Answer:
There are 50 ASE certification tests, covering almost every imaginable aspect of the automotive repair and service industry.
Explanation:
yww <33
Answer:
50421.6 m³
Explanation:
The river has an average rate of water flow of 59.6 m³/s.
Tributary B accounts for 47% of the rate of water flow. Therefore the rate of water flow through tributary B is:
Flow rate of water through tributary B = 47% of 59.6 m³/s = 0.47 * 59.6 m³/s = 28.012 m³/s
The volume of water that has been discharged through tributary B = Flow rate of water through tributary B * time taken
time = 30 minutes = 30 minutes * 60 seconds / minute = 1800 seconds
The volume of water that has been discharged through tributary B in 30 seconds = 28.012 m³/s * 1800 seconds = 50421.6 m³
Answer:

Explanation:
First, we will find actual properties at given inlet and outlet states by the use of steam tables:
AT INLET:
At 4MPa and 350°C, from the superheated table:
h₁ = 3093.3 KJ/kg
s₁ = 6.5843 KJ/kg.K
AT OUTLET:
At P₂ = 125 KPa and steam is saturated in vapor state:
h₂ =
= 2684.9 KJ/kg
Now, for the isentropic enthalpy, we have:
P₂ = 125 KPa and s₂ = s₁ = 6.5843 KJ/kg.K
Since s₂ is less than
and greater than
at 125 KPa. Therefore, the steam is in a saturated mixture state. So:

Now, we will find
(enthalpy at the outlet for the isentropic process):

Now, the isentropic efficiency of the turbine can be given as follows:

Answer:
The correct option is;
B) Metamorphic Rocks
Explanation:
Zoisite, which is also referred to saualpite, is a metamorphic rock which is a hydroxy sorosilicate mineral formed from other types of rocks such as sedimentary, metamorphic and ingenious rocks in the process of their metamorphism under the presence high temperatures and pressures and mineral fluids which are hot
Zoiste is named after Sigmund Zois by Abraham Gottlob Werner in 1805 when Sigmund Zois sent Abraham Gottlob Werner the mineral specimen from Saualpe in 1805
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