A Hardware Independent Real-time Ethernet for Motion Control Systems

Ethernet for Manufacture Automation Control (EtherMAC) is a new kind of real-time Ethernet used in motion control systems. It adopts a line topology with a standard industrial computer based master node and field-programmable-gate-array based slave nodes. EtherMAC employs one slave node to manage cycle communication and clock synchronization, so the real-time demand for its master node can be greatly reduced and dedicated hardware is no longer mandatory. Its distributed clock compensation mechanism can get synchronization accuracy in nanosecond order. The advantages of industrial computer and field programmable-gate-array are combined with EtherMAC, so that high control performance can be achieved

Effect of anlotinib combined with camrelizumab on clinical efficacy and short-term prognosis in male patients with advanced gastric cancer

This study aimed to investigate the effects of combining anlotinib with camrelizumab on clinical efficacy and short-term prognosis in male patients with advanced gastric cancer. A total of 88 male patients admitted to our hospital between May 2019 and March 2022 with advanced gastric cancer were included and randomly assigned to Group A (treated with anlotinib alone) or Group B (treated with anlotinib combined with camrelizumab) using the envelope method, with 44 patients in each group. Their clinical efficacy, vascular endothelial growth factor (VEGF) and programmeddeath-1 (PD-1) expression on Cluster of differentiation 4+ (CD4+) and cytotoxic Tlymphocyte (CD8+ T) cells in peripheral blood, immune function parameters, tumor markers, incidence of adverse reactions and survival time were compared. The results showed that the patients in Group B had significantly higher objective response rate (ORR) and disease control rate (DCR), superior PD-1 in VEGF, CD4+ T cells and CD8+ T cells, significantly improved immune function indicators and tumor markers (Carbohydrate antigen 50 (CA50), carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA21-1)), and significantly longer progression-free survival and overall survival than Group A. In addition, no significant difference in the incidence of adverse reactions between the two groups was observed. Therefore, the combination of anlotinib and camrelizumab could be a clinically beneficial treatment option and recommended for male patients with advanced gastric cancer as it can effectively control tumor progression, improve clinical efficacy and prolong their survival without increasing adverse reactions

Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder

BackgroundAlterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification.MethodsSeventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance.ResultsExperimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification.ConclusionThese findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings

Research on Three-dimensional Coupled Neutronics Thermo-mechanics Model for Dynamics Analysis of Fast-spectrum Micro-reactor

Fast-spectrum micro-reactor has many technical advantages, such as high energy density, good mobility, fast deployment speed, long life and so on. Because of the characteristic of compact structure and high role of leakages in the neutron balance in fast-spectrum micro-reactor core, geometric deformation caused by temperature change and mechanical effect under external force is the main source of reactivity feedback. So it is very important to carry out transient analysis based on neutronics-thermo-mechanics coupling to accurately simulate the dynamic characteristics of fast-spectrum micro-reactor core. Based on the open source multi-physics coupling framework MOOSE, a three-dimensional coupled neutronics thermo-mechanics model for the dynamics analysis of fast-spectrum micro-reactors was developed in this paper. The thermo-mechanics coupling was solved by using the JFNK (Jacobian-Free Newton Krylov) method. Then the neutronics model was coupled with thermal-mechanics model by Picard iteration method through the MultiApp and Transfer system based on MOOSE framework. The neutronics solver can directly simulate the neutronics behavior of deformed nuclear reactor configurations including automatic adapting of the cross sections for account for density change. In this paper, this method was applied to the simulation of the supercritical transient of Goidva-Ⅰ with three-dimensional coupled neutronics thermo-mechanics model. Time evolution of fission rate, average temperature rise, surface displacement and surface velocity in the transient with an initial period of 16.2 μs were evaluated. The numerical result of fission rate during the transient was compared with the experimental data, and an overall good agreement was observed. From the numerical results, it can be seen that the fission rate increases, then reaches a maximum value with the positive reactivity introduced. Goidva-Ⅰ is a typical compact, fast-spectrum reactors, reactivity feedback is dominated by core deformation. The quickly accumulated thermal power causes the core temperature increasing, the reactor materials expand, resulting in a larger geometry and reduced material densities. Fission rate then drops due to loss of criticality caused by the thermal expansion. Due to the rapid increase and decrease of fission power, the core is in a state of compression and expansion one after another, and the thermal inertia effect leads to the oscillation of core surface displacement and velocity. At the same time, due to the change of core reactivity caused by deformation, the core power also produces periodic oscillations after 500 μs. The results show that this method can accurately consider the reactivity feedback effect caused by the thermal expansion, which lays a foundation for further multi-physics coupling safety analysis of fastspectrum micro reactors

A Modelling Study for Predicting Life of Downhole Tubes Considering Service Environmental Parameters and Stress

A modelling effort was made to try to predict the life of downhole tubes or casings, synthetically considering the effect of service influencing factors on corrosion rate. Based on the discussed corrosion mechanism and corrosion processes of downhole tubes, a mathematic model was established. For downhole tubes, the influencing factors are environmental parameters and stress, which vary with service duration. Stress and the environmental parameters including water content, partial pressure of H2S and CO2, pH value, total pressure and temperature, were considered to be time-dependent. Based on the model, life-span of an L80 downhole tube in oilfield Halfaya, an oilfield in Iraq, was predicted. The results show that life-span of the L80 downhole tube in Halfaya is 247 months (approximately 20 years) under initial stress of 0.1 yield strength and 641 months (approximately 53 years) under no initial stress, which indicates that an initial stress of 0.1 yield strength will reduce the life-span by more than half

Impact of Inter-Regional Transport in a Low-Emission Scenario on PM_2.5 in Hubei Province, Central China

In 2020, when the novel coronavirus disease 2019 (COVID-19) broke out as a global pandemic, cities in Hubei Province first went into lockdown on 23 January and resumed work and production on 20 March. From February to March 2020, human activities in Hubei decreased significantly, with the average particulate matter smaller than 2.5 μm (PM2.5) concentration standing at 40 μg/m3, which is 21% lower than the expected based on a linear fitting trend in thePM2.5 concentration in Hubei. By using the empirical orthogonal function (EOF) method, this paper comparatively analyzes the spatial-temporal variations of Hubei’s PM2.5 concentration anomaly in February and March 2020 and the same periods of 2016–2019. The results show that the daytime peak of the PM2.5 daily variation in Hubei in a low-emission scenario during COVID-19 declined significantly, to which human activities contributed the most. However, during nighttime, the PM2.5 peak became more prominent, and the meteorological conditions had a more noticeable effect on the PM2.5 concentration. In addition, during COVID-19, there was a great drop in PM2.5 pollution accumulated from local sources within the urban circle of Wuhan City, while an increase was seen in central-western Hubei due to the inter-regional pollutant transport. Thus, the high PM2.5 concentration center in the urban circle of Wuhan disappeared, but the pollution transport channel cities in central-western Hubei remained as high-PM2.5-concentration centers

Effects of Applying the Implicit Particle Fuel Model for Pebble-bed Reactors

ABSTRACT In the pebble-bed high temperature gas-cooled reactor, there exist randomly located TRISO coated fuel particles in the pebbles and randomly located pebbles in the core, which is known as the double stochastic heterogeneity. In the previous research, the regular lattice pattern was used to approximately simulate the pebble unit cells because the difficulties in modeling the randomly located TRISO geometric. This work aimed at to quantify the stochastic effect of high-temperature gas cooled pebble-bed reactor unit cells, and in view of the strong ability to carry out the accurate simulation of random media, the implicit particle fuel model of Monte Carlo method is applied to analyze to the difference between regular distribution and random distribution. Infinite multiplication factors of the pebble-bed reactor unite cells were calculated by the implicit particle fuel model and simple cube regular lattice pattern at different TRISO packing factor from 0.5%-50%. The results showed that the simple cube regular lattice pattern underestimates the infinite multiplication factors for most packing fractions, but overrates the infinite multiplication factors when the packing fraction is very low