Stability Analysis and Reinforcement of the Existing Karst Cave Passing through Yujingshan Tunnel

High-speed Railway tunneling in karst terrain presents a huge challenge to the engineer including the identification, stability analysis and reinforcement of the karst cavities. The Cheng-Gui high-speed railway tunnel had to pass through the largest karst cave discovered in tunnel construction. To guaranteeing the tunnel construction safety, a series of corresponding prevention and control measures are put forward. To begin with, geological drilling, electromagnetic method and surface electrical resistivity tomography are adopted to detect and delineate the underground karst zone. Based on the detection results, this paper has put forward strategies to make the pre-support of karst cave and the main technical of those strategies include: the side-walls or in the crown was applied with shotcret (C40 steel fiber concrete); use expanding-shell pre-stressed hollow anchor rod and pre-stressed cable reinforcement; fix steel-mesh-bolting; the shotcrete sealing was applied. Moreover, if instabilities would develop in the side-walls, it should be sufficient to stabilize the cavities, to do dental cleaning of the broken rocks, and fill the voids with shotcrete or pumped lean concrete. At last, systematic grouting treatment around the excavated section, and was excavated with the layer-step method. The solutions presented here may provide guidance for the design and construction of high-speed railway tunnels to be implemented affected by karst processes. The technical validation of the proposed solutions was demonstrated by the successful completion of the Yujingshan tunnel. Please click Additional Files below to see the full abstract

Blood transcriptome reveals immune and metabolic-related genes involved in growth of pasteurized colostrum-fed calves

The quality of colostrum is a key factor contributing to healthy calf growth, and pasteurization of colostrum can effectively reduce the counts of pathogenic microorganisms present in the colostrum. Physiological changes in calves fed with pasteurized colostrum have been well characterized, but little is known about the underlying molecular mechanisms. In this study, key genes and functional pathways through which pasteurized colostrum affects calf growth were identified through whole blood RNA sequencing. Our results showed that calves in the pasteurized group (n = 16) had higher body height and daily weight gain than those in the unpasteurized group (n = 16) in all months tested. Importantly, significant differences in body height were observed at 3 and 4 months of age (p < 0.05), and in daily weight gain at 2, 3, and 6 months of age (p < 0.05) between the two groups. Based on whole blood transcriptome data from 6-months old calves, 630 differentially expressed genes (DEGs), of which 235 were upregulated and 395 downregulated, were identified in the pasteurized compared to the unpasteurized colostrum groups. Most of the DEGs have functions in the immune response (e.g., CCL3, CXCL3, and IL1A) and metabolism (e.g., PTX3 and EXTL1). Protein-protein interaction analyses of DEGs revealed three key subnetworks and fifteen core genes, including UBA52 and RPS28, that have roles in protein synthesis, oxidative phosphorylation, and inflammatory responses. Twelve co-expression modules were identified through weighted gene co-expression network analysis. Among them, 17 genes in the two modules that significantly associated with pasteurization were mainly involved in the tricarboxylic acid cycle, NF-kappa B signaling, and NOD-like receptor signaling pathways. Finally, DEGs that underwent alternative splicing in calves fed pasteurized colostrum have roles in the immune response (SLCO4A1, AKR1C4, and MED13L), indicative of potential roles in immune regulation. Results from multiple analytical methods used suggest that differences in calf growth between the pasteurized and unpasteurized groups may be due to differential immune activity. Our data provide new insights into the impact of pasteurization on calf immune and metabolic-related pathways through its effects on gene expression

Holocene sediment source analysis and paleoclimatic significance of core KZK01 from the eastern part of the Beibu Gulf

Identifying the sources of sediments is of great significance in reconstructing Holocene paleoclimate evolution in the Beibu Gulf and in understanding the characteristics of regional responses to changes in global climate. The Holocene paleoclimatic evolutionary history of the Beibu Gulf was investigated by chronological, geochemical, and mineralogical means using the sediments of Core KZK01 from the eastern part of the Beibu Gulf. The rare earth element (REE) distribution curves, (Gd/Yb)N and (La/Yb)N discriminant diagram, and (Gd/Lu)N and ∑LREE/∑HREE discriminant diagram indicated that the detrital materials in the eastern part of the Beibu Gulf primarily originated from Hainan Island and its proximal sources, with considerable contributions from Taiwan and Pearl River materials. Source analysis of clay minerals showed that Luzon Island was the main source of smectite, followed by Hainan Island. Rivers in Taiwan were the main sources of illite in the study area, followed by the Red River. The Red River was the main contributor of chlorite, followed by the Pearl River. Kaolinite mainly originated from Hainan Island and Guangxi. Coastal currents, surface currents, and warm currents were the main drivers of material transport. Paleoclimatic variations since the Holocene in the Beibu Gulf were divided into three stages: 12–9 cal kyr BP, 9–1.3 cal kyr BP, and 1.3 cal kyr BP to the present. During different stages of climatic evolution, drought was often accompanied by cold and humidity coexisted with warmth, and cold-dry-warm-humid alternation is characterized by significant phases. The illite crystallinity clearly recorded the extreme cold events, such as Bond Events (except Bond6) and the Younger Dryas, and the change trend was essentially consistent with the regional climate record, reflecting the control of global climate change on the process of land–sea interaction in the tropical region. Furthermore, it highlights the great potential of illite crystallinity as a proxy indicator for reconstructing the surface chemical weathering processes of the region

Satellite Image for Cloud and Snow Recognition Based on Lightweight Feature Map Attention Network

Cloud and snow recognition technology is of great significance in the field of meteorology, and is also widely used in remote sensing mapping, aerospace, and other fields. Based on the traditional method of manually labeling cloud-snow areas, a method of labeling cloud and snow areas using deep learning technology has been gradually developed to improve the accuracy and efficiency of recognition. In this paper, from the perspective of designing an efficient and lightweight network model, a cloud snow recognition model based on a lightweight feature map attention network (Lw-fmaNet) is proposed to ensure the performance and accuracy of the cloud snow recognition model. The model is improved based on the ResNet18 network with the premise of reducing the network parameters and improving the training efficiency. The main structure of the model includes a shallow feature extraction module, an intrinsic feature mapping module, and a lightweight adaptive attention mechanism. Overall, in the experiments conducted in this paper, the accuracy of the proposed cloud and snow recognition model reaches 95.02%, with a Kappa index of 93.34%. The proposed method achieves an average precision rate of 94.87%, an average recall rate of 94.79%, and an average F1-Score of 94.82% for four sample recognition classification tasks: no snow and no clouds, thin cloud, thick cloud, and snow cover. Meanwhile, our proposed network has only 5.617M parameters and takes only 2.276 s. Compared with multiple convolutional neural networks and lightweight networks commonly used for cloud and snow recognition, our proposed lightweight feature map attention network has a better performance when it comes to performing cloud and snow recognition tasks

Characterization of feed efficiency-related key signatures molecular in different cattle breeds.

Feed efficiency is a major constraint in the beef industry and has a significant negative correlation with residual feed intake (RFI). RFI is widely used as a measure of feed efficiency in beef cattle and is independent of economic traits such as body weight and average daily gain. However, key traits with commonality or specificity among beef cattle breeds at the same level of RFI have not been reported. Accordingly, the present study hypothesized that signatures associated with feed efficiency would have commonality or specificity in the liver of cattle breeds at the same RFI level. By comparing and integrating liver transcriptome data, we investigated the critical signatures closely associated with RFI in beef cattle using weighted co-expression network analysis, consensus module analysis, functional enrichment analysis and protein network interaction analysis. The results showed that the consensus modules in Angus and Charolais cattle were negatively correlated, and four (turquoise, red, tan, yellow) were significantly positively correlated in Angus liver, while (turquoise, red) were significantly negatively correlated in Charolais liver. These consensus modules were found to be primarily involved in biological processes such as substance metabolism, energy metabolism and gene transcription, which may be one of the possible explanations for the difference in feed efficiency between the two beef breeds. This research also identified five key candidate genes, PLA2G12B, LCAT, MTTP, LCAT, ABCA1 and FADS1, which are closely associated with hepatic lipid metabolism. The present study has identified some modules, genes and pathways that may be the major contributors to the variation in feed efficiency among different cattle breeds, providing a new perspective on the molecular mechanisms of feed efficiency in beef cattle and a research basis for investigating molecular markers associated with feed efficiency in beef cattle

Assessment of commutation failure in HVDC systems considering spatial-temporal discreteness of AC system faults

Abstract This paper presents a novel commutation failure (CF) assessment method considering the influences of voltage magnitude drop, phase shift, and spatial-temporal discreteness of AC system faults. The commutating voltage-time area is employed to analyze the spatial-temporal discreteness of AC system faults causing CF in high-voltage direct current systems, and the influences of fault position and fault time on CF are revealed. Based on this, a novel CF criterion is proposed, further considering the influence of voltage phase shift and the spatial-temporal discreteness. Then this research develops a new CF assessment method, which does not rely on electromagnetic transient simulations. A real case from the China Southern Power Grid is used to verify the practicability of the proposed method by comparing with simulation results obtained using PSCAD/EMTDC

Wideband Low-Profile Patch Antennas Using High-Dielectric Fluids and Hybrid Metal Structure

A novel liquid patch with hybrid metal structures has been presented to address the fundamental performance limitation of microstrip patch antenna in terms of bandwidth versus antenna thickness and size. Our proposed design example has realized over 33% impedance bandwidth with only 0.06 _0 antenna height through a multi-mode integration and improved impedance matching method. This work has shown state-of-the-art performance for wideband low-profile patch antennas, demonstrating significant advantages brought by the new type of antenna design using hybrid liquid dielectric and/or metal structures