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Grade 300 austenitic steel components are subjected to irradiation-assisted stress corrosion cracking hazard, in the context of nuclear pressurized water reactor operation. In this paper, it is assumed that inter-granular cracking susceptibility primarily depends on sub grain plasticity mechanisms, controlling the slip band thickness and spacing achieved for a plastic strain level imposed. A computational model evaluating the related grain-boundary stress evolutions is developed and evaluated, using actual post-irradiation observations. In practice, we calculate a quantitative damage susceptibility factor R depending on the characteristic local slip band arrangements and the grain to grain misorientation. The statistical R evolutions obtained are consistent with the strain localization and related intergranular crack susceptibility trends, under corresponding irradiation and deformation conditions.

Super-resolution (SR) in medical imaging is an emerging application in medical imaging due to the needs of high quality images acquired with limited radiation dose, such as low dose Computer Tomography (CT), low field magnetic resonance imaging (MRI). However, because of its complexity and higher visual requirements of medical images, SR is still a challenging task in medical imaging. In this study, we developed a deep learning based method called Medical Images SR using Generative Adversarial Networks (MedSRGAN) for SR in medical imaging. A novel convolutional neural network, Residual Whole Map Attention Network (RWMAN) was developed as the generator network for our MedSRGAN in extracting the useful information through different channels, as well as paying more attention on meaningful regions. In addition, a weighted sum of content loss, adversarial loss, and adversarial feature loss were fused to form a multi-task loss function during the MedSRGAN training. 242 thoracic CT scans and 110 brain MRI scans were collected for training and evaluation of MedSRGAN. The results showed that MedSRGAN not only preserves more texture details but also generates more realistic patterns on reconstructed SR images. A mean opinion score (MOS) test on CT slices scored by five experienced radiologists demonstrates the efficiency of our methods.

Considering the safety issues of the traditional UO2-Zr fuel, a variety of accident-tolerant fuel (ATF) candidates have been proposed in recent years. Among the several ATFs, U3Si2, and UN are the two promising candidates for fuel materials owing to their high thermal conductivity and high uranium density. The FeCrAl alloy and the SiC/SiC composite material are the two promising candidates for cladding owing to their high oxidation resistance and high strength. In order to quantitatively evaluate the performance of ATFs, this study summarizes the physical models of typical ATF cladding materials (FeCrAl and SiC) and pellet materials (UN and U3Si2). Then a three-dimensional non-linear finite element method is applied to simulate the thermal-mechanical behavior of several typical fuel-cladding combinations, including UO2-FeCrAl, UN-FeCrAl, U3Si2-FeCrAl, U3Si2-Zr, and U3Si2-SiC. The important physical quantities, such as the fuel centerline temperature, the deformation of the pellet and the cladding as well as the pellet-cladding mechanical interaction (PCMI) were studied. The fission gas release model was also verified and improved.

Background The precise annotation and accurate identification of the topography of fascicles to the end organs are prerequisites for studying human peripheral nerves. New method In this study, we present a feasible imaging method that acquires 3D high-resolution (HR) topography of peripheral nerve fascicles using an iodine and freeze-drying (IFD) micro-computed tomography (microCT) method to greatly increase the contrast of fascicle images. Results The enhanced microCT imaging method can facilitate the reconstruction of high-contrast HR fascicle images, fascicle segmentation and extraction, feature analysis, and the tracing of fascicle topography to end organs, which define fascicle functions. Comparison with existing methods The complex intraneural aggregation and distribution of fascicles is typically assessed using histological techniques or MR imaging to acquire coarse axial three-dimensional (3D) maps. However, the disadvantages of histological techniques (static, axial manual registration, and data instability) and MR imaging (low-resolution) limit these applications in reconstructing the topography of nerve fascicles. Conclusions Thus, enhanced microCT is a new technique for acquiring 3D intraneural topography of the human peripheral nerve fascicles both to improve our understanding of neurobiological principles and to guide accurate repair in the clinic. Additionally, 3D microstructure data can be used as a biofabrication model, which in turn can be used to fabricate scaffolds to repair long nerve gaps.

There has been increasing interest in applying gait recognition systems to real lives. Currently, the main obstacle hindering the appearance of gait recognition system is high complexity and suffering from dress and carrying variance. In this paper, it proposes a gait recognition method based on multi-link gravity center track (ML-GCT) that has low computation cost but robust to dress and carrying. It divides human silhouette into multiple links and calculates the GCT of each link. In order to improve the recognition accuracy, it takes Fisher's ratio as the importance score (IS) to rank body regions, then completes matching on the ranked regions. Test shows that the method is capable of achieving excellent recognition under different dress and carrying conditions.