration. This finding challenges the common view that digital payment adoption followed a continuously accelerating path, instead showing that the pandemic induced a discrete upward shift without altering the underlying growth trajectory. The estimated effects remain robust across multiple model specifications, including dynamic ITS models, seasonal adjustments, alternative break dates, exclusion of overlapping usage variables, and parsimonious infrastructure-only models. Inflation and ATM usage consistently show negative associations with digital payment intensity, highlighting the role of macroeconomic stability and cash substitution in shaping payment behavior. The study therefore offers a more nuanced interpretation of post-pandemic digital adoption by showing that the main effect of COVID-19 was a one-time level shift rather than a lasting change in growth dynamics. Focusing on aggregate usage intensity rather than access or account ownership, it provides a system-level perspective on digital payment behavior in response to large-scale shocks. Overall, the evidence suggests that the pandemic period coincided with a discrete upward realignment in digital payment usage in Saudi Arabia, reflecting the interaction between crisis-driven behavioral change and strong pre-existing digital infrastructure under Vision 2030. Full article

Background/Objectives: Cardiovascular disease (CVD) is the leading cause of death in China. While obesity and dietary patterns are well-established factors, the independent association between overall dietary quality and CVD prevalence—specifically whether this link persists regardless of Body Mass Index (BMI)—requires further clarification. Furthermore, the behavioral and cognitive correlates that drive dietary quality, such as health literacy, remain insufficiently explored. This study evaluated the association of dietary quality with self-reported CVD among Chinese adults, independent of BMI, and identified the key behavioral and cognitive factors associated with dietary adherence in this population. Methods: This cross-sectional study surveyed 975 Chinese adults through anonymous questionnaires and collected self-reported data on CVD, BMI, dietary quality, and health literacy. One-way analysis of variance (ANOVA) and the chi-square test were used to compare the characteristics between groups, and multivariate Logistic regression was used to analyze the association between dietary quality and the odds of CVD, sequentially adjusting for variables such as BMI, physical activity. Results: Higher dietary quality was independently associated with lower odds of CVD (Model 3: OR = 0.879, 95% CI: 0.845–0.915, p < 0.001). Notably, this inverse association remained significant after adjusting for BMI, which itself showed no significant association with CVD prevalence in the multivariable model. Regarding population profiling, poor dietary quality was significantly related to regular smoking (p < 0.05), whereas age, gender, residence, employment status, and BMI showed no significant associations with dietary quality categories. Furthermore, health literacy (p < 0.05) and physical activity (p < 0.05) showed positive associations with superior dietary quality. Conclusions: Dietary quality is a significant independent factor inversely associated with CVD prevalence, regardless of obesity status. Suboptimal dietary habits cluster among smokers and individuals with lower health literacy and physical activity levels, showing a stronger association with cognitive and behavioral factors than with demographic or occupational characteristics. Interventions should prioritize enhancing health literacy and addressing the clustering of unhealthy behaviors to effectively address the cardiovascular burden in the Chinese population. Full article

Background: Hippotherapy, a sensorimotor-rich intervention proposed for children with Autism Spectrum Disorder (ASD), is suggested to influence executive function (EF). However, the underlying electrophysiological mechanisms, particularly changes observed in resting-state Electroencephalography (EEG), remain underexplored. Methods: A total of forty-eight children with ASD, aged 9–12 years, participated in this quasi-experimental, non-randomized pre-test–post-test study. Participants were assigned to either a standardized 12-session hippotherapy program (n = 24) or a waitlist Control group (n = 24). EF was evaluated pre- and post-intervention using validated measures: the Wisconsin Card Sorting Test, Stroop Color–Word Test, Corsi Block-Tapping Task, and Tower of London. Resting-state EEG data (19 channels, 250 Hz) were recorded before and after the intervention and analyzed for spectral power, pairwise Pearson correlation, phase-based functional connectivity using the Phase Lag Index (PLI), and directed effective connectivity using Phase Transfer Entropy (PTE). EEG effects were tested with linear mixed models in MATLAB (fitlme), with the measured values in each ROI as the dependent variable, group and time as fixed effects, and SubjectID included as a random intercept; EF outcomes were analyzed with ANCOVA/MANCOVA, adjusting post-test scores for baseline. The assumptions of homogeneity of slopes, Levene’s test, and the Shapiro–Wilk test were examined, and the Holm–Bonferroni correction together with partial η² effect sizes were reported. Results: Following baseline adjustment, the hippotherapy group showed substantial and statistically significant improvements across all EF measures compared with controls partial η² range = 0.473–0.855; all adjusted p < 0.001; e.g., Stroop Incongruent Reaction Time (F(1,45) = 265.80, p < 0.001, ηp² = 0.855). EEG analyses revealed localized Group × Time interaction effects involving frontal delta power as well as selected alpha-, theta-, and beta-band connectivity measures within frontally anchored networks. In addition to these focal interaction effects, the hippotherapy group exhibited a narrower distribution of pre–post EEG changes across spectral power and connectivity metrics compared with controls, indicating greater temporal consistency in resting-state electrophysiological dynamics across sessions. Because group allocation was non-random (based on scheduling feasibility and parental preference), results should be interpreted as associations rather than causal effects. While the hippotherapy group exhibited significant EF improvements and relative stabilization in EEG spectral and connectivity metrics, particularly in frontal delta/theta/alpha/beta bands, a direct mapping between individual EEG changes and behavioral gains was not observed. Conclusions: A standardized 12-session hippotherapy program was associated with substantial improvements in EF and with relative stabilization of resting-state electrophysiological dynamics in children with ASD. However, the direct mechanistic link between these EEG and behavioral changes warrants further investigation. Larger randomized trials employing active control conditions, task-evoked electrophysiological measures, and extended longitudinal follow-up are needed to confirm efficacy, clarify mechanisms, and establish the durability of effects. Full article

This study proposes a machine learning framework tailored to the institutional characteristics of Taiwan’s stock market, aiming to enhance forecasting accuracy for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The model employs the Radius Neighbors Regressor with a dynamic radius-based similarity measure and integrates domain-specific features including technical indicators, volume–price relationships, and Qualified Foreign Institutional Investor (QFII) activity. A custom 60-day input window with a 20-day forecast horizon is applied to capture medium-term market dynamics. The framework was evaluated through extensive backtesting and real-world validation with the TAIEX Futures. The results demonstrate that the model achieves a peak directional accuracy of 85.1% under optimal parameter settings. Moreover, trading simulations confirm its practical viability, yielding a cumulative return on investment (ROI) of approximately 1600% during the short-term evaluation period (2023–2025) and nearly 2000% in the long-term evaluation (2019–2025), even after accounting for transaction costs and stop-loss mechanisms. These findings indicate that combining historical pattern similarity with institutional investor behavior substantially improves predictive power and profitability. Nevertheless, the framework remains constrained by its reliance on Taiwan-specific institutional features and historical trading data, limiting generalizability. Future research should extend applications to other markets while incorporating macroeconomic variables, corporate fundamentals, and news-driven signals to enhance adaptability. Full article

This research provides an extensive evaluation of liquefaction induced by earthquakes in the Ionian Islands, specifically Lefkada, Cephalonia, Ithaki, and Zakynthos, through the compilation of a liquefaction inventory and GIS-based liquefaction susceptibility index (LiSI) maps. A total of 49 liquefaction sites from 20 causative earthquakes confirm that liquefaction is a recurrent geohazard in the area, primarily affecting coastal and low-lying areas with unconsolidated post-alpine deposits. The relationship between earthquake magnitude and maximum epicentral distance of observed liquefaction is consistent with global empirical datasets, indicating that moderate to strong earthquakes (M_w = 5.9–7.4) can induce liquefaction at considerable distances. The susceptibility model integrates eleven conditioning variables, classified as geological and geomorphological variables, hydrological indices and optical satellite imagery-derived data, within an analytic hierarchy process (AHP) framework. Lithology, age, and geomorphological unit emerged as the dominant conditioning variables. The LiSI maps confirm the zones previously identified in the inventory. Model validation and sensitivity analysis including confusion matrix components, key performance metrics and ROC analysis in coarser grid sizes demonstrate performance ranging from excellent (Zakynthos) to moderate (Lefkada and Cephalonia), while remaining inconclusive for Ithaki due to data limitations. The model exhibits generally conservative behavior, characterized by high precision and specificity but variable sensitivity, while it is largely stable across spatial resolutions in most cases. Full article

Levoglucosan (LG), a tracer of biomass-burning air pollution, was measured in PM₁₀ particulate matter during a year-long study at an urban background site in Zagreb, Croatia. It is known that the atmospheric lifetime of LG is not constant and undergoes degradation through reactions with hydroxyl radicals, ozone, photooxidation, etc. In this study, daily variations in LG were examined and evaluated in relation to NO₂, O₃, and meteorological conditions, including temperature, relative humidity, solar irradiance, UV index, and wind characteristics. The annual mean PM₁₀ concentration was 22 µg m⁻³, while LG average was 0.312 µg m⁻³, both exhibiting pronounced seasonal variability. Elevated LG levels occurred during winter and autumn, consistent with residential wood combustion and stable atmospheric conditions, whereas markedly lower concentrations were observed in spring and summer. Moderate correlations of LG with PM₁₀ and NO₂ indicate contributions from combustion sources, while weak wind speeds and limited dispersion favored pollutant accumulation. In contrast, significant negative relationships were found between LG and ozone, temperature, and UV index. The results revealed non-linear behavior and an exponential decrease in LG with increasing oxidant levels, suggesting pseudo–first-order degradation driven by enhanced photochemical activity and hydroxyl radical formation. These findings highlight the importance of considering both emission patterns and atmospheric processing when using levoglucosan as a tracer of biomass burning in urban environments. Full article

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha⁻¹) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO₂ assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds. Full article

Background/Objectives: Depression is a common and consequential comorbidity among adults with diagnosed diabetes. Prior research has largely emphasized individual health behaviors, with less attention to emotional burden, social context, or theory-driven interpretation. The Multi-Theory Model (MTM) of Health Behavior Change offers an integrative framework for examining behavioral, emotional, and environmental correlates of health outcomes. This study applied MTM to examine correlates of lifetime diagnosed depression among U.S. adults with diagnosed diabetes. Methods: This cross-sectional study analyzed 2023 Behavioral Risk Factor Surveillance System (BRFSS) data from 19,967 adults with diagnosed diabetes, representing approximately 30 million U.S. adults after survey weighting. Lifetime diagnosed depression was assessed based on respondents reporting that a health professional had told them they had a depressive disorder, representing a lifetime history of depression rather than current depressive symptoms. Independent variables were organized into behavioral, emotional, and environmental domains consistent with MTM. Survey-weighted descriptive analyses, Rao–Scott χ² tests, and nested survey-weighted logistic regression models were conducted. Results: The weighted prevalence of lifetime diagnosed depression among adults with diagnosed diabetes was 24.3%. In the fully adjusted MTM-guided model, emotional and environmental domains showed the strongest associations with lifetime diagnosed depression. Frequent mental distress was associated with substantially higher odds of depression (adjusted odds ratio ≈ 10.4, p < 0.001). High social or economic stress and fair or poor self-rated health remained independently associated (p < 0.001). Behavioral factors, including physical activity, smoking, and body mass index, were attenuated after adjustment. Conclusions: Lifetime diagnosed depression among adults with diagnosed diabetes was more strongly associated with emotional burden and adverse social conditions than with health behavior alone, supporting the integration of distress screening and context-responsive interventions into diabetes care. Full article

Background and Objectives: Cyberchondria (CYB) has been associated with health anxiety and anxiety sensitivity (AS); however, its role in panic disorder (PD) remains unclear. This study aimed to compare CYB and AS levels between patients with PD and healthy controls and to examine their associations with PD severity. Materials and Methods: This cross-sectional case–control study included 71 patients with PD and 69 age- and sex-matched healthy controls. Participants completed the Cyberchondria Severity Scale (CSS), Anxiety Sensitivity Index-3 (ASI-3), and Beck Anxiety Inventory (BAI). PD severity was assessed using the Panic Disorder Severity Scale (PDSS). Group comparisons were additionally conducted using analysis of covariance (ANCOVA), controlling for relevant sociodemographic and clinical variables. Pearson correlation and hierarchical multiple regression analyses were performed. Results: Patients with PD had significantly higher CSS (80.70 ± 22.71 vs. 60.62 ± 17.22) and ASI-3 total scores (35.66 ± 17.87 vs. 12.25 ± 10.18) than healthy controls. In the PD group, CYB was positively correlated with AS (r = 0.38, p < 0.01), whereas no significant association was found between CYB and PD severity (r = 0.09, p > 0.05). AS showed a moderate positive correlation with PD severity (r = 0.46, p < 0.01). In hierarchical regression analyses, CYB did not predict PD severity. Adding AS significantly increased the explained variance; however, in the final model, only general anxiety severity (BAI) remained a significant predictor of PD severity. Conclusions: Patients with PD exhibit elevated levels of CYB and AS, which are positively associated with each other. Nevertheless, PD severity appears to be primarily driven by general anxiety symptoms rather than CYB. These findings suggest that CYB may represent a parallel maladaptive coping behavior rather than a direct determinant of symptom severity, with potential implications for assessment and intervention. Longitudinal studies are warranted to clarify causal relationships. Full article

This study identifies and explains road safety behavior patterns in Colombia using explainable artificial intelligence (XAI). Based on 9232 records and 38 variables from the Territorial Survey of Road Safety Behavior, the CRISP-DM methodology was applied, including data cleaning, normalization, encoding, and feature selection. XGBoost, Random Forest, Bagging, and AdaBoost models were evaluated, incorporating three domain-specific indices: Distraction Index (DI), Risky Road Interaction Index (RRI), and Normative Compliance Index (NCI). AdaBoost achieved the best overall balance (Precision = 0.78; Recall = 0.75; F1-score = 0.77), simultaneously reducing false positives and false negatives. SHAP analysis revealed that environmental and infrastructure factors (lighting, traffic signals, intersections, congestion, perceived crime) explain more variance than self-reported behaviors (mobile phone use, alcohol consumption, speeding). The complementary indices indicated above-average distraction levels, high exposure to risky interactions, and low compliance in specific segments. These findings enable the prioritization of targeted interventions (improvements in lighting and crossings, focused enforcement, and educational campaigns) and support operation with thresholds adjusted to error costs, providing traceable decision support for public road safety policies. Overall, the proposed approach integrates prediction and explainability to enable actionable decisions and continuous monitoring aimed at reducing traffic accidents. Full article

Background and Objectives: The aim of this study is to evaluate impulsivity in childhood anxiety disorders and to examine its relationship with anxiety sensitivity and emotion regulation. Materials and Methods: The study group consisted of a total of 60 children aged 8–12 years diagnosed with generalized anxiety disorder (GAD, n = 30) and other anxiety disorders (n = 30). The control group consisted of 40 healthy children of similar age without a psychiatric diagnosis. Data collection forms included the Barratt Impulsiveness Scale Short Form (BIS-S), the Children’s Anxiety Sensitivity Index (ASI-3), the Emotion Regulation Checklist (ERC), and The Screen for Child Anxiety Related Emotional Disorders (SCARED). Results: Our study found no significant differences in BIS-S scores between GAD, other anxiety disorders, and the control group. The total/physical and ERC subscales of the ASI-3 were higher in the generalized anxiety disorder and other anxiety disorder group than in the control group. However, there were no significant differences in the social dimension and cognitive dimension scores of the ASI-3. It has been determined that anxiety sensitivity does not significantly mediate the relationship between emotion regulation and impulsivity, and that emotional variability/negativity is directly and completely related to impulsivity. Conclusions: Our study suggests that children with anxiety disorders experience greater difficulties in regulating their emotions compared to healthy children, and that emotional variability is directly related to impulsivity. In this context, enhancing emotion regulation skills in anxiety disorders may prove to be a pivotal factor in the efficacy of treatment and the maintenance of behavioral control. Full article

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

High-velocity fluid flow in porous media frequently exhibits non-Darcy behavior, where inertial losses lead to nonlinear pressure gradient velocity behavior. Predicting the Forchheimer coefficient β remains challenging because β varies sensitively with pore geometry and is often not constrained by porosity and permeability alone. This study develops a structure-based method to estimate β using intrinsic descriptors obtained from the Darcy regime flow characterization and image-based geometry analysis. A set of two-dimensional granular porous media was generated with controlled variations in porosity, particle size distribution, and grain size variability. Single phase simulations are simulated with a body-force multiple-relaxation-time lattice Boltzmann method. The transition from Darcy flow to non-Darcy flow is identified from the velocity and pressure gradient response, and β is determined by fitting the inertial flow regime. Two tortuosity responses were observed. In uniform media, hydraulic tortuosity remained nearly constant in the Darcy regime and then gradually decreased. In disordered media, hydraulic tortuosity first increased with the onset of recirculation and then decreased as dominant flow paths became stable. Based on these results, a dimensionless inertial factor was correlated with porosity, intrinsic hydraulic tortuosity, and a pore structure index derived from specific surface area and hydraulic pore size. The resulting model predicts β from permeability and structural descriptors. The resulting correlation provides β estimates from Darcy permeability and geometry descriptors. Validation with quasi-two-dimensional microfluidic pillar array data showed that the model captured both the magnitude and relative ordering of β for the tested geometries. The proposed framework should be regarded as a proof of concept for idealized granular porous media and quasi-two-dimensional structured systems. Full article