Search Results (133,652)

Background: Intensive Care Unit (ICU) readmission and in-hospital mortality are critical indicators of patient outcomes following ICU discharge. Patients readmitted to the ICU often face worse prognosis, higher healthcare costs, and prolonged hospital stays. Identifying high-risk patients is essential for optimizing post-ICU care and resource allocation. Methods: This two-phase study included the following: (1) a retrospective analysis of ICU survivors in a mixed medical–surgical ICU to identify risk factors associated with ICU readmission and in-hospital mortality, and (2) a prospective validation of a newly developed predictive model: the Worse Outcome Score (WOScore). Data collected included demographics, ICU admission characteristics, severity scores (SAPS II, SAPS III, APACHE II, SOFA), interventions, complications and discharge parameters. Results: Among 1.190 ICU survivors, 126 (10.6%) were readmitted to the ICU, and 192 (16.1%) died in hospital after ICU discharge. Key risk factors for ICU readmission included Diabetes Mellitus, SAPS III on admission, and ICU-acquired infections (Ventilator-Associated Pneumonia (VAP) and Catheter-Related Bloodstream Infection, (CRBSI)). Predictors of in-hospital mortality were identified: medical admission, high SAPS III score, high lactate level on ICU admission, tracheostomy, reduced GCS at discharge, blood transfusion, CRBSI, and Acute Kidney Injury (AKI) during ICU stay. The WOScore, developed based on the results above, demonstrated strong predictive ability (AUC: 0.845 derivation, 0.886 validation). A cut-off of 20 distinguished high-risk patients (sensitivity: 88.1%, specificity: 73.0%). Conclusions: ICU readmission and in-hospital mortality are influenced by patient severity, underlying comorbidities, and ICU-related complications. The WOScore provides an effective, easy-to-use risk stratification tool that can guide clinicians in identifying high-risk patients at ICU discharge and guide post-ICU interventions, potentially improving patients’ outcomes and optimizing resource allocation. Further multi-center studies are necessary to validate the model in diverse healthcare settings. Full article

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings. Full article

This study develops an integrated framework for groundwater pollution source identification by coupling Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and the Mantel test, with the Qujiang River Basin as a case study. The framework enables a full-process assessment, encompassing qualitative identification, quantitative apportionment, and spatial validation of pollution drivers. Results indicate that groundwater chemistry is primarily influenced by three categories of sources: natural rock weathering, agricultural and domestic activities, and industrial wastewater discharge. Anthropogenic sources account for 73.7% of the total contribution, with mixed agricultural and domestic inputs dominating (38.5%), followed by industrial effluents (35.2%), while natural weathering contributes 26.3%. Mantel test analysis further shows that agricultural and domestic pollution correlates strongly with intensive farmland distribution in the midstream area, natural sources correspond to carbonate outcrops and higher elevations in the upstream, and industrial contributions cluster in downstream industrial zones. By integrating PCA, PMF, and Mantel analysis, this study offers a robust and transferable framework that improves both the accuracy and spatial interpretability of groundwater pollution source identification. The proposed approach provides scientific support for regionalized groundwater pollution prevention and control under complex hydrogeological settings. Full article

Landslides are common in mountainous regions and can significantly affect human life and infrastructure. The aim of this study is to analyze the role of hydrothermally altered rocks in generating ground instability and triggering debris flows in the Canoas microbasin, Sierra de Angangueo, within the Monarch Butterfly Biosphere Reserve. We characterized the unaltered (andesite) and altered (andesitic breccia) rocks from the landslide scarp through fieldwork and laboratory analysis. The altered rock exhibited an extremely low simple compressive strength of 0.47 ± 0.05 MPa. In contrast, the unaltered rock exhibited a higher strength of 36.26 ± 18.62 MPa and lower porosity. Petrographic analysis revealed that the unaltered rock primarily consists of an andesitic groundmass with plagioclase and orthopyroxene phenocrysts partially altered to sericite and kaolin. In comparison, the altered rock contains a matrix rich in clay, iron oxides, and completely replaced phenocrysts. The andesitic breccia has a high proportion of clay and silt and displays soil-like mechanical properties, making it vulnerable to saturation collapse during heavy rainfall. This research offers valuable insights into geological risk management in mountainous volcanic regions. The findings demonstrate that the presence of hydrothermally altered andesitic breccia with weak geomechanical properties was the critical factor that triggered the Canoas debris flow, underscoring hydrothermal alteration as a key control of slope instability in volcanic settings. Full article

All over the world, the problem of ensuring the safety of pedestrians, who are the most vulnerable road users, is becoming more acute due to urbanization and the growth of micromobility. In 2013, according to WHO data, more than 270 thousand pedestrians were dying each year worldwide (accounting for 22% of all traffic accidents). Currently, experts report that around 1.3 million people die every year globally from road crashes. The roads in developing countries are particularly hazardous, according to experts, because the increase in the number of vehicles far exceeds the development of road infrastructure and safety systems. Since the risk of hitting a pedestrian depends on many factors that can have different natures, and the severity of the consequences can be determined by a set of other factors, the risk of an accident can only be reduced by influencing all these factors in a comprehensive manner. The novelty of our approach is to create an intelligent system that will gradually accumulate all the best practices into a single complex aimed at reducing the risk of an accident with pedestrians and the severity of the consequences if an accident does occur. The distinction lies in offering an integrated system where each module addresses a particular task, so by mitigating risks at every stage, one achieves a synergistic outcome. From the analysis of existing and applied developments, it is known that many specialists mainly solve a narrowly focused problem aimed at ensuring the one subsystems sustainability in the “vehicle-infrastructure-driver-pedestrian” system. Some of these ideas are given as practical examples. The relevance of the designated problem increases with the emergence of autonomous vehicles and smart cities, the sustainability of which depends on the sustainable interaction between all road users. As experience shows, only the implementation of comprehensive solutions allows us to solve strategic problems, including improving road safety. Here, by complex solutions we mean solutions that combine technical issues, as well as environmental, social, and managerial aspects. To account for different kinds of effects, indicator systems are developed and composite indices are computed to choose the most rational solution. The novelty of our approach consists in combining within a unified DSS algorithms for assessing the efficiency of the proposed solution with respect to technological soundness, environmental sustainability, economic viability, social acceptability, as well as administrative rationality and computation of interrelated effects resulting from implementing any given project. In our opinion, the proposed system will lead to a synergistic effect due to the integrated application of various developments, which will ensure increased sustainability and safety of the transport system of smart cities. Our paper proposes a conceptual approach to addressing pedestrian safety, and the examples provided illustrate how the same model or algorithm can lead to positive changes from different perspectives. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Background/Objectives: The diagnosis of Mycobacterium tuberculosis complex (MTBC) and nontuberculous mycobacterial (NTM) infections is accomplished by three main diagnostics methods: smear microscopy, culture, and molecular testing. Diagnostic algorithms used by laboratories can significantly impact clinical and infection control management. Current Canadian Tuberculosis Standards recommend the use of nucleic acid amplification testing (NAAT) for smear-positive patients and smear-negative patients upon request. An alternative algorithm is to utilize NAAT in the Panel approach on all samples, pulmonary and extrapulmonary, to potentially reduce time to diagnosis and treatment. This alternative approach was implemented in November 2019 at the Newfoundland and Labrador Public Health and Microbiology Laboratory (NL PHML) using a laboratory-developed multiplex real-time PCR (LDT m-qPCR) assay targeting Mycobacterium spp. (Myco spp.) and MTBC, performed in parallel with smear and culture. Methods: To investigate the impact of this alternate testing approach, we conducted an observational retrospective analysis of laboratory diagnostic and treatment data, recognizing that temporal changes in epidemiology, clinical practice, and laboratory workflow may also have influenced outcomes. To complete this, study data from three years before and four years after implementation were gathered. Results: The sensitivity/specificity of the smear, m-LDT qPCR-MTBC, m-LDT qPCR-Myco spp., and culture assays in this study were 18.1%/100%, 96.7%/99.8%, 47.6%/99.0%, and 96.8%/100%, respectively. The gold standard utilized for these calculations was clinical diagnosis for active MTBC disease and culture for NTM infections, recognizing that the use of clinical diagnosis may introduce subjectivity. The Panel approach reduced the time to diagnosis of tuberculosis MTBC by 29 days (p < 0.0001) for NL PHML, and when modelled for a laboratory with rapid culture identification, diagnosis was reduced by 14 days (p = 0.003). Among non-empirically treated tuberculosis patients, the time to treatment was decreased by 25.5 days (p < 0.001). For NTM infections, rapid diagnostics only affected one patient’s treatment. This finding agrees with clinical management guidelines, which do not routinely utilize rapid diagnostics for the diagnosis of disease or treatment decisions. The cost implications of additional NAAT testing were calculated to be an increase of CAD 23.62 per sample. Conclusions: Our findings support the adoption of a molecular assay for MTBC as an initial diagnostic tool to decrease time to diagnosis and time to treatment, depending on local epidemiology and irrespective of smear status. Utilizing a molecular assay for genus level identification of NTM had minimal impact on clinical management suggesting its limited diagnostic utility in a broad population setting. Full article

Background: Post-colonoscopy syndrome is an emerging clinical entity characterised by the onset of gastrointestinal symptoms following a colonoscopy. The current management of this syndrome has not yet been established, although probiotics have been proposed. The therapeutic potential of a combination nutraceutical compound based on HBQ-Complex^®, butyrate, and probiotics (Lactobacillus acidophilus, Bifidobacterium animalis, and Lactiplantibacillus plantarum) in this setting remains unknown. Methods: A retrospective, multicentre, observational study was conducted in adult patients undergoing colonoscopy in the absence of known gastrointestinal diseases, assessing the onset of upper and lower gastrointestinal symptoms post-colonoscopy immediately after the procedure (T₀), at 2 weeks (T₁), and 4 weeks (T₂) thereafter, using a VAS (0–10). Two groups were analysed, one undergoing nutraceutical supplementation and a control group. Results: A total of 599 patients were included (64.9% receiving nutraceutical supplementation and 35% in the control group). Several variations were observed involving the treated group compared to the control for abdominal pain (59.9% vs. 33.3%), meteorism (64.9% vs. 35.1%), diarrhoea (46.9% vs. 19.5%), and bloating (59.3% vs. 26.7%) (p < 0.001 for all). Logistic regression analysis showed a reduction in constipation (OR: 3.344) and bloating (OR: 3.791) scores. Conclusions: Nutraceutical supplementation based on this combinational compound was associated with a reduction in gastrointestinal symptoms arising after colonoscopy, suggesting potential benefit in this setting. These findings pose a rationale for controlled prospective studies to confirm such evidence in broader clinical settings. Full article

Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds. Full article

Depressive spectrum disorders are considered among the most common in the general population. Major depressive disorder and persistent depressive disorder (or dysthymia) are the most recognized, but other depressive disorders exist with varying or no specificity. The main difference between major depressive disorder and dysthymia lies in the duration and intensity of symptoms. Improving our understanding of its etiology and pathogenesis must be a priority for health and safety. Given the complexity of the evidence in the literature, it was deemed useful to provide a comprehensive summary of the neuroanatomical dysfunctions currently identified, with particular attention to the anterior and medial cingulate cortex, dorsolateral and ventromedial prefrontal cortex, posterior parietal cortex, insula, amygdala, and hippocampus. Significant neural network alterations include hyperconnectivity of the default mode network (DMN), impairment of the executive control network (ECN), and dysfunction of the salience network (Salience Network). Neurophysiological markers reveal frontal alpha asymmetries and front-striatal metabolic alterations. Studying neural correlates is essential to deepen our understanding of the depressive spectrum and the development of personalized therapeutic interventions, including noninvasive neurostimulation techniques and target-specific pharmacological therapies, opening new avenues for translational research in neuropsychiatric settings. Full article

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations. Full article

Objectives: The aim of this study was to compare the effects of eight-week hamstring stretching programs, implemented at different times during physical education classes (i.e., warm-up, cool-down, and both periods), on primary schoolchildren’s back-saver sit-and-reach scores. Methods: A total of 275 schoolchildren (141 females and 134 males; age 8.82 ± 1.63 years) were divided into four groups: the WUG performed stretching during warm-up, the CDG during cool-down, and the MXG during both. The NSG followed the standard classes of physical education without any stretching. During physical education classes WUG, CDG, and MXG performed a 4 min stretching program twice a week. Hamstring extensibility was assessed before and after the program using the back-saver sit-and-reach test. Results: The CDG is the one that achieved statistically significant improvements compared with the WUG, MXG, and NSG (p ≤ 0.01; d = 0.50–0.71). Moreover, the CDG statistically increased the percentage of schoolchildren achieving healthy hamstring extensibility from pre-intervention (49%) to post-intervention (66%). Conclusions: This knowledge could guide teachers to design programs that guarantee feasible and effective development of hamstring extensibility in the physical education setting. Full article

The growing use of experimental radiopharmaceuticals for targeted radionuclide therapy (TRT) highlights the need for robust “in house” radiolabeling protocols. Among these, PSMA-ALB-56 is a PSMA ligand incorporating an albumin-binding moiety to enhance pharmacokinetics, which showed promise for prostate cancer treatment. This study investigated manual radiolabeling conditions of this vector molecule with lutetium-177 and developed a corresponding automated synthesis protocol. Manual experiments on low activities explored buffer systems and antioxidants, identifying sodium acetate buffer and L-methionine as optimal, achieving radiochemical purities above 97% with excellent stability over 48 h. However, when these conditions were transposed directly to an automated process on a GAIA^® module with activities > 2 GBq, radiochemical purity dropped below 70% due to significant radiolysis. This result emphasized that conditions optimized at low activities are not directly transferable to high-activity automated production, and highlighted the crucial role of antioxidant concentration. An optimized automated method was subsequently developed, integrating a solid-phase extraction purification step, higher antioxidant levels during radiolabeling and formulation, and a larger final product volume. These changes led to radiochemical purities above 98.9% and excellent product stability over 120 h for 3 test batches. The presence of high concentrations of methionine and ascorbic acid was essential to protect against radiolysis. This work underscores the importance of adjusting radiolabeling strategies during process scale-up and confirmed that antioxidant concentration is essential for successful ¹⁷⁷Lu radiolabeling. The optimized automated method developed here for [¹⁷⁷Lu]Lu-PSMA-ALB-56 may also be adapted to other radiopharmaceuticals in development for TRT. Full article

The quasisymmetric minimality for fractal sets is a hot research topic for scholars focused on the fractal geometry and quasisymmetric mappings. In this paper, we study the quasisymmetric minimality on packing dimension for homogeneous perfect sets. By using some mathematical tools such as the mass distribution principle, we find that a special class of homogeneous perfect sets with packing dimension 1 is quasisymmetrically packing minimal. Our result generalizes the results in the references. Full article

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area. Full article