Experimental Treatment with Favipiravir for COVID-19: An Open-Label Control Study

There is currently an outbreak of respiratory disease caused by a novel coronavirus. The virus has been named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the disease it causes has been named coronavirus disease 2019 (COVID-19). More than 16% of patients developed acute respiratory distress syndrome, and the fatality ratio was 1%–2%. No specific treatment has been reported. Herein, we examined the effects of favipiravir (FPV) versus lopinavir (LPV)/ritonavir (RTV) for the treatment of COVID-19. Patients with laboratory-confirmed COVID-19 who received oral FPV (Day 1: 1600 mg twice daily; Days 2–14: 600 mg twice daily) plus interferon (IFN)-α by aerosol inhalation (5 million international unit (IU) twice daily) were included in the FPV arm of this study, whereas patients who were treated with LPV/RTV (Days 1–14: 400 mg/100 mg twice daily) plus IFN-α by aerosol inhalation (5 million IU twice daily) were included in the control arm. Changes in chest computed tomography (CT), viral clearance, and drug safety were compared between the two groups. For the 35 patients enrolled in the FPV arm and the 45 patients in the control arm, all baseline characteristics were comparable between the two arms. A shorter viral clearance median time was found for the FPV arm versus the control arm (4 d (interquartile range (IQR): 2.5–9) versus 11 d (IQR: 8–13), P < 0.001). The FPV arm also showed significant improvement in chest CT compared with the control arm, with an improvement rate of 91.43% versus 62.22% (P = 0.004). After adjustment for potential confounders, the FPV arm also showed a significantly higher improvement rate in chest CT. Multivariable Cox regression showed that FPV was independently associated with faster viral clearance. In addition, fewer adverse events were found in the FPV arm than in the control arm. In this open-label before-after controlled study, FPV showed better therapeutic responses on COVID-19 in terms of disease progression and viral clearance. These preliminary clinical results provide useful information of treatments for SARS-CoV-2 infection.     

门架式堆取料机在铜冶炼中的生产实践

针对铜冶炼工艺的特点,从门架式堆取料机对前后工序衔接、结构、原理出发,介绍了其在熔炼炉系统和吹炼炉系统中的应用情况。经过试生产期的生产实践证明,门架式堆取料机具有场地利用率高、取料量大,自动化程度高和安全环保等优点。     

摇摆运动下多环路主冷却剂系统自然循环流量的稳定性分析

海洋核动力平台因其突出的安全性已成为当今核能领域热点研究问题之一，但在海洋等非惯性条件下会使一回路系统的热工特性发生变化。针对此问题，本文对几种典型的一回路系统在摇摆条件下的自然循环流量波动特性进行计算分析。分别建立典型的双环路、三环路、四环路的一回路系统模型布置方案，并同时考虑摇摆中心的位置，根据流体动量守恒方程，得到不同状态下一回路系统内的流量变化规律。对于单堆双环路系统，摇摆中心在船上/中/下部位置时，环路流量波动幅度分别为13.2%/11.2%/9.5%，堆芯流量波动幅度分别为0.9%/0.8%/0.6%；对于单堆三环路系统，摇摆中心在船下部时，环路流量和堆芯流量波动幅度分别为9.2%和0.8%；对于单堆四环路系统，摇摆中心在船下部时，布置方案1和方案2的环路流量波动幅度分别为9.5%和9.2%，堆芯流量波动幅度分别为0.9%和0.7%。计算结果表明：采用单堆双环路的设计布置方案是最有利于系统稳定性的。     

300M超高强钢在中性盐雾环境中的腐蚀行为及机制

摘要：为了研究300M超高强钢在中性盐雾环境中的腐蚀行为及腐蚀机制，采用失重法，宏观、微观腐蚀形貌分析，三维表面轮廓分析及电化学分析的研究方法，来表征腐蚀实验现象并进行分析。结果表明：300M超高强钢在中性盐雾环境中的腐蚀产物为FeOOH、Fe2O3、Fe(OH)3和Fe3O4；腐蚀速率随着腐蚀时间逐渐降低，腐蚀后期（72h）腐蚀速率降低50%；腐蚀初期以点蚀为主，点蚀坑通过横向扩展，逐渐发展为后期的均匀腐蚀，腐蚀表面形貌呈沟壑状；外腐蚀层对基体的保护能力很弱，Cr元素在锈层靠近基体的一侧偏聚使内腐蚀层具有一定的抗腐蚀性。     

基于双离合变速器电控单元的Volcano CAN开发

Volcano 是集汽车电子网络设计、实现和测试于一体的完整工具链。它包括整车网络规划及优化设计工具VNA,网络节点(TCU等)实现工具VTP以及网络测试工具Tellus。本文着重介绍了基于TCU的VTP。     

Interlayer Engineering of Molybdenum Trioxide toward High‐Capacity and Stable Sodium Ion Half/Full Batteries

Orthorhombic molybdenum trioxide (MoO₃) is one of the most promising anode materials for sodium‐ion batteries because of its rich chemistry associated with multiple valence states and intriguing layered structure. However, MoO₃ still suffers from the low rate capability and poor cycle induced by pulverization during de/sodiation. An ingenious two‐step synthesis strategy to fine tune the layer structure of MoO₃ targeting stable and fast sodium ionic diffusion channels is reported here. By integrating partially reduction and organic molecule intercalation methodologies, the interlayer spacing of MoO₃ is remarkably enlarged to 10.40 Å and the layer structural integration are reinforced by dimercapto groups of bismuththiol molecules. Comprehensive characterizations and density functional theory calculations prove that the intercalated bismuththiol (DMcT) molecules substantially enhanced electronic conductivity and effectively shield the electrostatic interaction between Na⁺ and the MoO₃ host by conjugated double bond, resulting in improved Na⁺ insertion/extraction kinetics. Benefiting from these features, the newly devised layered MoO₃ electrode achieves excellent long‐term cycling stability and outstanding rate performance. These achievements are of vital significance for the preparation of sodium‐ion battery anode materials with high‐rate capability and long cycling life using intercalation chemistry.     

嵌入式实时操作系统内核混合代码的自动化验证框架

"如何构造高可信的软件系统"已成为学术界和工业界的研究热点.操作系统内核作为软件系统的基础组件，它的安全可靠是构造高可信软件系统的重要环节.为了确保操作系统内核的安全可靠，将形式化方法引入到操作系统内核验证中，提出了一个自动化验证操作系统内核的框架.该验证框架包括：（1）分别对C语言程序和混合语言程序（C语言和汇编语言）进行验证；（2）在混合语言程序验证中，为汇编程序建立抽象模型，并将C语言程序和抽象模型粘合形成基于C语言验证工具可接收的验证模型；（3）从规范中提取性质，基于该自动验证工具，对性质完成自动验证；（4）该框架不限于特定的硬件架构.成功地运用该验证框架对两种不同硬件平台的嵌入式实时操作系统内核μC/OS-II进行了验证.结果显示：利用该框架在对两个不同的硬件平台上内核验证时，框架的可重复利用率很高，高达到88%，虽然其抽象模型需要根据不同的硬件平台进行重构.在对基于这两种平台的操作系统内核验证中，分别发现了10~12处缺陷.其中，在ARM平台上两处与硬件相关的问题被发现.实验表明，该方法对不同硬件平台的同一个操作系统分析验证具有一定的通用性.     

Calorimetric study and thermodynamic description of Ga-Ge-Li liquid alloys

This publication contains the thermodynamic results received by the drop calorimetry method. The experiments were conducted for four different cross sections, at the temperature of 1080 K. The investigated alloys were as follows: (Ga_0.75Li_0.25)_1-xGe_x, (Ge_0.50Li_0.50)_1-xGa_x, (Ga_0.50Li_0.50)_1-xGe_x, (Ga_0.25Li_0.75)_1-xGe_x. The mixing enthalpy changes measured for all four cross sections of the Ga-Ge-Li system are characterized by negative deviations from the ideal solutions. The Muggianu model with the ternary interaction parameters was applied to elaborate the experimental data of the mixing enthalpy change with the use of the optimized thermodynamic parameters of the binary systems available in the literature.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.