Answer & Explanation:
//written in java
public class Main {
public static void main(String[] args) {
//String stored in a variable named phrase
String phrase = "Brainly";
//Checking whether the first character is in upper case or not
if (Character.isUpperCase(phrase.charAt(0)))
System.out.println("capital");
else
System.out.println("not capital");
}
}
I don’t know if this helps, but here are three types of main flows: flow of material/goods, flow of money/cash, and flow of information.
Answer:
348 + 395 = 743
Hence, together they have 743 pennies and not 653 pennies. And we cannot perform the rounding, as that is the case when we have the decimal number or the float number. Only then we have the digits after the decimal. And if it's more than 5, we add 1 to the previous or else leave the number as it is. And we go on performing from right to left, and till the number of decimal places, we need to round off. However, here its purely an integer, and hence we cannot round off, as that will result in a significant loss, and which is not acceptable. However, if we want to round off before decimal places as well, then in that case 743 will be $7s, and 653 pennies will be 6+1= $7s, and if this level of loss is acceptable then we can assume that they have the same sum of money. However, here the answer is given in pennies, and hence this is not the case. And hence the answer given in the question is not correct.
Explanation:
The answer is self-explanatory. And since both are numbers, rounding is not required(as explained in the answer section), as it is required in case of decimal and float(as explained in the answer section). And as explained in the answer section, if we can tolerate very heavy loss, then the numbers as well can be rounded off as explained in the answer section. But that is not the case here, as the answer is given in pennies.
Answer: Accenture is developing a tool to help businesses detect gender, racial and ethnic bias in artificial intelligence software. 5 It lets users define the data fields they consider sensitive—such as race, gender or age—and then see the extent to which these factors are correlated with other data fields.
Explanation:
