Optimizing Data Processing with Concurrent Futures

You are tasked with processing a large list of numerical data. Each processing step is CPU-bound and can be executed independently. To improve performance, you need to implement a concurrent solution using Python's concurrent.futures module. The goal is to apply a given process_item function to each item in the input list, collect all results, and handle any potential exceptions that might occur during the processing of individual items.

Your implementation should:

• Utilize a ThreadPoolExecutor (or ProcessPoolExecutor if you prefer, but ThreadPoolExecutor is sufficient for this example).
• Submit each item for processing concurrently.
• Collect the results of all successful operations.
• Identify and log any exceptions that occur during the processing of an individual item without stopping the entire execution.
• Return a list of processed results (or None for failed items if a specific value is needed for failures, but for this task, just ensure failures are logged and not in the final successful results list).