Showing results for phenomena Background
GitHub Repo
https://github.com/Sohana1234/quantumphysics
Sohana1234/quantumphysics
Particle physics generates huge amounts of experimental data. Challenge: Distinguish signal events (rare, interesting) from background events (noise). This classification helps in the discovery of new particles & phenomena
GitHub Repo
https://github.com/SurathiBharath/web-page-1
SurathiBharath/web-page-1
<!DOCTYPE html> <html> <head> <style> ul { list-style-type: none; margin: 0; padding: 0; overflow: hidden; background-color: hsl(0, 0%, 0%); } li { float: left; } li a { display: yellow; padding: 8px; } </style> </head> <hr><hr> <body bgcolor="yellow"> <ul> <li><a href="#home">Home</a></li> <li><a href="#back">back</a></li> <li><a href="#next">next</a></li> <li><a href="#about">About</a></li> </ul> <hr><hr> <img src ="C:\Users\Venkatram\Desktop\HTML\img.JFIF" alt="LA" width="400" height="400"> <P>Nature, in the broadest sense, is the physical world or universe. <br>"Nature" can refer to the phenomena of the physical world, and also to life in general.<br> The study of nature is a large, if not the only, part of science. <br>Although humans are part of nature, human activity is often understood as a separate category from other natural phenomena.</P> </body> </html>
GitHub Repo
https://github.com/paulbeka/level-4-project
paulbeka/level-4-project
Cellular automata, like the famous "Game of Life", exhibit stable phenomena such as oscillators (periodic patterns) and spaceships (periodic patterns with translation). It is typically extremely difficult to find these structures, and much effort has been spent in finding efficient search algorithms to find structures. See https://arxiv.org/abs/cs/0004003 for a background paper on existing search approaches. This project will examine how deep learning could be used to accelerate CA search. The use of convolutional neural networks offers a structure which might be able to intelligently guide search algorithms to find novel structures.
GitHub Repo
https://github.com/suguna1993/Particle-Physics-Event-Classification-using-Random-Forest-ML-model-Signal-Vs-Background.
suguna1993/Particle-Physics-Event-Classification-using-Random-Forest-ML-model-Signal-Vs-Background.
The goal is to develop a model that can accurately predict whether a given set of experimental features corresponds to a signal or background event. Successful classification contributes to the advancement of particle physics research by automating the identification of events that may indicate the presence of specific particles or phenomena.
GitHub Repo
https://github.com/SurathiBharath/project-
SurathiBharath/project-
<!DOCTYPE html> <html> <head> <style> ul { list-style-type: none; margin: 0; padding: 0; overflow: hidden; background-color: hsl(0, 0%, 0%); } li { float: left; } li a { display: yellow; padding: 8px; } </style> </head> <hr><hr> <body bgcolor="yellow"> <ul> <li><a href="#home">Home</a></li> <li><a href="#back">back</a></li> <li><a href="#next">next</a></li> <li><a href="#about">About</a></li> </ul> <hr><hr> <img src ="C:\Users\Venkatram\Desktop\HTML\img.JFIF" alt="LA" width="400" height="400"> <P>Nature, in the broadest sense, is the physical world or universe. <br>"Nature" can refer to the phenomena of the physical world, and also to life in general.<br> The study of nature is a large, if not the only, part of science. <br>Although humans are part of nature, human activity is often understood as a separate category from other natural phenomena.</P> </body> </html>
GitHub Repo
https://github.com/saurabhpende/Particle_Physics_Event_Classification
saurabhpende/Particle_Physics_Event_Classification
I develop a model that can accurately predict whether a given set of experimental features corresponds to a signal or background event. Successful classification contributes to the advancement of particle physics research by automating the identification of events that may indicate the presence of specific particles or phenomena.
GitHub Repo
https://github.com/aritradey97/processing4
aritradey97/processing4
It is an animated representation of the big-bang theory.The model describes how the universe expanded from a very high density and high temperature state,[5][6] and offers a comprehensive explanation for a broad range of phenomena, including the abundance of light elements, the cosmic microwave background, large scale structure and Hubble's Law.
GitHub Repo
https://github.com/Lspringer24/Citi-Bike
Lspringer24/Citi-Bike
# Tableau Homework - Citi Bike Analytics ### Before You Begin * This assignment will be saved to your tableau public account rather than github. * If you haven't already, be sure to create a tableau public account [here](https://public.tableau.com/s/). * The free tier of tableau only lets you save to their public server. This means that each time you save your file it will be uploaded to your tableau public profile. * You are able to load and continue working on the same workbook. * When you are finished with your assignment, you will turn in the URL to your tableau public workbook along with any additional files used for your analysis. ## Background  Congratulations on your new job! As the new lead analyst for the [New York Citi Bike](https://en.wikipedia.org/wiki/Citi_Bike) Program, you are now responsible for overseeing the largest bike sharing program in the United States. In your new role, you will be expected to generate regular reports for city officials looking to publicize and improve the city program. Since 2013, the Citi Bike Program has implemented a robust infrastructure for collecting data on the program's utilization. Through the team's efforts, each month bike data is collected, organized, and made public on the [Citi Bike Data](https://www.citibikenyc.com/system-data) webpage. However, while the data has been regularly updated, the team has yet to implement a dashboard or sophisticated reporting process. City officials have a number of questions on the program, so your first task on the job is to build a set of data reports to provide the answers. ## Task **Your task in this assignment is to aggregate the data found in the Citi Bike Trip History Logs and find two unexpected phenomena.** **Design 2-5 visualizations for each discovered phenomena (4-10 total). You may work with a timespan of your choosing. Optionally, you may merge multiple datasets from different periods.** **The following are some questions you may wish to tackle. Do not limit yourself to these questions; they are suggestions for a starting point. Be creative!** * How many trips have been recorded total during the chosen period? * By what percentage has total ridership grown? * How has the proportion of short-term customers and annual subscribers changed? * What are the peak hours in which bikes are used during summer months? * What are the peak hours in which bikes are used during winter months? * Today, what are the top 10 stations in the city for starting a journey? (Based on data, why do you hypothesize these are the top locations?) * Today, what are the top 10 stations in the city for ending a journey? (Based on data, why?) * Today, what are the bottom 10 stations in the city for starting a journey? (Based on data, why?) * Today, what are the bottom 10 stations in the city for ending a journey (Based on data, why?) * Today, what is the gender breakdown of active participants (Male v. Female)? * How effective has gender outreach been in increasing female ridership over the timespan? * How does the average trip duration change by age? * What is the average distance in miles that a bike is ridden? * Which bikes (by ID) are most likely due for repair or inspection in the timespan? * How variable is the utilization by bike ID? **Next, as a chronic over-achiever:** * Use your visualizations (does not have to be all of them) to design a dashboard for each phenomena. * The dashboards should be accompanied with an analysis explaining why the phenomena may be occuring. **City officials would also like to see one of the following visualizations:** * **Basic:** A static map that plots all bike stations with a visual indication of the most popular locations to start and end a journey with zip code data overlaid on top. * **Advanced:** A dynamic map that shows how each station's popularity changes over time (by month and year). Again, with zip code data overlaid on the map. * The map you choose should also be accompanied by a write-up unveiling any trends that were noticed during your analysis. **Finally, create your final presentation** * Create a Tableau story that brings together the visualizations, requested maps, and dashboards. * This is what will be presented to the officials, so be sure to make it professional, logical, and visually appealing. ## Considerations Remember, the people reading your analysis will **NOT** be data analysts. Your audience will be city officials, public administrators, and heads of New York City departments. Your data and analysis needs to be presented in a way that is focused, concise, easy-to-understand, and visually compelling. Your visualizations should be colorful enough to be included in press releases, and your analysis should be thoughtful enough for dictating programmatic changes. ## Submission Your final submission should include: * A link to your Tableau Public workbook that includes: * 4-10 Total "Phenomenon" Visualizations * 2 Dashboards * 1 City Official Map * 1 Story * A text or markdown file with your analysis on the phenomenons you uncovered from the data. ## Assessment Your final product will be assessed on the following metrics: * Analytic Rigor * Readability * Visual Attraction ## Hints * You may need to get creative in how you combine each of the CSV files. Don't just assume Tableau is the right tool for the job. At this point, you have a wealth of technical skills and research abilities. Dig for an approach that works and just go with it. * Don't just assume the CSV format hasn't changed since 2013. Subtle changes to the formats in any of your columns can blockade your analysis. Ensure your data is consistent and clean throughout your analysis. (Hint: Start and End Time change at some point in the history logs). * Consider building your visualizations with small extracts of the data (i.e. single files) before attempting to import the whole thing. What you will find is that importing all 20+ million records of data will create performance issues quickly. Welcome to "Big Data." * While utilizing all of the data may seem like a nice power play, consider the time-course in making your analysis. Is data from 2013 the most relevant for making bike replacement decisions today? Probably not. Don't let overwhelming data fool you. Ground your analysis in common sense. * Remember, data alone doesn't "answer" anything. You will need to accompany your data visualizations with clear and directed answers and analysis. * As is often the case, your clients are asking for a LOT of answers. Be considerate about their need-to-know and the importance of not "cramming in everything". Of course, answer each question, but do so in a way that is organized and presentable. * Since this is a project for the city, spend the appropriate time thinking through decisions on color schemes, fonts, and visual story-telling. The Citi Bike program has a clear visual footprint. As a suggestion, look for ways to have your data visualizations match their aesthetic tones. * Pay attention to labels. What exactly is "time duration"? What's the value of "age of birth"? You will almost certainly need calculated fields to get what you need. * Keep a close eye for obvious outliers or false data. Not everyone who signs up for the program is answering honestly. * In answering the question of "why" a phenomenon is occurring, consider adding other pieces of information on socioeconomic or other geographic data. Tableau has a map "layer" feature that you may find handy. * Don't be afraid to manipulate your data and play with settings in Tableau. Tableau is meant to be explored. We haven't covered all that you need -- so you will need to keep an eye out for new tricks. * Treat this as a serious endeavor! This is an opportunity to show future employers that you have what it takes to be a top-notch analyst. * Good luck! ### Copyright Data Boot Camp (C) 2019. All Rights Reserved.
GitHub Repo
https://github.com/arrow-time/Fixed-Quantum-Chiral-Background-in-Space
arrow-time/Fixed-Quantum-Chiral-Background-in-Space
This single postulate provides a unified explanatory foundation for several fundamental physical phenomena: 1.Parity Violation; 2.Instability of Antiparticles and Matter-Antimatter Asymmetry: Antiparticles; 3.Higgs Mechanism as Quantum Chiral Lock; 4.Dark Matter Formation
GitHub Repo
https://github.com/rudykmckee/A-n-Body-Closed-Form-Solution