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Basic SQL

2024-09-03T00:12:00.002+05:30

Guide to SQL for beginners:-

• Consider the following MOVIE table and write the SQL queries based on it.

Movie_ID	MovieName	Type	ReleaseDate	ProductionCost	BusinessCost
M001	Dahek	Action	2022/01/26	1245000	1300000
M002	Attack	Action	2022/01/28	1120000	1250000
M003	Looop Lapeta	Thriller	2022/02/01	250000	300000
M004	Badhai Do	Drama	2022/02/04	720000	68000
M005	Shabaash Mithu	Biography	2022/02/04	1000000	800000
M006	Gehraiyaan	Romance	2022/02/11	150000	120000

• Display all information from movie.

• Display the type of movies.

• Display movieid, moviename, total_eraning by showing the business done by the movies.

• Display movieid, moviename and productioncost for all movies with productioncost greater thatn 150000 and less than 1000000.

• Display the movie of type action and romance.

• Display the list of movies which are going to release in February, 2022.

1 Consider the given table patient and Write following queries:

• Display the total charges of patient admitted in the month of November.

• Display the eldest patient with name and age.

• Count the unique departments.

• Display an average charges.

3.Suppose your school management has decided to conduct cricket matches between students of Class XI and Class XII. Students of each class are asked to join any one of the four teams – Team Titan, Team Rockers, Team Magnet and Team Hurricane. During summer vacations, various matches will be conducted between these teams. Help your sports teacher to do the following:

• Create a database “Sports”.

• Create a table “TEAM” with following considerations:

• It should have a column TeamID for storing an integer value between 1 to 9, which refers to unique identification of a team.

• Each TeamID should have its associated name (TeamName), which should be a string of length not less than 10 characters.

• Using table level constraint, make TeamID as the primary key.

• Show the structure of the table TEAM using a SQL statement.

• As per the preferences of the students four teams were formed as given below. Insert these four rows in TEAM table:

• Row 1: (1, Tehlka)

• Row 2: (2, Toofan)

• Row 3: (3, Aandhi)

• Row 3: (4, Shailab)

e) Show the contents of the table TEAM using a DML statement.

f) Now create another table MATCH_DETAILS and insert data as shown below. Choose appropriate data types and constraints for each attribute.

MatchID	MatchDate	FirstTeamID	SecondTeamID	FirstTeamScore	SecondTeamScore
M1	2021/12/20	1	2	107	93
M2	2021/12/21	3	4	156	158
M3	2021/12/22	1	3	86	81
M4	2021/12/23	2	4	65	67
M5	2021/12/24	1	4	52	88
M6	2021/12/25	2	3	97	68

4. Write following queries:

a) Display the matchid, teamid, team score who scored more than 70 in first inning along with team name.

b) Display matchid, teamname and secondteamscore between 100 to 160.

c) Display matchid, teamnames along with matchdates.

d) Display unique team names

e) Display matchid and matchdate played by Anadhi and Shailab.

5. Consider the following table and write the queries:

itemno	Item	dcode	qty	unitprice	stockdate
S005	Ballpen	102	100	10	2018/04/22
S003	Gel Pen	101	150	15	2018/03/18
S002	Pencil	102	125	5	2018/02/25
S006	Eraser	101	200	3	2018/01/12
S001	Sharpner	103	210	5	2018/06/11
S004	Compass	102	60	35	2018/05/10
S009	A4 Papers	102	160	5	2018/07/17

a) Display all the items in the ascending order of stockdate.

b) Display maximum price of items for each dealer individually as per dcode from stock.

c) Display all the items in descending orders of item names.

d) Display average price of items for each dealer individually as per doce from stock which average price is more than 5.

e) Display the sum of quantity for each dcode.

21 Write a MySQL connectivity program in Python to

• Create a database school

• Create a table students with the specifications -

ROLLNO integer, STNAME character(10) in MySQL

and perform the following operations: o Insert two

records in it o Display the contents of the table

ALL BASIC COMMANDS FOR SQL:

Blue=commands heading , red=command , default=black

Creation and display of database and table:

Create database <database_name>;

Show databases;

Use <database_name>;

Create table <table_name>(<column_name > columndatatype)

>(column data types can be int,char(limit),varchar(limit),decimal(4,2))

Inserting values into table:

(let’s assume we are making table for student details)

Insert into <table_name>(R_no , S_name,Percentage,ADM_date) values(1,”Aman”,96.64,”DD/MM/YYYY”)

Desc <table_name>;

For changes in table:

Drop database <database_name>;

Drop table <table_name>;

Drop column <column_name>;

Update <table_name> set Sname=”Ram” where Sname=”Aman”;

Alter table <table_name> add <column_name> <datatype> <size>;

Alter table change [<column_name>] new_name varchar(20);

Display Commands:

Select *from <table_name>;

Select Sname,R_no,marks from <table_name>;

Where and Distinct Clause:

Select *from <table_name> where Sname=” ” ;

“” “” “” “” where marks> ;

Select distinct name from <table_name>;

Select score/500*100 as “scored_percantage” from <table_name>;

Between and Not Between clause:

Select name,Roll_no,marks from <table_name> where marks between/not between 80 and 90;

In and Not in ,identifying datatypes clause:

Select *from <table_name> where Roll_no in (1,2,3);

“” “” “” “” not in

“” “” “” “” name like “A%”; (if name starts with A)

“” “” “” “” “_ _ _%”; (atleast 3 charcters)

“” “” “’ “” “%jeet%”; (name contain substring ‘jeet’)

Order by clause:

Order by ordering_func,(default ascending , for descending “desc”)

Select R_no,name from <table_name> where marks>75 order by name/name desc;

Delete(for deleting rows):

Delete from <table_name> where name=”Ram”;

Aggregate func:

Select max(score) as “total_marks” from <table_name>;

‘’ min( ) ‘’

‘’ Sum( ) ‘’

‘’ avg( ) ‘’

Group by:

Select name,dept_no,count(*) from <table_name> group by dept_no,

Select name,dept having sum( ) between 80 and 90;

Table Join:

Select *from <table1>,<table2>; (cartesian product)

Select patient_no , Dname from <table1>,<table2> where p_no=d_no;

We've Moved!

2021-07-14T02:45:00.003+05:30

Skillocity is now moving from proskillocity.blogspot.com to skillocity.in ! A new website, a new mission, a new purpose with the same team! Come visit us and enjoy the new look! We're waiting...!

Mesothelioma Disease Detector

2021-06-29T20:25:00.005+05:30

I recently found another disease detection dataset on the UC Irvine Machine Learning Repository and decided to have a go at this one. However this dataset is not used that often for disease detection as I have never heard of it before and never even came across it before discovering it while reading a research paper. I'd like to shed some light on this dataset and present the Mesothelioma Disease Detector Web App.

This web app detects if you have Mesothelioma or not depending upon various parameters such as Platelet Count, Blood Lactic Dehydrogenise, Alkaline Phosphatise, Total Protein, Albumin, Glucose, Pleural Lactic Dehydrogenise, Pleural Protein, Pleural Albumin, Pleural Glucose and C-reactive Protein. I'll explain more about these parameters later on in the article. This web app works on the Random Forest Classifier algorithm and is majorly coded in python. Mesothelioma is a type of cancer that occurs in the thin layer of tissue that covers the majority of our internal organs (mesothelium). Mesothelioma is an aggressive and deadly form of cancer.

The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. We trained the dataset using 20% of it as our test size. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with patients having mesothelioma and patients not having mesothelioma).

This web app was a learning curve for us and has improved our knowledge about Machine learning significantly. We hope to deploy more apps in the future and share them with you. Feel free to add onto this project and don't hesitate to drop by any suggestions. The link for the Mesothelioma Disease Detector web app is as follows :

https://share.streamlit.io/skillocity/mesothelioma-/main/app.py

About the dataset: Malignant mesotheliomas (MM) are very aggressive tumors of the pleura. These tumors are connected to asbestos exposure,

However it may also be related to previous simian virus 40 (SV40) infection and quite possible for genetic predisposition.
Molecular mechanisms can also be implicated in the development of mesothelioma.
Rural living is associated with the development of mesothelioma. Soil mixtures containing asbestos, known as
â€˜white-soilâ€™ or â€˜corakâ€™ can be found in Anatolia, Turkey and â€˜Lutoâ€™ in Greece.
Mesotheliomaâ€™s disease data set were prepared at Dicle University Faculty of Medicine in Turkey.
Three hundred and twenty-four Mesothelioma patient data. In the dataset, all samples have 34 features.

Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have Mesothelioma or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

Mesothelioma Awareness Day is Sept. 26. On this day, patients, family members, doctors and the mesothelioma community raise awareness of the rare cancer to help find a cure. Supporters wear blue and may wear mesothelioma awareness wristbands or ribbons.t was established in 2004 by the Mesothelioma Applied Research Foundation. It wasn’t until 2010 that Congress first declared September 26 as National Mesothelioma Awareness Day. On that note, lets raise awareness for Mesothelioma and show our support for Mesothelioma awareness and help many patients around the world.

Hepatic Disease Detector

2021-06-27T00:18:00.003+05:30

My fascination for machine learning apparently never seems to fade away and here I am with another app related to disease detection in machine learning. I created this and the last web app together after discovering both those datasets and successfully deployed both of them. This app is a hepatic disease detector and detects if you have a liver disease using machine learning.

This web app predicts if you have Hepatitis, Fibrosis, Cirrhosis or no hepatic disease based upon a number of parameters such as age, Albumin, Alkaline Phosphate, Alanine Aminotransferase, Aspartate Aminotransferase, Bilirubin, Serum Cholinesterase, Cholestrol, Creatinine, Gamma-Glutamyl Transferase and Prothrombin. I will explain the meaning of these terms as you read through the article. This web app works on the random forest classifier algorithm and is coded majorly in python.

The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. The sidebar sliders help in changing the values of the parameters for determination of the result. The graphs compare the values of the patient with others ( both with hepatic disease and non-hepatic disease patients). It also provides the accuracy of the result which is greater than 90%.

A value of 0 on the graph indicates that the patient has no hepatic diseases, a value of 1 indicates that he/she has hepatitis, a value of 2 indicates that he/she has fibrosis and a value of 3 indicates that he/she has cirrhosis. This web app was a learning curve for us and has improved our knowledge about Machine learning significantly. We hope to deploy more apps in the future and share them with you. Feel free to add onto this project and don't hesitate to drop by any suggestions. The link for the diabetes detector web app is as follows : https://share.streamlit.io/braxtonova/hepatitis/main/app.py

About this dataset: This dataset was donated by G.Gong of Carnegie Mellon University via Bojan Cestnik of Jozef Stefan Institute, Slovenia. The parameters include Albumin (a protein made by the liver which keeps keep fluid in the bloodstream so it doesn't leak into other tissues), Alkaline Phosphate (an enzyme in a person's blood that helps break down proteins), Alanine Aminotransferase (an enzyme found mostly in the cells of the liver and kidney. High levels of this enzyme may cause liver damage), Aspartate Aminotransferase (an enzyme mostly found in the heart and liver. High levels of this enzyme indicate that the liver / muscle cells might be injured), Bilirubin (yellowish pigment that is made during the normal breakdown of red blood cells. Bilirubin passes through the liver and is eventually excreted out of the body. Higher than normal levels of bilirubin may indicate different types of liver or bile duct problems.), Serum Cholinesterase (a blood test that looks at levels of 2 substances that help the nervous system work properly. They are called acetylcholinesterase and pseudocholinesterase. Your nerves need these substances to send signals.), Cholestrol (High cholesterol can limit blood flow, increasing the risk of a heart attack or stroke), Creatinine ( the waste product of creatine, which the muscles use to make energy.High levels in the blood might indicate that the kidneys are not working correctly.), Gamma-Glutamyl Transferase ( a transferase that catalyzes the transfer of gamma-glutamyl functional groups from molecules such as glutathione to an acceptor that may be an amino acid, a peptide or water) and Prothrombin (a protein made by the liver. It is one of several substances known as clotting (coagulation) factors).

Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have breast cancer or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

World Hepatitis Day is commemorated each year on 28 July to enhance awareness of viral hepatitis, an inflammation of the liver that causes a range of health problems, including liver cancer.There are five main strains of the hepatitis virus – A, B, C, D and E. Together, hepatitis B and C are the most common cause of deaths, with 1.3 million lives lost each year. Amid the COVID-19 pandemic, viral hepatitis continues to claim thousands of lives every day.This year’s theme is “Hepatitis-free future,” with a strong focus on preventing hepatitis B among mothers and newborns.

World Hepatitis Day is one of 11 official global public health campaigns marked by the World Health Organization (WHO). The inaugural International Hepatitis C Awareness day, coordinated by various European and Middle Eastern Patient Groups and Baby Muriel, took place on October 1, 2004. However, many patient groups continued to mark 'hepatitis day' on disparate dates. For this reason in 2008, the World Hepatitis Alliance in collaboration with patient groups declared May 19 the first global World Hepatitis Day.

Following the adoption of a resolution during the 63rd World Health Assembly in May 2010, World Hepatitis Day was given global endorsement as the primary focus for national and international awareness-raising efforts and the date was changed to July 28 (in honour of Nobel Laureate Baruch Samuel Blumberg, discoverer of the hepatitis B virus, who celebrates his birthday on that date). The resolution resolves that "28 July shall be designated as World Hepatitis Day in order to provide an opportunity for education and greater understanding of viral hepatitis as a global public health problem, and to stimulate the strengthening of preventive and control measures of this disease in Member States."

The month of May is designated as Hepatitis Awareness Month in the United States, and May 19th is Hepatitis Testing Day. On that note, lets raise awareness for hepatic health and increase awareness about hepatic diseases.

Breast Cancer Detection Web App

2021-06-27T00:16:00.005+05:30

Among the many applications of machine learning, one is of particular interest to me. The use of disease detection in machine learning has the potential to help a large number of people in the world and the advent of machine learning and computer vision in the past few years have definitely transformed the fields of medicine, finance, biotechnology and more. The use of disease detection methods using machine learning and computer vision has a number of applications in the medical sector and its use is only expected to grow exponentially as we develop better methods and models. he value of machine learning in healthcare is its ability to process huge datasets beyond the scope of human capability, and then reliably convert analysis of that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes, lower costs of care, and increased patient satisfaction.

Many leading tech companies and universities have been doing research on the use of AI in the medical sector. For example, Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a deep learning algorithm to identify skin cancer. Such revolutionary and pioneering research motivates enthusiasts of machine learning and computer vision like me to study more and more about these practices and the methods used to develop them.

I am here with an example of a disease detection app which detects if you have breast cancer based upon a number of features such as radius, age, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry and fractal dimension. I will explain more about these parameters shortly. This web app is based on machine learning and uses the Random Forest Classifier Classification Algorithm. The app is coded majorly in python.

We have deployed the app using Streamlit. It is an open source framework that allows data science teams to deploy web apps fairly easily. It's one of the best hosting services I've used and it's great for quick and easy deployment of web apps. The app is coded in python. The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. The graphs compare the values of the patient with others ( both with cancerous and non-cancerous patients). It also provides the accuracy of the result which ranges from around 90-95%.

A value of 0 on the graphs represents a benign i.e. non-cancerous tumor and a value of 1 represents a malignant i.e. a cancerous tumor. This web app was a learning curve for us and has improved our knowledge about Machine learning significantly. We hope to deploy more apps in the future and share them with you. Feel free to add onto this project and don't hesitate to drop by any suggestions. The link for the Breast Cancer Detection web app is as follows : https://share.streamlit.io/braxtonova/cancer/main/app.py

About the dataset: The dataset used is the Wisconsin Breast Cancer dataset created by researchers at the University of Wisconsin. It consists of the following parameters: radius (mean of distances from center to points on the perimeter), texture (standard deviation of gray-scale values), perimeter, area, smoothness (local variation in radius lengths), compactness (perimeter^2 / area - 1.0), concavity (severity of concave portions of the contour), concave points (number of concave portions of the contour), symmetry and fractal dimension ("coastline approximation" - 1). For those of you who are not familiar with the terms in statistics, my article about Exploratory Data Analysis can be a good starting point.

I will provide a brief idea about contours and the coastline paradox (one of my favorite mathematical paradoxes) in this article. In layman terms, an outline representing or bounding the shape or form of something is called a contour. However, we state this is calculus and linear algebra as: a line joining points on a diagram at which some property has the same value. A contour line (also isoline, isopleth, or isarithm) of a function of two variables is a curve along which the function has a constant value, so that the curve joins points of equal value. It is a plane section of the three-dimensional graph of the function f(x, y) parallel to the (x, y)-plane.Contour lines are curved, straight or a mixture of both lines on a map describing the intersection of a real or hypothetical surface with one or more horizontal planes. I'd also like to mention about contour integrals, which is a method of evaluating certain integrals along paths in the complex plains. Contour integration is also closely related to complex analysis, application of the residue theorem, Cauchy Integral formula etc.

I could talk about these all day but lets move onto the coastline paradox. The coastline paradox revolves around the seemingly simple notion that the coastline of a landmass does not have a well defined length. This results from the fractal curve-like properties of coastlines, i.e., the fact that a coastline typically has a fractal dimension (which in fact makes the notion of length inapplicable). The first recorded observation of this phenomenon was by Lewis Fry Richardson and it was expanded upon by Benoit Mandelbrot. The measured length of the coastline depends on the method used to measure it and the degree of cartographic generalization.

Breast Cancer Awareness Month, also referred to in the United States as National Breast Cancer Awareness Month, is an annual international health campaign organized by major breast cancer charities every October to increase awareness of the disease and to raise funds for research into its cause, prevention, diagnosis, treatment and cure. The National Breast Cancer Awareness month was founded in 1985 as a partnership between the American Cancer Society and the pharmaceutical divisions of Imperial Chemical Industry (now a part of Astrazeneca). The aim of this was to promote mammography as the most effective weapon in the fight against breast cancer. Let's support this initiative and promote the awareness of this disease among the masses.

Note: Some of you'll mentioned that the prediction is always a malignant tumor, that might be the case as the dataset contains relatively a less number of benign data points. Although, if you vary the values of texture and radius you should see the prediction come out as benign for certain cases.

Exploratory Data Analysis using Pandas Profiling

2021-06-26T23:13:00.004+05:30

In this article and web app, we are going to talk about data science in its true meaning- the analysis of big data, its visualization and representation and schematic analysis of that collected data. One of my first projects in Data Science was related to data analysis and specifically, exploratory data analysis using different libraries. I'm going to tell you about one such exploratory data analysis using the Pandas Profiling Report.

If you've taken any courses on statistics (and by that I mean certain advanced courses that touch upon topics like probability distribution, Gaussian functions and normal distributions) you would have come across data analysis at some point or he other. Exploratory data analysis is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. It was first proposed by an American mathematician John Tukey (who was also known for his notable work in Fast Fourier Transform Algorithm, Tukey range test, Tukey lambda distribution etc). However EDA is different from IDA( Initial Data Analysis)

The objectives of EDA are to:

Suggest hypotheses about the causes of observed phenomena

Assess assumptions on which statistical inference will be based

Support the selection of appropriate statistical tools and techniques

Provide a basis for further data collection through surveys or experiments

There are many really amazing books related to exploratory data analysis such as the very first 'Exploratory Data Analysis' book written by John Tukey in 1970; 'Exploratory Data Analysis with MATLAB, written by Angel R Martinez, Jeffrey Solka and Wendy L Martinez; 'Exploratory Data Analysis using R' by Ronald K Pearson, Graphical Exploratory Data Analysis' written by A. G. W. Steyn, Rolf Stumpf, and Stephen Henry Charles Du Toit; 'Exploratory Data Analysis in Empirical Research: Proceedings of the 25th Annual Conference of
the Gesellschaft Für Klassifikation E.V., University of Munich, March 14–16, 2001' and many more. Many high quality youtube videos have also been made on data analysis.

Coming onto this app, this app basically takes in a dataset given by the user and performs exploratory data analysis on it using the pandas profiling report and displays the outcome in the form of a large number of statistics such as number of variables, number of observations, total memory size, variable types, interactions, correlations, missing values, provides descriptive statistics including mean, standard deviation, skewness and much more. We have also provided a sample dataset to run the app. It is the Pima Indian Diabetes Dataset (I have an app and article related to that which has already been uploaded).

Pandas profiling is an open source python module and helps us to do quick and efficient EDA . However pandas profiling is not the best choice for large datasets as it takes a significant time to analyze larger datasets. Pandas profiling gives us an in-depth analysis of numerical variables covering quantile and descriptive statistics. It displays quartile values which measure the distribution of the ordered values in the dataset above and below the median, shows the interquartile range, standard deviation, coefficient of variation, mean absolute deviation and skewness.

For those of you who are not familiar with the statistical terms, here are a few definitions. A quartile is a type of quantile which divides the number of data points into four parts, or quarters, of more-or-less equal size. The interquartile range, also called the midspread, middle 50%, or H‑spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles, he standard deviation is a measure of the amount of variation or dispersion of a set of values (A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range.), The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The coefficient of variation (CV) is a statistical measure of the dispersion of data points in a data series around the mean. The coefficient of variation represents the ratio of the standard deviation to the mean.

Link of the webapp: https://share.streamlit.io/skillcode-ml/eda2/main/app.py

Financial Stock Price Web App

2021-05-29T11:35:00.008+05:30

Data science is spread across our lives and we are surrounded by its applications. We unknowingly use the vast number of applications of data science and machine learning such as recommender systems, house price prediction, data monitoring and data analysis etc. We are in fact surrounded by data and probably have played around with it many a times (especially if you're a math loving person). So here's another web app in yet another vital application of data science.

This is a financial stock price web app. It shows the closing financial stock price values for S and P 500 companies. S and P 500 companies are 500 of the largest companies listed on stock exchanges in the US. The S and P 500 stock market index comprises of 505 common stocks issued by 500 large cap companies. The stocks of these companies trade either on the NYSE (New York Stock Exchange) or NASDAQ (National Association of Securities Dealers Automated Quotations). NYSE is an auction market whereas NASDAQ is a dealer's market. Some of the S and P 500 companies included in the dataset are Alphabet, Apple, Amazon, Facebook, Microsoft etc.

This web app displays information about the respective user input company (from the list of S and P 500 companies) and provides its stock chart along with other company share statistics. It displays information of the company such as its GICS sector, GICS sub-industry, headquarters location, CIK, etc. Users can analyze data over the past multiple years of the respective company.

Simple moving averages have also been provided so that users can compare the present value with the average value of stocks in a particular range. This helps to determine whether an asset price will continue or if it will reverse a bull or bear trend. Historical company shares along with company share data statistics have also been provided.

Yahoo Finance and Yahoo Finance API was used to obtain the data. Users can also move their cursor over the stock charts to get the exact date and value of the stock price. Yahoo finance provides financial data, stock quotes, press releases etc. It alsooffers other tools for personal finance management.

Added feature: I added time series forecasting using fbprophet. Many people are unable to pip install that. This might be due to some reasons: fbprophet requires a C++ compiler, so if you're operating on Windows, better use anaconda instead of VScode. Fbprophet also requires pystan so make sure that is working and check for the compatibility of those two versions. For any other build issues, contact us or drop a comment. You can also check out Stack Overflow for additional errors during installation of fbprophet: https://stackoverflow.com/questions/56701359/running-setup-py-install-for-fbprophet-error. Other solutions might also be available on Github.

Coming onto the update, yes I've finally added time series forecasting for the stocks and now the app can predict the opening and closing stock price values of companies over a period of 1 to 15 years. Time series data using rangeslider can also be seen to find out historical opening and closing stock values. The forecast plot and components have also been provided. Stock values and predictions can also be seen on a weekly basis apart from their yearly values and trends.

This web app helped me to improve my experience in Machine Learning and definitely helped in my future projects. Feel free to add onto this project and don't hesitate to drop by any suggestions. Hope you enjoy the app!

Link of the app: https://share.streamlit.io/braxtonova/stockpred/main/app.py

Penguin Classification and Prediction Web App

2021-05-29T10:12:00.009+05:30

After a friend told me about the Streamlit library, I instantly started working on multiple data science related projects which not only increased my abilities in data science, but made me more adept with the use of programming languages such as Python, MATLAB and HTML. At the time of writing this article, users can only deploy 3 web apps on Streamlit from a particular account. But, if you request them you can potentially also deploy more web apps.

So coming onto this project, it's similar to the Iris Classification and Prediction Web App and this dataset is used as an alternative to the classic Iris dataset given by R.A. Fisher. It's a penguin classification and prediction web app. It successfully classifies and predicts the species of Palmer penguin based upon a number of user input features such as bill length, bill depth, body mass, flipper length etc. The prediction is also based upon the gender of the penguin and the island group (Bisoce, Dream and Torgersen) where it is located.

Just like most of my other web apps, this has a simple design and structure to improve UI/UX for user convenience. The sidebar sliders help in changing the values of the parameters for determination of the result. It also displays the prediction probability along with the predicted output of the Palmer penguin species.

This web app also displays the image of the predicted species. We have 3 different images for all three species which are displayed depending upon the predicted species based upon user input of parameter values.

Link of the app: https://share.streamlit.io/skillocity/penguin/main/prediction.py

About the dataset: This dataset was created by Dr. Kristen Gorman and members of the Palmer Station, Antarctica (LTER). Palmer is one of the three US Antarctic Stations governed by the Antarctic Treaty of 1959. The Palmer Station is an interdisciplinary polar marine research program established in 1990.

The dataset was uploaded by Allison Horst and it is available by CC-0 license in accordance with the Palmer Station LTER Data Policy and the LTER Data Access Policy for Type 1 data. The dataset contains data for 344 penguins. There are 3 diffrent species of penguins in this dataset, collected from 3 islands in the Palmer Archipelago, Antarctica.

Heart Disease Detector Web App

2021-05-19T22:42:00.011+05:30

Yes, you heard that right. We are back with more Machine Learning content and another web app (Yup, machine learning is definitely very cool). This app is similar to our first web app i.e. Diabetes Detector. This is another disease detection web app which detects if you have any cardiovascular diseases (CVD) based on certain features such as age, resting blood pressure, cholestrol, maximum heart rate achieved, ST depression induced by exercise and number of major vessels coloured by flouroscopy. ST depression refers to a finding on an electrocardiogram, wherein the trace in the ST segment is abnormally low below the baseline. Fluoroscopy is an imaging technique that uses X-rays to obtain real-time moving images of the interior of an object. This app is another example of the use of machine learning and data science in the field of medicine which I find very innovative.

This web app is based on machine learning and uses the Random Forest Classifier algorithm. The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with patients having heart diseases and not having heart diseases). It also provides the accuracy of the result. This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have a heart disease or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

This web app helped build upon my experience and comfort over machine learning and its vast applications. It is one of our many projects in the field of computer science and specifically data science. We hope to deploy more articles in the future and share them with you. Please feel free to add on and contribute to the project and don't hesitate to drop any suggestions. The link for the web app is: https://share.streamlit.io/pranav-coder2005/heartdisease/main/app.py

Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have any cardiovascular disease or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

The awareness month for heart health in the United States is February. National wear red day is observed on the first Friday of February of each year to increase awareness about heart diseases. World Heart Federation, World Health Organization and many other such organizations run campaigns dedicated to the prevention, diagnosis and control of cardiovascular diseases.The World Heart Federation is a nongovernmental organization based in Geneva, Switzerland, formed in 1972 In 1978 the International Society of Cardiology merged with the International Cardiology Federation to form the International Society and Federation of Cardiology. The federation hosts the World Congress of Cardiology. World Heart Day" was founded in 2000 to inform people around the globe that heart disease and stroke are the world’s leading causes of death. World Heart Day is celebrated every year on 29 September. On that note, let's increase cardiovascular health awareness and make the world much healthier.

Skillocity's Art Gallery

2021-05-16T00:23:00.006+05:30

Members of Skillocity are unique and talented in their own way. One of our team member, Roshan Kumar, has worked on a number of art and design projects and he's made multiple pieces of art which are undeniably brilliant. He has also participated in and won multiple art contests here in India. I have personally been following his art since the past few years and he is very impressive at it. Following are some of his drawings. We'll keep updating this page for as long as he provides us with more of his projects.

Created by: Roshan Kumar, Creator at Skillocity

Iris Classification and Prediction Web App

2021-05-11T15:18:00.018+05:30

One of my primary interests in the field of computer science is Machine Learning. Machine Learning has no bounds and here, the sky is not the limit. It has applications in many important sectors such as biotechnology, medicine, aviation etc. This is my second web app on Machine Learning and I'm working hard to successfully deploy many more Machine Learning apps. All of these projects are free to use and I welcome any suggestions and/or additions to the project.

One of the main reasons why I love Machine Learning is that it perfectly incorporates the beauty of mathematics and computer science to successfully integrate and develop a particular application. As I mentioned in the previous article, Machine Learning not only requires a good understanding of coding, it also requires in depth knowledge in Maths. So for the people out there who question the use of Maths in Computer Science, this is a classic example as to why that's not always the case. Having a good knowledge about calculus and Linear Algebra definitely helps in Data Science.

After the positive response of our first web app, Diabetes Detector, I decided to explore Machine Learning in greater depth and decided to deploy many more apps related to it in the future. So if you're interested in Machine Learning and data science, hey we're on the same page. Although I'm still a junior in high school so most of you might be more experienced than me.

So coming onto this app, this is an Iris Classification and Prediction App. It classifies and successfully predicts the type of Iris flower based upon a number of user input features such as Sepal Length, Petal Length, Sepal Width and Petal Width. It uses the Random Forest Classifier algorithm.

The web app uses a simple design and structure. I tried to make it as simple as possible so that it could be deployed easy. The sidebar sliders help in changing the values of the parameters for determination of the result. It also displays class labels, their corresponding index number and the prediction probability along with the predicted species of the Iris Flower.

Another interesting feature about this web app is the presence of the image of the predicted species. We have 3 different images for all three species which are displayed depending upon the predicted species based upon user input of parameter values.

This web app helped me to improve my experience in Machine Learning and definately helped in my future projects. Feel free to add onto this project and don't hesitate to drop by any suggestions. Hope you enjoy the app!

Link of the app: https://share.streamlit.io/pranav-coder2005/iris_flower_predictor/main/app.py

About the dataset : The Iris dataset was used in R.A. Fisher's paper written in 1936, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository. It is an example of linear discriminant analysis. This dataset is also knows as Anderson's Iris Dataset because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species.

Based on Fisher's linear discriminant model, this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines.

It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other.

Iris is a genus of 260–300 species of flowering plants with showy flowers. Its name is derived from the Greek word for Rainbow (yes, we are quite interested in languages and cultures) which in coincidentally also the name for the Greek goddess of rainbow-Iris. Apart from being the scientific name, Iris is also widely used as a common name for all Iris species.

Dataset License: Open Data Commons Public Domain Dedication and License (PDDL)

Projects of Team Skillocity

2021-04-17T12:45:00.006+05:30

The developers of our team, Pranav Sawant and Anshuman, have undertaken many projects especially in the field of CS and Physics. These projects highly boosted our academic repertoire and helped us to gain a lot of knowledge and experience. By working on these projects we not only got better at the respective subjects but also had a lot of fun! We hope to undertake more projects in the future and further enhance our knowledge and understanding in the educational field. Here are the highlights of some of our projects :

1) Skillocity : We started Team Skillocity in January 2021 with an aim to share our knowledge with other like minded readers. We are fortunate to have amassed nearly 10,000 viewers in just a few months and this project is still going strong. All of our projects are connected to Team Skillocity and this was our first project as a team. We will continue to share our knowledge with more readers and hope to post relevant and interesting content.

2) PWA (Powerful WebApp) deployment : This was a mini project undertaken by our developers and now our readers can now add a shortcut to our blog which runs through chrome, on their device. Usually, if you add a shortcut for a website, it would just add a shortcut to a single webpage you added a shortcut to. But in our case, after extensive coding, we have enabled our user the ability to add a shortcut to the entire website to make it easier to access, without the need of opening Chrome. This makes it feel just like an app!

With this feature, our users can now enjoy an app like experience on their laptop or phone without having to worry about our "app's" memory usage, since it uses almost none.

That's it! Check your home screen and you should find an icon with our logo. You can now access our blog just by clicking on the icon without opening chrome and typing out our website address.

More about this project : https://proskillocity.blogspot.com/2021/02/new-feature-announcement.html

3) SkillChem : SkillChem was one of the toughest projects undertaken by our developers and it required extensive programming in Android Studio to build our first native app. The app is now ready and can be accessed on the Amazon App Store and also from a google drive that we have shared.

After about 2 months of debugging and fixing errors and finding the right algorithm, we were finally able to deploy the app for public use.

More about this project : https://proskillocity.blogspot.com/2021/04/official-launch-of-skillchem.html

4) Diabetes Detector WebApp : This web app was created by our developers and is a classic application of machine learning in the medical field. Machine Learning has no limits and it can be deployed in multiple sectors and can greatly help in the lives of common people just like you and me. This web app is hosted on streamlit. The dataset for training the data was provided to us by Kaggle. The machine learning algorithm used is Random Forest Classifier.

More about this project : https://proskillocity.blogspot.com/2021/04/official-launch-of-our-first-web-app.html

5) Special Research on Habitability of planets : Since our developers (especially Anshuman) are interested in a number of subjects and not just CS, we did a small research on the habitability of planets. It can be used to qualitatively compare and help in determining the habitability potential of a planet. A number of parameters such as Spectral Class, Mass, Radius and Temperature of the planet were taken into account and an index was formulated. We called this index as the Narlikar Index, named after the Indian astrophysicist Jayant Narlikar.

More about this project : https://proskillocity.blogspot.com/2021/04/habitability.html

Diabetes Detector Web App

2021-04-16T12:23:00.023+05:30

Machine Learning - a concept that most people struggle to comprehend and most of them who do comprehend would have many different perceptions about it. I myself have been working on Machine Learning and its algorithms for about 6 months and after some rigorous training in Machine Learning and its subsidiary subjects such as Statistics and Linear Algebra, I've finally started to get a hold of it (even though a lot of work is yet to be done).

We launched our first native app -SkillChem- yesterday and today here we are with our first web app. This app is a diabetes detector and detects whether a particular patient has diabetes depending upon various parameters such as glucose, insulin, diabetes pedigree function etc. According to the creators of the dataset, the diabetes pedigree function scores likelihood of diabetes based upon family history of the disease. This web app is based on Machine Learning and works on the Random Forest Classifier algorithm.

The web app uses interactive visual and graphical interpretations to display the outcome and compare the input parameters given by the user. We trained the dataset using 20% of it as our test size. The sidebar sliders help in changing the values of the parametres for determination of the result. The graphs compare the values of the patient with others ( both with diabetic and non-diabetic patients). It also provides the accuracy of the result.

For those who want to learn about Machine Learning, its applications and algorithms, Professor Andrew Ng has a great course for it on coursera. He explains about the multiple machine learning algorithms and applications in depth. It's a good course for people of all ages looking to expand their knowledge in Machine Learning. The course is completely free to study. The link for the course is : https://www.coursera.org/learn/machine-learning

https://share.streamlit.io/pranav-coder2005/diabetes_detector/main/diabetes_detector_app.py

About the dataset : This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

The datasets consists of several medical predictor variables and one target variable, Outcome. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on.

Disclaimer: This is just a learning project based on one particular dataset so please do not depend on it to actually know if you have diabetes or not. It might still be a false positive or false negative. A doctor is still the best fit for the determination of such diseases.

November is the diabetes awareness month and world diabetes day is observed on 14 November of each year. Diabetes awareness groups spread awareness about the disease throughout the month through several programs, charitable events, campaigns etc. The purpose of it is to spread awareness about this disease all around the world and to fight it together. World Diabetes Day was created in 1991 by International Diabetes Federation and the World Health Organization in response to growing concerns about the ever increasing health threat posed by diabetes. 'The Nurse and Diabetes" was the theme was World Diabetes Day in 2020. The campaign aims to raise awareness around the crucial role that nurses play in supporting people living with diabetes. The blue circle is the universal symbol for diabetes. It was launched in 2006 to give diabetes a common identity. The symbol aims to support all existing efforts to raise awareness about diabetes. Let's show our support for diabetes awareness and help many patients around the world.

Habitability of Planets Using Narlikar Index : Special Research

2021-04-15T21:06:00.006+05:30

Is our insignificant little planet Earth the only one out there in the universe to be blessed with life? Could we find conscious beings out there in the vast unknown universe? Many such questions and theories exist about extra-terrestrial life , but unfortunately none of them have been proven to be true. So, can we find life on other planets? Well that’s a tough question and since the universe ( or the multiverse ) is so massive, we need to target certain planets in order to find life. That’s exactly the point that we are going to touch in this paper. What is the habitability of planets ? How can we make our eternal quest for life easier ? Which are the planets that presumably have life and how do we find them ? Many such questions would be answered in this paper.

What is habitability, how is it defined and what are the parameters and factors that define it ? The ability of a planet to develop, harbour and maintain an environment suitable to life is termed as planetary habitability. Additionally, environments that do not contain life can also be habitable.

There are many requirements for life such as sunlight, water, minerals etc. Additionally, several criterions must also be met for a planet to be able to host life. Some of these characteristics include mass, radius, spectral class, temperature of the planet, luminosity, biochemicals, composition, orbital properties, chemical reactions etc. Geological, geophysical and astrobiological properties also play a key role in determining the habitability potential of a planet.

The definitions of a habitable planet are based upon our current understanding of the subject and since a lot of research is being done on it, the measures and criterions of habitability can be modified as and when necessary.

The orbital range for water to exist in its liquid form at the surface of the planet is known as the Habitable Zone. These zones are also called Goldilocks Zones. Such zones are ideal for the presence of life. Additionally, habitats on which water is present on the surface is ideal for life.

We propose an index called Narlikar Index, named after the famous Indian Astrophysicist Jayant Narlikar. The Narlikar Index is based upon various parameters on which each of the potential habitable planets can be graded. The Narlikar Index can have values ranging from zero to four. The following table shows the Narlikar Index and its corresponding qualitative value of habitability.

Narlikar Index	Qualitative Value of Habitability

3-4	Highly Habitable
2-3	Habitable
1-2	Unhabitable
0-1	Highly Unhabitable

The objectives of the Narlikar Index are as follows :-

1. 1) To propose a model that will help study potentially habitable planets.

2. 2) To classify astronomical objects more easily.

3. 3)To compare the relative habitability of one planet with respect to the other.

The parameters of the Narlikar Index are as follows :-

1) Spectral Class : It is a method of classification of stars based on spectral analysis of its electromagnetic emission spectra, which gives information about the surface temperature and elementary nuclear reactions within the star. This implies that a habitable star must have a temperature between 3700°C to 6700°C which corresponds to late F or G to mid-K spectral class type.

2) Mass : Planets with less mass would find it difficult to harbour life for multiple reasons. Planets with a low mass would have a lower force of gravity, which would mean a low escape velocity hereby enabling particles to escape from the atmosphere. Planets lacking a thick atmosphere would also lack biochemical substances that are important for life. They would also have very poor insulation capabilities, providing less protection against dangerous objects such as asteroids. Lack of density of the atmosphere would also prevent water from existing in its liquid form.

A planet with a larger mass would also have a larger iron core, making it mineral rich. This iron core would enable an active magnetic field hereby protecting the planet from harmful phenomena such as cosmic radiations. If the planet is not shielded from cosmic radiations and stellar wind, planetary atmosphere would cease to exist. Also, life would be bombarded with ionized particles, making it difficult to survive.

3) Radius : Ideally a planet should have a radius between 0.5R to 2.5R for it to host life. Planets having a smaller radius would eventually end up being geologically dead. They would hence lack important life sustaining materials resulting from plate tectonics, earthquakes, volcanoes etc.

R = Radius of planet / radius of Earth.

4) Temperature of the Planet : The temperature of a planet influences how quickly atoms and molecules move. Low temperature results in slower chemical reactions, effectively slowing down the speed of many necessary life processes. Low temperatures also make liquid water unavailable as water would be found in its solid state. At high temperatures, DNA and RNA start to break up. Hence, we need a suitable temperature for the planet in the range of -25°C to 100°C.

A planet needs to additionally satisfy 4 basic criteria for it to have a high habitability potential :-

1. It needs to be in the habitable zone of its star.

2. It cannot be a rogue planet.

3. It should not be around any kind of neutron star or black hole.

The following tables illustrate the calculations of the Narlikar Index based upon its factors.

A) Radius of Planet

Case 1) Radius of planet ≥ Radius of Earth

Radius of Planet	Narlikar Index for Radius

More than 30R	0
24R to 30R	0.5
18r to 24R	1
15R to 18R	1.5
12R to 15R	2
8R to 12R	2.5
5R to 8R	3
2.5R to 5R	3.5
1R to 2.5R	4

Here R = Radius of Planet / Radius of Earth

Case 2) Radius of Planet < Radius of Earth

Radius of Planet	Narlikar Index for Radius

Less than 0.38R	0
0.38R to 0.40R	0.5
0.40R to 0.41R	1
0.41R to 0.43R	1.5
0.43R to 0.45R	2
0.45R to 0.47R	2.5
0.47R to 0.48R	3
0.48R to 0.50R	3.5
0.50R to 1R	4

Here R = Radius of Planet / Radius of Earth

B) Mass of Planet

Case 1) Mass of Planet ≥ Mass of Earth

Mass of Planet	Narlikar Index for Mass

More than 318M	0
272.72M to 318M	0.5
227.44M to 272.72M	1
182.16M to 227.44M	1.5
136.88M to 182.16M	2
91.6M to 136.88M	2.5
46.32M to 91.6M	3
1.30M to 46.32M	3.5
1M to 1.30M	4

Here M = Mass of Planet / Mass of Earth

Case 2) Mass of Planet < Mass of Earth

Mass of Planet	Narlikar Index for Mass

Less than 0.002M	0
0.002M to 0.06M	0.5
0.06M to 0.10M	1
0.10M to 0.14M	1.5
0.14M to 0.18M	2
0.18M to 0.22M	2.5
0.22M to 0.26M	3
0.26M to 0.30M	3.5
0.30M to 1M	4

Here M = Mass of Planet / Mass of Earth

C) Temperature of the Nearest Star

Case 1) Temperature of the Nearest Star ≥ 5200°C

Temperature of the Nearest Star	Narlikar Index for Star Temperature

More than 50000	0
43810-50000	0.5
37630-43810	1
31440-37630	1.5
25260-31440	2
19100-25260	2.5
12890-19100	3
6700-12890	3.5
5200-6700	4

Note: All temperatures are in °C

Case 2) Temperature of the Nearest Star < 5200°C

Temperature of the Nearest Star	Narlikar Index for Star Temperature

Less than 2600	0
2600-2770	0.5
2770-2840	1
2840-3010	1.5
3010-3190	2
3190-3360	2.5
3360-3530	3
3530-3700	3.5
3700-5200	4

Note: All temperatures are in °C

D) Average Temperature of the Planet

Case 1) Average Temperature of the Planet ≥ 0°C

Average Temperature of Planet	Narlikar Index for Planet Temperature

More than 520	0
460 to 520	0.5
400 to 460	1
340 to 400	1.5
280 to 340	2
220 to 280	2.5
160 to 220	3
100 to 160	3.5
0 to 100	4

Note: All temperatures are in °C

Case 2 : Average Temperature of the Planet < 0°C

Average Temperature of the Planet	Narlikar Index for Planet Temperature

Less than -200	0
-200 to -175	0.5
-175 to -150	1
-150 to -125	1.5
-125 to -100	2
-100 to -75	2.5
-75 to -50	3
-50 to -25	3.5
-25 to 0	4

Note: All values are in °C

Final Narlikar Index ( or simply Narlikar Index ) = ( N₁ + N₂ + N₃ + N₄ ) / 4

Where N₁ = Narlikar Index for Radius

N₂ = Narlikar Index for Mass

N₃ = Narlikar Index for Star Temperature

N₄ = Narlikar Index for Planet Temperature

This index, can in general, be applied to any planet to find its habitability. Let us look at a few planets to get a better understanding of this index.

Narlikar percentage = (Narlikar Index)*25

Note: Whenever while calculating the Narlikar Index, if we get Narlikar Index ≤ 3.0, then we divide it by 2.5 to get the actual Narlikar Index.

1) Earth : Earth is the only astronomical object that has been discovered to have life with conditions being nearly perfect for a variety of life forms. It performs exceptionally well scoring 4 on the Narlikar Index making it a highly habitable planet.

Radius : 1R

Mass : 1M

Temperature of the Sun : 5500°C

Average temperature of Earth : 14°C

Narlikar Index : 4.000

Narlikar Percentage : 100%

2) Kepler-296e : Kepler-296e performs relatively well scoring 3.625 on the Narlikar Index, making it a highly habitable planet orbiting Kepler-296. It is in the Draco constellation.

Radius : 1.750R

Mass : 2.96M

Temperature of Kepler-296 : 3167°C

Average temperature of Kepler-296e : -6°C

Narlikar Index : 3.625

Narlikar Percentage : 90.625%

3) Kepler-11b : Kepler-11b performs relatively poorly scoring 1.15 on the Narlikar Index, making it an unhabitable planet orbiting Kepler-11. It is in the Cygnus constellation.

Radius : 1.83R

Mass : 2.78M

Temperature of Kepler-11 : 5563°C

Average temperature of Kepler-11b : 627°C

Narlikar Index : 1.15

Narlikar Percentage : 28.75%

4) Kepler-20b : Kepler-20b performs relatively poorly scoring 1.15 on the Narlikar Index, making it an unhabitable planet orbiting Kepler-20. It is in the Lyra constellation.

Radius : 1.868R

Mass : 9.7M

Temperature of Kepler-20 : 5193°C
Average temperature of Kepler-20b : 741°C

Narlikar Index : 1.15

Narlikar Percentage : 28.75%

5) Kepler-442b : Kepler-442b performs relatively well on the Narlikar Index scoring 3.750, making it a highly habitable planet orbiting Kepler-442. It is in the Lyra constellation.

Radius : 1.34R

Mass : 2.3M

Temperature of Kepler-442 : 4129°C

Average temperature of Kepler-442b : -40°C

Narlikar Index : 3.75

Narlikar Percentage : 93.75%

6) Gliese 667Cc : Gliese 667Cc performs relatively well on the Narlikar Index scoring 3.625, making it a highly habitable planet orbiting Gliese 667C. Gliese 667Cc is most probably tidally locked to a star. It is in the Scorpius constellation.

Radius : 1.54R

Mass : 3.709M

Temperature of Gliese 667C : 3427°C

Average temperature of Gliese 667Cc : 4.3°C

Narlikar Index : 3.625

Narlikar Percentage : 90.625%

7) Wolf 1061c : Wolf 1061c is also known as WL 1061c. It performs relatively well on the Narlikar Index scoring 3.25, making it a highly habitable planet orbiting Wolf 1061. It is in the Ophiuchus constellation.

Radius : 1.6R

Mass : 4.3M

Temperature of Wolf 1061 : 3069°C

Average temperature of Wolf 1061c : -50°C

Narlikar Index : 3.25

Narlikar Percentage : 81.250%

8) Kepler-20e : Kepler-20e was the first planet found that was having a radius smaller than that of Earth while orbiting a sun like star. It orbits around the star Kepler-20. It performs quite poorly on the Narlikar Index scoring 1.2, making it an unhabitable planet orbiting Kepler-20. It is in the Lyra constellation.

Radius : 0.868R

Mass : 1.03M

Temperature of Kepler-20 : 5193°C

Average temperature of Kepler-20e : 770°C

Narlikar Index : 1.2

Narlikar Percentage : 30%

9) Kapteyn b : Kapteyn b is the oldest known potentially habitable planet. . It performs relatively well on the Narlikar Index scoring 3.125, making it a highly habitable planet orbiting Kapteyn’s star. It is in the Pictor constellation.

Radius : 1.6R

Mass : 4.8M

Temperature of Kapteyn’s star : 3297°C

Average temperature of Kapteyn b : -68°C

Narlikar Index : 3.125

Narlikar Percentage : 78.125%

10) Gliese 832c : Gliese 832c is a tidally locked planet having no plate tectonics. It performs relatively well on the Narlikar Index scoring 3.625, making it a highly habitable planet orbiting Gliese 832. It is in the Grus constellation.

Radius : 1.5R

Mass : 5.4M

Temperature of Gliese 832 : 3347°C

Average temperature of Gliese 832c : -20°C

Narlikar Index : 3.625

Narlikar Percentage : 90.625%

The conclusions of the Narlikar Index are as follows :-

1. A high Narlikar Index is ideal for habitability.

2. We should target planets with a high Narlikar Index.

3. The higher the Narlikar Index, the more similar the planet is with Earth.

Pranav Sawant

Anshuman

Guitar - The Coolest Instrument

2021-04-15T19:23:00.000+05:30

Most guitarists and music lovers (including me) consider guitar to be the coolest instrument. This is true to a certain extent although I enjoy other instruments too such as the Piano. Piano might be the most elegant and royal instrument out there but guitar is arguably the coolest instrument. You can play almost anything you want on the guitar, a unique distinction that only a few instruments have.

Learning guitar is probably a slightly more challenging task. Well after all its a skill and as I said for tennis in my previous article, there is no way to master a skill overnight. One has to be persistent and work hard to get a hold of the skill. Guitar is arguably a tougher instrument to learn than most other instruments and most musicians would agree. The intricacies of guitar and the dynamic play that it brings are definitely a joy to watch and experience but its also that much tougher to play it.

Learning the guitar can be a very tiring and often boring process (especially in the early days), but just like tennis, the joy of being able to play an instrument and to master a skill is invaluable. Guitar brings the spirit of optimism and can light up the most sulky souls and enlighten people. It is truly a joy to play the guitar and watch maestros exhibit their skills.

There are different types of guitars : classical, acoustic, Hawaiian and the electric guitar. Each brings something new with it and brings a lot of variety into music. Skilled musicians can often play multiple instruments and a decent number of guitarists can play more than one type of guitar.

I started to play the guitar when I was 11 years old. I was fortunate enough to learn at the renowned Calcutta School of Music and learnt to play the classical guitar. In Kolkata, I took part in multiple concerts including the Crescendo and other Calcutta School of Music concerts. I used to thoroughly enjoy these and it was a great opportunity to relax and enjoy the beautiful music being played.

I also gave 3 Trinity exams and received distinction in grades initial, 1 and 2 by the Trinity School of Music, London. Interacting with other guitar players and learning about the world class guitarists not only boosted my interest in guitar but also motivated me to practice more and get better.

While in Kolkata, I also learnt to play the Acoustic guitar and I enjoy playing both of these to this day. Guitar has helped me immensely even in my academic life. I can relax and rejuvenate in a matter of minutes as and when I want. Whenever I have to study a boring subject, I usually include it with playing the guitar so that I don't get bored while also managing to complete my tasks.

I have been playing the guitar since almost 5 years and now I can play a large number of songs and classical pieces. It does take hard work, but once the skill is developed it stays for a lifetime (unless you stop practicing for years). I can now play just about any song I want after hearing it a few times (yes, even gratitude by Amin Toofani if you were wondering). My mom also used to play the guitar, so that was a natural inspiration for me to get better at guitar.

Guitar is a tough skill to master, there's absolutely no doubt about it. But it's probably my most cherished skill that I can count upon anytime. In the early days I used to struggle for days on end trying to get a hold of fingerstyle and flamenco on the classical guitar. I used to also struggle a lot with the strumming patterns on the acoustic guitar. I couldn't get an upstrum to save my life! And even after I started to get an idea of fingerstyle, strumming, flamenco and the various dynamics and theory of music, I struggled again! I could barely manage to sing a line while strumming. The words just couldn't come out of my mouth.

But I worked on my guitar skills quite a lot, especially during the pandemic to lighten up the mood, and eventually got to a level where most can't. I don't want to discourage anyone by writing this article and the previous one, in fact if you have a chance go for it and grab the opportunity to learn with both hands. But don't give up, never give up cause if there's something guitar and tennis have taught me is to never give up, keep working hard and one day it's going to pay off.

There are a lot of musicians that I enjoy. Fabio Lima, Sungha Jung, Igor Presenyakov and Acoustic Trench are some of the guitarists that I admire. I also love many other musicians such as One Direction, One Republic, Bon Jovi, Imagine Dragons, Linkin Park and Avril Lavigne. There are quite a few highly under rated musicians that are unbelievably talented such as Sean Koch, Greyson Chance and James TW.

There is so much more to the world of guitar and music and the sky is definitely not the limit in this field. The creativity and talent of the musicians of today are beyond what words can describe. Music has helped so many people get through their tough times and it has brought a smile to millions of people on the planet. On that note, let's try to learn an instrument and keep the spirit going.

The Royal Sport named Tennis

2021-04-15T17:41:00.005+05:30

Many consider tennis to be the most royal and elegant sport. Well for some people like me it is definitely true. Tennis players and fans would agree that tennis brings a high degree of joy into their lives. We have seen so many cases of devoted an die hard fans in tennis. For a game that has been on the professional stage for about 150 years, tennis has undergone a significant amount of change. With better technology, the game has truly evolved and made the game more enjoyable.

But I'm not here to discuss about tennis in general, I'm here to tell you about how I learnt to play tennis and how you can too! Yes, you, the person reading this article at this very moment. I started playing tennis at the age of 8 years old. Yup, a late bloomer. I started playing in Pune at the Poona Club Tennis Courts. My first coach was a person named Rahul. We trained with each other for about 3 years and my game fairly improved during my time in Pune as I started to play with better players. Towards the end of those 3 years, I had a decent foundation in tennis and I could play with most people. But, my game significantly lacked in certain areas. I had very poor technique, my strokes were rusty and my footwork was terrible.

After those 3 years in Pune, I attended a one month Tennis camp in Bangalore. It was really fun and I spent quite some time on court. I improved my mental toughness and this helped me a lot later on,l not only while playing at the National level but also in my day to day life. After the one month camp, I headed off to Kolkata.

Many consider Kolkata to be the home of Indian sports. After my 2 year stay in Kolkata, I can totally agree. My time in Kolkata drastically changed my game. From a raw rusty player I became a well developed player who could play with literally anybody and take out most. In Kolkata, I trained with India's top coaches including the legendary Akhtar Ali. Akhtar Ali, former Davis Cup player and Captain, helped me to significantly improve various parts of my game. I developed a deadly forehand with a serve that most couldn't read. On my day, I could take out almost anybody with the dangerous forehand+serve combination. I also worked on my backhand a lot and I was quite comfortable with the shot (although it was still a weakness which became my strength later on).

I used to train for about 2-3 hours regularly at South Club and worked hard to improve my game. Kolkata was definitely the turning point for me and I learnt a lot from this city in general. I played at a lot of other clubs too such as Ordnance Club, which again helped me a lot with their quality of training and a good player group. I played a number of tournaments in Kolkata, most of them being at the National level. I even won a trophy (Kolkata has a very high level of competition when it comes to Tennis) and came agonizingly close on multiple occasions. I even qualifies for the Road to Wimbledon qualifiers, which was a pretty big achievement for me.

The level of tennis in Kolkata was of a really high standard which made it even more enjoyable to play with the best. The rigorous training and hard work eventually paid off as my game drastically improved. I also played a number of really fun parent-child doubles matches in Kolkata. After 2 years I eventually moved to Lucknow.

In Lucknow, I initially trained at the La Martiniere Tennis Academy for about a year. After which I trained with many good players and mostly played for recreation and the joy of the beautiful sport. Then came 2020 and along with it the pandemic.I had to halt tennis due to lockdown for a few months but after it resumed I started playing with my mom. My mom is also a former nationals player and still can take on a good player.

I trained with her for almost a year and to our surprise both of us improved a lot. I worked hard on my backhand and its now more lethal than ever. I have a single handed backhand which makes it tough against lefties but us very deadly. I have a deceptive slice, good strokes and I can hit winners from just about anywhere on the court.

I also improved my serve a lot during this one year, making it one of my most deadly shots. I started to serve much more faster and more accurately developed a strong kick serve which often bails me out of tough situations. My mom's game also improved quite a bit after training for a year. Her fitness and backhand are now much better than before. Therefore this one year was highly beneficial for the both of us.

Alright alright enough about me, let's talk about how you can improve your own game. Many consider tennis to be a really tough sport and that it takes years to master it. I actually agree with that but it definitely pays off once you've developed the skill. There's no shortcut to learn a sport overnight and you will definitely have to work hard and put in a lot of hours on the court with balls all around you.

A lot of coaches stress upon technique which is alright to a certain extent but you shouldn't give away your natural game. I made that mistake and suffered due to it. Yes, there's no doubt about it that technique is very important but we have to remember that the player who plays better wins and not the player with the better technique. Many professional players have an unconventional technique such as Medvedev. Even Rafa's game when he returns the serve with a looped ball is not a very common thing to do, but it pays off and it pays off really well! He's not won 20 slams for nothing.

Working on your weaknesses is again a must. A lot of players procastinate and are hesitant to work on their weaknesses, but we've gotta face it once and for all. Watch youtube videos, see grand slam mathces, work with your coaches but don't shy away from your weaknesses. A small negligible weakness against good players can also cost you the match. I've literally seen people with killer forehands but they just don't have a backhand! They will just refuse to hit a backhand and will get angry at you if you ever hit a ball to their backhand.

Fitness is another thing that is very important especially in a sport such as tennis where matches can even go upto 5 hours. If you're not fit then there's no way that you're going to come even close to competing with good players. Most professional players and coaches would agree that 70% of tennis is nothing but fitness and fitness plays a massive role in your game. Many players know this but don't pay much heed to it since they assume that fitness is over rated and the strokes are the real thing. A good level of fitness also helps is preventing injuries and that's a major reason why players such as Federer, Djokovic and Nadal have been able to sustain their longevity.

And lastly, have fun! This is the most important aspect and if you're not enjoying the game, you wouldn't like to put in that amount of hard work. Tennis has taught me a number of invaluable lessons which have helped me to a great extent in my academic life. So just enjoy the game, learn from your setbacks and live the royal elegant sport of tennis as you make your way into the tennis community.

Official Launch of our first Native App - SkillChem

2021-04-15T15:22:00.032+05:30

Hello readers,

We apologise for not posting anything significant lately. We were actually working on multiple CS based projects including the programming intensive Android App Development. Yes!!! You heard that right, we are finally here with our very first app. The Skillocity blogsite is just one part of a larger framework of Team Skillocity, under which we intend to produce more projects. These projects have been a learning curve for us and we hope that the tools we develop would help a variety of people.

So finally, after about 2 months of puzzling over on Android Studio, we are finally here with our very first app. We've named it SkillChem. SkillChem is majorly a chemical equation balancer and it can balance a wide variety of complex chemical equations including Redox equations. The app also contains the Periodic Table and the Reactivity Series for easy reference. The following images will provide a glimpse of our app.

We have built our app using Java, HTML, and JavaScipt in Android Studio. We faced a number of errors while making the app but this made our repertoire much stronger. We are now more experienced in CS with a good hold of Android App Development and we will hopefully be make to make more such projects in the future (could be limited due to time constraints).

The chemical equation balancer algorithm is based on a slightly modifed version of the Gauss-Jordan elimination method to give us integral solutions. For more info about the App development you could either install the app using the link given at the end of the article.

We hope that SkillChem satisfies your needs to solving chemical equations cause frankly it can get quite frustrating to balance long redox reactions. We ourselves would get quite bored doing it and therefore you have SkillChem which will solve it in an instant. Phew, CS knowledge does help once in a while.

After the successful deployment of our PWA(Powerful Webapp) this is the next big thing for Team Skillocity and hopefully one of many. We highly appreciate your support and look forward to doing many more cool things in the future!

The links for the app are as follows :

1) Amazon App Store (you'd have to download Amazon's appstore for this, and quite unstable)

2) We've also uploaded it on a Google Drive, you can access it from there too (Recommended)

After downloading the apk file for the app from either of the options above when you proceed to open the app, it might show a Play Protect warning with a message similar to "Google Play Store does not recognize the develop of the app". Please don't freak out, it's just because we haven't uploaded the app on Play Store. To upload the app on play store, we need to create a Google Play Console account which requires some fee. We would have it there in the future. But you can easily ignore the warning if you get one, because we assure you the app DOES NOT have any malware in it.

If you still feel uncertain, or have any queries or installation problems, please feel free to contact us @ proskillocity@gmail.com

So that's it from this update and we hope to see you soon!

How Computers Work: The Logic behind!

2021-02-27T01:47:00.017+05:30

When I say "world-changing", what is it that comes to your mind? Discoveries? Pandemics? People?

Of all these, there is one invention that changed the modern world the most: Computers!

What makes the world today different from that of the 18th century or even before that? The use of computers and the services possible through computers, like the Internet!

Computers are made to automate every aspect of human life and maybe eventually represent humans itself.

They are controlling everything in the modern world. From making a phone call, to heavy calculations, discoveries, and implantations within humans!

The breakdown of a Computer (No, not emotional one, it is not stressed):

Let's start with what a computer is. According to Oxford Languages, "A computer is an electronic device for storing and processing data, typically in binary form, according to instructions given to it in a variable program."

Let's break the definition down:

A computer is an electronic device, which runs on electric energy supplied externally. Why? Because a computer does physical work and doing physical work requires energy. Simple conservation laws acting there.

Next thing is that "a computer stores and processes data". A very important aspect of human intelligence is memory. We can remember data and use it to analyse patterns and go about our daily work. Modern computer circuits are just like a human brain. They have memory! Now, by memory I do not mean storage. Devices like optical drives or magnetic hard disks cannot give a computer its memory. Memory is the intrinsic property of a circuit to remember data and actively process it. Memory and processing go hand-in-hand. Storage is when the processed data is no longer needed and can be saved in a storage device. For a comparison, memory is when you've been asked to bring your classwork notebook to school and forget easily. Storage is when you write it down in your school diary and can stop worrying about remembering it. Storage devices are much simpler to make than achieving memory. Similar to how easy it is to make a diary than neural circuits in your brain for memory. The neural circuit counterpart for computers are 'Logic Gates' (explored later in the article!).

Computer circuits don't work unless a program asks it to do so.

A program is a set of instructions, written in a special language, to communicate with a computer. For writing a program, an algorithm (or logic) has to be decided first on the basis of which a program would work. These instructions are what tells the computer what to do. A computer doesn't understand human languages. Languages have been developed by humans in which a program can be passed on to a computer. These are known as programming languages and can be used to code any set of instructions by humans, like a program (aka app here) to text someone.

The language of Gods, uh, I mean...Computers:

A little detail I skipped is that computer circuits are electronic machines so only understand electric signals. There can be two states of an electrical signal: on(high voltage state) or off(low voltage state). To interpret the high/low voltage states, a language was needed. Base 2 number system or binary digits turned out to be the best choice since it only has 2 digits: 1 and 0. On state was assumed to be a 1 and off state was assumed to be a 0. Now, just two digits cannot solve problems using normal high-school algebra since that type of algebra used multiple variables. So, a different approach was needed. A different type of mathematics. Boolean Algebra turned out to be the chosen one. Why? Because the variables used in Boolean Algebra were only 2! → true or false. So, it was easy to use binary digits as these variables and use Boolean Algebra to solve the problem in hand. 1 was chosen to hold the 'true' value while 0 held the 'false' value. Remember, the computer still only understands electric voltage. We chose the values to be able to use a method for solving. So now, when we mean true, it means a high voltage for a computer, and when we mean false, it means a low voltage for a computer. Now, let's understand how Boolean Algebra would help us solve problems.

Boolean Algebra is logic based mathematics devised by George Boole to provide a true/false proof for statements. The solution was based on making logical arguments for a certain expression (which has to be simplified to a final answer) and assigning a true/false variable to each argument based on if it is actually true or false. Finally performing certain operations on arguments (or variables that represent the argument) for the final output. There are three fundamental operations in Boolean Algebra: AND, OR, NOT. Each of these operations requires Boolean inputs (the true/false variables) and give the true/false output based on the rules of operation (similar to how multiplication takes numbers and multiplies them). We choose what operation to be performed (similar to how we choose to add or multiply).

A description based on rules for each operation:

AND: returns a true only and only if both input values are true.

OR: returns a true if at least one input is true.

NOT: takes a single input and returns the opposite as output.

The circuits for these operations are known as 'Logic gates' and are the fundamental block of computer problem solving and are explained later in the article (memory of a computer is connected to logic gates!).

Programming Languages - Past and Future:

In the 40's computers were used primarily to reduce effort in world war and the computers could only perform arithmetic operations and so the machines were designed based on the type of problem to be solved using it. The problem was later fed to the computer using machine code. Machine code is binary code written based on the computer's internal design. Since binary digits (0 and 1) are voltages (low and high), it was easy to control the input using electrical switches. The programs had to be first written in English (the program at this level was called pseudo-code) and then converted to machine code manually.

This conversion was done by hand based on some opcode (operation code) tables. An opcode is a single instruction which the circuit could execute. The opcode tables contained keywords(the instruction) which specified the operation to be performed and the binary translation of these keywords. Early programmers had to specify the memory addresses (like an address A_1 would mean the value stored in that location on the circuit register) of the numbers/values because the computers were incapable of working with variables (modern day programmers could just write A=12 and use A rest of the time without worrying about the actual location of the variable). Opcode commands came to be known as mnemonics. An opcode table should make it easier to understand:

Since this process was tedious, by the late 1950's programmers came up with higher level languages which were more human intelligible. Opcodes were named operands and could be used in statements. So instead of coding out in binary, programmers could give statements like 'ADD LOAD A_16 AND LOAD A_15' (no variables yet so manually instruct the computer to load a value stored in a particular address). However, computers still cannot understand any language but binary. But being the persistent beings humans are, they solved this issue by coding some programs in binary (so that computers can understand it directly). These programs read the text based statements of higher languages and converted them into binary. In a way, these programs 'assembled' higher level code into binary, and so we're known as assemblers. These assemblers enabled the programmer to write more sophisticated code and worry less about computer mechanics and avoid or hide unnecessary details (abstraction of details).

Since memory addresses still had to be used, updating code became a problem. Adding a little value meant making quite a lot of changes into the actual code and if a value isn't needed you couldn't delete it without making a lot of changes. Considering the still developing circuits, even a little unused value would mean significant power usage by the circuit. Dr. Grace Hopper, an US naval officer who worked upon HARVARD MARK 1 computer during world war 2, wanted to make computers a greater necessity and developed a high level language called A-0. This language used variables and was way advanced than any of the high level languages ever came into existence prior to it.

So now, programmers could type something like 'A=15; B=23; C=A+B; '. This made it way easier to write more sophisticated codes without worrying about the memory addresses or the circuitry.

However, assemblers could not 'assemble' it since it used statements which required multiple binary level functions to be used for a single keyword of A-0 language. Assemblers can only convert when a direct conversion of assembly code into binary code is available, both of which mean the same thing and just are written differently. This wasn't the case with A-0 and so a new program named a compiler was made, which could convert the A-0 written code (known as source code) into assembly language which could be converted by an assembler into binary code. Modern day compilers can directly convert the source code in a language to binary code.

This was so advanced, in fact, that her co-workers were skeptical and would not use this language believing that computers could only do arithmetic and not actual programs, and so this language died out quickly.

Another genius Alan Backus who worked at IBM (leading computer manufacturing company of that time) developed a new language named Fortran and a compiler for this language. In 1957, this language could be used on IBM computers. This was a major hurdle as no common programming language existed which could be used by everyone on any computer. A committee was made in 1959, to develop a common business oriented programming language and thus COBOL language was born. Because of non uniformity in the architecture of circuitry of different computers, a different compiler was needed but these compilers accepted the same COBOL source code. This opened up opportunities of innovation as computers started to be used for a variety of tasks. This also invoked mass involvement in choosing programming as a career.

As computer hardware advanced, programming languages got better, faster, and easier with time. With ALGOL and BASIC in the 60's, C and SMALLTALK (one thing I'm bad at) in the 70's, C++ and Objective C in the 80's, Python and JAVA in the 90's, it doesn't feel like language design would ever end. So many languages have been developed in the 21st century alone, it feels like the process would only end with us thinking and a code for it being written in the future, making everyone a programmer (what would I do then? Quit my job?) . The CPU circuits still only understand binary and are based on that however. So, never forget what the device you're reading this on is doing for you internally!

So, to sum up, a flowchart of how a source code in a modern programming language gets converted for machine to understand would look something like this:

Programs, Circuits, and Arithmetic:

Anshuman, I get a little bit now, but how do these programs in the form of binary codes get executed by the computer?! How do computers calculate using electric signals?!! (Remember logic gates?)

Now we come to the most intriguing and clever part of it all. The fundamentals of computer science! Ladies and gentlemen, I present to you …. Logic Gates!

Logic gates are the induction of Boolean operations in circuitry. These are actual little blocks physically present in your device. Different combinations of logic gates make RAM (temporary computer memory). Let's understand how logic gates (or the operations) actually work. The three main logic gates are AND, OR, NOT named after the operations they perform. Since there are less number of possible output/input cases for the gates and all of the gates take true/false {1/0 in circuit use} as inputs and return true/false outputs {1/0 (high/low voltage) as circuit output}, an output based input table (known as truth table) can be made for these and would look something like this:

(Keep in mind the description of the respective operations)

Truth table for AND gate:

Truth table for OR gate:

Truth table for NOT gate is pointless since it just reverses the input.

An XOR gate is a gate formed by combination of NOT, OR, AND gates. Truth table for an exculsive OR (XOR) gate is as below.

A NOR gate is a gate formed by combining NOT gate and OR gate. The truth table is as below:

An XNOR gate is another such gate which has a NOT gate with XOR gate and so it produces just the opposite results than XOR. The truth table is below:

Now, imagine we already know of the hardware used to make these gates.(that thing would require a separate article).

These gates when combined in different combinations, produce adder, subtractor circuits, memory circuits, and can perform all sorts of arithmetic.

Let's take an adder circuit as an example ( the actual circuit diagram would get too complex for the article).

It would take as many inputs as long as they don't exceed 4 binary digits of data. The maximum number which can be represented using 4 bits is 15 in decimal system.

We can make 4-bit adders, 8-bit adders and so on. A 4-bit adder means it can take 4 binary digits as inputs. Similarly an 8-bit adder can take 8 binary digits as inputs. But that does not mean an 8-bit adder is only twice as good than a 4-bit one. An 8 bit adder has 4 more digits t than a 4 bit one and since, in a Base 2 system, each bit holds 2 times the value than the previous bit, the maximum number which can be represented using 8 bits is 255. The conversion process of binary to decimal numbers, hence looks something like this :

So how do modern computers represent millions of digits? They use higher bit and n number of processors.

Simply put, the number of bits typically refers to the length of the character representation that is used to build instructions or data. 4 bits permit 64 distinct characters usage while 8 bits permit 256 characters or instructions usage.

The modern computer circuits are generally 32-bit or 64-bit.

This is all arithmetic though! How do computers execute programs? Say, light up a screen a certain color.

The thing is, circuits already have a pathway for everything. So there is a path to a pixel (the things that show color) on your screen, it just isn't getting electric connection to light up. Whenever we write a program, the computer performs calculations to find that path and finding that path means completing it. Once the path is complete, electric current is sent to it, and voila, it lights up! The process obviously is a lot of more complicated but this is the basic principle behind it.

So finally! What is the circuit made of? What is the physical device that makes these gates possible? What is it that actually stores the data now that we know how to store data? What is that physical thing which does physical work and so requires physical energy? Well, that would be answered in the next part of the "How Computers Work" series.

PWA (Powerful WebApp) deployment for Skillocity

2021-02-25T02:42:00.016+05:30

As a team which believes in providing our users with the most powerful and easy to use tools, we are announcing a feature which would enable easy access of our blog directly from the home screen of your phone!

You're probably thinking we are launching an app, right?

Well we also believe in surprising our audience, and so it is not exactly an app.

An app would require extra memory space on your device and would not work on laptops/desktops but only phones/tablets.

Instead, you can now add a shortcut to our blog which runs through chrome, on your device. Usually, if you add a shortcut for a website, it would just add a shortcut to a single webpage you added a shortcut to. But in our case, after extensive coding, we have enabled our user the ability to add a shortcut to the entire website to make it easier to access, without the need of opening Chrome. This makes it feel just like an app!

With this feature, you could just have an app like experience for our blog on your laptop or phone, without having to worrying about our "app's" memory usage, since it uses almost none.

Steps to add this (a must try!) -->

1) Open our website on Chrome (suggested opening a new tab with our website and read off the instructions from this).

2) Click on the three dots besides the 'number of tabs open' box.

3) Scroll and find 'Add to Home Screen' option in the menu.

4) Edit the name to just be 'Skillocity' and click on 'ADD' option in the next pop-up box.

5) Click on 'ADD' option again in the next pop-up.

That's it! Check your home screen and you should find an icon with our logo. You can now access our blog just by clicking on the icon without opening chrome and typing out our website address.

Thoughts of our team members:

Pranav Sawant

A very economical and much needed feature to increase viewer engagement.

Anshuman Shukla

A very user-friendly and modern feature which represents our philosophy perfectly. No Chrome needed and thus removing a middle level. Just the user and our blog.

Tennis GOAT Debate

2021-02-22T18:54:00.032+05:30

Who is the greatest of all time ? It’s a question that gets posed in every sport from soccer to tennis. Because believe it or not, the only thing fans and the media are interested in, is debates and lists. Tennis is no exception; the world’s best have been competing in the Open Era since 1968. Hence, the list of potential candidates for the GOAT( Greatest of All Time) has only gotten longer. I’ll be talking about the men’s side of the debate in this article cause the women’s side deserves a separate article on it too!

When the first decade of the 21^st century loomed, no one could have foreseen that three players would dominate men’s tennis for more than two decades ! For most tennis fans, the GOAT debate revolves around three players which are Roger Federer, Rafael Nadal and Novak Djokovic. But have they really done enough to place themselves in a league of their own, head and shoulders above legends such as Rod Laver, Arthur Ashe, Pete Sampras, Andre Agassi, Bjorn Borg, Jimmy Connors, Ivan Lendl, Boris Becker, John McEnroe and others ? Well, that’s a tricky question as advances in sport science, technology and nutrition have made it a bit easier for the players of this generation to sustain their longevity in the sport. Let’s have a look at some stats.

At the time of writing this article, Roger Federer and Rafael Nadal lead the pack with 20 grand slams each with Novak Djokovic closing in on 18. They have won 58 of the last 70 grand slams. Most people believe that grand slams are the deciding factor to decide which player is the GOAT, the player with most grand slams wins the race, as simple as that. But for fans who have been following tennis, it’s a different and complicated story. Let’s have a look at our three major contenders and why each of them should be the GOAT.

Swiss Talent and Elegance

Roger Federer:

Starting with Roger Federer, the Swiss is the oldest of the pack turning 40 in 2021. He also holds the record with Rafael Nadal for most slams. But he has some significant other achievements which seem almost impossible to ever break. He holds the record for most weeks at number one in the world with 310 weeks at the top of the sport. He was also at the top for 237 weeks consecutively which makes it even more impressive ! He’s also the only player to win 2 grand slams 5 times each, he was the champion at Wimbledon from 2003-07 and ruled the US open from 2004-08. Federer has won a record 8 slams at Wimbledon, 6 at the Australian Open, 5 at the US open and a solitary slam at the French Open which he won in 2009. Federer has also reached 31 grand slam finals with 10 consecutive, both being all time records. He also has 46 semi-final and 57 quarter final appearances to his name, the most in the open era.

Federer also has other records such as a record of 71 hard court titles, a 56-match winning streak on hard courts. Federer’s most successful surface is grass where he has won a record 19 titles including an all-time 10 titles at Halle and 8 at Wimbledon. Federer also has the longest grass court winning streak as he won 65 consecutive matches on grass from 2003-08. Due to his success on grass courts, Federer is considered by many as the greatest grass court player of all time. Federer is the only player to register at least 10 titles on clay, grass and hard courts. At the prestigious ATP World Tour finals, Federer has a record of 6 titles and 10 final appearances.

Federer was selected by fellow players as winner of the Stefan Edberg Sportsmanship award 13 times ( 2004-09, 2011-2017). Fans voted for him in 2020 to receive the ATP Fans Favourite Award for a 18^th straight year ( since 2003). Since his grand slam winning debut at Wimbledon in 2003, Federer has won a record total of 39 ATP World Tour Awards. Federer is by far the most elegant, classy and technically superior player of all time. He has wowed tennis fans for over two decades and is still going strong. Fans and experts alike have called him as one of the best ambassadors of not only tennis but sport in general. Thus, Roger Federer is a clear fan favourite and will always remain number one in the minds and hearts of most tennis fans worldwide.

El Matador from Mallorca

Rafael Nadal:

Talking about Rafael Nadal, the Spaniard from the island of Mallorca has won a record 20 Grand Slam singles titles which ties him with Roger Federer. Nadal completed the Career Grand Slam and the Career Golden Slam, becoming the youngest player in the Open Era to achieve this feat, having won all four majors and the Olympic title by the age of 24 years, 3 months and 10 days. Known as the “King of Clay”, Nadal won the French Open 9 times in his first 10 attempts, with a match record of 66-1 (98.5%-win rate) and is viewed by many analysts as one of the greatest feats in the Open Era. Nadal won the French Open on his first attempt as a 19-year-old in 2005 and went on to make three consecutive title defences from 2005 to 2008; he defeated then world No. 1 Roger Federer in three consecutive finals from 2006 to 2008 (he also defeated Federer in the 2005 semi-finals) and again in the 2011 final (Nadal is the only player to defeat Federer in four finals at the same major.

Nadal is also the only player to beat Federer in the finals of 3 different grand slams, the French Open, the Australian Open, and Wimbledon). His sole loss at the French Open during this period came at the hands of Sweden's Robin Söderling in 2009 in the round of 16. Nadal then went on to avenge his loss to Söderling by defeating him in the 2010 French Open final, and then notched four consecutive title defences from 2011 to 2014 (an Open Era record). Rafa furthered his legend in history when he won "La Decima", a 10th title in Paris in 2017, where he didn't drop a set and lost only 35 games, only three shy of Borg's record of 32 games lost. He holds a match record of 100–2 at the French Open (never taken to five sets in the final) from 2005 to 2020 achieving a win percentage of 98.0% and is the only player to achieve this type of dominance at any single Grand Slam tournament. Nadal won the French Open an all-time record of 13 times and is the first, and only, player in history, male or female, to win 13 grand slams at a single major (no other male player has won more than 8 titles at a single major in the Open Era).

Additionally, Nadal is 25-0 in best of 5 matches on clay at other events, bringing his total match record in best of 5 on clay to 125-2, a win percentage of 98.4%. Nadal didn't lose a single semi-final on clay courts for 12 years (52-0) from the 2003 Croatia Open (lost to Carlos Moya) to the 2015 Rio Open (lost to Fabio Fognini). He has won 25 Masters 1000 titles, 13 Grand Slam titles, and an Open Era record 60 titles on clay. He won at least 1 Masters 1000 title for ten consecutive years from 2005 to 2014 and is the only player to achieve this type of consistency in the Open Era. He owns the Open era records of most consecutive years of winning 1+ ATP singles titles (17 years from 2004-2020) and most consecutive years of winning 2+ ATP singles titles (16 years from 2005-2020).

He owns the longest single surface win streak by a male having won 81 consecutive matches on clay courts from 2005 to 2007, 16 better than Federer's record on grass, and 25 better than Federer's record on hard courts. Nadal also won a record 8 consecutive Monte Carlo Masters 1000 titles from 2005 to 2012, and a record overall 11 titles including his three-peat from 2016 to 2018. Nadal has also won a record 9 Masters 1000 titles in Rome, and 11 titles at the ATP 500 Barcelona Open. In 2010, Nadal won the French Open plus all three-clay court Masters 1000 events (Monte Carlo, Rome, Madrid) in the same calendar year, thus becoming the first, and only, player to complete the "Clay Slam".

Nadal won at least one Grand Slam tournament for 10 consecutive years (2005 – 2014) having broken the previous men's record of 8 consecutive years. He holds the record for most titles at 3 different ATP Tour levels: ATP 500 (Barcelona-11), Masters 1000 (Monte Carlo-11), and Grand Slams (French Open-13).

Nadal’s success does not hold strictly to the clay courts. Over the course of his career, he has won 400+ hard court matches as well as 400+ clay court matches. He is the only player, regardless of gender, to have recorded 400+ match wins on both hard and clay courts. He has won 13 French Open titles, 2 Wimbledon titles, 1 Australian Open title and 4 US Open titles. Nadal also owns the Open era record of most consecutive years qualifying for the year-end ATP Finals at 16 years in a row. Nadal has the Open Era record for best career winning percentage (for minimum 500 wins) at 83.25% (999-201 record). Nadal is the only player to be ranked ATP world number one in three different decades, in the 2000s, 2010s, and 2020s. Due to Nadal’s physicality, never die attitude, fighting spirit and jaw dropping athleticism, he’s loved by tennis fans all over the world.

The Balkan Beast

Novak Djokovic :

Coming to our final candidate Novak Djokovic, the Serb is a seventeen-time Grand Slam Champion and six-time year-end World No.1. He is the only one in tennis history to hold all four Grand Slams on three different surfaces at once. Djokovic is also the only player to win all ATP tour Elite tournaments. Djokovic has won 36 titles in the ATP Masters tournaments, the most of any player. By 2018, he had won Masters titles across all 9 venues where Masters tournaments are held, becoming the first player to achieve the "Career Golden Masters". Djokovic is widely viewed as one of the greatest hardcourt players of the Open Era, if not the greatest. He has the record of most hardcourt Majors and most masters titles.

Djokovic is one of two players (with Robin Soderling) to defeat Rafael Nadal at the French Open. He is also the only player to defeat Nadal in all three clay court Masters events. Djokovic is the player with the most clay-match wins over Nadal and the one who ended Nadal's consecutive run of 8 Monte Carlo titles in the 2013 final. Djokovic is the only player to defeat Federer and Nadal in all four Grand Slams. He's also the only one to beat both in multiple Grand Slam finals, multiple Master finals and in the final of Year-End Championship. He is the only player to defeat Federer in three Wimbledon finals (2014, 2015 and 2019) and the only player to defeat Nadal in four Grand Slam finals (Wimbledon 2011, US Open 2011 and Australian Open 2012, 2019).

Djokovic has won a record 9 Australian Open titles, 5 Wimbledon titles, 3 US Open titles and one French Open title. Djokovic leads the head to head against both Roger and Rafa and has dominated the past decade winning 16 slams. Djokovic is widely regarded as one of the best players in the world due to his versatility, ability to play unbelievably well in pressure situations and consistency.

But is the GOAT debate worth having in the first place ? Most tennis pundits believe that its disrespectful to pick a winner and choose between the big three, and we as tennis fans are doing disservice to the players and the sport. It’s true that we cannot pick the GOAT before each of them hangs up their rackets. Many people believe that the number of grand slams is not the most important stat and others such as most weeks at number one matters more. This validates a very important question too, which is the most important stat for determining the GOAT ? This depends on person to person and hence we see different answers to the GOAT debate from different people.

Most people would agree that we cannot compare players of different generations as we wouldn’t be able to see prime Federer vs prime Borg or prime Djokovic vs prime McEnroe, so it’s pointless to even say that the big 3 lead the GOAT race. Arguably, if legends such as Rod Laver, Bjorn Borg, Jimmy Connors and others enjoyed the superior use of technology that we use today, they might have been the ones at the top. There are a lot of other factors that also should be taken into consideration while determining the GOAT. Rivalries and charisma also play a big role in shaping tennis history. Battling social issues (especially in the 20th century) should also be taken into account.

Essentially, it all comes down to unquantifiers. No matter how many newspapers, websites or articles come up with formulas to determine the GOAT, it boils down to the individual feelings of the fans. Therefore, instead of debating and deliberating on who the GOAT is, let’s just enjoy the game of tennis cause we’re going to miss them when they retire. It’s important to respect all the legends of the sport as they have contributed massively to the game and are true ambassadors of the sport. To cap it up, the GOAT debate will most likely continue as long as the sport exists cause records are meant to be broken and the question of who truly is the GOAT will most likely end as one of the questions who no one knows the answer to.

Special thanks:

Cam Williams, President, Tennis Talk
Tennis Paris Brothers

Vectors: A Physicist's perspective

2021-02-02T22:45:00.017+05:30

PDF file of this document available for download!

Mathematical interpretation of everyday reality of all linear algebra boils down to matrices and all of matrices boils down to vectors – Anonymous

Vectors, what really are they? To answer this question, there are three main ways of looking at it. One is the physics perspective; one is the Computer Science perspective and one is the Mathematical perspective. As always, we’re going to explore this topic both on an intuitive and mathematical level. Let’s tackle the Physics perspective in this article which is arguably the most trivial and easy to understand. More complex vector equations and operations will however not be included in this article (although I personally love linear algebra and higher applications of vector fields and tensor mechanics).

A vector is a physical quantity that has both magnitude and direction. Some examples of vectors are displacement, velocity, force, electric field, magnetic field, momentum, acceleration etc. Usually, in physics a vector is represented by a capital letter with an arrow over it. The magnitude of vector A is represented by |A|. A vector can be represented by an arrow parallel to the direction of the vector. The length and direction of the arrow thus represent the magnitude and direction of the vector respectively. To find the angle between two vectors, both vectors are drawn from one point in such a manner that tails of both the vectors coincide.

There are numerous kinds of vectors which we’ll encounter. A vector of unit magnitude is called a unit vector and the notation for it in the direction of A is “A cap” or “A caret”. Thus, A vector=|A|(A cap). Unit vectors along the x, y and z axes are called basis vectors for the standard three-dimensional coordinate system are represented as i cap, j cap and k cap respectively.

A vector having zero magnitude is called a null vector or a zero vector. The zero vector has no specific direction, the position of the vector of origin is a zero vector and they are only of mathematical importance having no physical meaning. Vectors are equal if they have the same magnitude and direction. The negative of any vector is a vector having equal magnitude acting in the opposite direction.

Vectors having the same initial points are called concurrent or co-initial vectors. Vectors that lie in the same plane are known as co-planar vectors.Vectors are said to be orthogonal if the angle between them is 90°. Hence, the unit vectors i cap, j cap and k cap are also called orthogonal unit vectors.

Scalars like distance, speed, density, energy etc can be added, subtracted, multiplied or divided using basic mathematical rules. However, when we come to vectors the things get slightly more interesting. The product of a vector A and a scalar n is nA whose magnitude is n times the magnitude of A and is in the same direction of A if n is positive and is in the opposite direction if n is negative. Further, if m and n are two scalars, (m+n)A= mA+nA and m(nA)=n(mA)=(mn)A. The division of a vector A by a non-zero scalar n is nothing but the multiplication of A by 1/n. Addition and subtraction of vectors is where things start to get slightly more mathematical. But still, as I promised its very trivial stuff.

There are a few ways to compute the addition or subtraction of vectors, the most common one is the parallelogram law of vector addition. Let R be the resultant of two vectors A and B. According to this law, the resultant R is the diagonal of the parallelogram of which A and B are the adjacent sides. Magnitude of R is given by, R=(A²+B²+2ABcosφ)^1/2. Here φ is the angle between A and B. The angle of R can be found out by angle α or β of R with A or B. Tanα=Bsinφ/A+Bcosφ and tanβ=Asinφ/B+Acosφ. It can be mathematically proven using range of trigonometric functions that R is maximum when φ=0° and minimum when φ=180°. R_max=A+B and R_min=A-B.

Another way of computing this is the triangle law of vector addition. According to this law, if tail of one vector is placed at the head of the other, their sum or resultant R is drawn from the tail end of the first to the head end of the other. Therefore, R=A+B=B+A.

Subtraction of vectors is nothing but reversing the direction of one vector and then adding it. Hence, A-B=A+(-B). In this case, the angle between the two vectors is 180-φ and hence the resultant A-B=(A²+B²-2ABcosφ)^1/2. For the direction of this resultant vector, we can find tanα=Bsinφ/A-Bcosφ and tanβ=Asinφ/B-Acosφ.

To classify as a vector, a physical quantity must not only have direction and magnitude, but it should also follow the triangle/parallelogram law of vector addition. For example, current has magnitude and direction, but it is still classified as a scalar as it doesn’t follow the triangle/parallelogram law of vector addition and is just added up like any other scalar. This concept can be generalised in the polygon law of vector addition which states that if a vector polygon be drawn placing the tail end of each succeeding vector at the head or the arrow end of the preceding one, their resultant R is drawn from the tail end of the first to the head or arrow end of the last. Thus, here R=A+B+C+D.

Two or more vectors which when compounded in accordance with the parallelogram law of vector R are said to be the components of vector R. Components of a vector can be made in any mutually perpendicular directions. But in general, we mostly use the standard coordinate system. Thus, the vector R can also be written as R = R_x(i cap)+ R_y(j cap)+ R_z(k cap). Here, R_x, R_y,R_z are components of R along the x, y and z axes respectively. The magnitude of R can be given as R=(R_x²+R_y²+R_z²)^1/2.

The vector R makes an angle α with the x axis, β with the y axis and γ with the z axis. Here, cosα=R_x/R, cosβ=R_y/R and cosγ=R_z/R. Cosα, cosβ and cosγ are also called direction cosines of R with the x, y and z axes respectively. An important identity to be noted here is that cos²α+cos²β+cos²γ=1.

Another interesting concept that stems up is the position vector. To locate the position of any point P in a plane or space, generally a fixed point of reference called the origin O is taken. The vector OP is the position vector of P with respect to O. If the coordinates of P are (x, y), then the position vector of point P with respect to O is OP=r=x(i cap) + y(j cap). However, there are two key points to be noted over here. For a point P, there is one and only one position vector with respect to O and the position vector of point P changes if the position of point O is changed.

Coming on to the multiplication of vectors, it is here that things start to get more interesting especially for Physics and Math lovers. The product of two vectors, is of two kinds i.e. the dot product and the cross product. The dot product, also called the scalar product of two vectors A and B is denoted by A.B and is read as A dot B. It is defined as the product of the magnitudes of the two vectors A and B and the cosine of their included angle φ. Thus, A. B=|A||B|cosφ.

Few properties of the dot product are to be kept in mind. They are commutative and distributive. Hence, A.B=B.A and A.(B+C)=A.B+A.C; A.A=A²; i.i=j.j=k.k=1; i.j=j.k=k.i=0. Component of B along A is A.B/|A| and component of A along B is A.B/|B|. Two vectors are perpendicular if their dot products are zero. Another important computational identity for the dot product is (a₁(i cap)+b₁(j cap)+c₁(k cap)).(a₂(i cap)+b₂(j cap)+c₂(k cap))=a₁a₂+b₁b₂+c₁c_2. The dot product is also called as the scalar product as the result of a dot product is always a scalar. There are many uses of the dot product in physics such as: W=F.S, P=F.V, U_E=M.B, U_B=M.B and many more.

Coming onto the cross product, it is denoted by AxB and is read as A cross B. It is defined as a third vector C which is perpendicular to both A and B and whose magnitude is equal to the product of the magnitudes of the vectors A and B and the sine of their included angle φ. Thus, C=AxB=|A||B|sinφ(n cap). Here (n cap) is the unit vector in the direction of vector C.

There are quite a few rules of finding the direction of vector C, one of them is the right-hand screw rule. It states that the vector C is normal to the plane of A and B and points in the direction in which a right handed screw would advance when rotated about an axis perpendicular to the plane of the two vectors in the direction from A to B through the smaller angle φ between them. Alternatively, we can state this rule as if the fingers of the right hand be curler in the direction in which vector A must be turned through the smaller included angle φ to coincide with the direction of vector B, the thumb points in the direction of C.

There are a few important properties of the cross product. AxB = -BxA, the cross product of two parallel or anti parallel vectors in zero. Thus ixi=jxj=kxk=0. The cross product is also distributive. Hence, Ax(B+C) = AxB + AxC. Another important identity is: ixj=k, jxk=i, kxi=j. We can compute the cross product with the help of a determinant as well.

There are many applications of the cross products in physics itself such as: τ=rxF, L=rxP, F_B=q(vxB) and many more. Another interesting result is that two vectors can be proved as parallel or anti parallel to one another if both the vectors bear a constant ratio. For instance, vector A=a₁(i cap)+b₁(j cap)+c₁(k cap) is parallel to vector B= a₂(i cap)+b₂(j cap)+c₂(k cap) if a₁/a₂ = b₁/b₂ = c₁/c₂ = constant value. If this constant value is positive, the vectors A and B are parallel and if the constant is negative, the vectors A and B are anti parallel.

A physical quantity need not always be a vector or a scalar. We can generalize it as a tensor. A tensor is defined as a mathematical object that describes a multilinear relationship between sets of algebraic objects related to a vector space. A tensor is characterised by its rank, a scalar is a tensor of rank 0 while a vector is a tensor of rank 1.

Tensors are used in many fields of physics such as stress, elasticity, fluid mechanics, moment of inertia, electromagnetism, magnetic susceptibility, premittivity, general relativity and many more. In physics, a tensor can occur at multiple points of an object and the tensors may vary from one location to another. This paved the way for the concept of a tensor field. A tensor field assigns a tensor to each point in space. Several times tensor fields are just simply called tensors. Tensor fields are used in many fields of physics and maths such as Differential geometry, general relativity, algebraic geometry and many more.

Moment of inertia has two forms, a scalar form (when the axis of rotation is known) and a more general tensor form which does not require the axis of rotation. Stress is neither a scalar nor a vector, it is a tensor. Area can behave either as a scalar or a vector depending upon the circumstances. Vectors associated with linear or directional effect are called polar vectors and those associated with rotation about an axis are called axial vectors. For example; force, linear velocity, linear acceleration etc are polar vectors whereas angular velocity, angular acceleration etc are axial vectors. Another couple of interesting proofs can be derived. The area of a triangle can be computed as |AxB|/2, similarly the area of a parallelogram would be |AxB|.

Another interesting application of the multiplication of vectors is the scalar triple product. Here, A. (BxC)=B.(CxA)=C.(AxB) is called the scalar triple product of the vectors A, B and C. It is a scalar quantity and the volume of a parallelepiped bounded by the three vectors is given by the scalar triple product of these vectors. A parallelepiped is nothing but a solid body who’s each face is a parallelogram. The scalar triple product of A, B and C can also be written as [ABC]. If the scalar triple product of three vectors is zero, this implies that they are coplanar.

Another interesting concept I’d like to mention is of the vector triple product. The vector triple product is interpreted as the cross product of one vector with the cross product of the other two. The following relationship is called the triple product expansion: Ax(BxC) = (A.C)B-(A.B)C.

Vectors are frequently used in physics, maths and engineering fields but there are also quite a few day to day applications of vectors. For example in sports when a player throws a ball or hits it, vectors are inevitably involved in that situation. In such a case, we can determine the range, maximum height and time period of flight of the ball. Even while crossing a river, a boat often drifts due to the flow of water. Here too vectors are involved.

Vectors are used in many sports and also in much cooler applications such as the determination of force required to move a body resting on a surface with friction. Vectors also have many applications in the fields of thermodynamics and electromagnetism. There are many such more uses of vectors in our everyday life and we have been knowingly or unknowingly using vectors all the time! That was the Physicists perspective and I’ll soon be back with the more generalised mathematical form of it.

Fast News : Earth's Artificial “Mini-Moon” 2020 SO to say a final Adieu!

2021-01-30T12:36:00.017+05:30

The object known as 2020 SO was first discovered on September 17 2020 and was suspected to be an artificial (man-made) object due to its low relative velocity. This suspicion was later confirmed by NASA's Infrared Telescope Facility in December of 2020 when the observatory found the object's spectrum to match that of stainless steel. Calculations have shown that this object is a rocket booster launched in 1966(54 years ago! Feeling old yet?) as part of the Surveyor 2 mission to explore our primary (and the best!) Moon!

It was captured by our planet when it entered Earth's Gravity Sphere on 8th November 2020 through Lagrange point L2 (a point of equilibrium b/w two massive bodies; in this case the Sun and the Earth).

It made a close approach on December 1, 2020 coming at a 50,000 km perigee(closest point in orbit). For reference, our Telecom satellites are at 36,000 km circular orbit.

It is set to make a final approach on 2nd February of 2021 and leave Earth's Gravity Sphere, through L1 Lagrange point, forever.

It probably was missing Earth and wanted to see its launch pad for the last time or say goodluck to its counterparts on the Moon (aww no I'm not crying, you are).

Being the fairly massive planet Earth is, it keeps capturing space objects. As a matter of fact it has another small rock orbiting it, named 2020 CD3 and a number of more insignificant objects in the Lagrange points.

This incident does show us the level of space debris we're polluting our solar system with, let alone the debris in orbit around the Earth which has to be constantly monitored to avoid collision with working satellites, unless we clear it. Space exploration comes with a cost, and we have to pay it, sooner or later.

A video by Tony Dunn showing the beautifully weird path of 2020 SO:

https://proskillocity.blogspot.com/2020/08/is-vision-of-colony-on-mars-before-end_22.html

https://proskillocity.blogspot.com/2019/08/how-do-we-know-shape-of-our-galaxy_21.html

What exactly is the Heisenberg Uncertainty Principle?

2021-01-25T18:08:00.041+05:30

Natural science, does not simply describe and explain nature, it is part of the interplay between nature and us – Werner Heisenberg

Some of you readers might have read about the Heisenberg Uncertainty Principle in high school or maybe even in college if you’ve attended courses on Quantum mechanics and Atomic Chemistry. But here we’re going to touch this topic both on an intuitive and mathematical level. Life has many trade-offs, the way my professor explained this to me is a very intuitive way of understanding this topic.

Let’s say you’re observing the routine of your mom and after a week, you realized that she’s gone to the Church on Saturday. Now, based on just this information, can we predict that your mom goes to the Church every Saturday? Of course not, we need further time for observation. Let’s say you then observe her for a month and notice that she goes to the church every Saturday. Based on this we might be able to make a statistical prediction, that your mom goes to the Church every Saturday. But still, we can’t be a 100% sure. Now let’s say you observe her for a year and see that she goes to the Church every Saturday. Based on these observations, we can say that there’s a high probability that your mom goes to the Church every Saturday, but we still cannot be a 100% sure as it’s entirely possible that your mom decides to go the school next Saturday. Hence, we see that we would require an infinite amount of time to perfectly predict whether your mom goes to the Church every Saturday. Thus, there is a trade-off between the frequency of her visits and time.

This is an intuitive example of a real-world situation, like what our beloved Heisenberg Uncertainty Principle says. Introduced in 1927 by the German physicist Werner Heisenberg, the principle states that the more precisely the position of a particle is determined, the less precisely it’s momentum can be predicted from initial conditions and vice versa. This can majorly only be applied in the quantum realm and cannot be applied to everyday macroscopic objects like you and me.

So, basically what the Heisenberg Uncertainty Principle wants to tell us is that let’s say there’s an electron revolving around its orbit and if we accurately measure it’s velocity with 100% precision, we have absolutely no idea where the exact position the electron is located ! How cool is that! in such a case where we know a particle’s velocity, it’s position can be graphed as a probability function. Hence the exact location of the electron is not certain but is rather expressed as a probability.

Mathematically we can say that the probability of finding an electron at a point within an atom is directly proportional to |Ψ²| at that point. |Ψ²| is known as probability density and is always positive. |Ψ²| can also be interpreted as the probability of finding an electron in a unit volume. Hence, the probability of finding the electron in an infinitesimally small volume dV = |Ψ²|dV. Ψ (pronounced as psi) is also called the amplitude function and has a few interesting properties that can be mathematically proven. It in continuous, single valued and finite. Also, since the probability of finding the electron in the entire volume that we’ve assumed is 1, we can also write the integral equation ʃ|Ψ²|dV=1 with limits from -∞ to ∞.

Ψ is basically the mathematical representation of waves in quantum mechanics and is called wave function, its value depends on the coordinates of the electron in the atom and doesn’t have any physical meaning. It is found in many other equations too including the Schrodinger’s Equation (that deserves a separate article on it altogether), radial probability density, radial probability distribution etc.

For some of you math freaks, here’s an interesting triple integral. Considering the position of the electron in three dimensions, we can also state that the probability of finding it in that three-dimensional space is 1. Hence, we have the triple integral, ʃʃʃ|Ψ²|dx dy dz =1 with limits from -∞ to +∞ in each integral. If a wave satisfies that triple integral, it is said to be a normalized wave function.

The concept of treating the electron both as a particle and a wave is called wave particle duality (this too deserves an independent article). Coming back to the Heisenberg Uncertainty Principle, we can mathematically represent it as Δx Δp ≥ h/4∏. Here Δx represents uncertainty in position and Δp represents uncertainty in momentum, h is called the Planck’s constant and its value is 6.62 X 10^-34 Joule second.

Therefore, this equation says that the product of uncertainty in position and uncertainty in momentum must always be greater than or equal to h/4∏. This essentially means that if uncertainty in position is very less i.e., we exactly know where the particle is located with a high degree of precision, then uncertainty in momentum must be very high to satisfy the equation i.e. we know very little about its momentum. And since momentum and velocity are closely related (p=mv), we don’t know its exact velocity in this case.

In the realm of quantum mechanics, we call variables such as position and momentum as conjugate or complementary variables. This in mathematics means that these variables such as position and momentum become Fourier transform duals. Other examples of conjugate variables would be time and frequency, Doppler and range etc. The important thing to be remembered is that the Heisenberg Uncertainty Principle can only be applied in the Quantum World on microscopic bodies showing wave particle duality. It cannot be applied on everyday macroscopic objects like you and me because macroscopic objects tend to have a high degree of particulate nature with an infinitely small wavelength.

Heisenberg formulated the Uncertainty Principle at Niels Bohr Institute in Copenhagen. According to Albert Einstein, randomness is a reflection of our ignorance and inability to comprehend a fundamental physical reality. On the other hand, Niels Bohr debated that probability distributions are irreducible and fundamental. Einstein and Bohr deliberated over the Heisenberg uncertainty principle for many years.

Heisenberg came to a very beautiful conclusion that formed as a base for further studies in the field of Quantum Mechanics. He theorized that absolute determinism was impossible as it required both position and momentum of the particle. This paved the way for the use of probabilistic determination of a particle due to the relation between position and momentum. This concept forms the base for the Copenhagen Interpretation of Quantum Mechanics.

A particularly interesting way to interpret the uncertainty principle is an illustration of how we see everyday objects. You can view and read this article due to photons of light which strike the surface and then bounce away. These photons inevitably transfer some energy to the surface it has bounced from. This energy will change the momentum of the electron on the surface. This way, we will be able to determine its momentum but not position. On the other hand,since quantum particles move at high speeds, it may no longer be in the same place. In either case, the uncertainly principle works and we cannot exactly determine the position and momentum of the electron.

The uncertainty principle explains many phenomenons that cannot be explained using classical physics. For example, in atoms, negatively charged electrons orbit the positively charged nucleus. According to the theory of classical electrodynamics, the two opposite charges should attract each other eventually making the electron fall out of orbit and collapse in a spiral way. In such cases, the uncertainty principle makes things a lot simpler. If the electron gets too close to the nucleus, then its position can be precisely determined. But since its position can be accurately defined, its momentum would have a large error in measurement. Phew! Heisenberg saved us there.

The uncertainty principle can also explain alpha decay. Alpha particle is a helium nucleus having two protons and two neutrons emitted by a heavy nuclei such as Uranium-238. These alpha particles are usually present inside the nucleus and require a very high amount of energy to break out. But since an alpha particle inside the nucleus has a well defined position, it doesn't have a well defined velocity. This means that there is a very small chance that it might escape from the nucleus. This result sin alpha decay and radioactivity is observed. This process is known as Quantum Tunneling.

One of the most intriguing results of the uncertainty principle is about vacuums. Vacuum in general sense means the absence of matter. However, in the quantum realm, things are not that straight-forward. Here, Heisenberg's uncertainty principle is expressed in the form of energy and time. Thus, we can only accurately measure either the energy or time but not both. In this case, energy and time are conjugate variables.

Uncertainty, which although seems very complex and some mystical theory, is fairly easy to understand and we probably wouldn't be here if this principle wouldn't exist. Werner Heisenberg received the Noble Prize for Physics in 1932 for his work in Quantum Mechanics. Another interesting thought example given by Albert Einstein disproves the Heisenberg Uncertainty Principle. Let’s say we have two balls, stick them together. Now spring them out so that both go in mutually opposite directions. Now, we can find the position of the first ball and not the momentum. Similarly, we can find the momentum of the second ball but not its position. But since the momentum of the second ball is mathematically related to the first ball, we can find the momentum of the first ball too! And voila, we hence can know both the exact position and momentum of both objects. I’ll leave this thought experiment open to interpretation to our readers.

Is the Vision of a colony on Mars before the end of the decade possible?

2020-08-22T16:46:00.001+05:30

With the recent 150m hop of Starship prototype SN5, Elon's ambition (and our desire!) of colonizing Mars in the 2030s looks very real. If the development and testing were to continue at this pace, Starship should be ready for its maiden voyage before 2025. We'd be ready with the capability to launch humans, no doubt, but would we ready with the human systems?

A round trip to Mars would roughly take about 21 months: about 18 months in deep space and 3 on Mars. Starship would probably be able to take us to Mars a little faster, possibly, in 6 months. But even that span could cause serious damage to the health of an astronaut if not protected from.

So, what is this radiation that we're talking about? We're talking about ionizing or high-energy radiation. It includes X-rays, gamma rays, high energy protons, and cosmic radiation which on interaction with a surface would produce secondary radiation(alpha and beta particles) by knocking off electrons!

These could be very hazardous especially since the amount of cosmic rays entering our solar system has increased due to the period of shrinking heliosphere, the bubble around our solar system created by solar wind pressure. (more on that in a later article). Oh, and did I give an honorable mention to the good ol' Coronal Mass Ejections (or CME's) by our dear Sun? If one is to occur during a deep space mission, the hazards for astronauts would increase by a good factor. Though the Orion capsule would be equipped with the HERA sensor, or Hybrid Electronic Radiation Assessor, to provide a warning if crew members need to take shelter in the middle of the crew module with storage bags as a cover in the case of a radiation event, such as a solar flare. The fear of an instrument failure and/or a partial mission failure remains.

For a fact, a solar storm event occurred between Apollo 16 and 17 missions. Had it occurred during one of those, history would have been a little different.

Why worry about radiation though? Did we not go to the Moon with minimal protection? Well, yes we did, but the duration of the Apollo missions was roughly 8 days. On Earth, due to its magnetosphere, on average humans experience about 2.4 millisieverts a year(varies based on location). The problems due to radiation are assumed to start at about 2.5 rads or 500 milli SV. But that isn't accurate enough since radiation measurement accounts for a lot of factors. The radiation the Apollo astronauts experienced averaged at about 1.14 rads or about 10millisieverts. To put this into perspective, an abdominal CT scan exposes you to about 7mSV. Eating a banana exposes you to about 0.1 microsieverts of radiation from radioactive potassium in it. In an airplane at 30,000 feet, the level of radiation is up to 3 microsieverts. Astronauts aboard the ISS receive about 80 millisieverts(or 80,000 micro SV).

The minimal protection on Apollo spacecrafts did come with a cost. Due to radiation, Apollo astronauts experienced bright flashes in their eyes due to cosmic rays hitting at the back of the retina creating Cherenkov radiation( a type of radiation caused due to charged particles moving faster than the speed of light in a particular medium) which could have played a part imparting 39 Apollo-era astronauts some form of cataract. Out of these 36 flew on high radiation moon landing missions.*Citation

According to a study in 2016 by Michael D. Delp and collaborators, the Cardiovascular Disease(CVD) mortality rate among Apollo lunar astronauts is 4-5 times higher than LEO astronauts. The number of deaths due to CVD was almost 5 times higher in Apollo astronauts. Note that several Apollo astronauts also had flown on Skylab, Gemini or Mercury missions.

Radiation, specifically high atomic number 56Fe, can also alter cellular physiology through DNA (deoxyribonucleic acid) mutations. Astronauts may experience increased thirst for water which would deplete this precious resource at a faster rate.

There are some positive effects of radiation(weird, right? oxymoron! hah) which include a little better adaptation of the immune system and increased telomers' length, which is related to cell-repair and aging. The longer, the better!

The best way to protect the craft would be to build an artificial magnetic field around the craft, followed by some passive protection though layers of aluminium or metal hydrides.

Astronauts would also have to carry exercise machines since the zero-g environment would reduce their bone density. ISS has 3(a bicycle, a treadmill, and a weightlifting machine of them on which astronauts workout for at least 6 hours a day. Their vision would also decline but that isn't a huge problem as it could be corrected by power glasses.

NASA revealed it's newest space-suits for Artemis generation in 2019, so that's a plus.

The Exploration Extra Vehicular Mobility unit:

Credit: NASA

The Orion Crew Survival suit:

Credit: NASA

Both these suits are supposed to be the best NASA space-suits yet enabling extended deep space missions to Moon and Mars.

So, can Starship or Orion even carry the resources for a Mars mission? According to NASA, a crew of six astronauts would require about 1360 tons of resources for a round-trip Mars mission! No rocket has that capability. So, cancel Mars and eat popcorns while streaming Netflix?

Meh, not so fast!

One possible way is to send astronauts with enough resources for the first six months, while simultaneously launching two more Starships with resources for while they're on Mars and the returning journey or, maybe, sending astronauts in a crew of 3 on two Starships. Starship also comes with in-orbit fueling capacity. Engineers and mission designers would have thought on that, hopefully.

During this pandemic induced lockdown of 2-3 months, people got frustrated and anxious and their state of mind was far from stable. Would you go into one for 21 months with no internet? Hell NO! Right? The astronauts traveling to Mars would. They would be in a live connection in the beginning but as the latency of communication increases with distance, it would be more of a passive communication mostly in the form of recorded messages. The psychological effects could be traumatizing for a normal human. Astronauts would think weird thoughts. We have seen the perspective of astronauts change when they return from space missions. They become more of a deep thinker. But thinking too deep may be the cause of anxiety and lack of motivation. They would question the objectives of the mission itself. Of course, we're speculating the worst-case scenario. They'd still have movies, games, and other recreational activities. It is also really important for the astronauts to have a strong connection among themselves. In the end, it's the endurance of the astronaut that matters as the other problems are in the process of being figured out by our awesome engineers.

Once we're on the Red Planet, it would be easy to build a colony as the technology for most purposes exists already. MOXIE is a Carbon Dioxide to Oxygen converting experiment, an experimental version of which is already aboard the Perseverance Rover launched last month! It could easily be scaled to meet our oxygen demands. Carbon Dioxide along with water-ice could also be used to make fuel for Starship after being extracted from the atmosphere. Caves, and other geographical structures along with inflatable modules could be our homes far from Earth. Though, due to radiation, we might have to go underground in the initial phase. Researches show that martian soil is perfect for making bricks. Just add some polymer plastics into soil and microwave it, and bricks are ready! Even some plants with modification to martian soil could be cultivated. And of course, the cost of Starship launches could come down to as low as $2 million per launch and as cheap as $2000 per Kg! It would make delivering food and other resources much feasible to our fellow earthlings on our sister planet.

Even though nuking Mars is some way off, the technology for terraforming the planet exists, from placing a giant magnet in front of Mars in the L1 Lagrange point to melting polar ice caps!

1: Reduces Solar Wind stripping of the already thin atmosphere.Benefits of placing an artificial magnet:

2: Reduces High-Energy radiation to the surface.

3: Stabilizes the equilibrium of the atmosphere.

We may not have a fully-fledged colony as long as the 2040s but the dream of being multi-planetary isn't far off. We sure as hell would be multi-planetary before the end of the decade. Humans have always achieved their ambition. This one would mark the start of a new age, a new chapter in the history of mankind. We'll show that the chance of living given to us hasn't gone to vain.

https://proskillocity.blogspot.com/2021/04/habitability.html

https://proskillocity.blogspot.com/2019/08/how-do-we-know-shape-of-our-galaxy_21.html