Search Results (6,008)

Background and objectives: Non-surgical deaths in the Emergency Department (ED) occur in the context of severe acute pathology and frequently under conditions of limited diagnostic time and incomplete clinical information. Data integrating ante-mortem clinical assessment with medico-legal autopsy results remain scarce, particularly in Central and Eastern Europe. Materials and Methods: We conducted a retrospective, monocentric descriptive clinicopathological study including 45 consecutive non-surgical deaths occurring in the Emergency Department of a tertiary care hospital between January 2019 and December 2023. Clinical, biological, and temporal data were retrospectively analyzed and correlated with complete medico-legal autopsy findings in order to establish the cause of death and to assess clinicopathological concordance. Results: The mean patient age was 74.3 years, and the median time from ED admission to death was 142 min. Cardiovascular disease was the most frequent cause of death in this cohort (35.6%), followed by sepsis (22.2%), non-COVID respiratory causes (15.6%), and SARS-CoV-2 infection (17.8%). Complete clinicopathological concordance was observed in 37.8% of cases, while partial concordance predominated (57.8%). Total discordance was rare (4.4%). Autopsy findings frequently demonstrated multisystem involvement, particularly in deaths attributed to sepsis and COVID-19. Conclusions: In this descriptive, autopsy-based cohort, non-surgical deaths in the Emergency Department were associated with advanced disease severity and rapid clinical deterioration, limiting complete etiological clarification prior to death. The high rate of partial clinicopathological concordance may reflect the complexity of terminal pathophysiological mechanisms encountered in emergency settings. Systematic clinicopathological correlation through autopsy remains essential for understanding selected cases of acute non-surgical mortality in selected, rapidly fatal ED cases. Full article

Background/Objectives: Topical treatment of cutaneous tuberculosis (CTB) requires reliable models to evaluate dermal drug release and diffusion, particularly for fixed-dose combinations (FDCs) with contrasting physicochemical properties. Human skin remains the reference standard but poses ethical, logistical, and reproducibility challenges. This study investigated the suitability of Strat-M^® synthetic membranes as an alternative to human skin for assessing the simultaneous release and diffusion of clofazimine (CFZ) and pyrazinamide (PZA) from a topical FDC, and aimed to develop an optimized dermal emulsion using a Quality-by-Design (QbD)-informed formulation development tool. Methods: Self-emulsifying dermal emulsions containing CFZ and PZA were developed following QbD principles. Preformulation studies included drug solubility screening, oil phase selection, and pseudoternary phase diagram construction to identify stable emulsion regions. Formulations were characterized for droplet size, polydispersity index, zeta potential, viscosity, self-emulsification efficiency, and thermodynamic stability. Eight stable emulsions were identified, of which four were selected for in vitro drug release studies. The peppermint oil-based emulsion (PPO415) was further evaluated in comparative diffusion studies using Strat-M^® membranes and ex vivo human skin (Caucasian and African). Results: PPO415 demonstrated favorable physicochemical properties, including high CFZ solubility, uniform droplet distribution, and suitability for dermal application. Comparative diffusion studies showed that Strat-M^® underestimated the partitioning of lipophilic CFZ while overestimating the diffusion of hydrophilic PZA relative to human skin. These differences were attributed to compositional and structural disparities between synthetic membranes and biological skin. Conclusions: Strat-M^® membranes show potential as a reproducible and ethical in vitro screening tool during early-stage formulation development for topical FDCs. However, ex vivo human skin remains essential for accurately predicting dermal drug distribution and therapeutic performance. Full article

The genetic diversity and population structure of five tapertail anchovy (Coilia nasus) populations—four wild populations from the Yangtze River (Taizhou, Anqing, Shanghai, Hukou) and one cultured population from Yangzhong—were analyzed using 18 highly polymorphic microsatellite loci. All loci exhibited high polymorphism, with genetic parameters as follows: mean number of alleles = 20.567, expected heterozygosity = 13.506, Shannon information index = 2.743, and polymorphic information content = 0.9624. F-statistics ranged from 0.02898 to 0.05714, indicating varying degrees of genetic differentiation between all populations. Analysis of molecular variance revealed that 4% of the total genetic variation was attributable to differences among populations, 23% to variation among individuals within populations, and 73% to within-individual genetic variation. A UPGMA phylogenetic tree based on Nei’s genetic distance showed that the Shanghai population clustered first with Anqing, followed by Taizhou, Hukou, and finally Yangzhong. Additionally, discriminant functions developed from microsatellite data enabled accurate population assignment for all individuals. These findings provide critical insights into the genetic relationships and structure of C. nasus populations, offering valuable implications for their conservation and management. Full article

This exploratory mixed-methods study examined whether a narrative-driven digital gamification platform, FantasyClass, grounded in the MDA (Mechanics–Dynamics–Aesthetics) framework and Bartle’s player typology (used as a cohort-level design input), was associated with science attitudinal change in preservice primary teachers. The quantitative component employed a one-group pretest–posttest (pre-experimental) within-participant design using a validated 22-item attitudes questionnaire (N = 23), structured across three temporal dimensions: past (retrospective experiences), present (current perceptions), and future (teaching expectations). Significant improvements were observed across all attitudinal dimensions with large effect sizes, most notably in students’ future expectations and confidence to teach science. Exploratory correlation analyses indicated that participants’ perceived motivational value of narrative and immersion elements was moderately associated with Future-dimension attitudinal gains. Qualitative thematic analysis of open-ended responses (n = 15) revealed enhanced motivation, reduced science anxiety, more positive perceptions of physics and chemistry, and strong intentions to adopt game-based and gamified strategies in future teaching practice. Convergence across quantitative and qualitative strands suggests that structurally coherent, player-type-informed narrative gamification may be associated with attitudinal transformation and early professional identity development in STEM teacher education, while recognizing that the design does not permit causal attribution. Full article

Large-scale collection and annotation of sensitive facial data in real-world traffic scenarios face significant hurdles regarding privacy protection, temporal consistency, and high costs. To address these issues, this work proposes an integrated method specifically designed for sensitive information anonymization and semi-automatic image annotation (AIA). Specifically, the Nullface anonymization model is applied to remove identity information from facial data while preserving non-identity attributes including pose, expression, and background that are relevant to downstream vision tasks. Secondly, the Qwen3-VL multimodal foundation model is combined with the Grounding DINO detection model to build an end-to-end annotation platform using the Dify workflow, covering data cleaning and automated labeling. A traffic-sensitive information dataset with diverse and complex backgrounds is then constructed. Subsequently, the systematic experiments on the WIDER FACE subset show that Nullface significantly outperforms baseline methods including FAMS and Ciagan in head pose preservation and image quality. Finally, evaluation on object detection further confirms the effectiveness of the proposed approach. The accuracy achieved by the proposed method reaches 91.05%, outperforming AWS, and is almost identical to the accuracy of manual annotation. This demonstrates that the anonymization process maintains critical semantic details required for effective object detection. Full article

Vitellaria paradoxa subsp. paradoxa C.F.Gaertn., is one of the most important components of sub-Saharan agroforestry systems, providing to rural communities, especially women, with socio- economic, environmental, and nutritional benefits. Despite its importance, the species is threatened and remains semi-domesticated. To better preserve and improve this resource, the genetic diversity and structure of 88 mother trees originated from Senegal and Burkina Faso were studied by analysing 17 phenotypic traits and 3196 SNP markers. The results revealed similar level of observed heterozygosity (Ho) between the Senegalese and Burkinabe populations (Ho = 0.16), whereas the average number of alleles per population (Na) and the expected heterozygosity (He) ranged from 0.33 to 0.34 and 0.38 to 0.39, respectively, indicating moderate to low genetic diversity. Furthermore, the polymorphic information content ranged from 0.15 for Senegal to 0.25 for Burkina Faso. Both ADMIXTURE and cluster analysis delineated our collection into two groups depending on the origin. The AMOVA showed that the highest fraction of variation was within individual, indicating a very low genetic differentiation (Fst = 0.0006) between population. At the phenotypic level, the G2 cluster representing the Senegalese genepool recorded the highest performance in terms of nut and kernel attributes, cariten and unsaponifiable matters contents, while higher crude fat, Diglyceride, Triglyceride, and Triacylglycerol Mono Stearoyl Olein Stearin contents were observed in the Burkina Faso collection (G1). The present findings on the species’ genetic diversity and genetic structure constitute a good start to strengthen the species tree improvement and conservation programs. Full article

Accurate precipitation mapping is essential for effective disaster management; however, individual radar, satellite, and numerical weather prediction products often struggle in the topographically complex terrain of South Korea. This study proposes a high-resolution (~500 m) daily precipitation fusion framework that integrates Korea Meteorological Administration (KMA) radar, Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), and Local Data Assimilation and Prediction System (LDAPS) data. The framework employs a Random Forest model augmented with a monthly Empirical Cumulative Distribution Function (ECDF) correction. Auxiliary predictors are incorporated to enhance physical interpretability and stability, including terrain attributes to represent orographic effects, land-cover information to account for surface-related modulation of precipitation, and seasonal cyclic signals to capture regime-dependent variability. These predictors complement dynamic precipitation inputs and enable the model to effectively capture nonlinear spatiotemporal patterns, resulting in improved performance relative to individual radar, IMERG, and LDAPS products. Evaluation against Automated Synoptic Observing System (ASOS) observations yielded a correlation coefficient of 0.935 and a mean absolute error of 3.304 mm day⁻¹ in a Leave-One-Year-Out (LOYO) validation for 2024. Regional analyses further indicate substantial performance gains in complex mountainous areas, including the Yeongdong–Yeongseo region, where the proposed framework markedly reduces estimation errors under challenging winter conditions. Overall, the results demonstrate the potential of the proposed fusion framework to provide robust, high-resolution precipitation estimates in regions characterized by strong topographic and seasonal heterogeneity, supporting applications related to hazard analysis and hydrometeorological assessment. Full article

Heritage buildings pose persistent challenges for documentation and conservation due to their geometric complexity, material heterogeneity, and the fragmentation of spatial and semantic datasets. To address these limitations, this study proposes a geospatially enabled HBIM–GIS framework that integrates hybrid photogrammetric survey data with semantic modeling and spatial analysis to support evidence-based conservation planning. A multi-source acquisition strategy combining terrestrial digital photogrammetry (TDP), Unmanned aerial vehicle digital photogrammetry (UAVDP), and spherical photogrammetry (SP) was employed to capture accurate geometric and semantic information across multiple spatial scales. Staged point-cloud fusion (UAVDP → TDP via ICP; SP → UAV–TDP via SICP) generated a high-density, georeferenced composite, achieving RMS residuals below 0.013 m and resulting in an integrated dataset exceeding 360 million points. From this composite, authoritative 2D drawings and a reality-based 3D HBIM model were developed, while GIS thematic mapping translated heterogeneous observations into structured, queryable layers representing materials, cracks, detachments, deformations, and construction phases. The proposed framework enabled the spatial diagnosis of deterioration mechanisms, revealing moisture-driven decay from plinth to mid-wall and concentrated cracking at openings and architectural transitions; side-to-side cracks accounted for approximately 55% and 65% of mapped fissures on the most affected façades. By embedding these diagnostics as element-level attributes within the HBIM environment, the framework supports precise localization, quantification, and prioritization of conservation interventions, ensuring material-compatible and location-specific decision making. The applicability of the framework is demonstrated through its implementation on a complex historic mosque in Yemen, validating its robustness under constrained access and resource-limited conditions. Overall, the study demonstrates that geospatially integrated HBIM–GIS workflows provide a reproducible, scalable, and transferable solution for the sustainable documentation and conservation of heritage buildings, supporting long-term monitoring and informed management of cultural heritage assets worldwide. Full article

Background: The nursing profession is currently facing a critical challenge with a noticeable decline in interest among young people to pursue nursing as a career. Objectives: This study examined academics’ perceptions of factors driving low enrollment in Greek baccalaureate nursing programs and explored incentives that could motivate young people to pursue nursing careers. Methods: We performed a cross-sectional study. We collected our data during October 2025 through an anonymous questionnaire. Source population included all academics in the nine nursing departments in Greece. Response rate was 54.2% (90 out of 166). Results: We classified the factors contributing to the low interest in baccalaureate nursing education programs into four groups: (a) poor working conditions, (b) negative social and cultural perceptions, (c) educational constraints, and (d) impact of the COVID-19 pandemic. Academics identified negative social and cultural perceptions of nursing and poor working conditions as the primary drivers of low interest in baccalaureate nursing programs. The COVID-19 pandemic was viewed as having a moderate influence on young people’s career choices, while educational constraints were considered least important overall. Academics in nursing departments based in Greece’s capital perceived the pandemic’s impact as more substantial than colleagues outside the capital and attributed greater importance to educational constraints. Respondents without prior clinical nursing experience emphasized educational barriers more strongly. To attract students, academics prioritized improving working conditions, increasing salaries, and expanding scholarships and support. Conclusions: Academics reported that unfavorable nursing work environments, intensified during COVID-19, influence students’ career choices, underscoring the need for urgent policy and organizational actions informed by this study and existing evidence. Full article

Seismic monitoring is a crucial step in ensuring the safety and resilience of building structures. The implementation of effective monitoring systems, particularly across large-scale, complex building clusters, is currently hindered by the limitations of traditional sensor placement methods, which suffer from low efficiency, high subjectivity, and difficulties in replication. This paper proposes an innovative AI-based Automated Layout Method for seismic monitoring devices, leveraging building geometric recognition to provide a scalable, quantifiable, and reproducible engineering solution. The core methodology achieves full automation and quantification by innovatively employing a dual-channel approach (images and vectors) to parse architectural floor plans. It first converts complex geometric features—including corner coordinates, effective angles, and concavity/convexity attributes—into quantifiable deployment scoring and density functions. The method implements a multi-objective balanced control system by introducing advanced engineering metrics such as key floor assurance, central area weighting, spatial dispersion, vertical continuity, and torsional restraint. This approach ensures the final sensor configuration is scientifically rigorous and highly representative of the structure’s critical dynamic responses. Validation on both simple and complex Reinforced Concrete (RC) frame structures consistently demonstrates that the system successfully achieves a rational sensor allocation under budget constraints. The placement strategy is physically informed, concentrating sensors at critical floors (base, top, and mid-level) and strategically utilizing external corner points to maximize the capture of torsional and shear responses. Compared with traditional methods, the proposed approach has distinct advantages in automation, quantification, and adaptability to complex geometries. It generates a reproducible installation manifest (including coordinates, sensor types, and angle classification) that directly meets engineering implementation needs. This work provides a new, efficient technical pathway for establishing a systematic and sustainable seismic risk monitoring platform. Full article

A newly commercialized single-row multi-fan autonomous unmanned ground vehicle (UGV) sprayer, for use in trellised tree fruit crops, was tested to better understand air and spray patterns prior to wide-scale adoption in the modern apple orchard systems typical to Washington State. This sprayer was equipped with five brown and yellow Albuz ATR80 nozzles per fan (QM-420, Croplands Quantum). The fans were installed in a Q8 configuration, with eight fans (four on each side) staggered near the front and back as a stack to increase vertical span. Air velocity and spray delivery patterns of the commercialized sprayer unit were assessed in laboratory using a customized smart spray analytical system. Previous field trails of this sprayer unit revealed a hardware issue with electric proportional valve controls in fan-nozzle assembly, resulting in uneven spray deposition across V-trellised canopy. Post issue resolution, the sprayer characterization data showed an average Symmetry of 91%, and 84% for air velocity and spray volume delivery on either side. An average Uniformity of 57% and 48%, respectively was recorded for pertinent sprayer attributes across the spray height. Overall, after optimization, the UGV sprayer is suitable for efficient agrochemical application in modern orchard systems. Further evaluation of labor savings, biological efficacy gains from autonomous operation, and a full economic analysis would better inform grower adoption. Commercial viability of this UGV sprayer could also be improved by added features such as variable-rate application enabled by real-time crop sensing or task-map integration. Full article

Automated machine learning (AutoML) platforms are increasingly adopted in manufacturing to support data-driven decision-making. However, systematic and reproducible evaluations of their practical applicability remain limited. This study presents a controlled benchmarking framework for comparing three selected cloud-based AutoML platforms: Google Vertex AI, Microsoft Azure ML and IBM Watsonx, in the context of raw material demand forecasting for mold manufacturing. A synthetic dataset was generated to reflect essential operational characteristics of industrial production, including batch-based manufacturing, inventory-triggered replenishment and delivery lead times. While the underlying bill of materials logic is deterministic, the interaction of production variability and inventory dynamics introduces nonlinear and time-dependent behavior. All platforms were evaluated using identical data splits, chronological cross-validation and consistent performance metrics to ensure fair comparison and prevent information leakage. Results indicate moderate predictive performance, which is attributed to embedded operational complexity. Performance differences between platforms are marginal, highlighting that practical considerations such as feature handling, deployment readiness and computational effort may be more influential than raw accuracy. Although synthetic data limit external validity, the proposed framework provides a reproducible and transparent basis for applied evaluation of AutoML platforms. Future work will incorporate real industrial data and robustness testing under non-stationary and disrupted production conditions. Full article

The Atacama Desert in northern Chile is characterized by its naturally high metal concentrations; however, human activities have significantly increased their availability and concentration in aquatic environments. In the Loa River basin, copper mining is the main economic activity, and the extremely arid conditions contribute to high levels of evaporation and salinity. This study evaluated the concentrations of As, Cd, and Cu in the 63 µm sediment fraction from three areas, Lequena, La Finca, and Quillagua, during the years 2014, 2015, 2017, and 2023. Contamination levels were assessed using multiple approaches, including the Geoaccumulation Index (Igeo), the Enrichment Factor (EF), the Pollution Load Index (PLI), and the mean Probable Effect Concentration Ratio (m-PEC-Q). The results showed that Lequena (upper river zone) had no evidence of anthropogenic contamination over time; however, the ecological risk assessment highlighted the significant natural contribution of arsenic, representing a potential risk to the ecosystem. In contrast, La Finca (mid-river zone) and Quillagua (river mouth) showed significantly high levels of contamination. The Geoaccumulation Index consistently classified these sites as “moderately” to “heavily” contaminated or “heavily contaminated” for arsenic, while the Enrichment Factor indicated “very high enrichment” for arsenic, reflecting a strong anthropogenic influence. Ecological risk assessments indicated a persistent 76% probability of toxicity at La Finca throughout all sampling years, a level also observed at Quillagua in 2017 and 2023, with concentrations frequently exceeding international sediment quality guidelines. These patterns are attributed to the proximity of mining activities in the middle zone and the downstream transport of contaminated sediments to the river’s mouth, resulting in persistently high ecological risks over time. This study provides important baseline information for pollution control and ecological safety in the Loa River basin. Full article

Accurately forecasting demand for intermittent items is essential for effective inventory control, improved service levels, and cost reduction. This study focuses on highly sparse, irregular, and volatile demand patterns and proposes a generalizable multi-source data-driven framework for intermittent demand forecasting, using automotive spare parts as a representative application scenario. The proposed framework integrates Transformer networks, multi-graph convolutional networks (GCNs), and a Mamba-based feature fusion module. The Transformer captures long-term temporal dependencies in historical demand sequences, while the multi-graph GCN incorporates prior knowledge—including traffic geography, socioeconomic indicators, and environmental attributes—to model spatial correlations across multiple supply nodes. The Mamba-based fusion module then integrates temporal and spatial features into a unified representation, enhancing predictive accuracy and robustness. Extensive experiments on real-world datasets of automotive spare parts in China show that the proposed framework exhibits competitive and often superior performance compared with TiDE, FSNet, Informer, and DLinear across multiple forecasting horizons (3-, 6-, and 9-step), as measured by RMSE, MAE, and $R^2$. The proposed approach provides a practical and adaptable solution for forecasting intermittent demand, offering valuable support for dynamic inventory management. Full article

Assessment of water resource spatial equilibrium (WRSE) is crucial for regional sustainable development, yet traditional methods always face difficulties in quantifying systemic differences and resolving their internal uncertainties. Accordingly, this study proposes a novel multi-attribute decision-making (MADM) model that integrates symmetric interaction entropy (SIE) with connection numbers (CNs) within a variable-weight framework. Firstly, information differences between alternatives and an ideal state were quantified by SIE, then these differences were decomposed into certain and uncertain components through the “identity–difference–opposition” (IDO) idea of CNs. In addition, a variable-weight mechanism was incorporated to enhance the model’s adaptability to regional characteristics. Applied to evaluate the WRSE in the Beijing–Tianjin–Hebei (BTH) region from 2014 to 2023, the model reveals that Hebei maintains the most favorable equilibrium state, with a partial identity potential or equal potential, followed by Beijing, while Tianjin predominantly exhibits partial opposite potential due to pronounced conflicts between its resource endowment and industrial structure. The proposed model not only enhances the sensitivity and interpretability of evaluation results but also facilitates the identification of key vulnerable indicators, thereby providing a scientific basis for formulating differentiated regional water governance strategies. Full article