You can program a non-recursive Fibonacci function in C using a iterative approach with a for loop. Here's a simple and straightforward example:

```c
#include

void fibonacci(int n) {
int t1 = 0, t2 = 1, nextTerm;

// Displaying the first two terms
printf("Fibonacci Series: %d, %d, ", t1, t2);
nextTerm = t1 + t2;

for (int i = 3; i <= n; ++i) {
printf("%d, ",nextTerm);
t1 = t2; // Updating the terms for the next round
t2 = nextTerm;
nextTerm = t1 + t2; // Calculating the next term
}
}

int main() {
int n;

printf("Enter the number of terms: ");
scanf("%d", &n);

fibonacci(n);
return 0;
}
```

This program first displays the first two terms of the Fibonacci sequence. Then, it enters the loop where `nextTerm` is calculated as the sum of `t1` and `t2`. Then, `t1` and `t2` are updated for the next round and the next term is calculated again. This process continues until it reaches the nth term. The non-recursive nature of the function lies in the loop; instead of calling itself to calculate the next term, it simply uses the previously calculated terms. This way, the program avoids the extra computational effort and possible stack overflows associated with recursive functions.

Remember to always include proper error checks and edge cases handling, such as when n is less than 1.

In Python, decorators are a type of design pattern that allows you to add new behavior to an existing object without modifying its structure. They are a very powerful tool that simplifies the code and makes it easier to read and maintain.

At a high level, decorators are simply a way to wrap a function or method with another function to extend or change its behavior. They are represented with the '@' symbol.

Here's a simple example:

```python
def my_decorator(func):
def wrapper():
print("Something is happening before the function is called.")
func()
print("Something is happening after the function is called.")
return wrapper

@my_decorator
def say_hello():
print("Hello!")

say_hello()
```

When you call say_hello(), it's not just the say_hello() function that gets called. Instead, it's the wrapper() function inside my_decorator() that gets called. It calls say_hello() as part of its execution, providing the extra behavior before and after the call.

In this example, the @my_decorator line is just a convenient shorthand for the following:

```python
say_hello = my_decorator(say_hello)
```

This is what's known as syntactic sugar in Python. It makes your code concise and easier to read.

Remember that the purpose of using decorators is to extend the behavior of the decorated function without permanently modifying it. The decorator simply provides a way to wrap extra functionality around the function it decorates.

For effective error and exception handling in Python, consider the following practices:

1. Use Try/Except Blocks: Fundamental in Python, they allow you to catch potential errors and exceptions, handling them in ways you define.

```python
try:
do_something()
except Exception as e:
handle_exception(e)
```

Avoid bare exceptions which catch all types of errors, as it can make debugging harder.

2. Specific Exceptions: Catch exceptions as specific as possible. It’s always better to catch the exact exception you are looking for.

```python
try:
do_something()
except IOError as e:
handle_IO_error(e)
```

3. Use The 'Else' Clause: 'Else' Clause in try/except block will only run if no exception was raised in the try block. It’s a good place to put code which depends on the success of the try block.

4. 'Finally' For Clean-Up: Placing clean-up code in a finally block is a good practice. It's always executed whether an exception occurred or not.

5. Exception Propagation: Allow exceptions to propagate up when they can’t be handled at the current level.

6. Logging: Logging exceptions can help to track errors and debug easily. You may use the built-in Python logging module.

7. Custom Exceptions: In some cases, you might want to define your own exception types. This can increase code clarity and robustness.

These are some of the practices, but depending on the context of your code it might change. Always test your exceptions thoroughly in your unit tests. Remember, exception handling should focus more on handling the exception rather than suppressing it.

In Python, `if __name__ == "__main__":` is used to determine whether the script is being run directly or it's being imported as a module. When a script is run directly, Python sets the `__name__` variable to be `"__main__"`, allowing the code under the `if __name__ == "__main__":` to run. If the script is being imported as a module, `__name__` is set to the module's name and the code under the `if __name__ == "__main__":` statement won't run.

This is used for code that you only want to execute if the file is being run directly, not when it's imported as a module in another script. For example, tests or other code e.g., print statements that checks the functionality of the script 'directly' can be put under this clause to avoid them being run when the script is imported as a module elsewhere.

So to summarize, use `if __name__ == "__main__":` to prevent certain parts of code from being run when the module is imported.

In Python, you can manage dates and times using the built-in `datetime` module.

First, you need to import it:

```python
import datetime
```

Here are some basic uses:

1. **Current date and time:** You can get the current date and time using `datetime.now()`.
```python
>>> print(datetime.datetime.now())
2019-07-19 13:19:12.991924
```

2. **Creating specific date:** You can create specific datetime by providing year, month, day as arguments to the `datetime()` function. You can provide further arguments for hours, minutes, seconds, and microseconds.
```python
>>> print(datetime.datetime(2020, 1, 1))
2020-01-01 00:00:00
```

3. **Accessing Date Attributes:** You can access the year, month, day, etc. from a datetime object using the attributes - year, month, day, hour, minute, second.
```python
>>> dt = datetime.datetime(2020, 1, 1)
>>> print(dt.year, dt.month, dt.day)
2020 1 1
```

4. **Timedelta:** Timedeltas represent duration, or the difference between two dates/times.
```python
>>> dt1 = datetime.datetime(2021, 1, 1)
>>> dt2 = datetime.datetime(2020, 1, 1)
>>> dt_delta = dt1-dt2
>>> print(dt_delta.days)
366
```

Remember, the `strftime()` method can be used to format a datetime object into a string of your desired format, and `strptime()` can be used to parse a string into a datetime object.

You can read the [official Python documentation](https://docs.python.org/3/library/datetime.html) for more in-depth information!

Recursion, in programming, is a method of problem-solving where the solution to a problem depends on smaller instances of the same problem. Recursion involves a function calling itself while a condition is met.

Here's a simple example using a function that calculates the factorial of a number:

```python
def factorial(n):
if n == 1:
return 1
else:
return n * factorial(n-1)
```

This function takes in a number `n`. If `n` is 1, it returns 1 (because the factorial of 1 is 1). However, if `n` is greater than 1, the function calls itself with the argument `n-1`. This means it will continue to multiply the current number, `n`, with the factorial of `n-1` until it hits the base case, `n` equals 1, in which it stops calling itself.

For example, if you call `factorial(5)`, the function unfolds like this:

factorial(5)
5 * factorial(4)
5 * 4 * factorial(3)
5 * 4 * 3 * factorial(2)
5 * 4 * 3 * 2 * factorial(1)
5 * 4 * 3 * 2 * 1
120

Therefore, calling `factorial(5)` returns `120`.

Note: Recursive functions need to have a base case that stops the recursion, or else they will keep calling themselves indefinitely, leading to a stack overflow error.

Remember recursion is just a tool. Some problems naturally lend themselves to recursive solutions (like tree traversals or problems based on divide and conquer strategy), but that doesn't mean recursion is the best or only way to solve them.

The `map()` function in Python allows you to apply a function to every item in an iterable (like a list or tuple) and returns a map object which can be converted into a list or other iterable. This can be useful when you need to perform the same operation on every item of a list.

The general syntax of `map()` is:

`map(function, iterable)`

Here, `function` is a function that will be applied to every item of `iterable`.

Here is a simple example of `map()` function which will multiply each item in the list by 2.

```python
def multiply_by_two(x):
return x * 2

my_list = [1, 2, 3, 4, 5]
result = map(multiply_by_two, my_list)
```

Immediately after using `map()`, result is a map object: `

The `super` keyword in Java is a reference variable used to refer to the immediate parent class object. It is primarily used in two scenarios:

1. **Method Overriding** - If a method is overridden in a child class, the `super` keyword allows you to call the method of the parent class from the overridden method in the child class.

2. **Constructor Invocation** - During constructor chaining, you can use the `super` keyword to call the parent class constructor from the child class's constructor. As a rule, this call to the parent class constructor must be the first line in the child class constructor.

For example, consider this parent class:

```java
class ParentClass {
void display() {
System.out.println("ParentClass display");
}
}
```
And this child class:

```java
class ChildClass extends ParentClass {
void display() {
super.display(); // this calls the display method of ParentClass
System.out.println("ChildClass display");
}
}
```
If you create an object of the `ChildClass` and call its `display` method, it first calls the parent class's `display` method with `super.display()`, then continues with its own implementation, outputting:

```
ParentClass display
ChildClass display
```

For constructor invocation:

```java
class ParentClass {
ParentClass(){
System.out.println("ParentClass Constructor");
}
}
class ChildClass extends ParentClass {
ChildClass(){
super(); // this calls the ParentClass constructor
System.out.println("ChildClass Constructor");
}
}
```

When you create an object of `ChildClass`, it will first call the `ParentClass` constructor, then its own:

```
ParentClass Constructor
ChildClass Constructor
```

Remember that `super` can also be used to access parent class fields if they are hidden by fields in the child class, but accessing fields this way is generally considered bad practice because it breaks encapsulation.

Improving the performance of Java code can be achieved in various ways. Here are a few practices that could help you:

1. **Efficient Algorithms and Data Structures**: Always look for the most efficient algorithms and use suitable data structures for your needs.

2. **Avoid Creating Unnecessary Objects**: Creating objects in Java is costly in terms of processing time. Use objects sparingly and release them for garbage collection as soon as they are no more needed.

3. **Use Primitives Instead of Wrapper Classes**: When you have an option, use primitive types instead of wrapper classes. This will reduce memory overhead and will also be faster.

4. **Use StringBuilder for Strings Concatenation**: String is immutable in java. If you do lots of String concatenation, consider using StringBuilder, it will be faster than concatenating Strings using '+' operator.

5. **Use Multithreading**: Java supports multithreading. If your application is suited for this, using multithreading can greatly improve performance by spreading the work over several CPU cores.

6. **JVM Options**: Use JVM options to tweak the behavior of the runtime, such as -Xmx to increase maximum allowed memory or -server to enable server mode for speed optimized JVM.

7. **Use Profiling Tools**: Tools like VisualVM or JProfiler can help you identify bottlenecks in your application, and optimize your code.

Remember, premature optimization is the root of all evil. Always measure before you start optimizing, and always optimize based on evidence, not on guesswork.

Java annotations are a type of metadata that can be added to your code. They give additional information to the JVM, compilers, or frameworks, enhancing code functionality or giving binding instructions without changing the actual behavior. You might see them used in frameworks like Spring and Hibernate, or APIs like JAX-RS and JAXB.

When to use annotations?

1. **Code Simplification**: Reduces the need for boilerplate code. For example, the `@Override` annotation in Java tells the compiler that the subsequent method overrides a method from the superclass, reinforcing good coding practices.

2. **Runtime Processing**: Annotations can be processed by your application at runtime. For example, libraries like JUnit make use of this to run unit tests that are marked with the `@Test` annotation.

3. **Compile-time checks**: Annotations can be used to instruct the compiler to enforce certain conditions, acting as a form of syntactical metadata. For instance, the `@NonNull` annotation can be used to tell the compiler to emit an error if the annotated element may be null.

4. **Frameworks Integration**: Frameworks like Spring heavily use annotations like `@Component`, `@Service`, etc., for dependency injection, event handling, and more.

Learn more about them and get used to their syntax because Java annotations can be highly beneficial for reducing code complexity, dependency identification, testing, and more.