The choice between AVL and red-black trees often depends on the functional characteristics of the application and the frequency of the operations being performed. AVL trees tend to have a more balanced structure, suitable for scenarios where search operations are frequent, while red-black trees might be preferred for scenarios with more frequent insertion and deletion operations.

In bubble sort, where each iteration places the largest unsorted element to its correct position, n-1 iterations are required to sort an array of size n, making a total of (n-1) + (n-2) + ... + 1 iterations.

Breadth-First Search (BFS) explores nodes level by level, starting from the source node and moving to their neighbors. It systematically visits all the neighbors at the current depth before moving on to nodes at the next level.

The bottom-up approach is commonly used in dynamic programming to efficiently compute Fibonacci numbers. It involves solving smaller subproblems first and using their solutions to build up to the solution of the original problem, often utilizing an array or table to store intermediate results.

"Increasing" in the Longest Increasing Subsequence (LIS) problem refers to arranging elements in ascending order. The goal is to find the longest subsequence where elements are in increasing order.

Linear search is a versatile algorithm that can be applied to search for elements, values, or objects in collections other than arrays. It is not limited to specific data types and can be used in various scenarios for searching unsorted data.

Adapting BFS for a maze with obstacles can be done by incorporating a heuristic approach, similar to A* Algorithm. A* considers both the cost to reach a point and an estimate of the remaining distance to the goal. In the context of a maze, this modification helps BFS navigate efficiently around obstacles, making it more effective for pathfinding in complex environments compared to the traditional BFS approach.

Linear search is preferable over binary search in scenarios where the array is small or not sorted. In such cases, the simplicity of linear search can be more efficient than the overhead involved in binary search, especially for small datasets or unsorted arrays where the linear search can terminate as soon as the element is found.

Dijkstra's Algorithm would be more suitable for the scenario because it not only finds the shortest path but also considers the weights or distances between destinations. In a delivery service, the distances between locations (nodes) are likely to vary, making Dijkstra's Algorithm more appropriate than BFS, which does not consider edge weights.

Topological sorting is extensively used in scheduling tasks in project management. It ensures that tasks are executed in the correct order based on dependencies, helping in efficient project completion. For example, if Task B depends on Task A, topological sorting ensures Task A is scheduled before Task B.