Search Results (2,154)

Background: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and is associated with increased mortality, prolonged length of stay (LOS), and greater resource utilization. Widely used AF risk scores were developed for stable outpatient populations and have limited applicability in critically ill patients. This study aimed to (1) characterize ICU patients with AF, (2) develop and temporally externally validate machine learning models to predict ICU mortality and ICU LOS, and (3) identify early clinical factors associated with these outcomes using interpretable methods. Methods: Adult ICU patients with AF from MIMIC-IV (n = 20,058) were used for model development with grouped cross-validation, and MIMIC-III (n = 11,475) served as a temporal external validation cohort. Predictors included demographics, admission characteristics, vital signs, laboratory values, vasoactive support, and AF-related medications available within the first 24 h of ICU admission. Eight classification algorithms were evaluated for ICU mortality, and six regression algorithms were evaluated for ICU LOS. Discrimination was primarily assessed using the area under the receiver operating characteristic curve (AUC) and average precision (AP), with additional threshold-dependent metrics reported to characterize operating-point behavior under low event prevalence. Probability-threshold optimization using out-of-fold predictions was applied to the primary mortality model. LOS performance was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R²). Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The median age was 75 years, and ICU mortality was 8.9%. For mortality prediction, the XGBoost model demonstrated preserved discrimination on temporal external validation (MIMIC-III) (AUC = 0.743; AP = 0.226). At the default probability threshold (0.50), recall and F1 scores were low due to low event prevalence; applying a prespecified F1-optimized threshold derived from the development cohort improved sensitivity while maintaining overall discrimination. For ICU LOS, models explained little variance on temporal validation; LightGBM performed best, but the explained variance was low (MAE = 88.9 h; RMSE = 163.9 h; R² = 0.038), indicating that the first 24-h structured data provide an insufficient signal to accurately predict ICU LOS, likely due to downstream clinical and operational factors. SHAP analysis identified clinically plausible predictors of mortality and prolonged ICU stay, including reduced urine output, renal dysfunction, metabolic derangement, hypoxemia, early vasopressor use, advanced age, and admission pathways. Full article

With the improvement of computing power in terminal devices and their widespread application in emerging technology fields, ensuring secure access to terminals has become an important challenge in the current network environment. Traditional security authentication and trust evaluation methods have many shortcomings in dealing with dynamic and complex network environments, such as limited ability to respond to new threats and inability to adjust evaluation strategies in real time. In response to these issues, this article proposes a dynamic weighted authentication trust evaluation method driven by multimodal data under zero trust architecture. The method introduces user operation risk values and time coefficients, which can dynamically reflect the behavior changes of users and devices in different times and environments, achieving more flexible and accurate trust evaluation. In order to further improve the accuracy of the evaluation, this article also uses the dynamic entropy weight method to calculate the weights of the evaluation indicators. By coupling with the evaluation values, the terminal access security authentication trust score is obtained, and the current authentication trust level is determined to ensure the overall balance of the trust evaluation results. The experimental results show that compared with traditional evaluation algorithms based on information entropy and collaborative reputation, the average error of the method proposed in this study has been reduced by 87.5% and 75%, respectively. It has significant advantages in dealing with complex network attacks, reducing security vulnerabilities, and improving system adaptability. Full article

Background/Objectives: Children and adolescents with intellectual disabilities (ID) have an elevated burden of obesity and cardiometabolic risk, yet factors associated with high blood pressure (BP) in this group remain insufficiently described. This study assessed the prevalence of hypertension (HTN) and isolated systolic hypertension (ISH) at a single visit and examined anthropometric and body composition correlates of elevated BP in children with ID. Methods: A cross-sectional study was conducted among 461 children and adolescents with ID aged 7–18 y attending special education schools in southeastern Poland. Anthropometric indicators (BMI, waist circumference [WC], hip circumference [HC], and waist-to-height ratio [WHtR]) and body composition parameters (BF%, MM%, FFM%, TBW%) were measured using standardized procedures. BP was assessed three times during one visit, and the average of the second and third readings was used. Receiver operating characteristic (ROC) analyses were used for exploratory assessment of discriminatory performance of anthropometric and body composition parameters, and multivariable logistic regression examined associations with elevated BP (HTN + ISH). Results: Overall, 13.9% of participants had HTN and 10.4% had ISH (combined prevalence: 24.3%). Abdominal obesity was present in 39.5% of participants, and elevated HC in 28.2%, both more common in girls. Higher BP categories were associated with greater WC, HC, BMI, and BF%, and lower MM%, FFM%, and TBW% (p < 0.0001). HC showed the highest discriminatory accuracy for HTN + ISH (AUC = 0.844), followed by MM%, BF%, and FFM%, whereas WHtR demonstrated limited discriminatory performance in ROC analyses. In multivariable models, WHtR ≥ 0.5 was associated with increased odds of elevated BP (OR = 4.25), whereas higher TBW% (≥55.38%) was inversely associated with elevated BP (OR = 0.17) in the total sample; similar patterns were observed in sex- and age-stratified analyses. Conclusions: Children with ID show a high prevalence of elevated BP at a single visit, including HTN-range and ISH-range values. Anthropometric indicators, particularly HC and WHtR, and BIA-derived body composition parameters reflecting higher fat mass and lower lean tissue proportion were associated with elevated BP. These exploratory findings suggest that simple anthropometric and body composition measures may help identify individuals who warrant further BP assessment, although longitudinal studies with repeated measurements are required before clinical application. Full article

The Zhundong coalfield in Xinjiang contains vast reserves and is a crucial source of thermal coal. However, the Zhundong coal has a high content of alkali and alkaline earth metals, which makes it prone to ash deposition and slagging in boilers, thereby limiting its large-scale utilization. Fluidized-bed preheating is an emerging clean combustion technology that can reduce the slagging and fouling risks associated with high-alkali coal by modifying its fuel properties. This study employs circulating fluidized-bed preheating technology to treat high-alkali coal, with a focus on investigating the effect of the preheated air equivalence ratio on fuel preheating modification. Through microscopic characterization of both the raw coal and preheated char, the release and transformation behaviors of elements and substances during the preheating process are revealed. The results demonstrate that fluidized preheating promotes alkali metal precipitation, and increasing the preheated air equivalence ratio (λ_Pr) enhances gas production and elemental release, with a volatile fraction mass conversion of up to 84.57%. As the λ_Pr value increased from 0.28 to 0.40, the average temperature in the preheater riser increased from 904 °C to 968 °C. Compared to the raw coal, the specific surface area of the preheated char was enhanced by a factor of 3.6 to 9.1 times, with a more developed pore structure and less graphitization, thus enhancing the surface reactivity of the preheated char. The increase in λ_Pr also facilitated the conversion from pyrrolic nitrogen to pyridinic nitrogen, thus improving combustion performance and facilitating subsequent nitrogen removal. These findings provide essential data support for advancing the understanding of preheating characteristics in high-alkali coal and for promoting the development of efficient and clean combustion technologies tailored for high-alkali coal. Full article

The November 2025 extreme rainfall event associated with Tropical Cyclone Ditwah caused catastrophic flooding and landslides across Sri Lanka. This study presents a national-scale statistical and Intensity–Duration–Frequency (IDF)-based assessment of the event using long-term rain gauge observations, extreme value analysis, and climate scenario-based projections. The 24-h rainfall data from 46 stations were analyzed for 1-, 2-, and 3-day durations. Historical annual maximum series were extracted and compared with the 2025 event to identify record-breaking extremes. Rainfall volumes were also estimated and compared with the island’s Average Annual Rainfall (AAR) and volumes from major flood events in 2010 and 2016. The November 2025 event exceeded historical maxima at 14 stations, with estimated return periods frequently surpassing 1000 years. The cumulative rainfall volume from 26–28 November accounted for 15.8% of Sri Lanka’s AAR. Updated IDF curves incorporating the event showed marked upward shifts, with intensities at some locations matching or exceeding projections under high-emission climate scenarios. The results highlight the inadequacy of existing design standards in capturing emerging extremes and the need for urgent updates to Sri Lanka’s national IDF relationships to support climate-resilient flood risk management and infrastructure planning. Full article

Background/Objectives: Inflammation may play a critical role in the pathogenesis of gestational diabetes mellitus (GDM). However, evidence linking early-pregnancy cytokines to subsequent GDM risk remains inconsistent, with most prior research focusing only on CRP, IL6, and TNFα. In this study, we expand on prior work by evaluating a broader range of immune markers and assessing sociodemographic factors as potential moderators. Methods: Data from a prospective U.S. pregnancy cohort (n = 308) were analyzed. Twenty cytokines were quantified in maternal first-trimester plasma using the MILLIPLEX High-Sensitivity Human Cytokine Magnetic Bead Panel. One-hour oral glucose (50 g) tolerance test (OGTT) values assessed at an average gestational age of 27.7 weeks (SD = 2.9) and GDM diagnosis were abstracted from medical records. Multivariable linear and logistic regression models were used to examine associations between cytokines and 1 h 50 g OGTT levels or GDM diagnosis, adjusting for key sociodemographic factors. Interactions terms were included to evaluate whether sociodemographic factors moderated cytokine–GDM relationships. Results: Sixteen women (5.1%) were diagnosed with GDM. Higher first-trimester high-sensitivity-IL6 levels were significantly associated with increased 1 h 50 g OGTT values (b = 3.76; 95% CI: 0.21, 7.32; p = 0.04) and greater odds of GDM (OR = 2.36; 95% CI: 1.17, 4.77; p = 0.02). These associations were more pronounced among Non-Hispanic White women compared to Non-Hispanic Black women (p for interaction = 0.03) and potentially those with normal weight or underweight during early pregnancy compared to overweight or obese women (p for interaction = 0.08). Conclusions: Elevated inflammatory markers, particularly high-sensitivity IL6, in early pregnancy are linked to impaired glucose metabolism and increased GDM risk later in pregnancy. These relationships appeared stronger in Non-Hispanic White women and women with normal weight or underweight during early pregnancy, underscoring the potential to develop serology-based early identification and prevention strategies. Full article

Background/Objectives: Patients with WPW syndrome have a risk of sudden cardiac death that can be assessed using an electrophysiological study. In symptomatic patients, the preferred route is intracardiac, whereas in asymptomatic children, transesophageal. Our study aimed to evaluate the risk using a transesophageal study, considering a threshold age of 12 years for sedation. Methods: We investigated 41 asymptomatic WPW children with a mean age of 12.5 ± 4.4 years (range 1 to 18 years old), with 48.8% being male. We determined three values: (1) the accessory pathway effective refractory period (APERP), (2) the minimal cycle length demonstrating 1:1 conduction through the accessory pathway, and (3) the shortest RR interval between two consecutive pre-excited beats during atrial fibrillation. Results: Children under 12 years had a mean age of 7.5 ± 2.5 years, while those over 12 years had a mean age of 15.5 ± 1.9 years. Sedation was administered exclusively to children under 12 years of age. Orthodromic reentrant tachycardia was induced in four children, and atrial fibrillation was induced in 14 children. Comparing the group under 12 with the group over 12, the mean APERP was 296 ± 38 ms vs. 286 ± 45 ms (p = 0.48), the average 1:1 conduction over the accessory pathway was 287.3 ± 41 ms vs. 282 ± 46 ms (p = 0.71), and the average shortest pre-excited RR interval during atrial fibrillation was 280 ms vs. 262 ms years (p = 0.75). Conclusions: Asymptomatic children under 12 years of age showed a lower incidence of inducible atrial fibrillation. They had accessory pathways with reduced risk, except one, and no children under 12 years underwent catheter ablation. Full article

Background/Objectives: Improper landing mechanics in Taekwondo can lead to non-contact injuries such as ankle sprains and knee ligament tears, highlighting the necessity for objective methods to evaluate landing stability and injury risk. Electromyography (EMG) enables the examination of muscle activation patterns; however, conventional analyses based on simple averages have limited predictive value. Methods: This study analyzed EMG signals recorded during single-leg landings (45 cm height) in 30 elite male Taekwondo athletes. Participants were divided into regular exercise groups (REG, n = 15) and non-exercise groups (NEG, n = 15). Signals were segmented into two phases. Eight features were extracted per muscle per phase. Classification models (Random Forest, XGBoost, Logistic Regression, Voting Classifier) were used to classify between groups, while regression models (Ridge, Random Forest, XGBoost) predicted continuous muscle activation changes as injury risk indicators. Results: The Random Forest Classifier achieved an accuracy of 0.8365 and an F1-score of 0.8547. For regression, Ridge Regression indicated high performance (R² = 0.9974, MAE = 0.2620, RMSE = 0.4284, 5-fold CV MAE: 0.2459 ± 0.0270), demonstrating strong linear correlations between EMG features and outcomes. Conclusions: The AI-enabled EMG analysis can be used as an objective measure of the study of the individual landing stability and risk of injury in Taekwondo athletes, but its clinical application has to be validated in the future by biomechanical injury indicators and prospective cohort studies. Full article

Background: Sleep is a key determinant of recovery and performance in elite athletes, yet its optimization extends beyond sleep duration alone and encompasses multiple subjective and physiological dimensions. Environmental factors, including the sleep surface, represent modifiable components of sleep that may influence perceived sleep quality. This study aimed to examine whether an individually adjustable modular sleep system improves subjective sleep quality in elite athletes and whether alterations in objective sleep metrics, circadian timing, or nocturnal autonomic physiology accompany such changes. Methods: Forty-three elite athletes participated in this pre–post-intervention study (without a control group). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while objective sleep and physiological parameters were recorded using a wearable device (Oura Ring, 3rd generation). Outcomes were averaged across three consecutive nights at baseline (T0) and post-intervention (T1). Baseline values were derived from the final three nights of a standardized pre-intervention monitoring period (minimum 7 nights), and post-intervention values from the final three nights following a standardized intervention exposure period (minimum 14 nights). Statistical analyses included paired frequentist tests and complementary Bayesian paired-sample analyses. Results: Subjective sleep quality improved significantly following the intervention, with a mean reduction in PSQI score of 0.67 points (p < 0.001). In contrast, no meaningful changes were observed in total sleep time (p = 0.28), REM duration (p = 0.26), circadian timing (p = 0.47), or nocturnal minimum heart rate (p = 0.42), as supported by the absence of physiological changes in these parameters. Conclusions: It seems that an individually adjustable sleep system can be able to improve perceived sleep quality in elite athletes without disrupting sleep architecture, circadian regulation, or nocturnal autonomic function. In athletes whose sleep duration and physiological sleep metrics are already near optimal, such micro-environmental interventions may offer a feasible, low-risk means of enhancing recovery by targeting subjective sleep quality. This dimension dissociates from objective sleep measures. Optimizing the sleep surface may therefore represent a practical adjunct to existing recovery strategies in high-performance sport. Full article

Accurate prediction of individual treatment effects in survival analysis is often complicated by the presence of competing risks and the inherent unobservability of counterfactual outcomes. While machine learning models offer improved discriminative power, they typically lack rigorous guarantees for uncertainty quantification, which are essential for safety-critical clinical decision-making. In this paper, we introduce CONFIDE (CONFormal Inference for Distribution-free Estimation), a novel framework that bridges causal inference and conformal prediction to construct valid prediction sets for cause-specific cumulative incidence functions. Unlike traditional confidence intervals for population-level parameters, CONFIDE provides individual-level prediction sets for time-to-event outcomes, which are more clinically actionable for personalized treatment decisions by directly quantifying uncertainty in future patient outcomes rather than uncertainty in population averages. By integrating semi-parametric hazard estimation with targeted bias correction strategies, CONFIDE generates calibrated prediction sets that cover the true potential outcome with a user-specified probability, irrespective of the underlying data distribution. We empirically validate our approach on four diverse medical datasets, demonstrating that CONFIDE achieves competitive discrimination (C-index up to 0.83) while providing robust finite-sample marginal coverage guarantees (e.g., 85.7% coverage on the Bone Marrow Transplant dataset). We note two key limitations: (1) coverage may degrade under heavy censoring (>40%) unless inverse probability of censoring weighted (IPCW) conformal quantiles are used, as demonstrated in our sensitivity analysis; (2) while the method guarantees marginal coverage averaged over the covariate distribution, conditional coverage for specific covariate values is theoretically impossible without structural assumptions, though practical approximations via locally-adaptive calibration can improve conditional performance. Our framework effectively enables trustworthy personalized risk assessment in complex survival settings. Full article

Background/Objectives: The triglyceride-to-High-density lipoprotein cholesterol (TG/HDL) ratio is an established marker of atherogenic dyslipidaemia and insulin resistance. Although its association with subclinical atherosclerosis has been reported, the relative contributions of long-term TG/HDL burden and visit-to-visit variability to carotid intima media thickness (CIMT) remain unclear. This study aimed to evaluate the differential associations of the longitudinal mean and temporal variability of the TG/HDL ratio with CIMT. Methods: This retrospective single-center observational cohort study included 260 adult patients with at least three years of longitudinal lipid measurements and a standardized carotid ultrasonography assessment. The longitudinal mean TG/HDL ratio and variability indices, including standard deviation, coefficient of variation, average real variability and variability independent of the mean, were calculated. CIMT was measured using B-mode ultrasonography. Associations were assessed using correlation analyses, multivariable linear regression, joint category analyses and stratified analyses according to statin therapy. Results: The longitudinal mean TG/HDL ratio was independently associated with increased CIMT after adjustment for traditional cardiovascular risk factors. In contrast, TG/HDL variability indices showed no independent association with CIMT and did not improve model performance beyond the mean TG/HDL ratio. Restricted cubic spline analysis demonstrated a significant non-linear association between TG/HDL mean and CIMT, suggesting a threshold-dependent relationship. Joint category analyses demonstrated higher CIMT values in groups with elevated TG/HDL mean regardless of variability status. A significant interaction was observed between TG/HDL variability and statin therapy (p for interaction = 0.011). Conclusions: These findings indicate that cumulative exposure to atherogenic dyslipidaemia, reflected by the long-term mean TG/HDL ratio, is more strongly associated with subclinical carotid atherosclerosis than short-term lipid fluctuations. Full article

Rainstorm floods constitute one of the major natural hazards threatening the safe and stable operation of power grid facilities. Constructing a rapid and accurate prediction model is of great significance in order to enhance the disaster prevention capacity of the power grid. This study proposes a rapid prediction model for urban rainstorm flood targeting power grid facilities based on deep learning. The model utilizes computational results of high-precision mechanism models as data-driven input and adopts a dual-branch prediction architecture of space and time: the spatial prediction module employs a multi-layer perceptron (MLP), and the temporal prediction module integrates convolutional neural network (CNN), long short-term memory network (LSTM), and attention mechanism (ATT). The constructed water dynamics model of the right bank of Liangshui River in Fengtai District of Beijing has been verified to be reliable in the simulation of the July 2023 (“23·7”) extreme rainstorm event in Beijing (the July 2023 event), which provides high-quality training and validation data for the deep learning-based surrogate model (SM model). Compared with traditional high-precision mechanism models, the SM model shows distinctive advantages: the R² value of the overall inundation water depth prediction of the spatial prediction module reaches 0.9939, and the average absolute error of water depth is 0.013 m; the R² values of temporal water depth processes prediction at all substations made by the temporal prediction module are all higher than 0.92. Only by inputting rainfall data can the water depth at power grid facilities be output within seconds, providing an effective tool for rapid assessment of flood risks to power grid facilities. In a word, the main contribution of this study lies in the proposal of the SM model driven by the high-precision mechanism model. This model, through a dual-branch module in both space and time, has achieved second-level high-precision prediction from rainfall input to water depth output in scenarios where the power grid is at risk of flooding for the first time, providing an expandable method for real-time simulation of complex physical processes. Full article

Pediatric obesity is a growing public health concern, significantly increasing the risk of metabolic and cardiovascular comorbidities. Background/Objectives: This study aims to explore the burden of obesity, its associated comorbidities, and resting metabolic rate (RMR) assessed by indirect calorimetry among children and adolescents in a cohort of 223 participants from Nord-East of Romania. Methods: A cross-sectional study was conducted among 223 children and adolescents (aged 4–18 years) who were diagnosed with obesity at Saint Mary Emergency Children’s Hospital Iași. Anthropometric measurements, clinical assessment, and biochemical parameters were recorded. RMR was measured by indirect calorimetry, using the Fitmate Pro Metabolic Technology (Cosmed, Rome, Italy), under a stable environment for 15 min, following a fasting period of minimum 6–8 h. Data were analyzed using SPSS 22.0, applying descriptive statistics and Pearson correlations. Results: A total of 223 participants were included in the analysis, with a mean age of 12.03 ± 3.32 years (range 4–17 years) and a mean body mass index (BMI) of 31.21 ± 5.84 kg/m². The average RMR was 1687.5 ± 425.5 kcal/day, with higher values in males compared with females. RMR showed significant positive correlations with age (r = 0.60), BMI (r = 0.51), waist circumference (r = 0.67), and fat mass measured with a three-site formula technique (r = 0.51) and systolic (r = 0.45) and diastolic blood pressure (r = 0.19), all with p < 0.001. A weak inverse correlation was observed between RMR and the fitness index (r = −0.24, p < 0.001), indicating an association between lower fitness scores and higher RMR values. RMR showed no significant correlation with fasting glucose or lipid levels, indicating that metabolic rate was more influenced by body composition than by biochemical markers. Conclusions: Pediatric obesity is strongly linked to multiple comorbidities, emphasizing the need for early detection and targeted interventions. Higher BMI and central adiposity were associated with increased RMR. Indirect calorimetry provides valuable insights into the metabolic profile of children with obesity and can inform individualized management strategies. Full article

Post-earthquake fires often cause more severe losses than the earthquakes themselves, highlighting the critical role of water distribution networks (WDNs) in mitigating fire risks. This study proposed an improved assessment framework for the post-earthquake firefighting functionality of WDNs. This framework integrates a WDN firefighting simulation model into a cloud model-based assessment method. By combining seismic damage and firefighting scenarios, the simulation model derives sample values of the functional indexes through Monte Carlo simulations. These indexes integrate the spatiotemporal characteristics of the firefighting flow and pressure deficiencies to assess a WDN’s capability to control fire and address fire hazards across three dimensions: average, severe, and prolonged severe deficiencies. The cloud model-based assessment method integrates the sample values of functional indexes with expert opinions, enabling qualitative and quantitative assessments under stochastic–fuzzy conditions. An illustrative study validated the efficacy of this method. The flow- and pressure-based indexes elucidated functionality degradation owing to excessive firefighting flow and the diminished supply capacity of a WDN, respectively. The spatiotemporal characteristics of severe flow and pressure deficiencies demonstrated the capability of firefighting resources to manage concurrent fires while ensuring a sustained water supply to fire sites. This method addressed the limitations of traditional quantitative and qualitative assessment approaches, resulting in more reliable outcomes. Full article

Asymmetry, defined as unequal neuromuscular activation or mechanical performance between contralateral limbs, plays a critical role in cycling efficiency and injury risk. While kinematic and kinetic measures are commonly used to assess asymmetry, surface electromyography (EMG) signals offer an additional perspective on neuromuscular asymmetry. This study evaluated muscle activation asymmetry during cycling using the Normalized Symmetry Index (NSI), a metric that quantifies differences in kinematics and kinetics between limbs, where higher values indicate greater asymmetry. NSI was calculated from EMG recordings of seven lower-limb muscles under two test conditions: step incremental and constant-power cycling to task failure. Twenty recreationally active participants performed both tests on a stationary ergometer while EMG data were collected bilaterally. Step incremental cycling resulted in a significant reduction in NSI for key muscles in the quadriceps group: vastus medialis (from 44% to 21%, p < 0.001), vastus lateralis (from 45% to 22%, p = 0.002), rectus femoris (from 54% to 24%, p < 0.001), and biceps femoris (from 52% to 29%, p = 0.003). No significant changes were observed for the tibialis anterior, soleus, or gastrocnemius medialis. In contrast, under constant-power conditions, NSI values remained unchanged over time for all muscles (all p > 0.05), with average NSI values ranging from 12% to 30%, indicating consistent bilateral activation. These findings highlight the sensitivity of surface EMG in detecting workload-dependent changes in muscle activation asymmetry and suggest that higher cycling intensities, compared to lower ones, may promote more balanced engagement of primary cycling muscles. Full article