clinical coding - Science topic

Without knowing any info on the system it is hard to help regarding MFT, in particular.

But for DFT usually people try to employ, directly, a broken-symmetry approach on systems with two coupled spins. If the system has more than two magnetic atoms you'd need to solve a particular set of equations that can be obtained considering the electronic system, as you can read in some papers like

This kind of modelling is possible using Gaussian, Orca, well, any of the major packages that we use for DFT.

Bhavya G. , I found the solution thanks to the NCBI team. You actually have to blast your coding sequence and get exon locations by comparing them with sequences submitted previously. Then you can either make a feature table indicating the exon regions; or make an amino acid FASTA file by joining the exon translations. I am attaching files shared by NCBI team. You can contact [email protected] for assistance.

It's a bit strange to plot adjusted p-values in a volcanoplot... but that's a different topic.

Please use a logarithmic y-axis or log adj.P in your second plot to make them more comparable. Otherwise the gaps you see in the vocanoplot won't be visible in the lower plot.

Sara Majeed Have you checked mathworks libraries?

Jhon Paul Ambit When you quote ChatGPT or any other AI, you should note this, just as you would with any other reference to an outside source.

Sorry, I don't really know where to find it.

Hey there Muhamad Hazim Ahmad Ghazali! Sure thing, I've got you Muhamad Hazim Ahmad Ghazali covered. Here's a snippet of Silvaco code for a Graphene Field Effect Transistor (GFET) simulation:

```silvaco

# GFET Simulation

# Material Definitions

material graphene

mobility = 2000

density = 1e13

temperature = 300

conductivity = 2.5e-5

eps = 0.1

tref = 300

mstar = 0.2

mumin = 2000

bfield = 0.05

contact mumin

# Mesh Definitions

mesh x 1.0e-9

# Device Definitions

device gfet

substrate SiO2

top_contact metal

bottom_contact metal

material graphene

length = 100e-9

width = 50e-9

thickness = 1e-9

doping n 1e17

# Simulation Settings

solve init

solve equilibrium

solve balance

solve dc vds = 0 1 0.1 vgs = 0 1 0.1

# Output

output current_vgs_vds

```

This code sets up a basic GFET simulation, defining the material properties, mesh, device parameters, and simulation settings. You Muhamad Hazim Ahmad Ghazali can tweak these parameters according to your specific requirements.

Feel free to give it a try and let me know if you Muhamad Hazim Ahmad Ghazali need any further assistance or clarification! Happy simulating!

I was looking for a similar code, finally, I found out how to do it using SPEI package guide in R. After extracting precipitation and PET in your grid cells, first, you need to prepare your CWB (climatic water balance) matrix which is calculated by subtracting precipitation by PET in each grid cell (Precipitation - PET). CWB matrix should have a column for each grid cell and rows containing corresponding dates. I have attached screenshots of precipitation CWB. Also, keep in mind that your CWB should be NA-freeUsing. USinng the below code you can simply calculate SPEI at various time scales.

library(raster)

library(terra)

library(dplyr)

library(SPEI)

library(ggplot2)

setwd("")

sts <- read.csv("centerlatlon.csv")

PET <- raster::brick("pet.nc", varname="Ep")

pet_data <- raster::extract(PET, cbind(x=sts$X, y=sts$Y))

pet_df <- t(pet_data)

#write.csv(pet_df, "PET.csv")

precipitation=raster::brick("precipitation.nc", varname="precipitation")

precipitation_data=raster::extract(precipitation,cbind(x=sts$X, y=sts$Y))

precipitation_df=t(precipitation_data)

# write.csv(precipitation_df, "precipitation.csv")

cwb_df <- precipitation_df - pet_df

cwb_df <- as.data.frame(cwb_df)

# to fill NA values in CWB, Iterate through each row of the matrix

for (i in 1:nrow(cwb_df)) {

# Iterate through each column, starting from the second to the second last

for (j in 2:(ncol(cwb_df) - 1)) {

# Check if the current cell is NA

if (is.na(cwb_df[i, j])) {

# Initialize an empty vector to hold non-NA adjacent values

adjacent_values <- numeric(0)

# Add the previous column's value if it's not NA

if (!is.na(cwb_df[i, j - 1])) {

adjacent_values <- c(adjacent_values, cwb_df[i, j - 1])

}

# Add the next column's value if it's not NA

if (!is.na(cwb_df[i, j + 1])) {

adjacent_values <- c(adjacent_values, cwb_df[i, j + 1])

}

# If there are any non-NA adjacent values, calculate the mean and replace the NA

if (length(adjacent_values) > 0) {

cwb_df[i, j] <- mean(adjacent_values)

}

}

}

}

cwb_df <- as.matrix(cwb_df)

# Calculate SPEI for a 12-month scale

spei_results <- spei(cwb_df, 12)

#plot(spei_results)

spei_values <- spei_results$fitted

spei_df <- as.data.frame(spei_values)

Hello all,

I appreciate the responses.

I managed to find a solution on Linux using Wine (though not specifically version for Linux yet).

All is well.

Thanks once more,

Abdelmalek

import numpy as np # Replace with your file pathfilename = "path/to/uavsar.grd" # Read data based on file format (e.g., data type, number of elements) with open(filename, "rb") as f: data = np.fromfile(f, dtype=np.float32) # Adjust data type as needed # Reshape data based on number of rows, columns, and polarization channels # (Information on these might be in a separate file or require manual parsing)data = data.reshape((rows, columns, num_channels)) # Access specific polarization channel (e.g., VV)vv_channel = data[:, :, 0] # Assuming VV is the first channel (adjust indexing) # Further processing and analysis of the data

You can use spaCy NER (named entity recognition models) or hugging face transformers, depending on the topic each model is trained to detect.

Hope it helps,

Az

If you are certain your projections are correct, you can use dis_conv_tol to reduce the convergence threshold

I am looking for a software/code ( open source ) that can facilitate Lg Q attenuation algorithm based two stations method.

Most compressive sources on grounded theory are books, of which I would recommend Charmaz, Constructing Grounded Theory, which has a thorough discussion of the progression from initial open coding to axial coding and then to theoretical coding. As this suggests, coding is quite a specific (and demanding) process in grounded theory.

In terms of what what to call the analysis method in this article, as I said before, I would call it "inspired by grounded theory," just because they did mention a constant comparative approach. (Note that both returning the transcripts to the participants and coding as a group are generic processes in qualitative research, and thus do not affect the definition of which method was used.)

% Economic Dispatch using Genetic Algorithm

% Define parameters

num_units = 3; % Number of generating units

P_min = [100, 150, 200]; % Minimum power output for each unit

P_max = [300, 400, 500]; % Maximum power output for each unit

a = [0.01, 0.015, 0.02]; % Cost coefficients

b = [5, 7.5, 10]; % Cost coefficients

c = [50, 75, 100]; % Cost coefficients

load_demand = 700; % Total load demand

% Define genetic algorithm options

options = optimoptions('ga', 'Display', 'iter', 'MaxGenerations', 100, 'PopulationSize', 50);

% Define fitness function

fitness_function = @(x) total_cost(x, P_min, P_max, a, b, c, load_demand);

% Solve Economic Dispatch problem using genetic algorithm

[x, fval] = ga(fitness_function, num_units, [], [], [], [], P_min, P_max, [], options);

% Display results

fprintf('Optimal power output of generating units:\n');

disp(x);

fprintf('Total cost: %.2f\n', fval);

% Define fitness function

function total_cost = total_cost(P, P_min, P_max, a, b, c, load_demand)

total_cost = sum(a .* P.^2 + b .* P + c); % Calculate total generation cost

penalty = max(0, P - P_max) + max(0, P_min - P); % Calculate penalty for violating power constraints

total_cost = total_cost + sum(penalty.^2); % Add penalty to the total cost

imbalance_penalty = abs(sum(P) - load_demand); % Penalty for load-demand imbalance

total_cost = total_cost + imbalance_penalty; % Add imbalance penalty to the total cost

end

Thank you so much for your reply, David. It certainly answered my question.

Hi Fedhasa,

You can download it from:

https://cran.r-project.org/src/contrib/Archive/climdex.pcic/climdex.pcic_1.1-11.tar.gz

and install it by selecting Package Archive File.

MicroRNA (miRNA) encoding genes produce non-coding RNA molecules that regulate gene expression post-transcriptionally by binding to target mRNAs while protein-coding genes produce mRNA transcripts that are translated into proteins. MiRNA genes often produce long primary transcripts (pri-miRNAs) processed into precursor miRNAs (pre-miRNAs) and mature miRNAs, whereas protein-coding genes contain exons and introns and undergo transcription, mRNA processing, and translation. The transcription of both types of genes is regulated, but miRNA genes may have distinct regulatory elements. MiRNA biogenesis involves processing steps mediated by specific complexes, leading to mature miRNA incorporation into RNA-induced silencing complexes (RISC). MiRNAs function post-transcriptionally to destabilize or repress the translation of target mRNAs by binding to complementary sequences in their 3' UTRs, while proteins translated from protein-coding genes perform diverse cellular functions. These differences highlight the distinct roles, structures, regulations, biogenesis pathways, and functions of miRNA-encoding genes compared to protein-coding genes.

Xi Fang, It seems like you're attempting to analyze the effects of both the product type and the gender of the seller on consumers' perceptions. Your aim is to see if there are differences in consumer perceptions when faced with different combinations of product type (service vs. normal product) and seller gender (female vs. male).

1. You're on the right track by considering interaction effects between the product type and the gender of the seller.

2. Make sure your model meets the assumptions of the statistical test you're using. For example, in regression analysis, you need to check for multicollinearity, normality of residuals, linearity, and homoscedasticity. Violations of these assumptions can affect the reliability of your results.

3. Ensure your sample size is adequate to detect the effects you're interested in. Small sample sizes can reduce statistical power, making it difficult to detect significant effects.

4. If you're still not finding significant results, consider exploring other potential moderators or factors that could influence consumer perceptions.

Hello Md. Tanvir Hossain.

Nowadays, the method that used qe2boltz.py is outdated. You can follow the methods to install Boltztrap2 as follow:

Watch all playlist, there are a few calculations that you can replicate.

Best regards,

Ricardo Tadeu

import numpy as np

sieve_sizes = np.array(#Data)

percent_retain = np.array(#Data_Percentage)

cumulative_percentage = np.cumsum(percent_retain)

index_d10 = np.argmax(cumulative_percentage >= 10)

index_d50 = np.argmax(cumulative_percentage >= 50)

index_d90 = np.argmax(cumulative_percentageTry >= 90)

# Corresponding sieve sizes

d10 = sieve_sizes[index_d10]

d50 = sieve_sizes[index_d50]

d90 = sieve_sizes[index_d90]

print(f"D10: {d10} µm")

print(f"D50: {d50} µm")

print(f"D90: {d90} µm")

Try this One I hope this is helpful.

Hello Tadesse Lemma

What is your objective with Boltztrap? Following recent tutorials, it looks like this qe2boltz.py and other python scripts are already out of date. I was able to successfuly follow this:

Check this video and the sequences. They are a introduction of newer versions of Boltztrap2. Also, take care with your python version. You are using python2 inside Aiida, aren't you? If you get newer version of Boltztrap2, python3 is needed.

Best regards,

Ricardo Tadeu

دراسة مهمة

Hi Sara Majeed

To address the issue of obtaining all zeros as the output from your MATLAB code, I'll provide a revised version of the core simulation loop and critical functions. Please note that without the complete definitions of `generate_channel1`, `generate_channel2`, and `generate_channel3`, I'll focus on general improvements and common mistakes that could lead to zeros.

The main points of focus will be on ensuring proper matrix dimensions, handling complex numbers correctly, and ensuring that the operations are logically and mathematically sound. Here's a simplified and corrected approach:

Simplified and Corrected Core Simulation Loop

This revision assumes that your channel generation functions (`generate_channel1`, `generate_channel2`, and `generate_channel3`) work as intended and return matrices of appropriate sizes and values.

matlab codes :

% Clear environment and close figures

clear all; close all; clc; warning('off');

% System Parameters

Num_users = 8;

Ns = 2;

Fc = 60e9;

lambda = 3e8 / Fc;

TX_ant = 256;

RX_ant = 16;

N = 256;

SNR_dB_range = -10:5:20;

% Initialize spectral efficiency storage

Rate_SU = zeros(1, length(SNR_dB_range));

% Initialization of channel matrices (for illustration)

H_los = zeros(Num_users, RX_ant, TX_ant);

% Reflection coefficient vector

v = exp(1i*2*pi*rand(N, 1));

% Main Simulation Loop

ITER = 500; % Number of iterations for averaging

for iter = 1:ITER

% Example channel generation (Replace with actual function calls)

% H_los is just for illustration, replace with actual channel generation

for u = 1:Num_users

H_los(u, :, :) = (randn(RX_ant, TX_ant) + 1i*randn(RX_ant, TX_ant)) / sqrt(2); % Example LOS channel

end

% Implement the logic for effective channel calculation here

% This is a placeholder showing a loop over users

for u = 1:Num_users

% Placeholder for effective channel calculation

% Example: H_eff(u,:,:) = H_los(u,:,:) + ... (your logic here)

end

% Calculate and update rates (Replace these placeholders with your logic)

for SNR_dB = SNR_dB_range

SNR = 10^(SNR_dB/10);

% Example rate calculation (replace with your algorithm)

Rate_SU = Rate_SU + log2(1 + SNR); % Placeholder for actual rate calculation

end

end

% Averaging over iterations

Rate_SU = Rate_SU / ITER;

% Plotting (Example)

figure; plot(SNR_dB_range, Rate_SU, '-o');

xlabel('SNR (dB)'); ylabel('Spectral Efficiency (bps/Hz)');

title('Spectral Efficiency vs. SNR');

grid on; legend('Example Rate');

Key Changes and Considerations:

1. **Initialization**: Make sure variables are initialized correctly, and matrices have appropriate sizes before they are used in calculations.

2. **Matrix Operations**: Ensure the matrix dimensions match for operations. Use `.*` for element-wise multiplication if needed.

3. **Complex Numbers**: MATLAB handles complex numbers natively. Ensure you're using them correctly in calculations, particularly when generating channels and applying the reflection coefficient vector.

4. **Channel Generation Functions**: Without the specific implementation of `generate_channel1`, `generate_channel2`, and `generate_channel3`, ensure these functions return correctly sized matrices and that the channels are modeled accurately.

5. **Spectral Efficiency Calculation**: This example simplifies the rate calculation. Replace placeholders with your actual algorithm, ensuring that operations on matrices and vectors are correctly performed.

6. **Debugging**: Use `disp(size(variable))` to print out variable sizes during debugging, ensuring they match expectations.

Remember, this revision is meant to guide you in debugging and correcting your code rather than being a direct drop-in replacement. Adjustments will be needed based on the specific functionalities of your channel generation functions and the exact algorithms you're implementing for hybrid beamforming.

What is the current file extension (the stuff after the dot in "somefile.wut") of the file, and what is the desired extension?

To accurately determine the reason for the yellow light and ensure the proper functioning of your NanoDrop 8000, I recommend consulting the instrument's user manual or contacting the manufacturer's technical support for assistance. They can provide specific guidance based on the instrument's design and any error codes or messages displayed.

Dear Xinyuan Cui

The profile is:

https://www.researchgate.net/profile/Doris-Entner/research You can try but I am afraid this is not a regular user of RG. I think it is better to use the contact info mentioned here:

Best regards.

an example code snippet in Python using the scikit-learn library to perform DBSCAN clustering on protein data. This code assumes that you have your protein data stored in a pandas DataFrame, where each row represents a protein and each column represents a feature (e.g., amino acid composition, structural properties, etc.).

import pandas as pd from sklearn.cluster import DBSCAN from sklearn.preprocessing import StandardScaler # Load protein data into a pandas DataFrame (replace 'protein_data.csv' with your file) protein_data = pd.read_csv('protein_data.csv') # Preprocess the data (optional but recommended) scaler = StandardScaler() scaled_protein_data = scaler.fit_transform(protein_data) # Perform DBSCAN clustering eps = 0.5 # Set the maximum distance between two samples for them to be considered as in the same neighborhood min_samples = 5 # Set the number of samples in a neighborhood for a point to be considered as a core point dbscan = DBSCAN(eps=eps, min_samples=min_samples) labels = dbscan.fit_predict(scaled_protein_data) # Output the cluster labels print("Cluster labels:", labels)

This code performs the following steps:

You may need to adjust the parameters (eps and min_samples) based on your specific dataset and requirements. Experiment with different parameter values to find the optimal clustering solution for your protein data.

Ensure that your protein data is appropriately formatted and contains numerical features suitable for clustering analysis. Additionally, consider performing feature selection or dimensionality reduction techniques before applying DBSCAN if your dataset has high-dimensional features.

Please follow me if it's helpful. All the very best. Regards, Safiul

If you're encountering issues running Trimmmomatic v0.39 on Windows 11, here are some steps you can take to troubleshoot the problem:

By following these steps, you should be able to troubleshoot and resolve issues with running Trimmmomatic v0.39 on Windows 11. If you continue to encounter difficulties, don't hesitate to seek further assistance from relevant support channels.

Please follow me if it's helpful. All the very best. Regards, Safiul

In attachment an example for a slip-plot design using SAS.

see also:

or:

# Import necessary modules

import os

from AquaCrop_OSPy import RunAquaCrop

# Set input and output directories

input_dir = 'input_data'

output_dir = 'output_data'

# Run AquaCrop-OSPy simulation

RunAquaCrop(input_dir, output_dir)

# Generate irrigation schedule

irrigation_schedule = generate_irrigation_schedule(output_dir)

# Save irrigation schedule to a file

output_file = os.path.join(output_dir, 'irrigation_schedule.csv')

irrigation_schedule.to_csv(output_file)

Thank you. I will check in due time.

We have uploaded several codes in Matlab central, feel free to browse through them, there are a couple on chaotic image encryption, and many more on chaotic random bit generators https://uk.mathworks.com/matlabcentral/fileexchange/?q=profileid:28825692

Please find the (commented) code here:

```

import numpy as np

def safy_simulation(num_samples, true_yield_mean, true_yield_std, measurement_error_std): # Generate true yield values

true_yield = np.random.normal(true_yield_mean, true_yield_std, num_samples) # Simulate measurement error

measurement_error = np.random.normal(0, measurement_error_std, num_samples) # Simulate measured yield by adding measurement error to true yield

measured_yield = true_yield + measurement_error

return true_yield, measured_yield

# Example usage, this is for you to change:

num_samples = 2000

true_yield_mean = 50

true_yield_std = 5

measurement_error_std = 2

true_yield, measured_yield = safy_simulation(num_samples, true_yield_mean, true_yield_std, measurement_error_std)

# Print a few samples

for i in range(5):

print(f"Sample {i + 1}: True Yield = {true_yield[i]:.2f}, Measured Yield = {measured_yield[i]:.2f}")

```

I think they remove the need for programming skills and make data analysis much easier to do quickly and efficiently! For the future, I look forward to considering adding more no-code functions to meet a wider range of research needs. Just like the no-code platforms used before, a lot of time will be spent on data processing and analysis, and with no-code tools It will make our work easier and easier

Courts have challenges when interpreting smart contracts. Smart contracts have a unique language and logical structure that experts cannot simply translate into standard human languages. Smart contracts' automatic execution may lead to changes in how they are interpreted forensically, maybe requiring the creation of a "reasonable coder" assessment. Defects in smart contracts are usually analyzed within the context of breach of contract or unjust enrichment principles rather than as grounds for nullification. It is important to assess how smart contracts affect interpretation systems by comparing them to standard-form agreements and applying appropriate interpretation criteria. The nature of smart contracts, whether they function as independent legal agreements or as instruments to carry out legal agreements impacts how they are understood. The legal system's compatibility with smart contracts and their possible application to intricate legal services in remote regions are crucial factors to address.

same problem , I'm getting errors

anyone could help please ?

you can deploy logistic regression algorithm for this task

Some ideas and associations:

You state “human can only aspire to fluency in so many different languages before mixing up words due to code switching”. I don’t know if this is true at all. On what research is it based? In my own situation, I am fluent in 5 languages and do not mix up words or get confused to which language a word belongs to.

Fluency in language corresponds to numeracy, that has been demonstrated. Children who are read a lot of stories in pre-school, for example, were no better in general subjects compared to other children, but they were better at maths later on. Both implicate logical thinking. Without logical thinking humans cannot string words into a longer narrative either.

People choosing coding or computer sciences may prefer to work individually and not in groups. That is a different dynamic, social vs solo, than proficiency at languages.

Lazaros Moysis thank you for your response and needful behaviour.

Using greedy algorithm we can find dominating sets for a graph

pip install setar

import numpy as np

import setar

# Generate some sample data

np.random.seed(0)

n = 200

x = np.random.randn(n)

# Introduce a threshold at index 100

x[100:] += 1

# Fit a SETAR model

model = setar.SETAR(x, p=2, d=0, q=0, k=1)

model.fit()

# Print the estimated threshold and coefficients

print("Estimated Threshold:", model.threshold)

print("Estimated Coefficients:", model.coeffs)

Use this in ayny Python Enviroment

https://gmsh.info/

You can use Gmsh for generating mesh for any FEA solver

Check Point In Surface within Gmsh

Thanks, but the discussion mentions that there is no official support for DPD in GROMACS, so I am still looking for third-party codes.

Design Patterns as a toolbox are complementary to Object-Oriented Programming and Advanced Object-Oriented Design (following SOLID acronym principles).

Follow:

Solving time fractional differential equations with delay using the Crank-Nicolson method involves discretizing the equation in both time and space. The Crank-Nicolson method is a numerical scheme that combines implicit and explicit methods to approximate the solution. Below is an example MATLAB code to solve a time fractional differential equation with delay using the Crank-Nicolson method. Note that this is a basic template, and you may need to adapt it to your specific equation and boundary/initial conditions.

% Parameters alpha = 0.5; % Fractional order (0 < alpha < 1) T = 1; % Total simulation time dt = 0.01; % Time step N = T / dt; % Number of time steps % Space domain L = 10; % Length of the spatial domain dx = 0.1; % Spatial step x = 0:dx:L; % Spatial grid % Initial condition u0 = sin(pi * x / L); % Crank-Nicolson method A = sparse(N + 1, N + 1); rhs = zeros(N + 1, 1); for n = 1:N % Discretized fractional derivative with delay if n > 1 u_delayed = u(:, n - 1); else u_delayed = u0; end D_alpha = fracderiv(x, u(:, n), alpha) + fracderiv(x, u_delayed, alpha); % Crank-Nicolson discretization A(n, n) = 1; A(n, n + 1) = -0.25 * dt * D_alpha; A(n + 1, n) = 0.25 * dt * D_alpha; A(n + 1, n + 1) = 1; % Right-hand side rhs(n) = u(:, n); rhs(n + 1) = u(:, n + 1); % Solve the linear system u(:, n + 1) = A \ rhs; end % Plot the results figure; surf(x, (1:N + 1) * dt, u'); xlabel('Space (x)'); ylabel('Time (t)'); zlabel('u(x, t)'); title('Numerical Solution using Crank-Nicolson Method'); % Fractional derivative function function D_alpha = fracderiv(x, u, alpha) N = length(x); h = x(2) - x(1); D_alpha = zeros(N, 1); for i = 2:N-1 D_alpha(i) = (u(i+1) - u(i-1)) / (2 * h) + ... alpha / (h * gamma(2 - alpha)) * (u(i+1) - 2 * u(i) + u(i-1)); end % Forward and backward differences at boundaries D_alpha(1) = (u(2) - u(1)) / h; D_alpha(N) = (u(N) - u(N-1)) / h; end

After trying the "automatic coding" via ChatGPT feature in ATLAS.ti, I don't think AI is much use for coding. But this implies that you want to do inter-rater reliability, which is only of value when you have done the kind of content analysis where you want to count codes. Other wise, more interpretive versions of qualitative analysis accept the subjectivity of the researcher.

With regard to trustworthiness, one possibility would be member checks (Lincoln and Guba, 1985).

By the above code, irrespective of protein size the grid box size will be considered as 20x20x20. End of the vina execution, most of the complex shows binding affinity "0" or much less, as the active site will be out of the grid box range. Better increase the grid box size (SIZE_X,Y,Z) up to 60 or 120 each, depending on the maximum proteins (chains in PDB code) size of each complex, and try to run VINA again. Then you may get binding energy values of maximum protein-ligand complexes (sometimes for all).

However, this will not mimic the experimental structure correctly, since you handling bulk protein-ligand (separately) complexes docking with the common configuration file same time.

To plot a Summary Receiver Operating Characteristic (SROC) curve for your meta-analysis using a bivariate random effects logistic regression model, you will need to follow several steps. Since you're using WinBUGS for your analysis, you'll need to extract the necessary parameters from your model's output and then use a statistical software or programming language like R or Python to plot the curve. Here's a general guide to follow:

1. Extract Model Output from WinBUGS

Parameter Estimates: After running your bivariate random effects logistic regression model in WinBUGS, extract the parameter estimates. These would typically include estimates for the logit of sensitivity and specificity, and their corresponding variances and covariances.

2. Compute Point Estimates and Confidence Intervals

Sensitivity and Specificity: Convert the logit estimates of sensitivity and specificity back to the probability scale. This can be done using the logistic transformation.

Variance-Covariance Matrix: Use the variance and covariance estimates to construct a variance-covariance matrix for the sensitivity and specificity estimates.

3. Plotting the SROC Curve

Choose a Software/Tool: While WinBUGS is not designed for plotting, you can use R or Python. Both have packages/functions for creating SROC curves.

Data Preparation: Prepare your data (sensitivity, specificity, and their variances/covariances) in a format suitable for the chosen software.

@Amani Al-Rubkhi

Hi, Wenwen Han

Please, reduce the search space (center dimensions) of the target protein.

Thanks!

Not sure, but you may check this template at Overleaf service:

Hey there A.M. Alshamy!

Getting your hands on the PHITS (Particle and Heavy Ion Transport code System) is a pretty straightforward process. You A.M. Alshamy can download the latest version directly from the official PHITS website or repository. Just hit up their download section, and you'll find the necessary files.

Make sure you A.M. Alshamy select the version that suits your requirements and system specifications. Once you've got the code, follow the installation instructions provided in the documentation. They usually have a detailed guide on setting up and running PHITS on different platforms.

Remember, if you A.M. Alshamy run into any snags during the installation or usage, the user community and forums associated with PHITS are excellent resources. Feel free to tap into those for assistance and insights from fellow researchers, like me.

Now, go ahead, dive into the world of PHITS, and let me know if you A.M. Alshamy need more info or have any specific questions!

Dear Matthew Femi Adeboye

If you want to apply logistic regression, your variables must be qualitative with a score of 2 * 2 To be able to calculate the relative risk factor and odds ratio.

There you have an example where we used logistic regression

Benefit from commenting on the results and displaying the tables

To understand the idea more..

Hello. From the CMhyd software, you can perform microscale statistical methods to extract the daily rainfall of climate change scenarios.

Hello, did you find the answer to this question? Thank you so much

The Cambridge Structural Database (CSD) or the Crystallographic Open Database (COD) can provide you with the CCDC (Cambridge Crystallographic Data Centre) or JCPDS (Joint Committee on Powder Diffraction Standards) codes for specific materials. There is no universal code for cobalt metal-organic frameworks (MOFs) or zinc MOFs since these databases contain many structures.

If you have a particular cobalt or zinc-based MOF in mind, it's best to search the databases directly using the compound's name, chemical formula, or structural details. You can also refer to published literature or scientific papers that describe the MOFs you are interested in, as they often include identifying codes or references to crystallographic databases.

Suppose you have the crystallographic data (such as X-ray diffraction data) for a specific cobalt or zinc MOF. In that case, you can generate a COD or CCDC code by depositing the data into the appropriate database.

Greetings, everyone! I'm currently utilizing OSLO, an optical design software. Unfortunately, I haven't found any details regarding the magnification of the objective lens. Additionally, I have uncertainties about the enclosed area (red dott), which I've included in an image. If anyone has any insights, please share them with me.

Murtadha Shukur Thank you so much

It helped a lot.

Thank you!

If you have analytical calculations, post it and i will try to model it in MATLAB.

Dear Rezvan Rahimi,

The link attached might be helpful

Best regards,

Bhanu

Rohit Agarwal If you are in the process of learning machine learning, I suggest you start with one of many books on the subject. The way you choose to start is a dead end, regardless of the type of relationship between y and x (linear or non-linear, one or multiple variables).

I suggest that you start instead with the use of the so-called Iris data set. It is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher. This set became a typical test case for many statistical classification techniques in machine learning such as support vector machines. The data set consists of 50 samples from each of the three species of Iris. The Iris data set is widely used as a beginner's dataset for machine learning purposes. Several other classic data sets have been used extensively, such as (among others):

I will try to cover some of the major themes and principles that need to be addressed in an AI ethics code. These are some suggestions for developing a code of ethics for AI:

Safety and Security: AI systems should be secure, safe, and robust. They should not harm humans physically, mentally, or financially either intentionally or unintentionally. Risks need to be assessed and mitigated.

Transparency and Explainability: AI systems should be transparent and their decision-making processes should be explainable to users. Their workings, data sources, and purposes should not be opaque or unexamined.

Fairness and Non-discrimination: AI systems should be fair, impartial, and unbiased. They should not discriminate on the basis of race, gender, age, ethnicity, ability, or other protected characteristics. Sensitive training data should be carefully vetted.

User Privacy: The private data of users should be securely collected, processed, and stored in compliance with privacy laws and only for purposes which users have consented to. Users should have access to and control over their data.

Reliability and Safety: AI systems, especially those used in critical situations, should reliably operate as intended. Safety and impact assessments are important for identifying and mitigating potential risks or harms.

Accountability and Oversight: Organizations deploying AI should be accountable for auditing and monitoring their systems to ensure compliance with ethical principles. Government oversight and regulation may also be warranted for particular high-risk applications.

Sustainable and Socially Beneficial AI: AI should benefit people and the planet over mere profit. Applications should align with social priorities and UN Sustainable Development goals like reducing poverty, improving health outcomes, and promoting human rights.

you visit mathworks.com to get more actions

John Kevin Malagum Padro Syed Fahad Ahmed

Basic susceptible-infected-recovered (SIR) model with scipy.optimize module to find the optimal control strategy.

## import needed Python libraries

import numpy as np

from scipy.integrate import odeint

from scipy.optimize import minimize

# Define the SIR model

def sir_model(y, t, beta, gamma, u):

S, I, R = y

dSdt = -beta * S * I + u

dIdt = beta * S * I - gamma * I

dRdt = gamma * I

return [dSdt, dIdt, dRdt]

# Define the objective function to minimize

def objective(u, t, y0, beta, gamma):

y = odeint(sir_model, y0, t, args=(beta, gamma, u))

I = y[:, 1]

return -I[-1] # maximize the final number of infected individuals

# Define the constraints for the control variable

def constraint(u):

return 1 - u # enforce u <= 1

# Set initial conditions and parameters

y0 = [0.99, 0.01, 0.0] # initial values of S, I, R

beta = 0.2 # infection rate

gamma = 0.1 # recovery rate

T = 100 # time horizon

t = np.linspace(0, T, T+1) # time grid

# Perform optimization

u0 = 0.5 # initial guess for the control variable

result = minimize(objective, u0, args=(t, y0, beta, gamma),

constraints={'type': 'ineq', 'fun': constraint, 'jac': lambda x: -1})

# Extract the optimal control strategy

u_opt = result.x

# Simulate the SIR model with the optimal control

y_opt = odeint(sir_model, y0, t, args=(beta, gamma, u_opt))

# Plot the results

import matplotlib.pyplot as plt

plt.plot(t, y_opt[:, 0], label='S')

plt.plot(t, y_opt[:, 1], label='I')

plt.plot(t, y_opt[:, 2], label='R')

plt.xlabel('Time')

plt.ylabel('Population')

plt.legend()

plt.show()

```

SIR model is defined as a set of ordinary differential equations (ODEs) in the `sir_model` function. The objective function `objective` is defined to maximize the final number of infected individuals by adjusting the control variable `u`. The constraint function `constraint` enforces the constraint `u <= 1`. The initial conditions and model parameters are set, and the optimization is performed using the `minimize` function from `scipy.optimize`. The optimal control strategy `u_opt` is extracted from the optimization result, and the SIR model is simulated with this control strategy. Finally, the results are plotted using `matplotlib.pyplot`.

Good luck: partial credit AI.

Can you provide the code here so that we can tell you what parameter to change for push. In NAMD, changing the sign of velocity you can do the push or pull.

It indicates that there were errors during the compilation process of your SIMULINK model using dSpace 1104.

A few general troubleshooting steps:

1. Check for missing or incompatible blocks: Make sure that all the blocks used in your SIMULINK model are compatible with the dSpace 1104 hardware and software versions you are using. Some blocks may not be supported or may require specific configurations.

2. Verify the model configuration settings: Double-check the model configuration settings to ensure that they are correctly set up for the dSpace 1104 hardware. Pay attention to parameters such as sample time, solver settings, and target hardware settings.

3. Review the generated code: If the error occurs during the code generation process, inspect the generated code for any errors or inconsistencies. Look for any specific error messages or warnings that can help pinpoint the problem area.

4. Update software and drivers: Ensure that you have the latest version of dSpace software and drivers installed. Outdated software versions can sometimes cause compatibility issues with newer versions of MATLAB and SIMULINK.

5. Consult the dSpace documentation and support: The dSpace documentation and support resources can provide valuable insights and solutions for common issues. Check their documentation, user guides, and forums for any relevant information regarding your specific error message.

Good luck: partial credit AI

Hello Michele Kosremelli Asmar , I came across your post about qualitative data collection in Arabic and the subsequent analysis in French or English. I'm curious to know what approach you eventually adopted and what feedback or suggestions the jury provided. Could you please share the outcome or any insights gained from your deliberations on this matter?

I am conducting a research adopting a thematic method for analysis, and my data is in Moroccan Darija, a dialect of Arabic spoken in Morocco and I opted to translate my data just like you and I am afraid I can't use either Nvivo or Atlas Ti for coding the data.