Search Results (13,369)

Taiwan has set an ambitious target of net-zero carbon emissions by 2050, relying heavily on offshore wind capacity of 13.1 GW by 2030 and 40–55 GW by 2050. Floating offshore wind (FOW) is expected to play a central role in meeting these targets, particularly in deep-water areas where fixed-bottom technology is technically constrained. This study combined S-curve modeling for capacity projections, learning curves for cost estimation, and input–output analysis to quantify economic and environmental impacts under three deployment scenarios. Our findings indicate that FOW development provides substantial economic benefits, particularly under the high-growth scenario. During the construction phase through 2040, total output is projected to exceed NTD 1.97 trillion, generating more than NTD 1 trillion in gross value added (GVA) and over 470,000 full-time equivalent (FTE) jobs. By 2050, operations and maintenance (O&M) output is expected to reach approximately NTD 50 billion, supporting roughly 14,200 jobs and about NTD 13.8 billion in income. Annual CO₂ reduction could reach up to 10.4 Mt by 2050 under the high-growth scenario, or about 6.86 Mt under the low-growth case, demonstrating the potential of FOW to drive industrial development while advancing national decarbonization. Full article

This study presents an experimental and numerical investigation into the mechanical behavior of corrugated steel–concrete composite bridge decks with composite dowel shear connectors. Four full-scale specimens were fabricated and subjected to flexural tests to obtain and analyze the load–deflection and load–strain curves. A finite element model was developed and validated against the experimental results. The validated model was subsequently applied to analyze the load-carrying process and to perform parametric sensitivity analysis. The effects of the concrete strength grade, steel strength, corrugated steel plate thickness, concrete slab thickness, and corrugated steel plate height on the ultimate bearing capacity were evaluated. The results indicate that corrugated steel–concrete composite bridge decks were subjected to concrete shear failure. The ultimate bearing capacity of the bridge deck reached approximately 3.36 times the design value, demonstrating a high safety reserve. Throughout the entire flexural failure process, the shear connectors performed effectively, with only minimal relative slip observed at the steel–concrete interface. At the instance of failure, only partial areas of the corrugated steel plate yielded. To fully exploit the structural potential, the key design parameters require rational coordination. Full article

In order to explore the influence of different current forms on the protection effect of cathodic protection systems for intelligent test piles of oil and gas gathering and transportation pipelines, X80 steel was taken as the research object to simulate the soil corrosion environment, and cathodic protection performance test experiments were carried out under three current forms: direct current (DC), conventional pulse (P) and high-frequency pulse (HP). Through a polarization curve test, electrochemical impedance spectroscopy (EIS) analysis, surface morphology observation and corrosion rate test, the effects of three current forms on cathodic polarization effect, polarization resistance, corrosion product composition and protection efficiency were compared. The results show that high-frequency pulse current can make the pipeline steel reach the protection potential in a shorter time, and under the same average current density, its polarization resistance is 23.6% and 15.8% higher than that of DC and conventional pulse, respectively. The anti-interference ability of conventional pulse current is better than that of DC. In the presence of stray current, the fluctuation amplitude of protection potential is only 21.1% of DC. The protection stability of DC is good, but the polarization speed is slow, and the phenomenon of “over protection” easily occurs in the process of long-term protection. Combined with economic analysis, high-frequency pulse current has significant advantages in high-corrosion-risk environments. Conventional pulse is suitable for stray current interference areas, while DC is more suitable for long-distance pipeline protection with low corrosion risk. The research results can provide a theoretical basis and technical support for the selection of the current form of pipeline cathodic protection systems. Full article

Background/Objectives: Cyclosporine A (CsA) is a key immunosuppressant in post-transplantation therapy protocol characterized by large interindividual and intraindividual pharmacokinetic (PK) variability and a narrow therapeutic range necessitating therapeutic drug monitoring (TDM) to prevent graft rejection and minimize side effects. TDM data can be used for developing PK models with the objective of identification and quantification of variability factors that contribute to the differences in CsA concentrations. Methods: Retrospectively collected data from medical records of 58 patients (children and young adults) regarding CsA blood concentrations, concomitant medications, and laboratory findings of significance were used for the population PK model development in NONMEM^® (version 7.5) with first-order conditional estimation method with interaction (FOCE-I). Simulation of the concentrations and area under the curve (AUC) was performed in the web application e-campsis^®. RStudio (version 4.5.0) was used for the purpose of descriptive statistics analysis and graphs plotting. Results: A one-compartment model with first-order absorption and elimination best described the data. Value of clearance (CL/F) was estimated to be 15 L/h, and volume of distribution (V/F) was 71.1 L for a typical patient weighing 40 kg. Interindividual variability (IIV) on CL/F and V/F was 34.91% and 43.05%, respectively. Interoccasional variability (IOV) was 12.25%. Body weight (WT) was introduced allometrically on CL/F and V/F, with the estimated exponent of 0.89 for CL/F and 1 (fixed) for V/F. According to the final model, CL/F decreases with increasing haemoglobin (HGB) value. A difference of almost 22.5% in CL/F was observed among patients’ HGB values reported in the study. Conclusions: Our findings indicate that HGB levels significantly influence CsA PK, particularly minimum concentration (C_min), highlighting the importance of regular HGB levels monitoring together with CsA levels. Full article

Background: Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide. Mitochondria-associated endoplasmic reticulum membranes (MAMs) have recently emerged as critical mediators in cardiovascular pathophysiology; however, their specific contributions to CHD pathogenesis remain largely unexplored. Objective: This study aimed to identify and validate MAM-related biomarkers in CHD through integrated analysis of transcriptomic sequencing data and Mendelian randomization, and to elucidate their underlying mechanisms. Methods: We analyzed two gene expression microarray datasets (GSE113079 and GSE42148) and one genome-wide association study (GWAS) dataset (ukb-d-I9_CHD) to identify differentially expressed genes (DEGs) associated with CHD. MAM-related DEGs were filtered using weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis, Mendelian randomization, and machine learning algorithms were employed to identify biomarkers with direct causal relationships to CHD. A diagnostic model was constructed to evaluate the clinical utility of the identified biomarkers. Additionally, we validated the two hub genes in peripheral blood samples from CHD patients and normal controls, as well as in aortic tissue samples from a low-density lipoprotein receptor-deficient (LDLR−/−) atherosclerosis mouse model. Results: We identified 4174 DEGs, from which 3326 MAM-related DEGs (DE-MRGs) were further filtered. Mendelian randomization analysis coupled with machine learning identified two biomarkers, DHX36 and GPR68, demonstrating direct causal relationships with CHD. These biomarkers exhibited excellent diagnostic performance with areas under the receiver operating characteristic (ROC) curve exceeding 0.9. A molecular interaction network was constructed to reveal the biological pathways and molecular mechanisms involving these biomarkers. Furthermore, validation using peripheral blood from CHD patients and aortic tissues from the Ldlr−/− atherosclerosis mouse model corroborated these findings. Conclusions: This study provides evidence supporting a mechanistic link between MAM dysfunction and CHD pathogenesis, identifying candidate biomarkers that have the potential to serve as diagnostic tools and therapeutic targets for CHD. While the validated biomarkers offer valuable insights into the molecular pathways underlying disease development, additional studies are needed to confirm their clinical relevance and therapeutic potential in larger, independent cohorts. Full article

Background: Preoperative chemotherapy is increasingly used for colorectal liver metastases (CRLM), but simple risk stratification tools for routine practice remain limited. We developed a simple risk model to predict outcomes after curative-intent CRLM resection, including in patients receiving preoperative chemotherapy. Methods: We retrospectively analyzed 115 patients who underwent initial curative-intent liver resection for CRLM at two centers. Factors associated with recurrence-free survival (RFS) and overall survival (OS) were evaluated using Cox proportional hazards models and log-rank tests. Model performance was benchmarked against the Beppu nomogram and Fong’s clinical risk score using the area under the curve (AUC). Outcomes were also assessed based on response to preoperative chemotherapy. Results: Having ≥3 CRLMs was the only independent predictor common to both OS and RFS. Among patients with 1–2 CRLMs, the largest tumor diameter being ≥5 cm independently predicted RFS. A composite high-risk definition (≥3 CRLMs, or 1–2 CRLMs with a diameter ≥ 5 cm) independently predicted recurrence (HR 2.05, p = 0.007) and overall mortality (HR 2.24, p = 0.017). The AUCs were similar to the Beppu nomogram for recurrence (0.68 vs. 0.70 (p = 0.683) at 36 months, 0.66 vs. 0.68 (p = 0.766) at 60 months) and to Fong’s score for survival (0.59 vs. 0.64 (p = 0.430) at 36 months, 0.65 vs. 0.74 (p = 0.074) at 60 months). Among patients receiving preoperative chemotherapy (n = 72), high-risk status was associated with poorer RFS (HR 3.11, p < 0.001) and OS (HR 2.80, p = 0.010). Within this subgroup, progressive disease (PD) was associated with worse outcomes than disease control (CR/PR/SD). Conclusions: This two-variable, rule-based model provides an easy-to-use tool for postoperative risk stratification after CRLM resection, and incorporating chemotherapy response may further refine prognostication. Full article

To address the application requirements of Visual Simultaneous Localization and Mapping (VSLAM) in intracavitary environments and the scarcity of gold-standard datasets for deep learning methods, this study proposes a hybrid attention mechanism combined with U-Net for vascular branch point extraction in endoluminal images (SuperVessel). The network is initialized via transfer learning with pre-trained SuperRetina model parameters and integrated with a vascular feature detection and matching method based on dual branch fusion and structure enhancement, generating a pseudo-gold-standard vascular branch point dataset. The framework employs a dual-decoder architecture, incorporates a dynamic up-sampling module (CBAM-Dysample) to refine local vessel features through hybrid attention mechanisms, designs a Dice-Det loss function weighted by branching features to prioritize vessel junctions, and introduces a dynamically weighted Triplet-Des loss function optimized for descriptor discrimination. Experiments on the Vivo test set demonstrate that the proposed method achieves an average Area Under Curve (AUC) of 0.760, with mean feature points, accuracy, and repeatability scores of 42,795, 0.5294, and 0.46, respectively. Compared to SuperRetina, the method maintains matching stability while exhibiting superior repeatability, feature point density, and robustness in low-texture/deformation scenarios. Ablation studies confirm the CBAM-Dysample module’s efficacy in enhancing feature expression and convergence speed, offering a robust solution for intracavitary SLAM systems. Full article

Background: Atherosclerosis and its associated chronic inflammation of the arterial wall disrupt fatty acid metabolism, leading to changes in plasma fatty acid composition. These alterations can be used to improve disease diagnosis and risk stratification by the development and application of specific lipidomic indices. Objectives: The objectives of this study are to evaluate the performance of conventional fatty acid indices and enhance diagnostic efficiency in atherosclerosis by introducing novel index based on plasma PUFA n-6 and n-3 content (Omega-6/3 Balance Index, O6/3-BI), as well as the perspective SFA/MUFA ratio (stearic/oleic acid ratio, C18:0/C18:1n-9) and a logit function combining PUFA and SFA/MUFA biomarkers. Methods: Plasma fatty acids were quantified by LC-MS/MS in healthy controls (n = 50) and patients with carotid atherosclerosis (n = 52), stratified by atorvastatin, rosuvastatin, or no statin therapy. The conventional indices (the Omega-3 Status (EPA + DHA), AA/EPA, and the omega-6/omega-3 ratio), and pathway ratios (C18:0/C18:1n-9; and C20:4n-6/C22:4n-6), as well as the newly introduced PUFA index and combined PUFA-SFA/MUFA logit function, were calculated. Their diagnostic performance for distinguishing atherosclerosis was assessed by a receiver operating characteristic (ROC) analysis with the cross-validation and calculation of Cliff’s Δ effect size. Results: The conventional parameters demonstrated a poor to low discrimination ability of the atherosclerosis patients’ groups from healthy controls (area under the ROC curve, AUC 0.548–0.711). In statin-treated patients, these conventional markers lost significance. The newly introduced PUFA index and SFA/MUFA ratio demonstrated improved patients’ discrimination with AUC 0.734–0.780 for the former and strong predictive power with AUC 0.831–0.858 for the latter marker and maintained their diagnostic value under statin therapy. The most significant positive effect size was observed for the SFA/MUFA ratio with Cliff’s Δ = 0.67–0.71. The combined PUFA-SFA/MUFA logit function also demonstrated a strong predictive power with AUC= 0.880 (Cliff’s Δ = −0.76), outperforming any single index. Conclusions: The newly introduced lipidomic index based on the PUFA content, SFA/MUFA ratio, and a logit function combining PUFA-SFA/MUFA biomarkers demonstrated a substantially better discrimination of atherosclerosis-related fatty acid metabolic disturbances than conventional fatty acid biomarkers. Full article

Background/Objectives: To thoroughly compare acoustic rhinometry (AR) with computed tomography (CT) cross-sectional areas that are approximately perpendicular to the direction of the nasal airflow (CT-CSA). Methods: We retrospectively examined subjects scheduled for functional nasal surgery, along with preoperative CT and AR. CT-CSAs were assessed in several nasal planes in the first 5 cm of the nasal airway. Area sizes and distances of the CT-CSAs from the columella served to create a CT curve analogous to the AR curve. AR curves were digitized. We examined the correlation and agreement (using the Bland–Altman method) between CT curves and digitized AR curves, as well as between selected CT-CSAs and the first two-encountered AR minimal cross-sectional areas (AR-MCA₁ and AR-MCA₂). Correlation was investigated by univariate analysis of variance and Pearson’s correlation. Agreement was examined by the Bland–Altman method. Results: In 33 subjects, the correlation of digitized AR with CT was moderate (r = 0.76; p < 0.001). AR, in general, underestimated the actual nasal area by 15%. AR-MCA₁ and AR-MCA₂ were closest to the CT-CSA of the nasal valve and the incisive canal, respectively. We noted a mainly moderate correlation between the CT-CSA of the nasal valve and AR-MCA₁ (all r > 0.59; all p < 0.001) in contrast to the weaker correlations between the CT-CSA of the incisive canal and AR-MCA₂. Conclusions: AR may underestimate the actual nasal area by 15%. AR-MCA₁ and AR-MCA₂ were closest to the CT-CSA of the nasal valve and the incisive canal, respectively. Full article

Background/Objectives: This study examined the clinical utility of diffusion-weighted whole-body magnetic resonance imaging with background body signal suppression (DWIBS) for differentiating infectious from non-infectious aortitis. Methods: The study included 32 patients with suspected inflammatory aortitis who underwent non-contrast computed tomography (NCCT) and magnetic resonance imaging. We evaluated the diagnostic performance of DWIBS using the spinal cord as a reference, NCCT, and their combination. The diagnosis of infectious aortitis was adjudicated based on imaging, clinical, and laboratory findings. We conducted a sensitivity analysis using a stricter definition of infectious aortitis that required both surgical and microbiological confirmation. Results: Fifteen patients were diagnosed with infectious aortitis. The sensitivity, specificity, and areas under the receiver operating characteristic curves were 93.3%, 70.6%, and 0.82, respectively, for NCCT; 93.3%, 76.5%, and 0.85, respectively, for DWIBS; and 86.7%, 94.1%, and 0.90, respectively, for the combination of both modalities. In the sensitivity analysis, the combined DWIBS and NCCT approach demonstrated a specificity of 87.5% and a sensitivity of 70.8%. Conclusions: DWIBS using the spinal cord as a reference appears to be a promising diagnostic tool for differentiating infectious from non-infectious aortitis, especially when combined with NCCT. Full article

A well-designed layout for urban parks plays a crucial role in constructing livable cities and enhancing residents’ well-being. The provision of age-friendly park access is fundamental to building an elderly-friendly city. However, previous studies have lacked comprehensive analyses that integrate the distribution of the elderly population, park accessibility, park quality, environmental characteristics, and social equity within a unified framework. Specifically, the supply–demand imbalance mechanism underlying the spatial variations in accessibility has not been adequately addressed. This study employs an improved two-step floating catchment area (2SFCA) method, combined with Lorenz curves and urban park-adapted Gini coefficients, to examine the supply–demand relationship and allocation differences between the elderly population and parks at the neighborhood and community levels. The analysis highlights issues related to equity and accessibility and explores their spatial disparity and influencing factors. The key findings are as follows: (1) The classic 2SFCA model exhibits significant biases in evaluating park supply–demand relationships, accessibility, and equity at a fine-grained scale, indicating the necessity of high-precision modeling. (2) Park accessibility in the Old City of Nanjing follows a dual-ring pattern of high accessibility, contrasted with clustered areas of low accessibility, while accessibility equity shows a central–peripheral gradient. Overall equity is relatively low, with good walking accessibility within only about one-third of communities. (3) Park supply levels, neighborhood construction year, and plot ratios are the primary factors influencing park accessibility for elderly residents. The comprehensive aging index is positively correlated with the equity in park layout, whereas housing prices and neighborhood size do not exhibit a simple linear relationship with park accessibility or equity for elderly residents. These findings provide a comprehensive and realistic perspective for understanding elderly park accessibility and equity, offering decision-making references for enhancing urban livability, managing an aging society, and formulating spatial equity policies in the future. Full article

The unique natural environment and climate of tropical island regions present significant challenges to construction. Under these variable natural conditions and complex construction processes, identifying and analyzing potential risks that could lead to vulnerabilities in construction safety systems and clarifying their transmission pathways remains a pressing issue. To fill this research gap, a GV-IB model for vulnerability analysis of construction safety systems in tropical island building projects (CSSTIBPs) was established. This model constructs a vulnerability analysis index system for tropical island construction safety systems based on the Grey Relational Analysis (GRA) and Vulnerability Scoping Diagram (VSD), considering exposure, sensitivity, and adaptability. By combining the artificial fish swarm algorithm with the K2 algorithm and the EM algorithm, an Improved Bayesian Network (IBN) is constructed to analyze and infer the influencing factors and disaster chains of vulnerability in tropical island construction safety systems. The IBN can effectively overcome the dependence on node order and data gaps in traditional Bayesian Network construction methods. The effectiveness of the model is verified by analyzing Hainan Island, China. The research results show that (a) The IBN stability verification showed an Area Under ROC Curve (AUC) of 0.783 > 0.7, indicating high effectiveness in identifying vulnerability factors. (b) Within the vulnerability measurement nodes of the CSSTIBPs, the influence on the system decreases in the following order is exposure (0.41), sensitivity (0.31), and adaptability (0.03). (c) Emergency response time, safety training, hazard identification time, accident response time, and duration of severe weather are key factors affecting the vulnerability of CSSTIBPs. Full article

Acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease where current clinical practice guidelines remain focused on traditional cytogenetic markers. Despite recent advances demonstrating excellent diagnostic accuracy for gene expression signatures, a discontinuity exists between biomarker validation and clinical implementation. This study aimed to develop and validate a multiparametric gene expression signature using digital PCR (dPCR) to accurately diagnose pediatric ALL, with potential utility for monitoring measurable residual disease (MRD). We analyzed 130 bone marrow aspirates from pediatric patients from four clinical groups: non-leukemia, MRD-negative, MRD-positive and leukemia characterized by immunophenotype. Gene expression of an 8-gene panel (JUP, MYC, NT5C3B, GATA3, PTK7, CNP, ICOSLG, and SNAI1) was quantified by dPCR. The diagnostic performance of individual markers was assessed, and a Random Forest machine learning model was trained to classify active disease. The model was validated using a 5-fold stratified cross-validation approach. Individual markers, particularly JUP, MYC, and NT5C3B, showed good diagnostic accuracy for distinguishing leukemia from non-leukemia. However, integrating all eight markers into a multivariate Random Forest model significantly enhanced performance. The model achieved a mean cross-validated area under the curve (AUC) of 0.908 (±0.041) on receiver operator characteristic (ROC) analysis and 0.961 (±0.019) on Precision–Recall (PR) analysis, demonstrating high reliability and a favorable balance between sensitivity and precision. The integrated model achieved high sensitivity (88.9%) for detecting active disease, particularly at initial diagnosis. Although specificity was moderate (65.0%), the high positive predictive value (PPV 85.1%) and accuracy (81.5%) confirm the clinical utility of a positive result. While the panel showed promising performance for distinguishing MRD-positive from MRD-negative samples, the limited MRD-positive cohort size (n = 11) indicates that validation in larger MRD-focused studies is required before clinical implementation for treatment monitoring. This dPCR-based platform provides accessible, quantitative detection without requiring knowledge of clonal shifts or specific genomic landscape, offering potential advantages for resource-limited settings such as those represented in our Mexican pediatric cohort. Full article

Background: As a newly recognized type of cell death implicated in cancer, ferroptosis plays multiple roles in tumor biology. Here, we sought to construct a prognostic framework for EC on the basis of ferroptosis-related long non-coding RNAs (FerlncRNAs), microRNAs (FermiRNAs), and mRNAs (FRGs) for endometrial cancer (EC). Methods: Transcriptomic profiles of tumors and matched clinical data for 544 EC patients were retrieved from TCGA-UCEC. A prognostic framework was generated through Cox regression, integrating ferroptosis-linked lncRNAs, miRNAs, and mRNAs. EC cases were stratified into groups with high or low predicted risk based on ferroptosis-related gene expression. The model’s prognostic utility was examined through Kaplan–Meier (K–M) analysis and receiver operating characteristic curves. Results: A prognostic model based on 16 RNAs, including 10 FerlncRNAs, 2 FermiRNAs, and 4 FRGs, was developed. Analysis using K–M plots showed that high-risk patients experienced shorter overall survival than their low-risk counterparts (p < 0.001). The model’s area under curve (AUC) values were 0.731, 0.749, and 0.768 at 1-, 3-, and 5-year time points, surpassing those of standard clinical parameters. Furthermore, in an external validation cohort, these signature RNAs were associated with EC prognosis. Conclusions: The novel ferroptosis-related lncRNA–miRNA–mRNA prognostic model provides a basis to assess clinical prognosis in EC patients. Full article

This study employed tree-based machine learning (ML) algorithms to predict low-level wind shear (LLWS) at Jeju International Airport (ICAO: RKPC). Hourly meteorological data from 47 observation stations across Jeju Island, collected between 2019 and 2023, were split into training (60%), validation (20%), and test (20%) sets to develop individual prediction models for lead times ranging from 1 to 6 h. A probabilistic prediction model was developed by assigning weights to individual models according to their true skill statistic performance. Validation using an independent 2024 dataset showed that the light gradient boosting machine-based probabilistic model exhibited the highest predictive performance, achieving an area under the receiver operating characteristic curve of 0.883. The Shapley additive explanation analysis identified wind components (U, V) as key variables, contributing over 50%, with the significance of pressure and temperature slightly increasing over long-term prediction times (4–6 h). In addition, spatial analysis revealed that nearby airport stations were more influential for short-term prediction times (1–2 h), whereas Mount Halla and offshore stations north of the airport gained greater influence for medium-to long-term prediction times (3–6 h). The ML-based LLWS prediction model offers high accuracy and interpretability, supporting stepwise warning systems and aiding aviation decision-making. Full article