Search Results (39,065)

Background: Hepatitis C Virus (HCV) remains a global health challenge as WHO elimination targets are not achievable in most countries, mainly due to the high number of undiagnosed individuals. In Italy, where national elimination efforts are ongoing, regional disparities further hinder progress. This study aimed to characterize the hidden burden of chronic HCV infection across t he territory of the Province of Salerno, Southern Italy, to suggest a novel municipal-level screening approach, with implications for national strategies. Methods: We analyzed records of residents diagnosed with chronic HCV infection and linked to care between 2015 and 2022. Data included age, sex, municipality of residence, HCV genotype, and fibrosis stage. Observed prevalence was compared with expected prevalence derived from national/regional benchmarks. Municipalities were categorized as urban or rural based on the resident population. Results: A total of 3528 cases were identified across 139 municipalities. Patients had a mean age of 63 years, and 54% were male. Half were diagnosed at an advanced stage (F3–F4), with genotype 1b being predominant. The hidden burden increased with age and showed a higher prevalence in rural areas compared to urban ones, with values of about 7 vs. 3 per 1000 inhabitants respectively. Logistic regression analysis identified age, male sex, urban residence, and genotype 1b as factors associated with advanced fibrosis or cirrhosis. Conclusions: This is the first Italian study to apply a standardized municipal-level classification to quantify the hidden burden of HCV. The model identifies underdiagnosed areas, highlights urban–rural disparities (a higher degree of underdiagnosis in rural areas versus a higher frequency of late diagnosis in urban ones), and provides a replicable tool for precision public health. Its adoption could enhance national HCV elimination efforts by supporting targeted screening, optimized resource allocation, and equitable access to care. Full article

Background: Stomatitis is a common adverse event associated with targeted therapies for hormone receptor-positive, HER2-negative (HR+/HER2–) breast cancer, particularly those inhibiting the PI3K/AKT/mTOR pathway. While mTOR-inhibitor-associated stomatitis is well established, less is known about its occurrence with other kinase inhibitors in real-world settings. We performed a pharmacovigilance disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) to evaluate stomatitis reports for alpelisib, capivasertib, everolimus, and palbociclib. Methods: Events were identified using four term sets—Stomatitis, Original Trial Terms (OTT), Comprehensive Trial Terms (CTT), and Stomatitis-Associated Main Terms (SAMT)—which reflect varying definitions and medical terminologies. Disproportionality analyses using reporting odds ratio (ROR), proportional reporting ratio (PRR), and Information Component (IC) were calculated with 95% confidence intervals. Results: All agents showed ROR and PRR >1, indicating higher odds and reporting proportions of stomatitis compared with other drugs. These findings were confirmed by IC analysis. Everolimus demonstrated the strongest association (ROR: 30.72 [29.61–31.88]), followed by alpelisib (ROR: 13.11 [11.79–14.58]) and palbociclib (ROR: 11.73 [11.35–12.11]). Capivasertib had the lowest reporting odds (ROR: 3.14 [1.81–5.43]), though limited by fewer reports. Differences between CTT and SAMT were minimal (~2%). Conclusions: These results support the use of the SAMT as an efficient screening tool. Furthermore, these findings underscore the need for optimized stomatitis detection and continued monitoring, particularly for PI3K and mTOR inhibitors, in both clinical trials and postmarketing surveillance. Full article

The present paper reports the comparison of the ship motions and added resistance assessment using high fidelity RANSE simulations in virtual towing tank LincoSim, using 2D strip theory as implemented in ShipX v4.4.0 and 3D BEM potential flow software Hydrostar v8.2.1. All calculations are performed for a medium-sized RoPax ferry of Levante Ferries fleet, which operates daily routes in the Ionian Sea. Calculations by ShipX are performed in frequency domain (using strip-theory and direct pressure integration) and in time domain. The high-fidelity RANSE seakeeping modeling is based on the open-source CFD code OpenFOAM v12 using a standardized framework, tailored to take advantage of HPC facilities and based on a forcing zone formulation. The CFD simulations are performed for six wave periods in head and beam seas at the constant wave height of 3 m. Comparison of the obtained results shows that potential-flow methods are very efficient and reliable tools, suitable for the massive calculations in the first stages of the project. High-fidelity RANSE modeling seems to be more suited for selected cases such as analysis of roll and added resistance in beam waves. Full article

Understanding the nonlinear mechanical behaviour of mitral valve chordae tendineae is critical for accurate biomechanical modelling in cardiac simulations. This study integrates high-resolution 3D finite element analysis with experimentally derived Cauchy stress–Green–Lagrange strain data to capture both material and geometric nonlinearities. A one-dimensional formulation incorporating strain-dependent elasticity and large deformation kinematics was developed and validated against 3D simulations in COMSOL Multiphysics. Calibrated using experimental stress–strain data and validated against high-fidelity 3D finite element simulations in COMSOL, it reveals that neglecting transverse deformation overestimates axial force by 7%. Cross-sectional area reduction during stretch remained consistently around 12%, underscoring the importance of Poisson effects. A polynomial fit to the strain-dependent modulus of elasticity enables efficient force prediction with excellent agreement to experimental data. These results advance the mathematical modelling of biological tissues with nonlinear hyperelastic behaviour, providing a foundation for patient-specific simulations and real-time predictive tools in cardiovascular engineering. Full article

The 2020s represent both the digital decade and the pivotal period in the fulfillment of long-standing commitments made by public, private, and institutional actors in favor of sustainable development. In the manufacturing context, Product Lifecycle Management (PLM) systems are used during the design phase to reduce product environmental footprint. However, only a few studies have thoroughly identified the environmental impacts associated with these technological solutions. This study proposes a sensitivity analysis of five environmental impact categories associated with two PLM system architectures and three mitigation scenarios. To this end, we use an engineering school as a representative PLM system case study, relying on the Life Cycle Assessment (LCA) methodology and leveraging specialized tools that enable the execution and comparative analysis of multiple LCA scenarios. Our results consistently identify the manufacturing and usage phases of PLM system users’ equipment as the main contributors of the PLM system to climate change, acidification, and the depletion of abiotic mineral and metal resources. End-of-life contributes significantly to particulate matter impact, and usage phase, in a nuclear mix country, to ionizing radiation. The policy of purchasing and reselling reconditioned users’ equipment is clearly identified as a key lever for reducing the magnitude of these five environmental impacts. Full article

During the Fused Deposition Modeling (FDM) molding process, temperature changes are nonlinear and instantaneous, which is a key parameter affecting FDM printing efficiency, molding accuracy, warpage deformation, and other factors. This study presents a finite element simulation framework that integrates ANSYS Parametric Design Language (APDL) with birth–death element technology to investigate the temperature evolution and thermomechanical behavior during the FDM process. The framework enables dynamic simulation of the complete printing and cooling cycle, capturing the layer-by-layer material deposition and subsequent thermal history. Results indicate that temperature distribution follows a gradient pattern along the printing path, with rapid heat dissipation at the periphery and heat accumulation in the central regions. Thermomechanical coupling analysis reveals significant stress concentration at the part bottom (310 MPa) and progressive strain increase from bottom (3.68 × 10⁻⁵ m) to top (2.95 × 10⁻⁴ m). Experimental validation demonstrates strong agreement with numerical predictions, showing maximum temperature deviations below 8% and strain distribution errors within 5%. This integrated approach provides an effective tool for predicting thermal-induced deformations and optimizing FDM process parameters to enhance part quality. Full article

This study presents an integrated experimental and numerical investigation of the Refill Friction Stir Spot Welding (RFSSW) process applied to dissimilar aluminum alloys. The primary objective is to evaluate the mechanical and thermal behavior of the joints and to identify key process parameters influencing weld quality. Experimental welding trials were performed on aluminum alloy sheets using RFSSW, followed by shear testing and metallographic analysis to assess joint integrity, microstructure evolution, and fracture behavior. Infrared thermography and temperature sensors were employed to monitor heat distribution during welding. In parallel, a finite element model was developed to simulate the thermal cycle and stress distribution within the welded region. The numerical results showed good agreement with the experimental data, particularly regarding peak temperature and cooling trends at specific distances from the tool center. The findings demonstrate that RFSSW can successfully join dissimilar aluminum alloys with minimal defects when optimized parameters are applied. The combination of experimental observations and FEM simulation provides valuable insights into the underlying thermomechanical phenomena and offers a foundation for further process optimization. Full article

Background/Objectives: Impaired angiogenesis and vascular dysfunction are central features of chronic diseases such as cardiovascular disorders, neurodegeneration, and metabolic syndrome. Vascular endothelial growth factor (VEGF) plays a pivotal role in vascular repair and metabolic regulation, yet its responses to exercise remain inconsistently reported. This study aimed to systematically compare the effects of different exercise modalities and doses on VEGF levels across diverse populations. Methods: This review was registered in PROSPERO (CRD42025643709) and followed PRISMA guidelines. PubMed, Web of Science, Embase, and Cochrane Library were searched until 16 January 2025. Eligible studies were randomized or quasi-experimental trials reporting exercise-induced changes in serum/plasma VEGF. Data were extracted and assessed independently using JBI tools. Exercise types were categorized and doses standardized as metabolic equivalents (METs). Network meta-analysis was performed in Stata17.0 (SMD as effect size), with SUCRA used for ranking. Dose–response relationships were examined by meta-regression (remr package), and publication bias was assessed via funnel plots. Results: Twenty-eight studies (N = 1138) were included. In healthy adults, lower-limb resistance training produced the greatest VEGF increase, with benefits observed above ~600 METs-min/week and peaking near 1950 METs-min/week. Among obese individuals, combined aerobic and resistance training under hypoxic conditions showed the highest VEGF response, though dose-specific effects were not significant. In patients with chronic conditions, upper-limb resistance training within 756–950 METs-min/week was most effective, displaying a U-shaped dose–response relationship. No substantial publication bias was detected. Conclusions: The VEGF response to exercise appears to be influenced by both population characteristics and training dosage. High-intensity lower-limb resistance training may provide greater benefits for healthy adults, while obese individuals might experience enhanced responses with combined training under hypoxic conditions. For clinical populations, moderate-dose upper-limb resistance training may be particularly beneficial. Large-scale, long-term trials are needed to further clarify and refine exercise prescriptions targeting VEGF-mediated vascular adaptations. Full article

Background: Electrocardiographic (ECG) abnormalities are common in acute pulmonary embolism (PE), but the prognostic significance of P wave morphology remains unclear. Early identification of high-risk patients is critical for guiding therapy and monitoring. Methods: We retrospectively analyzed 300 patients with confirmed PE. P wave morphology (normal, biphasic, notched, peaked) was evaluated for association with in-hospital mortality using chi-square and logistic regression, adjusted for age, sex, PESI score, and oxygen saturation. Results: Mortality differed significantly across P wave groups (χ² = 35.3, df = 3, p < 0.001). In univariate analysis, biphasic (OR 15.38, 95% CI 5.02–47.10, p < 0.001) and peaked (OR 7.21, 95% CI 2.35–22.10, p = 0.001) morphologies were strongly associated with mortality, whereas notched P waves were not (OR 1.44, 95% CI 0.16–12.87, p = 0.743). After adjustment, biphasic (OR 14.87, 95% CI 4.77–46.37, p < 0.001) and peaked (OR 6.58, 95% CI 2.11–20.53, p = 0.001) shapes remained independent predictors. Age, sex, PESI score, and oxygen saturation were not significant in multivariable analysis. Conclusions: Biphasic and peaked P wave morphologies on ECG are strong predictors of in-hospital mortality in patients with PE. Routine assessment of P wave shape may provide a simple tool for early risk stratification, warranting validation in prospective cohorts. Full article

Promoting investment in Carbon Capture, Utilization, and Storage (CCUS) is essential for mitigating carbon emissions and combating climate change. This paper explores the uncertainties and environmental benefits associated with CCUS, integrating the frameworks of pollution right trading and carbon trading. A model for coal-fired power plant investment decisions on CCUS is developed and solved using the Least Squares Monte Carlo method, with results being robust beyond approximately 6000 simulation paths. Applied to a 600 MW ultra-supercritical coal-fired power plant in Shaanxi, China, our findings indicate that investment leads to a loss of CNY 1200.4 million in the absence of both environmental benefits and market trading mechanisms. A positive investment value of CNY 462 million with an optimal timing in the 10th year is achieved only when both environmental benefits and trading mechanisms are present. Furthermore, with only carbon trading, the option value is marginal (CNY 64.8 million), and investment remains unprofitable without government subsidies. Sensitivity analysis highlights that government subsidies significantly impact investment motivation. An initial carbon price of approximately CNY 95 per ton triggers immediate investment, while higher capture proportions and utilization levels positively affect decision-making. This study provides analytical tools for investment decisions in CCUS across multiple scenarios, serving as a reference for policymakers in designing emission reduction strategies. Full article

Pesticide poisoning remains a significant public health issue, characterized by high morbidity and mortality, particularly among patients presenting to the emergency department. This study aimed to develop a 14-day in-hospital mortality prediction model for patients with acute pesticide poisoning using early clinical and laboratory data. This retrospective cohort study included 1056 patients who visited Soonchunhyang University Cheonan Hospital between January 2015 and December 2020. The cohort was randomly divided into train (n = 739) and test (n = 317) sets using stratification by pesticide type and outcome. Candidate predictors were selected based on univariate Cox regression, LASSO regularization, random forest feature importance, and clinical relevance derived from established prognostic scoring systems. Logistic regression models were constructed using six distinct feature sets. The best-performing model combined LASSO-selected and clinically curated features (AUC 0.926 [0.890–0.957]), while the final model—selected for interpretability—used only LASSO-selected features (AUC 0.923 [0.884–0.955]; balanced accuracy 0.835; sensitivity 0.843; specificity 0.857; F1.5 score 0.714 at threshold 0.450). SHapley Additive exPlanations (SHAP) analysis identified paraquat ingestion, Glasgow Coma Scale, bicarbonate level, base excess, and alcohol history as major mortality predictors. The proposed model outperformed the APACHE II score (AUC 0.835 [0.781–0.888]) and may serve as a valuable tool for early risk stratification and clinical decision making in pesticide-poisoned patients. Full article

Understanding the spatial patterns of ethnic inter-embeddedness is essential for promoting sustainable development in multi-ethnic regions. This study develops a novel “Clustered-Boundary-Cellular” typological model to interpret the spatial differentiation of traditional villages in China’s Hehuang region. Using an integrated approach that combines GIS spatial analysis (Kernel Density Estimation, Ripley’s K-function, and Standard Deviational Ellipse), spatial statistics (Global Moran’s I), and other statistical tests (Kruskal–Wallis tests and multinomial logistic regression), we categorized and analyzed 153 nationally designated traditional villages. The results indicate the following: (1) The villages exhibit significant spatial differentiation, falling into three distinct scenarios. Clustered–Isolation villages (107/153, 69.9%) are predominantly located in topographically constrained areas and display strong spatial clustering; Boundary–Permeation villages (24/153, 15.7%) are distributed along transport corridors and show the highest road density (0.55 km/km²); Cellular–Symbiosis villages (22/153, 14.4%) occur in multi-ethnic cores areas and exhibit a relatively random spatial distribution. (2) This differentiation results from the synergistic effects of multidimensional drivers: natural environmental constraints (notably elevation and proximity to rivers), religious–cultural adaptation (Global Moran’s I analysis confirms the strong clustering of Tibetan and Salar groups, reflecting distinct religious spatial logics), and economic transition dynamics (transportation infrastructure serves as a key catalyst). This study demonstrates the value of the proposed model as an analytical tool for diagnosing ethnic spatial relations. The findings offer important insights and spatial guidance for formulating context-sensitive strategies for sustainable governance, cultural heritage preservation, and ethnic integration. Full article

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments. Full article

Accurate prediction of the International Roughness Index (IRI) is critical for road safety and maintenance decisions. In this study, we propose a novel Physics-Aware Informer (PA-Informer) model that integrates the efficiency of the Informer structure with physics constraints derived from partial differential equations (PDEs). The model addresses two key challenges: (1) performance degradation in short-sequence scenarios, and (2) the lack of physics constraints in conventional data-driven models. By embedding residual PDEs to link IRI with influencing factors such as temperature, precipitation, and joint displacement, and introducing a dynamic weighting strategy for balancing data-driven and physics-informed losses, the PA-Informer achieves robust and accurate predictions. Experimental results, based on data from four climatic regions in China, demonstrate its superior performance. The model achieves a Mean Squared Error (MSE) of 0.0165 and R² of 0.962 with an input window length of 30 weeks, and an MSE of 0.0152 and R² with an input window length of 120 weeks. Its accuracy is superior to that of other models, and the stability of the model when the input window length changes is far better than that of other models. Sensitivity analysis highlights joint displacement and internal stress as the most influential features, with stable sensitivity coefficients (S_p ≈ 0.89 and S_p ≈ 0.81). These findings validate the PA-Informer as a reliable and scalable tool for predicting pavement performance under diverse conditions, offering significant improvements over other IRI prediction models. Full article

Machine learning algorithms are essential tools for developing Mineral Prospectivity Models (MPMs), enabling a data-driven approach to mineral exploration. This study integrated airborne geophysical, topographic, and geological data with a mineral system framework to build MPMs for iron oxide–copper–gold (IOCG) and hydrothermal nickel deposits in the Southern Copper Belt of the Carajás Province, Brazil. Seven machine learning algorithms were tested using stratified 10-fold cross-validation: Logistic Regression, k-Nearest Neighbors, AdaBoost, Support Vector Machine (SVM), Random Forest, XGBoost, and Multilayer Perceptron. SVM delivered the highest classification accuracy and robustness, highlighting new mineralized zones while minimizing false positives and negatives, and accounting for geological complexity. SHapley Additive ExPlanations (SHAP) analysis revealed that structural controls (e.g., faults, shear zones, and geochronological contacts) exert a stronger influence on mineralization patterns than lithological factors. The resulting prospectivity maps identified geologically distinct zones of IOCG and hydrothermal nickel mineralization, with high-probability closely aligned with major structural corridors oriented E–W, NE–SW, and NW–SE. Results also suggest an indirect association with volcanic units, Orosirian A1-type granites and Neoarchean A2-type granites. Full article