Explain with evidence. The identity of liquid “B” is water. First, it smaller like nothing, it was odorless. Water is also odorless. Secondly, the average class density of 0.98g/cm3 and my individual density of 0.99g/cm3 are very close to the density of water, which is 1.00g/cm3z Is density a characteristic property of matter?
Using linguistic values (words or sentences) expresses less specific than numerical ones but it is closely related to the way that humans express and use their knowledge. In order to deal with the uncertainty and vagueness in the linguistic evaluation, many researchers have applied Fuzzy set theory to convert linguistic variable to fuzzy number. According to the linguistic variable (Li 1999), Linguistic term Triangular fuzzy number Very low (0,0,0.25) Low (0,0.25,0.5) Medium (0.25,0.5,0.75) High (0.5,0.75,1) Very high (0.75,1,1) Triangular fuzzy number is defined as follows: DELPHI Triangular Fuzzy Cognitive Map: Algorithm: Suppose we have m experts E1,E2,...Em and n criterion C1,C2...Cn using domain experts opinion obtain the n X n fuzzy relational matrices A1,A2,...AM that represent relation between the criterion A=(a_ij^((k)) )_(n×n) k = 1 , 2 , . . .
The K value was calculated to be 4.7. In the second method for solving for K, use the values calculated for the variables Q, K, and n and plug into Equation 1. By doing this the calculated value was 4.645. The value of K calculated in excel was calculated to be
Again, PT and RK are drawn parallel to OX so that they intersect RS and QM at T and K respectively. We can observe that PTR and RKQ are similar triangles and so we can write as PT∶RK = PR∶RQ …(i) TR∶KQ = PR∶RQ …(ii) ⇒ PT/RK = PR/RQ = m/n [From (i)] Since PT = NS = OS – ON = x-x_1, And RK = SM = OM – OS = x_2-x Therefore we can write, ( x-x_1)/(x_2-x) = m/n ⇒ nx-nx_1=mx_2-mx ⇒ mx+nx=mx_2+nx_1 ⇒ x(m+n)=mx_2+nx_1 ⇒ x= (mx_2+nx_1)/(m+n) TR/KQ = PR/RQ = m/n [From (ii)] Since TR = SR – ST = SR – NP = y-y_1, And KQ = MQ – MK = MQ – SR = y_2-y Therefore we can write, ( y-y_1)/(y_2-y) = m/n ⇒ ny-ny_1=my_2-my ⇒ my+ny=my_2+ny_1 ⇒ y(m+n)=my_2+ny_1 ⇒ y=
The value >3 reflects a good model fit while a value >6 indicates a permissible and average fit model. In the above table, the value of chi-square=5.5 indicates that the model of average fit for the data. Please solve the following problems on the Nike data. Consider only the following variables: awareness, attitude, preference, intention, and loyalty toward Nike. 4-1.
4.1.SIMPLE LINEAR REGRESSION ANALYSIS Taking the number of clusters assisted as the independent variable and the funds allocated as the dependent variable, a scatter plot can be drawn using the data to show the pattern of relationship between the two variables. Null Hypothesis, Ho: There is no statistically significant correlation between the number of units and fund allocation; β=0 Alternative Hypothesis, Ha: There is statistically significant correlation between the number of units and fund allocation; β≠0 Using the table, the regression equation is as follows: Y= -4.1+0.31X Figure 3: Simple linear regression The graph distinctly shows the positive relation between the two variables. However, the relation is very weak. The slope coefficient equal to 0.31 shows that 1 unit increase in theindependent variable increases the dependent variable by only a miniscule amount of 0.31 unit. 4.2 .ANALYSING CORRELATION COEFFICIENT ( R2) R = 0.183 As R is very close to 0, we can conclude that the linear relationship is negligible, i.e.
The cutoff level of 0.70 recommended for theory testing research (Nunnally and Bernstein, 1994) was used as standard. The items not contributing significantly to the reliability were eliminated for parsimony purposes. We recomputed of alpha values for the remaining items , further again elimination few of these which did not adhere to standards & this in turn helped in improving alpha values. Now, corrected-item total correlation of all the items was more than 0.6 and these contributed significantly to the reliability. To establish reliability, apart from Chronbach alpha we used composite reliability (CR) as suggested by Hair Jr. et al.
Histogram equalization is computed using the transformation function which is obtained from the whole input image. The transformation function is a function of cumulative density function which is calculated from the probability density function and range of gray level values to which the input image needs to be mapped. The probability distribution function is calculated from the intensity values of the input image pixels. The draw backs of GHE is that it does not considerably increase the dynamic range of the gray level value after histogram equalization. The Histogram of the Histogram equalized image contains gaps.
Results of Genetic Algorithm T CONCLUSIONS This work correlates the Response Surface Methodology with Genetic Algorithm for face milling operation. Based on the experimental and theoretical work, the following conclusions were arrived. The hybridization of RSM and GA are the effective methodology for optimization of machining parameters in face milling operation. The performance test of developed models has less percentage of difference with experimental results. The overall accuracy rate of present approach for MRR and SR are 99% and 86% respectively.
Xiang’s experimental results reveal that cyclones with a smaller cone diameter result in a slightly higher collection efficiency when compared to cyclones with a bigger cone tip diameter. Change in pressure drop was not significant when the cone size was varied. W.Peng et al  studied the best modeling assumptions for cyclones and swirl tubes by CFD and LDA. It has been found that tangential velocity is axially constant in the cylinder-on-cone cyclone whereas the tangential vecity decreases slightly as it descends down the cyclone. Radial velocity is fairly uniform over the length of the separation zone in the cylinder-on-cone cyclone except for a localized lip leakage.