To study how different times of laser shocks affect the micro-dimple, surface residual stress and microstructure of E690 high-strength steel, the finite element software ABAQUS was used to simulate the surface evolution process of the sample. E690 high-strength steel specimens were impacted by pulsed laser with a power density of 7.96 GW/cm(2). Three-dimensional surface morphology was measured by an optical profiler. The surface residual stress and FWHM values of laser-shocked zone were measured by an X-ray diffraction residual stress tester, and the microstructure and morphology of the impacted area were characterzal by using TEM. Results show that when pulse laser with a power density of 7.96 GW/cm(2) impacts 1-4 times, the resultant depths present an increasing trend between 10 and 40 am. A comparison of the simulated and measured results of three-dimensional surface topography in depth-wise direction suggests that the error rate falls within a reasonable range. When pulse laser impacts 2 times and more, test values of residual compressive stress of the specimens tend to be consistent in all directions. FWHM values gradually increase and tended to be equal with the impact being performed 4 and 3 times. TEM images and electron diffraction patterns of specimens that has been impacted by pulse laser for 2 times show nanocrystals forming on the surface of the micro-dimple.
Ba(Sr1/3Ta2/3)O-3 (BST) ceramic was synthesized by a solid-state reaction method. The phase stability, microstructural evolution, and mechanical and thermal properties of the BST ceramic were investigated and characterized to evaluate the potential application of BST as a top coating material for thermal barrier coatings (TBCs). The results show that BST can maintain a stable hexagonal perovskite structure up to 1600 degrees C. Anisotropic growth of the grains above 1400 degrees C was observed. Its low elastic modulus and high fracture toughness suggest a high damage tolerance for the BST ceramic. In addition, the moderate coefficient of thermal expansion and superior heat insulation capability of the BST ceramic provide this ceramic the potential to serve as a top coating material of TBCs at higher temperature.
A real crack to be assessed in a RPV is generally a shallow crack subjected to biaxial far-field stresses. However, the fracture toughness Ke or Je, which is an important material property for the structural integrity assessment of RPV containing cracks, are usually tested on deep cracked compact tension [C(T)] or single-edged bending [SE(B)] specimens under uniaxial loading. The fracture toughness data do not reflect the realistic biaxial loading state that the cracks are subjected to. Cruciform bending [CR(B)] specimen is therefore developed to simulate the biaxial stress state. In this paper, a series of finite element (FE) simulations of the CR(B) specimens containing different semi-elliptical cracks are conducted, Stress-strain curves of materials of different yield strength and hardening behavior reflecting the variation in the mechanical properties of RPV steels due to aging or temperature change are implemented into the finite element models. The J-A2 theory is applied to analyze the crack tip constraint. The results show that the biaxial effect is material property dependent and affected by load levels.
Reactor pressure vessel (RPV) is considered to be irreplaceable, which is the most limiting factor for the lifetime of a nuclear power plant. This paper aims to introduce our project for the evaluation of the irradiation embrittlement for the Chinese RPV forging. The forging manufactured in China was irradiated in the high fluence engineering test reactor. Tensile tests, Charpy impact tests and fracture toughness tests in terms of master curve To were carried out for the material subjected to different irradiation fluences. Comparison of the mechanical properties of the irradiated materials and the materials without irradiation is made. The irradiation resistance of the materials in our project is also compared with the data for the irradiated RPV steels in the literatures.
The occurrence and development of many diseases are accompanied by a change in the mechanical properties of the human body across different length scales. The word "elastodiagnosis'' coined in this review paper indicates that the elastic cue, i.e., the variation in the elastic properties (including linear elastic, viscoelastic, hyperelastic, poroelastic properties and so on) of cells, tissues or organs, can be used in the diagnosis of a disease. This review is organized into sections based on the use of elastodiagnosis in different diseases, including monitoring the development of liver fibrosis, assessing artery stiffening, determining the stage of chronic kidney disease (CKD) and detecting cancers. Emphasis is given to the challenges involved in understanding and characterizing the variation in the mechanical properties of both healthy and diseased tissues, and future perspectives for improving and developing elastodiagnosis methods are discussed. (C) 2019 Elsevier Ltd. All rights reserved.
Recently, convolutional neural network (CNN) based single image super-resolution (SISR) solutions have demonstrated significant progress on restoring accurate high-resolution image based on its corresponding low-resolution version. However, most state-of-the-art SISR approaches attempt to achieve higher accuracy by pursuing deeper or more complicated models, which adversely increases computational cost. To achieve a good balance between restoration accuracy and computational speed, we make simple but effective modifications to the structure of residual blocks and skip-connections between stacked layers, and then propose a novel energy-aware training loss to adaptively adjust the restoration of high-frequency and low-frequency image regions. Extensive qualitative and quantitative evaluation results on benchmark datasets verify the effectiveness of the proposed techniques that they significantly improve SISR accuracy while causing no/ignorable extra computational loads. (C) 2019 Elsevier B.V. All rights reserved.
Recently, the convolutional neural network (CNN) has achieved great progress in many computer vision tasks including object detection, image restoration, and scene understanding. In this paper, we propose a novel CNN-based saliency detection method through dense recurrent connections and residual-based hierarchical feature integration. Inspired by the recent neurobiological finding that abundant recurrent connections exist in the human visual system, we firstly propose a novel dense recurrent CNN module (D-RCNN) to learn informative saliency cues by incorporating dense recurrent connections into sub-layers of convolutional stages. Then we present a residual-based architecture with short connections for deep supervision which hierarchically combines both coarse-level and fine-level feature representations. Our end-to-end method takes raw RGB images as input and directly outputs saliency maps without relying on any time-consuming pre/post-processing techniques. Extensive qualitative and quantitative evaluation results on four widely tested benchmark datasets demonstrate that our method can achieve more accurate saliency detection results solutions with significantly fewer model parameters.
In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.
When a malfunction occurs in a marine gas turbine, its thermal efficiency will decrease slightly, and the gas path fault is often difficult to distinguish. In order to solve this problem, based on the second law of thermodynamics, the endogenous irreversible loss (EIL) model of the marine gas turbine is established, and the exergy loss analysis under normal conditions is carried out to verify the accuracy of the model. The fault diagnosis of gas turbine gas path based on EIL is proposed, and a simulation experiment conducted on a three-shaft marine gas turbine demonstrated that the proposed approach can detect and isolate gas path fault accurately under different operating conditons and enviroments.
Metacaspase (MC), which is discovered gene family with distant caspase homologs in plants, fungi, and protozoa, may be involved in programmed cell death (PCD) processes during plant development and respond abiotic and biotic stresses. To reveal the evolutionary relationship of MC gene family in Rosaceae genomes, we identified 8, 7, 8, 12, 12, and 23 MC genes in the genomes of Fragaria vesca, Prunus mume, Prunus persica, Pyrus communis, Pyrus bretschneideri and Malus domestica, respectively. Phylogenetic analysis suggested that the MC genes could be grouped into three clades: Type I*, Type I and Type II, which was supported by gene structure and conserved motif analysis. Microsynteny analysis revealed that MC genes present in the corresponding syntenic blocks of P. communis, P. bretschneideri and M. domestica, and further suggested that large-scale duplication events play an important role in the expansion of MC gene family members in these three genomes than other Rosaceae plants (F. vesca, P. mume and P. persica). RNA-seq data showed the specific expression patterns of PbMC genes in response to drought stress. The expression analysis of MC genes demonstrated that PbMC01 and PbMC03 were able to be detected in all four pear pollen tubes and seven fruit development stages. The current study highlighted the evolutionary relationship and duplication of the MC gene family in these six Rosaceae genomes and provided appropriate candidate genes for further studies in P. bretschneideri.
Amyloid fibrils have evolved from purely pathological materials implicated in neurodegenerative diseases to efficient templates for last-generation functional materials and nanotechnologies. Due to their high intrinsic stiffness and extreme aspect ratio, amyloid fibril hydrogels can serve as ideal building blocks for material design and synthesis. Yet, in these gels, stiffness is generally not paired by toughness, and their fragile nature hinders significantly their widespread application. Here we introduce an amyloid-assisted biosilicification process, which leads to the formation of silicified nanofibrils (fibril-silica core-shell nanofil-aments) with stiffness up to and beyond similar to 20 GPa, approaching the Young's moduli of many metal alloys and inorganic materials. The silica shell endows the silicified fibrils with large bending rigidity, reflected in hydrogels with elasticity three orders of magnitude beyond conventional amyloid fibril hydrogels. A constitutive theoretical model is proposed that, despite its simplicity, quantitatively interprets the nonmonotonic dependence of the gel elasticity upon the filaments bundling promoted by shear stresses. The application of these hybrid silica-amyloid hydrogels is demonstrated on the fabrication of mechanically stable aero-gels generated via sequential solvent exchange, supercritical CO2 removal, and calcination of the amyloid core, leading to aerogels of specific surface area as high as 993 m(2)/g, among the highest values ever reported for aerogels. We finally show that the scope of amyloid hydrogels can be expanded considerably by generating double networks of amyloid and hydrophilic polymers, which combine excellent stiffness and toughness beyond those of each of the constitutive individual networks.
Reducing the working temperature of solid oxide fuel cells is critical to their increased commercialization but is inhibited by the slow oxygen exchange kinetics at the cathode, which limits the overall rate of the oxygen reduction reaction. We use ab initio methods to develop a quantitative elementary reaction model of oxygen exchange in a representative cathode material, La0.5Sr0.5CoO3-delta, and predict that under operating conditions the rate-limiting step for oxygen incorporation from O-2 gas on the stable, (001)-SrO surface is lateral (surface) diffusion of O-adatoms and oxygen surface vacancies. We predict that a high vacancy concentration on the metastable CoO2 termination enables a vacancy-assisted O-2 dissociation that is 10(2)-10(3) times faster than the rate limiting step on the Sr-rich (La,Sr)O termination. This result implies that dramatically enhanced oxygen exchange performance could potentially be obtained by suppressing the (La,Sr)O termination and stabilizing highly active CoO2 termination.
Increased glycolysis in the lung vasculature has been connected to the development of pulmonary hypertension (PH). We therefore investigated whether glycolytic regulator 6-phosphofructo-2-kinase/fructose-2, 6-bisphosphatase (PFKFB3)-mediated endothelial glycolysis plays a critical role in the development of PH. Heterozygous global deficiency of Pfkfb3 protected mice from developing hypoxia-induced PH, and administration of the PFKFB3 inhibitor 3PO almost completely prevented PH in rats treated with Sugen 5416/hypoxia, indicating a causative role of PFKFB3 in the development of PH. Immunostaining of lung sections and Western blot with isolated lung endothelial cells showed a dramatic increase in PFKFB3 expression and activity in pulmonary endothelial cells of rodents and humans with PH. We generated mice that were constitutively or inducibly deficient in endothelial Pfkfb3 and found that these mice were incapable of developing PH or showed slowed PH progression. Compared with control mice, endothelial Pfkfb3-knockout mice exhibited less severity of vascular smooth muscle cell proliferation, endothelial inflammation, and leukocyte recruitment in the lungs. In the absence of PFKFB3, lung endothelial cells from rodents and humans with PH produced lower levels of growth factors (such as PDGFB and FGF2) and proinflammatory factors (such as CXCL12 and IL1 beta). This is mechanistically linked to decreased levels of HIF2A in lung ECs following PFKFB3 knockdown. Taken together, these results suggest that targeting PFKFB3 is a promising strategy for the treatment of PH.
Multispectral pedestrian detection is an important functionality in various computer vision applications such as robot sensing, security surveillance, and autonomous driving. In this paper, our motivation is to automatically adapt a generic pedestrian detector trained in a visible source domain to a new multispectral target domain without any manual annotation efforts. For this purpose, we present an auto-annotation framework to iteratively label pedestrian instances in visible and thermal channels by leveraging the complementary information of multispectral data. A distinct target is temporally tracked through image sequences to generate more confident labels. The predicted pedestrians in two individual channels are merged through a label fusion scheme to generate multispectral pedestrian annotations. The obtained annotations are then fed to a two-stream region proposal network (TS-RPN) to learn the multispectral features on both visible and thermal images for robust pedestrian detection. Experimental results on KAIST multispectral dataset show that our proposed unsupervised approach using auto-annotated training data can achieve performance comparable to state-of-the-art deep neural networks (DNNs) based pedestrian detectors trained using manual labels.