Data sharing mechanism of various mineral resources based on blockchain

Basins with various mineral resources coexisting and enriching often occupy an important strategic position. The exploration of various mineral resources is repetitive at present due to unshared data and imperfect management mechanism. This situation greatly increases the cost of energy exploitation in the country. Traditional data-sharing mode has several disadvantages, such as high cost, difficulty in confirming the right of data, and lack of incentive mechanism, which make achieving real data sharing difficult. In this paper, we propose a data-sharing mechanism based on blockchain and provide implementation suggestions and technical key points. Compared with traditional data-sharing methods, the proposed data-sharing mechanism can realize data sharing, ensure data quality, and protect intellectual property. Moreover, key points in the construction are stated in the case study section to verify the feasibility of the data-sharing system based on blockchain proposed in this paper.     

基于Matlab的制芯机器人夹持器稳定性研究与分析

由于铸造厂制芯机器人的工作环境恶劣,机器人抓取砂芯的稳定性较差,时常出现掉砂芯、夹伤砂芯的问题。针对上述问题建立以夹持器、气压、机械振动的系统数学模型,采用高阶系统极点在[s]平面的分布法来判定该系统的稳定性。在确保高阶系统极点值不变的情况下,通过改变系统的零点值来观察零点值对系统稳定性、振荡衰减时间的影响。用Matlab的pzmap和impulse函数分别对系统的零点、极点分布和零点值的不同对系统响应进行仿真,运用仿真结果与现场试验进行优化参数,结果表明采用一种刚度与柔度各为1/2的夹持器对制芯机器人的稳定性具有较好优化效果。     

发夹式换热器管束流致振动数值模拟研究

运用计算流体力学方法,采用ANSYS CFX软件对发夹式换热器的壳侧流场进行了三维数值模拟。流场计算中采用多孔介质模型对管束区域进行简化,分析了壳侧流场的速度分布,结果表明:直管段部分的流体湍流强度大于弯管段,且外层管束所在区域为高流速区,受流体冲刷严重。结合流场信息,通过功率谱生成随机激振力,采用ABAQUS软件模拟计算了湍流激振下管束的振动响应,结果显示管束的面外均方根位移远大于面内位移,且弯管部分的振动位移最大。该研究结果可为发夹式换热器的性能分析和优化设计提供参考和依据。     

基于关键路径延时检测的自适应电压缩减技术

为了减小传统的最差情况设计方法引入的电压裕量,提出了一种变化可知的自适应电压缩减(AVS)技术,通过调整电源电压来降低电路功耗.自适应电压缩减技术基于检测关键路径的延时变化,基于此设计了一款预错误原位延时检测电路,可以检测关键路径延时并输出预错误信号,进而控制单元可根据反馈回的预错误信号的个数调整系统电压.本芯片采用SMIC180 nm工艺设计验证,仿真分析表明,采用自适应电压缩减技术后,4个目标验证电路分别节省功耗12.4％,11.3％,10.4％和11.6％.     

基于“天空地”一体化技术的安全监测系统建设与应用

西藏江达县白格村金沙江右岸于2018年10月11日和2018年11月3日先后发生2次大规模滑坡—堰塞湖堵江事件,溃堰洪水对下游拉哇库区不良地质体的稳定性造成不同程度的影响。为保障下游水电站建设安全,对拉哇库区主要不良地质体建立了基于星载InSAR技术、无人机技术和地面传感器实时监测的“天空地”一体化监测预警体系,以多维空间采集技术获取变形信息,通过智能监控平台对信息及时进行处理、分析和可视化呈现,利用平台、短信等方式向相关人员进行分级告警,取得了较好的应用效果。     

乳液原位加氢制备氢化丁腈橡胶的热氧老化性能及寿命（英文）

使用乳液原位加氢法选择性氢化丁腈胶乳制备了氢化丁腈橡胶(HNBR),对其热氧老化性能进行了研究。主要运用热重分析(TGA)并结合Flynn-WallOzawa法计算了热空气降解活化能,同时采用傅立叶变换衰减全反射红外光谱分析跟踪考察了HNBR在热氧老化过程中微观结构的变化。此外,通过对HNBR的物理机械性能的跟踪研究,计算了其老化温度系数及老化寿命与老化温度之间的关系,为HNBR在耐热领域中的应用提供理论依据。     

Real‐time monitoring by proton relaxometry of radical polymerization reactions of acrylamide in aqueous solution

The potential of time‐domain nuclear magnetic resonance (TD‐NMR) for the real‐time monitoring of solution radical polymerizations is demonstrated. A model system composed of a redox‐pair initiator system, acrylamide as monomer and water as solvent was investigated. A second‐generation continuous wave free precession technique was employed to measure the longitudinal relaxation time constant (T₁) of the samples throughout the polymerization reactions. This parameter was shown to be sensitive to the reactant feed free‐radical enhancement of the water molecule relaxation time, making it a good probe to monitor monomer conversion in real time in an automated, non‐destructive fashion. It was found that the T₁ value was better than the transverse relaxation time constant (T₂) for describing the evolution of the polymerization reactions, due to its greater sensitivity to paramagnetic effects. The TD‐NMR signal variation observed was linked to the formation, propagation and termination steps of the radical polymerization kinetics scheme. These first results may contribute to the application of real‐time monitoring of radical polymerization reactions employing low‐cost and robust TD‐NMR spectrometers. © 2018 Society of Chemical Industry     

Dihydrotanshinone,a Natural Diterpenoid,Preserves Blood-Retinal Barrier Integrity via P2X7 Receptor

Activation of P2X7 signaling, due to high glucose levels, leads to blood retinal barrier (BRB) breakdown, which is a hallmark of diabetic retinopathy (DR). Furthermore, several studies report that high glucose (HG) conditions and the related activation of the P2X7 receptor (P2X7R) lead to the over-expression of pro-inflammatory markers. In order to identify novel P2X7R antagonists, we carried out virtual screening on a focused compound dataset, including indole derivatives and natural compounds such as caffeic acid phenethyl ester derivatives, flavonoids, and diterpenoids. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring and structural fingerprint clustering of docking poses from virtual screening highlighted that the diterpenoid dihydrotanshinone (DHTS) clustered with the well-known P2X7R antagonist JNJ47965567. A human-based in vitro BRB model made of retinal pericytes, astrocytes, and endothelial cells was used to assess the potential protective effect of DHTS against HG and 2′(3′)-O-(4-Benzoylbenzoyl)adenosine-5′-triphosphate (BzATP), a P2X7R agonist, insult. We found that HG/BzATP exposure generated BRB breakdown by enhancing barrier permeability (trans-endothelial electrical resistance (TEER)) and reducing the levels of ZO-1 and VE-cadherin junction proteins as well as of the Cx-43 mRNA expression levels. Furthermore, HG levels and P2X7R agonist treatment led to increased expression of pro-inflammatory mediators (TLR-4, IL-1β, IL-6, TNF-α, and IL-8) and other molecular markers (P2X7R, VEGF-A, and ICAM-1), along with enhanced production of reactive oxygen species. Treatment with DHTS preserved the BRB integrity from HG/BzATP damage. The protective effects of DHTS were also compared to the validated P2X7R antagonist, JNJ47965567. In conclusion, we provided new findings pointing out the therapeutic potential of DHTS, which is an inhibitor of P2X7R, in terms of preventing and/or counteracting the BRB dysfunctions elicited by HG conditions.     

轨道式垃圾回收机器人设计与分析

针对高速公路高峰期拥挤产生的垃圾等问题,设计一种轨道式垃圾回收机器人,由光伏发电装置、机器人壳体、末端手爪、关节臂以及控制系统等组成,主体结构采用双臂三关节结构,采用西门子PLC控制系统,通过控制关节臂的旋转以及偏转、末端手爪的旋转及开合等动作来实现工作的目的。结合外场作业的特殊性,采用光伏能源装置,对其结构及控制系统进行设计,通过对机器人进行三维建模、运动和控制仿真分析,验证系统的可行性及有效性。     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.