并行操作符可以改变后续流元素的流程，通过将流分成多个子流并同时处理这些子流的元素，从而实现并行处理的效果。下面是一个包含代码示例的解决方法：

import java.util.Arrays;
import java.util.List;

public class ParallelStreamExample {
    public static void main(String[] args) {
        List numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);

        // 串行流处理
        numbers.stream()
                .map(n -> {
                    System.out.println("Mapping: " + n + " Thread: " + Thread.currentThread().getName());
                    return n * 2;
                })
                .forEach(n -> System.out.println("Consuming: " + n + " Thread: " + Thread.currentThread().getName()));

        System.out.println("------------------------");

        // 并行流处理
        numbers.parallelStream()
                .map(n -> {
                    System.out.println("Mapping: " + n + " Thread: " + Thread.currentThread().getName());
                    return n * 2;
                })
                .forEach(n -> System.out.println("Consuming: " + n + " Thread: " + Thread.currentThread().getName()));
    }
}

输出结果：

Mapping: 1 Thread: main
Consuming: 2 Thread: main
Mapping: 2 Thread: main
Consuming: 4 Thread: main
Mapping: 3 Thread: main
Consuming: 6 Thread: main
Mapping: 4 Thread: main
Consuming: 8 Thread: main
Mapping: 5 Thread: main
Consuming: 10 Thread: main
Mapping: 6 Thread: main
Consuming: 12 Thread: main
Mapping: 7 Thread: main
Consuming: 14 Thread: main
Mapping: 8 Thread: main
Consuming: 16 Thread: main
Mapping: 9 Thread: main
Consuming: 18 Thread: main
Mapping: 10 Thread: main
Consuming: 20 Thread: main
------------------------
Mapping: 2 Thread: ForkJoinPool.commonPool-worker-1
Mapping: 1 Thread: ForkJoinPool.commonPool-worker-2
Consuming: 4 Thread: ForkJoinPool.commonPool-worker-2
Consuming: 2 Thread: ForkJoinPool.commonPool-worker-1
Mapping: 3 Thread: ForkJoinPool.commonPool-worker-3
Mapping: 4 Thread: ForkJoinPool.commonPool-worker-1
Consuming: 6 Thread: ForkJoinPool.commonPool-worker-3
Consuming: 8 Thread: ForkJoinPool.commonPool-worker-1
Mapping: 5 Thread: ForkJoinPool.commonPool-worker-2
Mapping: 6 Thread: ForkJoinPool.commonPool-worker-3
Consuming: 12 Thread: ForkJoinPool.commonPool-worker-3
Mapping: 7 Thread: ForkJoinPool.commonPool-worker-4
Consuming: 10 Thread: ForkJoinPool.commonPool-worker-2
Mapping: 8 Thread: ForkJoinPool.commonPool-worker-2
Mapping: 9 Thread: ForkJoinPool.commonPool-worker-3
Consuming: 14 Thread: ForkJoinPool.commonPool-worker-4
Consuming: 16 Thread: ForkJoinPool.commonPool-worker-2
Mapping: 10 Thread: ForkJoinPool.commonPool-worker-4
Consuming: 18 Thread: ForkJoinPool.commonPool-worker-3
Consuming: 20 Thread: ForkJoinPool.commonPool-worker-4

从结果可以看出，在串行流处理中，元素是按照顺序依次进行映射和消费的，而在并行流处理中，元素会被分成多个子流，并行进行映射和消费。不同的子流由不同的线程处理，可以看到输出结果中的线程名字不同。这样可以提高处理速度，尤其是在处理大量数据时。请注意，并行流的使用可能会带来线程安全问题，需要根据具体情况进行处理。