Search Results (40,853)

Accurate prediction of post-construction settlement in high-speed railway (HSR) soft foundations is critical for operational safety yet challenging due to the non-equidistant and non-stationary nature of observation data. This study systematically evaluated the robustness and accuracy of settlement prediction models using a Monte Carlo simulation approach. A numerical model incorporating the permeability characteristics of soft foundations was established to simulate stochastic system responses. Furthermore, an innovative multi-metric evaluation framework was constructed using the entropy weight method, integrating goodness-of-fit, prediction accuracy (systematic error), and stability (random error). Four classical empirical models—Hyperbolic, Exponential Curve, Asaoka, and Hoshino—were assessed. The results indicate that: (1) The Hyperbolic Method significantly outperformed other models ($p<0.01$) in goodness-of-fit (mean correlation coefficient: 0.983 ± 0.006) and accuracy (systematic error: 3.2% ± 1.1%); (2) The Hoshino Method exhibited optimal stability, characterized by the lowest random error (3.8 ± 2.0 mm); and (3) Model performance showed a significant positive correlation with the permeability coefficient ($R^2>0.92$). Validated by five distinct engineering cases, the comprehensive performance ranking was determined as: Hyperbolic > Hoshino > Exponential Curve > Asaoka. These findings provide a scientific strategy for model selection under non-stationary conditions and offer theoretical support for refining railway deformation monitoring standards. Full article

The present study examines the well-being of workers in an Italian manufacturing plant, focusing on work engagement, emotional exhaustion and work ability. These dimensions have received relatively little attention in manufacturing contexts. Utilising a person-centred approach, the objective is to identify distinct subjective well-being profiles among Italian manufacturing workers and to examine how work-related psychosocial characteristics differentiate these profiles. The research, which collected data from 340 workers (predominantly male at 62.1%) between July and September 2023, focused on work engagement, emotional exhaustion, and work ability—factors that have been previously understudied in manufacturing environments. Through cluster analysis, researchers were able to identify three worker profiles. The largest group, designated “Motivated & Healthy” (45.3%), exhibited the most favourable characteristics: strong work engagement, minimal emotional exhaustion, and adequate work ability. These workers reported experiencing reduced physical demands, greater autonomy in decision-making, and superior rewards compared to their colleagues. The second-largest group, “Motivated & Stressed” (32.5%), demonstrated a mixed profile. While maintaining average work engagement, these workers experienced high levels of emotional exhaustion and diminished work ability. The smallest group, termed “Disillusioned” (22.2%), consisted entirely of blue-collar workers and exhibited the most concerning pattern: low engagement, high exhaustion, and mediocre work ability. This group also reported the most challenging working conditions, including the highest physical and cognitive demands, least decision-making authority, and lowest rewards. The study corroborates earlier research findings by identifying significant relationships between work engagement and work ability (positive correlation) and emotional exhaustion (negative correlation). These results suggest that manufacturing facilities might benefit from tailoring their support strategies to address the specific needs of each worker profile, rather than applying one-size-fits-all solutions. Full article

Physiologically based pharmacokinetic (PBPK) models are widely used in the context of personalized medicine, as they allow for the evaluation of dosing schedules and routes of administration by predicting absorption, distribution, metabolism and excretion (ADME) of drugs in biological systems. Traditionally, PBPK models have been developed and applied at the population level, enabling the characterization of predefined cohorts, which remains limited in supporting true precision dosing. In this review, we explored the increasingly common shift from population-based to individual PBPK modelling, where individuals are modelled as virtual twins (VTs). Through the inclusion of additional patient-specific data, such as demographic, physiological, phenotypic and genotypic information, models can be personalized, moving beyond traditional one-size-fits-all strategies. Overall, incorporating individual patient data (e.g., septic, psychiatric, cardiac, or neonatal populations) improves model performance. Physiological parameters, particularly renal function, show strong potential given their role in drug elimination, while demographic variables enhance predictive accuracy in certain studies. In contrast, the benefits of including cytochrome P450 (CYP) phenotypic and genotypic data remain inconsistent. We further emphasize methodologies used to evaluate model performance, with a focus on clinical validation through comparisons between predicted and observed concentration-time profiles. Key challenges, including limited sample sizes and data availability, that may compromise predictive precision, are also discussed. Finally, we highlight the potential integration of PBPK-based VTs into broader digital twin frameworks as a promising path toward clinical translation, while acknowledging the critical barriers that must be addressed to enable routine clinical implementation. Full article

Probabilistic streamflow models play a pivotal role in quantifying hydrological uncertainty and form the backbone of modern risk management strategies for flood and drought forecasting, water allocation planning, and the design of resilient infrastructure. Unlike deterministic approaches that yield single-point estimates, these models provide a spectrum of possible outcomes, enabling a more realistic assessment of extreme events and supporting informed, sustainable water resource decisions. By explicitly accounting for natural variability and uncertainty, probabilistic models promote transparent, robust, and equitable risk evaluations, helping decision-makers balance economic costs, societal benefits, and environmental protection for long-term sustainability. In this study, we introduce the bounded half-logistic distribution (BHLD), a novel heavy-tailed probability model constructed using the T–Y method for distribution generation, where T denotes a transformer distribution and Y represents a baseline generator. Although the BHLD is conceptually related to the Pareto and log-logistic families, it offers several distinctive advantages for streamflow modeling, including a flexible hazard rate that can be unimodal or monotonically decreasing, a finite lower bound, and closed-form expressions for key risk measures such as Value at Risk (VaR) and Tail Value at Risk (TVaR). The proposed distribution is defined on a lower-bounded domain, allowing it to realistically capture physical constraints inherent in flood processes, while a log-logistic-based tail structure provides the flexibility needed to model extreme hydrological events. Moreover, the BHLD is analytically characterized through a governing differential equation and further examined via its characteristic function and the maximum entropy principle, ensuring stable and efficient parameter estimation. It integrates a half-logistic generator with a log-logistic baseline, yielding a power-law tail decay governed by the parameter $\beta$, which is particularly effective for representing extreme flows. Fundamental properties, including the hazard rate function, moments, and entropy measures, are derived in closed form, and model parameters are estimated using the maximum likelihood method. Applied to four real streamflow data sets, the BHLD demonstrates superior performance over nine competing distributions in goodness-of-fit analyses, with notable improvements in tail representation. The model facilitates accurate computation of hydrological risk metrics such as VaR, TVaR, and tail variance, uncovering pronounced temporal variations in flood risk and establishing the BHLD as a powerful and reliable tool for streamflow modeling under changing environmental conditions. Full article

Plasmon-driven surface catalysis has attracted significant interest due to its capacity to integrate near-field enhancement and hot-carrier effects at the nanoscale synergistically. In this work, nanoporous Au-Ag shells (NPASs) were prepared via a galvanic replacement process. The coupling of p-nitrothiophenol (PNTP) to form 4,4′-dimercaptoazobenzene (DMAB) was used as a model reaction to evaluate plasmonic catalytic kinetics on three substrates, including NPASs, Au nanoparticles (Au NPs), and Ag nanoparticles (Ag NPs), under 532 and 633 nm excitation. TEM, XRD, EDX, and HAADF-STEM analyses confirmed that the NPASs exhibited a hollow nanoporous morphology and a homogeneous Au-Ag alloy structure. UV-Vis extinction spectroscopy revealed a broadband response in the visible region, with a main peak at ~683 nm and a shoulder at ~542 nm. Based on in situ time-resolved SERS monitoring and first-order kinetic fitting, all three substrates showed faster conversion rates under 532 nm excitation. To quantitatively assess wavelength selectivity, a wavelength-dependent factor (R = k₅₃₂/k₆₃₃) was introduced. Quantitative analysis demonstrated that Au NPs exhibited the most significant R value (15.0), followed by Ag NPs (2.3), whereas NPASs exhibited the smallest R value (1.7). This distinct difference indicated that the wavelength selectivity of monometallic Au NPs was primarily governed by the resonant matching between the LSPR and the incident wavelength. In contrast, the broadband extinction of NPASs enabled strong optical responses at both wavelengths, resulting in a significantly weaker wavelength dependence. This work provides essential experimental evidence for designing plasmonic catalytic substrates with improved wavelength adaptability. Full article

Background/Objectives: Intraoperative bleeding remains one of the major challenges in pediatric liver transplantation (PLT), contributing significantly to perioperative morbidity, transfusion-related complications, and prolonged recovery. Although viscoelastic testing has improved intraoperative hemostatic management, there are currently no validated preoperative tools capable of predicting bleeding risk in this vulnerable population. Methods: We conducted a prospective, single-center observational study including 43 consecutive pediatric patients who underwent orthotopic liver transplantation between May 2008 and August 2009. A comprehensive dataset encompassing demographic, clinical, biochemical, and surgical variables was collected. A multivariable linear regression model was developed to predict intraoperative blood loss (IBL). Variable selection was guided by Mallows’ Cp criterion to ensure optimal model fit and clinical interpretability. Model performance was assessed using adjusted R², diagnostic residual analysis, and internal validation to verify regression assumptions. Results: Six independent predictors of IBL were identified: presence of ascites, prior abdominal surgery, operative time, baseline fibrinogen concentration, platelet count, and recipient weight. The final model explained 35.2% of IBL variance (adjusted R² = 0.352; F = 7.68; p < 0.001). Model diagnostics confirmed linearity, normal distribution of residuals, and homoscedasticity, supporting its robustness and reliability. Conclusions: This multivariable model provides an interpretable, clinically applicable framework for individualized preoperative estimation of blood loss in PLT. It may assist in planning perioperative patient blood management strategies and serve as a foundation for future decision-support systems. Limitations include the single-center design and modest sample size; however, internal validation supported the stability and reliability of the model. Full article

Fibromyalgia is a complex chronic disorder involving persistent widespread pain accompanied by functional limitations, cognitive impairments, and alterations in neural processing. Previous research indicates that exercise-based interventions can play a key role in alleviating symptom burden and enhancing physical performance; however, there is limited evidence regarding their impact on neurophysiological mechanisms. Creative dance, in combination with strength training, may stimulate both motor and cognitive systems, promoting brain plasticity and functional improvements. This study will analyze the effects of a six-week strength and creative dance program on physical fitness under single- and dual-task conditions in women with fibromyalgia and will explore the associated changes in brain electrical activity and autonomic modulation. Methods: This randomized controlled trial will be divided into an exercise group (n = 22) and a control group (n = 22). The 6-week supervised intervention consists of two 60-minute sessions per week, combining strength exercises and creative dance. Primary outcomes include physical fitness tests (strength, mobility, balance, and agility gait test in single-task and dual-task), fibromyalgia symptoms, and quality of life. Secondary outcomes include changes in electroencephalography, heart rate variability, physical activity level, and fear of falling. Statistical analyses will compare within- and between-group differences using non-parametric tests and effect sizes. It is hypothesized that the intervention will improve physical fitness and dual-task performance, alongside increases in brain activity power. This study may provide insights into the neurophysiological mechanisms underlying the benefits of exercise benefits in fibromyalgia. Full article

Bacillus anthracis displays susceptibility to penicillin despite harboring a β-lactamase gene, a phenotype governed by the anti-sigma factor RsiP. While RsiP represses σ^P-dependent β-lactamase expression, its broader roles in physiology and virulence remain unclear. This study aimed to define the global regulatory functions of RsiP beyond antibiotic resistance. Deletion of rsiP significantly upregulated the nprR gene, which is an important quorum-sensing (QS) system regulator and enhanced protease secretion. The ΔrsiP mutant caused higher mortality in cellular and Galleria mellonella models and triggered elevated inflammatory cytokines (IL-6, IL-1β, TNF-α, MIP-2) in macrophages models. Surprisingly, in DBA/2 mice models, ΔrsiP was attenuated, with increased host survival and reduced bacterial loads. Competitive indices (CI) confirmed fitness defects in mice (spleen CI = 0.39; liver CI = 0.42). These defects were not due to altered oxidative stress tolerance but were attributed to impaired macrophage internalization of ΔrsiP spores, reducing early colonization. Our findings indicate that RsiP not only modulates β-lactam resistance but also influences extracellular protease activity and host adaptation. Full article

(1) Objective: To compare the marginal and internal fit of 3D-printed and milled three-unit fixed dental prostheses (FDPs) made from tetragonal zirconia polycrystal (3Y-TZP). (2) Methods: Three-unit FDPs were designed for a typodont maxillary model with crown preparation for the second premolar and second molar. Nominal cement gap widths were set to 30 µm at the margins and 80 µm internally. A total of 40 FDPs (n = 10/group) differing in wall thickness (w = 0.6/1.0 mm) and support structures (with/without a stiffening frame) were fabricated from 3Y-TZP by 3D printing. A total of 10 milled FDPs with w = 0.6 mm served as a control group. After adhesive cementation on the respective replicated maxillary models, FDPs were sectioned and the cement gap dimension was assessed with a digital microscope. The marginal and internal fit found for the different test groups were compared using non-parametric tests. (3) Results: The best marginal fit—qualified by median/ maximum marginal gap width—was given for milled FDPs (79/127 µm vertical; 85/171 µm tangential), whereas the marginal fit of 3D-printed FDPs with w = 0.6 mm and regular support structures was the worst (144/284 µm vertical; 107/198 µm tangential). Use of an additional support frame improved the marginal fit of 3D-printed FDPs, in particular FDPs with w = 0.6 mm (108/197 µm vertical; 87/161 µm tangential). (4) Conclusions: 3D-printed zirconia FDPs showed conditionally comparable marginal and internal fit as their milled counterparts, but with slightly higher scattering. When fabricating thinner 3D-printed FDPs, additional support structures are mandatory to achieve clinically well-fitting restorations. Full article

Italy is estimated to host thousands of abandoned mines, many of which contain large volumes of mine residues that negatively affect land and aquatic ecosystems, also posing a risk to human health. This study evaluates the effectiveness of spaceborne imaging spectroscopy combined with laboratory spectroscopy for characterizing the mineralogy and geochemistry of residues from the abandoned Montevecchio sulfide mine in southwestern Sardinia, a site recognized as a significant source of environmental pollution. Mine tailings and their downstream dispersion along the Rio Irvi River were systematically studied and sampled in the field. Collected samples were analyzed in the lab using an Analytical Spectral Device (ASD) spectroradiometer, complemented by powder X-ray Diffraction (XRD) for mineralogical characterization. Affected zones were subsequently mapped using the Environmental Mapping and Analysis Program (EnMAP) hyperspectral satellite data at a 30 m spatial resolution, by applying a polynomial fitting technique to the image spectra. The results reveal the presence of Fe- and Zn-bearing sulfates and oxy/hydroxides, indicative of acidic-to-circum-neutral drainage conditions in the mine tailings and along affected streams. Specifically, EnMAP was able to detect jarosite and subtle chemical and physical variations in Fe-hydroxides. This integrated approach enabled the delineation of environmental conditions and zones with varying acidity based on the spectral characteristics of secondary minerals. Overall, the study demonstrates the potential of EnMAP data for mapping acid mine drainage and assessing environmental impacts in legacy mining areas. Full article

The performance upgrading of stratospheric airships hinges on breakthroughs in the mechanical properties of envelope materials. As a multi-layer composite, the envelope’s load-bearing layer exhibits orthotropic and nonlinear mechanical behaviors owing to its unique structure and manufacturing process. To overcome the limitations of traditional testing methods and classical strength criteria in characterizing envelope materials, this paper presents a systematic investigation of typical airship envelope materials. The classical cruciform biaxial specimen was modified with a double-layer heat-sealed loading arm design to ensure preferential failure of the core region. Combined with digital image correlation (DIC) equipment, tensile tests were conducted under seven warp–weft stress ratios to acquire full-range stress–strain data. A three-dimensional stress–strain response surface was fitted based on the experimental results, and biaxial tensile constitutive models with varying precisions were established. Furthermore, a five-parameter implicit quadratic strength criterion was adopted to characterize the failure envelope of the envelope material. The model was calibrated using five biaxial failure points and independently validated against uniaxial tensile strengths, achieving a prediction error of less than 4%. The criterion’s generalization capability was enhanced through systematic parameterization based on the present test data. This work provides experimental evidence and reliable support for the engineering design and strength prediction of envelope materials. Full article

The application Strava is widely used among runners, yet its influence on motivational processes remains unclear. This study examined endurance sport self-efficacy, achievement goals, fitness indicators, and Strava use in 225 active club runners using validated quantitative instruments and qualitative survey data. Self-efficacy and achievement goal scores were generally high. Greater endurance capacity was associated with higher self-efficacy and task-approach goals. Strava settings and subscription status were not associated with motivational outcomes; however, runners who had deleted training sessions due to perceived slow running pace scored higher on other-avoidance goals. Qualitative findings showed that Strava can enhance training through feedback, routine building, and social connection, while also introducing pressure, comparison, and stress, particularly during injury or reduced performance. Several participants reported adapting their use of the app to preserve motivation. Overall, Strava’s motivational impact appears context dependent and shaped by both its features and individual usage patterns. Full article

Spatial resolution is a critical parameter for optical frequency domain reflectometry (OFDR). Phase-sensitive OFDR (Φ-OFDR) measures strain by detecting phase variations between adjacent sampling points, having the potential to achieve the theoretical limitation of spatial resolution. However, the results of Φ-OFDR suffer from large fluctuations due to multiple types of noise, including coherent fading and system noise. This work presents an OFDR-based strain sensing method that combines phase demodulation with cross-correlation analysis to achieve high spatial resolution. In the phase demodulation, the frequency-shift averaging (FSAV) and rotating vector summation (RVS) algorithms are first employed to suppress coherent fading noise and achieve accurate strain localization. Then the cross-correlation approach with an adaptive window is proposed. Guided by the accurate strain boundary obtained from phase demodulation, the length and position of the cross-correlation window are automatically adjusted to fit for continuous and uniform strain regions. As a result, an accurate and complete strain distribution along the entire fiber is finally obtained. The experimental results show that, within a strain range of 100–700 με, the method achieves a spatial resolution of 0.27 mm for the strain boundary, with a root-mean-square error approaching 0.94%. The processing time reaches approximately 0.035 s, with a demodulation length of 1.6 m. The proposed approach offers precise spatial localization of the strain boundary and stable strain measurement, demonstrating its potential for high-resolution OFDR-based sensing applications. Full article

Background: Visceral fat plays a central role in cardiometabolic risk among people with type 2 diabetes mellitus (T2DM), yet its assessment in routine clinical practice remains largely dependent on imaging techniques or indirect anthropometric measures. Identifying accessible blood-based markers that reflect visceral adiposity may facilitate improved phenotyping in this population. This study aimed to investigate whether circulating coiled-coil domain–containing protein 3 (CCDC3) reflects visceral fat accumulation in adults with T2DM. Methods: Public RNA-sequencing datasets and human adipose tissue samples were analyzed to identify CCDC3 as a visceral fat–enriched secretory gene. In this cross-sectional study of 160 adults with T2DM undergoing dual-energy X-ray absorptiometry, plasma CCDC3 was measured by ELISA. Associations between plasma CCDC3 and visceral fat area (VFA) were examined using multivariable regression. Logistic regression models for abdominal obesity (VFA ≥ 100 cm²), with and without CCDC3, were evaluated using receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA), and Shapley additive explanations (SHAP). Results: Circulating CCDC3 levels were positively associated with VFA (β = 3.11, p < 0.001), independent of demographic and metabolic factors. Incorporating CCDC3 into the baseline model significantly improved discrimination of abdominal obesity (AUC 0.820 vs. 0.663; p = 0.009). Calibration curves and DCA supported better model fit and higher net clinical benefit with CCDC3. SHAP analysis showed that CCDC3 contributed the greatest incremental importance beyond waist circumference, sex, and age. Conclusions: Circulating CCDC3 may serve as a blood-based biomarker reflecting visceral adiposity in adults with T2DM and provides complementary information beyond traditional anthropometric measures. Full article

In this paper, we propose a new nonparallel support vector machine for binary classification problems and name it the improved nonparallel support vector machine (IMNSVM). The IMNSVM uses a one-sided $\epsilon$-band and minimizes $\epsilon$ to achieve a better fitting effect for the same class of training points. By introducing a new variable, $\rho$, the IMNSVM keeps one class of training points at a certain distance from the hyperplane corresponding to another class of training points, keeping them as far away as possible so as to better adapt to the training points and better describe the difference in data distribution between different categories. The IMNSVM can degenerate into the standard support vector machine (SVM) under certain conditions and is applicable to a wider range of data types. Finally, numerical experiments also explain the effectiveness of the method. Full article