To run a Minecraft server effectively, your hardware should meet minimum requirements:

1. **CPU**: Minecraft server primarily relies on the quality of the CPU. The server is single-threaded, meaning it performs one operation at a time. Hence high-frequency quad-core CPUs are recommended over low-frequency octa-core CPUs. A relative benchmark could be a newer i3 or a relatively older i5 or i7.

2. **RAM**: Requirements will vary depending on the size of your server. For a mini server with 1-4 players, a minimum of 1-2GB is necessary. However, larger servers with 50+ players need 6GB or more. Java is memory-heavy involving a considerable amount of garbage collection.

3. **Storage**: SSDs are recommended over HDDs due to faster read/write speed, which results in lower world save intervals and faster world loading. Disk size depends on the world size. Typically, 10-15GB should be sufficient for a small server.

4. **Network**: A stable and fast internet connection is essential. The upstream bandwidth matters more than the downstream one. For a small to medium server, a 5-10Mbps upload speed is recommended. However, this can vary significantly depending on the number of players and mods.

5. **OS**: Linux distributions such as Ubuntu and CentOS, as they have less overhead than Windows, are generally preferred for Minecraft servers.

Remember, before you start, make sure your hardware will be able to handle the specifications of the game. If too stressed, the gameplay will be less enjoyable for yourself and others. Keep the server updated and secured. Good luck hosting!

The runtime complexity of Bubble Sort is O(n^2) in any average or worst-case scenario, where n is the number of items being sorted. This means if you're doubling the amount of items you need to sort, the time it takes Bubble Sort to finish will roughly quadruple (2^2 = 4). This is because for each item in the dataset, you compare it to every other item.

However, in the best-case scenario (where the input is already sorted), Bubble Sort performs at O(n) time complexity. This linear runtime is due to the fact that the algorithm only needs to make one pass through the data set to confirm it's sorted.

Also, do note that while Bubble Sort is a simple sort algorithm to understand and implement, its high time complexity makes it impractical for large datasets. There are other sorting algorithms such as QuickSort, MergeSort, or HeapSort that perform better for a larger input size.

ArrayList and LinkedList in Java, both implement the List interface and their methods and results are almost identical. The main difference between them comes from the way they internally store the elements.

ArrayList is a dynamically resizing array, which means the elements are stored in adjacent memory locations and thereby allows constant time O(1) access to any elements as you can calculate offset directly. When an ArrayList grows beyond the capacity, a new array is created and the old data is moved to the new space, which can be a significant overhead for large arrays.

LinkedList, on the other hand, is made up of nodes where each node contains a reference to the data and the next node in the list. These nodes are not stored in adjacent memory spaces. Therefore, adding or removing an element only requires changing the node link, making these operations have constant time complexity O(1), assuming you have direct access to the node which usually is not the case. Because of the need to traverse the list from the start to our desired index, access time in LinkedList is O(n).

So, in general:

- Use ArrayLists when you want good performance with random access of elements.
- Use LinkedLists when your primary use case is adding and removing a lot of elements and you don't need to access elements randomly often.

However, it’s important to note that ArrayList almost always outperforms LinkedList in practice, even in use-cases where LinkedList theoretically should be faster, because modern CPU architecture favors contiguous memory access.

In summary, ArrayList should be the default choice and LinkedList should only be used in very specific cases.

Understanding orders of magnitude in deep learning compute involves a strong grasp of how linear scaling rules apply to deep learning computing power.

1. **Training time for deep learning models scales linearly with the number of parameters**: If we have a model with 10 million parameters, it will generally take 10 times as long to train as a model with 1 million parameters.

2. **Training time decreases linearly with the amount of computing power**: With double the computing power, you can generally cut training time in half.

3. **Scaling loss**: Eventually, you reach a stage of diminishing returns. If your model is already large and you double its size, you might not see a full 2x improvement in performance. These diminishing returns can be attributed to several factors like overfitting or hardware limitations.

Deep learning compute isn't just about "bigger is better" - it's about balancing the requirements of your model (its size, the size of your dataset, the complexity of the problem you're trying to solve) with the resources you have available.

Some great resources for a deeper understanding include Andrej Karpathy's 'Deep Learning Scaling is Predictable, Empirically', which provides insights into how and why these linear scaling rules work, and the 'Deep Learning Book' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, which presents comprehensive coverage of the technical aspects of deep learning.

Eliminating memory leaks in Java requires a comprehensive understanding of how memory is allocated and de-allocated. Here are a few steps you can follow:

1. **Understand the Basics:** In Java, memory leaks occur when objects are created but never garbage collected because the application still maintains a reference to them. Always care about the lifecycle of every object you create.

2. **Identify Memory Leaks:** The key to identifying memory leaks is through the use of profiling tools. Java's built-in profiling tool (JConsole) can provide a basic view of your heap usage. For a more detailed analysis, tools like VisualVM, Eclipse MAT, etc., can help you to identify suspicious objects which are causing memory leaks.

3. **Fix the Leak:** Once you've found the potential leak, you can analyse the code to check where the reference is still maintained. Listeners are one of the most common causes of memory leaks in GUI applications—make sure you are removing all listeners.

Some good practices:

- Use the latest version of Java if possible. Each new release usually comes with improvements in garbage collection and heap management.
- Avoid unnecessary object creation. Always favor primitives over wrappers.
- Use appropriate data structures. Incorrect choice could cause unnecessary memory consumption.
- Consciously use caching, if you forget to put a size limit or a proper eviction policy, you'll get a memory leak quickly.
- Redefine the scope of variables to as small as possible, which will make those objects eligible for garbage collection as soon as possible.
- Be careful with long-living collections that tend to accumulate elements over the application's lifetime (an example can be static collections).

Remember to test thoroughly after you've made these optimizations. Fixing a memory leak can often have unintended side effects, so it's essential to verify that your fixes have not adversely affected your application.

Multithreading in Java refers to the concurrent execution of two or more parts of a program to maximize the utilization of the CPU. Each part of such program is called a thread, and each thread defines a separate path of execution.

Consider an example in which you are building a text editor. One thread might look after the user interface, the second thread might be saving the document after every few seconds, and the third thread might be fetching and displaying help information, all concurrently.

Multithreading can be very beneficial and can be used:

1. When tasks are independent of each other and can run in parallel without blocking each other.
2. To utilize the CPU better, as single-threaded programs may not fully utilize the CPU because they would be waiting for I/O or user interactions etc.
3. To make the programs more responsive. For example, a text editor can be saving a file in the background while the user continues to edit the document.

However, multithreading should be avoided:

1. When tasks are not independent and must share common resources. This can lead to issues of deadlocks and race conditions.
2. Multithreaded programs can be harder to design, write, debug and maintain.
3. Multithreading may not yield any benefits if the program is I/O bound, instead of CPU bound, as the I/O operations would then become the bottleneck.

Remember, if used correctly, multithreading can greatly improve the efficiency of a program but if used incorrectly, it can lead to unpredictable results. It's all about how and where you use it.

Throwing exceptions and returning error codes both have their place in Java, depending on the context:

1. Throwing exceptions is generally preferred in Java due to several reasons.

a. Clarity: Exceptions separate error handling code from regular code, making it easier to read and maintain.

b. Providing Details: Exceptions can carry detailed information about the problem occurred, as they can hold various types of data.

c. Propagation: Exceptions can be propagated up the call stack (re-thrown multiple times), allowing higher levels of your application to handle them if necessary. Error codes just return to the immediate caller.

d. Standardization: Exceptions are part of the Java core library, it is a common and universally understood way to deal with errors.

2. Returning Error codes is generally less preferred but can be effective in certain scenarios:

a. Performance overhead: If you're in a performance-critical section of code and an 'error' is anticipated as a part of normal flow, return codes could avoid the overhead of exceptions handling mechanism.

b. Expected Errors: When errors are expected outcome (like validations), returning error codes can be seen as returning valid result of an operation rather than an exception.

Remember this though, overuse of either can lead to "exception hell" or "error code hell". It's all about balancing and understanding the needs of your application. Design with the caller in mind, if an error scenario is an "exceptional situation", throw exception. If not, return a suitable error code.

Python is used extensively across various industries for a multitude of applications.

1. Web and Internet Development: Django and Flask are two very popular frameworks for developing web applications in Python.

2. Scientific and Numeric Computing: Libraries like NumPy, SciPy, and pandas are used in data analysis and scientific computing.

3. Machine Learning & Artificial Intelligence: Libraries like TensorFlow, PyTorch, and Scikit-Learn enable development in these fields.

4. Data Analysis and Visualization: Python is widely used for data crunching and visualization with libraries such as pandas, matplotlib, and seaborn.

5. Game Development: Pygame is a set of Python modules designed for writing video games.

6. Software Development: Python is often used as a support language for software developers, for build control and management, testing, and in many other ways.

7. Finance and Trading: Python plays a significant role in Finance industry applications like risk management, pricing etc., and is also extensively used in algorithmic trading.

8. Automation: Python is also famous for its simplistic programming syntax which makes it suitable for scripting and automation tasks.

9. Cybersecurity: Python's vast library ecosystem and scripting capabilities make it a popular choice for cybersecurity professionals for penetration testing, threat analysis, etc.

10. Education: Last but not least, Python is often used as a beginner's language in many introductory programming courses due to simplicity of its syntax and widespread usage in the industry.

Beyond these, Python is versatile and can be used in virtually any field that needs programming. Its adaptability to different needs and ease of learning are some of the reasons why it's so popular.

A `SyntaxError` in Python means that the Python interpreter is unable to understand your code because it is not properly formatted or incorrect according to Python's language rules.

Here are steps you can take to debug and fix `SyntaxError`:

1. **Read the error message**: Python `SyntaxError` also provides an error message, which can give you a hint about what's wrong. It often points to the exact line and place in the line where the error occurred.

2. **Check your syntax**: Python syntax rules must be followed for the code to be executed. Check for common syntactical issues such as:
- Unbalanced parentheses, braces, brackets, quotes or improper use of the colon.
- Mixing tabs and spaces for indentation.
- Code written in wrong language version (Python 2 vs Python 3).

3. **Use a IDE/Text editor**: Most modern Python IDEs or text-editors have syntax highlighting and linting which can catch `SyntaxError` before execution of the code.

4. **Break Down Your Code**: If your script is long, break it down. Comment out some parts and run it again. This may help pinpoint where the error is coming from.

5. **Consult Python Documentation**: If you're using a new Python function or feature for the first time and encountering errors, check out its usage in Python Documentation.

Remember, every developer, regardless of experience, encounters syntax errors. It's a normal part of programming. Keep debugging!

Python 2 and Python 3 have a number of differences, here are principal ones:

1. **Print statement**: In Python 2, "print" is treated as a statement rather than a function. Python 3 requires parentheses around the items to be printed: print("Hello World").

2. **Division behavior**: Python 2 performs floor division with the / operator when both operands are of integer type. However, in Python 3, / always performs true division, and // is now used for floor division.

3. **Unicode Support**: Python 2 has ASCII str() types by default, separate unicode(), but no byte type. On the other hand, Python 3 has Unicode (utf-8) strings and a separate bytes type.

4. **Syntax and Libraries**: Python 3 syntax is simpler and easily understandable whereas in Python 2, syntax is comparatively complex. Also, many old libraries don’t work without modification on Python 3.

5. **Error Handling**: In Python 2, there is 'StandardError' which is base class for all built-in exceptions except 'StopIteration' and 'SystemExit'. Python 3 removes 'StandardError', and 'Exception' is base class for all built-in exceptions.

6. **Iteration**: Python 3's range function behaves like Python 2's xrange. They both provide a way to generate a list of integers for you to use, however you please. The main difference is that range returns a Python list object and xrange returns an xrange object.

Which one should you use? Python 2 was officially discontinued (end of life) in 2020, so for any new projects it's highly recommended to use Python 3.
Developer community and libraries are focused more on Python 3 now, and you'll get better community support for Python 3 and its newer features.

This is just the tip of the iceberg. The languages have evolved independently over time, and there are other minor distinctions, most of them under the hood. I'd recommend checking out the official documentation for each version if you're curious, but hopefully this list will give you a ground to start.