Search Results (2,636)

Sudden tailings dam breaches trigger large-scale heavy metal compound pollution in coupled surface water–groundwater systems, requiring systematic full-cycle ecological damage quantification tools applicable to diverse contamination types. This study constructs an integrated full-cycle ecological damage assessment framework for sudden water pollution accidents, integrating three core modules: multi-model pollutant migration prediction, multi-scale aquatic biological damage diagnosis, and three-dimensional ecological-economic loss accounting. The framework adopts a modular design that can potentially accommodate heavy metals (Cd, Cr, As, Pb) and organic pollutants such as polycyclic aromatic hydrocarbons (PAHs), with standardized molecular, individual, and population-level biological endpoints and corresponding pollutant dose–response templates reserved as reference calculation modules. However, applicability beyond this case has not been validated and requires case-specific calibration. To verify the operability and accuracy of the proposed integrated system, a typical tailings dam leakage incident dominated by hexavalent chromium (Cr(VI)) and arsenic (As) pollution was selected as the practical validation case; all field monitoring, pollutant simulation, and final economic loss quantification in this case exclusively rely on on-site measured Cr(VI) and As data, while Cd and PAH-related biological response curves and remediation cost formulas retained in the manuscript only serve as illustrative universal template components of the framework rather than case-measured results. For the Cr(VI)/As pollution case, the advection–diffusion model simulation revealed that the Cr(VI) contamination plume horizontally spread 250 m within 48 h and extended to 560 m after seven days, and anaerobic groundwater environments drove the transformation of toxic mobile trivalent arsenic (As(III)) from primary pentavalent arsenic. The calibrated SWAT model achieved Nash–Sutcliffe efficiency (NSE) coefficients of 0.75 for dissolved Cr(VI) and 0.68 for particulate As. The graph theory-based rapid prediction model cut computation duration down to minutes; when validated against independent field monitoring data, it yielded an average relative error of 14.2%, and its consistency with the SWAT model reached 10.5% relative deviation, satisfying the accuracy requirement for emergency early warning. Field biological monitoring demonstrated substantial ecological impairment: metallothionein (MT) expression in fish tissues was markedly elevated (the reported 6.2-fold induction value derives from standard Cd exposure template tests within the framework, with analogous MT upregulation also observed for field Cr(VI)/As co-stress), and benthic community Shannon diversity declined by over 50% in polluted river reaches. The standardized Ecological Damage Index (EDI) of the case was calculated as 480.2, indicating severe aquatic ecosystem damage, with total comprehensive ecological and economic losses reaching 17.25 million CNY. This study innovatively couples high-precision physical transport models with fast emergency prediction algorithms and establishes a complete multi-tier biological indicator chain linking molecular biomarkers to community integrity metrics; the three-dimensional loss accounting system integrating ecosystem service impairment, restoration expenditure, and post-pollution recovery loss realizes closed-loop full-cycle damage evaluation. The proposed framework, demonstrated for Cr(VI) and As pollution, has a modular design that may potentially be extended to other pollutants such as Cd and PAHs by adjusting model parameters, providing a quantitative reference for emergency disposal, pollution remediation, and ecological compensation of water contamination accidents, although further validation across different pollutants and hydrological settings is required. Full article

With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks. Full article

As the primary carrier for oil and gas transportation, pipelines are critical for the entire industry. Pipelines are continuously subjected to corrosion and abrasion in the oil and gas delivery process, leading to gradual wall thickness reduction, shortened service life, and deteriorated operational safety. Ultrasonic testing has been widely adopted for monitoring pipeline wall thickness. Conventional ultrasonic transducers possess rigid configurations, which hinder large-area inspection and exhibit poor adaptability to complex curved components. In contrast, flexible ultrasonic sensors show prominent advantages, with their small size, light weight, and excellent conformal contact with curved surfaces. Flexible piezoelectric thin-film sensors have been used in a wide range of fields. As one of the most representative piezoelectric polymers, poly(vinylidene fluoride–trifluoroethylene) (P(VDF-TrFE)) combines favorable piezoelectric coefficients and intrinsic flexibility, making it popular. Some research groups have investigated the influences of modified filler particles, doping ratios, and fabrication process optimization on the performance of P(VDF-TrFE)-based piezoelectric composites, while others have concentrated on the practical applications of existing flexible piezoelectric sensors. This study emphasizes a rapid customized fabrication strategy for flexible sensors instead of single-specification standardized probes; hence, it does not share the same comparison benchmark as conventional fixed-dimension sensors. Systematic research on flexible piezoelectric thin-film sensors is presented, including piezoelectric material modification, substrate design, laminated structural design, fabrication workflows, establishment of the testing platform, and the development of matched circuit systems. The material preparation and manufacturing processes are optimized, and a scalable technical route for fabricating flexible piezoelectric sensors is proposed. Using this route, flexible piezoelectric thin-film sensors can be rapidly tailored for different application scenarios to satisfy diverse engineering demands. Multiple experiments were conducted on pipeline samples with varying wall thicknesses and curvatures. The results verify that the sensor reaches a measurement precision of 0.01 mm, meeting the demands of high-precision pipeline structural health monitoring. Full article

This paper investigates a segmented reconfigurable intelligent surface (RIS)-enabled backscatter communication riding over ambient non-orthogonal multiple-access (NOMA) signals. For a practical hardware, a simultaneous adjustment of the reflection coefficients of the RIS elements in phase and continuously in amplitude is physically not feasible, contradicting the conventional symbiotic approach of continuously adjusting the amplitude of the reflection coefficient from 0 to one. To address this bottleneck, a novel on–off mechanism of the RIS’s reflecting elements for symbiotic backscatter NOMA systems is proposed. To begin with, the coexistence outage probability and the ergodic capacity of the proposed system are analyzed for diverse dispersed end users and the corresponding performance boundaries in the high signal-to-noise-ratio (SNR) regime are subsequently characterized. In addition, Monte Carlo simulations are provided to verify the correctness of the proposed analytical framework. Finally, the numerical results show that the transmission effectiveness of the proposed on–off mechanism approaches that of the ideal continuous one with an increase in the number of RIS elements. The findings also reveal that the proposed on–off mechanism offers the advantage of reduced reflection coefficient control vis-à-vis the deployment of the underlying RIS-enabled symbiotic backscatter system without the need to adjust the reflection coefficient in amplitude and in phase simultaneously. Full article

Water vapor (H₂O) acts as both an essential thermodynamic driver and a primary source of chemical radicals in the middle atmosphere, playing an irreplaceable role in maintaining Earth’s radiative balance and indicating long-term climate variability. In this study, 24 years (2002–2025) of H₂O measurements from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on board the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite are systematically analyzed to characterize the H₂O spatiotemporal distribution throughout the middle atmosphere (specifically within the 20–80 km altitude range), with a focus on elucidating its evolutionary patterns across time, altitude, and latitude. Building upon this analysis, an empirical model for the bimonthly mean water vapor volume mixing ratio (VMR) is constructed based on actual measurements. Employing a nonlinear least-squares fitting algorithm, time-series fitting is performed on the data within distinct altitude and latitude grids. Consequently, a mathematical analytical expression for the time series was derived for each latitudinal band at every altitude grid point, alongside the determination of corresponding fitting parameter sets. By integrating these parameterized formulas and derived parameters, a comprehensive empirical H₂O VMR model spanning multiple altitude layers and a broad latitudinal range was ultimately established. Validation results demonstrate that the empirical model exhibits high consistency with the original observational data. The coefficients of determination ($R^2$) generally exceed 0.7 and strictly remain $\ge$ 0.6 in all cases. Furthermore, the model demonstrates strong linear correlation with actual observations (Pearson correlation coefficients typically exceeding 0.8) and maintains low bias, as evidenced by small root mean square errors (mostly < 0.35 ppmv) and mean absolute errors (mostly < 0.25 ppmv) across diverse spatial grids. These evaluation metrics collectively indicate excellent goodness-of-fit and robust reconstruction capabilities. This model provides a reliable empirical reference for investigating the spatiotemporal evolution of middle atmospheric H₂O VMR and serves as a potential data foundation for future optimizations of relevant radiative transfer models. Full article

Introduction: The Kumamoto sub-breed of Japanese Brown cattle is a small population facing increasing inbreeding and declining effective population size driven by the intensive use of a limited number of elite sires. Methods: We analyzed 811 Japanese Brown cows genotyped using a 30K SNP array, retaining 19,745 SNPs after quality control. We calculated and compared ten genomic inbreeding estimators, including SNP-by-SNP and segment-based measures. Population structure was first assessed by rearing region using principal component analysis (PCA) and was then evaluated using ADMIXTURE-based clustering and distance-based hierarchical clustering, from which representative subsets were retained under predefined filtering criteria and further examined using PCA and neighbor-joining (NJ) tree analyses. Finally, we calculated the contribution of each ADMIXTURE group to total gene and allelic diversity, and integrated these two measures into a final conservation index after Z-score standardization. Results: ROH- and HBD-based estimators showed high concordance, whereas allele-frequency-dependent SNP-by-SNP estimators exhibited distinct distributions. Rearing region did not explain the main genetic structure. Instead, four representative ADMIXTURE-based groups, supported by independently identified family groups, captured the major genetic structure associated with paternal backgrounds. ADMIXTURE group 2 made the largest contribution to both gene and allelic diversity, and showed lowest genomic inbreeding. Conclusions: These findings suggest that ROH- and HBD-based estimators may serve as valuable indicators of genomic inbreeding in the Kumamoto sub-breed of Japanese Brown cattle; paternal background has played an important role in shaping the current genomic structure; and ADMIXTURE group 2, mainly associated with the Haru-yama-to/-sakae sire background, may provide a valuable breeding resource for limiting future inbreeding accumulation and maintaining genetic diversity. Full article

Compact sub-6 GHz and wireless local area network (WLAN) multiple-input multiple-output (MIMO) antennas require broad impedance coverage and low inter-port coupling within limited footprints. This work presents a two-port Koch-fractal U-slot antenna with a defected ground structure (DGS) on RT/duroid 5880. The design evolves from a rectangular monopole through Koch-edge shaping, U-slot loading, and ground-plane defects. The fabricated two-port prototype exhibits a measured −10 dB impedance bandwidth of 3.07–6.02 GHz, covering n78, n79, and WLAN, while the measured inter-port isolation exceeds 18.13 dB. The fabricated single-element prototype provides measured realized gains of 1.92, 2.34, and 2.05 dBi at 3.5, 4.7, and 5.5 GHz, respectively. Measurement-derived MIMO metrics yield an envelope correlation coefficient not exceeding 0.002, diversity gain close to 10 dB, channel capacity loss of 0.07–0.10 bits/s/Hz, mean effective gain near −3.1 dB with zero port imbalance, and acceptable in-phase total active reflection coefficient behavior. WLAN-band quadrature phase-shift keying tests at 5.18, 5.50, and 5.825 GHz produce error vector magnitude values of 5.4–9.1%, with derived bit error rate estimates below 10⁻⁶ under an additive white Gaussian noise assumption. The design provides wide measured bandwidth, good isolation, low correlation, and WLAN-band signal-domain validation in a simple printed structure. Full article

Smartphone accelerometry could enable longitudinal home-based balance testing, yet its reliability across tasks and outcome measures must be established. We assessed the test–retest reliability and measurement precision of a custom smartphone balance application. Sixty-nine healthy, community-dwelling middle-aged and older adults (ages 41–76 years) performed a 5 s home-based balance assessment weekly for 3 weeks. The assessment was directed by the application, which generated accelerometer-based sway metrics. Participants performed two 30 s trials each of feet together and tandem stance (eyes open, eyes closed) and single leg stance (eyes open only). Intraclass correlation coefficients (ICCs) and relative standard error of measurement (SEM%) were calculated for time-domain and frequency-domain measures. Outcomes achieved good-to-excellent reliability when averaged across three weekly assessments (ICC = 0.59–0.95), particularly mediolaterally. Time-domain measures, particularly mean acceleration (ICC = 0.84–0.92; SEM% = 11.1–23.5%), were the most reliable and precise outcomes overall, while frequency-domain measures showed lower reliability and precision at higher spectral bands (ICC as low as 0.59; SEM% up to 41.5%). As standing tasks became more difficult, reliability and precision declined correspondingly. We conclude that minimally supervised, home-based balance assessment can produce reliable, precise sway measures in healthy, screened, iPhone-owning middle-aged and older adults. This application carries potential for large-scale, remote balance monitoring for research purposes, though validation in more diverse and higher-risk populations will be needed before clinical application can be recommended. Full article

Machine Learning (ML) models effectively capture complex, nonlinear, multimodal, and time-dependent patterns in indoor environments. In thisstudy, the relationship between indoor CO₂ concentrations and environmental variables in different areas of a university hospital was investigated using ML methods. The dataset is a total of 114, including 80% train and 20% test. CO₂ concentration measured at 16:00 was defined as the target variable, while eight inputs (number of occupants, room volume, room floor area, average relative humidity, average temperature, average CO₂ concentration, the change in CO₂ concentration, room orientation) were selected as model inputs. Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF) and Linear Regression were applied to predict CO₂ concentration. Model performance was evaluated based on prediction accuracy criteria, such as Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), so it enabled a comparative consideration of their effectiveness. When CO₂-related variables were included, RF achieved the best performance (R² = 0.804, MAPE = 8.34%, MAE = 70.65, RMSE = 104.24), followed by XGBoost (R² = 0.776, MAPE = 9.07%, MAE = 78.10, RMSE = 111.44). In contrast, ANN (R² = 0.250, MAPE = 19.62%, MAE = 159.81, RMSE = 203.72) and Linear Regression (R² = 0.175, MAPE = 19.44%, MAE = 169.37, RMSE = 213.77) showed comparatively lower predictive performance. Overall, the results indicate that tree-based ML models can provide promising predictive performance and practical insights for indoor air quality monitoring and management in healthcare facilities, while their applicability and generalizability could be further strengthened through future evaluations involving data from diverse healthcare environments. Full article

Extreme climate events, particularly precipitation extremes, pose significant risks to ecosystems, infrastructure, and socio-economic systems globally. In Peru, the diversity of its climate, driven by its complex topography, makes it highly vulnerable to such events, especially in the Andes and Amazon regions. This study evaluates the performance of 25 CMIP6 GCMs in simulating extreme precipitation events during both the wet and dry seasons at the national level. Gridded precipitation data from the PISCO product and CMIP6 model simulations for the period 1981–2014 were used to estimate extreme precipitation indices, including Rx1day, Rx5day, SDII, CDD, CWD, R10mm, and PRCPTOT. Performance was assessed using statistical metrics such as PBIAS, NRMSE, and the Pattern Correlation Coefficient (PCC), integrated through a TOPSIS ranking. Results indicate that NorESM2-MM, MPI-ESM1-2-LR, and CESM2 exhibit the best performance, achieving TOPSIS scores above 0.8. These models show high spatial correlation (PCC frequently >0.8) and relatively low biases. In contrast, models like FGOALS-g3 and CanESM5 show significant limitations, with PBIAS exceeding 80% in Rx1day and Rx5day and TOPSIS scores below 0.5. The ensemble reveals a persistent ‘drizzle bias,’ with wet day frequency (R1mm) generally overestimated by 20–40% in the wet season and by 40–80% during the dry season across most CMIP6 models. Furthermore, indices of temporal persistence (CWD and CDD) remain the most challenging, with CWD overestimations often exceeding 100–200%. These findings highlight the critical need for statistical or dynamical downscaling, together with bias correction, before using CMIP6 projections for local adaptation strategies in the Andes and Amazon regions. Full article

The FMD is an endangered species under first-class national protection in China. Comparative genomic investigation of microsatellite (SSR) in CDS may provide insights into adaptive evolutionary mechanisms and may inform conservation management strategies for captive populations. Here, we analyzed the FMD genome alongside five closely related ruminants: cattle (Bos taurus), red deer (Cervus elaphus), white-tailed deer (Odocoileus virginianus), sheep (Ovis aries), and goat (Capra hircus). Through genome-wide bioinformatic identification, we systematically compared the abundance, density, structural categories, repeat motifs, chromosomal distribution, and pathway enrichment analysis of SSR-containing genes in CDS. Furthermore, we performed synteny analysis and evaluated population genetic diversity. A total of 2509 SSRs in CDS were identified in the FMD, with a relative density of 62.61 loci/Mb. Trinucleotide SSRs were overwhelmingly dominant (88.46%) in the FMD. Notably, the FMD exhibited the highest relative abundances of both tetranucleotide and pentanucleotide repeats among the six species (2.37 and 2.18 loci/Mb, respectively), with pentanucleotide abundance approximately 5.6- to 9.1-fold higher than that of the other species. Chromosomal mapping revealed the highest SSR density in CDS regions on chromosome 27, while SSR-containing genes exhibited a heterogeneous pattern characterized by localized clustering. Synteny analysis demonstrated relatively conserved syntenic relationships between the FMD and goat, sheep, and cattle, with moderate conservation also observed with red deer and white-tailed deer, suggesting that SSR-containing genes in ruminants may remain highly conserved during chromosomal rearrangements. GO and KEGG analyses indicated that SSR-containing genes across all species were predominantly enriched in transcriptional regulation, RNA processing, and signal transduction pathways. Specifically, the FMD showed enrichment patterns associated with hypoxia response, mRNA processing, and epigenetic regulation, which may reflect lineage-specific transcriptional patterns, though the functional involvement of these SSRs remains to be experimentally validated. In addition, the five primer pairs screened in this study exhibited high polymorphism, with a mean polymorphism information content (PIC) of 0.93. The observed heterozygosity (Ho) was significantly lower than the expected heterozygosity (He), and the mean inbreeding coefficient (FIS) was 0.57, indicating heterozygote deficiency and an elevated risk of inbreeding in this captive FMD population. Collectively, our findings provide preliminary insights into the conserved patterns of microsatellite evolution and lineage-specific divergence in ruminants, offering a reference framework for comparative genomics and adaptive evolution research, as well as practical molecular markers for genetic management of captive populations. Full article

This study develops a computer-aided molecular design (CAMD) framework for selecting solvents to extract octacosanol from multicomponent sugarcane wax. The solvent-selection problem is formulated as a mixed-integer nonlinear programming problem in which candidate solvents are assembled from UNIFAC functional groups and evaluated using the net distribution coefficient and net solvent selectivity. Four metaheuristic solvers—ant-colony optimization (ACO), simulated annealing (SA), efficient ant-colony optimization (EACO), and efficient simulated annealing (ESA)—are compared in terms of solvent quality and computational efficiency. EACO and ESA replace selected pseudo-random samples with Hammersley sequence samples to improve multidimensional sampling uniformity. All four solvers identify the same highest-ranked candidate, while ACO and EACO require substantially fewer objective-function evaluations than SA and ESA. The SA-based methods provide greater diversity among lower-ranked candidates. Relative to their conventional counterparts, EACO reduces the computational cost by 14.2% and ESA by 16% while preserving the leading solvent candidates. Overall, the CAMD framework consistently identifies promising candidates, including ethane and propanal, for octacosanol extraction, and quasi-random sampling improves the efficiency of both metaheuristic approaches. Full article

Background/Objectives: Empirical research exploring the linkages between community leisure facility diversity, distinct facility categories, and mental health outcomes among older populations remains scarce, especially regarding gender-specific variations. Methods: Grounded in social capital theory, this study establishes an analytical framework to unpack the underlying mechanisms of these relationships. Based on a sample of 10,270 adults aged 60 and older drawn from the 2023 CLASS, structural equation modeling is applied to investigate the correlational patterns, indirect transmission pathways, and gender heterogeneity embedded in these associations. Results: The results indicate that community leisure facility diversity is positively correlated with better mental health status among older adults, with a total association coefficient of 0.052. Social networks partially mediate this correlational pattern, with an indirect association value of 0.010. Five facility categories exhibit significant positive total associations with mental health, namely senior activity rooms (0.038), fitness centers (0.053), chess and card rooms (0.031), libraries (0.028), and community canteens (0.043), while their indirect association pathways present divergent features. Notably, the linkages between community leisure facilities and mental health are more pronounced for older men. Five facility types show significant positive associations with male mental health, whereas for older women, only senior activity rooms and fitness centers yield significant correlational patterns. Conclusions: These findings suggest that community leisure facility planning can be optimized by shifting from simple quantity expansion to refined, targeted provision with gender-sensitive design. Adopting gender-informed strategies, particularly prioritizing socially supportive facilities and reducing usage barriers for older women, offers a valuable direction for building age-friendly communities and promoting healthy aging. Full article

The temperature field of a high-voltage direct-current (HVDC) cable governs its current rating and insulation lifetime and must therefore be predicted accurately across diverse operating conditions. Finite-element (FE) simulation is accurate but too costly for repeated evaluation, whereas data-driven reduced-order models (ROMs) often extrapolate poorly beyond the training-current range. This paper proposes a physics-structured POD–neural ROM to address this limitation. Specially, proper orthogonal decomposition (POD) compresses the three-dimensional temperature-rise field into a few modal coefficients, which are predicted from the operating conditions by a neural network. The key innovation is to embed the Joule-heating law directly into the architecture: the leading coefficient is represented as a current-squared factor multiplied by a learned current-independent shape. This construction guarantees the correct current scaling of the dominant mode, including its zero-current limit and extrapolation beyond the training range. On FE data for an eight-layer cross-linked polyethylene cable, the model achieves 2.4% mean relative error under current extrapolation and remains below 5% at twice the maximum training current, outperforming Gaussian-process, dynamic-mode-decomposition, autoregressive, and black-box baselines. The full field is evaluated in approximately one millisecond per condition, with a cost independent of the training-set size. Controlled ablations show that the improvement arises from structurally enforcing the scaling law rather than merely supplying $I^2$ as an input feature. Embedding known physical scaling into a surrogate architecture therefore provides a principled route to reliable extrapolation. Full article

Although coronary CT angiography (CCTA) is widely utilized for diagnosing coronary artery disease (CAD), automated CCTA image segmentation is frequently hindered by sparse annotations, pseudo-label noise, and under-delineated fine branches. To mitigate these issues, we present PCDW-Net, a semi-supervised segmentation framework that couples perturbation consistency with discrepancy-aware weighting for enhanced label-scarce performance. Utilizing Adaptive Multi-scale Attention Fusion Network (AMAF-Net) as the backbone within a teacher-student architecture, the network applies diverse perturbations to unlabeled samples, leveraging a consistency loss to promote feature invariance. Simultaneously, a pixel-level discrepancy-aware weighting scheme serves to suppress erroneous pseudo-labels. Evaluated on the public ASOCA and private CTA40 datasets using 10% and 20% annotated fractions, the model was evaluated using Dice similarity coefficient (DSC) and Average Symmetric Surface Distance (ASSD). Under the 20% labeling constraint, PCDW-Net yielded a DSC of 85.36% on ASOCA and 83.48% on CTA40, superior to both Mean Teacher (MT) and Mutual Consistency Network+ (MC-Net+). Ablation studies confirmed the efficacy of each module. Overall, the framework effectively leverages unlabeled volumetric data to yield precise vessel boundary delineations. Full article