Search Results (146,575)

Background/Objectives: Temporomandibular disorders (TMD) are common in pediatric patients, yet limited data exist on orthodontists’ knowledge, confidence, and clinical practices related to pediatric TMD. This cross-sectional study aimed to characterize orthodontists’ perceived knowledge, confidence, training, and practice patterns, and examine associations between routine screening behaviors and perceived confidence. Methods: A 34-item anonymous survey was distributed to orthodontists and orthodontic residents enrolled in or graduated from U.S. Commission on Dental Accreditation (CODA)-accredited programs. The survey assessed perceived knowledge, confidence in screening, diagnosis, and management of pediatric TMD, adequacy of residency training (on 0–10 numerical rating scale), frequency of routine TMD screening and examination practices, and referral patterns. Respondents were compared in study outcomes according to years of clinical practice with ANOVA. Respondents were categorized according to frequency of TMD screening (always/some of the time vs. sometimes/never) and compared in study outcomes using independent t-tests. Results: Out of 83 respondents, perceived knowledge (56.8 ± 26.9), confidence with screening (62.0 ± 30.5), diagnosis (59.4 ± 29.8), and management (50.8 ± 30.9) of pediatric TMD were moderate. Less than half of respondents (45.8%) reported routinely screening pediatric patients using standardized screening questions. Orthodontists who reported routine screening demonstrated significantly greater perceived knowledge and confidence in screening, diagnosis, and management compared with those who screened less frequently (all p’s ≤ 0.018, effect size between 0.57 and 0.78). Greater use of specific history-taking and clinical examination components was also associated with higher perceived confidence (all p’s between 0.001 and 0.046, effect size between 0.53 and 1.01). Confidence differed by years in practice, with lower scores reported among residents and mid-career practitioners (p < 0.05). Conclusions: Variability exists in orthodontists’ perceived knowledge, confidence, and clinical practices regarding pediatric TMD. Routine screening was associated with greater perceived competence. These findings highlight potential alignment between structured screening behaviors and self-reported confidence and may inform educational strategies in orthodontic training. Full article

Wild mushroom consumption is widespread worldwide and remains an important cause of foodborne intoxication. This concise review analyzes recent literature on the geographic distribution of poisonous wild mushrooms and the epidemiological patterns of intoxication reported in Asia, Europe, and the Americas. Most poisoning incidents occur as a result of the misidentification of toxic species as edible during mushroom foraging. Alongside well-known poisonous mushrooms, several newly identified toxic species have been reported in recent years. The available epidemiological evidence demonstrates clear regional clustering of poisoning incidents, pronounced seasonal peaks associated with mushroom growth, and a predominance of cases in populations where wild mushroom foraging is a traditional practice. Amatoxin-containing species of the genus Amanita remain the leading cause of severe and fatal intoxications worldwide. Overall, the analyzed studies indicate that wild mushroom poisoning continues to represent a significant food safety and public health concern, particularly in Asia and parts of Europe. Improved toxicological surveillance, public awareness, and timely clinical management are essential for reducing morbidity and mortality associated with these intoxications. Full article

Siltation in irrigation canals adversely affects overflow capacity and accessibility, making its identification crucial for dredging, prevention, and maintenance, among other purposes. In this study, the siltation risks of Golmud irrigation canals were assessed from three perspectives: hydrodynamic impact, anthropogenic impact, and greening impact. The assessment factors included sediment deposition risk, bed erosion risk, proximity to public administration and services, proximity to residential areas, proximity to commercial services, and proximity to green spaces. The entropy weight method and TOPSIS method were employed to calculate the comprehensive siltation risk level, with model validation confirming a high overall accuracy of 94%. The results showed that among the six factors, proximity to public administration and services had the greatest influence on siltation, with a weight of 0.29. Additionally, the most vulnerable siltation locations were primarily in the city center, reflecting the susceptibility of urban areas to anthropogenic activities. This study develops a rapid and objective risk-scanning tool that couples hydrodynamics with land-use factors, providing a standardized technical pathway for the checking of large-scale urban infrastructure. Full article

Open and reusable road-safety microdata remain scarce in Latin America, particularly when incident records combine detailed temporal information, geocoded event locations, and a clear pathway for extracting fatal outcomes. This article documents a curated administrative dataset for Medellín, Colombia, containing 702,540 reported road-traffic incidents recorded between 1 January 2008 and 31 August 2025. The dataset includes 13 variables describing incident identifier, date, time, incident class, severity, interpolated address, geographic coordinates (latitude and longitude), and planning-unit identifiers. Although the complete dataset contains three severity levels—property damage only, injured, and fatal—it also enables the construction of a fully reproducible fatality subset by filtering incidents classified as fatal, yielding 2762 records. The database covers 21 planning units (communes) in Medellín and includes named neighborhood information for 394 neighborhoods in the complete dataset and 274 neighborhoods in the fatal subset. Spatial completeness is high for administrative data: geographic coordinates are available for 93.63% of all records and 90.77% of fatal incidents. To keep the emphasis on dataset documentation, this data descriptor focuses on compact statistical tables and an illustrative grouped logistic regression model of fatal outcomes. The dataset, accompanied by a complete data dictionary and reproducible R script, is intended to support secondary research in road-traffic safety, spatial epidemiology, transportation planning, urban mobility, and public health. Full article

To meet the requirement of real-time bridge crack detection, this paper proposes a lightweight detection model based on YOLOv7-tiny. First, an edge-preserved image enhancement method is proposed. It effectively enhances the image contrast and preserves the structural features of crack edges. This provides a high-quality data foundation for the detection network. Second, a LWCSP module is introduced. This module integrates hybrid convolution and shuffle operations. It reduces the model’s parameter count and computation. Simultaneously, it maintains strong feature representation capability. A good balance between detection performance and efficiency is achieved. Finally, an improved SWise-IoU is proposed to optimize the bounding box regression in YOLOv7-tiny. This method dynamically evaluates sample quality. It enables differentiated gradient adjustment for samples of different qualities. This promotes sufficient learning of sample features by the model, thereby improving detection accuracy. Experimental results show that the proposed model delivers strong performance on a public bridge crack dataset. Compared to the baseline, the mAP@0.5 is 12.1 higher, and model size, parameter count, and FLOPs are reduced by 7.3%, 8.03%, and 10%, respectively. The final model size is only 11.4 MB, and mAP@0.5 is 86.1%, suitable for a real-time crack detection task. Full article

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

The increasing frequency of global forest fires necessitates rapid and accurate detection methods. This study proposes a forest fire detection and segmentation framework based on the MST++ hyperspectral reconstruction model to improve the accuracy and robustness of wildfire monitoring under complex environmental conditions. The proposed method first reconstructs hyperspectral images from RGB inputs using an MST++ model trained on the NTIRE 2022 RGB-to-hyperspectral dataset (950 paired samples), followed by fire and smoke segmentation based on spectrally sensitive bands. For segmentation experiments, 118 flame images from the BoWFire dataset and 100 manually annotated smoke images from public datasets (D-Fire and DFS) were used. Quantitative results demonstrate that the proposed MST++-based method significantly outperforms the conventional U-Net baseline. In flame segmentation, MST++ achieved an IoU of 76.90%, an F1 score of 86.81%, and a Kappa coefficient of 0.8603, compared to 44.42%, 58.15%, and 0.5625 for U-Net, respectively. For smoke segmentation, MST++ achieved an IoU of 91.76% and an F1 score of 95.66%, surpassing U-Net by 17.08% and 10.32%, respectively. In fire–smoke overlapping scenarios, MST++ maintained strong robustness, achieving an IoU of 89.64% for smoke detection. These results indicate that hyperspectral reconstruction enhances discrimination capability among flame, smoke, and complex backgrounds, particularly under low-light and overlapping conditions. The proposed framework provides a reliable and efficient solution for early forest fire detection and demonstrates the potential of hyperspectral reconstruction approaches in disaster monitoring applications. Full article

Background: ESG activities are increasingly regarded as a critical prerequisite for the long-term survival of humanity. Global and regional efforts have been undertaken to develop and control ESG activities; however, national differences (institutional and social schemes, level of economic development) are still considered to account for most of the variance in ESG performance. On this basis, a research gap was identified and verified to determine whether legal regulations have an impact on the quality of ESG reporting in Poland. The study was further extended by investigating whether taxonomic disclosures affect the quality of ESG reporting. Methods: The CATI and CAVI methods were applied, resulting in the collection of 325 valid responses. In the first stage of the research, the diversity of respondents’ answers was analyzed, according to their sector of activity, using a one-factor analysis of ANOVA variance with Welch and Brown–Forsythe corrections. In the second stage, the Games–Howell Test was employed to determine which sectoral responses differed significantly. The third stage was focused on diagnosing the impact of the sector of activity on respondents’ answers by calculating the eta-squared ratio. Results: The existence of a positive impact of ESG regulatory development on the quality of reporting disclosures was confirmed; nevertheless, this impact was assessed as moderate or weak. When more detailed taxonomic disclosures were considered, no significant influence on the quality of ESG disclosures was identified. An analysis of responses across sectors led to the conclusion that the sectoral perspective does not exert a meaningful influence on stakeholders’ opinions. Conclusions: The presented results are useful at the regulatory level, both internationally and nationally, as they partly legitimize the simplifications and exemptions currently being introduced in ESG reporting. At the same time, while highlighting the potential of the regulations under review, they point to the need for additional efforts to strengthen their impact by enhancing communication and, based on informing and promoting new solutions, emphasizing their potential positive effects and benefits, as well as considering the scope of reporting through selective application. The findings presented are also useful for educational purposes and to other researchers for comparative purposes, providing a basis for research into other determinants of ESG reporting quality. Full article

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need by developing and validating a deep learning-based diagnostic support tool designed to enhance the objectivity and reproducibility of this classification. In this work, we address a key challenge in computational pathology—the tendency of attention mechanisms to overfit by concentrating on limited features—by systematically evaluating a direct regularization method within multiple instance learning (MIL) models. The models were trained and validated using 10-fold cross-validation on a public training set of 538 whole-slide images and further tested on an independent public dataset for the more challenging task of molecular subtype classification. We utilized features from a foundational model pre-trained on histopathology data to represent tissue morphology. Our findings demonstrate that directly regularizing the attention mechanism with a stochastic approach provides a statistically significant improvement in accuracy and generalization, highlighting its power as a robust technique to mitigate overfitting for this clinical task. In direct contrast to the reported variability in manual assessment, our final model achieved high consistency and accuracy, with a balanced accuracy of 0.854 and a Cohen’s Kappa of 0.791. The model also demonstrated strong generalization on the molecular classification task. Its attention mechanism provides visual heatmaps for pathologist review, fostering interpretability and trust. We have developed a highly accurate and generalizable artificial intelligence tool that directly addresses the challenge of interobserver variability in ovarian cancer classification. Its performance highlights the potential for artificial intelligence to serve as a decision support system, standardizing histopathological assessment. Full article

Subway public spaces have been identified as a vital medium for showcasing urban culture. The design quality of these spaces has been shown to have a profound influence on passengers’ spatial perception and cultural experience. However, amid rapid urbanization, subway stations commonly face issues such as homogeneous spatial interfaces and unclear cultural themes, resulting in diminished station identifiability. This study integrates post-use evaluation with Importance–Performance Analysis (IPA) to establish an assessment and optimization pathway aimed at systematically identifying and prioritizing key design elements for enhancing cultural identifiability. Taking Tianjin Gulou Station as a case study, user feedback collected through questionnaires identified 12 indicators influencing identifiability satisfaction. The reliability and validity of the questionnaire were confirmed through validity analysis and paired-sample t-tests, while IPA was employed to clarify improvement priorities. The results indicate that the overall perceived importance of cultural identifiability at Gulou Station significantly exceeds satisfaction levels. Landmark installations, art walls, and vertical transportation fall within the “high importance-low satisfaction” quadrant, which is identified as a primary area of focus for enhancement. Basic interface elements such as flooring and ceilings require enhancement, while transfer entrances and station name walls constitute advantageous designs warranting preservation. Based on the findings of the present study, three targeted design strategies are proposed: enhancing spatial perception, constructing cultural continuity, and integrating multidimensional experiences. These approaches seek to address the “spatial-cultural” perception gap, providing actionable pathways for the distinctive renewal of subway spaces. Full article

Background: Euthyroid sick syndrome (ESS) is commonly diagnosed in children during acute metabolic stress such as diabetic ketoacidosis (DKA). Nevertheless, the association of ESS with clinical outcomes has not been fully established. This study aimed to address the association between ESS and duration of hospital stay among pediatric patients presenting with DKA. Methods: This retrospective cohort study included 176 children admitted with confirmed DKA. Baseline clinical, biochemical, and outcome data, including complications and time to discharge, were collected. Kaplan–Meier survival analysis and Cox proportional hazards regression models were used to assess factors associated with duration of hospital stay. Results: Children were classified based on thyroid function tests at admission into ESS (n = 112, 63.6%) and non-ESS (n = 64, 36.4%). Children with ESS were younger [median age 10.0 (6.5–13.5) years vs. 14.0 (11.5–16.0) years; p < 0.001], had lower median weight [31.0 (20.5–44.5) Kg vs. 40.5 (34.5–49.5) Kg; p < 0.001], had lower median BMI [18.0 (16.5–20.0) kg/m² vs. 19.0 (17.5–20.5) kg/m²; p = 0.007), and slightly lower mean pH at admission [7.1 ± 0.1 vs. 7.2 ± 0.1, p = 0.016]. Free T3 (2.4 (2.0–3.4) vs. 5.1 (4.2–5.5) pmol/L), Free T4 (12.0 (10.7–14.1) vs. 14.4 (14.0–16.2) pmol/L), and TSH 1.8 (1.1–2.9) vs. 2.7 (1.7– 3.2) mIU/L) were significantly lower in ESS patients (p < 0.001 for all). Impaired consciousness occurred exclusively in the ESS group (8.9% vs. 0%, p = 0.034). Median hospital stay was longer among ESS patients, with over a quarter hospitalized for ≥5 days (26.8% vs. 0%; p < 0.001). Kaplan–Meier analysis showed significantly prolonged hospitalization for ESS patients (log-rank p < 0.0001). Patients with ESS [hazard ratio (HR) = 0.31; 95% CI, 0.21–0.45; p < 0.001], pediatric intensive care unit admission [HR = 0.49; 95% CI, 0.29–0.83; p = 0.008], moderate DKA [HR = 0.51; 95% CI, 0.30–0.87; p = 0.014], and severe DKA [HR = 0.28; 95% CI, 0.14–0.57; p < 0.001] were associated with prolonged hospital stay. Conclusions: ESS is significantly associated with prolonged hospital stays in children with DKA. Early identification of ESS may help guide monitoring strategies and discharge planning. Full article

Traditional palliative care education may limit the development of clinical competencies and attitudes required to alleviate suffering and improve quality of life. Gamification has been proposed as an alternative educational strategy in this field. Background/Objectives: This study aimed to assess the association between gamification-based intervention and palliative care knowledge among nursing students at a public university. Methods: This single-group, pre–post-intervention study was conducted in the Nursing Program of the Universidad Técnica del Norte, Ecuador, including 136 students from the accessible population. Palliative care knowledge was assessed before and after the intervention using the validated Palliative Care Quiz for Nursing (PCQN-SV). Student satisfaction and Moodle usability were assessed using a 10-item Likert-type questionnaire. The gamified educational intervention was delivered online over 60 h. Data were analyzed using descriptive statistics and Wilcoxon signed-rank tests for paired comparisons, and exploratory logistic regression analyses were conducted to evaluate contextual differences across hospitals. Statistical significance was set at α = 0.05. Results: The mean age was 22.9 years (SD = 1.89), and 73.5% were female. Knowledge scores increased significantly after the intervention (Wilcoxon signed-rank test, p < 0.001; r = 0.35). The proportion of students achieving sufficient knowledge (≥13 correct responses) increased from 27.2% (37/136) at baseline to 49.3% (67/136) post-intervention. Contextual analysis indicated variability across clinical training sites, with Lago Agrio showing higher odds of sufficient knowledge (aOR = 3.25; 95% CI [1.26–8.41]; p = 0.015). Conclusions: The gamified intervention was associated with increased palliative care knowledge among nursing students. Heterogeneity across hospitals suggests that contextual factors may influence the magnitude of change. Full article

Background and Objectives: Psychological resilience is increasingly recognized as a determinant of how patients respond to surgical stress, yet its role in perioperative medicine remains poorly defined. This narrative review aims to synthesize current evidence on resilience in surgical populations from a psychobiological perspective, spanning conceptual models, measurement approaches, clinical correlates, biological mechanisms, and intervention strategies. Materials and Methods: This narrative review was conducted to examine psychological resilience in adult surgical populations from an integrated psychobiological and perioperative perspective. A structured literature search was performed in December 2026 using PubMed, Scopus, and PsycInfo, combining resilience-related constructs with surgical, perioperative, biological, and clinical outcome keywords. Eligible publications included observational, longitudinal, interventional, translational, and conceptually relevant studies addressing resilience in adult surgical settings. Evidence was synthesized qualitatively across predefined domains, including conceptualization and measurement of resilience, associations with perioperative outcomes, neuroendocrine and inflammatory mechanisms, and resilience-modulating interventions within perioperative and Enhanced Recovery After Surgery (ERAS) frameworks. Results: Contemporary models conceptualize resilience as a dynamic, context-dependent process supported by interacting psychological, biological, and social factors. In surgical cohorts, higher resilience is consistently associated with better patient-reported outcomes, including quality of life, pain control, and emotional adjustment, and in some studies with survival and functional recovery. Preoperative depression, anxiety, maladaptive coping, and low social support converge as components of a broader “resilience profile” linked to poorer postoperative trajectories. Biologically, resilient phenotypes are characterized by more regulated hypothalamic–pituitary–adrenal and autonomic responses and reduced inflammatory activation. Psychological therapies, prehabilitation programs, and selected pharmacological strategies show convergent, though heterogeneous, signals of benefit and can be interpreted as indirect resilience-enhancing interventions. Conclusions: Resilience appears to be a clinically meaningful, potentially modifiable construct that links psychosocial functioning, biological vulnerability, and postoperative outcomes. Incorporating resilience assessment into preoperative risk stratification and systematically embedding resilience-building strategies within perioperative and ERAS pathways may support more personalized, psychologically informed surgical care. Prospective, multidomain studies are needed to validate measurement tools, clarify mechanisms, and test resilience-targeted interventions in surgical populations. Full article

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework. Full article