(Table 1) KMO is a sampling adequacy index (range from 0 to 1), with high values (between 0.6 and 1.0) indicating that Exploratory Factor Analysis is appropriate (Tabachnick and Fidell, 2007). Since, the KMO sampling adequacy of this research data is high at 0.870 and the significance of Bartlett’s Test of Sphercity is appropriate, thus the test results provide sufficient evidence to support the appropriate use of Exploratory Factor Analysis for factors that have an impact on determining the effectiveness of Cold Chains scale
By using two main algorithms: holistic based and component based. And the system consist of mainly four components 1) Eye detection and alignment , 2) Use of feature descriptors , 3) Genetic algorithm , 4) Sum of score fusion. Holistic based algorithm In holistic based algorithms faces are compared by considering the whole face simultaneously. Strength of the holistic algorithm is that it characterizes both facial composites and mugshots with local descriptor based features. The working of holistic algorithm pipeline is given in fig.
It should be noticed that the data extracted from MATLAB2011 is expressed in pixel which is then converted into millimeter. Excel (2010) software took the data directly from MATLAB. In the next stage the data were statistically analyzed in SPSS16 software. For color assessment, the images were also analyzed in the RGB and L*a*b* color spaces in MATLAB2011 software (Fig5). The word RGB is an abbreviation for red–green–blue colors.
Each of the analyte will have its own Rf value under certain circumstances. The separation of the phospholipid classes can be improved by two-dimensional chromatography. This technique requires developing the TLC plate in a direction, then dried, and developed in a solvent mixture at a 90 ° the first development (Singh and Jiang,
In brief, the PCR amplification of endogenous control and the test genes were performed in triplicate 10µL reaction mixtures contained 10ng cDNA, 0.5µLof 2.5µM each forward and reverse primers, 5µLof 2XFaststart SYBR Green master mix (Roche,Cat. No.6924204001) for the sample (+RT), 10ng total RNA for non-reverse transcriptase (-RT) and water for non template control (NTC), respectively. The reaction was performed in Light Cycler®96 (Roche Diagnostics, Switzerland) using LightCycler® 480 Multiwell plate 96; clear (Roche
(1983) The adequacy of the model for the best fitting of experimental data was evaluated by obtaining the coefficient of determination R2 and least RMSE and percent mean relative deviation of modulus (E%) using following equations: E(%)=100/N ∑_(i=1)^N▒[(Experimental value-predicted value)/(Experimental value)] Chi Square=χ^2=∑_(i=1)^N▒[〖(Experimental value-predicted value)〗^2/((N-n))] Where, n = no. of unknown and N= number of data points RMSE=Root mean square error=∑_(i=1)^N▒[√(〖(Experimental value-predicted value)〗^2/N) ] The nonlinear regression analysis was performed by using Statsoft Statistica (18.104.22.168 EN). RESULT AND DISCUSSION The effect of all process variables including solution concentration, solution temperature and immersion time on mass transfer kinetics namely water loss and solute gain was investigated. The detailed description of effect of process variables on mass transfer kinetics and its modeling has been discussed as
Themes will be made according to the research objectives and the findings collected from the case study would be crosschecked against the literature reviewed. In order to process any quantitative information, MS Excel graphs will be used for the accomplishment of the numerical results. These cross-referenced research findings will be valid enough to generalise it on the larger population. Qualitative analysis transforms theories in a form of statements and concepts focusing on interpretations. The qualitative analysis for the study is consisting of discussion of secondary data and results obtained from the interviews and survey questionnaire (Brecher & Harvey, 2002; Weber, Shils, Finch, Antonio, & Sica, 2011; Bergh & Ketchen, 2009; Krishnaswamy, Sivakumar, & Mathirajan,
The nonlinear system simulation is done for the following reference and disturbance signals. At t = 10 min the feed rate F increases from 1 to 1:2, at t = 100 min the feed composition zF increases from 0:5 to 0:6 and at t = 200 min the set point in yD increases from 0:99 to 0:995. The time response of the distillate yD for the case of the reduced-order µ-controller is given in Figure. It is seen from the Figure that the disturbances are attenuated well and the desired set point is achieved
They used FERET, AR face and AT&T database and obtained an accuracy of about 75-80%. Xiaoyang Tan et.al proposed a method where they used self-organizing map(SOM) to represent each individual face image, then they used two strategies for learning the SOM topological map where a single SOM map was trained for all samples and then a separate SOM map was used for each class. They then used soft K-nearest neighbor classifier for recognition from SOM topological map. They used AR face database and obtained an accuracy of about
Descriptive statistics- descriptive statistics are used to describe the basic features of data and it provides simple summaries about the sample and the measures used in the study. Percentages, means central tendency (mean, median and mode) and means of dispersion (standard deviation, range, and mean deviation) Descriptive statistics-Mean, standard deviation, frequency and percentage distribution will be calculated based on the obtained scores. Inferential statistics- Inferential statistics help in drawing inferences from the data, e.g. finding the differences, relationship and association between two or more variables by the help of the parametric and non-parametric statistical tests(Z-test, t-test, ANOVA, chi-square test) Chi-square tests-Non-parametric test is used to find out the association between two quantitative variables. Chi –square tests to study the association between knowledge on coronary artery disease and selected demographic characteristics.