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The melt refining process of Martensitic stainless steel smelting is closely related to the characteristics of the matrix; the formation mechanism and distribution of carbides, especially, influence the mechanical properties of steel. In this study, three different melt refining processes of 440 stainless steel were used for analysis: aluminum reduction process (440 M-Al), silicon reduction process (440 M-Si), and electro slag remelting (440 C-ESR). The results show the main carbides of 440 stainless steel after three different melt refining processes are M7C3 and M23C6. The carbides of 440 M-Al and 440 M-Si are fine particles; the carbide of 440 C-ESR is a small block. The 440 M-Al carbides are micron- and nanoscale-sized particles. The micron-sized carbides solidify and aggregate at the unit cell boundary, and the structure is Cr7C3. There are gaps between the carbide particles, and the solid solution carbon content of the adjacent matrix is high, which makes the hardness of the matrix greater than that of the carbide aggregation area. On the contrary, the matrix of 440 C-ESR solid dissolves many alloying elements, and the fine lump carbide is also Cr7C3. The carbide diffusion interface layer of 440 C-ESR can improve the bonding characteristics of the interface of the carbide and the matrix, thus enhancing the mechanical properties.

An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed.

BackgroundDue to the influence of the COVID-19 pandemic, conventional face-to-face academic conferences have been restricted, and many of these conferences have moved onto the internet. ObjectiveThe aim of this study was to investigate the virtual conferences in the field of urology during the COVID-19 pandemic and provide suggestions for better organization of such conferences. MethodsA cross-sectional survey was conducted from May 30 to June 15, 2020, in China. Our team designed a 23-item questionnaire to investigate the conferences attended by urologists during the COVID-19 pandemic. SPSS 22.0 (IBM Corporation) was applied to analyze the data collected. ResultsA total of 330 Chinese urologists participated in our survey, and the response rate was 89.7% (330/368). Among the participants, 40.9% (135/330) were associate chief physicians. The proportion of participants who took part in conventional face-to-face academic conferences decreased from 92.7% (306/330) before the COVID-19 pandemic to 22.1% (73/330) during the pandemic (P

BackgroundIn view of repeated COVID-19 outbreaks in most countries, clinical trials will continue to be conducted under outbreak prevention and control measures for the next few years. It is very significant to explore an optimal clinical trial management model during the outbreak period to provide reference and insight for other clinical trial centers worldwide. ObjectiveThe aim of this study was to explore the management strategies used to minimize the impact of the COVID-19 epidemic on oncology clinical trials. MethodsWe implemented a remote management model to maintain clinical trials conducted at Beijing Cancer Hospital, which realized remote project approval, remote initiation, remote visits, remote administration and remote monitoring to get through two COVID-19 outbreaks in the capital city from February to April and June to July 2020. The effectiveness of measures was evaluated as differences in rates of protocol compliance, participants lost to follow-up, participant withdrawal, disease progression, participant mortality, and detection of monitoring problems. ResultsDuring the late of the first outbreak, modifications were made in trial processing, participant management and quality control, which allowed the hospital to ensure the smooth conduct of 572 trials, with a protocol compliance rate of 85.24% for 3718 participants across both outbreaks. No COVID-19 infections were recorded among participants or trial staff, and no major procedural errors occurred between February and July 2020. These measures led to significantly higher rates of protocol compliance and significantly lower rates of loss to follow-up or withdrawal after the second outbreak than after the first, without affecting rates of disease progression or mortality. The hospital provided trial sponsors with a remote monitoring system in a timely manner, and 3820 trial issues were identified. ConclusionsWhen public health emergencies occur, an optimal clinical trial model combining on-site and remote management could guarantee the health care and treatment needs of clinical trial participants, in which remote management plays a key role.

Wheat can be infected by various pathogens, resulting in a variety of disease symptoms. Early detection of the pathogen infection before symptom appearance is essential for disease monitoring and warning, disease control and the improvement of wheat production. Wheat stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is an important airborne fungal disease. This study investigated the possibility of thermal infrared imaging technology used for early detection of wheat stripe rust since the Pst infection can result in changes of transpiration rate of wheat leaves and thus may result in changes in leaf temperature. The healthy wheat plants were subjected to artificial spray inoculation with Pst urediospore suspension in an artificial climate chamber. Thermal infrared images and data on leaf temperature of Pst-infected wheat plants in incubation period (no symptom appearance) were collected daily using thermal infrared imaging technology. Meanwhile, healthy wheat plants and diseased wheat plants with symptoms infected with Pst were chosen as controls. The variation of thermal infrared images and leaf temperature of wheat plants was analyzed. The results showed that the infection with Pst resulted in a decrease in temperature at the infected parts of wheat leaves, but the leaves of healthy wheat plants and diseased wheat plants with symptoms had no obvious changes in average temperature and maximum temperature difference (MTD) at different parts of leaves. On 5 days post inoculation (dpi), the positions of the leaves of Pst-infected wheat plants in incubation period in the thermal infrared images appeared light yellow and these Pst-infected wheat plants could be distinguished from the healthy plants by visual observation of thermal infrared images. On 8 dpi, there were yellow spots appearing in the thermal infrared images of Pst-infected wheat plants in incubation period. Then on 12 dpi, the yellow spots turned green in the thermal infrared images and there were small uredinia appearing on the wheat leaves. Data analysis of leaf temperature showed that on 3 dpi, there were significant differences (P<0.05) between the leaves of Pst-infected wheat plants in incubation period and the leaves of healthy wheat plants in average temperature and MTD, and that the leaves of Pst-infected wheat plants exhibited a decease in average temperature by 0.08℃, lower than the leaves of healthy wheat plants, but exhibited an increase in MTD by 0.04℃, higher than the leaves of healthy wheat plants. So wheat plants in incubation period without disease symptoms could be distinguished from the healthy wheat plants via temperature difference. With the increase of the number of days after inoculation, the difference in average temperature and MTD between the leaves of Pst-infected wheat plants and the leaves of healthy wheat plants gradually increased. On 12 dpi, the average temperature of the leaves of Pst-infected wheat plants decreased by 1.22℃ than that of the leaves of healthy wheat plants, and MTD of the leaves of Pst-infected wheat plants increased by 1.58℃ than that of the leaves of healthy wheat plants. Meanwhile, with the increase of the number of days after inoculation, the difference in average temperature and MTD between the leaves of Pst-infected wheat plants in incubation period and the leaves of diseased wheat plants with symptoms gradually decreased. And on 12 dpi, the average temperature of the leaves of Pst-infected wheat plants exhibited 0.27℃ higher than that of the leaves of diseased wheat controls, and MTD of the leaves of Pst-infected wheat plants was 0.30℃ lower than that of the leaves of diseased wheat controls. The results indicated that Pst infection could be detected by visual observation of thermal infrared images at 7 days before the disease symptom apparently appeared and that the infection could be detected by analyzing temperature difference based on thermal infrared images at 9 days before the disease symptom was observed. The results demonstrated that early detection of wheat stripe rust can be realized using thermal infrared imaging technology. This study provides a new method based on thermal infrared imaging for early detection of wheat diseases. [ABSTRACT FROM AUTHOR]

To deal with the issue of error ambiguity-resolving under high phase noisy circumstance in multi-baseline phase interferometer, this paper presents an improved algorithm of successive recursive ambiguity-resolving based on the vector average filtering of phase-difference. The proposed method calculates the vector average values of the multi-point phase-difference. The impact of phase noise on ambiguity-resolving is reduced by averaging vectors between channels so as to improve the accuracy of phase ambiguity estimation at all levels. The correct ambiguity resolving could be achieved even in higher phase noise conditions. The computer simulation results demonstrate the merits of presented algorithm [ABSTRACT FROM AUTHOR]