Strength of pure alumina ceramics above 1 GPa

Up to now, commercially available alumina ceramics were claimed to have strength between 400 and 550 MPa. However, our study shows strength ~ 2 times higher for commercially available alumina than commonly believed. The average and characteristic strength, measured on 31 pure alumina ceramic discs by ball on three balls (B3B) test, were 1205 ± 93 MPa and 1257 MPa, respectively, with a Weibull modulus of m = 11.8. Tested specimens were in form of discs with a diameter of 5 mm and thickness 0.5 mm. The grain size distribution of the alumina is bimodal with an average grain size of ~ 850 nm measured at the surface. The fracture reveals a mixed transgranular / intergranular failure mode. To avoid incorporation of additional flaws, the discs were tested as sintered. The characteristic flexural strength measured in B3B was recalculated according to Weibull theory for standard 4-point bending bars of size 3 × 4 × 45 mm as bend 856 MPa. The measured strength of nearly 900 MPa shows the potential of strength for high purity alumina ceramics.     

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.     

User activities and the heterogeneity of urban space:The case of Dahiyat Al Hussein park

Urban researchers have maintained a constant interest in the complexity and continuity of urban space usage. Some have applied actor–network theory (ANT) to investigate the heterogeneity of spaces and present them through the networks of their users’ activities. However, these accounts are predominantly limited in examining the extent to which these spaces may be heterogeneous when exploring such networks. This paper draws on recent ANT scholarship, which employs an ethnographic research conducted in a main park in a housing project at Dahiyat Al Hussein in Amman, Jordan. The findings describe the complex and unpredictable negotiations that occur within spaces by documenting the varieties and interrelations among user activity networks within this common and shared urban space. This research reveals the extent to which spaces, parks in this case, may be heterogeneous by unpacking their usage. The conclusions and insights assert the necessity of paying attention to design detail and creating designs that are responsive to evolving user activities.     

Three-phase four-wire shunt active power filter based on the SOGI filter and Lyapunov function for DC bus control

The three-phase four-wire shunt active power filter (SAPF) was developed to suppress the harmonic currents generated by nonlinear loads, and for the compensation of unbalanced nonlinear load currents, reactive power, and the harmonic neutral current. In this work, we consider instantaneous reactive power theory (PQ theory) for reference current identification based on the following two algorithms: the classic low-pass filter (LPF) and the second-order generalized integrator (SOGI) filter. Furthermore, since an important process in SAPF control is the regulation of the DC bus voltage at the capacitor, a new controller based on the Lyapunov function is also proposed. A complete simulation of the resultant active filtering system confirms its validity, which uses the SOGI filter to extract the reference currents from the distorted line currents, compared with the traditional PQ theory based on LPF. In addition, the simulation performed also demonstrates the superiority of the proposed approach, for DC bus voltage control based on the Lyapunov function, compared with the traditional proportional-integral (PI) controller. Both novel approaches contribute towards an improvement in the overall performance of the system, which consists of a small rise and settling time, a very low or nonexistent overshoot, and the minimization of the total harmonic distortion (THD).     

黄浦江下游吴淞口站低潮位重现期研究

黄浦江为感潮河流,低潮位重现期的确定对黄浦江航道的设计具有重要意义。为了准确估计黄浦江低潮位的重现期,以黄浦江下游吴淞口站年最低潮位序列为例,在对样本进行三性审查的基础上,分别采用传统的数理统计方法和时变矩方法进行水文频率分析。结果表明:吴淞口站年最低潮位序列在1996年发生了变异;还现修正后的序列服从位置参数线性时变的GEV模型,低潮位存在缓慢的上升趋势;一致性条件下百年一遇低潮位约为0.261 m,在非一致性条件下其重现期增大为150 a。非一致性条件下的重现期增大说明黄浦江现有的航道设计标准偏安全,航道通航保证率提高。     

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。     

网络安全威胁因素及其常见网络安全技术分析

社会经济的快速发展促进了科学技术水平的显著提升，社会生产生活各个领域当中对计算机网络的应用程度不断提升，当前，网络已经成为了人们日常生活与工作的重要组成部分。为此，加强对网络安全等因素影响研究就显得格外重要。网络环境关系着用户的使用质量，更加影响人们的工作效率，加强对网络安全技术的研究时解决相关问题的核心内容。本文针对网络安全威胁因素以及常见的网络安全技术进行了简要分析。     

Restructuring bank networks after mergers and acquisitions: A capacitated delocation model for closing and resizing branches

During restructuring processes, due to mergers and acquisitions, banks frequently face the problem of having redundant branches competing in the same market. In this work, we introduce a new Capacitated Branch Restructuring Model which extends the available literature in delocation models. It considers both closing down and long term operations׳ costs, and addresses the problem of resizing open branches in order to maintain a constant service level. We consider, as well, the presence of competitors and allow for ceding market share whenever the restructuring costs are prohibitively expensive.We test our model in a real life scenario, obtaining a reduction of about 40% of the network size, and annual savings over 45% in operation costs from the second year on. We finally perform a sensitivity analysis on critical parameters. This analysis shows that the final design of the network depends on certain strategic decisions concerning the redundancy of the branches, as well as their proximity to the demand nodes and to the competitor׳s branches. At the same time, this design is quite robust to changes in the parameters associated with the adjustments on service capacity and with the market reaction.     

Combination of nuclear magnetic resonance spectroscopy and nonlinear methods to analyze the copolymerization of phosphonic acid derivatives

We demonstrate in this study that the combination of modern inline monitoring methods [here: inline nuclear magnetic resonance (NMR)] with simulations gains more exact and profound kinetic results than previously used methods like linearization without that combination. The ¹H-NMR spectroscopic data (more than 100 data points) are used to construct the copolymerization diagram. The reactivity ratios are obtained applying the van Herks nonlinear least square method. The examination of the radical copolymerization of 2-hydroxyethyl methacrylate (HEMA) with (2-{[2-(ethoxycarbonyl)prop-2-en-1-yl]oxy}ethyl) phosphonic acid (ECPPA) as important adhesive monomer used in dentistry yields reactivity ratios of r_HEMA = 1.83; r_ECPPA = 0.42. The copolymerization diagram reflects nonideal, non-azeotropic copolymerization. The sequence distribution of the obtained by Monte Carlo simulation indicates the generation of statistical copolymers. As an important finding, it is demonstrated that the repeating units responsible for etching and adhesion are arranged over the whole polymer chain, which is necessary to achieve proper functionality. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48256.     

Modelling of black carbon statistical distribution and return periods of extreme concentrations

Eight datasets of 1-h black carbon (BC) concentrations measured in Warsaw agglomeration (Poland), at urban background and sub-urban sites, and in Racibórz, a small town in Upper Silesia district (regional background site) were analyzed to evaluate BC levels, daily profiles and statistical distributions of concentrations in Central-Eastern European region. The observed mean levels ranged from 1483 ng m⁻³ in suburban site during summer to 3358 ng m⁻³ in regional background site in winter. Observed diurnal patterns were bimodal in the locations dominated by traffic emissions, but unimodal, with elevated evening peak in individually heated residential area. Three theoretical frequency distributions were applied to fit analyzed datasets separately. The lognormal distribution was the most appropriate to represent the middle-range values, while the high concentrations were satisfactorily predicted by the type I two-parameter exponential distribution which was used to estimate the return periods of extreme concentrations for winter months.