In today's online social networking, one can google social network and stumble upon an array of different results. Each of these choices is often associated with a common cause for people to network. The majority of today's social networks are focused more towards teenagers and young adults as a virtual "hangout" and discussion area. However, there is a lack in one crucial area of social networking development and that is the medical/healthcare industry. One demographic not directly accounted for in the social networking age is people with the need for acquiring medical or healthcare related information and for connecting with others on this level.In addition, web based social networks are limited to access restrictions. Users are usually required to access from a PC or laptop. This problem is slowly being taken care of with the emergence of mobile social networking. Milestone mobile devices such as the Apple iPhone and the Google/HTC G1 phone have helped jump start this transition. The paper looks into the new aspects how to design a special wireless mobile system (mCare) based on social networking platform in healthcare.
Long efforts have been made to enable machines to understand human language. Nowadays such activities fall under the broad umbrella of machine comprehension. The results are optimistic due to the recent advancements in the field of machine learning. Deep learning promises to bring even better results but requires expensive and resource hungry hardware. In this paper, we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines. Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines.
The effects of water extractable pentosans (WEP) and water unextractable pentosans (WUP) on pasting properties in flours of eight different hard white spring wheat (HWSW) cultivars was studied. WEP and WUP isolated from a hard wheat flour were added to each of the cultivars at 1% and 2% level. The results indicated that WEP exhibited a pronounced effect on pasting properties as compared to WUP and variety. Univariate analysis of variance (ANOVA) was used to evaluate sources of variation. The variety significantly (P<0.001) influenced all the pasting parameters. WUP caused significant (P<0.001) variation in paste viscosities (except breakdown). WEP influenced more pronouncedly the hot paste, cold paste, breakdown and setback viscosities with F values-221.802, 214.286, 98.073 and 120.159, respectively. Variety-by-WEP interaction exhibited significant (P<0.01) influence on pasting time, peak, hot paste and cold paste viscosities. Whereas, variety-by-WUP interaction only significantly (P<0.001) influenced the pasting-time and -temperature. Duncan's test was used to analyze the significant difference (P<0.05) within the variety. The results revealed that WUP did not induce significant (P<0.05) influence on all the pasting parameters, whereas, WEP influenced significantly (P< 0.05) the paste viscosities of some of the varieties. It was also found that the addition of WEP remarkably reduced the setback, hot paste, cold paste viscosities and increased the breakdown viscosity in all cultivar flours. The effect of WEP was greater at higher level of supplementation on paste viscosities.
In order to learn the concept of statistical techniques one needs to run real experiments that generate reliable data. In practice, the data from some well-defined process or system is very costly and time consuming. It is difficult to run real experiments during the teaching period in the university. To overcome these difficulties, statisticians developed simple and very economical experiments, which can be performed in the class by the students. Stone studied the variation, bias, stability and statistical quality control through the Blind Paper Cutting (BPC) experiment. In this article, the Blind Paper Cutting experiment is demonstrated on the basis of basic principles of experiment design. A BPC experiment is performed considering different factors, and important factors to optimise the response are identified through complete factorial design. The appropriate response model using important factors has been constructed.
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