M15 Kuala Lumpur stats & predictions

Daniil Medvedev vs. Emerging Talent

This match offers an intriguing contrast between established prowess and budding potential. Medvedev's experience could give him an edge over younger competitors who are still finding their footing on clay courts.

• Tactics: Medvedev should exploit his aggressive baseline play while keeping an eye out for opportunities at the net.
• Predictions: Given Medvedev’s current form, he is likely to emerge victorious unless his opponent manages an unexpected upset through clever tactics or sheer determination.davidschneider1998/Physics-Projects<|file_sep# Physics Projects Repository This repository contains physics projects I have completed throughout my high school career. <|repo_name|>davidschneider1998/Physics-Projects<|file_sep### Summary ### The goal of this project was create a model that predicts how many people would die per year from COVID-19 if there were no social distancing measures implemented. ### Method ### In order to predict how many people would die per year if there were no social distancing measures implemented I created two models: one assuming exponential growth (no social distancing) until herd immunity was reached then linear growth afterwards; another assuming logistic growth until herd immunity was reached then linear growth afterwards. To fit these models I used real world COVID-19 case data collected by Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data). To fit these models I also needed death rate information which was obtained from https://www.worldometers.info/coronavirus/. I used Python code written by David Schneider (https://github.com/davidschneider1998) (see "Python Code" section below) which took as input three arguments: 1) number of days since first reported case (which I set equal to zero) 2) number of days since first reported death (which I set equal to zero) 3) number of days after which herd immunity is achieved (which varied depending on model) Using these inputs it generated two plots: one showing how cases would grow over time assuming exponential growth until herd immunity was achieved then linear growth afterwards; another showing how cases would grow over time assuming logistic growth until herd immunity was achieved then linear growth afterwards. Both plots also included actual case numbers based on real world COVID-19 case data collected by Johns Hopkins University. I assumed that each infected person would eventually die from COVID-19 so I multiplied my predicted number cases by death rate information obtained from https://www.worldometers.info/coronavirus/. ### Results ### For my models I assumed that herd immunity would be achieved after approximately two years since COVID-19 first appeared (~730 days). Using this assumption my results showed that approximately ~50 million people would die per year if there were no social distancing measures implemented. ### Conclusions ### My results suggest that without social distancing measures ~50 million people would die per year due to COVID-19. ### Limitations ### The biggest limitation with my model is that it assumes every infected person will eventually die due COVID-19 which may not be true. Another limitation is that it assumes everyone has equal probability of getting infected regardless if they live alone or live with others. ### Python Code ### python #!/usr/bin/env python import numpy as np import pandas as pd import matplotlib.pyplot as plt def main(): # Import real world COVID-19 case data data = pd.read_csv('csse_covid_19_data/csse_covid_19_time_series/time_series_covid_19_confirmed_global.csv') # Create array containing total number cases per day cases_per_day = np.sum(data.iloc[:,11:], axis=0).to_numpy() # Create array containing cumulative number cases per day cumulative_cases_per_day = np.cumsum(cases_per_day) # Set starting point for modeling starting_point_for_modeling = np.argmax(cumulative_cases_per_day > .01*cumulative_cases_per_day[-1]) # Calculate average daily percent increase over last seven days before starting point for modeling daily_percent_increase_last_seven_days_before_starting_point_for_modeling = ((cumulative_cases_per_day[starting_point_for_modeling]/cumulative_cases_per_day[starting_point_for_modeling -7])**(.1)-1)*100 # Define function describing exponential growth until herd immunity reached then linear growth afterwards def exponential_growth_until_herd_immunity_reached_then_linear_growth_afterwards(days_since_first_case,days_since_first_death,days_until_herd_immunity): if days_since_first_case <= days_until_herd_immunity: return cumulative_cases_per_day[starting_point_for_modeling]*(1+daily_percent_increase_last_seven_days_before_starting_point_for_modeling/100)**(days_since_first_case-days_since_first_death) else: return cumulative_cases_per_day[starting_point_for_modeling]*(1+daily_percent_increase_last_seven_days_before_starting_point_for_modeling/100)**(days_until_herd_immunity-days_since_first_death)+(days_since_first_case-days_until_herd_immunity)*(cumulative_cases_per_day[starting_point_for_modeling]*daily_percent_increase_last_seven_days_before_starting_point_for_modeling/100/(1+daily_percent_increase_last_seven_days_before_starting_point_for_modeling/100)**(days_until_herd_immunity-days_since_first_death)) # Define function describing logistic growth until herd immunity reached then linear growth afterwards def logistic_growth_until_herd_immunity_reached_then_linear_growth_afterwards(days_since_first_case,days_since_first_death,days_until_herd_immunity,k): if days_since_first_case <= days_until_herd_immunity: return k/(1+(k/cumulative_cases_per_day[starting_point_for_modeling]-1)*np.exp(-daily_percent_increase_last_seven_days_before_starting_point_for_modeling/100*days_since_first_case)) else: return k/(1+(k/cumulative_cases_per_day[starting_point_for_modeling]-1)*np.exp(-daily_percent_increase_last_seven_days_before_starting_point_for_modeling/100*days_until_herd_immunity))+(days_since_first_case-days_until_herd_immunity)*(k*daily_percent_increase_last_seven_days_before_starting_point_for_modeling/(100*(k-cumulative_cases_per_day[starting_point_for_modeling]))*np.exp(-daily_percent_increase_last_seven_days_before_starting_point_for_modelismodell%auntiimmunit_dailysincefirstcasel%daiyuntilherdimmunitry)) # Fit exponential model using curve_fit function from scipy library popt_exp,[pcov_exp] = curve_fit(exponential_growth_until_herd_immunity_reached_then_linear_growth_afterwards,[starting_points],cumulative_cases_per_day[startinstarting_points:],bounds=(0,np.inf),maxfev=5000,p0=[365,.03]) k_optimal_logistic,model_covariance_matrix_logistic] = curve_fit(logistic_growth_until_heard_immunity_reached_then_linear_growth_afterwards,[starting_points],cumulative_cases_per_day[startinstarting_points:],bounds=(0,np.inf),maxfev=5000,p0=[365,.03]) # Generate arrays containing predicted number cases per day using fitted exponential model parameters predicted_number_of_cases_exponential=np.zeros((len(cumulative_cases_per_day))) for i in range(len(cumulative_cases_perecursive_number_of_days)): predicted_number_of_cases_exponential[i] = exponential_growuntil_heard_immmunity_reached_then_linear_growth_afterwards(i,i-startsngpointformodellngpointformodelnoghtogether,popt_exp[i]) predicted_number_of_caselogistic=np.zeros((len(cumulative_caselogistic))) for i in range(len(cumlative_caselogistic)): predicted_number_of_caselogistic[i]logisticsgrowthuntilheardimmunityreachedthenlineargrowthafterwards(i,i-startinstartingpoints,k_optimal_logisitc[i]) <|file_sep># Physics Projects Repository This repository contains physics projects I have completed throughout my high school career. <|repo_name|>davidschneider1998/Physics-Projects<|file_sep**Summary** The purpose of this project was twofold: Firstly we wanted find out what effect adding different amounts NaCl had on boiling point elevation. Secondly we wanted find out what effect adding different amounts NaCl had on freezing point depression. **Method** In order complete our experiment we performed two separate trials: In our boiling point elevation trial we added varying amounts NaCl solution ranging from pure water (0 g NaCl added) all the way up to saturated salt solution (~36 g NaCl added). We did this three times resulting in three sets of data points. In our freezing point depression trial we added varying amounts NaCl solution ranging from pure water (0 g NaCl added) all the way up upto saturated salt solution (~36 g NaCl added). We did this four times resulting four sets of data points. We measured boiling points using thermometer placed inside boiling water bath. We measured freezing points using thermometer placed inside ice water bath. After collecting our raw data we plotted graphs showing relationship between amount NaCl added vs boiling/freezing points. We fitted lines through these graphs using least squares method. From slopes obtained via least squares method we calculated molality values corresponding with each line fit. From molality values corresponding with each line fit we calculated van't Hoff factors corresponding with each line fit. From van't Hoff factors corresponding with each line fit we determined whether or not ion pair formation occured between sodium cations & chloride anions present within salt solutions. **Results** Our results show that boiling point elevations ranged between ~101°C & ~102°C whereas freezing point depressions ranged between ~ -2°C & ~ -20°C depending upon amount salt added (& respective molality value associated). Furthermore our results indicate van't Hoff factor values ranged between ~2 & ~2.5 depending upon amount salt added (& respective molality value associated). These results suggest ion pair formation occurred because van't Hoff factor values were greater than theoretical value expected if no ion pair formation occurred (~2) **Conclusions** Our experiment shows adding increasing amounts NaCl caused boiling point elevations & freezing point depressions consistent w/theoretical predictions made based upon colligative properties principles taught within chemistry courses taught at high school level. <|repo_name|>ZhangDengxiao/vscode-extension-sqlfmt<|file_sep