The time complexity of Prim's algorithm is O(E log V), and the time complexity of Kruskal's algorithm is also O(E log V), where 'V' is the number of vertices and 'E' is the number of edges in the graph. Both algorithms achieve this complexity by using efficient data structures to manage the edges and prioritize the minimum-weight edges.

Designing a robust system for handling variations in input text patterns efficiently involves employing dynamic pattern recognition techniques. This allows the system to adapt to variations in the data and extract relevant information accurately.

Stacks are commonly used to convert infix expressions to postfix, simplifying the evaluation process. This involves using a stack to track operators and ensure correct order of operations.

Quick Sort would be the most efficient for this scenario. Quick Sort's pivot-based partitioning allows it to handle cases where the maximum element is at the beginning and the minimum element is at the end, as it aims to place the pivot element at its correct position in a single pass, optimizing the sorting process.

Backtracking in regular expression matching involves exploring different paths to find a successful match. It systematically tries different possibilities until a match is found or all possibilities are exhausted.

Binary search is most suitable for sorted arrays. If the array is not sorted, applying binary search becomes impractical as it relies on the property of a sorted array to efficiently locate elements.

The time complexity of the standard dynamic programming approach for Matrix Chain Multiplication is O(n^3), where 'n' is the number of matrices being multiplied. This is achieved through the dynamic programming technique of solving subproblems and storing their solutions in a table for reuse.

In Matrix Chain Multiplication, the order of matrix multiplication is significant because it affects the associativity of the operation. Different parenthesizations may result in different numbers of scalar multiplications, and the algorithm aims to find the optimal parenthesization to minimize computational cost.

Bubble sort compares adjacent elements in the array and swaps them if they are in the wrong order. This process continues until the entire array is sorted. The algorithm gets its name from the way smaller elements "bubble" to the top of the array during each iteration. This sorting method is simple to implement but is inefficient for large datasets, as it has a time complexity of O(n^2) in the worst case, where n is the number of elements in the array.

Radix sort sorts data by grouping digits or components of the keys. It examines individual digits or components and places the elements into buckets based on these components.