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Dynamic drape is the unique property that allows the fabric to be bent in multiple directions. Silk fabric has good hand feeling and draping property. In this paper, we select 15 kinds of silk fabrics to measure its structural, mechanical and draping parameters for studying the impact factors of dynamic draping property about silk fabric. We make a correlation analysis with the measured fabric parameters then obtain the factors sequence that affects silk fabric's dynamic draping property. Experimental result shows that the draping property of silk fabric is affected by many performance parameters. In particular, the latitudinal tensile property has the greatest influence among all these parameters.

The investor sentiment hypothesis in behavioral finance tells us that emotions can cause to their decision-making. And social network has become a popular venue for sharing opinions. Here we want to discover whether there is a correlation between the sentiment index derived from Sina microblog and the 5-minute closing value of Shanghai Composite Index(SCI) in one day. In this paper, we put forward an efficient way to gather investor information and construct stock-oriented sentiment lexicons that measure positive vs. negative mood. Specifically, we got everyday blogs from many stock investor’s microblog using Sina API and designed an approach to the quantification of the text comments. In order to further analysis, we used three trading day’s real-time data to see the effects of our sentiment index computation. On the basis of two sequences, we tried to use Pearson Correlation to make analysis at different level. Then we applied our method to an intra-day market scale of three days to find that the max coefficient achieved 0.73 which means significant correlation and the minimum also got 0.56 that shows moderate relationship. Our results indicate that investor sentiment on social network has a positive correlation with SCI movement.

The correct selection of performance metrics is one of the most key issues in evaluating classifier's performance. Although many performance metrics have been proposed and used in machine learning community, there is not any common conclusions among practitioners regarding which metric to choose for evaluating a classifier's performance. In this paper, we attempt to investigate the potential relationship among some common used performance metrics. Based on definitions, We first classify seven most widely performance metrics into three groups, namely threshold metrics, rank metrics, and probability metrics. Then, we focus on using Pearson linear correlation and Spearman rank correlation to investigate the relationship among these metrics. Experimental results show the reasonableness of classifying seven common used metrics into three groups. This can be useful for helping practitioners enhance understanding about the different relationships and groupings among the performance metrics.

We employ a new technique comprised of multiple simultaneous Large-Eddy Simulations (LES) to analyze the dynamics of turbulence, with emphasis on jet noise. Unlike methods which seek to use correlation of signals at different locations, here we isolate and tag perturbations in different regions of interest and examine the manner in which they are modulated and filtered by the turbulence. The technique essentially uses two (or more) simultaneous, synchronized Large Eddy Simulations (LES). One, denoted baseline, provides the natural perturbations existing in the jet. These are processed and injected into a twin to provide a perturbation boost, which can then be tracked in the twin. Several simulations on a Mach 1.3 cold jet demonstrate the effectiveness of the approach in determining the self and mutual interactions in core turbulence that ultimately yield the near acoustic field. Detailed 1-D and 2-D statistical analyses yield unique insight into the nature of intermittency, spatio-temporal correlations, wave-packet generation and directivity.

The purpose of this study was to predict 10kV cable failures based on deficiency data, and provide data basis for maintenance adjustment. Deficiency Data Analysis (DTA) tree is built on history data. Then probability distribution of deficiency and failure data is constructed considering the family characteristics. Kullback-Leibler distance is used to analyze the correlation between deficiency and failure, which is used to predict failure based on monitoring and testing data. Shenzhen Nanshan power supply company adopted the algorithm analysis achievement, as a result, the cable failure number drop 18% comparing to last year. It is proved that this algorithm can be used to predict malfunctions of cables, then according maintenance could be carried out and enhance system reliability.

This paper introduces a method to analyze the reason of heavy metal pollution. At first do correlation analysis between the 8 kind of metal, the results showed that: there are significant positive correlation between Cu and Cr, Ni and Cr, Pb and Cd, Pb and Cu, which shows they may come from same pollution source. Next do the factor analysis. The results showed that the contribution of the first five factors to total variation accounted for 87.7%. After analysis, we can get that Cr, Ni, Cd, Pb is generated by smelting and machining metal in industrial park. Hg and Zn is mainly generated by automobile exhaust in the trunk road. As is generated by fertilization in the mountain farmland.

The quality of 2-year-old Dendrobium officinale Kimura et Migo from four different latitudes Weifang City, Shandong Province (36°78'N, 119°02'E), Wujiang City, Jiangsu Province (31°08'N, 120°66'E), Wenzhou City, Zhejiang Province (28°27'N, 121°05'E) and Menghai City, Yunnan Province (21°59'N, 100°24'E) was analyzed in order to understand the effect of latitude, temperature, humidity, light intensity on its quality. It has important theoretical and practical significance to optimize its cultivation technology and improve its active ingredient content. The results showed that the ecological factors had effects on the quality of 2-year-old D. officinale. The quality of 2-year-old D. officinale in Weifang City, Shandong Province equals to or is higher than that in source regions. D. officinale can be widely planted in high latitude regions such as Weifang City, Shandong Province. The ecological factors for growth D. officinale in high latitude regions are as following: temperature three basis points are 4.7-17.2-32.3°C, mean temperature difference is 4.7-20.5°C, effective accumulated temperature is 1831°C, mean humidity is 69.7-89.5% and mean light intensity is 1.9-19.5 Klux.

The nutrient of 2-year-old Dendrobium officinale Kimura et Migo from four different latitudes Weifang City, Shandong Province (36°78'N, 119°02'E), Wujiang City, Jiangsu Province (31°08'N, 120°66'E), Wenzhou City, Zhejiang Province (28°27'N, 121°05'E) and Menghai City, Yunnan Province (21°59'N, 100°24'E) was analyzed in order to understand the effect of latitude, temperature, humidity, light intensity on its nutrient. The results showed that the ecological factors had effects on the nutrient of 2-year-old D. officinale. The nutrient copper and zinc content increased with the increase of latitude, effective accumulated temperature. The magnesium and zinc increased with the increase of humidity. The nutrient of 2-year-old D. officinale in WF equals to or is higher than that in the other places.