DADT | Implementation of Disjoint Author-Document Topic Model | Topic Modeling library

I have a parent component which has timer component inside it. Timer starts at 15 minutes and count downs till 0. When my timer shows time as 0 I want to trigger a submit button event, submit button is inside Quiz Component (Quiz Component is also a child component of Parent Component). I found probably I can use MutationObserver when p tag changes. I am not sure whether it's the correct and only approach or there is better way to achieve this.

Parent Component:

I am learning how to use GEKKO for parameter estimation problems and as a first step, I am developing example problems that I have previously implemented using Scipy minimization routines. These have been done following the information available in APMonitor.com and the courses available within. The current problem is a batch reactor simulation of a methanol-to-hydrocarbons process obtained from: http://www.daetools.com/docs/tutorials-all.html#tutorial-che-opt-5

The model description can be followed in the code described further below, but the elementary steps considered are:

• A --> B
• A + B --> C
• C + B --> P
• A --> C
• A --> P
• A + B --> P

where experimental data is available for concentrations of A, C, and P as a function of time. The goal of the model is to estimate the rate constants for the six elementary reactions (k1-k6). The difficulty I am encountering right now is that my GEKKO model and my Scipy.optimize

• based models lead to different parameter estimates, despite using the same experimental data and initial guesses for the parameters. I have also compared this model to one developed using gPROMS and Athena Visual Studio, with the scipy model agreeing with the parameter estimates obtained with these closed-sourced programs. The estimated parameters for each program are shown below:

• Scipy model (L-BFGS-B optimizer): [k1 k2 k3 k4 k5 k6] = [2.779, 0., 0.197, 3.042, 2.148, 0.541]

• GEKKO model (IPOPT optimizer): [k1 k2 k3 k4 k5 k6] = [3.7766387559, 1.1826920269e-07, 0.21242442412, 4.130394645, 2.4232122905, 3.3140978171]

Interestingly, both models lead to the same objective function value of 0.0123 at the end of the optimization and look similar in the plots of species concentration vs time. I have tried changing GEKKO's optimizer and tightened tolerances to 1E-8 to no avail. My guess is that my GEKKO model isn't properly set up, but I cannot find the issue with it. Any help would be appreciated in pointing me to possible issues that may be leading to the model discrepancies. I attach the two scripts below:

I have an initial value problem that needs to be solved; the differential equations are derived from a dictionary that looks like:

I am trying to solve a large system of differential equations using solve_ivp.

I have a dynamic system where I have an expression for the residual of the time derivate but not an analytic expression for what the time derivative actually is: sum(da_i/dt) ** 2 = f(a). I can solve this by performing an optimization at every time step. I think I can recast this into a least squares problem and solve for every value at every time step in a single solve. I think this is a linear casting but I'm not 100% sure about that. How can I form this system of equations to efficiently solve for the values across all times in one solve?

So I'm writing my masters thesis and I have to model the BR (Briggs-Rauscher iodine-iodide oscillation) reaction and I have some problems with this code. Everytime I run(the same code) it, I get a different plot and an error. I tried different points and different variations but still the same.

I am trying to execute a bash script on my server from a radio button and submit button on my web page. There are two radio buttons and each will execute a different bash script (test1.sh test2.sh). Here is what i have so far.

Webpage HTML:

Sorry for the awkward choice of words in the title.

The thing is: I have a concrete model with a continuous set variable to express time and a very simple ode, where the derivative of a variable, let's call it dadt, equals the difference between two other variables b and c which are not dependent on a (in other words, a is just the difference of b and c multiplied by time). Unfortunately, it seems that the programm applies the intgration backwards meaning that if dadt is,say, 20 at time=20s, then a increases by 20*dadt between 0s and 20s. This way, the differential equation constraint does not really apply to time=0s and therefore renders the optimization useless. Is it intended this way or did I make a mistake along the way? I should add the Lagrange-Radau collocation. Is it the this kind of collocation's standard way of interpreting the direction of the integration, meaning "backward"?

Thanks.