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Enhancing artistic style classification through a novel ArtFusionNet framework
Identification of dominant global and local modes behind dynamic stiffness valleys in BIW structures via modal contribution and ESE
This study proposes a novel methodology that integrates modal contribution analysis with Element Strain Energy (ESE) distribution to identify the dominant modes causing dynamic stiffness variations in Body-in-White (BIW) structures. As Noise, Vibration, and Harshness (NVH) performance becomes increasingly critical in automotive design, accurately identifying the sources of dynamic stiffness deficiencies in the early design stages is imperative. This research addresses a significant gap in existing literature, where traditional methods struggle to distinguish between global and local modes in high-frequency, dense modal environments. By systematically analyzing the impact of both global and local modes on dynamic stiffness at key vehicle body connection points, our findings demonstrate the critical importance of prioritizing higher-frequency modes with localized strain energy concentrations for early-stage structural analysis. The proposed approach effectively tackles challenges such as modal overlap and frequency discrepancies, thereby enhancing the precision of NVH diagnostics and providing a reliable framework for targeted structural optimization. Consequently, this work offers a substantial advancement in the understanding and enhancement of NVH performance in automotive body structures, contributing to more efficient and effective design processes.
A new biomarker combination differentiates viral from bacterial infections and helps monitoring response to antibiotics in hospitalized children
Development and validation of a nomogram for all-cause mortality in osteoporosis patients over five years
Purpose Osteoporosis significantly increases fracture risk and mortality, yet robust tools for predicting long-term mortality in this population are lacking.This study aimed to develop and validate a nomogram for predicting 5-year all-cause mortality among patients with osteoporosis. Methods A retrospective cohort study was conducted using data from 2,165 osteoporosis patients sourced from the NHANES database (2007–2023; training set) and 304 patients from Dandong Central Hospital (2017–2024; validation set). Potential risk factors were analyzed through LASSO regression, followed by multivariate logistic regression, to identify independent predictors.A nomogram was constructed employing significant predictors. Finally, the C-index, ROC curve, calibration curve, and decision curve analysis were utilized to validate the model in both the training and validation sets. Results In the study population, 192 patients died in the training set and 36 patients died in the experimental set. At the same time, we collected detailed baseline demographic data. Specifically, the age distribution of the training set was 56.07 ± 17.62, and that of the experimental set was 57.11 ± 18.34. Among them, 49.52% of the training set were male, and 50.99% of the experimental set were male. During the study period, we recorded 228 deaths. Seven independent predictors of 5-year all-cause mortality were identified: increased Age (OR=1.090,95%Cl: 1.115–2.313), Male gender (OR=1.606,95%Cl: 1.071–1.109), Smoking (OR=1.945,95%Cl: 1.289–2.933), higher FBG (OR=1.006,95%Cl: 1.002–1.010), higher Uric acid (OR=1.177,95%Cl: 1.039–1.332); Alcohol use (OR=0.583,95%Cl: 0.410–0.827) and higher BMI (OR=0.946,95%Cl: 0.909–0.985) were protective. The resulting nomogram demonstrated strong discriminatory ability in both the training set (AUC = 0.834) and validation set (AUC = 0.862). In the validation set, the precision rate was 0.514, the recall rate was 0.5, and the F1-score was 0.507. Calibration plots and the Hosmer-Lemeshow test indicated good agreement between predicted and observed outcomes (p > 0.05). Decision curve analysis confirmed significant clinical utility across a wide range of risk thresholds. Conclusion This study developed and validated a novel nomogram incorporating seven common clinical factors, which can predict the 5-year all-cause mortality risk in patients with osteoporosis. Although the tool demonstrated good performance and has the potential to assist in clinical risk stratification and personalized management, there are still some limitations in the study design. Therefore, its clinical applicability should be interpreted with caution until further external validation.
Quantitative phenolic profiling and protective effects of grape seed extract on mancozeb-induced cellular and genetic toxicity
Integration of mathematical modeling and economics approaches to evaluate strategies for control of Salmonella Dublin in a heifer-raising operation
Salmonella Dublin infections in heifer-raising operations (HROs) cause animal health and economic losses for these operations and represent a pathogen source for dairy farms obtaining replacement heifers from HROs. To improve control of S. Dublin, we (i) developed a mathematical model of S. Dublin transmission on a HRO, (ii) evaluated the vaccine effectiveness and cleaning improvements for controlling the infection, and (iii) evaluated the influence of infection and control strategies on the HRO’s operating income. We developed a modified Susceptible-Infected-Recovered-Susceptible model of S. Dublin spread in a batch-stocking HRO post-introduction of an index case, with stochasticity introduced through Monte Carlo simulations. Epidemiological outcomes (S. Dublin-induced deaths and abortions during raising and S. Dublin carriers and asymptomatic infections among raised replacement heifers) and operating income per 100-head raised on a HRO over a 2-year simulation were compared between control scenarios. We validated our model against S. Dublin infection data in cattle. Partial rank correlation coefficient analysis and classification trees were used to determine parameter influence on model outcomes. Our model predicts a median of 37 carriers and 92 asymptomatic infections among raised replacement heifers out of 2,330 heifers that departed the operation by the end of the 2-year simulation period, suggesting a relevant role of HROs in spreading S. Dublin. Increasing barn floor cleaning frequency (to a maximum of 12x per day) meaningfully reduced the S. Dublin epidemiological outcomes and improved the HRO’s operating income. Depending on the cost of cleaning, the median operating income increased between 1.2% to 10.6% in the first year when cleaning 12x per day compared to baseline (cleaning 1x per week). In most cost scenarios, predictions do not support using a vaccine that solely reduces mortality, even when paired with stringent cleaning measures. The developed model is expected to aid efforts to control S. Dublin in HROs.
A high efficiency active X2G boost converter with hybrid optimized proportional integral controller for PV powered EV charging applications
Abstract Photovoltaic (PV) integration with Electric Vehicles (EV) plays a pivotal role in promoting sustainable transportation and fostering clean energy ecosystems. However, the development of high-efficiency, high-gain power converters and robust control strategies remains a critical challenge. This paper presents an innovative approach to enhancing PV based EV charging systems through the development of a novel Active X2G Boost Converter, which uniquely integrates a quasi Z-source network (qZSN) with a voltage multiplier stage in a single-switch configuration. This design significantly improves voltage gain, energy conversion efficiency and reduces component stress, addressing key limitations in existing converter topologies. A standout feature of the proposed system is the implementation of a Hybrid Siberian Tiger–Meerkat Optimized Proportional-Integral (HSTM-PI) controller, a newly introduced metaheuristic control strategy that blends the global search capability of Meerkat Optimization Algorithm (MOA) with local refinement strength of the Siberian Tiger Optimization (STO). This hybridization ensures faster convergence, enhanced dynamic voltage regulation and greater control robustness under fluctuating solar and load circumstances. The system also incorporates a bidirectional converter, battery storage, and grid synchronization through a three-phase inverter. Comprehensive mathematical modelling, MATLAB simulation and experimental validation confirm the system’s effectiveness, achieving a notable 96% efficiency, outperforming conventional designs in both regulation precision and operational reliability.
Land consolidation drives changes in soil bacterial community structure and promotes positive bacterial interactions
Understanding how land consolidation influences bacterial community structure and interactions is essential for advancing ecological restoration and sustainable land management. However, current knowledge remains insufficient regarding the micro-scale ecological effects of this comprehensive management practice. This study investigated an arable land consolidation project in the Yangtze River Delta plain, where 50 soil samples were collected from consolidated (n = 36) and non-consolidated (n = 14) paddy fields. A comparative analysis was conducted to evaluate the impacts of land consolidation on soil microbial community structure and ecological processes. The findings revealed that land consolidation significantly altered edaphic factors, with moisture content, pH, total nitrogen, and organic matter identified as the dominant drivers of bacterial community structure. Consolidation also reduced the influence of spatial heterogeneity on community composition. Variance partitioning analysis showed edaphic variables (8.84%) and land consolidation (4.34%) significantly contributed to the variations in bacterial community structure. Consolidated fields exhibited a better fit to the neutral community model, suggesting that land consolidation may improve the migration ability of bacterial communities by enhancing soil homogeneity and reducing habitat isolation. The broader niche breadth observed in land consolidation fields further indicated that bacterial communities possess stronger environmental adaptability and community stability, providing a foundation for positive interactions. Interaction analyses revealed higher species coexistence after land consolidation, characterized by stronger positive cohesion and a higher proportion of positive associations in co-occurrence networks. Collectively, these findings suggest that land consolidation could promote positive bacterial interactions, potentially enhancing overall community stability.
Bayesian optimization of capillary pressure data in hydraulic flow units using NMR
A diffusion of innovations measurement scale for reinvention, relative advantage, compatibility, complexity, trialability and observability
Diffusion of innovations (DOI) theory identifies critical factors that influence technology adoption rates and offers a predictive model for understanding how innovations spread through populations. While DOI theory encompasses six key perceptual characteristics (relative advantage, compatibility, complexity, trialability, observability, and reinvention), most empirical research operationalizes only Rogers’ five core attributes, rarely integrating reinvention despite its theoretical importance for understanding post-adoption adaptation. This research develops and validates a comprehensive scale measuring all six DOI characteristics, with particular attention to the reinvention construct. Through three independent samples (n = 2,019), we test the scale’s validity within a nomological network, creating an adaptable instrument for studying innovation diffusion that captures the full scope of DOI theory.
Current mosquitoes evolved more recently than previously thought
Correlation analysis between frequency of gastrointestinal bleeding episodes and abnormal coagulation indexes in digestive system tumors
Active expiration reduces hypercapnia in lung failure – results of the prospective interventional ActiveEx study and development of a prototype device for automated application
Background This study investigates the efficacy of Intermittent Abdominal Pressure Ventilation (IAPV) and Expiratory Rib Cage Compression (ERCC) in reducing hypercapnia among critically ill, mechanically ventilated patients. It also assesses the feasibility of automating these techniques using a self-developed, ventilator-synchronized device on a dummy. Methods This single-arm feasibility study was conducted in two intensive care units at Charité – Universitätsmedizin Berlin, including critically ill patients with hypercapnic lung failure, with additional lab testing at the Technical University of Berlin. Manual IAPV and ERCC were applied to patients, and automation feasibility was tested on a dummy using prototype device. Primary outcomes included changes in tidal volume and partial pressure of carbon dioxide (PaCO2) and device effectiveness at different PEEP levels. Results In nine hypercapnic patients, manual IAPV increased tidal volume from 5.72 to 8.85 mL/ kg predicted bodyweight (p < 0.001, relative effect (CI) 0.93 (0.79–1.06)) and ERCC from 5.79 to 8.13 mL/ kg predicted bodyweight (p < 0.001, relative effect (CI) 0.93 (0.80–1.05)). PaCO2 reduced after 20 minutes with both techniques (IAPV: from 65 to 52 mmHg, p < 0.01, relative effect (CI) 0.15 (0.01–0.28); ERCC: from 61 to 51 mmHg, p= < 0.01, relative effect (CI) 0.22 (0.07–0.37)). A transient decrease in oxygenation was fully and rapidly reversible. The automated device doubled tidal volumes in dummy simulations, with greater effectiveness at higher PEEP-levels. Conclusion Manual and automated ventilator-synchronized IAPV and ERCC were associated with improved ventilation. Their potential role in managing hypercapnic respiratory failure—such as in weaning failure, obstructive lung disease, or neuromuscular weakness—remains a subject for future clinical research. German Registry of Clinical Trials (DRKS00027397)