answer:```python def multiply_matrices(a, b): result = [[0, 0], [0, 0]] result[0][0] = a[0][0] * b[0][0] + a[0][1] * b[1][0] result[0][1] = a[0][0] * b[0][1] + a[0][1] * b[1][1] result[1][0] = a[1][0] * b[0][0] + a[1][1] * b[1][0] result[1][1] = a[1][0] * b[0][1] + a[1][1] * b[1][1] return result def matrix_power(matrix, n): if n == 1: return matrix if n % 2 == 0: half_pow = matrix_power(matrix, n // 2) return multiply_matrices(half_pow, half_pow) else: half_pow = matrix_power(matrix, n // 2) return multiply_matrices(multiply_matrices(half_pow, half_pow), matrix) def fibonacci(n): if n == 0: return 0 fib_matrix = [[1, 1], [1, 0]] result_matrix = matrix_power(fib_matrix, n - 1) return result_matrix[0][0] # Example usage: n = 10 print(fibonacci(n)) ``` This code uses matrix exponentiation to calculate the nth Fibonacci number efficiently. The Fibonacci recurrence relation can be represented as a matrix: ``` | F(n+1) F(n) | = | 1 1 | * | F(n) F(n-1) | ``` By raising this matrix to the power `n-1`, we can calculate the nth Fibonacci number. The `multiply_matrices` function multiplies two 2x2 matrices, and the `matrix_power` function calculates the power of a matrix using a divide-and-conquer approach. Finally, the `fibonacci` function uses these helper functions to calculate the nth Fibonacci number. Note: This code assumes that `n` is a non-negative integer. If `n` can be negative or a non-integer, additional error checking is needed.

answer:<tool_call>[{name: get_liquidity_value, arguments: {symbol: BTC}}, {name: get_liquidity_value, arguments: {symbol: ETH}}, {name: get_liquidity_value, arguments: {symbol: XRP}}, {name: spac_sec_filings, arguments: {symbol: ITAC}}, {name: spac_sec_filings, arguments: {symbol: PSTH}}]</tool_call>

answer:Here's how you could implement this function: ```python def invert_preserve_order(l, m): inverted_dict = {} for value in l: inverted_dict[value] = m.get(value) return inverted_dict ``` The `invert_preserve_order` function takes a list `L` and a dictionary `M` as input. It iterates through each value in the list `L`, and creates a new dictionary `inverted_dict` with the value as the key and the value from the original dictionary `M` as the value.

answer:Analyzing the provided data, we can observe a positive correlation between the hours studied and test scores. Students who studied for longer periods tend to have higher test scores, while those who studied for shorter periods tend to have lower test scores. The data shows that students who studied for 10 hours or more had test scores above 85, with many scoring above 90. In contrast, students who studied for 4 hours or less had test scores below 70, with some scoring as low as 30. However, it's worth noting that there is some variation in the data, particularly among students who studied for 6 hours. Their test scores ranged from 72 to 85, indicating that hours studied alone may not be the sole determining factor for test performance. Additionally, there appears to be a plateau effect, where students who studied for 12 hours or more did not significantly outperform those who studied for 10 hours. This could suggest that there is a point of diminishing returns, where additional study time does not necessarily lead to significantly better test scores. While there is a clear positive relationship between hours studied and test scores, the data suggests that other factors, such as individual aptitude, learning style, and the effectiveness of study methods, may also play a role in determining test performance.