Search Results (11,321)

Background/Objectives: Traumatic bladder rupture often accompanies pelvic fracture, making it uncertain whether differences between extraperitoneal bladder rupture (EPBR) and intraperitoneal bladder rupture (IPBR) reflect the rupture site itself or overall trauma burden. Prior multi-institutional evidence has focused on EPBR. We therefore examined whether rupture site was associated with hospital length of stay after accounting for concomitant pelvic fracture and injury severity. Methods: We retrospectively reviewed 46 adults with definite traumatic bladder rupture treated at a single regional trauma center from January 2014 through December 2024. Rupture site was the primary exposure, hospital length of stay was the primary outcome, and intensive care unit (ICU) length of stay was the secondary outcome. Parsimonious exploratory log-linear models included rupture site, concomitant pelvic fracture, and Injury Severity Score (ISS). Results: Twenty-one patients had IPBR, 25 had EPBR, and 26 had concomitant pelvic fracture. In the adjusted primary-outcome analysis, rupture site was not clearly associated with hospital length of stay (EPBR versus IPBR adjusted ratio, 1.02; 95% CI, 0.59–1.76). The secondary adjusted analysis likewise showed no clear association with ICU length of stay (adjusted ratio for ICU days + 1, 1.31; 95% CI, 0.77–2.21). In unadjusted comparisons, pelvic fracture was observed more frequently with EPBR (72.0% versus 38.1%; p = 0.021; FDR q = 0.078), and EPBR was associated with longer hospital stay (median, 44.0 versus 24.0 days; p = 0.024; FDR q = 0.078) and ICU stay (8.0 versus 4.0 days; p = 0.007; FDR q = 0.037). Among 42 surgically treated patients, EPBR was associated with a longer admission-to-repair interval (adjusted ratio for days + 1, 2.21; 95% CI, 1.28–3.82). Conclusions: After adjustment for concomitant pelvic fracture and ISS, rupture site was not clearly associated with hospital or ICU length of stay. The conditional admission-to-repair finding may reflect complex trauma-care pathways rather than diagnostic delay. The frequent coexistence of bladder rupture and pelvic fracture reinforces the clinical importance of careful bladder assessment in patients with severe pelvic trauma. Full article

With the rapid expansion of AR/VR-based digital platforms, there is an increasing demand for the automated generation of size-varied 3D shoe assets. However, conventional CAD-based linear scaling and PCA-based global statistical shape models are limited in their ability to capture the non-uniform and locally non-linear deformations observed in insole contours. This study proposes a size-adaptive insole contour generation framework that integrates image-based contour extraction, B-spline parametric representation, and type-specific SVR-based local displacement regression. By decomposing control-point displacements into tangent–normal components, the proposed method directly models non-linear curvature variations associated with size progression without relying on dimensionality reduction. Quantitative evaluations under an eight-fold leave-one-insole-out (LOIO) protocol show that the proposed method achieves a mean Hausdorff distance of 4.73 mm, a Chamfer distance of 1.76 mm, and an IoU of 0.929. It significantly outperforms the no-clustering ablation in both the Hausdorff distance (6.13 mm; $p=0.032$) and IoU ($p=0.033$), as well as the PCA-based kernel ridge regression baseline across all three metrics ($p<0.05$). No statistically significant differences were observed between the proposed method and the ratio scaling, Gaussian process, or thin plate spline baselines ($p>0.05$). PCA-Linear showed a small numerical advantage in the Hausdorff distance (4.16 mm), but the difference was not statistically significant ($p=0.187$). A sensitivity analysis further reveals the existence of a practical control-point density region that balances geometric fidelity and model complexity. This work provides a data-driven parametric approach for standard size-based digital grading automation and establishes a technical foundation for future extension to full 3D shoe mesh generation. Full article

The accurate determination of vitamin B12 in fortified food products presents considerable analytical challenges due to its low concentrations (μg kg ^–1) and its binding to proteins in dairy products. This study aimed to develop a reliable and accurate method for determining vitamin B12 in yogurts that offers a robust, sensitive, and accurate tool for the routine quantification of vitamin B12 in complex dairy matrices, contributing to nutritional assessment and quality control in the food industry. Therefore, sample preparation including hydrolysis at high temperatures in an excess of cyanide, and utilization of SPE cartridges for analyte purification and preconcentration is essential. UHPLC-ESI-MS/MS analysis was conducted using a C18 column in positive ionization mode (ESI+). The experimental procedure was meticulously optimized, examining extraction conditions, such as the solvent used, the extraction time, and the effectiveness of SPE cartridges. The optimized method demonstrated excellent linearity (R² > 0.990), high recoveries (e.g., 82.7–96.6%), good precision (RSD < 20%), and adequate sensitivity (LOD: 0.38 μg kg ⁻¹ and LOQ: 1.1 μg kg ⁻¹). The method was successfully validated according to the Eurachem Guide, evaluated for uncertainty and corroborated through successful interlaboratory testing in cereal and milk matrices. Full article

High-dimensional monitoring and subspace analysis require contamination-resistant dependence summaries with subquadratic state complexity. Coordinatewise clipping limits individual entries but allows total row contribution to grow with dimension. Dual-Bound Fast Outlier-Robust Correlation Estimation combines fixed median-based calibration, coordinate clipping, parallel and residual contraction, and Frequent Directions sketching to estimate a normalized transformed second moment. Conditional on calibration, an exact-arithmetic companion has a simultaneous row envelope and an error decomposition for sampling, whole-row replacement, and sketch approximation. The implemented dense output is the Gram matrix of a column-normalized sketch factor, which is positive semidefinite in exact arithmetic and shares its factor with the subspace route. Active and peak calibration states are linear in dimension for fixed rank and calibration size before dense-output materialization. Minimax development over 112 configurations selected the reported parameter tuple. In matched-window synthetic experiments, Dual-Bound reduced mean projection error by 0.0858 under casewise and 0.0150 under coordinatewise Cauchy replacement relative to a tuned median-absolute-deviation version of Sketch-based Fast Outlier-Robust Correlation Estimation; clean and bounded structural differences were $-0.0015$ and $-0.0020$. Results depended on configuration and mechanism. External projection and donor-balanced reconstruction errors remained between 0.97 and 1.00, so biological recovery and population validation were not demonstrated. The analysis provides no unconditional calibration or convergence guarantee, no useful guarantee in terms of the cell-fault fraction, and no end-to-end implementation or unconditional floating-point guarantee. These results establish a focused design for conditional transformed-scatter sketching in high-dimensional streams. Full article

Australia’s Great Barrier Reef is a national icon, World Heritage Area, and under sustained threat from climate change and terrestrial land-use. It is also a complex AUD 95 billion asset generating 77,000 jobs and contributing AUD 9 billion annually to Australia’s economy, a 69% increase over the 2017 valuation despite climate pressures and coral bleaching events. With 1.2 million residents in the GBR catchment, reef health directly impacts local economies and communities. We propose that alignment of reef investments with collaborative regional resilience through a Great Barrier Reef Economic Zone will improve the resilience of the regions sustained by and who care for the GBR. More inclusive than standard linear mechanistic thinking, this circular approach will leverage the GBR’s international status to attract continuing investment in regional resilience and sustainable growth and improve GBR management outcomes. Full article

Most predictive approaches in educational data mining rely on complex models whose opacity limits practical adoption by classroom teachers, creating a gap between model sophistication and classroom usability. This gap is particularly acute at the class-group level, where institutional gradebook data are routinely aggregated for teacher-level planning but rarely modelled with an explicit account of when model complexity is actually justified. This paper addresses that gap: its novelty is to provide a structural explanation, grounded in group-level academic dynamics, for why linear models are highly competitive, rather than merely adequate, for this type of data, and to test this account empirically. An eight-year longitudinal dataset (2013/2014–2020/2021) from a Spanish secondary school—1070 class-group records across 32 subjects—was used to compare linear regression and Random Forest for final grade prediction, a Random Forest classifier against an XGBoost classifier for academic risk detection, and SHAP (SHapley Additive exPlanations)-based explainability, validated through Leave-One-Course-Out (LOCO) cross-validation. Within this dataset, linear regression consistently matches or outperforms Random Forest in both scenarios (R² = 0.857 with two assessments; R² = 0.740 with one), explained by stable cohort dynamics—baseline grades, teaching continuity, group composition—that produce a linear temporal structure (Spearman ρ > 0.81) leaving little predictive return for ensemble complexity in this setting. For the passing class, the Random Forest classifier achieves F1 = 0.972 with high inter-cohort stability (LOCO F1 ∈ [0.944, 0.984]); for the minority at-risk class, it outperforms XGBoost (F1 = 0.69 vs. 0.57), a gap consistent with the benefit of explicit class-imbalance handling, though fully disentangling this from a possible ensemble-family effect is left for future work. The 2019/2020 cohort is statistically anomalous (Mann–Whitney U, p < 0.001), reflecting an exogenous shift in the grade-generating process under emergency evaluation rather than evidence against the linearity account under normal conditions. Simple, transparent models operating on routinely collected gradebook data deliver actionable early-warning signals within the digital competence of most practising teachers; group-level prediction additionally protects student identity by ensuring no individual is labelled at-risk, combining predictive utility with ethical design. Full article

Background: Endotoxemia and oxidative stress may contribute to adverse body-composition changes in patients receiving maintenance hemodialysis, but their relationships with adiposity and muscularity remain incompletely defined. We investigated whether the concentrations of circulating lipopolysaccharide (LPS), soluble NOX2-derived peptide (sNox2-dp), and hydrogen peroxide (H₂O₂) showed differential associations with bioimpedance-derived body-composition compartments and explored their relationships with normalized protein catabolic rate (nPCR), a surrogate of protein intake. Methods: In this observational, cross-sectional study, adult patients receiving maintenance hemodialysis were enrolled at a single center. Serum LPS and sNox2-dp and H₂O₂ concentrations were measured before dialysis. Body composition was assessed by bioimpedance analysis; fat mass (FM) was used as an index of adiposity, whereas intracellular water indexed to height squared (ICW/h²) was used as a proxy of muscularity. Associations were examined using Spearman correlation and multivariable linear regression adjusted for age, sex, and nPCR. Results: A total of 58 participants were included with a median age of 73 years; 64% were male and mean body mass index was 24.6 ± 4.2 kg/m². LPS correlated with sNox2-dp (rho = 0.420, p = 0.001), whereas sNox2-dp correlated with H₂O₂ (rho = 0.352, p = 0.007); the LPS–H₂O₂ association was borderline (rho = 0.259, p = 0.050). sNox2-dp correlated positively with fat-free mass, total body water, ICW, and ICW/h², whereas LPS correlated with FM (rho = 0.305, p = 0.023). Participants with nPCR > 0.89 g/kg/day had higher LPS concentrations than those with lower nPCR (p = 0.015), and nPCR correlated with ICW/h² (rho = 0.36, p = 0.007). In multivariable analysis, LPS remained independently associated with FM (β = 0.210, p = 0.046). nPCR was positively associated with ICW/h² at the threshold of statistical significance (β = 1.393, p = 0.050), whereas sNox2-dp showed a nonsignificant positive trend (p = 0.060). Conclusions: In maintenance hemodialysis, endotoxemia and NOX2 activation showed differential associations with body composition: LPS with adiposity and sNox2-dp with muscularity. Higher nPCR was associated with both greater muscularity and higher LPS concentrations, suggesting a complex relationship between protein intake, the gut–oxidative-stress axis, and body composition. Full article

To address the quantitative distortion problem caused by the primary internal filtration effect (PIFE) resulting from coexisting substances in the fluorescence detection of aromatic pollutants, this study proposes a multi-component concentration quantification correction model that integrates transmittance absorbance and lateral (90°) fluorescence intensity. Using styrene as the target analyte and anthracene/phenanthrene as representative interferents, by establishing a coupled framework of excitation light decay dynamics and fluorescence emission, the nonlinear PIFE problem was transformed into a linear regression task. The experimental results show that: under the coexistence of anthracene and phenanthrene, the model reduces the detection deviation of styrene from 40 to 63% to within 10%, and the correction accuracy of the three-component mixed system is increased by 3–5 times. This work provides a theoretical framework for fluorescence quantitative analysis in complex systems, and also offers a new method for high-precision online monitoring of aromatic organic pollutants. Full article

Visible and synthetic aperture radar (SAR) images exhibit substantial nonlinear radiometric differences and geometric deformations due to their fundamentally different imaging mechanisms, making high-precision registration between the two modalities a long-standing challenge in remote sensing image processing. Existing deep learning-based cross-modal registration methods mostly adopt purely convolutional architectures or hybrid convolution–Transformer frameworks, which struggle to achieve a favorable trade-off between long-range dependency modeling and computational efficiency. Moreover, current methods generally rely only on heterogeneous cross-modal supervision for end-to-end training, while overlooking the geometric deformation priors embedded in intra-modal consistency. To address these issues, this paper proposes MTCPNet, a Mamba-based registration network with tri-branch consistency projection for SAR–visible image registration. Specifically, a feature consistency projection module is designed to project SAR and visible images into a modality-invariant shared feature space, with a feature consistency loss introduced to explicitly constrain cross-modal geometric alignment. A Mamba-based hybrid architecture serves as the feature extraction backbone, integrating the linear-complexity long-range dependency modeling of selective state space models with the local contextual representation of window-based self-attention. Under a tri-branch training paradigm, intra-modal consistency supervision and cross-modal matching supervision are jointly incorporated to optimize network parameters. Experimental results demonstrate that MTCPNet consistently outperforms state-of-the-art methods on multiple benchmark datasets, providing a promising solution for high-precision multisource remote sensing image registration. Full article

There is a growing need for high-efficiency power electronic converters that can effectively convert energy, regulate voltages, and enhance power quality in standalone power generators, as the use of renewable energy sources and battery energy storage devices increases. The quasi-impedance-source inverter (qZSI) has attracted significant interest due to its single-stage buck-boost operation, continuous input current, reduced reliance on passive elements, and increased reliability. In this paper, the control strategy and implementation of the qZSI with battery assistance for standalone photovoltaic energy generation are discussed. To analyze the operational characteristics and design the control strategy of the qZSI, the system equations are linearized around the nominal operating point to develop a small-signal model, from which the direct current (DC) side and alternative current (AC) side transfer functions are derived and used as the basis for controller design. Using the proposed model, hybrid controllers are designed to control the shoot-through duty cycle, maintain DC link voltage stability, and battery charging to achieve stable power generation. Furthermore, the SPWM technique is applied to produce AC power with minimal harmonic content and higher efficiency. Application results show stable dynamic behavior, effective battery energy management, improved voltage regulation, and reduced harmonic distortion in the output waveform. The main contribution is a low-complexity coordinated PI and PR control framework for standalone battery-assisted qZSI operation, experimentally validated under DC- and AC-side disturbances without requiring an additional battery-side power-conversion stage. Full article

Seismic site response analysis plays a fundamental role in predicting earthquake ground motions at the ground surface by accounting for soil effects on wave propagation. Although advanced nonlinear methods can accurately simulate complex soil behaviour, their use in routine engineering practice is challenging. Consequently, the Equivalent Linear (EQL) method remains widely adopted despite relying on simplifying assumptions, particularly the definition of the effective shear strain (ESS), which governs the selection of strain-compatible soil properties. An inappropriate ESS may lead to over- or underestimation of soil nonlinearity and, consequently, of the seismic response. This issue is especially relevant for mine tailings, whose cyclic behaviour remains poorly understood despite the significant risks associated with earthquake-induced failures of tailings storage facilities. This study investigates the influence of the ESS assumption using an in-house EQL code through a detailed case study and a parametric analysis comprising 2106 simulations, in which seventeen surface ground-motion intensity measures are evaluated as a function of the ESS coefficient, $R_{\gamma }$. The results confirm general trends reported in the literature but also identify cases in which neither the magnitude nor the direction of the effects of varying $R_{\gamma }$ can be inferred from $R_{\gamma }$ alone, as they depend on the interaction between the deposit response and the frequency content of the input motion. Consequently, while the conventional value of $R_{\gamma }=0.65$ is generally adequate for routine analyses, parametric studies are recommended for critical structures such as tailings storage facilities. Full article

Industrial water treatment plants are increasingly dependent on cyber–physical systems (CPS) and automated control processes for their operational safety and efficiency. However, the embedding of digital control networks exposes these critical infrastructures to sophisticated cyber–physical attacks, including malicious tampering with chemical dosing units and physical actuators. This paper proposes a robust, AI-enhanced anomaly detection framework designed to identify multi-stage malicious activities in water treatment systems using real-world industrial datasets. The proposed system is developed and validated on the Secure Water Treatment (SWaT) dataset, which contains multivariate sensor and actuator time-series data collected from a fully operational physical testbed under both normal operations and targeted cyber–physical attacks. First, high-frequency sensor noise is filtered, and cross-channel measurement reliability is maximized using a Kalman filter-based sensor fusion module. Subsequently, the fused-state vector is analyzed using an unsupervised Isolation Forest algorithm optimized for high-dimensional boundary isolation. To eliminate false negatives caused by stealthy, low-amplitude data injections that bypass purely statistical models, a deterministic, rule-based verification layer derived from physical process control logic is integrated. By integrating a discrete linear Kalman filter with an unsupervised Isolation Forest and deterministic physical rules, the framework effectively suppresses high-frequency sensor noise, achieving a 67.8% reduction in root mean square error (RMSE), while maintaining high detection accuracy across complex industrial attack scenarios. Experimental results demonstrate that the proposed hybrid framework yields superior detection capability, achieving a Precision of ≈95%, a Recall of ≈93%, a scenario-level F1-score of 94.1 % (alongside a sample-level F1-score of 21.5 %) and an edge inference latency of 0.6 ms, effectively demonstrating its suitability for deployment within simulated real-time industrial edge computing environments. The findings further confirm that combining statistical machine learning, state-space sensor fusion, and invariant physical process logic provides a resilient defense paradigm for securing critical industrial infrastructure against modern cyber–physical threats. Full article

Vegetation change is regarded as a key indicator of environmental change and ecosystem functional evolution. In mountainous regions, vegetation dynamics arise from complex and non-linear interactions among climate, topography, land use, and human activities, yet the mechanisms governing these interactions remain poorly understood. Clarifying long-term vegetation trajectories and their interacting controls is essential for understanding ecosystem structure and function. In this study, we integrated machine learning and causal modeling by combining random forest (RF) and structural equation modeling (SEM) to quantify both the relative importance and the direct and indirect pathways of natural and anthropogenic drivers of vegetation change in the Longnan region from 2000 to 2020. The RF results showed that the selected driving factors explained 82.62% and 72.39% of the spatial variation in vegetation cover in 2000 and 2020, respectively, with climatic and anthropogenic factors ranking as the most important drivers. Although ecological restoration activities contributed to an overall improvement in vegetation conditions, land-use heterogeneity and ecological constraints imposed by high elevation jointly produced contrasting local responses, resulting in vegetation degradation in surrounding areas. SEM revealed that the net influence of anthropogenic activities shifted from positive to negative over time, mainly due to land-use change, indicating a reorganization of human–vegetation interactions. Climate effects remained positive, with precipitation having a stronger influence than temperature. Topography moderated vegetation responses, as slopes below 40° favored vegetation growth. Soil effects shifted from positive to negative, likely associated with changes in soil organic matter. By jointly applying RF and SEM, this study captures both non-linear responses and causal pathways, providing a system-level perspective on the complex mechanisms underlying vegetation change. Full article

Accurate battery state estimation is essential for electric-vehicle battery management systems (BMSs), directly improving their safety, durability, and operational reliability. This study proposes an integrated degradation-diagnosis framework that is, to our knowledge, among the first to combine electrochemical impedance spectroscopy (EIS), direct-current internal resistance (DCIR), and state of health (SoH) within a single, quantitative, low-complexity analysis of a hybrid-vehicle NCM811 lithium-ion battery module. Cycling-test data measured at 0, 400, 800, and 1200 cycles were reanalyzed using power-law regression, end-of-life (EOL) extrapolation, and cross-metric correlation analysis; the dataset was then extended to 2000 cycles (six checkpoints in total) to test the reliability of long-term lifetime prediction. Three findings are experimentally demonstrated. First, the ohmic resistance remained essentially constant during cycling, whereas the interfacial resistance increased by +422.7%, identifying interfacial (not bulk) resistance growth as the dominant degradation pathway. Second, power-law models substantially outperformed conventional exponential models for RE, DCIR, and SoH (R² = 0.998, 0.999, and 0.990, respectively, vs. R² = 0.870 for the exponential SoH model); extending the dataset from four to six checkpoints narrowed the resulting EOL model-form uncertainty from a 3.5-fold to a 1.6-fold discrepancy (2776 vs. 9831 cycles, narrowing to 3124 vs. 4908 cycles). Third, a strong linear relationship between DCIR and SoH (R² = 0.956) was obtained, indicating that resistance-only monitoring can approximate SoH without full impedance measurement. Beyond these demonstrated results, the proposed framework offers potential value for SoH estimation, battery condition diagnosis, and state-estimation algorithm development in advanced BMSs; these broader applications have not been experimentally validated in this study and are discussed as directions for future work. Full article

In this study, an electrochemical sensor based on a graphitized carboxylated multi-walled carbon nanotube/polyaniline (G-COOH-MWCNTs/PANI) composite was developed for the highly sensitive detection of Pb²⁺ in seawater. A G-COOH-MWCNTs/PANI composite dispersion was prepared via a solution blending method and subsequently drop-cast onto a glassy carbon electrode (GCE) to fabricate the modified electrode. Differential pulse anodic stripping voltammetry (DPASV) was employed for the quantitative determination of Pb²⁺. The morphology of the composite was characterized by scanning electron microscopy (SEM), while the electrochemical behavior of the modified electrode was investigated using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Critical experimental parameters, including the type and pH of the supporting electrolyte, deposition potential, deposition time, and loading amount of the composite film, were systematically optimized. In addition, the optimal concentration ratio of G-COOH-MWCNTs to PANI was determined using an orthogonal experimental design. Under the optimized experimental conditions, the proposed sensor exhibited a linear response toward Pb²⁺ over the concentration range of 25–220 μg/L, with the regression equation Ip = 2.757C + 4.316 (R² = 0.997). The limit of detection (LOD), calculated at a signal-to-noise ratio (S/N) of 3, was 0.0337 μg/L. The sensor also demonstrated excellent reproducibility (relative standard deviation, RSD = 1.7%), satisfactory anti-interference capability, and good long-term stability, retaining 94.1% of its initial response after 35 days of storage. Spike recovery experiments using real seawater samples yielded recoveries ranging from 96.73% to 99.73%, with RSD values below 3%, indicating excellent accuracy and precision in complex seawater matrices. These results demonstrate that the proposed sensor enables accurate determination of Pb²⁺ in seawater without complicated sample pretreatment and exhibits considerable potential for applications in marine environmental monitoring of heavy metal contamination. Full article