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.     

Design and performance study of phase‐locked loop using fractional‐order loop filter

The present work reports the realization of an analog fractional‐order phase‐locked loop (FPLL) using a fractional capacitor. The expressions for bandwidth, capture range, and lock range of the FPLL have been derived analytically and then compared with the experimental observations using LM565 IC. It has been observed that bandwidth and capture range can be extended by using FPLL. It has also been found that FPLL can provide faster response and lower phase error at the time of switching compared to its integer‐order counterpart. Copyright © 2014 John Wiley & Sons, Ltd.     

Seismic behavior of geosynthetic-reinforced retaining walls backfilled with cohesive soil

This study investigates the seismic performance of geosynthetic-reinforced modular block retaining walls backfilled with cohesive, fine grained clay-sand soil mixture. Shaking table tests were performed for three ½ scaled (wall height 190 cm) and ¼ scaled model walls to investigate the effects of backfill type, the influence of reinforcement length and reinforcement stiffness effects. The El Centro and Kobe earthquake records of varying amplitudes were used as base acceleration. Displacement of the front wall, accelerations at different locations, strains on the reinforcements, and the visual observations of the facing and the backfill surface were used to evaluate the seismic performance of model walls. The model walls were subjected to rigorous shaking and the walls did not exhibit any stability problems or signs of impending failure. The maximum deformations observed on the models with cohesive backfill was less than half of the deformation of the sand model. The load transfers between the geogrid and cohesive soil was comparable to that of sand and hence the needed reinforcement length was similar as well. As a result; the model walls with cohesive backfills performed within acceptable limits under seismic loading conditions when compared with granular backfilled counterparts.     

致密油藏注天然气提高采收率研究现状

随着我国石油需求量不断增加,致密油藏的开发也愈发重要。如何高效开发致密油藏是一项重点难点,其中致密油藏注天然气提高采收率是一个极具潜力的研究方向。因此,本文着重介绍以天然气作为能量补充介质在国内外的研究现状和应用现状,并且从两相特征等方面总结了理论研究中的一些机理,对致密油藏注天然气提高采收率的发展前景进行了一定的展望。     

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.     

Smart firefighting construction in China: Status,problems, and reflections

“Smart firefighting” construction as a part of the “smart city” has been a concern of the public security and fire agencies at all levels. In this study, the status, problems, and reflections of “smart firefighting” construction in China are discussed. A recent survey indicates that China has launched its smart firefighting construction and initially created a new perspective on its regional smart firefighting work based on three main aspects: intelligent disaster perception by Internet of Things (IoT) construction, intelligent disaster prevention by big data construction, and intelligent disaster disposal by emergency rescue platform construction. However, the current smart firefighting construction in China still has some prominent problems such as the data interconnectivity and normalized management of various platforms, the extensibility of smart firefighting platforms, and the intelligent level of smart firefighting researches, which need to be solved urgently. Therefore, we argue that smart firefighting construction in China should establish data interconnectivity, industrial normalized management, 2D/3D geographic information interaction and extension, high-integration fire protection theory, and many other aspects in the near future and truly realize firefighting visualization and efficient data applications for 4D time space. This study could provide valuable reference for smart firefighting and smart city construction.     

Temperature- and frequency-dependent hysteresis scaling behaviour and phase transitions in a [001]c 0.73Pb(Mg1/3Nb2/3)O3-0.27PbTiO3 single crystal

The dielectric properties and bipolar polarization-electric field (P-E) and strain-electric field (S-E) dynamic hysteresis of a relaxor [001]_c 0.73Pb(Mg_1/3Nb_2/3)O₃-0.27PbTiO₃ (PMN-0.27PT) single crystal were investigated to reveal more details of the temperature-induced phase transitions. Different linear scaling relations for ferroelectric hysteresis area <A>, coercive field E_c, saturation polarization P_s and remnant polarization P_r versus temperature τ were measured in different temperature regions. For each measurement frequency f, all hysteresis parameters were found to decrease linearly with temperature in the temperature range of the single rhombohedral (R) phase or tetragonal (T) phase, and the rate of decrease in the T phase was observed to be much larger than the corresponding rate in the R phase. In the temperature range near the R-T phase transition, the exponent α in the power law <A>∝f ^α for the R phase was found to be smaller than that for the T phase, and the magnitude of α depended strongly on temperature when the crystal was in the R-T coexisting phase state. Our experimental and theoretical results indicate that the difference in the activation energy and dipole moment in the R and T phases may lead to the observed discrepancy for the P-E and S-E hysteresis behaviour in different temperature regions.     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

Corrosion behaviors of Cr2AlC/α-Al2O3 composites in 3.5 wt. % NaCl aqueous solution

Cr₂AlC and its composites containing α-Al₂O₃ (6.1 and 15.2 wt %) were prepared by hot pressing and their corrosion behaviors in air-saturated 3.5 wt % NaCl aqueous solution were investigated by electrochemical testing methods. It was revealed that the secondary phase of Al₂O₃ particles mainly distributed along grain boundaries of Cr₂AlC matrix. The potentiodynamic polarization measurements showed that the corrosion current densities of these Cr₂AlC composites were lower than that of the pure Cr₂AlC. The Aluminum in Cr₂AlC was prone to be attacked more easily. When immersed at open circuit potential (OCP), Al readily slipped out from Cr₂AlC matrix into NaCl solution in the form of dissoluble species. But in the case of polarization, regardless of potentiostatic polarization or potentiodynamic polarization, more de-intercalated Al, reacted with the electrolyte to form corrosion products of Al₂O₃ and/or AlOOH and deposited on the sample surface. For Cr₂AlC/α-Al₂O₃ composites, the presence of Al₂O₃ weakened the corrosion along grain boundaries by partly blocking the permeation of electrolyte and inhibiting the anodic dissolution process.     

Development and monitoring of an integrated bioprocess for production of a potential malaria vaccine with Pichia pastoris

An engineered variant of the yeast Pichia pastoris was used to produce the potential malaria vaccine D1M1. This paper describes the implementation of an expanded bed adsorption chromatography step into a sequential cultivation process of the yeast in order to link protein expression, cell release, and product capture of the secreted protein in a fully automated plant. The process is operated with a semi-continuous strategy for an integrated production. Process improvements have been achieved using design of experiments. Resulting purities of product achieved up to 87% with recoveries of 51% in a single downstream operation. Furthermore, a multivariate analysis of historical batch data was used to develop a golden batch model for process monitoring.