Quantifying discrepancies in opinion spectra from online and offline networks

Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online-offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining.Comment: 10 pages, 4 figure

New Power Quality Index in a Distribution Power System by Using RMP Model

In this paper, a new power quality index (PQI), which is directly related to the generation of distortion power from nonlinear harmonic loads, is introduced to determine their harmonic pollution ranking in a distribution power system. The electric load composition rate (LCR) and the total harmonic distortion (THD) for the estimated currents on each harmonic load are used to define the proposed PQI. The reduced multivariate polynomial (RMP) model with one-shot training property is applied to realize the PQI. Then, the ranking of distortion power for each nonlinear load, which have adverse effect on the entire system, is determined. It is proved that the relative ranking based on the PQI matches that on the distortion power computed directly from each harmonic load

Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations

Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices

DECAY FACTOR WITH EXPERIMENTAL VARIABLES IN TWO CIRCULATING FLUIDIZED BED (CFB) RISERS

The effects of the riser inlet velocity, solid mass flux and particle size on the axial solid holdup profile and decay factor were investigated using two circulating fluidized beds (CFBs) with FCC (Geldart A) particles as the bed materials. Based on the experimental results from the two-CFBs, the axial solid holdup in the two CFBs were compared with the correlations of previous studies. Also, an empirical correlation was proposed for decay factor that exhibited a good agreement with experimental data

Improving bread quality using Deinococcus geothermalis glycogen branching enzyme

Glycogen branching enzyme(GBE) catalyzes transglycosylation reaction producing α-1,6-glucosidic linkages by cleaving an α-1,4-glucosidic linkage. Deinococcus geothermalis GBE (DgGBE) has the unique activity to form a large number of short oligosaccharide side chains(degree of polymerization 3~5) from the reaction with amylose. To observe the influence of DgGBE on bread quaility, we added 100 unit of the enzyme per kg of the flour at the step of mixing dough. During the fermentation, DgGBE treated dough showed 50~100% larger volume than control. After baking, the total volume and the specific volume of DgGBE treated loaf showed about 10% larger than those of control. The baked breads were sliced to 2cm of depth and stored in 25 degrees celcius, and then the texture was evaluated by texture analyzer during storage time. Hardness and Chewiness of DgGBE treadted bread increased slowly to compared with those of the control. DgGBE treated bread showed a significant effect on antistaling. 1. Shupeng Wua, Yu Liu , Qiaojuan Yan , Zhengqiang Jiang (2014) Gene cloning, functional expression and characterisation of a novel glycogen branching enzyme from Rhizomucor miehei and its application in wheat breadmaking. Food Chemistry 159 (2014) 85-94 2. José Manuel Amigo , Arantxa del Olmo Alvarez , Merete Møller Engelsen , Henrik Lundkvist , Søren Balling Engelsen (2016) Staling of white bread crumb and effect of maltogenic α-amylases. Part 1 : Spatial distribution and kinetic modeling of hardness and resilience. Food Chemistry 208 (2016) 318-32