Sunday, September 3, 2023

Single Sign-On (SSO)

Implementing Single Sign-On (SSO) across multiple domains. Here's a high-level overview of how you could approach this:

  1. Centralized Authentication Server (Main Domain):

    • Maintain a centralized authentication server on your main domain that handles the user login/authentication process. This server will issue tokens or session identifiers upon successful login.
  2. Authentication Protocol:

    • Use a standardized authentication protocol like OAuth 2.0 or OpenID Connect. These protocols allow you to securely authenticate users and issue tokens.
  3. Token Issuance:

    • When a user logs in through the main domain, the authentication server should issue a token (e.g., JWT) that contains information about the user and their authentication status.
  4. Cross-Domain Communication:

    • Implement Cross-Origin Resource Sharing (CORS) to allow other domains to make authenticated requests to the main domain. This enables secure communication between domains.
  5. Popup/Redirect Flow:

    • When a user on a secondary domain needs to authenticate, you can implement a popup or redirect flow to the main domain's login page.
    • The main domain's login page should handle the authentication process, and upon successful login, it can close the popup or redirect back to the secondary domain.
  6. Token Verification:

    • The secondary domain can receive the token from the main domain once the user is authenticated. This token can be used to verify the user's identity on the secondary domain.
    • The secondary domain should validate the token with the main domain's authentication server to ensure its authenticity and check the user's session.
  7. Session Management:

    • Sessions in web applications are typically domain-specific. However, you can implement shared session management by using a centralized session store or database.
    • When a user logs in on the main domain, their session information is stored in the centralized session store.
    • Other domains can query this centralized session store to check if a user is authenticated. This would require some communication between domains.
  8. Access Tokens vs. Session IDs:

    • Instead of sharing session IDs, it's better to use access tokens (JWTs) for authentication. JWTs are designed to be portable and can carry authentication information securely.
  9. Security Considerations:

    • Implement strong security measures to protect the tokens and ensure secure communication between domains. Use HTTPS, validate tokens on the server, and handle token expiration and revocation.

Remember that implementing SSO and cross-domain authentication can be complex, and security is paramount. You should carefully plan and design your authentication flow and consider using established authentication libraries or frameworks to ensure the highest level of security. Additionally, legal and privacy considerations, such as GDPR compliance, may also apply when handling user data across multiple domains.


How it can be done with Python

Certainly, I can provide you with a high-level description, sample code blocks, and sample data for each point in the Single Sign-On (SSO) implementation. However, please note that this is a complex task, and the actual implementation may vary based on your technology stack and requirements.

1. Centralized Authentication Server (Main Domain):

A centralized authentication server handles user login and token issuance.

Sample Data: No specific data to provide.

2. Authentication Protocol:

Use OAuth 2.0 or OpenID Connect for secure authentication.

Sample Code (OAuth 2.0): Sample code will depend on your chosen programming language and library. Below is a simplified example using a hypothetical library.

python
# Sample OAuth 2.0 server code (Python with Flask) from flask import Flask, request, jsonify app = Flask(__name__) # Endpoint for user login (authentication) @app.route('/login', methods=['POST']) def login(): # Authenticate the user (e.g., check username and password) # If authenticated, issue an access token access_token = 'sample-access-token' return jsonify({'access_token': access_token}) if __name__ == '__main__': app.run()

3. Token Issuance:

Issue a token (e.g., JWT) upon successful login.

Sample Data (JWT): The issued JWT may look like this:

css
Header: { "alg": "HS256", "typ": "JWT" } Payload: { "sub": "user123", "exp": 1630454400 # Expiration time (Unix timestamp) } Signature: HMACSHA256( base64UrlEncode(header) + "." + base64UrlEncode(payload), secret )

4. Cross-Domain Communication:

Implement Cross-Origin Resource Sharing (CORS) to allow secure communication between domains.

Sample Code (JavaScript):

javascript
// Sample CORS configuration for Express.js (Node.js) const express = require('express'); const cors = require('cors'); const app = express(); // Enable CORS for all routes app.use(cors()); // Your routes and logic here app.listen(3000, () => { console.log('Server is running on port 3000'); });

5. Popup/Redirect Flow:

Implement a popup or redirect flow for authentication.

Sample Code (JavaScript - Popup Flow):

javascript
// Sample JavaScript to open a popup for authentication const loginButton = document.getElementById('login-button'); loginButton.addEventListener('click', () => { const popup = window.open('https://main-domain.com/login', 'loginPopup', 'width=400,height=400'); // Handle the popup's communication and close logic });

6. Token Verification:

The secondary domain verifies the token issued by the main domain.

Sample Code (JavaScript):

javascript
// Sample JavaScript to verify the JWT on the secondary domain const token = 'sample-access-token'; // Send a request to the main domain for token verification fetch('https://main-domain.com/verify-token', { method: 'POST', headers: { 'Content-Type': 'application/json', }, body: JSON.stringify({ token }), }) .then(response => response.json()) .then(data => { if (data.valid) { // Token is valid; user is authenticated console.log('User is authenticated'); } else { // Token is not valid; user is not authenticated console.log('User is not authenticated'); } }) .catch(error => { console.error('Error verifying token:', error); });

7. Session Management:

Implement shared session management using a centralized store.

Sample Data (Centralized Session Store): Data in the centralized store may look like this:

json
{ "user123": { "authenticated": true, "session_data": { // Session data for the user } }, // Other user sessions }

8. Access Tokens vs. Session IDs:

Use access tokens (JWTs) for authentication, as shown in "Sample Data (JWT)" above.

9. Security Considerations:

Ensure secure communication (HTTPS), token validation on the server, token expiration checks, and secure handling of sensitive information.

Please note that these are simplified examples, and actual implementation details will depend on your technology stack and specific requirements. Additionally, you may need to use libraries or frameworks tailored to your chosen programming language and platform for authentication and token management.


How it can be done with php

Here is the sample PHP code blocks, and sample data for each point in the Single Sign-On (SSO) implementation using PHP. Please note that this is a high-level overview, and the actual implementation may vary based on your specific requirements and PHP framework (if any).

1. Centralized Authentication Server (Main Domain):

A centralized authentication server handles user login and token issuance.

Sample Data: No specific data to provide.

2. Authentication Protocol:

Use OAuth 2.0 or OpenID Connect for secure authentication.

Sample Code (OAuth 2.0):

Here's a simplified example of an OAuth 2.0 server using PHP with the Slim framework:

php
<?php use Slim\Factory\AppFactory; require __DIR__ . '/vendor/autoload.php'; $app = AppFactory::create(); // Endpoint for user login (authentication) $app->post('/login', function ($request, $response, $args) { // Authenticate the user (e.g., check username and password) // If authenticated, issue an access token $access_token = 'sample-access-token'; return $response->withJson(['access_token' => $access_token]); }); $app->run();

3. Token Issuance:

Issue a token (e.g., JWT) upon successful login.

Sample Data (JWT): The issued JWT may look like this:

css
Header: { "alg": "HS256", "typ": "JWT" } Payload: { "sub": "user123", "exp": 1630454400 // Expiration time (Unix timestamp) } Signature: HMACSHA256( base64UrlEncode(header) + "." + base64UrlEncode(payload), secret )

4. Cross-Domain Communication:

Implement Cross-Origin Resource Sharing (CORS) to allow secure communication between domains.

Sample Code (PHP - Slim Framework):

CORS middleware can be added to your Slim application:

php
use Slim\Middleware\CorsMiddleware; $app->add(new CorsMiddleware([ 'origin' => ['https://domain2.com', 'https://domain3.com'], 'methods' => ['GET', 'POST', 'PUT', 'DELETE'], ]));

5. Popup/Redirect Flow:

Implement a popup or redirect flow for authentication.

Sample Code (JavaScript - Popup Flow):

This part remains the same as in the previous example (JavaScript code for opening a popup).

6. Token Verification:

The secondary domain verifies the token issued by the main domain.

Sample Code (PHP):

php
<?php // Sample PHP code to verify the JWT on the secondary domain $token = 'sample-access-token'; // Send a request to the main domain for token verification $verificationUrl = 'https://main-domain.com/verify-token'; $data = json_encode(['token' => $token]); $ch = curl_init($verificationUrl); curl_setopt($ch, CURLOPT_CUSTOMREQUEST, "POST"); curl_setopt($ch, CURLOPT_POSTFIELDS, $data); curl_setopt($ch, CURLOPT_RETURNTRANSFER, true); curl_setopt($ch, CURLOPT_HTTPHEADER, [ 'Content-Type: application/json', ]); $response = curl_exec($ch); curl_close($ch); $verificationResult = json_decode($response, true); if ($verificationResult['valid']) { // Token is valid; user is authenticated echo 'User is authenticated'; } else { // Token is not valid; user is not authenticated echo 'User is not authenticated'; }

7. Session Management:

Implement shared session management using a centralized store.

Sample Data (Centralized Session Store): This part remains the same as in the previous example (JSON data for centralized session store).

8. Access Tokens vs. Session IDs:

Use access tokens (JWTs) for authentication, as shown in the "Sample Data (JWT)" section above.

9. Security Considerations:

Ensure secure communication (HTTPS), token validation on the server, token expiration checks, and secure handling of sensitive information.

Please note that these are simplified examples, and actual implementation details will depend on your specific requirements and the PHP framework you're using (if any). Additionally, you may need to use libraries or components tailored to PHP for authentication and token management.

Friday, July 14, 2023

Quantum Computing

Quantum Computing

Quantum computing is an emerging technology that utilizes the principles of quantum mechanics to solve complex problems beyond the capabilities of classical computers. Today, major corporations are providing access to real quantum hardware, which was once only imagined by scientists three decades ago. They continuously improve superconducting quantum processors and advance software and quantum-classical orchestration to achieve the speed and capacity necessary for transformative quantum computing.

These quantum machines differ significantly from the classical computers that have been in existence for many decades. Here's a basic introduction to this transformative technology.

Here's how it works:

Classical computing, powering devices like laptops and smartphones, relies on bits, units of information that can store a zero or a one. In contrast, quantum computing is built on quantum bits or qubits, which can store zeros and ones. Qubits can simultaneously represent various combinations of zero and one, known as superposition.

When classical computers solve problems with multiple variables, they perform calculations sequentially whenever a variable changes. Each calculation represents a single path to a single result. In contrast, quantum computers possess a larger working space, enabling them to explore an enormous number of paths simultaneously. This capability makes quantum computers significantly faster than classical computers.

How do quantum computers solve problems?

Classical computers operate with a limited set of inputs, follow an algorithm, and deliver a single answer. The bits encoding the inputs do not share information with each other. Quantum computers, on the other hand, are different. When data is input into qubits, these qubits interact with other qubits, allowing for numerous simultaneous calculations. This is why quantum computers can work much faster than classical computers. However, quantum computers do not provide a single definitive answer like classical computers; instead, they offer a range of possible answers.

The first concrete evidence that quantum computers could tackle problems too complex for classical computers came in 2019 when Google announced a major breakthrough. Their quantum computer solved a problem in 200 seconds that would have taken a classical computer 10,000 years.

Google's new quantum computer is supposed to remarkable 241 million times more powerful than its predecessor. It performs calculations in an instant that would require the best existing supercomputers 47 years. Google's new quantum computer achieves results in a fraction of the time that the world's leading supercomputers would take—47 years.

Usage of Quantum Computing in Various Medical Areas

Quantum computing has the potential to bring about significant advancements in various medical areas. Here are some potential medical areas within endocrinology where quantum computing could provide a leap in findings, along with possible advancements they could achieve:

  1. Hormone regulation and signaling (endocrinology): Quantum computing simulations could provide a more detailed understanding of hormone regulation and signaling pathways. By accurately modeling the complex interactions and dynamics of hormones, receptors, and intracellular signaling cascades, researchers can gain insights into the mechanisms underlying endocrine disorders and develop targeted interventions.

  2. Personalized hormone therapies (endocrinology): Quantum computing can optimize the design of personalized hormone therapies by analyzing a patient's unique hormone profile, genetic information, and lifestyle factors. This optimization could lead to more effective treatment strategies tailored to individual patients, considering their specific hormone levels, receptor sensitivities, and metabolic characteristics.

  3. Drug discovery and development (Pharmacology): Quantum computing simulations can accelerate the discovery and development of novel hormone-based drugs. By accurately modeling the interactions between hormones and their target receptors, researchers can identify potential drug candidates with improved efficacy and reduced side effects. This could lead to the development of new therapies for various endocrine disorders.

  4. Precision diagnostics: Quantum computing's computational power can improve diagnostic accuracy in endocrinology. By analyzing large datasets, including genetic information, hormone levels, and patient health records, quantum algorithms can identify patterns and biomarkers associated with specific endocrine disorders. This could enhance early detection and improve diagnostic precision.

  5. Systems biology and network analysis: Quantum computing simulations can model complex biological systems involved in endocrine regulation, such as gene regulatory networks and metabolic pathways. This can help unravel the intricate relationships and dynamics within these systems, leading to a deeper understanding of endocrine processes and the identification of novel therapeutic targets.

  6. Data analysis and integration: Quantum computing's ability to process and analyze large datasets can enable more comprehensive and integrative analyses of endocrine data. This could involve integrating hormone data with other biological data types, such as genomics, proteomics, and clinical data, to reveal novel insights and associations that were previously challenging to uncover using classical computing methods.

Usage of Quantum Computing in Endocrinology

In the context of analyzing and predicting outcomes of changes in hormones, quantum computing could offer advantages in several areas:

  1. Simulation of complex systems: Hormone regulation involves intricate biochemical networks and interactions. Quantum computers could simulate these systems more accurately and efficiently, enabling researchers to gain a deeper understanding of hormone dynamics and their effects on the body.

  2. Optimization of hormone therapies: Developing optimal hormone treatment strategies often involves searching through vast solution spaces. Quantum computing algorithms, such as quantum annealing or variational algorithms, could assist in optimizing hormone therapies by exploring numerous possibilities simultaneously.

  3. Machine learning and pattern recognition: Quantum computers have the potential to enhance machine learning algorithms used in endocrinology research. They can process and analyze large datasets more quickly, enabling the discovery of hidden patterns and correlations within hormone-related data.

  4. Quantum chemistry simulations: Quantum computers can simulate molecular structures and interactions, offering insights into how hormones bind to receptors, undergo chemical reactions, or interact with other molecules. These simulations could aid in drug discovery and the development of novel hormone-based therapies.

Drug Discovery and Development

Quantum computing holds potential for analyzing and making findings in drug discovery and development. Quantum computing can offer advantages in this field through the following approaches:

  1. Molecular simulation and modeling: Quantum computers can simulate the behavior of molecules with a level of accuracy that surpasses classical computers. They can provide more detailed insights into molecular structures, dynamics, and interactions. By simulating the behavior of drugs and their target receptors, quantum computers can aid in predicting binding affinities, understanding molecular mechanisms, and optimizing drug designs.

  2. Quantum chemical calculations: Quantum computers can perform complex quantum chemical calculations, such as electronic structure calculations and molecular dynamics simulations, with greater efficiency compared to classical methods. These calculations enable the exploration of chemical reactions, the prediction of reaction rates, and the assessment of drug stability and toxicity.

  3. Optimization of drug candidates: Quantum computing algorithms, such as quantum optimization algorithms or quantum machine learning, can be employed to optimize drug candidates. These algorithms can explore vast solution spaces to identify compounds with desired properties, such as high potency, selectivity, and reduced side effects. Quantum computers can expedite the process of evaluating and refining drug candidates to enhance their efficacy and safety profiles.

  4. Virtual screening and database analysis: Quantum computers can be utilized in virtual screening techniques to analyze large databases of compounds. By leveraging quantum algorithms, they can efficiently identify potential drug candidates that interact favorably with specific target receptors. Quantum computing can accelerate the screening process, enabling the exploration of a larger chemical space and the identification of novel lead compounds.

  5. Quantum-enhanced machine learning: Quantum machine learning techniques can be applied to drug discovery and development. Quantum computers can process and analyze large datasets more efficiently, extracting valuable insights from complex biological and chemical data. Quantum machine learning algorithms can aid in target identification, predicting drug responses, and optimizing treatment strategies.

Precision Diagnostics

Quantum computing has the potential to contribute to precision diagnostics by enabling advanced analysis and making findings in various ways:

  1. Data analysis and pattern recognition: Quantum computing can process and analyze large and complex datasets in precision diagnostics, such as genomics, proteomics, patient records, and clinical data. Quantum algorithms can efficiently search for patterns, correlations, and biomarkers that might be difficult to detect using classical computational methods. This can aid in identifying disease signatures, predicting disease risk, and personalizing diagnostic approaches.

  2. Quantum machine learning: Quantum machine learning algorithms can be utilized to enhance precision diagnostics. By leveraging the parallelism and computational power of quantum computers, these algorithms can process and analyze multi-dimensional data, identify relevant features, and develop predictive models for disease diagnosis. Quantum machine learning has the potential to improve accuracy and speed in identifying disease subtypes and predicting treatment responses.

  3. Quantum-assisted imaging analysis: Quantum computing can assist in analyzing medical imaging data, such as MRI, CT scans, or microscopy images. Quantum algorithms can be employed to enhance image processing, feature extraction, and image segmentation tasks. This can help in identifying subtle abnormalities, improving image resolution, and enabling more accurate diagnostic interpretations.

  4. Simulation of biological systems: Quantum computers can simulate complex biological systems at the molecular level, offering insights into disease mechanisms and interactions between biomolecules. By accurately modeling biochemical pathways, protein interactions, and genetic variations, quantum computing can contribute to understanding disease progression, identifying drug targets, and optimizing treatment strategies for precision diagnostics.

  5. Integration of multi-modal data: Quantum computing can assist in integrating diverse types of data, such as genomic, proteomic, and clinical information, to develop a comprehensive view of an individual's health profile. Quantum algorithms can facilitate the integration and analysis of multi-modal data sources, leading to more accurate and holistic diagnostic assessments.

Systems Biology and Network Analysis

Quantum computing can play a role in analyzing and making findings in systems biology and network analysis by leveraging its computational power and parallelism. Here's how quantum computing can be used in these areas:

  1. Modeling complex biological systems: Quantum computing can simulate the behavior of complex biological systems, such as gene regulatory networks, metabolic pathways, and signaling cascades. Quantum algorithms can capture the quantum nature of biological processes and provide more accurate representations of their dynamics. This enables researchers to gain deeper insights into the behavior of biological systems, understand how molecules interact and influence each other, and study emergent properties.

  2. Network analysis and optimization: Quantum computing can be employed to analyze biological networks, such as protein-protein interaction networks or gene co-expression networks. Quantum algorithms can identify key network components, detect patterns, and uncover hidden relationships within the network structure. This can help in understanding the underlying mechanisms of diseases, identifying potential drug targets, and optimizing therapeutic interventions.

  3. Pattern recognition and data integration: Quantum computing's ability to process large datasets and extract patterns can be valuable for systems biology and network analysis. Quantum algorithms can handle multi-dimensional data and integrate diverse data types, such as genomics, proteomics, and clinical information. This facilitates a comprehensive analysis of biological systems, allowing for the identification of biomarkers, disease signatures, and potential therapeutic approaches.

  4. Optimization of biological processes: Quantum optimization algorithms can be utilized to optimize biological processes. This includes tasks like identifying optimal drug combinations, designing synthetic biological circuits, or optimizing metabolic pathways. By leveraging the inherent parallelism of quantum computing, researchers can explore a vast solution space and identify optimal solutions more efficiently.

  5. Predictive modeling and personalized medicine: Quantum computing can contribute to predictive modeling in systems biology. By analyzing multi-omic data and combining it with clinical information, quantum algorithms can develop predictive models for disease progression, treatment response, and patient outcomes. This can aid in personalized medicine by enabling tailored treatment strategies based on an individual's unique biological network and molecular profile.

Data Analysis and Integration

Quantum computing can play a role in analyzing and making findings in data analysis and integration in the medical field. Here's how quantum computing can be utilized in this context:

  1. Handling large and complex datasets: Quantum computing's computational power can efficiently process and analyze vast amounts of medical data, including endocrine data. It can handle multi-modal data from various sources, such as genomics, proteomics, clinical records, and imaging data. Quantum algorithms can expedite data analysis tasks, enabling researchers to extract meaningful insights from large and complex datasets.

  2. Pattern recognition and correlation analysis: Quantum computing algorithms can identify patterns, correlations, and associations within endocrine data. This includes detecting relationships between hormone levels, genetic variations, clinical symptoms, and treatment outcomes. Quantum algorithms can explore multiple data dimensions simultaneously, allowing for more comprehensive and accurate pattern recognition.

  3. Integrative analyses and data fusion: Quantum computing can facilitate the integration and fusion of diverse data sources. Quantum algorithms can combine endocrine data with other medical data types, such as genomics or proteomics, to enable integrative analyses. This integration can uncover hidden connections, identify novel biomarkers, and enhance the understanding of endocrine disorders and their underlying mechanisms.

  4. Machine learning and predictive modeling: Quantum machine learning algorithms can be applied to medical data, including endocrine data, to develop predictive models and support decision-making. Quantum computers can process and learn from large datasets more efficiently, enabling the development of accurate models for disease diagnosis, risk prediction, and treatment response.

  5. Privacy-preserving data analysis: Quantum computing can address privacy concerns by performing data analysis while preserving the privacy of sensitive information. Quantum secure multi-party computation protocols can enable collaborative data analysis across different institutions without directly sharing patient data. This allows for secure and privacy-preserving integrative analyses of endocrine data from multiple sources.

Harness the power of quantum computing in medical areas

To harness the power of quantum computing in various medical areas, careful consideration needs to be given to data modeling and setting up computations. Here are some potential areas and considerations for leveraging quantum computing in each area:

  1. Hormone regulation and signaling:

    • Data: Researchers would need to gather comprehensive data on hormone regulation pathways, signaling networks, and interactions between hormones and receptors. This may include data on molecular structures, protein-protein interactions, gene expression profiles, and cellular response dynamics.
    • Computation: Quantum computing simulations can be employed to model the complex dynamics of hormone regulation and signaling systems. This involves developing quantum algorithms that accurately represent the interactions between hormones, receptors, and intracellular signaling cascades.
  2. Personalized hormone therapies:

    • Data: Collecting patient-specific hormone profiles, genetic information, lifestyle factors, and response to treatments is essential. This data, along with clinical outcomes, can be used to create a comprehensive dataset for personalized hormone therapy optimization.
    • Computation: Quantum computing algorithms can be used to optimize personalized hormone therapies by exploring a vast solution space. These algorithms can consider multiple variables simultaneously, such as hormone levels, genetic factors, treatment modalities, and patient preferences, to determine the optimal treatment strategy for an individual.
  3. Drug discovery and development:

    • Data: Quantum computing in drug discovery would require extensive data on hormone-receptor interactions, molecular structures, and their impact on cellular processes. This includes data from experiments, clinical trials, molecular docking studies, and genomic information.
    • Computation: Quantum computing simulations can be used to explore molecular interactions, screen potential drug candidates, and predict their efficacy. Quantum algorithms can optimize the search for novel compounds, considering factors such as binding affinity, selectivity, and drug delivery mechanisms.
  4. Precision diagnostics:

    • Data: Comprehensive datasets comprising hormone levels, genetic information, clinical symptoms, and outcomes are crucial for precision diagnostics. This may involve integrating various data types, such as genomics, proteomics, and clinical data.
    • Computation: Quantum computing can assist in analyzing large-scale diagnostic datasets, identifying patterns, and correlations. Quantum algorithms can leverage the inherent parallelism of qubits to perform efficient pattern recognition and classification tasks, aiding in accurate and personalized diagnosis.

To fully utilize quantum computing in these areas, interdisciplinary collaborations between endocrinologists, computational scientists, and quantum computing experts are essential. They can work together to design appropriate data models, develop quantum algorithms, and implement efficient computations that harness the power of quantum computing for specific endocrinology applications. Additionally, ongoing advancements in quantum computing hardware and software will play a crucial role in supporting these endeavors.

What Needs to be done

However such development could be facilitated by countries like Singapore, UK, France, Germany, or the USA can take various steps such as.

  1. Establishing a platform: Create a dedicated platform or consortium that brings together professionals, researchers, and institutes from various countries. This platform can serve as a hub for collaboration, knowledge sharing, and resource pooling.

  2. Funding and grants: Governments can provide funding and grants to support research projects in quantum computing applied to endocrinology. This financial support can enable researchers to pursue innovative ideas, conduct experiments, and develop quantum computing algorithms and simulations specific to endocrine systems.

  3. Research partnerships: Foster collaborations between leading research institutions, universities, and private companies within the country and internationally. Encourage partnerships between experts in endocrinology, quantum computing, and computational science to exchange ideas, share expertise, and collectively tackle challenges in the field.

  4. Hosting conferences and symposiums: Organize international conferences, symposiums, and workshops that focus on the intersection of quantum computing and endocrinology. These events can serve as platforms for researchers, industry experts, and policymakers to present their findings, discuss advancements, and forge new collaborations.

  5. Talent development and education: Invest in quantum computing education and training programs to develop a skilled workforce capable of bridging the gap between endocrinology and quantum computing. Offer scholarships, fellowships, and specialized courses to attract talented individuals and nurture their expertise in both domains.

  6. Infrastructure development: Support the establishment of specialized laboratories, computing facilities, and quantum research centers equipped with the necessary hardware and software resources. This infrastructure will enable researchers to conduct experiments, simulations, and data analysis related to quantum computing and endocrinology.

  7. Policy and regulatory frameworks: Develop supportive policies and regulatory frameworks that address the ethical, legal, and privacy concerns associated with the application of quantum computing in endocrinology. Ensure compliance with data protection regulations and encourage responsible and transparent use of quantum computing technologies.

By implementing these facilitation measures, countries can create an enabling environment for collaboration, research, and advancements in the field of quantum computing applied to endocrinology. Such efforts can accelerate the development of innovative solutions and pave the way for groundbreaking discoveries in this promising intersection of science and technology.

Wednesday, July 27, 2022

What is Blockchain ?

 


What is Blockchain ?

Blockchain is a mechanism of storing data (storing information without edit capability), so information with regards to given subject item is stored using independent blocks of data in a decentralized environment with references to next and previous blocks, thus only when all blocks of data is retrieved itemized information could be derived. In an orthodox environment data repository is a Relational Database Management System (RDBMS), where information is scattered among multiple tables in the functionally and spatially most optimized manner. Key difference between the two is the key used in the RDBMS where with a single record key you could access, read, write , delete or modify any record related to the particular block of information, whereas Blockchain, environment there is no single key that allows you to retrieve information related to that particular event/transaction/block-of-information and moreover it allows only read and write ( immutable ) and once written, it cannot be modified/altered or deleted.

For an example, fund transfer situation; the login is a reference to your wallet and wallet has the references to blocks in the blockchain that creates your ledger. Hence for a given transaction blocks from multiple people will be linked together; 

where each block is encrypted with a public & private key. Since any transaction could be characterized as a give and take,  it will be recorded as a one node giving and other node receiving. As each wallet is decentralized and stored in a block chain in-order to transfer money wallet has to be reassembled. 

that can be done by the private key of the person who owns it, hence if you loose your private key, even-though it shows your balance you are unable to reassemble the required blocks and create a new record. Similarly receiving party also needs to reassemble their wallet to receive any money hence, they have to use their private key and re-assemble their wallet.

Full Stack of web services & How you choose your components !!

Full Stack of Web Services and How you choose your components !!

As full stack consist of two major components , namely:

  • Front end (client) and

  • Back end (Server).

The front or the client side is mainly user-interface of the web application that the user interacts with the system. Usually built with language tools such as HTML, CSS, JavaScript, Libraries etc. However there are exceptions.

Then again, the back end of the stack handles the data provided by the client and processes, and makes the connection between repositories+engines and the clients or the front end interface. Basically the web server/cloud that consists of servlets and repositories, that will use different programming languages like PHP, Python, Java, Perl, CGI, C,C++, Micro Languages etc, along with servers like :

  • Apache
    Apache The Apache HTTP Server, colloquially called Apache, is a free and open-source cross-platform web server software, released under the terms of Apache License 2.0. Apache is developed and maintained by an open community of developers under the auspices of the Apache Software Foundation that was established in 1999, the ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors. Our all-volunteer board oversees more than 350 leading Open Source projects, including Apache HTTP Server -- the world´s most popular Web server software. { source wikipedia }
  • NginX
    NginX Nginx (pronounced "engine X"), stylized as NGINX, nginx or NginX, is a web server that can also be used as a reverse proxy, load balancer, mail proxy and HTTP cache. The software was created by Igor Sysoev and publicly released in 2004. Nginx is free and open-source software, released under the terms of the 2-clause BSD license. A large fraction of web servers use NGINX often as a load balancer. { source wikipedia }
  • IIS
    IIS Internet Information Services (IIS, formerly Internet Information Server) is an extensible web server software created by Microsoft for use with the Windows NT family.[2] IIS supports HTTP, HTTP/2, HTTPS, FTP, FTPS, SMTP and NNTP. It has been an integral part of the Windows NT family since Windows NT 4.0, though it may be absent from some editions (e.g. Windows XP Home edition), and is not active by default. { source wikipedia }
  • OpenResty
    OpenResty OpenResty is a web platform based on nginx which can run Lua scripts using its LuaJIT engine. The software was created by Yichun Zhang. It was originally sponsored by Taobao.com before 2011 and was mainly supported by Cloudflare from 2012 to 2016. Since 2017, it has been mainly supported by OpenResty Software Foundation and OpenResty Inc.
    OpenResty is designed to build scalable web applications, web services, and dynamic web gateways. The OpenResty architecture is based on several nginx modules which have been extended in order to expand nginx into a web app server to handle large number of requests. The concept of the OpenResty solution aims to run server-side web app completely in the nginx server, leveraging nginx event model to do non-blocking I/O not only with the HTTP clients, but also with remote backends like MySQL, PostgreSQL, Memcached, and Redis. { source wikipedia }
  • Cloudflare Server
    Cloudflare Server Cloudflare is most well known as a Content Delivery Network (CDN). Today it has grown past that and offers a range of services mostly covering networking and security.
    Their stated mission: To help build a better Internet.
    To understand that, consider your experiences with the Internet so far. I’m certain there have been instances where you encountered slow or unresponsive web pages. There are many reasons why this is so, but the end result is the same – your browsing experience is affected.
    Even worse, you may not have been able to access the content which you needed. That’s one of the main reasons why Cloudflare and other companies like it exist.
    Cloudflare owns and operates a massive network of servers. It uses these to help speed up websites as well as protect them from malicious attacks like DDoS. Ultimately, websites that use services like Cloudflare are safer and offer their users a better browsing experience. { source Article by: Timothy Shim }
    etc.
Makes up the back-end/Web Server

However when it comes to selecting the best combination we have to look at what each programming language is capable of such as PHP, Python, Perl and Ruby will take care of almost all kinds of database handling and data processing however if your requirement is beyond database manipulation; for an example if it is a real-time data manipulation like an IBMS where you have control your building services ( a gate control ), then you have to look at your hardware and server capabilities where you will have to write some low-level programs like C/C++ or micro C.

Data Flow and Definitions (compendium of the data & the flow)

Important fact is that, before trying to find out which is the best stack you are going to use, you have to have a compendium of the data-flow and its definitions. if you have real-time data then your stack will extend up to hardware level. Hence you have to come up with the hardware stack to bridge the communication between your web server and

SCADA Supervisory control and data acquisition (SCADA) is a control system architecture comprising computers, networked data communications and graphical user interfaces for high-level supervision of machines and processes. It also covers sensors and other devices, such as programmable logic controllers, which interface with process plant or machinery.
data bus (real time data bus that runs MODbus, DNP3, MQTT etc ). Thus, web server that will allow modular plugins or such control is essential.



Data Communication

Then you can work from the bottom where your data starts. if it is a data repository then you can start from required procedure calls or SCADA communication that will provide an API to the application layer communicate with the data objects. at this level using an abstract notation of your data is very use-full since it will give a easy conceptualization and designing of upper layers of the stack.

Client View/ User Interface (UI)

Then you have to look at the requirements of your User Interface, That could be a web browser or mobile app or any kind of IOT. But in most situations all these interfaces has the ability to handle the client-server communication via an API that supports XML or Json. thus we could assume that platform that runs the UI could be capable of handling

REST API A REST API (also known as RESTful API) is an application programming interface (API or web API) that conforms to the constraints of REST architectural style and allows for interaction with RESTful web services. REST stands for representational state transfer and was created by computer scientist Roy Fielding.
Hence we do not have worry much about clients side.

Web Server

When it comes to the web server, you have to make sure both requirements of the client side and also the data objects are met. In most cases these requirements can be handled but some web servers like IIS fails miserably. But in most cases Unix based web servers has the ruggedness make sure it functions as required. Then again two Unix based servers comes on top, namely

  • Apache
    Apache The Apache HTTP Server, colloquially called Apache, is a free and open-source cross-platform web server software, released under the terms of Apache License 2.0. Apache is developed and maintained by an open community of developers under the auspices of the Apache Software Foundation that was established in 1999, the ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors. Our all-volunteer board oversees more than 350 leading Open Source projects, including Apache HTTP Server -- the world´s most popular Web server software. { source wikipedia }
  • NginX
    NginX Nginx (pronounced "engine X"), stylized as NGINX, nginx or NginX, is a web server that can also be used as a reverse proxy, load balancer, mail proxy and HTTP cache. The software was created by Igor Sysoev and publicly released in 2004. Nginx is free and open-source software, released under the terms of the 2-clause BSD license. A large fraction of web servers use NGINX often as a load balancer. { source wikipedia }
Unfortunately Nginx does not have the capability to handles all the requirements when you compare Apache vs Nginx Theretofore Apache will be my pick that will support any type of stack you want to build.

Application

Application development mostly revolves around the language you pick and any language like PHP, Python, Java, Perl can be used to develop web service, or create Common Gateway Interface (CGI) scripts to handle lower levels functions like SCADA interfaces. Most important part is that choosing the correct language that suits your requirement. But in some situations you have pick multiple languages to achieve this. for an example achieving tasks that can be done with PHP, Python, Java or Perl could be done with C/C++ as well but it will be impractical. Therefore it is important that you select the correct language to achieve each task, where one might end up with multiple languages in one server.

one important fact is that it is always better to develop your application API spec that is formulated and confirms to the

REST API A REST API (also known as RESTful API) is an application programming interface (API or web API) that conforms to the constraints of REST architectural style and allows for interaction with RESTful web services. REST stands for representational state transfer and was created by computer scientist Roy Fielding.
that confirms to constraints of REST architectural style and allows for interaction with RESTful web services

By employing such methodology you can make sure that application stack and UI stack can be developed interdependently where mobile app developers, Web UI Developers and all other IOT developers has the freedom to continue their development without having to depend on each other.

Components of the Stack

In most cases web developers tend to create single application component that create many restrictions so it is important to modularize each stack component that allows additional stack components at later stage.

  • Data Layer
  • Application Layer
  • UI
  • App
  • IOT
As mentioned before Data Layer, Application Layer, and UI Layer components are developed in one stack layer that could functions as typical web service but when it come to additional components, you will go back and develop separate Data and Application layer components to handle other interfaces.

Adding Payment Gateway to your web site (Payment Gateway API)

Adding Payment Gateway to your web site (Payment Gateway API)
A typical Payment gateway API enables a web site (application developers) to efficiently enable and add credit card transaction processing capabilities to their products /shopping carts. Due to it´s need for the level of security, APIs are mainly developped as the C/C++ libraries. It is objectified in such a manner that can be used directly from C++ code or it can be wrapped using a high level scripting languages such as Perl and PHP that will give easy access to the libraries thus allowing web services running on Unix or Windows based operating systems, to enabe payment gateways.
However, gateways have gone a step further by web service that can be enabled just by wrapping payment UI using a JavaScript from the gateway server. where gateway server provides an API to communicate along with JavaScript wrapper to tokenize the payment information where token needs to be used within a given time period using the payment API that will confirm to a protocol like
REST API A REST API (also known as RESTful API) is an application programming interface (API or web API) that conforms to the constraints of REST architectural style and allows for interaction with RESTful web services. REST stands for representational state transfer and was created by computer scientist Roy Fielding.
.
That could provide more security and control for both server and the client, where server could manage which clients has the authority to access each service.

How it works

when a merchant want to enable internet payment gateway (IPG) functionality to their shopping cart they just have to insert the java script enabled object in which ever the place they want to complete the checkout and create a web service to receive the payment token.

one important fact that developers has to keep in mind is that they don´t get credit information via the web post but only a reference, that has to be used within a given time period. say for and example the checkout value is $100.00 and card is 4242 4242 4242 4242 with a specific expiry date then you will get a inference code like xxxx-xxxx-xxxxxxxxxxx-xxxx that will differ from one gateway to another.

then merchant´s web server has to make a request from the IPG server with reference code within a given period of time along with invoice number and other security keys, in-order to complete the payment

Hence :

  • security keys that needs to be used with IPG server will not be shared on the web but kept secured at the merchant server
  • users credit card information will be securely transferred to IPG server while developers does not have to worry about managing security
  • There will never be multiple charges on the credit card even if there are glitches on the network connectivity make the http request multiple times where one token can be used only once and one invoice number can be used only once.

Nerveless, sharing the card information with the different IPG providers every-time a client makes a internet payment cannot be eliminated with this mechanism and every merchant has to go through a rigorous application process to enable internet payment on their web site.

we are going eliminate these issues and provide more control to the card holder in each card payment not only in the internet payment arena, but also at payments at shops and places where merchants cannot afford to provide payment machines.