The Experts below are selected from a list of 6 Experts worldwide ranked by ideXlab platform

Nora Strasser - One of the best experts on this subject based on the ideXlab platform.

  • STUDENT EXPLORATION ACTIVITIES USING MATHEMATICA
    2013
    Co-Authors: Nora Strasser
    Abstract:

    Basic Mathematica concepts are described that allow the teacher to create student exploration activities. The Mathematica concepts introduced are easy to use and produce interactive activities for the students. The use of different Mathematica notebook styles is also discussed. Emphasis is on using the Manipulate command in Mathematica. Other Mathematica commands that are introduced include Plot, Plotrange, and PlotLabel. Using the Manipulate command in Mathematica allows the teacher to create interactive activities that are easy for students to use. Students can use these activities on any computer since the activities will use the computable document format (.cdf) that allows computers without Mathematica to run interactive Mathematica notebooks. Wolfram provides a free download of the Wolfram CDF Player on their website

Plos One - One of the best experts on this subject based on the ideXlab platform.

  • Time-course of the fluctuation of the signaling molecules displays a chaos-like oscillation.
    2014
    Co-Authors: Plos One
    Abstract:

    Diffusion of active cofactor binding signaling molecule (X) and of inactive cofactor binding signaling molecule (Z). The Appendix S1 presents the simulation parameters, with the notation of Eqs. (3.9). p is (a) 0.795, (b) 0.81, (c) 0.84, (d) 0.88, (e) 0.96, (f) 1.00, (g) 1.12, and (h) 1.16. The upper graph shows two parametric plots of X, and Z. Red, and blue lines in the lower graph represent the concentrations of X, and Z, respectively. The horizontal axis represents time (0 ≤ t ≤ 200) and the vertical axis represents the concentrations of X, and Z, respectively. When p exceeds 0.80, chaos-like oscillation is observed. Mathematica cord when p  =  0.795 (a) is shown below. Below is the simulation program when p  =  1.0253: D1  =  0.28 k2  =  0.00034580 a  =  800 b  =  656 c  =  100 d  =  100 e  =  100 f  =  100 p =  1.0523 D4  =  156 D5  =  156 R  =  1 X  =  k2/D1 Z  =  (k2 (D1∧2 R+ D4 k2))/(D1 (D1 p - D5 k2)) NDSolve[{Derivative[1][x][t]  =  =  -(R (D1 - a X) + 2 X D4 + D5 Z) x[t] + (R a - D4 + 2 c X + e Z) x[t]∧2 + (p - D5 X - b X - d X∧2 - f X Z) z[t] - (D5 + R b - e X + f Z) x[t] z[t] - (f X) z[t]∧2, Derivative[1][z][t]  =  =  (2 X D4 + D5 Z - c X∧2 - e X Z) x[t] + (D4 - 2 c X - e Z) x[t]∧2 + (D5 + 2 X d - e X + f Z) x[t] z[t] + (D5 X - p + d X∧2 + f X Z) z[t], x[0]  =  =  1.’*∧-6, z[0]  =  =  1.’*∧-6}, {x, z}, {t, 0, 30000}, MaxSteps -> 50000] g001  =  Plot[{X + x[t]}/. %, {t, 0, 200}, Plotrange -> All, PlotStyle -> {RGBColor[1, 0, 0]}, Plotrange -> ALL] g003  =  Plot[{Z + z[t]}/. %%, {t, 0, 200}, Plotrange -> All, PlotStyle -> {RGBColor[0, 0, 1]}, Plotrange -> All] g004  =  ParametricPlot[Evaluate[{X + x[t], Z + z[t]}/. %%%], {t, 0, 2000}, Plotrange -> All, AxesLabel -> {"X", "Z"}] Show [g001, g003, AxesLabel -> {"t", "X, Z"}].