Search

In the process of excavation of underground tunnels, water gushing is often accompanied. In order to avoid affecting the construction schedule and safety, it is necessary to accurately evaluate the drainage capacity. In this paper, the drainage capacity of the tunnel is reviewed and calculated, and the effects of longitudinal slope, steel arch and roughness are considered. The drainage capacity under different conditions is analyzed by the formula of uniform flow in open channel.

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model's analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model's geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks. [ABSTRACT FROM AUTHOR]

In deep-sea mining engineering, accurately predicting the energy required per unit length of pipeline to transport a unit mass of solids (dimensionless specific energy consumption, DSEC) is crucial for ensuring energy conservation and efficiency in the project. Based on our previous work, we utilized the machine learning (ML) and the computational fluid dynamics (CFD)–discrete element method (DEM) method to study the transport characteristics and flow field variations of gradated coarse particles in inclined pipes (gradated particles refer to solid particles mixed in specific size and quantity ratios). First, we collect 1185 sets of data from 13 experimental literature, and after analyzing and processing them, an ensemble model based on four other ML models is developed. Both for pure substance particles (PS) and mixed particles (MP), the prediction accuracy of this ensemble model is relatively higher (PSs are spherical particles with uniform size and density, and MPs are particles with different shapes, sizes, and densities). Then, the CFD-DEM process and the operating conditions include low flow velocity with low volume concentration (2 m/s and 2.5%), low flow velocity with high volume concentration (2 m/s and 7.5%), and high flow velocity with low volume concentration (4 m/s and 2.5%). Under conditions of low flow velocity and low concentrations, as well as high flow velocity and low concentrations, the DSEC hardly changes with the variation of the pipe inclination angle. Under low flow velocity and high-concentration conditions, as the pipe gradually becomes vertical, the value of DSEC gradually increases. [ABSTRACT FROM AUTHOR]

Investigating the aerodynamic performance and wake characteristics of wind farms under different levels of wake effects is crucial for optimizing wind farm layouts and improving power generation efficiency. The Large Eddy Simulation (LES)–actuator line model (ALM) method is widely used to predict the power generation efficiency of wind farms composed of multiple turbines. This study employs the LES-ALM method to numerically investigate the aerodynamic performance and wake characteristics of a single NREL 5 MW horizontal-axis wind turbine and three such turbines under different wake interaction conditions. For the single turbine case, the results obtained using the LES-ALM method were compared with the existing literature, showing good agreement and confirming its reliability for single turbine scenarios. For the three-turbine wake field problem, considering the aerodynamic performance differences under three cases, the results indicate that spacing has a minor impact on the power coefficient and thrust coefficient of the middle turbine but a significant impact on the downstream turbine. For staggered three-turbine arrangements, unilateral turbulent inflow to the downstream turbine causes significant fluctuations in thrust and torque, while bilateral turbulent inflow leads to more stable thrust and torque. The presence of two upstream turbines causes an acceleration effect at the inflow region of the downstream single turbine, significantly increasing its power coefficient. The findings of this study can provide methodological references for reducing wake effects and optimizing the layout of wind farms. [ABSTRACT FROM AUTHOR]

Growth-regulating factor (GRF) is a multi-gene family that plays an important role in plant growth and development and is widely present in plants. Currently, GRF gene members have been reported in many plants, but the GRF gene family has not been found in sweet potato. In this study, ten GRF genes were identified in sweet potato (Ipomoea batatas), twelve and twelve were identified in its two diploid relatives (Ipomoea trifida) and (Ipomoea triloba), which were unevenly distributed on nine different chromosomes. Subcellular localization analysis showed that GRF genes of sweet potato, I. trifida, and I. triloba were all located in the nucleus. The expression analysis showed that the expression of IbGRFs was diverse in different sweet potato parts, and most of the genes were upregulated and even had the highest expression in the vigorous growth buds. These findings provide molecular characterization of sweet potato and its two diploid relatives, the GRF families, further supporting functional characterization. [ABSTRACT FROM AUTHOR]

Background: Although the inherited risk factors associated with fatty liver disease are well understood, little is known about the genetic background of metabolic dysfunction-associated steatotic liver disease (MASLD) and its related health impacts. Compared to non-alcoholic fatty liver disease (NAFLD), MASLD presents significantly distinct diagnostic criteria, and epidemiological and clinical features, but the related genetic variants are yet to be investigated. Therefore, we conducted this study to assess the genetic background of MASLD and interactions between MASLD-related genetic variants and metabolism-related outcomes. Methods: Participants from the UK Biobank were grouped into discovery and replication cohorts for an MASLD genome-wide association study (GWAS), and base and target cohorts for polygenic risk score (PRS) analysis. Autosomal genetic variants associated with NAFLD were compared with the MASLD GWAS results. Kaplan–Meier and Cox regression analyses were used to assess associations between MASLD and metabolism-related outcomes. Results: Sixteen single-nucleotide polymorphisms (SNPs) were identified at genome-wide significance levels for MASLD and duplicated in the replication cohort. Differences were found after comparing these SNPs with the results of NAFLD-related genetic variants. MASLD cases with high PRS had a multivariate-adjusted hazard ratio of 3.15 (95% confidence interval, 2.54–3.90) for severe liver disease (SLD), and 2.81 (2.60–3.03) for type 2 diabetes mellitus. The high PRS amplified the impact of MASLD on SLD and extrahepatic outcomes. Conclusions: High PRS of MASLD GWAS amplified the impact of MASLD on SLD and metabolism-related outcomes, thereby refining the process of identification of individuals at high risk of MASLD. Supplementation of this process with relevant genetic backgrounds may lead to more effective MASLD prevention and management. [ABSTRACT FROM AUTHOR]