Answer:
ethics
Explanation:
The set of principles that guides the ordering of values is known as ethics. Scenario, challenge, or event in which a person has to choose between various morally correct or bad behaviors. The moral principles and norms that govern conduct in the business world. The rules or norms that regulate a specific individual or a group's behavior. It is also the ethical ideas and standards that society accepts as right or good as opposed to immoral or undesirable.
Answer:
The first automatic digital computer has been designed by the English mathematician and inventor Charles Babbage. Babbage developed the Analytical Engine Plans for the mid-1830s.
Explanation:
- Babbage has developed the concept of a digital, programmable computer and was a mathematician, philosopher, inventor, and mechanical engineer.
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Some regard Babbage as a "computer father"
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The inventing of its first mechanical computer, the difference engine, is attributable to Babbage, which eventually resulted in more complex electronic designs, although Babbage's Analytical Engine is the main source of ideas for modern computers. He was described as the "prime" among the numerous polymaths of his century by his varied work in another field.
Answer:
The answer is "None of these".
Explanation:
In the given question an array "sales[]" is declared, which contains 50 double type elements, and in the next line, an integer variable j is defined, which uses a for loop. In this question two options is given, in which both are not correct, that can be described as follows:
- In option (i), A loop is defined that, uses variable j which starts with 0 and ends with 48, So total elements are 48 that's why it is not correct.
- In option (ii), A loop will use variable j that, starts with 1 and ends with 49, That's why it is not correct.
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).
