Radix sort would be more efficient for sorting positive integers within a limited range like 1 to 1000. Its time complexity is O(nk), where 'n' is the number of elements, and 'k' is the number of digits in the largest number. In this scenario, the range is small, leading to a more favorable time complexity than merge sort.

A stack is a linear data structure that follows the Last In, First Out (LIFO) principle, meaning the last element added is the first one to be removed. It operates like a collection of elements with two main operations: push (to add an element) and pop (to remove the last added element).

The Ford-Fulkerson algorithm handles multiple sources and sinks by processing each source-sink pair independently. It performs iterations considering one source and one sink at a time, calculating flows and augmenting paths accordingly. The results are then aggregated to obtain the overall maximum flow for the entire network.

The Ford-Fulkerson algorithm can be adapted to handle graphs with multiple paths and sinks. This adaptability is essential for scenarios where there are multiple ways to route flow from the source to the sink. It involves augmenting the flow along different paths in each iteration until an optimal solution is reached.

The main advantages of using string compression techniques include improved data storage efficiency, reduced bandwidth usage, and faster data transmission. By eliminating repeated characters, the compressed string requires less space, making it beneficial in scenarios with storage or bandwidth constraints.

The longest common substring problem cannot be efficiently solved using the greedy approach. Greedy algorithms make locally optimal choices, and in this problem, a globally optimal solution requires considering the entire input space, making dynamic programming or other techniques more suitable.

To optimize bubble sort, one can implement a flag-based check that stops iterating when no more swaps are needed. This optimization helps in breaking out of the loop early if the array is already sorted, reducing unnecessary iterations and improving the overall efficiency of the algorithm.

The time complexity of DFS in the worst case is O(V + E), where V is the number of vertices and E is the number of edges in the graph. This is because DFS visits each vertex and edge exactly once in the worst case.

In BFS, to avoid infinite loops in graphs with cycles, a queue data structure is used to keep track of visited nodes. The queue ensures that nodes are explored in the order they are discovered, preventing cycles.

Recursive implementation of binary search involves breaking the problem into two subproblems at each step, making it a logarithmic algorithm with a time complexity of O(log n), where 'n' is the number of elements.