The correct answer is A. Familiar words for clues
Explanation:
Finding unfamiliar words is common while reading, especially in texts that belong to a specific field such as medicine, technology, etc. This can be handled through multiple strategies such as using a dictionary, guessing the meaning of the word based on its parts, and using context clues.
In this context, one of the easiest and most time-saving strategy is the use of context clues that implies using the familiar words as clues to guess the meaning of an unfamiliar word. This is effective because in most cases the meaning of an unknown word can be determined using the context of the word or words around the unknown word. Also, this strategy takes little time because you only need to analyze the sentence or paragraph where the unknown word is. Thus, the time-saving strategy to define unfamiliar words involves using familiar words for clues.
Answer:
-26
Explanation:
The given binary number is 1110 0101. Also given that the signed binary number is represented using one's compliment.
We begin by computing the 1s complement representation of 1110 0101 by inverting the bits: 00011010
Converting 00011010 to decimal, it corresponds to 26.
So the 1s complement of the original number is 26. This means that the original number was -26.
Answer:
The following code are:
public void dissolve() {
setRed(getRed()+1);
setGreen(getGreen()+1);
setBlue(getBlue()+1);
alpha+=1;
}
Explanation:
Here, we define the void type function "dissolve()" inside it, we set three function i.e, "setRed()", "setGreen()", "setBlue()" and then we increment the variable "alpha" by 1.
Inside those three mutators method we set three accessor methods i.e, "getRed()", "getGreen()" , "getBlue()" and increment these accessor by 1.
The values will not be returned by the mutator functions, the accessor will be returned the values.
Answer:
The above statement is FALSE
Augmented reality works with sensor based inputs from the real world.
It is an immersive perception of a real-world environment in which objects existing in the real world are augmented by computer-generated perceptual knowledge, often through multiple sensory modalities like visual, auditory, haptic, somatosensory and olfactory.
Based on the above description
