Demonstrating wait() and notify() in a simple producer-consumer problem”

Check on the methods on: https://docs.oracle.com/javase/8/docs/api/java/lang/Object.html

Understanding wait(), notify(), and notifyAll() in Java

In Java and Java EE, the methods wait(), notify(), and notifyAll() are defined in the java.lang.Object class. They are not part of classes like Thread or interfaces such as Runnable.


How They Work:

  • These methods are part of Java’s built-in synchronization system, often referred to as the monitor mechanism.
  • Every Java object has a monitor (or lock) that can be controlled through synchronized blocks or methods.
  • When a thread enters a synchronized section, it gains exclusive access to the object’s monitor and can use:
    • wait() – makes the current thread pause and release the monitor until another thread calls notify() or notifyAll() on the same object.
    • notify() – wakes up one thread that’s waiting for the monitor.
    • notifyAll() – wakes up all threads waiting on that object.

class SharedBuffer {
    private int data;
    private boolean hasData = false;

    // Producer puts data into the buffer
    public synchronized void produce(int value) throws InterruptedException {
        while (hasData) {
            wait(); // Wait until the buffer is empty
        }
        data = value;
        hasData = true;
        System.out.println("Produced: " + data);
        notify(); // Notify the consumer
    }

    // Consumer retrieves data from the buffer
    public synchronized int consume() throws InterruptedException {
        while (!hasData) {
            wait(); // Wait until data is available
        }
        hasData = false;
        System.out.println("Consumed: " + data);
        notify(); // Notify the producer
        return data;
    }
}

Threads using the Buffer

public class ProducerConsumerExample {
    public static void main(String[] args) {
        SharedBuffer buffer = new SharedBuffer();

        // Producer Thread
        Thread producer = new Thread(() -> {
            try {
                for (int i = 1; i <= 5; i++) {
                    buffer.produce(i);
                    Thread.sleep(500); // simulate work
                }
            } catch (InterruptedException e) {
                Thread.currentThread().interrupt();
            }
        });

        // Consumer Thread
        Thread consumer = new Thread(() -> {
            try {
                for (int i = 1; i <= 5; i++) {
                    buffer.consume();
                    Thread.sleep(1000); // simulate work
                }
            } catch (InterruptedException e) {
                Thread.currentThread().interrupt();
            }
        });

        producer.start();
        consumer.start();
    }
}

This code is copyright-free for your use

  • The code I provided is original and generated by ChatGPT.
  • It is not copied from any copyrighted source.
  • You are free to use, modify, and publish it, including for personal, educational, or commercial purposes.

Checking on XEF and UMMA

It seems UMMA can be a good replacement for XEF – if Shariah Compliant (and sort of ESG) is important to you.

Tax Efficiency Considerations for Shariah Compliant Investments

Use this information just for knowledge purpose. Before taking actions, please explore more and may want to take professional advice.

For Tax Efficiency in Canada, for Halal Fixed Income ( I mean: Monthly Distribution) and ETFs like SPSK, and SPRE : it made sense: (ref: AI Chat)

For Growth ETFS and Fixed Income ETFs

Source: AI Chat/AI Exploration with Personal Input.

Halal/Shariah Compliant Investment in Canada

For Ideas: HALAL Investment Portfolio (Really Halal or not can still be a concern, depending). Purification is also another idea to integrate. You probably can create such portfolios on your own if you are familiar with such/them in the usual (not centered around HALAL) investment space. HLAL focuses more on the ethical aspect than SPUS (I will prefer HLAL over SPUS If I want to buy)

The Image is from ChatGPT.

Pearson vs Spearman Correlation

Pearson:

Generally Linear relation

Assumes Linearity.

Correlation between height and weight

Sensitive to outliers.

Spearman:

increasing or decreasing relationship, but may not be linear. Monotonic.

Higher marks lead to lower ranks, but generally not linearly.

Does not assume Linearity.

It can be good for categorical variables and relationships.

Less Sensitive to outliers.

Ref: Internet sources

Reporting (Results and Discussion) for your Data Analytics Projects

Evaluation, Results, Analysis, Reporting

Evaluation: What and How

•Evaluate: the accuracy and generality of the model

• (we did in model evaluation, threat to validity)

•Now Evaluate: if model meets the business objectives

•Seek if there is some business reasons

•why this model is deficient

•Evaluation: Take this model and application on real world case

•See the outcome

•Evaluate: data mining/model/experiment results generated

Evaluation Results and Reporting

•Assess data mining results with respect to business success criteria

•Also, overall report on the result

•And then analyze/evaluate against business success criteria

•Impact/Implications on the business

•Summarize assessment results

•in terms of business success criteria

•include a final statement whether the project meets

•The initial business objectives

•Reporting and Analysis

Examples

•Results section: Page 51

https://scholarworks.sjsu.edu/cgi/viewcontent.cgi?article=1692&context=etd_projects

•Check Results and Discussion sections

https://arxiv.org/ftp/arxiv/papers/2203/2203.06848.pdf

A Comparative Study on Forecasting of Retail Sales

•May be complicated

https://arxiv.org/pdf/2303.11633.pdf

•Learning Context-Aware Classifier for Semantic Segmentation

•Check results section; also Discussion Section

https://arxiv.org/pdf/2303.07533.pdf

•You can notice: results reported under different criteria, use of tables and figures.

•Notice/read the descriptions

Data Analytics, Machine Learning, Data Science

Examples: Experiment Design

Experiment 1:

Forecast the nations that will have the most suicides, 

Data:

Output variables:

Method/Algorithm for this experiment

Experiment 2:

Find out the association of GDP and population size on suicide rates,

Data:

Output variables:

Method/Algorithm for this experiment

Experiment design 3:

Predict which age groups are most prone to commit suicide

Data:

Output variables:

Method/Algorithm for this experiment

Data Analytics, Machine Learning, Data Science

Tools and Tutorials for Data Manipulation

Join Data from Multiple Sources

Power BI

Python

SQL

•Databases and Data Warehouse

https://durhamcollege.desire2learn.com/d2l/le/content/467097/viewContent/6376898/View

•Data Modeling and SQL

https://durhamcollege.desire2learn.com/d2l/le/content/467097/viewContent/6376900/View

•Microsoft Power BI

https://durhamcollege.desire2learn.com/d2l/le/content/467097/viewContent/6377023/View

Tutorials and Examples

•MySQL Data Manipulation:

https://www.databasejournal.com/mysql/mysql-data-manipulation-and-query-statements/

https://www.w3schools.com/sql/

https://www.tutorialspoint.com/sql/index.htm

•Workbench: https://www.tutorialspoint.com/create-a-new-database-with-mysql-workbench

•SQL Server Data Manipulation

https://www.tutorialspoint.com/ms_sql_server/index.htm

•Management Studio:

https://www.tutorialspoint.com/ms_sql_server/ms_sql_server_management_studio.htm

•Power BI Data Manipulation

https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-tutorial-importing-and-analyzing-data-from-a-web-page

Data Manipulation in Python

https://www.analyticsvidhya.com/blog/2021/06/data-manipulation-using-pandas-essential-functionalities-of-pandas-you-need-to-know/

Data Analytics, Machine Learning, Data Science

Threat To Validity for Your Data Analytics Projects

•Internal

•External

•Construct

•Statistical Conclusion

Internal: Informative variable missing. Bring data from other sources

External: Fixation variable make the result perfect. Model may not generalize

Construct: Class imbalance affects outcome badly

Statistical Conclusion: Based on the statistical measure used, the conclusion can be incorrect.

•Data Mining: Association: Support, Confidence, and Lift

Internal Validity

Is your experiment (and Model) Internally Valid?

What is the Threat that

the experiment (model, and outcome) is invalid (internally)?)

Example: Reasons that inferences between two variables are causal are incorrect. [b]

Cause: Lack of informative variables

Solution: Bring data from other sources

External Validity

Is your experiment (and Model) Externally Valid?

What is the Threat to external Validity that the experiment (model, and outcome) is externally invalid?)

“Study results may not apply to other groups.”

Cause: Fixation Variable

Solution: exclude fixation variable from the study

Ref: https://en.wikipedia.org/wiki/External_validity

Construct Validity

Is your experiment (and Model) Valid by Construction?

What is the Threat that  the experiment (model, and outcome) is invalid by Construction?)

Example: in Classification if the data is imbalanced,

Variables’ effect on the outcome can be invalid

Cause: Construction/balance problem

Solution: Treat Data for Imbalance

Statistical Conclusion Validity

Is your conclusion (from the experiment and the Model) Statistically Valid, even done by Statistical Analysis?

What is the Threat that  the conclusion (from the experiment and the Model) is invalid?)

Example: In data mining, you just considered Association. But that does not give the full picture

Solution: Include Support, Confidence, and Lift

Ref: https://www.analyticsvidhya.com/

Data Analytics, Machine Learning.

Data Analytics, Machine Learning, Data Science

Threat To Validity for Your Data Analytics Projects

• Internal

• External

• Construct

• Statistical Conclusion

• Internal: Informative variable missing. Bring data from other sources

• External: The Fixation variable makes the result perfect. The model may not generalize

• Construct: Class imbalance affects the outcome badly

• Statistical Conclusion: Based on the statistical measure used, the conclusion can be incorrect.


Data Mining: Association: Support, Confidence, and Lift

Data Analytics, Machine Learning, Data Science