Some operating systems have a tree–structured file system but limit the depth of the tree to some small number of levels. What effect does this limit have on users? How does this simplify file system design (if it does)?

Answer:

sample output

Enter f0 = 440

440.0,466.1637615180899, 493.8833012561241,

523.2511306011974,  554.3652619537443

Explanation:

import math                                 // math library  

def RaiseToPower():                 //  RaiseToPower method

 r = math.pow(2,1/12)               //r = 2^(1/12)

f0 = float(input('Enter f0 : '))     //input from user

//a key has a frequency,sy f0

print(f0,end=' ')                      //Display statement

 for n in range(1,5):                //Iterate the loop up to  the next 4 higher key Hz

n = f0 * math.pow(r,n)         //calculate the fn value

print(fn,end=' ')                   //Display statement

 RaiseToPower()                //Call RaiseToPower method  

the answer is Language

Answer:

Essayons

Explanation:

Answer:

Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. Greedy algorithms are used for optimization problems. An optimization problem can be solved using Greedy if the problem has the following property: At every step, we can make a choice that looks best at the moment, and we get the optimal solution of the complete problem.

If a Greedy Algorithm can solve a problem, then it generally becomes the best method to solve that problem as the Greedy algorithms are in general more efficient than other techniques like Dynamic Programming. But Greedy algorithms cannot always be applied. For example, the Fractional Knapsack problem (See this) can be solved using Greedy, but 0-1 Knapsack cannot be solved using Greedy.

The following are some standard algorithms that are Greedy algorithms.

1) Kruskal’s Minimum Spanning Tree (MST): In Kruskal’s algorithm, we create an MST by picking edges one by one. The Greedy Choice is to pick the smallest weight edge that doesn’t cause a cycle in the MST constructed so far.

2) Prim’s Minimum Spanning Tree: In Prim’s algorithm also, we create an MST by picking edges one by one. We maintain two sets: a set of the vertices already included in MST and the set of the vertices not yet included. The Greedy Choice is to pick the smallest weight edge that connects the two sets.

3) Dijkstra’s Shortest Path: Dijkstra’s algorithm is very similar to Prim’s algorithm. The shortest-path tree is built up, edge by edge. We maintain two sets: a set of the vertices already included in the tree and the set of the vertices not yet included. The Greedy Choice is to pick the edge that connects the two sets and is on the smallest weight path from source to the set that contains not yet included vertices.

4) Huffman Coding: Huffman Coding is a loss-less compression technique. It assigns variable-length bit codes to different characters. The Greedy Choice is to assign the least bit length code to the most frequent character. The greedy algorithms are sometimes also used to get an approximation for Hard optimization problems. For example, the Traveling Salesman Problem is an NP-Hard problem. A Greedy choice for this problem is to pick the nearest unvisited city from the current city at every step. These solutions don’t always produce the best optimal solution but can be used to get an approximately optimal solution.

Answer:

False

Explanation:

The revenue has to be considered when commercializing technology. Social, political and market support determine whether there will be customers or not. So when applying scenario logic to technology commercialization we have to consider if there is market for the technology, if the society is willing ot pay for the technology anf if politics of the region will allow the business to operate.

Answer:

C) X=40.24 Y=24.33 Z=20.75

Explanation:

Answer:

The advantage and the disadvantage of the relevant query are illustrated in the explanation in the paragraph below.

Explanation:

Advantage:

• The benefit of creating a simultaneous server through spawning a mechanism seems to be that alternative methods are shielded against everyone else, which would be very necessary whenever the extremely database manages communication services entirely.

Disadvantage:

• The downside about creating a concurrent system through spawning a methodology seems to be that this process seems to be very expensive but using multicore processing systems should save this expense. It is also easier when using threads again for the aim of communicating between two or even more participants although we stop the kernel executing the correspondence.