Multi-scale distribution of coal fractures based on CT digital core deep learning

In order to realize high-precision and high-efficiency identification of multi-scale distribution characteristics of coal fractures, carry out the study of multi-scale distribution characteristics identification methods based on CT digital core deep learning. Industrial CT scanning system is used to collect a large number of coal original CT digital core information array, the CT digital core information array is converted into a two-dimensional gray-scale image and then it is divided into square images of different scales and the image brightness is enhanced to different levels as training samples, Finally, the construction and optimization of model parameters of AlexNet, ResNet-18, GoogLeNet and Inception-V3 models for the identification of CT-containing fractures are realized by Matlab platform. Study the recognition accuracy and verification accuracy of different model training under different number of training samples; Study the accuracy, calculation efficiency and training time of different models for images with different scales and brightness under the same training sample, obtain the optimal model for calculating the fractal dimension of two-dimensional CT images with fractures, then, the fractal distribution characteristics of each fracture image are calculated according to the statistical method of box-counting dimension, compared with the traditional binarization method and human eye recognition method, The applicability of the multi-scale distribution characteristics identification method of coal fractures based on CT digital core deep learning is verified. The result shows: ① ResNet-18 model is the optimal model for calculating the fractal dimension of two-dimensional CT images with cracks when the image sample is brightness 4 and the scale is 3.5 mm to 21 mm, the model has high accuracy and short training time in calculating the fractal dimension of two-dimensional CT fracture images. ② Compared with the traditional binarization method, the multi-scale recognition method of coal fracture based on CT digital core deep learning has the advantages of fast speed, high accuracy and is not easily affected by impurities in coal

UT-B-deficient mice develop renal dysfunction and structural damage

<p>Abstract</p> <p>Background</p> <p>Urea transporter UT-B is the major urea transporter in erythrocytes and the descending vasa recta in the kidney. In this study, we investigated the effects of long-term UT-B deficiency on functional and structural defect in the kidney of 16-and 52-week-old UT-B-null mice.</p> <p>Methods</p> <p>UT-B-knockout mice were generated by targeted gene disruption and lacked UT-B protein expression in all organs. The urinary concentrating ability of mice was studied in terms of daily urine output, urine osmolality, and urine and plasma chemistries. Changes in renal morphology were evaluated by hematoxylin and eosin staining.</p> <p>Results</p> <p>The UT-B-null mice showed defective urine concentrating ability. The daily urine output in UT-B-null mice (2.5 ± 0.1 ml) was 60% higher and urine osmolality (985 ± 151 mosm) was significantly lower than that in wild-type mice (1463 ± 227 mosm). The 52-week-old UT-B-null mice exhibited polyuria after water deprivation, although urine osmolality was increased. At 52 weeks of age, over 31% of UT-B-null mice exhibited renal medullary atrophy because of severe polyuria and hydronephrosis.</p> <p>Conclusions</p> <p>Long-term UT-B deficiency causes severe renal dysfunction and structural damage. These results demonstrate the important role of UT-B in countercurrent exchange and urine concentration.</p

Response and Energy Absorption of Concrete Honeycombs Subjected to Dynamic In-Plane Compression: A Numerical Approach

Foam concrete exhibits long stress plateau with increasing strain subjected to compression and absorbs a considerable amount of energy, making them promising for building and structure protection. In the present study, hexagonal concrete honeycombs are employed to approximately represent foam concrete, whose response and energy absorption subjected to dynamic in-plane compression are investigated with smooth particle hydrodynamics method. The response modes under low to high velocity crushing are numerically investigated, with which the critical velocity separating quasi-static response and progressive collapse mode is determined. Furthermore, the dynamic energy absorption capacity is examined and discussed

Research on High Power Factor Single Tube Variable Structure Wireless Power Transmission

Aiming at the problems existing in the current radio energy transmission system, we propose a wireless power transmission (WPT) system with the parallel–parallel (PP)-compensated structure. The transmitter of the transmission system adopts a separate topological structure to suppress the current shock and noise. In order to improve the efficiency of the WPT, reduce the static loss, and reduce the current oscillation loss on the power side, the input current ripple can be improved by two parallel phase-shifting methods. In this paper, two topological theories are analyzed, and the simulation and experiment results verify the correctness of these theories under both static and on-load conditions. After the final two-way phase-shift, 61.99% of the ripple is reduced. It provides a new approach for the design of WPT systems with PP structure

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R-s) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R-s) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R(s )was 44.07 t CO2 ha(-1) a(-1) R-s correlated significantly with soil temperature (P <0.01), which explained 69.7% of the variation in R-s at a diurnal scale. Soil moisture was correlated significantly with R-s on a daily scale except not during winter, indicating it affected R-s. A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R-s. The relationship between R-s and soil temperature during a day showed a clear hysteresis. R-s was significantly and positively (P <0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R-s.Peer reviewe

A multi-factor integrated method of calculation unit delineation for hydrological modeling in large mountainous basins

Hillslope-based distributed hydrological model has become an essential tool to simulate hydrological processes in mountainous areas, while how to properly delineate hillslope with key factors still remains to be answered. In this study, we propose a conceptually simple and computationally efficient method, the hillslope-asymmetry-elevation-band-aspect-based (HEA) delineation method, for large mountainous basins. Among these three factors, elevation band and hillslope aspect could represent the spatial heterogeneity of each hillslope in vertical and horizontal directions, respectively. More actual flow routing in each hillslope could be characterized due to the consideration of hillslope asymmetry and elevation band. The performance of HEA method is examined by conducting hydrological simulations with HEA-based basic calculation units (BCUs) in the Nu River basin in Southwest China. Simulated hydrographs agree well with the observations at different sites with Nash-Sutcliffe efficiency coefficient (NSE) greater than 0.75, indicating the HEA delineation method works well for the large mountainous basins. Further numerical experiments are carried out to quantitatively investigate the role of HEA delineation factors in influencing streamflow process and the contribution of homogeneity of underlying surface and meteorological forcing in influencing streamflow process in different aspects. The results show that: the total streamflow is overestimated (underestimated) without consideration of hillslope asymmetry (aspect); while it is overestimated (underestimated) in wet (dry) season without consideration of elevation band. In addition, reduced heterogeneity in underlying surface and meteorological forcing leads to underestimated streamflow in different aspects, of which about 80% and 20% can be attributed to underlying surface and meteorological forcing, respectively

Tree Species Classifications of Urban Forests Using UAV-LiDAR Intensity Frequency Data

The accurate classification of tree species is essential for the sustainable management of forest resources and the effective monitoring of biodiversity. However, a literature review shows that most of the previous unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)-based studies on fine tree species classification have used only limited intensity features, accurately identifying relatively few tree species. To address this gap, this study proposes developing a new intensity feature—intensity frequency—for the LiDAR-based fine classification of eight tree species. Intensity frequency is defined as the number of times a certain intensity value appears in the individual tree crown (ITC) point cloud. In this study, we use UAV laser scanning to obtain LiDAR data from urban forests. Intensity frequency features are constructed based on the extracted intensity information, and a random forest (RF) model is used to classify eight subtropical forest tree species in southeast China. Based on four-point cloud density sampling schemes of 100%, 80%, 50% and 30%, densities of 230 points/m2, 184 points/m2, 115 points/m2 and 69 points/m2 are obtained. These are used to analyze the effect of intensity frequency on tree species classification accuracy under four different point cloud densities. The results are shown as follows. (1) Intensity frequencies of trees are not significantly different for intraspecies (p > 0.05) values and are significantly different for interspecies (p Acer Buergerianum achieves a user accuracy (UA) of over 95% and a producer accuracy (PA) of over 90% for four density conditions. (3) The OA varies slightly under different point cloud densities, but the sum of correct classification trees (SCI) and PA decreases rapidly as the point cloud density decreases, while UA is less affected by density with some stability. (4) The priori feature selected by mean rank (MR) covers the top 10 posterior features selected by RF. These results show that the new intensity frequency feature proposed in this study can be used as a comprehensive and effective intensity feature for the fine classification of tree species

Self-assembled structure of sulfonic gemini surfactant solution

Sulfonate gemini surfactant is a new type of anionic gemini surfactant. The unique structure of double sulfonate endows the sulfonate gemini surfactant with superior surfactant properties, including lower critical micelle concentration (CMC), unusual decontamination ability, excellent stability in strong acid/alkali solution. In this paper, the self-assembled structure of gemini dodecyl sulfonate sodium, abbreviated as 12-2-12(SO3Na)2, is studied by using of dissipative particle dynamics (DPD) method. We have constructed a spring structure model of surfactant molecules, and the effect of length hydrophobic chain, the concentration of surfactants, ethanol addictive on the self-assembly behavior and critical micelle concentration (CMC) was investigated. The results show that with the increase of the concentration of surfactants in aqueous solution, spherical, wormlike and layered micelles appear in turn. With the increase of the length of the hydrophobic chain, the clusters of the surfactants become tighter and the larger clusters are presented at the lower concentration. It was found that the addition of ethanol molecule can enhance the solubility of hydrophobic group and thus inhibit the formation of the micelles