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

Day 27: Blender tutorial for making illustrations – Adding Subtitles and Annotations in Blender (for Illustrative Videos)

Step 1: Prepare Your Voiceover/Audio

1. Record Your Voiceover:
• Ensure your voiceover is clear and well-paced. If you’ve already added it to your video (as done in Day 19), make sure it is synced properly with your animations.
2. Transcribe Your Voiceover:
• Listen to your voiceover and type out the script for subtitles. This will be the text you'll use for the subtitles. Make sure the transcription is accurate.

Step 2: Switch to Blender’s Video Sequencer

1. Open the Video Editing Layout:
• In Blender, go to the "Video Editing" workspace from the top of the interface or by selecting it from the workspace dropdown menu.
2. Add Your Animation:
• Load the animated video created in previous days by clicking Add > Movie or Add > Image Sequence if you rendered your animations as image frames.

Step 3: Add Text Strips for Subtitles

1. Add Text Strips for Subtitles:
• In the timeline (lower area), go to Add > Text. A text strip will appear in the sequencer.
2. Adjust the Duration:
• Drag the text strip to match the timing of the relevant spoken words in your voiceover. Make sure the text strip starts and ends exactly when the dialogue is happening.
3. Edit the Text:
• With the text strip selected, click on the Modifiers tab in the properties area. Here you can type the subtitle text you want to display. Add the transcribed sentences, one by one.
4. Customize the Appearance:
• Change the font, size, and position of the text by selecting the text strip and adjusting the options under Strip > Transform or in the text strip properties panel. You can position the subtitles at the bottom of the screen, change their color, or add a background for better visibility.

Step 4: Sync Subtitles with Audio

1. Play Through the Sequence:
• Use the timeline play button or press Spacebar to play your video and make sure the subtitles appear at the correct times.
2. Adjust Timing if Needed:
• Adjust the text strips by dragging them along the timeline to fine-tune their start and end points to ensure synchronization with the voiceover.

Step 5: Add Annotations

1. Use Grease Pencil for Annotations:
• Switch back to the 3D View workspace, and use the Grease Pencil tool to draw or write annotations directly onto the video.
• Create a new Grease Pencil object, and then use the Draw Tool to write notes, labels, or arrows that highlight key parts of your illustrations.
2. Animate the Annotations:
• You can animate these annotations by using the keyframes function. For example, you can make an annotation appear at a specific time and disappear later. This will guide your audience to focus on the important parts of the visual content.
3. Add Text Annotations:
• Another option for adding annotations is through text objects. In the Add Menu, choose Text, and position it within the 3D scene to add notes or explanations. You can animate the text objects similarly, using keyframes.

Step 6: Preview and Fine-Tune

1. Preview the Full Video:
• Play through your entire animation to ensure that subtitles and annotations are appearing correctly at the intended moments and are synchronized with the voiceover.
2. Fine-Tune the Appearance:
• Adjust the text size, timing, and appearance of annotations if needed to improve clarity and readability.

Step 7: Rendering the Final Video with Subtitles

1. Set up the Render:
• Once satisfied with the timing of subtitles and annotations, set up the final render. Switch to the Output Properties panel, choose your desired format (MP4, AVI, etc.), and set the resolution and frame rate.
2. Render the Animation:
• Click Render > Render Animation to export the video with subtitles and annotations embedded.

Step 8: Export as SRT File (Optional)

• If you want to upload subtitles separately, you can manually create an SRT (SubRip Subtitle) file for platforms like YouTube. Tools like Subtitle Edit or Aegisub can be used to add the same subtitles, and the timings you adjusted in Blender can guide your SRT creation.

Source:

ChatGPT

Software Park is going to be established in DHA Islamabad/Rawalpindi

(Source: Pixabay)

In Islamabad/Rawalpindi, with DHA interchange there is DHA Expressway, with which Software Park, DHA Emaar, is going to be established. The process of land clearing has already been started. This software park is also closely linked to DHA Phase 5. In the DHA Phase 5, there are also some other important buildings, such as DHA headoffice, McDonald’s, and Novacare Hospital. Nevertheless, Software park is close to Caltex Petrol Pump. Close to this area, Emaar is also offering plots that are available at installments. For this, people have to give 25% downpayment to DHA Emaar and the rest is on quarterly installments. It is also interesting to note that during the last few years several banks and IT companies have shown interests in these areas. One of the reasons of the interest of these organizations in this area is that of software park and another reason is that of plot sizes, which range from 9 marlas to 60 marlas. Eventually, considering DHA in association with GT Road, DHA Down Town can be a good option

Source:
Property Gupshup - 🚨 LAND CLEARING of DHA Islamabad's 1️⃣st BUSINESS HUB | Property Gupshup - https://www.youtube.com/watch?v=6DmlvtmcuZQ

Day 5: 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 lavaan and semPlot packages. Then, defined the model using the following lines of codes:

 

 

# Define the SEM model

model <- '

  # Direct effects

  achievement ~ teaching_quality + peer_support + engagement

 

  # Indirect effects

  engagement ~ peer_support

 

  # Covariances (optional if you want to assess relationships between predictors)

  peer_support ~~ teaching_quality

'

 

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

 

# Simulate data (for the sake of demonstration)

set.seed(123)

n <- 200

peer_support <- rnorm(n, mean = 5, sd = 2)

teaching_quality <- rnorm(n, mean = 6, sd = 1.5)

engagement <- 0.5 * peer_support + rnorm(n, mean = 3, sd = 1)

achievement <- 0.6 * teaching_quality + 0.3 * peer_support + 0.4 * engagement + rnorm(n)

 

data <- data.frame(peer_support, teaching_quality, engagement, achievement)

 

Then, I fit the SEM model to the data:

 

# Fit the model

fit <- sem(model, data = data)

# Get a summary of the model

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

 

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

# Plot the SEM model

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

         nCharNodes = 0, color = list(lat = "blue", man = "green"),

         sizeMan = 6, sizeLat = 8)

Source:
ChatGPT

Day 26: Blender tutorial for making illustrations – Creating Complete Educational Videos

Objective: Combine all elements (diagrams, animations, voiceovers) and export a complete educational video.

---

1. Preparation: Gather All Materials

• Diagrams: Gather the diagrams and animations you’ve created in Blender during the previous days (flowcharts, mind maps, brainstorm diagrams).
• Voiceover Script: Prepare your script that corresponds to the visual content. You should match the timing of the voice with each diagram or animation.
• Background Audio (Optional): Choose soft background music or ambient sounds if you plan to include them.

2. Set Up the Blender Workspace

• Open Blender and organize all of your layers.
• Arrange your different elements (e.g., flowcharts, brainstorm diagrams) in the timeline to create a smooth flow between them.
• If you haven't already, create transitions between different scenes (for example, fade-in or fade-out between diagrams).

3. Refining Animations

• Go through each of the animated diagrams to ensure smooth transitions and movements.
• Refine the timing: Make sure that every animation (text appearing, diagram growing, camera movements) is timed correctly.
• Key Considerations:
• No abrupt cuts between scenes unless intentional.
• Adjust the speed of transitions based on your narration speed.

4. Add Voiceover to Blender

• Audio Setup: Go to the "Video Sequencer" within Blender.
• Import Audio: Drag and drop your pre-recorded voiceover file (e.g., .mp3 or .wav) into the timeline.
• Sync Audio and Visuals: Play back the animation with your voiceover, adjusting the timing of visuals to match the narration.
• Pro Tip: Make sure key visual elements (like text labels or flowcharts) appear right as you speak about them.

5. Polish with Background Audio (Optional)

• If using background music, import it into the same timeline and adjust the volume to ensure it doesn’t overpower your voice.
• Fade in/out the music where necessary (e.g., intro, outro).

6. Adding Subtitles (Optional)

• To improve accessibility, you can add subtitles within Blender using the Text Editor or add them in post-production software like DaVinci Resolve, if desired.

7. Final Video Export

• Settings:
• Render size: 1920 x 1080 (Full HD)
• Frame rate: 24-30 fps (based on your target platform)
• Format: MP4 with H.264 codec for compatibility and good compression.
• Go to the Render Properties in Blender.
• Under the Output Tab, set the correct file format and directory for export.
• Click Render Animation to create your final video.

8. Preview the Final Video

• Watch the entire video to ensure that all elements (diagrams, animations, voiceovers) are working seamlessly.
• Check for any timing issues between the voice and visuals.

9. Export and Save

• Save the project file for future edits.
• Export the final video again if any last-minute changes were made.

Outcome:

A complete educational video with flowcharts, animations, and voice narration, ready to be shared on your YouTube channel.

Source:
ChatGPT

Day 4: 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, essential libraries, including lavaan and semPlot are loaded. Then, an SEM model is specified showing how diet and exercise affect cardiovascular disease both directly and indirectly through mental health. Following lines of codes can be used:

 

library(lavaan)

library(semPlot)

 

# Model specification

model <- '

  # Direct effects

  mental_health ~ diet + exercise

  cardiovascular_disease ~ diet + exercise + mental_health

 

  # Indirect effects (via mental health)

  cardiovascular_disease ~ mental_health

'

 

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

 

set.seed(123)

n <- 200  # number of participants

 

# Simulate data

diet <- rnorm(n, mean = 50, sd = 10)

exercise <- rnorm(n, mean = 30, sd = 8)

mental_health <- 0.4 * diet + 0.6 * exercise + rnorm(n)

cardiovascular_disease <- 0.5 * diet + 0.3 * exercise + 0.7 * mental_health + rnorm(n)

 

# Combine into a data frame

data <- data.frame(diet, exercise, mental_health, cardiovascular_disease)

 

Then the lavaan function is used to fit the specified model to the data, using the following lines of codes:

 

fit <- sem(model, data = data)

 

# View the summary of the results

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

 

Then, the SEM can be visualized using the semPlot using the following lines of codes:

 

# Create a SEM plot

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

         sizeMan = 7, sizeLat = 10, asize = 2, color = "lightblue")

Source:
ChatGPT