Answer:
I've implemented this program using python
userinput = int(input("Length: "))
mylist = []
mylist.append(userinput)
for i in range(1,userinput+1):
userinp = int(input("Input: "))
mylist.append(userinp)
smallval = mylist[1]
for i in range(1,len(mylist)):
if smallval > mylist[i]:
smallval = mylist[i]
for i in range(1,len(mylist)):
mylist[i] = mylist[i] - smallval
for i in range(1,len(mylist)):
print(mylist[i],end=' ')
Explanation:
I've added the full source program as an attachment where I used comments to explain difficult lines
Answer:
The algorithm is as follows:
1. Start
2. Display "Input Item Price: "
3. Input Price
4. Markup = Price + 80% * Price
5. Selling_Price = 10% * Markup
6. Display "The Selling Price is "+Selling_Price
7. Stop
Explanation:
This signals the beginning of the algorithm
1. Start
This is a prompt that asks for the price of the item
2. Display "Input Item Price: "
This gets the price of the item
3. Input Price
This calculates the Markup (80% of the inputted price)
4. Markup = Price + 80% * Price
This calculates the selling price (10% of the markup)
5. Selling_Price = 10% * Markup
This prints the selling price
6. Display "The Selling Price is "+Selling_Price
This signals the end of the algorithm
7. Stop
Answer:
accounting system
Explanation:
The most common response variable modeled for cropping systems is yield, whether of grain, tuber, or forage biomass yield. This yield is harvested at a single point in time for determinate annual crops, while indeterminate crops and grasslands may be harvested multiple times. Although statistical models may be useful for predicting these biological yields in response to some combination of weather conditions, nutrient levels, irrigation amounts, etc. (e.g., Schlenker and Lobell, 2010, Lobell et al., 2011), they do not predict responses to nonlinearities and threshold effects outside the range of conditions in data used to develop them.
In contrast, dynamic cropping and grassland system models may simulate these biological yields and other responses important to analysts, such as crop water use, nitrogen uptake, nitrate leaching, soil erosion, soil carbon, greenhouse gas emissions, and residual soil nutrients. Dynamic models can also be used to estimate responses in places and for time periods and conditions for which there are no prior experiments. They can be used to simulate experiments and estimate responses that allow users to evaluate economic and environmental tradeoffs among alternative systems. Simulation experiments can predict responses to various climate and soil conditions, genetics, and management factors that are represented in the model. “Hybrid” agricultural system models that combine dynamic crop simulations with appropriate economic models can simulate policy-relevant “treatment effects” in an experimental design of climate impact and adaptation (Antle and Stockle, 2015).
Answer: The ASOS systems serves as the nation's primary surface weather observing network. ASOS is designed to support weather forecast activities and aviation operations
Answer:
It is science fiction
Explanation:
Rockets and stars relate to science
