Search Results (2,285)

Abdominal Aortic Aneurysm (AAA) remains a significant public health challenge, with an 82.1% increase in related fatalities from 1990 to 2019. In the United States alone, AAA complications resulted in an estimated 13,640 deaths between 2018 and 2021. In clinical practice, computed tomography angiography (CTA) is the primary imaging modality for monitoring and pre-surgical planning of AAA patients. CTA provides high-resolution vascular imaging, enabling detailed assessments of aneurysm morphology and informing critical clinical decisions. However, manual segmentation of CTA images is labor-intensive and time consuming, underscoring the need for automated segmentation algorithms, particularly when feature extraction from clinical images can inform treatment decisions. We propose a framework to automatically segment the outer wall of the abdominal aorta from CTA images and estimate AAA wall stress. Our approach employs a patch-based dilated modified U-Net model to accurately delineate the outer wall boundary of AAAs and Nonlinear Elastic Membrane Analysis (NEMA) to estimate their wall stress. We further integrate Non-Uniform Rational B-Splines (NURBS) to refine the segmentation. During prediction, our deep learning architecture requires $17\pm 0.02$ milliseconds per frame to generate the final segmented output. The latter is used to provide critical insight into the biomechanical state of stress of an AAA. This modeling strategy merges advanced deep learning architecture, the precision of NURBS, and the advantages of NEMA to deliver a robust and efficient method for computational analysis of AAAs. Full article

Non-uniform rational B-spline (NURBS) surfaces are effective for accurately modeling curved geometries, and research in this area has recently increased. In this study, point cloud data obtained from two challenging test environments (a convex wooden object and the widely used Stanford Bunny dataset from the literature) were used to predict the u and v parameter values corresponding to positions in the knot vectors, to determine the knot points of NURBS surfaces. The u and v parameters were predicted with accuracies of 92.60% and 93.20% for the wooden object, and 85.50% and 84.40% for the Stanford Bunny. The models’ decision-making processes were analyzed using explainable artificial intelligence (XAI) methods, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). Predicted knot points were compared with the calculated knot points, which are considered as actual, yielding root mean square errors (RMSE) of 0.09 mm for the wooden object and 0.02 mm for the Stanford Bunny. This study fills a gap in the literature by predicting knot points and providing XAI-based analyses, demonstrating that the approach effectively preserves the characteristic features of NURBS surfaces across different geometries. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

Background: The coronary slow flow phenomenon (CSFP) is an angiographic entity increasingly recognized in patients with angina and/or ischemia but non-obstructive coronary arteries (ANOCA/INOCA), associated with systemic inflammation, endothelial dysfunction, and microvascular abnormalities. The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a novel immunonutritional index that may reflect this multifactorial risk profile. Methods: This retrospective single-center case–control study included 122 patients with CSFP and 126 age- and sex-matched controls with normal coronary flow, all presenting with symptoms of chronic coronary syndrome. CSFP was diagnosed via corrected TIMI frame count. HALP and other inflammatory indices (NLR, PLR, SII, SIRI) were calculated from baseline laboratory values. Associations were evaluated using multivariable logistic regression, ROC analysis, and restricted cubic spline (RCS) modeling. Results: The HALP score was significantly lower in CSFP patients (mean 56.2 vs. 65.9, p < 0.001). In multivariable analysis, HALP was independently associated with CSFP (adjusted OR: 0.951; 95% CI: 0.930–0.972; p < 0.001), whereas NLR lost significance. PLR, SII, and SIRI remained independently associated. HALP showed the highest diagnostic performance (AUC: 0.698), significantly outperforming all other indices (DeLong p < 0.001). A HALP cutoff ≤ 56.4 provided 58.2% sensitivity and 77.0% specificity. RCS analysis demonstrated a significant non-linear inverse relationship (p for non-linearity = 0.034). Subgroup analyses confirmed consistent associations across age, sex, hypertension, and diabetes strata. Conclusions: The HALP score is independently associated with CSFP and outperforms traditional inflammatory indices. Its low cost and accessibility make it a promising tool for clinical risk stratification in ANOCA/INOCA patients, pending validation in multicenter prospective studies. Full article

Background/Objectives: Acute respiratory failure (ARF) is associated with a high mortality. This study aimed to explore the association of arterial partial pressure of carbon dioxide (PaCO₂) in relation to survival outcomes in mechanically ventilated patients with ARF. Methods: This multicenter retrospective cohort study integrated the data from the eICU Collaborative Research Database (eICU-CRD; n = 10,946), the Medical Information Mart for Intensive Care IV (MIMIC-IV; n = 6683), and clinical records from two university-affiliated intensive care units in China (n = 410). The patients were categorized into low, normal, and high PaCO₂ groups using a restricted cubic spline model to explore the relationship between PaCO₂ and mortality. The 28-day survival distributions among the three groups were compared using Kaplan–Meier curves, with statistical significance assessed via the log-rank test. A multivariable Cox proportional hazards model was constructed to evaluate the independent prognostic value of PaCO₂ for multiple complications. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for the low and high PaCO₂ groups relative to the normal PaCO₂ group. Results: A U-shaped relationship was observed between PaCO₂ and mortality, with both low PaCO₂ (<36.4 mmHg) and high PaCO₂ (>57.9 mmHg) associated with an increased mortality risk. Kaplan–Meier survival analysis demonstrated that patients in the intermediate PaCO₂ range (36.4–57.9 mmHg) exhibited the highest survival rate (65.2%), whereas those in the low and high PaCO₂ groups had significantly lower survival rates (60.0% and 63.2%) (log-rank test, p < 0.001). Adjusted survival analyses further revealed that complications such as sepsis and chronic kidney disease significantly influenced the mortality across PaCO₂ strata. Compared with the intermediate PaCO₂ group, the hazard of death increased by 25.5% in the low PaCO₂ group and by 18.9% in the high PaCO₂ group. Conclusions: This retrospective analysis indicates that arterial PaCO₂ levels within the optimal range are associated with improved survival in patients with acute respiratory failure (ARF) on mechanical ventilation, but prospective studies are needed to establish causality and consider potential confounding factors. Full article

Defects such as gaps, delamination, and the misalignment of fibres impair the performance of carbon fibre-reinforced composites and can lead to structural failure during operation. Eddy current testing has proven to be a suitable method for detecting these defects early in the manufacturing process. However, validated electromagnetic modelling techniques are required to develop new eddy current sensors and gain a better understanding of the eddy current signals caused by different defect sizes. This paper proposes a novel finite element modelling approach to better account for fibre heterogeneity using spline approximation. Further, adaptive mesh refinement is used to reduce FEM solution errors. A defect in the form of a gap is modelled by adjusting the spline approximation accordingly. Finally, the model also accounts for inter-laminar current paths between carbon fibre layers, which are determined by four-terminal resistance measurement. The results show that the electromagnetic properties of the structure can be successfully modelled. The simulation is validated by comparing the virtual scans with eddy current scans of dry carbon fibre fabric with and without artificially manufactured gaps. Full article

Background: Previous studies identified epicardial adipose tissue (EAT) as a metabolic risk factor for atrial remodeling. However, given the distinct physiological changes associated with aging, findings from the general population may not translate directly to older adults. This study aims to clarify the relationship between EAT and left atrial (LA) diameter in older adults specifically. Methods: This retrospective cross-sectional study was conducted among in an older adult cohort (aged ≥ 65 years) at Peking Union Medical College Hospital. The association between EAT and LA diameter was evaluated using multivariable linear regression, a generalized additive model, and restricted cubic spline (RCS) modeling. Results: Among 353 participants (median age 75 years), EAT was independently associated with LA diameter (β = 0.286, p < 0.001) after adjusting for confounders including age, BMI, and LDL-C. Notably, RCS analysis revealed a J-shaped relationship between EAT volume and LA dimensions. Specifically, when EAT exceeded 110.7 cm³, the LA diameter increased significantly by 0.22 mm per 10 cm³ increase in EAT (p = 0.004). Conclusions: EAT accumulation shows a non-linear association with left atrial remodeling in older adults, with an identifiable threshold at 110.7 cm³. EAT may be a valuable biomarker for cardiovascular risk stratification, suggesting that EAT burden monitoring could be beneficial in older populations. Full article

Rapid depressurization and warming during recovery can trigger stress in deep-sea microbes and accelerate RNA degradation. We developed a remotely operated vehicle (ROV)-oriented multi-sequence microbial sampler for 2000 m sampling (20 MPa, 2 °C) that integrates in situ filtration with immediate RNAlater injection (an RNA stabilization reagent), collecting up to 12 samples per dive. A Dirichlet sampling–B-spline–SVM framework was used to optimize the cam profile of the sequence trigger for robust actuation under geometric constraints and realistic tolerances in both manufacturing and assembly. Relative to the baseline 3-4-5 motion law, the optimized design reduces nominal peak driving torque by ~18–20% and lowers the maximum torque under tolerance perturbations; tests show a further ~10–25% reduction using a SiC ball–ZrO₂ block pair versus a MoS₂-lubricated titanium pushrod–ZrO₂ block pair. A Darcy–Forchheimer porous-media computational fluid dynamics (CFD) model predicts earlier clogging on the lower membrane and a fast-to-slow RNAlater displacement process; greater membrane resistance mismatch delays 95% displacement and increases RNAlater loss. Simulations and Rhodamine B tests suggest an RNAlater consumption of 0.9 L per parallel filter (one membrane per side), and 20 MPa chamber tests confirm stable operation and membrane retrieval. Full article

Background/Objectives: Traumatic experiences may lead to both negative and positive outcomes. Positive psychological changes following trauma are commonly referred to as posttraumatic growth (PTG). The present study aims to examine factors associated with posttraumatic growth among Syrian refugees who have been living in Turkey for an extended period. Methods: This cross-sectional study included a sample of 240 Syrian refugees. Participants completed the Posttraumatic Stress Disorder Checklist (PCL-5), the Posttraumatic Growth Inventory (PTGI), and the Mindful Attention Awareness Scale (MAAS). Path analysis was conducted to examine the effects of PTSD symptoms and mindfulness levels on posttraumatic growth. In addition, Multivariate Adaptive Regression Spline (MARS) analysis was used to identify threshold values for the contributions of these variables to posttraumatic growth. Results: The mean age of the participants was 36.9 ± 10.4 years, and 47% were female. The direct effect of PTSD symptoms on posttraumatic growth was negative and statistically significant (β = −0.291, p < 0.001). PTSD symptoms also had an indirect effect on posttraumatic growth through mindfulness (β = −0.254), resulting in a total effect of −0.545. According to the MARS model, when MAAS scores exceeded 78, mindfulness demonstrated a positive effect on posttraumatic growth. Conclusions: The findings indicate that PTSD symptoms among refugees are associated with posttraumatic growth through both direct and indirect pathways. Furthermore, mindfulness emerges as a key factor in understanding the development of posttraumatic growth in this population. Full article

This work introduces a new UHPB-spline computation approach to approximate the pseudo-parabolic problem of order three, which exposes parabolic as well as hyperbolic physical appearance. The approach is used for the discretization of spatial derivatives, which contributes to better accuracy and flexibility. For the discretization of the time derivative, the FDM is employed, endorsing computational proficiency. The anticipated approach has significantly enhanced accuracy. Contrasting various existing approaches, the proposed process is tailored for handling the difficulty in the third-order problem while preserving accuracy and stability over extensive problems. A detailed von Neumann stability analysis confirms its unconditional stability, which makes it robust, especially for long-term simulations, while the numerical ROC demonstrates the second-order convergence both in space and in time. Two expressive examples are considered to determine the accuracy and usefulness of the projected process. Compared to existing techniques, the combination of UHPB-spline functions together with the Crank–Nicolson method and FDM is evidence of an influential and consistent tool to solve pseudo-parabolic problems of higher orders. Full article

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems. Full article

Background: Currently, post-translational modification (PTM) search in MS/MS data is performed using either open modification search (OMS) or closed search (CS) algorithms. The OMS method allows for the determination of many PTMs and unknown mass-shifts in one run. In contrast, closed search algorithms are more sensitive but limited in the number of PTMs that can be specified in one search. Methods: In this manuscript, we propose an optimized Python algorithm based on the IdentiPy search engine that performs an automated sequential search for each PTM based on previous annotations from public databases and customized protein lists. We also determined the sufficient size of the search space to increase the significance of false discovery rate (FDR) estimation. We modified the FDR calculation algorithm by implementing a spline approximation of the ratio of the modified decoys, and by calculating error propagation to filter out unstable data and determine the cutoff value. Results: The results of this pipeline for a test dataset were comparable to previously published data in terms of the number of unmodified peptides and proteins. Additionally, we identified 13 different types of peptide PTMs and achieved an increase in relative protein coverage. Our filtration method based on spline transferred FDR showed a superior number of identified peptides compared to separate FDR. Conclusions: Our developed pipeline can be used as a standalone application or as a module of multiple PTM search in data analysis platforms. Full article

Kolmogorov–Arnold Networks employ learnable univariate activation functions on edges rather than fixed node nonlinearities. Standard B-spline implementations require $O\left(3KW\right)$ parameters per layer (K basis functions, W connections). We introduce shared Gaussian radial basis functions with learnable centers ${\mu }_k^{\left(l\right)}$ and widths ${\sigma }_k^{\left(l\right)}$ maintained globally per layer, reducing parameter complexity to $O(KW+2LK)$ for L layers—a threefold reduction, while preserving Sobolev convergence rates $O\left(h^{s-\Omega }\right)$. Width clamping at ${\sigma }_{min}={10}^{-6}$ and tripartite regularization ensure numerical stability. On MNIST with architecture $[784,128,10]$ and $K=5$, RBF-KAN achieves $87.8\%$ test accuracy versus $89.1\%$ for B-spline KAN with $1.4\times$ speedup and 33% memory reduction, though generalization gap increases from $1.1\%$ to $2.7\%$ due to global Gaussian support. Physics-informed neural networks demonstrate substantial improvements on partial differential equations: elliptic problems exhibit a $45\times$ reduction in PDE residual and maximum pointwise error, decreasing from $1.32$ to $0.18$; parabolic problems achieve a $2.1\times$ accuracy gain; hyperbolic wave equations show a $19.3\times$ improvement in maximum error and a $6.25\times$ reduction in $L^2$ norm. Superior hyperbolic performance derives from infinite differentiability of Gaussian bases, enabling accurate high-order derivatives without polynomial dissipation. Ablation studies confirm that coefficient regularization reduces mean error by 40%, while center diversity prevents basis collapse. Optimal basis count $K\in [3,5]$ balances expressiveness and overfitting. The architecture establishes Gaussian RBFs as efficient alternatives to B-splines for learnable activation networks with advantages in scientific computing. Full article

Background: Little is known about the relation between total alcohol intake and beverage types with the risk of heart failure (HF) and its subtypes in the veteran population. This study aims to examine the associations between total and type of alcohol consumption and risk of HF and its subtypes, namely HF with reduced [HFrEF] and preserved [HFpEF] ejection fraction, in a large cohort of US veterans. Methods: The study cohort included 401,348 Million Veteran Program participants with complete alcohol information collected through a survey and no HF at baseline. HF events were defined as 1 inpatient or 1 outpatient diagnosis code together with at least two ejection fraction (EF) measurements. We defined HFrEF as HF with left ventricular ejection fraction (LVEF) of ≤40% and HFpEF as heart failure with LVEF ≥ 50%. The associations between alcohol intake, type of beverage consumed (i.e., beer, wine, or liquor), and incidence of HF, HFpEF, and HFrEF were assessed using Cox proportional hazard models. Restricted cubic spline regression was used to assess for a dose–response association between alcohol consumption and the risk of HF. Results: Mean age was 65 years, and 91% were men. With a mean follow-up of 6.4 years, we observed 38,420 incident HF events (15,356 HFrEF, 19,047 HFpEF, and 4017 HF with an EF value of 41–49%). Compared to never drinkers, multivariable adjusted hazard ratios for HF were 0.90 (95% CI: 0.86, 0.94), 0.88 (95% CI: 0.84, 0.93), 0.86 (95% CI: 0.81, 0.91), 0.92 (95% CI: 0.86, 0.98), 0.95 (95% CI: 0.84, 1.06), and 1.08 (95% CI: 1.01, 1.15) for current drinkers of 0.1–0.5, 0.6–1, 1.1–2, 2.1–3, 3.1–4 drinks/day, and heavy drinkers (i.e., >4 drinks/day and/or those diagnosed with alcohol use disorder), respectively. We found a similar association between alcohol intake and risk of HFpEF and HFrEF, except heavy drinking was significantly associated with HFrEF (HR: 1.13, 95% CI: 1.02, 1.24), not HFpEF (HR: 1.05, 95% CI: 0.96, 1.13). Types of alcoholic beverage preference did not influence the alcohol-HF relation. Conclusions: Our data are consistent with a J-shaped relation between alcohol consumption and risk of heart failure, irrespective of subtypes. Full article

To address the limitations of traditional analytical modeling in capturing complex surface topographies, this paper presents comprehensive research on the error sensitivity mechanism, loaded tooth contact analysis (LTCA), and load-bearing contact characteristics of curved face gears based on high-precision point cloud modeling. The primary objectives are threefold: (1) to establish a high-fidelity topological reconstruction framework using Non-Uniform Rational B-Splines (NURBS) to bridge the gap between discrete data and finite element analysis (FEA); (2) to reveal the inherent mechanical response and sensitivity mechanism to spatial installation misalignments; and (3) to evaluate the contact performance and transmission error fluctuations under operational loads. Specifically, an analytical discretization method is proposed for point cloud generation, followed by a dual-path validation system integrating “rigid tooth contact analysis (TCA)” and “loaded FEA”. The results demonstrate that the proposed reconstruction achieves a superior accuracy with a Root Mean Square Error (RMSE) of 2.2 × 10⁻³ mm. Furthermore, shaft angle error is identified as the dominant sensitivity factor affecting transmission smoothness and edge contact, exerting a more significant influence than offset and axial errors. Compared with existing research on arc-tooth and helical face gears, this work provides a more robust closed-loop verification for curved profiles, revealing that material elastic deformation increases transmission error amplitude by 10.1% to 17.2%. These insights offer a theoretical reference for the high-precision assembly and tolerance allocation of helicopter transmission systems. Full article