Anomalous crystal structure of γ" phase in the Mg-RE-Zn(Ag) series alloys:Causality clarified by ab initio study

The crystal structure of the single-unit-cell thickness γ " phase,as a key strengthening phase in Mg-REZn(Ag) series alloys,has been extensively studied,and several structural models have been proposed in the past two decades.However,these reported models,and even the lattice constants at the same proposed structure,are scattered severely,which has led to considerable confusion and not available for further mechanical property simulation and prediction of Mg alloys containing this phase.In this study,by using first-principles calculations,the crystal structure of y" phase is clarified,resolving the discrepancies among different experiments,and its intrinsic mechanical properties have also been studied for the first time.It is verified that the γ " phase contains quasi-five atomic layers,instead of the previously reported tri-layer,and surprisingly,its crystal structure has many variants,which would change with the alloy composition.Besides,with the help of the simulated selected area electron diffraction(SAED) patterns,it is found that the atoms in the central layer remain partially ordered distribution,and this ordered extent primarily depends on the atomic ratio of RE:Zn(Ag) and the solute content in an alloy.That is,the ordered extent increases with decreasing the atomic ratio of RE:Zn(Ag) and/or increasing solute content of alloy,and vice versa.Ag and Zn dissolved in the γ" phase would produce almost opposed mechanical anisotropy for the γ " phase under the identical crystal structure,and the addition of Ag shows more efficient on increasing the shear modulus of γ" phase.     

Functional Causality between Oil Prices and GDP Based on Big Data

This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four quarters of each year. We discuss the causality in the sense of Granger, which requires the stationarity of the data. Thus, in the first Step, we test the stationarity by using the Monte Carlo test of a functional time series stationarity. Our main goal is treated in the second step, where we use the functional causality idea to model the co-variability between these variables. We show that the two series are not integrated; there is one causality between these two variables. All the statistical analyzes were performed using R software.     

飞行时间二次离子质谱对矿物表面性质的研究

随着易选矿石的逐步枯竭,低品位、嵌布粒度细等难选矿石的开发利用已成必然趋势。目前,矿物加工已经从实践生产阶段发展到了理论研究指导实践生产的阶段,因此具有分辨率高、信息量大、灵敏快速以及样品用量少等特点的矿物表层研究方法来支撑理论研究是必不可少的。飞行时间二次离子质谱法(TOF-SIMS)是近年来在矿物加工中使用较多的一种矿物表面化学分析方法,具有表面质谱、化学成像、深度剖析、快速灵敏及数据精准的优点,因此总结归纳了近年来TOF-SIMS在矿物加工中应用取得的成果,以期为学者提供可靠的理论依据及技术支持。     

Decay Rate Kinetics of Ozone Gas in Rice Grains

The present study was carried out to evaluate the effects of ozone on rice grains for the following three conditions: saturation time, decay rate, and half-life of ozone. Experiments were performed in different bed thicknesses (5 and 10 cm) and moisture content (11.4 and 14.2% wb) at atmospheric conditions. The lowest saturation time of ozone was 119 min, with the concentration of 516 ppm for rice grains ozonated at 5-cm bed thickness with 11.4% (wb) moisture content. The decay rate kinetics of ozone obtained were consistent with a first-order model. Regarding the half-life of ozone, the lowest value obtained was 6.78 min for rice grains ozonated at 10-cm bed thickness with 14.2% (wb) moisture content.     

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.     

Detection of correlation characteristics between financial time series based on multi-resolution analysis

Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.     

Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

Designed damping controller for SSSC to improve stability of a hybrid offshore wind farms considering time delay

This paper presents the stability improvement results of hybrid doubly fed induction generator (DFIG)-based and permanent magnet generator (PMG)-based offshore wind farms (OWFs) using a static synchronous series compensator (SSSC). An adaptive-network-based fuzzy inference system (ANFIS) controller of the proposed SSSC is designed to render adequate damping characteristics to the studied system. A frequency-domain approach based on a linearized system model using eigenvalue technique analysis is performed. A time-domain scheme based on a nonlinear system model subject to a three-phase short circuit fault at infinite bus with variations in the signal transmission delays has also been investigated to compare the damping of the studied system in cases of with and without controller. The simulation results with MATLAB/SIMULINK toolbox have been presented. It can be concluded from the simulation results that the proposed SSSC joined with the designed ANFIS damping controller can offer adequate damping performance to the studied hybrid DFIG-based and PMG-based OWFs under severe disturbance.     

Fraud detection in registered electricity time series

This paper analyses time series without the seasonal component of consumers’ power consumption at low voltage in order to detect fraud and illogical consumption by customers. Statistical process control is used, where the process represents the process of using electricity. XMR charts are used to indicate major changes (decrease) in registered customers’ consumption. Verification of this method was tested on time series of a set of customers who were caught stealing during a time series. It shows that symptoms of non-random factors in time series of customers are revealed in a high percentage, which indirectly confirms the method’s ability to successfully detect electricity fraud.