Search Results (19,921)

Ice-jam formation during winter low-flow conditions represents a persistent hydrotechnical hazard in small and medium-sized rivers of Central Europe. Despite extensive monitoring efforts, preventive structural measures remain insufficiently developed and rarely evaluated under real geomorphological constraints. This study proposes and hydraulically verifies a low-profile riverbed sill designed to suppress the initiation and stabilization of frazil and anchor ice during critical winter discharges. The analysis integrates 20 years of hydrological and water-temperature data (2004–2024), 26 detailed cross-sectional surveys, a high-resolution longitudinal profile derived from DMR 3.0, and a newly formulated Ice-Jam Risk Index (Iice) combining flow velocity, depth-to-width ratio and thermal deficit. Application to the Torysa River (rkm 42.8–43.6) revealed a clearly defined high-risk zone (rkm 43.20–43.38), where hydraulic conditions frequently fall below the critical thresholds for ice accumulation (U < 0.35 m·s⁻¹; h/B < (h/B)_crit; ΔT > 0.5 °C), indicating shallow and laterally widened channel sections prone to anchor-ice stabilization. Model simulations demonstrated that the proposed sill increases mean velocity by 22–35% during Q₆₅–Q₈₅ conditions, reducing the local I(ice) by 61%, while preserving the conveyance capacity for discharges above Q₅₀ and avoiding measurable backwater impacts upstream. Field-based morphology, risk index interpolation and hydraulic modeling all confirm that the structure effectively disrupts the formation of stable anchor-ice nuclei, which have historically triggered severe ice-jam floods in this reach (2011/12, 2016/17, 2021/22). The results show that a properly dimensioned low-profile sill provides a passive, low-cost, and transferable engineering solution for winter flood risk mitigation, outperforming reactive ice-management techniques while maintaining ecological and hydraulic compatibility with small natural rivers. The methodology is replicable for other rivers where supercooling, low-flow hydraulics and channel morphology jointly control ice-jam initiation. Full article

This study focuses on analyzing the dynamic process, strength, and orientation of risk spillovers in the Chinese banking system under the exogenous shock of the COVID-19 pandemic. Using daily closing prices of 25 representative banks, it stratifies the data into three periods: pre-, during-, and post-pandemic. Employing the HD-TVP-VAR-DY model and dynamic topological directed networks, this study captures the time-varying heterogeneity of risk spillovers and identifies systemic core nodes. The findings reveal that abrupt shocks significantly exacerbate systemic fragility. The primary risk transmitters in the pre-, during-, and post-pandemic periods were CCBs, JSCBs, and CCBs, while SOCBs and RCBs were the main net risk recipients. The interbank risk propagation exhibits a structural evolution pattern of “Concentration–Decentralization–Reshaping.” In the pre-pandemic period, the network was highly concentrated, forming a directional connectedness structure from JSCBs to large banks. During the pandemic, the network underwent significant decentralization, with CMBC and SZRCB emerging as pivotal spillover sources; risk flows shifted from directional to systemic diffusion, characterized by passive absorption. Post-pandemic, the network reverted to a hierarchical-driven pattern, with BOC becoming the core risk source, and the propagation dynamics shifted from passive absorption back to active spillover dominance. Full article

A database containing flow boiling heat transfer characteristics of various refrigerants inside micro-fin tubes with different structures under wide-range working conditions was built. Then the influencing mechanisms of refrigerant thermo-physical properties, fin structure and working conditions on nucleate boiling and forced-convection heat transfer characteristics were analyzed qualitatively. To reveal the actual heat transfer mechanism of refrigerant inside the micro-fin tube, some existing correlations were selected for evaluating the experimental data within the database. The comparison results indicate that there is no correlation achieving high-precision prediction for all experimental data and the prediction accuracy of correlation is influenced significantly by working conditions, particularly mass flux and heat flux. Finally, to acquire a general theoretical model, a new correlation was proposed based on the fitting mechanism of the Hamilton et al. correlation as it exhibits the most concentrated prediction deviation, which means the number of variables affecting correlation prediction effect is the least. After verification, it can be discovered that the average prediction deviation of the new correlation for all experimental data is less than ±30% when the two-phase fluid Reynolds number is less than 3500, which is enough to validate the application value of the theoretical model. Full article

Reliable, real-time river flow monitoring is essential for disaster prevention, but traditional in situ methods are costly and high-risk. Large-scale particle image velocimetry (LSPIV) offers a non-contact alternative, though its accuracy is often compromised by noise and non-water pixels, requiring intensive manual data processing. This study proposes an integrated framework for enhancing non-contact river surface velocity estimation by combining deep learning-based water surface segmentation with optimized LSPIV, using accessible smartphone imaging. The framework was tested on two urban rivers in Taichung, Taiwan. DeepLabV3+ was identified as the superior segmentation model based on MPA/PA and MIoU metrics. The DeepLabV3+-derived mask was integrated into the LSPIV workflow, which was optimized using a 32 × 32 pixels interrogation area (IA), reducing processing time by approximately 44%. By removing non-water pixels, the masked LSPIV yielded a 7% increase in mean surface velocity. This suggests that the inclusion of non-water elements diluted the average, underscoring their tendency to introduce a low-velocity bias in unmasked calculations. The overall validation showed mean absolute percentage errors below 6% compared to the radar velocimeter. Consequently, this integrated smartphone-based framework offers a cost-effective and precise solution for future large-scale deployment in urban flood monitoring and smart city hydrological management. Full article

Efficient condensation is fundamental for high-performance passive two-phase heat transfer devices, such as grooved heat pipes, which are widely used in thermal management for electronic, automotive, aerospace and energy systems. Enhancing condensation heat transfer requires precise control of the condensate distribution and liquid drainage, which can be achieved through the optimization of fin geometry. This study investigates the condensation heat transfer over rectangular, trapezoidal and inverted trapezoidal fins under horizontal and vertical downflow conditions for four refrigerants (R134a, R245fa, R290 and R717) by means of three-dimensional steady-state CFD simulations using the volume-of-fluid (VOF) method. The fin surfaces, inspired by grooved wick heat pipes, are aimed at improving condensate removal and overall condensation heat transfer. The numerical model is validated through comparison with experimental data taken from the literature. Numerical results show that ammonia achieves the highest condensation heat transfer, due to its favorable thermophysical properties. In horizontal flow, inverted trapezoidal and rectangular fins yield up to 10% higher heat transfer than trapezoidal fins, with the inverted trapezoid promoting a more uniform condensate film. Vertical downflow enhances gravity-driven drainage, producing thinner, more stable films and up to 88% higher local heat flow rates in the grooves. These results provide insights into the coupled influence of geometry, working fluid, and flow conditions on condensation mechanisms, offering useful guidelines for the design and optimization of condensers in passive heat transfer devices. Full article

Epoxy resins with excellent overall performance, are widely used in aerospace, automotive, and related fields, frequently in combination with reinforcing fibers to fabricate composites. To enable controllable epoxy processing for prepreg fabrication and composite forming, a rheological model and a curing kinetics model were developed and experimentally validated for an epoxy resin. Rotational rheometry was conducted to quantify the viscosity evolution with temperature and time, enabling construction of a corresponding rheological model. Comparison between model predictions and experimental measurements exhibited a high level of consistency across a wide temperature range. Furthermore, differential scanning calorimetry (DSC) was employed to measure heat-flow curves at different heating rates. The degree of curing was calculated from the heat-flow data, and an autocatalytic curing kinetics model was established based on a reaction kinetics formulation. And the accuracy of the model was verified by isothermal experiments. The developed rheological model provides a theoretical basis and practical guidance for resin processing and prepreg fabrication, whereas the curing kinetics model supports the design and control of curing and forming schedules for epoxy-matrix composites. Full article

Integrating enhanced oil recovery (EOR) with geological carbon storage presents a dual-strategy solution for sustainable hydrocarbon production and greenhouse gas emission mitigation. CO₂ flooding, particularly under miscible conditions, is a pivotal technology in this endeavor. This study employs advanced in situ nuclear magnetic resonance (NMR) imaging to visually and quantitatively investigate the pore-scale mechanisms of CO₂ flooding in fractured carbonate rocks from a Kazakhstan oilfield. By establishing a novel correlation between NMR data and pore throat size distribution, we quantify the lower limit of pore throat mobilization—a key parameter for evaluating storage and displacement efficiency. Results show that miscible CO₂ flooding significantly reduces this limit to the submicron scale (0.1 μm) in matrix rocks, dramatically improving oil recovery from small pores. However, fracture networks dominate fluid flow, leading to early gas breakthrough and severely limiting CO₂ penetration and miscible displacement in the matrix. The study provides pore-scale insights for optimizing CO₂ injection strategies to maximize both hydrocarbon recovery and CO₂ storage potential in complex carbonate formations. The study elucidates the microscopic mobilization mechanisms and remaining oil distribution patterns during CO₂ flooding in volatile reservoirs. Moreover, it represents an environmentally friendly methodology for mitigating greenhouse gas emissions. Full article

Circulant follicular helper T cells (cTfh) are a specialized subset of CD4⁺ T cells that induce immunoglobulin class switching and antibody secretion in plasma cells through the production of IL-21. To investigate the role of cTfh-like cells in the development of Sjögren’s disease (SjD), we analyzed the circulating Tfh-like cells, their production of IL-21 and IL-4, and the co-expression of ICOS, CXCR5, and CCR9 by flow cytometry, and evaluated their association with clinical characteristics of the disease. Percentages of CD4⁺ IL-21⁺ CXCR5⁺ ICOS⁺ CCR9⁺ IL-4⁺ T cells were analyzed in peripheral blood samples from 20 healthy controls (HCs) and 19 patients with SjD. Serum levels of IL-1β, IL-4, IL-6, IL-21, and sCD40L were assessed using a Luminex assay. Laboratory data included anti-Ro/La antibodies, immunoglobulin levels (IgA and IgG), focus score, disease duration, and ESDDAI/SSDDI scores. Decreased frequencies of CXCR5⁺ IL-21⁺ T cells and CCR9⁺ IL-4⁺ T cells were observed in the peripheral blood of patients with SjD. Heatmap analysis was used to identify correlations between cTfh-like cells and clinical parameters. Elevated proportions of cTfh-like cells were positively correlated with disease severity, inflammatory markers, and autoantibody production. High-dimensional analysis identified distinct subpopulations with differential expression of ICOS, CXCR5, CCR9 and IL-21, suggesting heterogeneity of these cells in SjD and their involvement in disease pathogenesis. Full article

The increasing use of engineered timber in modern architecture raises critical concerns about fire safety, particularly when combustible linings are exposed within compartments. Classical compartment fire framework, largely derived from non-combustible enclosures, may not adequately capture the dynamics introduced by materials such as cross-laminated timber (CLT). This study investigates how combustible linings influence the fluid dynamic fields of compartment fires derived from the thermal field using CFD simulations informed by experimental data. A series of configurations, from inert to fully lined compartments, were analysed to isolate the effect of burning boundaries. Results show a progressive intensification of fire conditions with additional combustible surfaces: upper-layer temperatures approach 900 °C, smoke layers thicken, and stratification becomes more pronounced. Velocity fields are similarly affected, with peak inflow and outflow velocities doubling compared to the inert case and new vortical structures emerging near burning walls. These findings highlight that exposed CLT significantly amplifies radiative and convective heat feedback, modifying both temperature distributions and flow patterns in ways not captured by the traditional framework based on the inverse opening factor. This underscores the need for performance-based fire design approaches integrating both thermal and fluid dynamic perspectives, ensuring safe implementation of timber in modern construction. Full article

Current carbon accounting in the power sector often relies on annual average emission factors, which suffer from ill-defined system boundaries, update delays, and insufficient temporal granularity. To address these limitations, this study introduces a high-spatiotemporal-resolution dynamic measurement model for grid carbon emission factors, grounded in carbon emission flow theory. Applied to a regional grid in northern China, the model employs nodal carbon–emission–flow balance to construct system-level matrix equations. This approach accurately traces the spatiotemporal transmission paths of carbon emissions, enabling refined, node-level, and hourly carbon accounting. A case study demonstrated that our model significantly outperformed existing static methods based on interprovincial power exchange in both resolution and accuracy. The results revealed pronounced spatiotemporal heterogeneity in grid emission factors: diurnal fluctuations reach up to 45% in maximum deviation, closely coupled with renewable energy output, while spatial disparities between high- and low-emission regions reach a factor of 4.7, highlighting the critical roles of generation mix and grid topology. This study confirms that high-resolution emission factors effectively overcome the biases of traditional methods, providing a critical data foundation for green electricity trading, demand-side response, and regionally differentiated emission-reduction policies. Our approach offers key methodological and policy insights for building new-type power systems and advancing carbon neutrality goals. Full article

Coronary artery fistulas (CAFs) are rare congenital coronary anomalies in children and are frequently detected incidentally; however, the optimal management of asymptomatic cases and long-term outcomes remain debated. We retrospectively evaluated patients <18 years who underwent cardiac catheterization and coronary angiography for CAF at a single tertiary center between 2003 and 2022, analyzing demographic, clinical, angiographic, procedural, and follow-up data; fistulas were classified using a modified Sakakibara system, and temporal changes in institutional clinical approach and device selection were also assessed. Forty-two patients (mean age 7.4 ± 5.9 years) were included, most of whom were asymptomatic (80.9%); the left coronary artery was the most frequent origin and 85% drained to right-sided chambers. Transcatheter closure was attempted in 25 patients and was technically successful in 23 (92%); complete occlusion was achieved angiographically in 61% immediately and exceeded 90% during follow-up due to spontaneous resolution of residual shunts. One patient required surgery for persistent moderate residual flow, and no major procedural complications, thrombotic events, or ischemic outcomes were observed. In selected children, transcatheter CAF closure is safe and effective, while conservative follow-up appears appropriate for small, hemodynamically insignificant fistulas, supporting individualized, anatomy-guided management. Full article

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. The hybrid quantum-inspired fuzzy controller is applied to a petroleum extraction column. The controller adopts fuzzy rule weights using a quantum-inspired optimization algorithm. Compared with classical PID and fuzzy controllers, it reduces settling time and solvent consumption. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted using synthetic data and varying operational parameters to evaluate control performance. The hybrid controller achieved a 0.7% reduction in phenol consumption and reduced temperature deviations by 2.2% compared to a baseline fuzzy controller. Energy savings ranged from 1% to 2% depending on the operating scenarios. These results were confirmed through repeated simulations and statistical analysis. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications. The statistical evaluation was based on repeated simulation runs and comparative performance metrics rather than physical experiments. Full article

This study analyses the transition from traditional barrier-based toll collection to a free-flow tolling (FFT) system on a national motorway corridor. The aim is to quantify how FFT affects mobility, safety and environmental performance when physical toll plazas are replaced by overhead gantries. Operational data at toll barriers and booths are first characterised in terms of traffic volumes, queue events and accident frequency, and a set of Key Performance Indicators is defined to describe both mobility and environmental effects. Travel times are modelled for light and heavy vehicles, distinguishing between electronic toll collection and manual payment, while demand variations are estimated using elasticities with respect to travel time. Environmental impacts are assessed through an energy-based model of deceleration, queueing and acceleration combined with fuel-specific emission factors for CO₂-equivalent and PM₁₀. The results show that removing physical toll plazas reduces queues by about 79.5% and is expected to reduce accidents in toll areas by roughly 50%, with CO₂-equivalent emissions at toll locations decreasing by up to 80% for light vehicles and 85% for heavy vehicles, and corridor-wide emissions also being significantly reduced, even when induced demand is considered. A final application to a photovoltaic green island on a decommissioned toll plaza illustrates how FFT can be coupled with infrastructure reuse to support cost-effective decarbonisation. Full article

This paper addresses the issue of coordination failures in fully mechanized mining equipment under complex operating conditions, which can lead to operational abnormalities and safety hazards. We systematically analyze the dynamic coordination relationships within the equipment system across three dimensions: temporal, spatial, and geometric. Centered on the coal flow, we establish a comprehensive “mining–transportation” coordination mathematical model covering the entire production process from the coal flow cut off by the shearer to the coal flow transported out by the conveyor. Building upon this foundation, a deep learning prediction method integrating long short-term memory (LSTM) and transformer architectures is proposed to construct an intelligent prediction model for the shearer traction speed. This model effectively captures temporal features and long-term dependencies within equipment operation data, enabling the prediction of critical operational parameters for fully mechanized mining systems. It significantly enhances the early identification and warning capabilities for equipment coordination failure states. The experimental results based on the operational data of fully mechanized mining systems show that the LSTM–Transformer model performs excellently in the prediction of traction speed. The mean square error (MSE) of prediction reached 0.041, the mean absolute error (MAE) was 0.122, and the coefficient of determination (R²) was 0.996, fully demonstrating the advantages of the model in terms of prediction accuracy and stability. This article provides a theoretical basis and technical support for the judgment of the operating status of coal mine working faces and the early warning of accident risks, which is of great significance for promoting the intelligent construction of coal mines. Full article

Traffic accident prediction is of great importance for intelligent transportation systems and public safety management. Unlike conventional traffic flow forecasting tasks, accident data are characterized by low occurrence frequency and highly imbalanced distributions, with near-zero values during most time periods and occasional concentrated bursts. Accident occurrences are also strongly influenced by daily and weekly periodic patterns, resulting in mixed characteristics of low baseline levels, abrupt peaks, and long-term trends. These properties make traditional time series forecasting methods based on stationarity assumptions or single-period modeling less effective. To address this issue, this study proposes a time series forecasting framework that integrates patch-aware local perception with global basis representation. Specifically, this study aims to improve traffic accident time-series forecasting accuracy under sparse and bursty conditions by integrating patch-aware local perception with global basis representation. The patch-level structure captures fine-grained fluctuations in accident sequences by modeling short-term local variations, while basis decomposition provides robust modeling of overall trends through a set of global latent components, leading to complementary effects at both local and global levels. Experimental results on the I-405 highway accident dataset demonstrate that the proposed model significantly outperforms baseline methods, reducing mean squared error (MSE) and mean absolute error (MAE) by approximately 9.7% and 12.6% compared with PatchTST, and by 22.3% and 28.2% compared with Basisformer. Furthermore, experiments on public benchmark datasets ETTh1 and Electricity show that the proposed method achieves comparable or superior performance to mainstream models, indicating its effectiveness and generalization ability across different types of time series scenarios. Full article