What to do or check while purchasing a plot?

(Source: Pixabay)

It seems good, if you would purchase a shop in a mall or somewhere, rather than purchasing a plot, as it would start giving you rent immediately. Or a house to give you rent or a land, which is on “theka.”

Nevertheless, one of the buying strategies to purchase plots is to purchase several smaller plots rather than purchasing a large land. In this way, your investment will be diversified and risk would be reduced.

It is also important to consult at least three people, three experts.

Do not rush, take your time. For instance, if you would be in hurry, you may purchase a dipped land or a hill, and these are useless in terms of building houses.

Think and work on location. For instance, people may look for the future developments regarding the location, and may purchase a plot. For instance, in Jinnah avenue, Islamabad, one kanal can be sold at about 100 crores.

Reverse commission can be used in terms of purchasing plots. For instance, you may go to the commission agent, and say that I want to purchase a plot at lower prices, and the lower the price of the plot, the more commission I will give to you.

It is also important to check the maintenance charges of the plot. In some cases, maintenance charges can be more than the price. In this case, you may give some amount of your rent (from houses or shops) in maintenance charges.

It is also important to check whether gas, water, and electricity are available.

Also check that the plot is not land locked, which is related to the point that the land is locked and streets or ways in the surrounding are locking the land to reach inside. If there is road with the plot, its resale value increases.

Do research, before purchasing a plot. In this case, you may go to zameen.com or olx, or offices of property agents. You may also ask neighbours. Eventually, do not go with rumours.

Source:

How To Buy A Plot | 10 Tips to buy Property in Pakistan - Azad Chaiwala - https://www.youtube.com/watch?v=Jdg9u0H47lA

A reason behind the decline in prices of plots

(Source: Pixabay.com)

In the real estate market, prices are down for the past several months. In this case, government policies obviously have some effect, the lack of commitment of most of the people involved in new housing projects is also an important factor behind the decline in prices. For instance, developers may say that this is our land; this is the time in which we would complete our work, etc. but they are not fulfilling their commitment. In this case, it seems imperative talk about them. In most of the cases, our marketing companies, for their own profits, don’t talk about them. It would also be important to note that most of the developers talk their money, which they have gotten from clients, in some other projects that result in the decline in the progress of the clients’ work.

Source:

MASS Global Marketing-MGM - Real Estate Fraud Alert | Real Estate Business in Pakistan #Pakistan #RealEstateBusiness - https://www.youtube.com/watch?v=PXlPktPNcjs

Prices in Block E, Phase 8, Bahria Town, Rawalpindi/Islamabad, Pakistan

 

(Source: https://bahriatown.com/)

Bahria Town, Phase 8, has Block E containing different parts, including E1, E2, and E3. E block has mostly the sizes of plots in the range of 10 marlas, though some plots are also available in the size of 1 kanal. The middle ring road side of this area is not only heighted but also has higher prices, such as in the range of 1.25 crores, while the other side from middle ring road has plots in the size range of 90 lacs. During the last 1.5 to 2 years, prices have gone down about 20 lac rupees. Nevertheless, all E1, E2, and E3 areas are possessionable. In the E1, the prices are in the range of 90 lacs to 1 crore. E1 is also a heighted area. In E1, some plots of 6 marlas are also available, the prices of which are in the range of 75 lacs. E2 and E3 have 5 marla plots. In E3, 5 marla plots are available in the price range of 60 lacs.

Source:

Bahria Town Rawalpindi Phase 8 E Block | Price Latest Updates | Advice Associates - Advice Associates - https://www.youtube.com/watch?v=y7f0xpBmp6c

Day 12: 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 learned and studied about Confirmatory Factor Analysis (CFA) while implementing a polychoric matrix and use the robust diagonally weighted least squares (RDWLS) method to evaluate the model fit using several fit indices such as RMSEA, CFI, TLI, SRMR, and chi-square/DF. This same thing can also be found in one of the research papers, such as that conducted Almeida et al. (2024).

Initially, I loaded the essential libraries and created supposed data using the following lines of codes:

 

library(lavaan)

library(semPlot)

library(psych)

 

set.seed(123)  # For reproducibility

n <- 200  # Number of participants

 

# Simulate ordinal data (Likert scale: 1 to 5)

item1 <- sample(1:5, n, replace = TRUE)

item2 <- sample(1:5, n, replace = TRUE)

item3 <- sample(1:5, n, replace = TRUE)

item4 <- sample(1:5, n, replace = TRUE)

item5 <- sample(1:5, n, replace = TRUE)

 

# Create a data frame

data <- data.frame(item1, item2, item3, item4, item5)

 

Then, using the psych package, the polychoric correlation matrix was determined:

 

polychoric_matrix <- polychoric(data)$rho

 

The CFA model was specified:

 

cfa_model <- '

  Factor1 =~ item1 + item2 + item3

  Factor2 =~ item4 + item5

'

 

fit <- cfa(cfa_model, sample.cov = polychoric_matrix, sample.nobs = n, estimator = "ML")

 

and the model fit was checked:

 

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

 

Eventually, the CFA model was visualized:

 

semPaths(fit, what = "std", edge.label.cex = 1.2, layout = "tree", style = "lisrel", rotation = 2)

 

 

Sources:

Almeida, D. M., Santos-de-Araújo, A. D., Júnior, J. M. C. B., Cacere, M., Pontes-Silva, A., Costa, C. P., ... & Bassi-Dibai, D. (2024). The best internal structure of the Diabetes Quality of Life Measure (DQOL) in Brazilian patients. BMC Public Health, 24(1), 580.

ChatGPT

Some of the most important sectors in DHA, Islamabad/Rawalpindi, Pakistan

(Source: https://www.dhai-r.com.pk/)

Considering the popular sectors of DHA Islamabad/Rawalpindi, Sector A, Sector B, Sector C, and Sector K, are among the most popular sectors. People living in these areas can have access to basic facilities, including banking, saloons, parks, schools, etc. Sector K is also near Golf Course. Considering the prices of most of the plots in Sectors A, B, and C, prices may range from 2.5 crores to 4.5 crores. Plots near main boulevard could be somewhat higher, such as near 3.25 crores. On the other hand, plots away from main boulevard and plots in low-lying areas can also be obtained at lower prices. On the other hand, plots in Sector K can be in the range from 3.5 crores to 4.5 crores.

Source:

Property Gupshup - 💥MOST WANTED sectors in DHA Islamabad Rawalpindi | Property Gupshup - https://www.youtube.com/watch?v=tFVtnk-XpWM

Day 11: 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, and defined the SEM model, as follows:

# Load the packages

library(lavaan)

library(semPlot)

 

# Define the SEM model

model <- '

  # Direct effect

  productivity ~ management + job_satisfaction + workplace

 

  # Indirect effect

  job_satisfaction ~ management

'

 

Then, a supposed data was developed, as follows:

# Simulate a dataset with the relevant variables

set.seed(123)  # For reproducibility

 

n <- 200  # Number of observations

 

# Simulate random variables for the model

management <- rnorm(n, mean = 5, sd = 2)  # Management style

job_satisfaction <- 0.5 * management + rnorm(n, mean = 0, sd = 1)  # Job satisfaction

workplace <- rnorm(n, mean = 4, sd = 1.5)  # Workplace environment

productivity <- 0.3 * management + 0.4 * job_satisfaction + 0.5 * workplace + rnorm(n, mean = 0, sd = 1)  # Productivity

 

# Combine into a data frame

data <- data.frame(management, job_satisfaction, workplace, productivity)

 

# Inspect the data

head(data)

 

Then, lavaan was used to fit the SEM model, as follows:

 

# Fit the model

fit <- sem(model, data = data)

 

# Display the summary of the SEM results

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

 

Eventually, the model is visualized using the semPlot package, as follows:

# Visualize the SEM model

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

         style = "ram", nCharNodes = 0, curvePivot = TRUE)

 

Source:

ChatGPT

Margalla Orchards by DHA in Islamabad, Pakistan

 

(Source: Zameen.com)

Margalla Orchards by DHA was launched in 2015. Presently, its transfer is taking place in the office of Federal Government Employee Housing Society, but soon its transfer will start taking place in the DHA, Phase 5 office, which is the main office of DHA. It is near Park Road. Before August, the prices of 1 kanal plots (files) in this area were in the range of 80 lacs to 1 crores. Now, after being linked to DHA, the prices are in the range of 1.6 crore or near or above this, such as above 2 crores. Investment in any area in this place can be of good value. It is about 8500 kanals’ area. Immediately, in its neighbor is Park Enclave, where market rates are above 5 crores at a distance and 6 crores in the start. Comsats University is located in its front. It has different sectors, including A, B, C, etc. Within 3 years, the prices of plots in these sectors can be doubled.

Source:

Real Ustad - 𝐌𝐚𝐫𝐠𝐚𝐥𝐥𝐚 𝐎𝐫𝐜𝐡𝐚𝐫𝐝𝐬 𝐛𝐲 𝐃𝐇𝐀 𝐈𝐬𝐥𝐚𝐦𝐚𝐛𝐚𝐝 - 𝐄𝐯𝐞𝐫𝐲 𝐅𝐢𝐥𝐞 𝐢𝐬 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 - https://www.youtube.com/watch?v=33zkpgH7Jdc

Day 10: 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 defined the SEM model using the following lines of codes:

 

# Load libraries

library(lavaan)

library(semPlot)

 

# Define the SEM model

model <- '

  # Direct effect of teacher experience on instructional methods

  InstructionalMethods ~ a * TeacherExperience

 

  # Direct effect of instructional methods on student performance

  StudentPerformance ~ b * InstructionalMethods

 

  # Direct effect of teacher experience on student performance

  StudentPerformance ~ c * TeacherExperience

 

  # Moderation: Classroom environment moderates the effect of instructional methods on student performance

  StudentPerformance ~ d * Interaction

 

  # Define indirect (mediation) effect

  indirect := a * b

 

  # Define total effect (direct + indirect effects)

  total := c + (a * b)

'

 

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

 

# Generate sample data (if real data is not available)

set.seed(123)

n <- 300

data <- data.frame(

  TeacherExperience = rnorm(n, mean = 10, sd = 5),

  ClassroomEnvironment = rnorm(n, mean = 3, sd = 1),

  InstructionalMethods = rnorm(n, mean = 0, sd = 1),

  StudentPerformance = rnorm(n, mean = 75, sd = 10)

)

 

Then a relationship is created and interaction terms are established, as follows:

 

# Create relationships and interaction term

data$InstructionalMethods <- 0.5 * data$TeacherExperience + rnorm(n)

data$StudentPerformance <- 0.3 * data$TeacherExperience +

                           0.6 * data$InstructionalMethods +

                           0.4 * data$InstructionalMethods * data$ClassroomEnvironment +

                           rnorm(n)

data$Interaction <- data$InstructionalMethods * data$ClassroomEnvironment

 

Then we fit the model and visualized the SEM model using the following lines of codes:

 

# Fit the model

fit <- sem(model, data = data)

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

 

# Visualize the SEM model

semPaths(fit,

         whatLabels = "std",

         layout = "tree",

         edge.color = "blue",

         sizeMan = 6,

         sizeLat = 8,

         fade = FALSE)

 

Source:

ChatGPT

Six important factors that can lead to increase in property rates and real estate business

 

The real estate market in Pakistan is going to go up during the next 4 to 6 months. The current account deficits in Pakistan would decrease that can help in improving the real estate market. This decrease can be linked to the amount/payment provided by IMF. Moreover, China, Saudi Arabia, and Russia are going to invest in Pakistan that can also help in improving economic stability. So, this foreign investment would help in the next few months.

Another point is that people from foreign countries would send money to Pakistan in the form of remittances that can also positively affect the real estate market. For instance, if we compare August from September, there has been about 29% increase in foreign remittances.

Another interesting thing is that inflation is very much controlled during the past several months. It is not increasing, and this is a positive sign. This helps people in thinking about things other than day to day operations, such as real estate.

It is also important to note that the devaluation of Pakistani Rupees in comparison to Dollar has stopped. Pakistani Rupee is very much stable at one place. This is interesting, as people may stop investing in money trading and may stop investing in other options, such as real estate.

The interest rate is also an interesting and important factor. It is gradually decreasing that can also lead to increased investment in other options. In 2025, it is said that interest rate would come in single digit.

Pakistan’s exports are also increasing that can also result in increased investment in real estate. Exports result in more inflow of dollars, which is beneficial.

Source:

Asaan Home - Nobody Tells You About Pakistan's Real Estate Crisis | Which Projects are Profiting BIG Right Now! -  https://www.youtube.com/watch?v=K6eCIlrayrM  

Day 9: 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, and defined the SEM model, using the following lines of codes:

# Load the libraries

library(lavaan)

library(semPlot)

 

# Define the SEM model

model <- '

  # Direct effects

  Housing_Demand ~ Economic_Conditions

  Property_Prices ~ Housing_Demand

 

  # Moderation (interaction term between Housing Demand and Interest Rates)

  Property_Prices ~ Housing_Demand * Interest_Rates

'

 

Then develop a supposed dataset using the following:

# Simulate data for the model

set.seed(123) # For reproducibility

N <- 300 # Sample size

 

Economic_Conditions <- rnorm(N, mean = 0, sd = 1)

Housing_Demand <- 0.6 * Economic_Conditions + rnorm(N, mean = 0, sd = 1)

Interest_Rates <- runif(N, min = 2, max = 5)  # Random interest rates between 2% and 5%

Property_Prices <- 0.8 * Housing_Demand + 0.4 * Housing_Demand * Interest_Rates + rnorm(N, mean = 0, sd = 1)

 

# Combine data into a dataframe

data <- data.frame(Economic_Conditions, Housing_Demand, Interest_Rates, Property_Prices)

 

Then I fit the SEM model using lavaan, as follows:

 

# Fit the SEM model

fit <- sem(model, data = data)

 

# Display the summary of the SEM model

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

 

Eventually, the model is visualized using semPlot, as follows:

# Plot the SEM model

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

         style = "lisrel", residuals = FALSE, intercepts = FALSE)

 

Source:
ChatGPT

Day 30: Blender tutorial for making illustrations – Upload and Share Your First Video

1. Finalize Your Video File

Before you upload your video, make sure it is in the correct format for YouTube. The most common format is MP4, which provides a good balance between file size and quality. You should have done this on Day 29, but double-check the following:

• Resolution: YouTube recommends 1080p (1920x1080) for standard videos.
• Frame rate: Use a consistent frame rate (e.g., 30fps).
• Aspect ratio: Standard aspect ratio is 16:9.

If everything is correct, you’re ready to proceed with the upload.

2. Log in to Your YouTube Account

• Visit YouTube Studio.
• Sign in using your Google account credentials.

3. Navigate to the Video Upload Section

• Once logged in, click the Create button (a camera icon with a plus sign), located at the top-right corner of the page.
• Select Upload Videos from the dropdown menu.

4. Upload Your Video

• Drag and Drop your video file into the upload area or click Select Files to browse for it manually on your computer.
• Wait for the video to upload. During the upload process, YouTube will show a progress bar.

5. Title Your Video

Choose a title that is SEO-friendly and accurately describes your content. Since your video is an illustrative educational video, include keywords such as "illustrative", "educational", and the topic covered in the video.

Example: "Illustrative Flowchart on Cell Division – Educational Video Tutorial"

6. Write a Description

The description is crucial for both viewers and search engines. Write a detailed and engaging description that explains what the video is about and what the audience will learn.

Example:

• Start with a short, attention-grabbing intro about the video.
• Mention the key concepts covered in the video.
• Add relevant keywords for SEO purposes.
• Include timestamps if the video has multiple sections.
• Finally, add any links to related content, including your social media or Patreon link.

7. Set Your Video Thumbnail

You can upload a custom thumbnail for your video, which is the image viewers will see before clicking on the video. A well-designed thumbnail can significantly increase click-through rates. Make sure it:

• Is visually appealing and informative.
• Reflects the content of your video (e.g., flowchart, illustrations).
• Has large, readable text if necessary.
• Is 1280x720 pixels in resolution.

8. Add Tags

Tags help YouTube understand the content of your video and recommend it to the right audience. Use tags related to the video's content.

• Example tags: "illustration", "Blender tutorial", "educational video", "flowchart", "Quranic content".

9. Select Playlists (Optional)

If you have playlists on your channel (e.g., educational series), you can add the video to relevant playlists. This helps organize your content and improve viewer retention.

10. Audience Settings

Select whether your video is made for kids or not. If your video is for a general audience, select "No, it’s not made for kids" unless the video content specifically targets children.

11. Set Video Visibility

YouTube allows you to choose when and how your video becomes visible:

• Public: The video is immediately visible to everyone.
• Private: Only you and people you invite can view the video.
• Unlisted: Only people with the link can view it.
• Scheduled: You can set a specific date and time for the video to go public.

If you want your video to be immediately available, choose Public.

12. Add End Screens and Cards (Optional)

End screens and cards can help promote other content on your channel. You can:

• Add related videos to the end screen.
• Suggest other playlists.
• Direct viewers to your Patreon or other social media.

13. Review and Publish

• Once you’ve filled out all the details, review them to ensure everything is correct.
• Click Publish (or Schedule if you selected that option).

14. Share Your Video

After publishing, share your video to increase its reach:

• Share on social media: Post on platforms like Twitter, Facebook, Instagram, and LinkedIn.
• Embed on your website or blog if you have one.
• Send to your email list if you have subscribers.
• Post the link in relevant online communities or forums where people might find the video valuable.

15. Monitor Video Performance

After uploading, keep an eye on the video’s performance in YouTube Studio:

• Check for views, likes, comments, and engagement statistics.
• Respond to comments to engage with your audience.

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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