Data quality and uncertainty assessment methodology for pavement LCA

In performing pavement life cycle assessment (LCA), users are facing various reports of energy intensity coefficient (EIC) of pavement materials which differ considerably among data sources and therefore alter the LCA results significantly. Instead of selecting a certain EIC without or of little explanation for the current pavement LCA practices, this study proposed a methodology to build probability density function (PDF) for EIC based on available data-set and their qualities. Each data was first evaluated about the data quality indicator (DQI) through data quality pedigree matrix and converted to PDF in modified Beta distribution form. Three weighting methods, the DQI one, coefficient of variation (COV) one and analytical hierarchy process (AHP) one, were developed to assign weightings for different data. Monte Carlo simulation was run with the weighted PDF of each data as input to obtain the ultimate PDF for EIC. A case study to estimate the bitumen’s EIC with eight data samples were performed using the proposed methodology. It is found (1): the estimates by the proposed methodology is of higher reliability (lower COV) compared to any single data due to utilisation of information of the overall data samples; (2) the AHP weighting method is most favoured despite the results of the three weighting methods are close; (3) the central estimates of bitumen’s EIC are between5.4~5.8 MJ/kg. The proposed methodology is helpful in aiding calculating EICs for pavement materials and capturing uncertainties in LCA results in a statistical sense.     

智能变电站设备管控大数据分析系统研究

结合数据挖掘、大数据技术在电力系统中的应用现状，分析和设计了一种智能变电站设备管控大数据分析系统。对变电站设备运行状态的大数据特征及基本框架进行了分析，并重点阐述了变电站设备运行状态大数据分析系统的数据集成与预处理、数据存储与处理、设备状态评估模型以及数据可视化展示。实验结果表明，所设计的大数据分析系统能够大幅度提升变电站设备数据分析智能化水平，为变电站管控稳定运行提供更加有效和实用性的技术支撑。     

Natural food mastication capability in preschool children according to their oral condition: A preliminary study

This study investigated, for the first time, the masticatory capability of preschool children using natural foods, and the impact of an early oral health alteration (early childhood caries: ECC) on the granulometry of ready-to-swallow food boluses. Thirteen children with ECC were compared to 13 preschool children with a healthy oral condition. Oral health criteria and NOT-S scores (Nordic Orofacial dysfunction Test-Screening) were recorded. For each child, number of masticatory cycles (Nc), chewing time (Ti), and frequency (Fq = Nc/Ti) were recorded during mastication of raw carrot (CAR), cheese (CHS) and breakfast cereals (CER) samples. Food boluses were collected by stopping children at their food-dependent individual swallowing threshold (Nc), and the median food bolus particle size value (D50) was calculated. Correlations were sought between oral health and masticatory criteria. In the ECC group, mean Fq values were significantly decreased for all three foods (p ≤ .001) and mean D50 values were significantly increased (p ≤ .001) compared to the control group (i.e., D50 CAR = 4,384 μm ± 929 vs. 2,960 μm ± 627). These alterations were related to the extent of ECC. The NOT-S mean global score was significantly increased in children with ECC (2.62 ± 1.37 vs. 1 ± 0.91 in the control group, p ≤ .01), due to “Mastication and swallowing” domain impairment. This study gives granulometric normative values for three foods in preschool children and shows the impact of ECC on D50 values. The progression of children's masticatory capability after dental treatment, and the impact of such modifications of sensory input on future eating habits should be explored.     

Learning Association Rules and Tracking the Changing Concepts on Webpages: An Effective Pornographic Websites Filtering Approach

We applied the decision tree algorithm to learn association rules between webpage’s category (pornographic or normal) and the critical features. Based on these rules, we proposed an efficient method of filtering pornographic webpages with the following major advantages: 1) a weighted window-based technique was proposed to estimate for the condition of concept drift for the keywords found recently in pornographic webpages; 2) checking only contexts of webpages without scanning pictures; 3) an incremental learning mechanism was designed to incrementally update the pornographic keyword database.     

基于大数据挖掘的用电行为分析

得益于智能电网的发展,电力大数据的价值与作用逐渐被挖掘。通过对电力大数据的深入挖掘与研究,可促进电网企业管理水平的进一步提升,为我国智能电网的发展提供助力。而依托于大数据挖掘的用户用电行为分析,可实现当前企业电网调控与需求侧的充分融合,为电力系统的稳定性运行提供保障。基于此,文章针对大数据挖掘的用电行为分析进行深入探析,以期为智能电网发展提供帮助。     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

基于数值微分核脉冲信号数字处理方法

核反应堆核测量系统测量探测器输出的核脉冲信号，该信号后沿拖尾很长，在计数率较高时容易产生信号堆积和基线漂移等问题，导致源区计数率测量上限仅能达到105 Hz左右。文中基于数值微分方法，采用数字处理技术，在时域上分析了核脉冲信号经过前置放大、信号成形、低通滤波和脉冲甄别后的输出，并利用探测器实测信号进行了仿真。仿真结果表明，基于数值微分的数字处理方法可以实现相邻0.4 μs脉冲信号的识别和测量，将源区测量计数率上限提升到2×106 Hz以上。      

基于大数据的C语言程序设计

C语言作为一种程序设计语言,在大数据组成的物联网和云计算中应用广泛,具有强大的适应性和兼容性,是大数据时代下,在IT行业中起到关键作用的程序设计语言。文章分析了基于大数据的C语言程序设计的必然性和应用策略,为C语言在大数据上的应用方向提供了参考依据。以汉字的应用为例,提出了解决C语言应用问题的思路。     

肉桂特色资源加工利用产业发展现状

肉桂是我国特色经济林树种，我国肉桂种植面积和年产量均居世界首位。我国肉桂主要分布在两广地区，其中广西为14.9万hm²，广东为8.9万hm²，两地面积占全国肉桂分布总面积的95%以上。肉桂既是著名的香料又是传统的名贵中药，具有极高的经济价值，发展肉桂产业对于促进农民增收、维护健康中国有着重要的意义。本研究综述了肉桂资源量、分布情况及主要利用部位，介绍肉桂主要产品（如桂皮，桂枝，肉桂粉及肉桂油）及加工技术现状、产品质量控制及其分析检测方法，分析了产业未来的发展趋势，并提出了产业发展的对策和措施：加强肉桂标准化示范基地建设，提高优质资源供给能力；提高肉桂加工利用技术水平，延长产业链；培育肉桂龙头企业，提高产品市场竞争力。为我国肉桂特色资源加工利用产业的可持续发展提供一定的思路。