Search

In the present research work, the 2.25Cr1Mo0.25V steel plates with a thickness of 112 mm were welded using the multi-pass submerged automatic arc welding process. The creep specimens were prepared from the base metal (BM) and weld metal (WM) in the welded joint after heat treatment process. The uniaxial creep tests were performed to investigate the creep deformation and rupture behaviors at 550°C under different applied stress levels. The microstructure and fracture surface morphology of crept BM and WM samples were also characterized using the scanning electron microscope with energy-dispersive X-ray spectroscopy. The results showed that typical three-stage creep deformation curves are observed in both BM and WM specimens, and the BM exhibits a faster deformation rate than the WM. Both the creep rupture time and uniaxial creep ductility are found to be increased with a decrease in applied stress. Furthermore, the relationship between the minimum creep rate and time to rupture of both BM and WM samples was obtained, and it can be described using a unified Monkman–Grant equation. In addition, it is found that the creep fractures of the BM and WM are a transgranular ductile failure. The creep damages of both materials are mainly associated with the microstructural degradations, that is, the initiation and coalescence of creep cavities at second phase particles such as carbide and inclusion particles along the loading direction.

Motivation The interaction between genetic variables is one of the major barriers to characterizing the genetic architecture of complex traits. To consider epistasis, network science approaches are increasingly being used in research to elucidate the genetic architecture of complex diseases. Network science approaches associate genetic variables’ disease susceptibility to their topological importance in the network. However, this network only represents genetic interactions and does not describe how these interactions attribute to disease association at the subject-scale. We propose the Network-based Subject Portrait Approach (NSPA) and an accompanying feature transformation method to determine the collective risk impact of multiple genetic interactions for each subject. Results The feature transformation method converts genetic variants of subjects into new values that capture how genetic variables interact with others to attribute to a subject’s disease association. We apply this approach to synthetic and genetic datasets and learn that (1) the disease association can be captured using multiple disjoint sets of genetic interactions and (2) the feature transformation method based on NSPA improves predictive performance comparing with using the original genetic variables. Our findings confirm the role of genetic interaction in complex disease and provide a novel approach for gene–disease association studies to identify genetic architecture in the context of epistasis. Availability and implementation The codes of NSPA are now available in: https://github.com/MIB-Lab/Network-based-Subject-Portrait-Approach Contact ting.hu@queensu.ca Supplementary information Supplementary data are available at Bioinformatics Advances online.

Aside from ultrahigh strength and elasticity, metallic glasses (MGs) possess a number of favorable properties. However, their lack of dislocation based plastic deformation mechanisms in crystalline metals and the resulting loss of ductility have restricted the engineering applications of MGs over the last 60 years. This review aims to provide an overview of deformation and failure mechanisms of MGs via formation and propagation of shear bands (SBs), with an emphasis on the control of SBs to promote strength-ductility synergy. With this goal in mind, we highlight some of the emerging strategies to improve the ductility of MGs. Topics covered include postprocessing techniques such as precompression, heterogeneity tuning, and rejuvenation, with a primary focus on recent progresses in structural design based methods including nanoglasses, notched MGs, and MG nanolattices, as future innovations toward strength-ductility synergy beyond the current benchmark ranges. This work was supported by National Natural Science Foundation of China (Award Nos. 11790293 and 11972278; Funder ID: 10.13039/ =501100001809).

Many human diseases can be attributed to genetic variations in the genome. Scientists have been identifying genetic variants associated with disease risks using population-based data. With this knowledge, an individual’s genetic liability to a disease can be estimated using the polygenic risk score (PRS), calculated based on their genotype profile. However, selecting the most predictive genetic variants is challenged by the high dimensionality of genomics data. Typically, hundreds of thousands of genetic variants are being tested on their association with a disease risk. Moreover, the effect of a genetic variant on a disease risk is often influenced by other variants. It is their interactions that contribute to a disease risk. In this research, we propose a feature selection method for PRS assessment that is able to search for combinations of genetic variants using a genetic algorithm and network science. Our method provides accurate predictive models for PRS computation, as well as useful insights into the intertwined relationships among a large number of genetic variants.