Search Results (806)

Background: Pediatric cancer incidence has been increasing in the United States, despite improvement in pediatric cancer survival. This steady increase in incidence trends is not completely understood but maybe associated with social and environmental factors. In this study we aimed to assess the cumulative incidence, temporal trends, and mortality rates in pediatric cancer. Additionally, we examined sub-group variability in both incidence and mortality rates. Methods: Data from Surveillance, Epidemiology, and End Results (SEER) −18 from 1973–2014 were used for the purpose of analysis in this study. Age-adjusted incidence rates were used to assess temporal trends in cancer among children aged <1–19 years. Univariable and multivariable binomial regression models were used to examine the association between race and cancer mortality while adjusting for potential confounders. Results: There were 92,594 cancer diagnoses during this period. White children comprised 74,758, (80.7%), black children 10,030, (10.8%), and other races 6648, (7.2%). Overall the age-adjusted cumulative incidence was slightly higher among white children (16.4%) than black children (12.4%) and other (13.0%). Children aged 15–19 years and those in metropolitan regions were more likely to be diagnosed with pediatric cancer. Relative to females, males were 16% more likely to die from the disease [adjusted Risk Ratio (aRR): 1.16, 95% Confidence Interval (CI): 1.09–1.22]. Additionally, compared to white children, black children had higher mortality rates [(aRR): 1.37, 99% CI: 1.23–1.52]. Conclusions: There is an increasing trend in pediatric cancer incidence; while white children have the highest incidence, black children and males indicated a survival disadvantage, indicative of racial and sex variability in overall pediatric cancer in the United States. Full article

New energy vehicles (NEVs) have gained increasing favor among NEV consumers due to their dual advantages of “low cost” and “environmental friendliness.” In recent years, the share of NEVs in the global automotive market has been steadily rising. For instance, in the Chinese market, the sales of new energy vehicles in 2024 increased by 35.5% year-on-year, accounting for 70.5% of global NEV sales. However, as the diversity of NEV brands and models expands, selecting the most suitable model from a vast amount of information has become the primary challenge for NEV consumers. Although online service platforms offer extensive user reviews and rating data, the uncertainty, inconsistent quality, and sheer volume of this information pose significant challenges to decision-making for NEV consumers. Against this backdrop, leveraging the strengths of the quasi OWA (QOWA) operator in information aggregation and interval basic uncertain linguistic information (IBULI) information aggregation and two-dimensional information representation of “information + quality”, this study proposes a large-scale group data aggregation method for decision support based on the IBULIQOWA operator. This approach aims to assist consumers of new energy vehicles in making informed decisions from the perspective of information quality. Firstly, the quasi ordered weighted averaging (QOWA) operator on the unit interval is extended to the closed interval $\left[0,\tau \right]$, and the extended basic uncertain information quasi ordered weighted averaging (EBUIQOWA) operator is defined. Secondly, in order to aggregate groups of IBULI, based on the EBUIQOWA operator, the basic uncertain linguistic information QOWA (BULIQOWA) operator and the IBULIQOWA operator are proposed, and the monotonicity and degeneracy of the proposed operators are discussed. Finally, for the problem of product decision making in online service platforms, considering the credibility of information, a product decision-making method based on the IBULIQOWA operator is proposed, and its effectiveness and applicability are verified through a case study of NEV product decision making in a car online service platform, providing a reference for decision support in product ranking of online service platforms. Full article

This study presents a novel approach to the parametrization of 3D PETRO FACIES and SEISMO FACIES using supervised and unsupervised learning, supported by a coherent structural and stratigraphic framework, to enhance understanding of the presence of hydrocarbons in the Dzików–Uszkowce region. The prediction relies on selected seismic attributes and well logging data, which are essential in hydrocarbon exploration. Three-dimensional seismic data, a crucial source of information, reflect the propagation velocity of elastic waves influenced by lithological formations and reservoir fluids. However, seismic response similarities complicate accurate seismic image interpretation. Three-dimensional seismic data were also used to build a structural–stratigraphic model that partitions the study area into coeval strata, enabling spatial analysis of the machine learning results. In the 3D seismic model, PETRO FACIES classification achieved an overall accuracy of 80% (SD = 0.01), effectively distinguishing sandstone- and mudstone-dominated facies (RT1–RT4) with F1 scores between 0.65 and 0.85. RESERVOIR FACIES prediction, covering seven hydrocarbon system classes, reached an accuracy of 70% (SD = 0.01). However, class-level performance varied substantially. Non-productive zones such as HNF (No Flow) were identified with high precision (0.82) and recall (0.84, F1 = 0.83), while mixed-saturation facies (HWGS, BSWGS) showed moderate performance (F1 = 0.74–0.81). In contrast, gas-saturated classes (BSGS and HGS) suffered from extremely low F1 scores (0.08 and 0.12, respectively), with recalls as low as 5–7%, highlighting the model’s difficulty in discriminating these units from water-saturated or mixed facies due to overlapping seismic responses and limited training data for gas-rich intervals. To enhance reservoir characterization, SEISMO FACIES analysis identified 12 distinct seismic facies using key attributes. An additional facies (facies 13) was defined to characterize gas-saturated sandstones with high reservoir quality and accumulation potential. Refinements were performed using borehole data on hydrocarbon-bearing zones and clay volume (VCL), applying a 0.3 VCL cutoff and filtering specific facies to isolate zones with confirmed gas presence. The same approach was applied to PETRO FACIES and a new RT facie was extracted. This integrated approach improved mapping of lithological variability and hydrocarbon saturation in complex geological settings. The results were validated against two blind wells that were excluded from the machine learning process. Knowledge of the presence of gas in well N-1 and its absence in well D-24 guided verification of the models within the structural–stratigraphic framework. Full article

(1) Background: Violence against healthcare professionals is becoming a growing concern for healthcare systems and a public health issue, and in Portugal it remains undocumented at a national level, leaving a critical knowledge gap. This scenario compromises the development of effective public policies and evidence-based institutional strategies, which are essential for guiding policymakers in the implementation of preventive measures and appropriate safety protocols to assess the nature, frequency, and key factors contributing to violence against healthcare professionals (doctors and nurses) in clinical settings. (2) Methods: This is a quantitative, descriptive, and cross-sectional study. The sample size was 440 professionals (n = 440). Between January and May 2024, healthcare professionals (physicians and nurses) working in four local health units located in the south of Portugal were invited to participate in this study via institutional e-mail. Data was collected using a structured questionnaire on the healthcare professional’s sociodemographic and work-related characteristics and aspects related to violence towards healthcare professionals in the workplace. Unconditional logistic regression models were fitted to compute crude odds ratios (ORs) and 95% confidence intervals (95%CIs) for the association between sociodemographic and work-related characteristics and violence at work. (3) Results: Nearly 40% of the healthcare professionals sampled reported having been victims of violence in the workplace, and, among these, the majority reported experiencing psychological violence (94.2%), followed by physical violence (46.2%), another type of violence (39.1%), and sexual violence (4.1%). Incidents were mostly occasional (65.5%), occurring during the daytime (51.5%) and on weekdays (84.8%). Healthcare professionals aged between 34 and 55 years old were approximately twice as likely to experience violence compared to those who were 56 years old or older (OR = 2.28; 95%CI 1.33–3.90). Also, those who had been with the organization for more than 4 years (5–7 years: OR = 2.37; 95%CI 1.05–5.37. ≥8 years: OR = 1.87; 95%CI 1.00–3.50), as well as those who worked shifts (OR = 1.84; 95%CI 1.25–2.72), reported incidents of violence more frequently. (4) Conclusions: The low response rate (12.5%) and cross-sectional design limit the generalizability of the results, which should be interpreted considering these methodological limitations. Workplace violence in Portugal is a reality, and it requires solutions. Information related to violent incidents must be comprehensively gathered to understand the full extent of the problem and develop prevention strategies based on potentially changeable risk factors to minimize the negative effects of workplace violence. Full article

The Unit Inverse Maxwell–Boltzmann (UIMB) distribution is introduced as a novel single-parameter model for data constrained within the unit interval ($0,1$), derived through an exponential transformation of the Inverse Maxwell–Boltzmann distribution. Designed to address the limitations of traditional unit-interval distributions, the UIMB model exhibits flexible density shapes and hazard rate behaviors, including right-skewed, left-skewed, unimodal, and bathtub-shaped patterns, making it suitable for applications in reliability engineering, environmental science, and health studies. This study derives the statistical properties of the UIMB distribution, including moments, quantiles, survival, and hazard functions, as well as stochastic ordering, entropy measures, and the moment-generating function, and evaluates its performance through simulation studies and real-data applications. Various estimation methods, including maximum likelihood, Anderson–Darling, maximum product spacing, least-squares, and Cramér–von Mises, are assessed, with maximum likelihood demonstrating superior accuracy. Simulation studies confirm the model’s robustness under normal and outlier-contaminated scenarios, with MLE showing resilience across varying skewness levels. Applications to manufacturing and environmental datasets reveal the UIMB distribution’s exceptional fit compared to competing models, as evidenced by lower information criteria and goodness-of-fit statistics. The UIMB distribution’s computational efficiency and adaptability position it as a robust tool for modeling complex unit-interval data, with potential for further extensions in diverse domains. Full article

Human gait analysis is essential for clinical evaluation and rehabilitation monitoring, particularly in post-stroke individuals, where joint kinematics provide valuable insights into motor recovery. While optical motion capture (OMC) is the gold standard, its high cost and restricted use in laboratory settings limit its accessibility. This study aimed to evaluate the accuracy of REEV SENSE, a novel magnetometer-free inertial measurement unit (IMU), in capturing knee and ankle joint angles during overground walking in post-stroke individuals using assistive devices. Twenty participants with chronic stroke walked along a 10-m walkway with their usual assistive device (cane or walker), while joint kinematics were simultaneously recorded using OMC and IMUs. Agreement between the systems was assessed using the mean absolute error, root mean square error, 95% confidence intervals, and Pearson’s correlation coefficient. Knee angles measured with the IMUs showed a strong correlation with the OMC (r > 0.9) and low errors (MAE < 5°), consistent with clinical acceptability. Ankle angle accuracy was lower for participants using walkers, while knee measurements remained stable regardless of the assistive device. These findings demonstrate that REEV SENSE IMUs provide clinically relevant kinematic data and support their use as a practical wearable tool for gait analysis in real-world or remote clinical settings. Full article

Certain environmental exposures are associated with COVID-19 incidence and mortality. To determine whether environmental context is associated with other COVID-19 experiences, we used data from the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Study data (n = 1785; three survey waves 2020–2022 for adults in the United States). Environmental context was assessed using self-reported climate stress and county-level air pollution, greenness, toxic release inventory site, and heatwave data. Self-reported COVID-19 experiences included willingness to vaccinate, health impacts, receiving assistance for COVID-19, and provisioning assistance for COVID-19. Self-reported climate stress in 2020 or 2021 was associated with increased COVID-19 vaccination willingness by 2022 (odds ratio [OR] = 2.35; 95% confidence interval [CI] = 1.47, 3.76), even after adjusting for political affiliation (OR = 1.79; 95% CI = 1.09, 2.93). Self-reported climate stress in 2020 was also associated with increased likelihood of receiving COVID-19 assistance by 2021 (OR = 1.89; 95% CI = 1.29, 2.78). County-level exposures (i.e., less greenness, more toxic release inventory sites, and more heatwaves) were associated with increased vaccination willingness. Air pollution exposure in 2020 was positively associated with the likelihood of provisioning COVID-19 assistance in 2020 (OR = 1.16 per µg/m³; 95% CI = 1.02, 1.32). Associations between certain environmental exposures and certain COVID-19 outcomes were stronger among those who identify as a race/ethnicity other than non-Hispanic White and among those who reported experiencing discrimination; however, these trends were not consistent. A latent variable representing a summary construct for environmental context was associated with COVID-19 vaccination willingness. Our results suggest that intersectional equity issues affecting the likelihood of exposure to adverse environmental conditions are also associated with health-related outcomes. Full article

Accurate prediction of the Air Quality Index (AQI) is crucial for protecting public health; however, the inherent instability and high volatility of AQI present significant challenges. To address this, the present study introduces a novel hybrid deep learning model, KL-PV-CBGRU, which utilizes Kalman filtering to decompose AQI data into features and residuals, effectively mitigating volatility at the initial stage. For residual components that continue to exhibit substantial fluctuations, a secondary decomposition is conducted using variational mode decomposition (VMD), further optimized by the particle swarm optimization (PSO) algorithm to enhance stability. To overcome the limited predictive capabilities of single models, this hybrid framework integrates bidirectional gated recurrent units (BiGRU) with convolutional neural networks (CNNs) and convolutional attention modules, thereby improving prediction accuracy and feature fusion. Experimental results demonstrate the superior performance of KL-PV-CBGRU, achieving R² values of 0.993, 0.963, 0.935, and 0.940 and corresponding MAE values of 2.397, 8.668, 11.001, and 14.035 at 1 h, 8 h, 16 h, and 24 h intervals, respectively, in Shijiazhuang—surpassing all benchmark models. Ablation studies further confirm the critical roles of both the secondary decomposition process and the hybrid architecture in enhancing predictive accuracy. Additionally, comparative experiments conducted in Beijing validate the model’s strong transferability and consistent outperformance over competing models, highlighting its robust generalization capability. These findings underscore the potential of the KL-PV-CBGRU model as a powerful and reliable tool for air quality forecasting across varied urban settings. Full article

Background and Objectives: Romania has experienced the highest measles incidence rate in the European Union since late 2023, driven by suboptimal measles–mumps–rubella (MMR) uptake. Contemporary data on bedside predictors of clinical deterioration are scarce. The objective was to characterise demographic, clinical and laboratory differences between severe and non-severe measles and derive a multivariable model for intensive-care-unit (ICU) admission. Methods: We undertook a retrospective cohort study at the “Victor Babeș” University Hospital for Infectious Diseases, Timișoara. All admissions from 1 November 2023 to 15 May 2025 with serological or RT-PCR confirmation and a complete baseline laboratory panel were included. Descriptive statistics compared ward-managed versus ICU-managed patients; independent predictors of ICU transfer were identified through logistic regression that incorporated age, vaccination status, leukocyte count, C-reactive protein (CRP) and interleukin-6 (IL-6). Results: Among 455 patients (median age 3.0 y, interquartile range [IQR] 1.0–7.0), 17 (3.7%) required ICU care. Vaccine coverage was 18.0% overall and 0% among ICU cases. Compared with ward peers, ICU patients exhibited higher leukocyte counts (8.1 × 10⁹ L vs. 6.0 × 10⁹ L; p = 0.003) and a near-five-fold elevation in IL-6 (18 pg mL vs. 4 pg mL; p < 0.001), while CRP, procalcitonin and fibrinogen were similar. ICU admission prolonged median length of stay from 5 days (IQR 4–7) to 8 days (5–12; p = 0.004). In multivariable modelling, IL-6 remained the sole independent predictor (odds ratio [OR] 1.07 per pg mL; 95% confidence interval [CI] 1.03–1.12; p = 0.001); the model’s AUC was 0.83, indicating good discrimination. Complete separation precluded reliable estimation of the protective effect of vaccination, but no vaccinated child required ICU care. Conclusions: A simple admission panel centred on IL-6 accurately identified Romanian measles patients at risk of critical deterioration, whereas traditional markers such as CRP and leukocyte count added little incremental value. Even a single documented MMR dose was associated with the complete absence of ICU transfers, underscoring the urgent need for catch-up immunisation campaigns. Integrating IL-6-guided triage with intensified vaccination outreach could substantially reduce measles-related morbidity and health-system strain in low-coverage EU settings. Full article

Aims: To investigate the global epidemiological characteristics of lower respiratory infection (LRI) burden caused by Streptococcus pneumoniae (SP) from 1990 to 2021. Methods: Using data from the Global Burden of Disease (GBD) study 2021, we systematically analyzed Streptococcus pneumoniae-related (SP-related) LRI burden, focusing on mortality, disability-adjusted life years (DALYs), and temporal trends by age, gender, geographic region, and socio-demographic index (SDI) quintiles. Decomposition analysis assessed the influence of epidemiological shifts, population growth, and aging on age-standardized mortality rates (ASMRs), while an autoregressive integrated moving average (ARIMA) model projected future trends. Results: Between 1990 and 2021, the global SP-related LRI death number decreased from 1,028,083 (95% uncertainty interval (UI): 923,782–1,146,074) to 505,268 (95% UI: 454,335–552,539), and the ASMR dropped from 19.28 (95% UI: 17.32–21.49) to 6.40 (95% UI: 5.76–7.00) per 100,000. The age distribution consistently exhibited a clear two-tiered pattern, gradually shifting from being predominantly composed of young children to being dominated by older adults. Disparities were stark across SDI quintiles, low-SDI regions exhibited up to 100-times-higher under-five mortality than high-SDI regions. Geographic distribution showed the highest ASMRs in sub-Saharan Africa and the lowest in Canada, the United States, and Australia, with Mongolia and Finland showing the largest reductions in mortality. Epidemiological changes were the most significant factor in ASMR reduction. Conclusions: The SP-related LRI burden has decreased globally but remains a major health concern, especially in low-SDI regions. Targeted public health interventions, particularly for neonates and elderly adults, are essential to address persistent disparities and further reduce mortality. Full article

Accurate information on waste composition is essential for strategic planning in waste management and developing environmental technologies. However, detailed analyses of individual waste containers are both time- and cost-intensive, resulting in a limited number of available samples. Therefore, it is crucial to apply statistical methods that enable reliable estimation of average waste composition and its variability, while accounting for territorial differences. This study presents a statistical approach based on territorial stratification, aggregating data from individual waste container analyses to higher geographic units. The methodology was applied in a case study conducted in the Czech Republic, where 19.4 tons of mixed municipal waste (MMW) were manually analyzed in selected representative municipalities. The method considers regional heterogeneity, monitors the precision of partial estimates, and supports reliable aggregation across stratified regions. Three alternative approaches for constructing interval estimates of individual waste components are presented. Each interval estimate addresses variability from the random selection of waste containers and the selection of strata representatives at multiple levels. The proposed statistical framework is particularly suited to situations where the number of samples is small, a common scenario in waste composition analysis. The approach provides a practical tool for generating statistically sound insights under limited data conditions. The main fractions of MMW identified in the Czech Republic were as follows: paper 6.7%, plastic 7.3%, glass 3.6%, bio-waste 28.4%, metal 2.1%, and textile 3.0%. The methodology is transferable to other regions with similar waste management systems. Full article

Little is known about the manifestation of cardiovascular diseases (CVD) among individuals with ocular cancer (OC), a population for whom reports on sex-based differences in survival remain inconsistent. We evaluated the occurrence of CVD mortality after the diagnosis of OC in the United States. We used data from 11,460 adults diagnosed with OC from 2000 to 2021 who were ≥18 years and were enrolled in the Surveillance, Epidemiology, and End Results program. We used competing risk models to estimate hazard ratios (HR) and 95% confidence intervals (CI). About 55% of adults were male, with uveal melanoma being the most common OC (72.1%). During a median follow-up of 5.4 years, 4561 deaths occurred, with 15% attributable to CVD. In models adjusted for sociodemographic and clinico-pathophysiological factors, male adults had elevated risk for CVD mortality (HR: 1.54, 95%CI: 1.31–1.81). The sex difference in CVD mortality was more prominent for adults diagnosed with OC before 65 years of age (HR: 2.15; 95%CI: 1.48–3.11). These associations remained largely unchanged in propensity score analysis. In this study of adults with OC, CVD deaths were higher among young and middle-aged males. Implementation of optimal cardiovascular health interventions after diagnosis of OC, especially among men, holds promise in enhancing survival in this population. Full article

Background/Objectives: Thrombotic thrombocytopenic purpura (TTP) is a microangiopathic hemolytic anemia exhibiting 90% mortality without prompt treatment. The aim of this study was to investigate the association of therapeutic plasma exchange (TPE)-treated TTP in end-stage renal disease (ESRD) patients with mortality, demographics, and clinical comorbidities. We queried the United States Renal Data System for ESRD patients starting dialysis between 1 January 2005 and 31 December 2018, using International Classification of Diseases (ICD)-9 and ICD-10 codes for thrombotic microangiopathy, with a TPE procedure code entered within 7 days. Methods: Cox proportional hazards models were used to assess mortality, adjusting for demographic and clinical factors. Results: Among 1,155,136 patients, increased age [adjusted odds ratio (OR) = 0.96, 95% confidence interval (CI): 0.94–0.96]; black race (OR = 0.67, CI: 0.51–0.89); and Hispanic ethnicity (OR = 0.43, CI: 0.28–0.66) were associated with a lower risk of TPE-treated TTP diagnosis, whereas female sex (OR = 1.59, CI: 1.25–2.02) and tobacco use (OR = 2.08, CI: 1.58–2.75) had a higher risk. A claim for TPE-treated TTP carried a lower risk of death (adjusted hazard ratio = 0.024, CI: 0.021–0.028). Female sex, black race, Hispanic ethnicity, and hypothyroidism were also associated with decreased all-cause mortality. Conclusions: These findings suggest that ESRD patients with TPE-treated TTP are significantly protected from mortality compared with ESRD patients without this diagnosis. Full article

E-Learning is an emerging dominant phenomenon in education, making the development of robust models that can accurately represent the dynamic behavior of learners in MOOCs even more critical. In this article, we propose the Temporal Sparse Attention-Gated Recurrent Unit (TSA-GRU), a novel deep learning framework that combines TSA with a sequential encoder based on the GRU. This hybrid model effectively reconstructs student response times and learning trajectories with high fidelity by leveraging tthe emporal embeddings of instructional and feedback activities. By dynamically filtering noise from student interactions, TSA-GRU generates context-aware representations that seamlessly integrate both short-term fluctuations and long-term learning patterns. Empirical evaluation on the 2009–2010 ASSISTments dataset demonstrates that TSA-GRU achieved a test accuracy of 95.60% and a test loss of 0.0209, outperforming Modular Sparse Attention-Gated Recurrent Unit (MSA-GRU), Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and TSA in the same experimental design. TSA-GRU converged in five training epochs; thus, while TSA-GRU is demonstrated to have strong predictive performance for knowledge tracing tasks, these findings are specific to the conducted dataset and should not be implicitly regarded as conclusive for all data. More statistical validation through five-fold cross-validation, confidence intervals, and paired t-tests have confirmed the robustness, consistency, and statistically significant superiority of TSA-GRU over the baseline model MSA-GRU. TSA-GRU’s scalability and capacity to incorporate a temporal dimension of knowledge can make it acceptably well-positioned to analyze complex learner behaviors and plan interventions for adaptive learning in computerized learning systems. Full article

Background/Objectives: This study aimed to evaluate sociodemographic factors, features of the acute infection, and post-infection health status in survivors of COVID-19, assessing their association with post-acute COVID-19 syndrome (PACS). Methods: A multidisciplinary public clinic in Brazil assessed COVID-19 survivors between June 2020 and February 2022. Patients were classified as having PACS or subacute infection (SI). Data on the history of the acute infection, current symptoms, physical examination, and laboratory findings were collected and analyzed using multivariate models with PACS as the outcome. Results: Among the 113 participants, 63.71% were diagnosed with PACS at a median of 130 days (IQR: 53–196) following acute symptom onset. Admission to the intensive care unit was more frequent among individuals with PACS than those with SI (83.3% vs. 65.0% respectively; p = 0.037). Symptoms significantly more prevalent in the PACS group when compared to the SI cohort included hair loss (44.4% vs. 17.1% respectively; p = 0.004), lower limb paresthesia (34.7% vs. 9.8% respectively; p = 0.003), and slow thinking speed (28.2% vs. 0.0% respectively; p < 0.001). Logistic regression revealed that only the time interval between the onset of acute symptoms and the clinical evaluation was independently associated with a PACS diagnosis (β = 0.057; 95% CI: 1.03–1.08; p < 0.001). Conclusions: Patients with PACS had a higher frequency of intensive care unit admission compared to those with subacute infection. However, in the multivariate analysis, the severity of the acute infection did not predict the final diagnosis of PACS, which was associated only with the time elapsed since symptom onset. Full article