Microstructure and mechanical properties of Mo-Nb microalloyed medium Manganese trip Steel by cyclic quenching

A novel cyclic quenching (CQ) and austenite reverse transformation (ART) was proposed for a Fe-0.25C-3.98Mn-1.22Al-0.20Si-0.19Mo-0.03Nb (wt.%) Mo-Nb microalloyed medium-Mn TRIP steel to improve strength and ductility. The results show that after twice cyclic quenching and ART exhibited optimum comprehensive properties, characterized by an ultimate tensile strength of 838 MPa, a total elongation of 90.8%, a product of strength and elongation (PSE) of 76.1 GPa ·%, and the volume fraction of austenite of approximately 62 vol.%

MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000x150,000 pixels; (2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data; and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods.Comment: 7 pages, 4 figures, accepted by 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023

Post-critical SsPmp and its applications to Virtual Deep Seismic Sounding (VDSS)—1: sensitivity to lithospheric 1-D and 2-D structure

Virtual Deep Seismic Sounding (VDSS) has recently emerged as a novel method to image the Moho and potentially other lithospheric boundaries. The behaviour of SsPmp, the post-critical reflection phase at the Moho that is utilized in VDSS, is rich with complexities not yet widely considered. Here, motivated by observations from the Ordos Plateau in North China, we use synthetic seismograms computed with a broad range of 1-D models to evaluate how different parts of the lithosphere along the ray path of SsPmp affect its phase, amplitude and arrival time. Our findings include: (1) When the crust–mantle boundary is a sharp discontinuity, the SsPmp phase shift relative to the direct S wave is controlled by lower-crustal V_p, upper-mantle V_p and ray parameter. This property indicates the possibility of using SsPmp to constrain V_p in the lower crust and uppermost mantle. (2) When the crust–mantle boundary is a velocity-gradient zone, SsPmp arrival times vary as different functions of ray parameter from cases with a sharp crust–mantle boundary, because different rays turn at different depths. This feature allows measurement of the vertical velocity gradient in the crust–mantle transition zone with SsPmp. (3) When the virtual source (location of S-to-P conversion at the free surface) is in a sedimentary basin, SsPmp amplitude can be significantly reduced due to low S-to-P reflected energy at the virtual source. This may cause the absence of SsPmp despite appropriate source–receiver geometry. In addition to 1-D models, we further conduct 2-D waveform modelling and find that the SsPmp arrival time relative to direct S is not only controlled by crustal thickness at the reflection point but also by lateral variation of V_s beneath the virtual source and receiver. Therefore, in areas with significant lateral heterogeneity in the lithosphere the accuracy of crustal-thickness measurements from SsPmp arrival times depends on our knowledge of the variability of lithospheric structure across a broad region

Convex Hull for Probabilistic Points

We analyze the correctness of an O(n log n) time divide-and-conquer algorithm for the convex hull problem when each input point is a location determined by a normal distribution. We show that the algorithm finds the convex hull of such probabilistic points to precision within some expected correctness determined by a user-given confidence value phi. In order to precisely explain how correct the resulting structure is, we introduce a new certificate error model for calculating and understanding approximate geometric error based on the fundamental properties of a geometric structure. We show that this new error model implies correctness under a robust statistical error model, in which each point lies within the hull with probability at least φ, for the convex hull problem

Study on rolling process and heat treatment of high strength ship plate steel EH40

Means of a tensile test studied the mechanical properties and microstructure of the experimental steel plate under different rolling processes, Charpy impact test, optical microscopy and scanning electron microscopy (SEM). The results show that the optimum thermomechanical control process (TMCP) is a heating temperature of 1200 °C, the best rolling temperature of 1180 °C. The thickness of the ship plate steel was rolled from 170 mm to 40 mm in the recrystallization zone by multi-channel time deformation, and then the thickness was decreased from 40 mm to 15mm in the non-recrystallization zone, the temperature waiting for a range 980 °C ~ 920 °C, the finish rolling temperature of 830 °C. After rolling and being cooled rapidly by laminar cooling, the cooling rate is about 12 °C/s and the final target temperature of 600 °C, which maintains the best state of steels. All data of the experimental steels have accelerated the international level, high-strength ship plate EH40 has been successfully trialed and met the practical requirements, all of these provide a solid foundation for further scientific research

A sugarcane mosaic virus vector for gene expression in maize

Zea mays L. ssp. mays (maize) is an important crop plant as well as model system for genetics and plant biology. The ability to select among different virus‐based platforms for transient gene silencing or protein expression experiments is expected to facilitate studies of gene function in maize and complement experiments with stable transgenes. Here, we describe the development of a sugarcane mosaic virus (SCMV) vector for the purpose of protein expression in maize. An infectious SCMV cDNA clone was constructed, and heterologous genetic elements were placed between the protein 1 (P1) and helper component‐proteinase (HC‐Pro) cistrons in the SCMV genome. Recombinant SCMV clones engineered to express green fluorescent protein (GFP), β‐glucuronidase (GUS), or bialaphos resistance (BAR) protein were introduced into sweet corn (Golden × Bantam) plants. Documentation of developmental time courses spanning maize growth from seedling to tasseling showed that the SCMV genome tolerates insertion of foreign sequences of at least 1,809 nucleotides at the P1/HC‐Pro junction. Analysis of insert stability showed that the integrity of GFP and BAR coding sequences was maintained longer than that of the much larger GUS coding sequence. The SCMV isolate from which the expression vector is derived is able to infect several important maize inbred lines, suggesting that this SCMV vector has potential to be a valuable tool for gene functional analysis in a broad range of experimentally important maize genotypes

Employee Competitive Attitude and Competitive Behavior Promote Job-Crafting and Performance: A Two-Component Dynamic Model

While competition has become increasingly fierce in organizations and in the broader market, the research on competition at an individual level is limited. Most existing research focuses on trait competitiveness. We argue that employee competitiveness can be state-like and can be demonstrated as an attitude toward and behavior representative of competition. We therefore propose a dynamic model with two separate components: competitive attitude and competitive behavior. Drawing upon self-determination theory and the person–environment interaction perspective, we examine how employee competitive attitude and competitive behavior can be influenced by both personal characteristics and team climate, which in turn leads to different work outcomes, as demonstrated in two studies. Study 1 developed measures for competitive attitude and competitive behavior. Study 2 collected data from salespeople in a large insurance company in three waves. The results showed that employee competitive attitude and behavior could be predicted by personality. Moreover, employee competitive attitude and behavior were related to sales performance in differential ways via job crafting, and these mediated relationships could be moderated by team climate. These findings support the two-component dynamic model combining competitive attitude and behavior, which helps promote understanding of the dynamics of competition and its consequences at the individual level. Theoretical and practical implications are also discussed

Post-critical SsPmp and its applications to Virtual Deep Seismic Sounding (VDSS)—1: sensitivity to lithospheric 1-D and 2-D structure

Virtual Deep Seismic Sounding (VDSS) has recently emerged as a novel method to image the Moho and potentially other lithospheric boundaries. The behaviour of SsPmp, the post-critical reflection phase at the Moho that is utilized in VDSS, is rich with complexities not yet widely considered. Here, motivated by observations from the Ordos Plateau in North China, we use synthetic seismograms computed with a broad range of 1-D models to evaluate how different parts of the lithosphere along the ray path of SsPmp affect its phase, amplitude and arrival time. Our findings include: (1) When the crust–mantle boundary is a sharp discontinuity, the SsPmp phase shift relative to the direct S wave is controlled by lower-crustal V_p, upper-mantle V_p and ray parameter. This property indicates the possibility of using SsPmp to constrain V_p in the lower crust and uppermost mantle. (2) When the crust–mantle boundary is a velocity-gradient zone, SsPmp arrival times vary as different functions of ray parameter from cases with a sharp crust–mantle boundary, because different rays turn at different depths. This feature allows measurement of the vertical velocity gradient in the crust–mantle transition zone with SsPmp. (3) When the virtual source (location of S-to-P conversion at the free surface) is in a sedimentary basin, SsPmp amplitude can be significantly reduced due to low S-to-P reflected energy at the virtual source. This may cause the absence of SsPmp despite appropriate source–receiver geometry. In addition to 1-D models, we further conduct 2-D waveform modelling and find that the SsPmp arrival time relative to direct S is not only controlled by crustal thickness at the reflection point but also by lateral variation of V_s beneath the virtual source and receiver. Therefore, in areas with significant lateral heterogeneity in the lithosphere the accuracy of crustal-thickness measurements from SsPmp arrival times depends on our knowledge of the variability of lithospheric structure across a broad region

A Simple Brain Storm Optimization Algorithm with a Periodic Quantum Learning Strategy

Brain storm optimization (BSO) is a young and promising population-based swarm intelligence algorithm inspired by the human process of brainstorming. The BSO algorithm has been successfully applied to both science and engineering issues. However, thus far, most BSO algorithms are prone to fall into local optima when solving complicated optimization problems. In addition, these algorithms adopt complicated clustering strategies such as K-means clustering, resulting in large computational burdens. The paper proposes a simple BSO algorithm with a periodic quantum learning strategy (SBSO-PQLS), which includes three new strategies developed to improve the defects described above. First, we develop a simple individual clustering (SIC) strategy that sorts individuals according to their fitness values and then allocates all individuals into different clusters. This reduces computational burdens and resists premature convergence. Second, we present a simple individual updating (SIU) strategy by simplifying the individual combinations and improving the step size function to enrich the diversity of newly generated individuals and reduces redundancy in the pattern for generating individuals. Third, a quantum-behaved individual updating with periodic learning (QBIU-PL) strategy is developed by introducing a quantum-behaved mechanism into SBSO-PQLS. QBIU-PL provides new momentum, enabling individuals to escape local optima. With the support of these three strategies, SBSO-PQLS effectively improves its global search capability and computational burdens. SBSO-PQLS is compared with seven other BSO variants, Particle Swarm Optimization (PSO), and Differential Evolution (DE) on CEC2013 benchmark functions. The results show that SBSO-PQLS achieves a better global search performance than do the other nine algorithms