answer:Here is a Python program that fulfills the requirements. The program uses a single pass through the binary string to find the longest consecutive sequence of zeros. It has a time complexity of O(n):```pythondef longest_zeros(bin_str): max_count = 0 count = 0 position = -1 for i in range(len(bin_str)): if bin_str[i] == '0': if count == 0: temp_position = i count += 1 if count > max_count: max_count = count position = temp_position else: count = 0 if max_count == 0: print("No zero sequence found") else: print("Longest sequence =", max_count, "Starts at position =", position)longest_zeros('110010001')```Output: "Longest sequence = 3 Starts at position = 5"The function `longest_zeros` takes a binary string `bin_str` as input and initializes variables to track the longest sequence of zeros (`max_count`), the current sequence of zeros (`count`), and the position of the longest sequence (`position`). It checks each character in the binary string. If a '0' is found, it increments the count and, if the sequence is longer than the previous longest, updates the maximum count and the starting position. If a '1' is found, it resets the count. Finally, if no sequence of zeros was found, it outputs an appropriate message; otherwise, it outputs the length and starting position of the longest sequence of zeros.

answer:Here is the high-level step-by-step guide for creating a TensorFlow-based deep learning algorithm, executing it using Google Cloud's ML Engine, and setting up an API endpoint.1. Create TensorFlow-based Deep Learning Model:First, you will need to create your deep learning model using TensorFlow. Here is a simple example of how you might do this:```pythonimport tensorflow as tf# define the modelmodel = tf.keras.models.Sequential([ tf.keras.layers.Dense(512, activation='relu', input_shape=(784,)), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10)])# compile the modelmodel.compile(optimizer='adam', loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])```2. Prepare Your DatasetIn order to train your model, you will first need to prepare your dataset. Depending on the complexity of your model and the size of your data, you might want to consider different ways of loading your data into TensorFlow.3. Train the ModelThe next step is to train your model. In TensorFlow, you do this using the `fit` method on your model:```python# train the model on the datamodel.fit(X_train, y_train, epochs=5)```4. Deploy Model to Google Cloud ML EngineNext, you need to deploy your trained model to Google Cloud ML Engine. To do this, you need to:- Export your trained TensorFlow model to a .pb file.- Upload your .pb file to a Google cloud storage bucket.- Create a new model in Cloud ML Engine and point it to your uploaded .pb file.The following commands can be used to do this:```bash## Change these as per your bucket and project settingsPROJECT_ID=your-project-idBUCKET_NAME=your-bucket-nameMODEL_NAME=your-model-nameVERSION_NAME=v1REGION=us-central1# Create a bucket to store your modelgsutil mb -l REGION gs://BUCKET_NAME# Upload your model to the bucketgsutil cp -r exported_model/ gs://BUCKET_NAME/{MODEL_NAME}# Create a model in Cloud ML Enginegcloud ai-platform models create {MODEL_NAME} --regions={REGION}# Create a version of the model in Cloud ML Enginegcloud ai-platform versions create {VERSION_NAME} --model={MODEL_NAME} --origin=gs://BUCKET_NAME/{MODEL_NAME} --runtime-version=2.1 --python-version=3.7 --framework=tensorflow```5. Create API EndpointOnce your model has been deployed to Google Cloud ML Engine, you can create an API Endpoint to enable the transmission of predictive analytics results. The API Endpoint can be used to make prediction requests to your deployed model.Use the following command to make API predictions:```bash# make a predictiongcloud ai-platform predict --model=MODEL_NAME --version=VERSION_NAME --json-instances=input.json```For this input.json would be a separate file that holds the new instances for which you would like to get predictions.Remember to replace the placeholders above (your-model-name, your-bucket-name, etc.) with the actual names you used.

answer:The regex that checks if a given string is containing only alphabets will be: `^[a-zA-Z]*`Explanation: - `^` is the start of the string.- `[a-zA-Z]` checks for all alphabets (both lower case and upper case).- `*` means zero or more of the previous expression.- `` is the end of the string. So the regex will only match if the entire string consists of the alphabets (either lower case or upper case) or is empty. If there are any other characters like numbers or special characters, it won't match.

answer:Add a background color to the list items in your CSS.Here's an example:```cssul li { background-color: #f5f5f5; /* replace with your color */}```Adjust the color (the `#f5f5f5` part) to match your site's design. Also note that Internet Explorer 6 is extremely outdated and is not used much anymore. It's generally a good idea to build your site to be used with more modern browsers, if at all possible.

answer:The code provided does not actually have any errors. It will execute correctly in Python and the output would be: `Result: 3.5`The code is dividing `x` by `y` (14 by 4) which results in the decimal 3.5. Python calculates this floating point number correctly.If you are looking for an integer division that outputs just the quotient without the decimal part, you would use `//` in Python instead of `/`, so the corrected script would be:```pythonx = 14y = 4z = x // yprint('Result:', z)```This would output `Result: 3`, because 14 divided by 4 equals 3 with a remainder. This Python operation discards the remainder and only gives the integer portion of the quotient. However, the initial code is not erroneous as per the standard Python rules and will run perfectly fine, it just depends on whether you want the result as a floating point number or an integer.

answer:Sure, Here is a Python console application that does the job for you:```pythondef alternate_ascii_code(input_string): new_string = '' for i in range(len(input_string)): if i%2 == 0: new_string += input_string[i] else: new_string += str(ord(input_string[i])) return new_stringif __name__ == "__main__": input_string = input("Enter a string: ") print("New string: " + alternate_ascii_code(input_string))```In this script, the `alternate_ascii_code` function iterates through the string character by character, and checks if the index of the character is even. If it's even, the function just appends the character to the `new_string`. If it's not (i.e., if it's odd), instead of appending the character, it appends the ASCII code of the character to the `new_string`. The `ord` function is used to get the ASCII code of a character.