answer:Here's the Python function to analyze the similarity between DNA sequences:```pythondef analyze_dna_sequences(sequences): similarity_dict = {} sequence_length = len(sequences[0]) for i in range(len(sequences)): for j in range(i+1, len(sequences)): matches = sum(1 for a, b in zip(sequences[i], sequences[j]) if a == b) similarity = (matches / sequence_length) * 100 similarity_dict[(i, j)] = similarity return similarity_dict```This function takes a list of DNA sequences as input and returns a dictionary with tuple keys representing the indices of the sequences being compared, and float values representing the percentage similarity between those sequences.Here's how the function works:1. The function initializes an empty dictionary `similarity_dict` to store the similarities.2. It gets the length of the first sequence, assuming all sequences have the same length.3. It uses two nested loops to iterate over all pairs of sequences.4. For each pair of sequences, it calculates the number of matching characters using the `zip` function and a generator expression.5. It calculates the percentage similarity by dividing the number of matches by the sequence length and multiplying by 100.6. It stores the similarity value in the `similarity_dict` dictionary, using a tuple of the indices of the sequences being compared as the key.7. Finally, it returns the `similarity_dict` dictionary.Note that this implementation assumes that all sequences have the same length. If this is not the case, you would need to modify the code to handle sequences of different lengths.

answer:def analyze_restaurant_cleanliness(restaurants, threshold): below_threshold = [] for restaurant in restaurants: if restaurant["rating"] < threshold: below_threshold.append(restaurant["name"]) return below_threshold

answer:def parse_articles(articles): result = [] for article in articles: companies = [] words = article.split() for word in words: if word.isalpha() and word.isupper(): companies.append(word) result.append(companies) return result

answer:Here's the Python function to determine if a given list of integers contains a contiguous subsequence (supernatural sequence) that sums up to a specific target number:```pythondef has_supernatural_sequence(nums, target): current_sum = 0 start = 0 for end in range(len(nums)): current_sum += nums[end] while current_sum > target: current_sum -= nums[start] start += 1 if current_sum == target: return True return False```Explanation:1. The function takes two arguments: `nums` (a list of integers) and `target` (the target sum).2. We initialize two variables: `current_sum` to keep track of the running sum, and `start` to keep track of the starting index of the current subsequence.3. We iterate through the list using a variable `end` as the index.4. In each iteration, we add the current element `nums[end]` to `current_sum`.5. If `current_sum` becomes greater than `target`, we subtract elements from the beginning of the current subsequence until `current_sum` becomes smaller than or equal to `target`. This is done using a `while` loop that subtracts `nums[start]` from `current_sum` and increments `start`.6. If `current_sum` becomes equal to `target`, it means we have found a supernatural sequence, so we return `True`.7. If the loop completes without finding a supernatural sequence, we return `False`.The time complexity of this solution is O(n), where n is the length of the input list `nums`. This is because we iterate through the list once, and the operations inside the loop (adding/subtracting elements) take constant time. The space complexity is O(1) since we only use a few extra variables and don't create any additional data structures that scale with the input size.

answer:def calculate_electricity_cost(daily_consumption, rate_per_kwh): total_consumption = sum(daily_consumption) rate_per_kwh_in_dollars = rate_per_kwh / 100 total_cost = total_consumption * rate_per_kwh_in_dollars return round(total_cost, 2)

answer:def order_creditors(creditors): # Sort the list of tuples based on the priority level sorted_creditors = sorted(creditors, key=lambda x: x[1]) # Extract the creditor names in the sorted order ordered_names = [creditor[0] for creditor in sorted_creditors] return ordered_names