Answer:
Young's modulus for this alloy of nickel is 1.997×10^11 N/m^2
Explanation:
Young's modulus = stress/strain
Stress = Force (F)/Area (A)
F = mg = 59 kg × 9.8 m/s^2 = 578.2 N
A = πd^2/4 = 3.142 × (0.19/100)^2/4 = 2.836×10^-6 m^2
Stress = 578.2/2.836×10^-6 = 2.039×10^8 N/m^2
Strain = extension/length = 2.86×10^-3 m/2.8 m = 1.021×10^-3
Young's modulus = 2.039×10^8/1.021×10^-3 = 1.997×10^11 N/m^2
A person would prefer to be able for the doctor/ nurse to visually see their symptoms and the problem, which a robot can not do, which would lead to a misdiagnosis
Explanation:
Step1
Factor of safety is the number that is taken for the safe design of any component. It is the ratio of failure stress to the maximum allowable stress for the material.
Step2
It is an important parameter for design of any component. This factor of safety is taken according to the environment condition, type of material, strength, type of component etc.
Step3
Different material has different failure stress. So, ductile material fails under shear force. Ductile material’s FOS is based on yield stress as failure stress as after yield point ductile material tends to yield. Brittle material’s FOS is based on ultimate stress as failure stress.
The expression for factor of safety for ductile material is given as follows:

Here,
is yield stress and
is allowable stress.
The expression for factor of safety for brittle material is given as follows:

Here,
is ultimate stress and
is allowable stress.
Answer:
a) 3.607 m
b) 1.5963 m
Explanation:
See that attached pictures for explanation.
Answer:
import pandas pd
def read_prices(tickers):
price_dict = {}
# Read ingthe ticker data for all the tickers
for ticker in tickers:
# Read data for one ticker using pandas.read_csv
# We assume no column names in csv file
ticker_data = pd.read_csv("./" + ticker + ".csv", names=['date', 'price', 'volume'])
# ticker_data is now a panda data frame
# Creating dictionary
# for the ticker
price_dict[ticker] = {}
for i in range(len(ticker_data)):
# Use pandas.iloc to access data
date = ticker_data.iloc[i]['date']
price = ticker_data.iloc[i]['price']
price_dict[ticker][date] = price
return price_dict