ANALYSIS OF FACTORS CAUSING WATER DAMAGE TO LOESS DOUBLE-ARCHED TUNNEL BASED ON TFN-AHP

In order to analysis the factors causing water damage to loess double-arched tunnel, this paper conducts field investigation on water damage to tunnels on Lishi-Jundu Expressway in Shanxi, China, confirms its development characteristics, builds an index system (covering 36 evaluation indexes for construction condition, design stage, construction stage, and operation stage) for the factors causing water damage to loess double-arched tunnel, applies TFN-AHP (triangular fuzzy number-analytic hierarchy process) in calculating the weight of indexes at different levels, and obtains the final sequence of weight of the factors causing water seepage to loess double-arched tunnel. It is found out that water damage to loess double-arched tunnel always develops in construction joints, expansion joints, settlement joints, and lining joints of tunnel and even around them; there is dotted water seepage, linear water seepage, and planar water seepage according to the trace and scope of water damage to tunnel lining. The result shows that water damage to loess double-arched tunnel mainly refers to linear water seepage, planar water seepage is also developed well, and partition and equipment box at the entrance and exit of tunnel are prone to water seepage; construction stage is crucial for controlling water damage to loess double-arched tunnel, atmospheric precipitation is the main water source, and the structure defect of double-arched tunnel increases the possibility of water seepage; the final sequence for weight of various factors is similar to the actual result

Depth-aware neural style transfer

Neural style transfer has recently received significant attention and demonstrated amazing results. An efficient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by defining and optimizing perceptual loss functions. Such methods are typically based on high-level features extracted from pre-trained neural networks, where the loss functions contain two components: style loss and content loss. However, such pre-trained networks are originally designed for object recognition, and hence the high-level features often focus on the primary target and neglect other details. As a result, when input images contain multiple objects potentially at different depths, the resulting images are often unsatisfactory because image layout is destroyed and the boundary between the foreground and background as well as different objects becomes obscured. We observe that the depth map effectively reflects the spatial distribution in an image and preserving the depth map of the content image after stylization helps produce an image that preserves its semantic content. In this paper, we introduce a novel approach for neural style transfer that integrates depth preservation as additional loss, preserving overall image layout while performing style transfer

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces.Comment: Corrected typo

Expression of semaphorin 3A and neuropilin 1 with clinicopathological features and survival in human tongue cancer

Objective: To investigate the association between semaphorin 3A (SEMA 3A) and its receptor neuropilin 1 (NRP1) and the clinicopathologic characteristics of patients with tongue cancer. Study Design: Forty-three tongue squamous cell carcinoma specimens were included. Immunohistochemical staining of SEMA3A and NRP1 was performed on 15 normal tongue epithelium specimens and the 43 tumour specimens. Immunoreactivity was evaluated based on the staining intensity and distribution score. Statistical analyses were performed using Chi-squared and Spearman tests and Kaplan-Meier analysis. Results: SEMA3A was significantly down-regulated in tongue cancer compared with normal tongue (P=0.025), while NRP1 was over-expressed in tumours (P=1 predicted shorter survival (P=0.045). Conclusions: Aberrant expression of SEMA3A and its receptor NRP1 might be involved in the development of tongue cancer and might be useful prognostic markers in this tumour type

A survey on deep geometry learning: from a representation perspective

Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth images, multi-view images, voxels, point clouds, meshes, implicit surfaces, etc. The performance achieved in different applications largely depends on the representation used, and there is no unique representation that works well for all applications. Therefore, in this survey, we review recent developments in deep learning for 3D geometry from a representation perspective, summarizing the advantages and disadvantages of different representations for different applications. We also present existing datasets in these representations and further discuss future research directions

Evidence synthesis of Chinese medicine for monkeypox: Suggestions from other contagious pox-like viral diseases

Background: Monkeypox, a zoonotic disease caused by an Orthopoxvirus, presents an etiology similar to smallpox in humans. Currently, there are no licensed treatments for human monkeypox, so clear and urgent research on its prophylaxis and treatment is needed. Objective: The purpose of this study was to explore the evidence of Chinese medicine for contagious pox-like viral diseases and provide suggestions for the multi-country outbreak management of monkeypox. Methods: The review was registered on INPLASY (INPLASY202270013). Ancient classics in China and clinical trials involving randomized controlled trials , non-RCTs, and comparative observational studies of CM on the prevention and treatment of monkeypox, smallpox, measles, varicella, and rubella were retrieved from the Chinese Medical Code (fifth edition), Database of China Ancient Medicine, PubMed, the Cochrane Library, China National Knowledge Infrastructure, Chongqing VIP, Wanfang, Google Scholar, International Clinical Trial Registry Platform, and Chinese Clinical Trial Registry until 6 July 2022. Both quantitative and qualitative methods were applied to present the data collected. Results: The use of CM to control contagious pox-like viral diseases was traced back to ancient Chinese practice cited in Huangdi’s Internal Classic, where the pathogen was recorded nearly two thousand years back. There were 85 articles (36 RCTs, eight non-RCTs, one cohort study, and 40 case series) that met the inclusion criteria, of which 39 studies were for measles, 38 for varicella, and eight for rubella. Compared with Western medicine for contagious pox-like viral diseases, CM combined with Western medicine showed significant improvements in fever clearance time (mean difference, −1.42 days; 95% CI, −1.89 to −0.95; 10 RCTs), rash/pox extinction time (MD, −1.71 days; 95% CI, −2.65 to −0.76; six RCTs), and rash/pox scab time (MD, −1.57 days; 95% CI, −1.94 to −1.19; five RCTs). When compared with Western medicine, CM alone could reduce the time of rash/pox extinction and fever clearance. Chinese herbal formulas, including modified Yinqiao powder, modified Xijiao Dihaung decoction, modified Qingjie Toubiao decoction, and modified Shengma Gegen decoction, were frequently applied to treat pox-like viral diseases and also showed significant effects in shortening the time of fever clearance, rash/pox extinction, and rash/pox scabs. Compared with Western medicine (placental globulin) or no intervention, eight non-randomized trials and observational studies on the prevention of contagious pox-like viral diseases showed a significant preventive effect of Leiji powder among high-risk populations. Conclusion: Based on historical records and clinical studies of CM in managing contagious pox-like viral diseases, some botanical drugs could be an alternative approach for treating and preventing human monkeypox. Prospective, rigorous clinical trials are urgently needed to confirm the potential preventive and treatment effect of Chinese herbal formulas