Some of the valuable societies in/near Islamabad, Pakistan

 

In Pakistan, there are some of the housing projects that may experience an increase in rates. Among the top-rated are those located near Chakri Road. These projects either experience the rates at a good level as that of previous level, or at a profit level. Among the different large projects facing good developments near Chakri Road are Capital Smart City. Another project is Phase 2, Faisal Town, in which one of the high level of developments has already been started. They are also legitimate developers, so the chances of higher rates are good. Blue World City project may also experience an increase in the rates of plots. Considering Islamabad, Park View City is experiencing good results. Overseas block in this area may experience good profits, especially those which moves from non-balloting to balloting. In the Gujjar Khan, New Metro City may also experience the return of good amount within the next six months to one year. It is also important to note that plots that are present on ground are already in good amount, while some of the plots which are only in files could be in negatives. Societies that are developing are experiencing good profits. For instance, in Faisal Hills, almost every file is in profit, as it is going through good development.

Source:

PROPERTY NAAMA - Rawalpindi & Islamabad Housing Society Next Year Profit & Loss Comparison | Market Analysis 2024 - https://www.youtube.com/watch?v=yx2w2M9N7Ds

Day 8: A challenge to learn basics of Structural Equation Modeling (SEM) using lavaan and semPlot packages in R

 

During the next 12 days, I will learn and repeat the basics of structural equation modeling (SEM) using lavaan and semPlot packages in R.

You can search my lavaan posts by typing: #UsmanZafarParacha_lavaan , and semPlot posts by typing: #UsmanZafarParacha_semPlot

=============

During this day, I loaded the essential libraries and prepared a data, using the following lines of codes:

 

# Load the required libraries

library(lavaan)

library(semPlot)

library(psych)

 

# Simulate data

set.seed(123)  # For reproducibility

n <- 300  # Number of samples

 

# Latent variables

Knowledge <- rnorm(n, mean = 0, sd = 1)

Attitudes <- 0.5 * Knowledge + rnorm(n, mean = 0, sd = 1)

Behavior <- 0.7 * Knowledge + 0.4 * Attitudes + rnorm(n, mean = 0, sd = 1)

 

# Observed indicators for each latent variable

Know1 <- 0.8 * Knowledge + rnorm(n, mean = 0, sd = 0.5)

Know2 <- 0.9 * Knowledge + rnorm(n, mean = 0, sd = 0.5)

Know3 <- 0.7 * Knowledge + rnorm(n, mean = 0, sd = 0.5)

 

Att1 <- 0.8 * Attitudes + rnorm(n, mean = 0, sd = 0.5)

Att2 <- 0.9 * Attitudes + rnorm(n, mean = 0, sd = 0.5)

Att3 <- 0.7 * Attitudes + rnorm(n, mean = 0, sd = 0.5)

 

Beh1 <- 0.8 * Behavior + rnorm(n, mean = 0, sd = 0.5)

Beh2 <- 0.9 * Behavior + rnorm(n, mean = 0, sd = 0.5)

Beh3 <- 0.7 * Behavior + rnorm(n, mean = 0, sd = 0.5)

 

# Create a data frame

data <- data.frame(Know1, Know2, Know3, Att1, Att2, Att3, Beh1, Beh2, Beh3)

 

The above code shows three observed variables for each latent factor. For example,

Knowledge: Know1, Know2, Know3

Attitudes: Att1, Att2, Att3

Behavior: Beh1, Beh2, Beh3

Then the SEM model is specified, using the following lines of codes:

 

# SEM model specification

model <- '

  # Measurement model

  Knowledge =~ Know1 + Know2 + Know3

  Attitudes =~ Att1 + Att2 + Att3

  Behavior  =~ Beh1 + Beh2 + Beh3

 

  # Structural model

  Attitudes ~ Knowledge

  Behavior  ~ Knowledge + Attitudes

'

The SEM model is fitted using lavaan, and visualized using the semPaths, as follows:

 

# Fit the SEM model

fit <- sem(model, data = data)

 

# Summarize the results

summary(fit, fit.measures = TRUE, standardized = TRUE)

 

# Plot the SEM path diagram

semPaths(fit, "std", layout = "tree", residuals = TRUE, nCharNodes = 7, edge.label.cex = 1.2)

 

Source:
ChatGPT

Day 29: Blender tutorial for making illustrations – Preparing a YouTube-Ready Video

 

Step 1: Review the Final Video

Before diving into YouTube optimizations, you’ll need to ensure that your video is polished and fully edited. Recheck:

• Voice Syncing: Ensure that your voiceover aligns with the visuals.
• Subtitles & Captions: If you added subtitles on Day 27, confirm they appear at the right times.
• Transitions & Animations: Make sure animations, transitions, and effects are smooth and visually appealing.
• Audio Quality: Ensure clear, crisp audio without background noise.

Step 2: Choose the Correct Video Format

YouTube supports multiple video formats, but the preferred and most widely compatible is MP4. Blender allows exporting animations in MP4 format.

1. Go to the "Output Properties" tab in Blender.
2. Under File Format, select FFmpeg Video.
3. Container: Choose MPEG-4 (MP4) for YouTube compatibility.
4. Codec: Ensure the video codec is set to H.264, which offers the best balance of quality and file size.
5. Audio Codec: If you added a voiceover, select AAC for the audio codec, which is also YouTube-friendly.

Step 3: Set Resolution & Frame Rate

For optimal YouTube video quality, adjust the resolution and frame rate in Blender:

• Resolution: Set the resolution to 1920x1080 pixels (Full HD). This is the standard for YouTube videos and ensures your content looks crisp.
• Frame Rate: YouTube videos should ideally be at 30 FPS (frames per second) or 60 FPS for smoother animations.
• You can change this under Dimensions > Frame Rate in Blender’s Output Properties.

Step 4: Adjust Bitrate for Video Quality

You want a good balance between file size and quality. When exporting your video, pay attention to the bitrate:

• Bitrate for Full HD: Set the bitrate to 10,000–12,000 kbps for high-quality 1080p video.
• You can set this in Blender under the "Encoding" options (part of the Output Properties tab).

Step 5: Add a Thumbnail

Thumbnails are crucial for YouTube videos, as they determine whether viewers click on your video. You can create a custom thumbnail in Blender using a still frame from your video or a separately designed image.

1. Select an appealing frame from your video or create a separate visual using text and graphics.
2. Export it as an image (preferably PNG or JPEG).
3. Make sure the thumbnail has a resolution of 1280x720 pixels.

Step 6: Optimize Video Title, Description, and Tags

Now that your video is YouTube-ready, let’s focus on the metadata to maximize visibility.

• Title: Create a clear, engaging, and descriptive title.
• Example: "Learn Medical Concepts with Blender Animations | Flowcharts & Diagrams Explained"
• Description: Add a detailed description summarizing the video content.
• Include relevant keywords like "Blender tutorial," "flowcharts," "educational diagrams," "Quran learning," or any other topic covered.
• Add your Patreon link, social media handles, or any links to additional resources.
• Tags: Use relevant keywords that describe your video. For example, "Blender," "educational videos," "flowcharts," "mindmaps," and your niche like "biology," "Quranic learning," etc.

Step 7: Video Settings for Uploading

Before uploading, double-check the following YouTube settings:

• Privacy Setting: Choose Public to make the video available to everyone, or Unlisted if you want to share it with select individuals first.
• Category: Select the correct category, such as Education.
• Monetization (Optional): If you’re eligible for monetization, turn this on.
• Video Language & Captions: Set the language of the video and enable captions if applicable.

Step 8: Video End Screens and Cards

Enhance engagement by adding:

• End Screens: Include clickable elements at the end of your video for viewers to subscribe, watch more videos, or visit your website.
• Cards: You can add interactive elements during the video to link to related content or playlists.

Step 9: Review and Upload

Once everything is set:

1. Upload your video using YouTube's "Upload" feature.
2. Double-check the preview of the video to ensure everything (title, description, thumbnail) appears correctly.
3. Publish your video.

Outcome

Your video is now YouTube-ready and will have the right quality, engaging metadata, and appealing visuals to attract and retain viewers.

Source:
ChatGPT

Day 7: A challenge to learn basics of Structural Equation Modeling (SEM) using lavaan and semPlot packages in R

During the next 12 days, I will learn and repeat the basics of structural equation modeling (SEM) using lavaan and semPlot packages in R.

You can search my lavaan posts by typing: #UsmanZafarParacha_lavaan , and semPlot posts by typing: #UsmanZafarParacha_semPlot

=============

During this day, I loaded essential libraries, including lavaan and semPlot, and then defined the SEM model, as follows:

 

library(lavaan)

library(semPlot)

 

# Define the SEM model

model <- '

  # Direct effects

  Z ~ b1*X + b2*Y

 

  # Indirect effects (X affects Z through Y)

  Y ~ a1*X

 

  # Calculate the indirect effect

  indirect := a1 * b2

 

  # Total effect of X on Z

  total := b1 + (a1 * b2)

'

 

These aspects can be explained as follows:

·        Z ~ b1X + b2Y: Disease risk (Z) is predicted by genetic predisposition (X) and environmental factors (Y).

·        Y ~ a1*X: Environmental factors (Y) are predicted by genetic predisposition (X).

·        indirect := a1 * b2: Defines the indirect effect of genetic predisposition (X) on disease risk (Z) through environmental factors (Y).

·        total := b1 + (a1 * b2): Defines the total effect of genetic predisposition (X) on disease risk (Z), which includes both the direct and indirect effects.

Then the supposed data is generated as follows:

# Set seed for reproducibility

set.seed(123)

 

# Simulate data with 300 observations

n <- 300

X <- rnorm(n, mean = 0, sd = 1)  # Genetic predisposition

Y <- 0.6 * X + rnorm(n, mean = 0, sd = 1)  # Environmental factors influenced by X

Z <- 0.5 * X + 0.7 * Y + rnorm(n, mean = 0, sd = 1)  # Disease risk influenced by X and Y

 

# Combine into a data frame

data <- data.frame(X = X, Y = Y, Z = Z)

 

Then, the SEM model is fitted using the following codes:

 

# Fit the SEM model to the data

fit <- sem(model, data = data)

 

# Summarize the model fit

summary(fit, fit.measures = TRUE, standardized = TRUE)

 

The model is visualized using the following lines of codes:

 

# Visualize the SEM model using semPlot

semPaths(fit, what = "std", layout = "tree", edge.label.cex = 1.2,

         nCharNodes = 6, edge.color = "blue", style = "lisrel")

Source:
ChatGPT

 

Day 28: Blender tutorial for making illustrations – Review and Final Touches

Step 1: Review Your Illustrations

• Go over all the visuals you’ve created for your video. Check every diagram, flowchart, mind map, or educational graphic for completeness and accuracy.
• Ask yourself: Is the information clear? Are the illustrations visually appealing and easy to follow?
• Ensure consistency in design:
• Consistent use of colors, text sizes, and fonts.
• Uniform line thickness and styles across diagrams.

Tip: If certain parts of your video seem unclear or crowded, consider simplifying or breaking them into smaller sections.

---

Step 2: Fine-Tune Your Animations

• Rewatch your animated sequences to ensure they are smooth and engaging.
• Check for any awkward movements, pauses, or transitions.
• Ensure animations are timed well to match the pace of your narration.
• If needed, use Blender’s Graph Editor to smooth out any jerky movements.
• Adjust ease-in and ease-out settings for smoother transitions.
• If an element appears too fast or slow, tweak the animation timeline accordingly.

Tip: Animations should enhance understanding. If they distract from the main message, consider simplifying or slowing them down.

---

Step 3: Optimize Your Voice Narration

• Listen to your voiceover carefully and note any sections where the audio is unclear, too fast, or doesn’t align well with the visuals.
• Re-record any problematic sections if needed. Use the same recording settings for consistency.
• Sync your voiceover with key visual moments in the video.
• Ensure important points in the narration are highlighted with corresponding visuals.

Tip: For better engagement, match your tone and pace with the educational content. If discussing complex topics, slow down slightly to help viewers absorb the information.

---

Step 4: Check for Audio-Visual Sync

• Watch the video with both visuals and audio playing together.
• Ensure that transitions between visuals are in sync with the narration.
• Verify that key points in your audio are highlighted with matching visual cues (e.g., text appearing, diagrams lighting up).
• Adjust the timing of animations if they’re too fast or slow compared to your voice.

---

Step 5: Refine the Background and Overall Design

• Look at the background and lighting in your illustrations and animations.
• Does the background complement the visuals, or is it too distracting? If it’s overwhelming, opt for something simpler or subtler.
• Ensure lighting is adding depth and clarity to the visuals, not overpowering them.

Tip: Adjust the brightness and contrast of your lighting to ensure the primary content stands out clearly.

---

Step 6: Test for Accessibility

• Add captions or subtitles for better accessibility, especially for non-native speakers or viewers with hearing impairments.
• Ensure that captions are accurately timed with the narration.
• If possible, include on-screen annotations for emphasis, such as arrows, highlights, or brief text explanations.

Tip: Use Blender’s text tools or a separate video editor to add clear and concise subtitles.

---

Step 7: Ensure File Quality and Format

• Set the correct resolution and export settings in Blender:
• For YouTube, use a resolution of 1920x1080 (1080p) for Full HD quality.
• Ensure the frame rate is consistent, typically 24-30 fps for educational content.
• Choose a video format like MP4 with H.264 encoding for efficient file size without losing quality.

Tip: Run a quick test export of a small section to ensure the quality is up to your standards before exporting the full video.

---

Step 8: Render a Final Video

• After making all adjustments, render the entire video.
• Monitor the rendering process to ensure no errors occur.
• Check the final rendered video for any last-minute issues like timing errors, visual glitches, or audio sync problems.

---

Step 9: Final Playback and Review

• Watch the entire video from start to finish as if you were the audience.
• Pay attention to the flow of the video: Does it feel smooth and logical? Does it effectively communicate the educational content?
• Ask yourself if you would find it engaging, informative, and easy to follow.

Tip: If possible, get feedback from someone else before moving to the next step. Fresh eyes can help spot issues you might have missed.

---

Step 10: Prepare for YouTube Upload

• Ensure all elements of the video (title screen, visuals, audio, captions) are finalized and ready for upload.
• Export a thumbnail that clearly represents the content of your video.
• Make sure it’s visually appealing and includes readable text (if applicable).

Source:
ChatGPT

Prices of different areas in Phase 8, Bahria Town, Rawalpindi, Pakistan

 

(Source: https://bahriatown.com/)

In Phase 8, Bahria Town, Rawalpindi, different categories of 5 marla plots are available. These types include 25x45 and 30x40. In the Ali Block, dimensions of 5 marla plots include 25x45, and these are available in the range of 80 lacs to 1 crore. Similarly, Overseas sector 5 has plots having the dimensions of 25x45, and these are available in the price range of 1 crore to 1.3 crores. M Block in Phase 8 is one of the most demanding and beautiful blocks, and its plots’ dimensions are 25x45, and price may range from 70 lacs to 90 lacs. The Rose Garden block has plots in the dimensions of 25x45. It has two zones, including Zone 1 and Zone 2. Zone 1 is developed. Plots in this area may range from 50 lacs to 60 lacs, and their dimensions are 30x40. Zone 2 is less developed, and is in depression area. Prices in this area may range from 40 lacs to 50 lacs. N block has plots’ dimensions of 25x45, and prices may range from 43 lacs and above. Plots are available on installments and 26 lacs are for down payments, and 16.5 lacs have to be paid during possession. Bahria Orchard is also an area in Phase 8, where plots’ dimensions are 25x45. Prices of plots in this area are in the range of 27 lacs to 30 lacs.

Source:

Reality 21 - Best Time to Invest in Bahria Town? Latest Market Trends & What is 5 Marla Plot Price in Bahria Town - https://www.youtube.com/watch?v=mvzWVwt_MrQ

Day 6: A challenge to learn basics of Structural Equation Modeling (SEM) using lavaan and semPlot packages in R

During the next 12 days, I will learn and repeat the basics of structural equation modeling (SEM) using lavaan and semPlot packages in R.

You can search my lavaan posts by typing: #UsmanZafarParacha_lavaan , and semPlot posts by typing: #UsmanZafarParacha_semPlot

=============

During this day, lavaan and semPlot packages are loaded. Then, an SEM model is defined. Following lines of codes can be used:

 

# Load packages

library(lavaan)

library(semPlot)

 

# Define the SEM model

model <- '

  # Direct effect

  retirement_planning ~ financial_literacy

 

  # Indirect effects

  financial_literacy ~ income_level

  savings_behavior ~ income_level + financial_literacy

  retirement_planning ~ savings_behavior

'

 

Then, a supposed data is prepared using the following lines of codes:

 

# Simulate example data

set.seed(123)

data <- data.frame(

  income_level = rnorm(100, mean = 50000, sd = 15000),  # Income level

  financial_literacy = rnorm(100, mean = 60, sd = 10),  # Financial literacy score

  savings_behavior = rnorm(100, mean = 5000, sd = 2000),  # Savings behavior

  retirement_planning = rnorm(100, mean = 70, sd = 15)  # Retirement planning score

)

 

The SEM model is fit in the data, and eventually the SEM model is visualized using the semPlot.

 

# Fit the SEM model

fit <- sem(model, data = data)

 

# View the summary of the model fit

summary(fit, standardized = TRUE, fit.measures = TRUE)

Source:
ChatGPT