Fracture toughness prediction of eutectic ceramic composite considering damage effect and transformation toughening

The toughness of eutectic ceramic composites is obtained by multiple toughening mechanisms involving crack-bridging and pull-out of rod-shaped eutectics, as well as stress-induced transformation toughening. In the loading procedure, damage will emerge in the rod-shaped eutectic. Firstly, the damage variables are defined by the microstructure of rod-shaped eutectic with aligned nano/micro- fibers. The maximum strain criterion is used for determining the loading function. According to the attenuation characteristic of eutectic rigidity, the critical fracture stress of the damage rod-shaped eutectic is obtained by damage variable maximizing. Secondly, we imagine the bridging load carried by the damage rod-shaped eutectics in the crack wake to produce a crack-closing force. The latter reduces the stress intensity in front of the crack. The pull-out work is given by the integral of the frictional force over the pull-out length. Bridging toughening mechanism and pull-out toughening mechanism of damage rod-shaped eutectics are constructed. Thirdly, defining a parabola transformation yield function, the transformation plastic strain increment is gotten by transformation plastic potential function. The screening impact of transformation particles for mixed-mode I-II crack is gained. And lastly, based on the crack-bridging and pull-out of rod-shaped eutectics, as well as stress-induced transformation toughening mechanisms, the added toughness scale with the inherent matrix toughness, the theoretical formula of fracture toughness of the eutectic ceramics composite is determined. The result shows that the fracture toughness is dependent on the aspect ratio of rod-shaped eutectic: the fracture toughness is minimum as the aspect ratio is equal to 0.3 and maximizing when the aspect ratio is equal to 14. The damages inside eutectics enlarge the incremental range of variation of the fracture toughness. The transformation particles exert a slight influence on the fracture toughness due to its less content

A Subabdominal MRI Image Segmentation Algorithm Based on Multi-Scale Feature Pyramid Network and Dual Attention Mechanism

This study aimed to solve the semantic gap and misalignment issue between encoding and decoding because of multiple convolutional and pooling operations in U-Net when segmenting subabdominal MRI images during rectal cancer treatment. A MRI Image Segmentation is proposed based on a multi-scale feature pyramid network and dual attention mechanism. Our innovation is the design of two modules: 1) a dilated convolution and multi-scale feature pyramid network are used in the encoding to avoid the semantic gap. 2) a dual attention mechanism is designed to maintain spatial information of U-Net and reduce misalignment. Experiments on a subabdominal MRI image dataset show the proposed method achieves better performance than others methods. In conclusion, a multi-scale feature pyramid network can reduce the semantic gap, and the dual attention mechanism can make an alignment of features between encoding and decoding.Comment: 19 pages,9 figure

Increased intestinal permeability with elevated peripheral blood endotoxin and inflammatory indices for e-waste lead exposure in children

Lead (Pb) entering the body through different channels can damage the function of intestinal mucosal barrier and cause the body stressful inflammatory response to enhance. This study conducted a cross-sectional study to investigate the effects of Pb exposure on intestinal permeability in children by measuring the level of bacterial endotoxin and index of inflammatory cell types in peripheral blood. From November to December 2018, we recruited 187 participants aged 3-6 years by stratified randomization, from an electronic-waste-exposed group (n = 82) and a referent group (n = 105). General demographic information, past history of the digestive system in child, and family situation were informed by children's guardians with questionnaires. Children in the exposed group showed lower weight, height, and body mass index while more diarrhea in a month. Blood Pb and plasma endotoxin were elevated in exposed children than referent children and the positive relationship between them was shown in all children [B (95% CI): 0.072 (0.008, 0.137), P = 0.033]. Peripheral monocyte counts and leukotriene B-4 (LTB4) levels were significantly increased in the exposed group. Endotoxin levels were positively correlated with neutrophils, monocytes, and LTB4 [B (95% CI): 0.054 (0.015, 0.093), 0.018 (0.005, 0.031), and 0.049 (0.011, 0.087), respectively, P < 0.05]. To sum up, the exposed children showed lower physical growth levels, poorer gut health, and increased intestinal permeability, which was related to high blood Pb and peripheral inflammatory indices. These results suggest the possible adverse impact of environmental Pb exposure on the intestinal health of children

Dronevision: An Experimental 3D Testbed for Flying Light Specks

Today's robotic laboratories for drones are housed in a large room. At times, they are the size of a warehouse. These spaces are typically equipped with permanent devices to localize the drones, e.g., Vicon Infrared cameras. Significant time is invested to fine-tune the localization apparatus to compute and control the position of the drones. One may use these laboratories to develop a 3D multimedia system with miniature sized drones configured with light sources. As an alternative, this brave new idea paper envisions shrinking these room-sized laboratories to the size of a cube or cuboid that sits on a desk and costs less than 10K dollars. The resulting Dronevision (DV) will be the size of a 1990s Television. In addition to light sources, its Flying Light Specks (FLSs) will be network-enabled drones with storage and processing capability to implement decentralized algorithms. The DV will include a localization technique to expedite development of 3D displays. It will act as a haptic interface for a user to interact with and manipulate the 3D virtual illuminations. It will empower an experimenter to design, implement, test, debug, and maintain software and hardware that realize novel algorithms in the comfort of their office without having to reserve a laboratory. In addition to enhancing productivity, it will improve safety of the experimenter by minimizing the likelihood of accidents. This paper introduces the concept of a DV, the research agenda one may pursue using this device, and our plans to realize one

Patterns of Immune Infiltration in Endometriosis and Their Relationship to r-AFS Stages

Background: Endometriosis (EMS) is an estrogen-dependent disease in which endometrial glands and stroma arise outside the uterus. Current studies have suggested that the number and function of immune cells are abnormal in the abdominal fluid and ectopic lesion tissues of patients with EMS. The developed CIBERSORT method allows immune cell profiling by the deconvolution of gene expression microarray data.Methods: By applying CIBERSORT, we assessed the relative proportions of immune cells in 68 normal endometrial tissues (NO), 112 eutopic endometrial tissues (EU) and 24 ectopic endometrial tissues (EC). The obtained immune cell profiles provided enumeration and activation status of 22 immune cell subtypes. We obtained associations between the immune cell environment and EMS r-AFS stages. Macrophages were evaluated by immunohistochemistry (IHC) in 60 patients with ovarian endometriomas.Results: Total natural killer (NK) cells were significantly decreased in EC, while plasma cells and resting CD4 memory T cells were increased in EC. Total macrophages in EC were significantly increased compared to those of EU and NO, and M2 macrophages were the primary macrophages in EC. Compared to those of EC from patients with r-AFS stage I ~ II, M2 macrophages in EC from patients with stage III ~ IV were significantly increased. IHC experiments showed that total macrophages were increased in EC, with M2 macrophages being the primary subtype.Conclusions: Our data demonstrate that deconvolution of gene expression data by CIBERSORT provides valuable information about immune cell composition in EMS

Robustness meets low-rankness: unified entropy and tensor learning for multi-view subspace clustering

In this paper, we develop the weighted error entropy-regularized tensor learning method for multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal and subspace structure discovery into one unified framework. Unlike most existing methods which focus only on the affinity matrix learning for the subspace discovery by different optimization models and simply assume that the noise is independent and identically distributed (i.i.d.), our WETMSC method adopts the weighted error entropy to characterize the underlying noise by assuming that noise is independent and piecewise identically distributed (i.p.i.d.). Meanwhile, WETMSC constructs the self-representation tensor by storing all self-representation matrices from the view dimension, preserving high-order correlation of views based on the tensor nuclear norm. To solve the proposed nonconvex optimization method, we design a half-quadratic (HQ) additive optimization technology and iteratively solve all subproblems under the alternating direction method of multipliers framework. Extensive comparison studies with state-of-the-art clustering methods on real-world datasets and synthetic noisy datasets demonstrate the ascendancy of the proposed WETMSC method

Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: limited clustering performance and high computational cost since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and interviews simultaneously. To address the above issues, this paper proposes the Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten-p norm is utilized to factorize the third-order tensor as the product of two small-scale thirdorder tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods