Quality Problems in Waters Used for Drinking Purposes in Italy

With a grant from the Italian Ministry of the Environment, the National Institute of Health (Istituto Superiore di Sanita) promoted and coordinated some activities aimed at determining the extent and the intensity of contamination of waters used for human consumption by some chemical agents, and describing causes and modalities of contamination and human health implications. The chemical agents examined were herbicides, nitrates, trihalomethanes, asbestos, manganese and fluoride. In this paper a first nationwide picture of these problems is reported.

Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy

There is growing interest in power quality issues due to wider developments in power delivery engineering.In order to maintain good power quality,it is necessary to detect and monitor power quality problems.The power quality monitoring requires storing large amount of data for analysis.This rapid increase in the size of databases has demanded new technique such as data mining to assist in the analysis and understanding of the data.This paper presents the classification of power quality problems such as voltage sag,swell,interruption and unbalance using data mining algorithms:J48,Random Tree and Random Forest decision trees.These algorithms are implemented on two sets of voltage data using WEKA software.The numeric attributes in first data set include 3-phase RMS voltages at the point of common coupling.In second data set,three more numeric attributes such as minimum,maximum and average voltages,are added along with 3-phase RMS voltages.The performance of the algorithms is evaluated in both the cases to determine the best classification algorithm,and the effect of addition of the three attributes in the second case is studied,which depicts the advantages in terms of classification accuracy and training time of the decision trees.

Clinical data quality problems and countermeasure for real world study

Real world study （RWS） has become a hotspot for clinical research. Data quality plays a vital role in research achievement and other clinical research fields. In this paper, the common quality problems in the RWS of traditional Chinese medicine are discussed, and a countermeasure is proposed.

Research on the Motives of the Qualification Problem of Assets Valuation Industry in China——Study Based on Questionnaire and Factor Analysis

The assets valuation industry in China is the product, which comes from the reform and open policy and the development of the socialist market economy. Under more than 20 years of development which begins to bud in the beginning of the nineteen nineties, the assets valuation industry has become the indispensable Intermediary trade of the socialist market economy system in China. With the scale of assets valuation agency enlarging and its rapidly growing business in recent years, however, some problems have been exposed gradually, which harm the social image of the industry of asset valuation seriously. So the social public and the relevant administrative departments pay close attention on the quality of the asset valuation. To know about what are the essential causes that affect the assets valuation industry managers＇ decision, this paper, through using some means which include literature research, interview and questionnaire investigation and utilizing the method of the factor analysis, relegates 24 questions which come from some answers of the respondents to three factors which are the practicing quality factor, the sting factor of an organization and the internal management factor of valuation organization. Furthermore, it will analyze the features of these factors which are divided into two factors that are internal motivation and external motivation, looking for the key cause of the assets valuation industry quality problem of China which purposes in order to promote the healthy development of valuation profession and much better standard the internal quality of valuation industry of China.

A robust principal component analysis-based approach for detection of a stator inter-turn fault in induction motors

Health condition monitoring of induction motors is important because of their vital role and wide us in a variety of industries.A stator inter-turn fault(SITF)is considered to be the most common electrical failure according to statisti-cal studies.In this paper,an algorithm for the detection of an SITF is presented.It is based on one of the blind source separation techniques called principal component analysis(PCA).The proposed algorithm uses PCA to discriminate between the faulty components of motor current signatures and motor voltage signatures from other components.The standard deviation of one of the decomposed vectors is used as a statistical SITF criterion.The proposed criterion is robust to non-fault conditions including voltage quality problems and large mechanical load changes as well as harmonic contaminants in the voltage supply.In addition,with a straightforward and low computational burden in the fault detection process,the proposed method is computationally efficient.To evaluate the performance of the proposed method,large numbers of practical and simulation scenarios are considered,and the results confrm the good performance,high degree of accuracy,and good convergence speed of the proposed method.