Backtracking in DFS involves reverting to the previous step and trying a different option when exploring a solution space. It is particularly useful in problems with multiple decision points and unknown paths.

In radix sort, the "radix" or base value is significant as it defines the number of digits in each element. The algorithm processes each digit individually based on this radix, creating a sorted sequence.

In this scenario, BFS would be the preferable choice. BFS explores neighboring locations first, ensuring that the shortest path is found before moving to more distant locations. It guarantees the shortest route for unweighted graphs, making it suitable for navigation systems. DFS, on the other hand, may find a solution faster in certain cases but does not guarantee the shortest path.

Radix sort involves sorting elements based on individual digits. Starting from the least significant digit (LSD) to the most significant digit (MSD), elements are grouped and rearranged. The process is repeated until all digits are considered, resulting in a sorted array. Pseudocode and implementation details provide a clearer understanding.

In the context of LCS, a subsequence is a sequence of elements that appear in the same order as in the original sequence but not necessarily consecutively. It allows for gaps between elements in the subsequence.

When sorting large datasets stored on disk, mitigating the risk of running out of memory involves using an in-memory caching mechanism. This mechanism allows frequently accessed data to be stored in memory, reducing disk I/O operations and minimizing the chance of memory exhaustion.

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 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.

"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.

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.

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.

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.