Search Results (951)

Background: In many inpatient settings, physician coverage is organized around single-attending responsibility, which can create challenges in supervision and workload distribution, particularly in procedurally intensive environments. To address these issues, our department introduced a junior–senior inpatient team model in which multiple physicians jointly share responsibility for hospitalized patients. This study examined physicians’ perceptions of how this restructuring influenced clinical workflow, supervision, and workload. Methods: We performed a mixed-methods cross-sectional survey two months after implementation. Twenty-two physicians (13 junior, 9 senior) completed five-point Likert-scale items and open-ended questions. Responses were analyzed using non-parametric group comparisons. Qualitative comments were examined thematically to identify recurring perspectives on supervision and workload. Results: Junior physicians reported more favorable perceptions across several domains. Significant differences between junior and senior physicians were observed for reassurance during off-site duties (p = 0.013) and perceived reduction in burden when managing critically ill patients (p = 0.002). Qualitative findings indicated that junior physicians experienced greater shared responsibility and easier access to consultation, whereas senior physicians described increased supervisory demands, responsibility extending beyond subspecialty areas, and heavier weekend or holiday duties. Both groups emphasized the importance of flexible patient redistribution during staffing variability. Conclusions: The junior–senior inpatient team model was associated with improved perceived accessibility of supervision and collective support for junior physicians while increasing supervisory demands on senior staff. These findings suggest the potential importance of workload-sensitive implementation strategies and explicit role definition when introducing physician team–based coverage in high-acuity inpatient settings. Full article

Energy flexibility in regulated users is examined as a structural property of demand, assessed through variability and disorder metrics derived from smart metering data. Using the coefficient of variation and normalized entropy, the analysis reveals stable routines during weekdays and greater heterogeneity in transitional periods such as evenings and weekends. Non-negative matrix factorization (NMF) is applied to extract latent user pro-files, which are subsequently clustered to uncover representative trajectories of consumption. Groups with bimodal or extended load distributions emerge as the most adaptable, highlighting the role of latent profiling in identifying flexibility potential. Simulations of partial load redistribution demonstrate that, while individual savings remain modest, aggregated benefits and improvements in reliability indicators (SAIDI, SAIFI, ENS) are significant. These findings confirm that flexibility is unevenly distributed across users and time, and that its quantification provides a strategic foundation for differentiated demand response schemes and the design of resilient, user-oriented energy systems. Full article

Observations of energy and carbon dioxide fluxes in the urban centres of rapidly developing countries remain limited. In this study, one year of eddy covariance measurements was conducted in the city centre of Hefei to investigate how underlying urban surfaces and human activities influence surface energy and carbon dioxide fluxes. A strong correlation was observed between net radiation and sensible heat flux, with both fluxes being significantly lower in winter. Abundant summer precipitation substantially enhanced latent heat flux. Anthropogenic heat flux and storage heat flux ranged from 30 to 350 W m⁻² and from −100 to 350 W m⁻², respectively. Improved energy balance closure was generally associated with more unstable atmospheric conditions, while increased urban surface heterogeneity was linked to poorer closure. Traffic was identified as a major contributor to carbon dioxide emissions, with annual emissions reaching 12.73 kg CO₂ m⁻² yr⁻¹ in the city centre. Carbon dioxide fluxes were significantly higher in winter and slightly lower on weekends compared to weekdays. In addition, the increasing adoption of new energy vehicles (NEVs) has contributed to a reduction in urban CO₂ fluxes. Overall, human activity in urban centres substantially enhances anthropogenic heat release and carbon dioxide emissions, thereby intensifying urban heat island effects and carbon emissions. Full article

To enhance the resilience and sustainability of urban metro systems under operational uncertainties and external disturbances, critical station identification and vulnerability assessment should be further investigated from the perspective of network science. In this paper, the presented comprehensive clustering algorithm and the Pearson correlation coefficient are adopted to explore the origin-destination (OD) passenger flow characteristics on different date classifications, and the different dates should be reasonably classified into three categories, including working day, weekends, and holiday. Meanwhile, this paper proposes the dynamic DomiRank algorithm and flow DomiGCN model to identify critical stations from network structure and function on different data classifications respectively, and further studies the vulnerability property of metro networks under simulated attacks. The Shanghai metro network is selected as case to prove the feasibility and correctness of the model. The results show that the dynamic DomiRank algorithm is relatively effective to identify critical stations from network structure, and the flow DomiGCN model is also relatively effective to identify critical stations from network function. Moreover, simulated attacks to these critical stations detected by the proposed methods can cause more damages than the other methods. These findings provide some supports for protection of metro infrastructure and contribute to the sustainable operation and development of urban rail transit systems. Full article

Background/Objectives: Competitive karting places high physiological and cognitive demands on drivers. This field study investigated the acute effects of racing on heart rate variability (HRV), perceived fatigue and neuromuscular function, and cognitive-motor performance during an official event held under persistent rain. Methods: Longitudinal, repeated-measures design across two conditions: control (race video viewing) and competition (qualifying and race). Four drivers (Junior, X30 Senior, X30 Super Shifter) were assessed pre/post-control and post-qualifying/race on Day 1 (Saturday) and pre/post-race only on Day 2 (Sunday). Measures included continuous heart rate, pre/post HRV (HRV4Training; rMSSD, SDNN), perceived fatigue (ROF), bilateral handgrip strength, and visuomotor performance (reaction times and accuracy). Results: On Day 1, SDNN and rMSSD decreased significantly after qualifying versus pre- and post-control (p < 0.05), remaining globally lower post-race; no changes emerged in frequency-domain indexes. Perceived fatigue, handgrip strength, and mean/max reaction times did not change significantly; an improvement in minimum reaction time was observed post-race versus post-control (p = 0.033). rMSSD consistently decreased after racing on both days (p < 0.05) with no day × time interaction observed, and accuracy improved on Sunday, reflected by more correct attempts (hits) and fewer failed attempts (errors) (p < 0.05). Conclusions: Racing was associated with lower time-domain HRV indices (rMSSD/SDNN), consistent with heightened autonomic activation without measurable decrements in handgrip-based neuromuscular function or cognitive-motor performance. The improved accuracy on Day 2 may be related to an increased level of physiological activation and readiness associated with race day. Routine HRV monitoring across race weekends is recommended to guide recovery decisions when subjective scales show limited immediate sensitivity. These findings are preliminary due to the small and heterogeneous sample and should be interpreted cautiously. Full article

The Venice lagoon is the largest in the Mediterranean Sea. The historic city of Venice, located on a cluster of islands in the centre of this lagoon, is an enchanting and iconic destination for national and international tourists. The historical centre of Venice and the other islands of the lagoon, such as Burano, Murano and Torcello, attract crowds of tourists every year. Transportation is provided by boats navigating the lagoon along a network of canals. The lagoon itself attracts visitors who enjoy various outdoor recreational activities in the open air, such as fishing and sunbathing. While statistics are available for the activities targeting the islands, no information is currently available on the spatio-temporal distribution of recreational activities across the lagoon waters. This study explores the feasibility of using Sentinel-2 satellite images to assess and map the spatio-temporal distribution of boats in the Venice Lagoon. Cloud-free Level-2A images have been selected to study seasonal (summer vs. winter) and weekly (weekends vs. weekdays) variabilities in 2023, 2024, and 2025. The RGB threshold filtering and the U-Net Semantic Segmentation were applied to the Sentinel-2 images to ensure reliable results. Two spatial indices were produced: (i) a Water Recreation Index (WRI), identifying standing boats in areas attractive for recreation; and (ii) a Water Transportation Index (WTI), mapping moving boats along the canals. Multi-temporal WRI maps allow areas with recurring recreational activities—that are significantly higher in the summer compared to winter, and on weekends compared to other weekdays—to be identified. The WTI identifies canal paths with higher traffic intensity with seasonal and weekly variations. The latter should be targeted by measures for traffic control to limit wave induced erosion, while the first could be subject to protection or development strategies. Full article

Background: Sleep deprivation may contribute to increased abdominal adiposity. Although weekend catch-up sleep is associated with various health outcomes, its role in abdominal adiposity remains unclear, particularly among female fixed-shift workers. Therefore, this study aimed to explore the association of workday sleep deprivation and weekend catch-up sleep with abdominal adiposity indicators in Brazilian female fixed-shift workers. Methods: A cross-sectional study was conducted on 450 female fixed-shift workers aged ≥ 18 years from a large industrial group in Southern Brazil. Abdominal adiposity indicators linked to cardiovascular risk were assessed: waist circumference (WC ≥ 88 cm), waist-to-height ratio (WHtR > 0.5), weight-to-waist index (WWI ≥ 11), conicity index (C-Index ≥ 1.27), and WC & Body Mass Index (combined WC ≥ 88 cm and BMI ≥ 30 kg/m²). Workday sleep deprivation was defined as <6 h (h) of sleep on workdays, and weekend catch-up sleep (absolute difference between weekend and workday sleep duration) was defined as >2 h longer sleep on weekends vs. workdays. Associations were estimated using a Poisson regression with robust variance adjusted for demographic, socioeconomic, behavioral, reproductive, and occupational confounders. Results: The mean age was 34.9 ± 9.9 years. The prevalence rates of abdominal adiposity were 45.3% for WC, 47.6% for WHtR, 26.2% for WWI and C-Index, and 28.7% for WC&BMI. Workday sleep deprivation and weekend catch-up sleep were reported by 27.1% and 43.3% of the participants, respectively. After adjustment for confounders, workday sleep deprivation was consistently associated with higher abdominal adiposity: Prevalence Ratio (PR) = 1.37 (95% CI: 1.10–1.69) for WC; 1.25 (95% CI: 1.02–1.53) for WHtR; 1.48 (95% CI: 1.07–2.04) for WWI; 1.43 (95% CI: 1.03–1.99) for C-Index, and 1.59 (95% CI: 1.17–2.16) for WC&BMI. Longer weekend catch-up sleep was positively associated with WHtR (PR = 1.24; 95% CI: 1.03–1.49) and WC&BMI (PR = 1.39; 95% CI: 1.04–1.85). Conclusions: Workday sleep deprivation was consistently linked to increased abdominal adiposity, whereas associations with longer weekend catch-up sleep were less consistent. These findings underscore the potential metabolic risk of insufficient sleep among female shift workers. Full article

Existing studies on urban public space vitality predominantly focus on single temporal scales or macro-urban levels, lacking a systematic understanding of day–night and weekday–weekend differentiation patterns at the meso-scale. This study examines 149 public spaces in the Yuexiu District, Guangzhou, employing Baidu heatmap data and the geographically weighted random forest (GWRF) model to analyze built environment impacts across four temporal scenarios. The SHAP interaction analysis is incorporated to quantitatively evaluate factor interdependencies and their temporal variations. Findings reveal significant spatiotemporal heterogeneity. Building density shows greater night-time importance while residential density exhibits enhanced daytime importance, particularly on weekend. Weekday–weekend comparison demonstrates contrasting spatial reorganization patterns, with weekday showing divergence and weekend showing convergence in factor importance distributions. The factor interaction analysis highlights stable synergistic relationships between density and diversity, alongside temporal transitions in density–residential density interactions from competitive to synergistic during night-time. Low-vitality public spaces are concentrated in peripheral areas with high building density but insufficient commercial facilities and functional mix. These findings deepen our understanding of the spatiotemporal mechanisms underlying public space vitality generation and the interaction effects among built environment factors, thereby providing an empirical foundation for the formulation of temporally adaptive planning strategies. Full article

Against the backdrop of China’s “dual-carbon” goals, accurate analysis and prediction of subway passenger flows are crucial for optimizing operational efficiency and advancing low-carbon urban transportation. Beijing’s subway network exhibits pronounced spatiotemporal heterogeneity across workdays, weekends, and holidays, yet existing studies often rely on static networks or single-scale temporal analyses, failing to capture dynamic flow evolution. To address this gap, this study develops a dynamic time-varying network framework with a 15 min temporal granularity, integrating sliding time-window analysis, node strength evaluation, and betweenness centrality for bottleneck identification. A Temporal–Spatial Fusion Gated Recurrent Unit (TSF-GRU) model is proposed to fuse temporal dependencies, spatial correlations, and network topology for short-term passenger flow forecasting. Results show distinct flow patterns: workdays feature a “concentrated commuting” dual peak, holidays a “steady continuous” leisure pattern, and weekends an “extended flexible” hybrid pattern. Station functions and bottleneck evolution vary dynamically across date types, with transportation hubs central on holidays/weekends and business nodes dominating workday peaks. The TSF-GRU model achieves a test-set MAPE of 7.62% and bottleneck prediction accuracy of 92.3%, outperforming traditional methods. This study provides a feasible pathway for refined, low-carbon subway operations in megacities and methodological support for achieving dual-carbon goals. Full article

Urban vitality is a crucial indicator of a city’s sustainable development and the quality of life of its residents. Investigating the spatiotemporal patterns and influencing mechanisms of urban vitality is essential for optimizing the built-environment and improving governance. Using the central urban area of Guiyang, China, as a case study, this research integrates multi-source urban sensing data to investigate the spatiotemporal patterns of urban vitality and their driving factors. Geographically weighted regression (GWR) and machine learning combined with SHapley Additive exPlanations (SHAP) are applied to capture spatial heterogeneity, nonlinear relationships, and threshold effects among influencing variables. Results show that urban vitality exhibits a Y-shaped, single-core, multi-center, and clustered spatial configuration, with slightly higher intensity on weekdays and similar diurnal rhythms across weekdays and weekends. The effects of influencing factors display strong spatial non-stationarity, characterized by a concentric gradient radiating outward from the historic Laocheng core. Building density (BD), residential point density (RED), normalized difference vegetation index (NDVI), and road density (RD) emerge as the dominant contributors to urban vitality, while topographic conditions play a relatively minor role. The relationships between key landscape and built-environment variables and urban vitality are highly nonlinear, with distinct threshold effects. By integrating spatial econometric modeling and explainable machine learning, this study advances methodological approaches for urban vitality research and provides practical insights for landscape-oriented urban planning and human-centered spatial design. Full article

Background: In-hospital cardiac arrest (IHCA) constitutes a high-impact clinical event, associated with substantial mortality, frequent neurological and functional impairment. There is a pressing need for primary IHCA studies that evaluate risk predictors, given the inherent challenges of IHCA data collection, previously unharmonized reporting frameworks, and the predominant focus of prior investigations on other domains. Among potential contributors, the “off-hours effect” has consistently been linked to poorer IHCA outcomes. Accordingly, we sought to examine whether in-hospital mortality after IHCA varies according to the time and day of occurrence within a tertiary academic center in Northern Greece. Methods: We conducted a single-center observational cohort study using a prospectively maintained in-hospital resuscitation registry at AHEPA University General Hospital, Thessaloniki. All adults with an index IHCA between 2017 and 2019 were included, and definitions followed Utstein-style recommendations. Results: Multivariable logistic regression adjusted for organizational, patient, and process-of-care factors demonstrated that afternoon/night arrests, weekend arrests, heart failure comorbidity, and need for mechanical ventilation were independent predictors of higher in-hospital mortality. Conversely, arrhythmia as the cause of IHCA and arrests occurring in the intensive care unit or operating room were associated with improved survival. Subgroup analyses confirmed consistent off-hours differences, with weekend events showing reduced 30-day and 6-month survival and worse functional status at discharge. Afternoon/night arrests were more frequent, characterized by longer response intervals and lower survival at both time points. Conclusions: Organizational factors during nights and weekends, rather than patient case mix, drive poorer IHCA outcomes, underscoring the need for targeted system-level improvements. Full article

Accurate prediction of Blood Glucose (BG) levels is essential for effective diabetes management and the prevention of adverse glycemic events. This study introduces a novel designed hybrid Gated Recurrent Unit-Transformer (GRU-Transformer) model tailored to forecast BG levels at 15, 30, 45, and 60 min horizons using only Continuous Glucose Monitoring (CGM) data as input. The proposed approach integrates advanced CGM feature extraction step. The extracted features are statistically the mean, the median, the maximum, the entropy, the autocorrelation and the Detrended Fluctuation Analysis (DFA). In addition, in order to define more enhanced and specific features, the custom 3-points monotonicity score, the sinusoidal time encoding, and the workday/weekend binary features are proposed in this work. This approach enables the model to capture physiological dynamics and contextual temporal patterns of Type 1 Diabetes (T1D) with great accuracy. To thoroughly assess the performance of the proposed method, we relied on several well-established metrics, including Root Mean Squared Error (RMSE), Coefficient of Determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Percentage Error (RMSPE). Experimental results demonstrate that the proposed method achieves superior predictive accuracy for both short-term (15–30 min) and long-term (45–60 min) forecasting. Specifically, the model attained the lowest average RMSE values, with 4.00 mg/dL, 6.65 mg/dL, 7.96 mg/dL, and 8.91 mg/dL and yielding consistently high R² scores for the respective prediction horizons. This new method distinguishes itself by continuously exceeding current prediction models, reinforcing its potential for real-time CGM and clinical decision support. Its high accuracy and adaptability make it a favorable tool for improving diabetes management and personalized glycemic control. Full article

Against the global backdrop of urbanization and sustainable development, urban parks—key public spaces for carbon sequestration, heat island mitigation, and public health promotion—have made their service performance a critical metric for evaluating urban human settlement quality. However, traditional evaluations relying on static questionnaires and aggregate indicators often fail to capture the spatiotemporal dynamics of park usage and complex supply–demand relationships. To address this gap, this study developed a three-dimensional dynamic evaluation model (“Vitality Level, Demand Matching, Service Supply”) for 59 urban parks in Gongshu District, Hangzhou, integrating multi-source data (mobile phone signaling, POIs, park vectors, demographic statistics). The model includes nine indicators (e.g., Temporal Activity Difference, Vitality Stability Index) with weights determined via the entropy weight method. Empirical results show: (1) Gongshu’s park service performance presents a “core-periphery” spatial disparity, with high-performance parks concentrated in central areas (e.g., West Lake Culture Square) due to convenient transportation and diverse functions; (2) Performance levels vary significantly between weekdays and weekends, with higher stability on weekdays and more pronounced supply–demand mismatches on weekends; (3) Time-series cross-validation and Monte Carlo simulations confirmed the model’s robustness. This framework shifts park research from “static quantitative description” to “dynamic performance diagnosis,” providing a scientific basis for refined planning and efficient management of parks in high-density cities. Full article

Isolated microgrids with green hydrogen storage offer a promising solution for supplying electricity to remote communities where conventional grid expansion is infeasible. Designing such systems requires balancing two conflicting objectives: minimizing installation and operation costs while maximizing supply reliability. This paper proposes a multi-objective optimization methodology, based on the Non-dominated Sorting Genetic Algorithm II, to determine the optimal sizing of multiple microgrid components. This sizing explicitly addresses both the power capacities (kW) (for photovoltaic panels, wind turbines, electrolyzers, and fuel cells) and the energy storage capacities (kWh and kg) (for batteries and hydrogen tanks, respectively), aiming to generate Pareto-optimal solutions that explore this trade-off. The proposed method evaluates the trade-off by minimizing two objectives: the Net Present Value, which includes investment, replacement, and maintenance costs, and the total expected interruption hours, derived from an hourly energy balance analysis. The methodology’s effectiveness is validated using four distinct case studies. Three of these are based on real locations with specific load profiles and climate data. To test the method’s robustness, a fourth case study uses a fictitious load profile, designed with pronounced seasonal variations and a clear distinction between weekday and weekend consumption. Our results demonstrate the method’s ability to identify efficient hybrid renewable topologies combining photovoltaic and/or wind generation, batteries, and hydrogen systems (electrolyzer, storage tank, and fuel cell). The obtained cost–reliability curves provide practical decision-support tools for system planners. Full article

Background/objective: Maxillofacial fractures continue to represent a significant public health issue, with incidence patterns shaped by regional and demographic variables. This study aimed to deliver a comprehensive 15-year epidemiological analysis of maxillofacial trauma cases in southern Hungary. Methods: The study included patients who received treatment for maxillofacial trauma at the University of Pécs from 2009 to 2023. Data collected encompassed demographic characteristics, injury etiology, fracture location and complexity, date of injury, presence of alcohol involvement, therapeutic interventions, postoperative complications and reasons, and number of fixation plates removed. Descriptive statistics and odds ratios were calculated, with statistical significance defined as p < 0.05. Results: Among 1948 patients (69.9% male), a total of 2826 fractures were reported, averaging 1.45 fractures per patient. The most frequently affected age group was 21–30 years; however, a notable increase in cases among the elderly was observed for recent years. Falls accounted for the highest proportion of injuries (44.4%), followed by assaults (28.3%) and traffic accidents (16.8%). Injuries predominantly occurred on weekends, with Saturdays being particularly common. Alcohol consumption was documented in 14.7% of cases. The condyle (27.9%), body (25.7%), and angle (25.0%) were the most common sites of mandibular fracture. The maxillary sinus and zygomatic body were the leading sites of maxillofacial fractures. Conservative treatment was implemented in 54.6% of all cases, whereas surgical intervention was more frequently required for mandibular injuries (76.7%). Plate removal was performed in 15.3% of patients. Conclusions: During the study period, the incidence of maxillofacial trauma demonstrated a consistent increase, accompanied by demographic changes indicative of an aging population and a reduction in assault-related cases. Falls—especially among older adults—became the leading cause of injury. These results emphasize the necessity for targeted prevention efforts, geriatric-specific trauma management, and the implementation of health policies tailored to regional needs. Full article