I would like to know how can Creative Urban Space reduce the usage of automobile? What will be the toolkits to reudce automobile by urban space?

I am working on DFT calculations on halide perovskites using VASP. My system consists of 56 atoms and is reported to possess monoclinic symmetry. I am performing k mesh optimization for this system using following INCAR (eg. at different k-meshes 1x1x1, 2x2x2 etc.)

ISTART = 0

ICHARG = 2

ISPIN = 2

# ENCUT = 363.00 eV

EDIFF = -0.000001

EDIFFG = -0.0001

IALGO = 38

IBRION = 1

POTIM = 0.5

NSW = 150

PREC = MEDIUM

LREAL = Auto

ADDGRID = T

NPAR = 8

However, my system is not getting converged even after these many ionic iterations. I tried copying CONTCAR to POSCAR and re-running vasp but at the end the OUTCAR isn't showing "reaching structural energy minimization". How can I obtain converged energy for this system? Can anyone please suggest the solution for this ?

l'utilisation de ROS est il nécessaire dans toutes les voitures autonomes ou non ?

Hello friends, have a good time

I had a question, thank you for your help

I want to write the code of this structure with Python,

I defined a functional model for CNN, LSTM and Deep auto encoder

Now I need to give the latent vector of autoencoder as input to extreme gradient boost,

I do not know how to do this to define this whole structure as a single model to give it input and predict output

I used the StackingClassifier command but it gave me an error:

The Functional estimator should be classifier

Thanks for guiding me on how to code this structure

I first construct a base model (default parameters) and obtain MAE (rfr base file for image).

# BASELINE MODEL

rfr_pipe.fit(train_x, train_y)

base_rfr_pred = rfr_pipe.predict(test_x)

base_rfr_mae = mean_absolute_error(test_y, base_rfr_pred)

Then I perform GridSearchCV to get best parameters and get the average MAE (rfr grid for image).

# RFR GRIDSEARCHCV

rfr_param = {'rfr_model__n_estimators' : [10, 100, 500, 1000],

'rfr_model__max_depth' : [None, 5, 10, 15, 20],

'rfr_model__min_samples_leaf' : [10, 100, 500, 1000],

'rfr_model__max_features' : ['auto', 'sqrt', 'log2']}

rfr_grid = GridSearchCV(estimator = rfr_pipe, param_grid = rfr_param, n_jobs = -1,

cv = 5, scoring = 'neg_mean_absolute_error')

rfr_grid.fit(train_x, train_y)

print('best parameters are:-', rfr_grid.best_params_)

print('best mae is:- ', -1 * rfr_grid.best_score_)

Then I fit the "best parameters" obtained to get an optimized MAE but the results are always worse than the base model MAE (opt rfr for image).

# OPTIMIZED RFR MODEL

opt_rfr = RandomForestRegressor(random_state = 69, criterion = 'mae', max_depth = None,

max_features = 'auto', min_samples_leaf = 10, n_estimators = 100)

opt_rfr_pipe = Pipeline(steps = [('rfr_preproc', preproc), ('opt_rfr_model', opt_rfr)])

opt_rfr_pipe.fit(train_x, train_y)

opt_rfr_pred = opt_rfr_pipe.predict(test_x)

opt_rfr_mae = mean_absolute_error(test_y, opt_rfr_pred)

Not just once but every time and in most of the models (linear regression, random forest regressor)! I guess there is something fundamentally wrong with my code else this problem wouldn't arise every time. Any idea what might be causing this?

Hello everyone, I participate in a project in which we intend to forecast the demand of customers With installed and photovoltaic panels. These are the so-called microinstallations, i.e. their power does not exceed 40 kw. Due to the fact that the distribution system operator installs two-way meters that measure consumption and delivery, we are not able to find out what the customer's auto consumption level is without additional metering. When it comes to forecasting the demand of these customers, usually after the installation of photovoltaics, customers most often decide to install additional receivers so that they can consume the energy produced. Increasingly, these receivers are heat pumps. Thus, the demand of such customers changes dynamically with the change in temperature. To solve this problem, we believe that it will be appropriate to use, for example, linear regression with explanatory variables such as temperature, sunlight, humidity. As for forecasting what customers give to the grid, in this case an efficiency matrix will be built Determining how much energy is given away at a given temperature and sunlight Along with taking into account the Parameters of the sun, i.e. azimuth and Height of the sun. I would like to know what methods do you use to solve this type of task?

regards

Paweł

I want to make a simulation for 400 time steps, how can I give in Ansys Workbench?

I have installed MGL tools as per the protocol, and the tool is working but am not able to view the molecule.

Can someone help me out with problem to solve it.

The suspension system of the automobile is very important device for stabilising the motion as well as reducing the effect of external disturbance. this is very important to give high comfortable for passengers. the suspension system consist of spring and damper elements. What is the design value of the damping ratio for the automobile?

and, How affect this value on the dynamic behavior of the vehicle?