Answer:
The answer is "Option C"
Explanation:
Base heights are related to it as measurements of height or z-values as they are extracted by mathematics which serves to define the ground of that same world. Once applying a datatype, it can be any entire amount, that will become the level of the function in meters beyond the ground, and wrong choices can be described as follows:
- In option A, It is a part of extra dimensions object, that's why it is wrong.
- In option B, It wrong because the offset tool allows the quick and easy offsetting of the lines.
- In option D, It emphasizes the recovery charts, that's why it is incorrect.
I have write a very simple code for you in python and i hope it will help you a lot.
def generateString(char, val):
print(char * val)
Explanation:
This is how you can create your function in python and this function will give you the desired output.
How to Call a function:
generateString('a',12)
this is how you can call the function to get output.
I hope you get the idea.
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:
D. Expert systems
Explanation:
Artificial intelligence (AI) also known as machine learning can be defined as a branch of computer science which typically involves the process of using algorithms to build a smart computer-controlled robot or machine that is capable of performing tasks that are exclusively designed to be performed by humans or with human intelligence.
Artificial intelligence (AI) provides smarter results and performs related tasks excellently when compared with applications that are built using conventional programming.
Generally, there are two (2) main characteristics of artificial intelligence (AI) systems and these include;
I. Non-algorithmic processing.
II. Symbolic processing.
In artificial intelligence (AI), the field of expert systems is the most important applied area because it models human knowledge.
Hence, expert systems represents knowledge as a set of rules.
Although, all expert systems are generally lacking in human capabilities and can only use inference procedures to proffer solutions to specific problems that would normally require human expertise or competence.
Some of the areas where expert systems can be applied are; monitoring, diagnosis, scheduling, classification, design, process control, planning, etc.
