Step 1: Understanding pH.
The pH scale is logarithmic and measures the concentration of hydrogen ions \([H^+]\). The formula for pH is: \[ {pH} = -\log[H^+] \] Thus, a solution with pH 7 has a hydrogen ion concentration of \([H^+] = 10^{-7}\) mol/L, and a solution with pH 6 has \([H^+] = 10^{-6}\) mol/L.
Step 2: Mixing the two solutions.
When two solutions are mixed, the resulting concentration of \([H^+]\) is the average of the two concentrations, as the volumes are equal. The combined concentration of hydrogen ions is: \[ [H^+]_{{mix}} = \frac{(10^{-7} + 10^{-6})}{2} = \frac{1.1 \times 10^{-6}}{2} = 5.5 \times 10^{-7} \] Step 3: Calculating the resulting pH.
Now, we calculate the pH of the resulting solution: \[ {pH}_{{mix}} = -\log(5.5 \times 10^{-7}) = 6.26 \] Therefore, the resulting pH of the mixture is approximately \(6.26\).
A color model is shown in the figure with color codes: Yellow (Y), Magenta (M), Cyan (Cy), Red (R), Blue (Bl), Green (G), and Black (K). Which one of the following options displays the color codes that are consistent with the color model?
While doing Bayesian inference, consider estimating the posterior distribution of the model parameter (m), given data (d). Assume that Prior and Likelihood are proportional to Gaussian functions given by \[ {Prior} \propto \exp(-0.5(m - 1)^2) \] \[ {Likelihood} \propto \exp(-0.5(m - 3)^2) \]
The mean of the posterior distribution is (Answer in integer)