Respan Dataset Explorer

Сбрасывают ли самолеты топливо на подлете?

Самолёты могут сбрасывать топливо в определённых обстоятельствах, но это обычно не происходит непосредственно на подлёте к аэропорту. Сброс топлива используется как мера безопасности, когда необходимо снизить вес самолёта перед экстренной посадкой. Это может потребоваться в ситуациях, когда взлётный вес самолёта превышает максимально допустимый для безопасной посадки.

Основные моменты, связанные со сбросом топлива:

1. **Обстоятельства**: Сброс топлива практикуется в основном на дальнемагистральных рейсах, где самолёт может быть загружен значительным количеством топлива. Это может произойти при возникновении технических неполадок, медицинских неотложных случаев на борту или других инцидентов, требующих возвращения в аэропорт вылета вскоре после взлёта.

2. **Процедура**: Сброс топлива происходит на высоте, обычно над безопасными и удалёнными от населения районами. Пилоты стараются выбрать такие маршруты, чтобы минимизировать воздействие на людей и окружающую среду.

3. **Экологические аспекты**: Топливо сбрасывается на достаточно большой высоте, где оно распыляется и в значительной степени испаряется до достижения земли, минимизируя риск экологического ущерба.

4. **Необходимость**: Сброс топлива не является обычной процедурой и используется только в экстренных случаях. Современные самолёты имеют высокую технологичность автоматизации и навигации, что позволяет избегать необходимости сбрасывать топливо в большинстве сценариев.

Важно отметить, что многие самолёты конструктивно способны приземляться с полным топливным баком, если необходимость в этом возникает, хотя это может потребовать дополнительных проверок и обслуживания после посадки.

Comment écrire 1949,96$ en lettre 

Pour écrire 1949,96 $ en lettres, on peut dire : « mille neuf cent quarante-neuf dollars et quatre-vingt-seize cents ».

How to reply to this : Hello Akshaye,
Congratulations for your confirmation.
I’m very happy to have you in the Mauritius Workplace team under Vandana’s leadership.
Thank you for your commitment and contribution which already added a lot of value for our internal customers. 
Very well done. Looking forward to pushing Office Services further in FY25 and more 😉! 
Kind regards.
JC

Hi JC,

Thank you for your kind words and warm welcome! I’m excited to be part of the Mauritius Workplace team and to work under Vandana’s leadership. I appreciate the recognition and am eager to contribute further to the success of Office Services as we move into FY25.

Looking forward to collaborating with all of you and achieving great things together!

Best regards,  
Akshaye

Chemistry of a tree leaf

The chemistry of a tree leaf is complex and involves various components and processes that allow the leaf to perform essential functions such as photosynthesis, respiration, and transpiration. Here are some key chemical aspects of a tree leaf:

1. **Photosynthesis:**
   - **Chlorophyll:** This green pigment is crucial for absorbing light energy, primarily from the sun, and converting it into chemical energy. Chlorophyll molecules capture light and use it to transform carbon dioxide (CO₂) and water (H₂O) into glucose (C₆H₁₂O₆) and oxygen (O₂).
   - **Carbon Dioxide and Water:** These are the raw materials for photosynthesis, taken up from the air through the stomata and from the soil through the roots, respectively.
   - **Glucose:** The sugar produced during photosynthesis serves as an energy source for the tree and as a building block for other organic molecules.
   - **Oxygen:** Released as a byproduct of photosynthesis into the atmosphere.

2. **Respiration:**
   - Leaves also undergo cellular respiration, where glucose and oxygen are used to produce energy in the form of ATP (adenosine triphosphate), with carbon dioxide and water released as byproducts.

3. **Transpiration:**
   - **Water Vapor:** Leaves lose water vapor to the atmosphere through small openings called stomata. This process helps in the uptake of nutrients and cooling of the plant.

4. **Structural Components:**
   - **Cellulose:** A polysaccharide that forms the structural framework of the plant cell wall, providing rigidity and strength to the leaf.
   - **Lignin:** Another structural component found in the cell walls, particularly in vascular tissues, contributing to the plant's structural integrity.

5. **Secondary Metabolites:**
   - **Flavonoids, Terpenes, and Alkaloids:** These are compounds that play roles in defense against herbivores and pathogens, attraction of pollinators, and protection from UV light.
   - **Phenolics:** Involved in plant defense and can act as antioxidants.

6. **Minerals and Nutrients:**
   - **Nitrogen, Phosphorus, Potassium, Magnesium,** and **Calcium:** Essential nutrients absorbed from the soil that play various roles in leaf function, such as chlorophyll production, energy transfer, and enzyme activation.

7. **Stomata Regulation:**
   - **Guard Cells:** These cells control the opening and closing of stomata to regulate gas exchange and water loss.

The interplay of these chemical components and processes allows leaves to effectively contribute to the survival and growth of the tree, playing pivotal roles in both the tree's physiology and the larger ecosystem.

use url join import requests
from parsel import Selector
from lib.dbdog import DBdog
from diskcache import Cache
import time
import json

UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36'

dbd = DBdog()  # Initialize DBdog instance


cache = Cache(directory='cache')

@cache.memoize()
def fetch_url(url):
    response = requests.get(url, headers={'user-agent': UA}, timeout=10)
    response.raise_for_status()
    
    return response.text

def get_results(url):
    html_content = fetch_url(url)
        # Use parsel to select and extract data
    # Parse the HTML content
    selector = Selector(text=html_content)

    # Initialize a dictionary to hold the results
    cities = []

    # Extract headings and corresponding cities
    for section in selector.css(".row.colbtm"):
        # Get the province name (section heading)
        province = section.css("h4.page-head-line::text").get()
        
        # Get cities under this province
        
        for li in section.css("li"):
            city_name = li.css("a::text").get()
            city_link = li.css("a::attr(href)").get()
            cities.append({"provice": province, "city": city_name, "link": city_link})
        

    # Convert the results to JSON
    json_output = json.dumps(cities, ensure_ascii=False, indent=4)

    # Print JSON output
    print(json_output)


url = 'http://mob.yinhangkahao.com/yidong/index.html'
a = get_results(url)

Here's an enhanced version of your script that uses the `urljoin` function from `urllib.parse` to handle relative URLs and a few other improvements for better code practice. I'll also ensure that the indentation and logical structure are correct. Here's the revised version:

```python
import requests
from parsel import Selector
from lib.dbdog import DBdog
from diskcache import Cache
import time
import json
from urllib.parse import urljoin

UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36'

dbd = DBdog()  # Initialize DBdog instance
cache = Cache(directory='cache')

@cache.memoize()
def fetch_url(url):
    response = requests.get(url, headers={'user-agent': UA}, timeout=10)
    response.raise_for_status()
    return response.text

def get_results(url):
    html_content = fetch_url(url)
    # Use parsel to select and extract data
    selector = Selector(text=html_content)
    
    # Initialize a list to hold the results
    cities = []

    # Extract headings and corresponding cities
    for section in selector.css(".row.colbtm"):
        # Get the province name (section heading)
        province = section.css("h4.page-head-line::text").get()
        
        # Get cities under this province
        for li in section.css("li"):
            city_name = li.css("a::text").get()
            city_link = li.css("a::attr(href)").get()
            if city_link:
                # Create fully qualified URL
                city_link = urljoin(url, city_link.strip())
            cities.append({"province": province, "city": city_name, "link": city_link})

    # Convert the results to JSON
    json_output = json.dumps(cities, ensure_ascii=False, indent=4)

    # Print JSON output
    print(json_output)

url = 'http://mob.yinhangkahao.com/yidong/index.html'
get_results(url)
```

### Key Enhancements:

1. **`urljoin` Usage**: The `urljoin` function has been incorporated to handle relative URLs correctly.
2. **Fixed Indentation**: Made sure that the indentation in `get_results` is consistent and correct.
3. **Variable Names**: Corrected the spelling from `"provice"` to `"province"` in the append statement.
4. **Whitespace and Cleanup**: Added `strip()` to clean up the `city_link` URL after extracting it.
  
Make sure to have the required libraries (`requests`, `parsel`, `lib.dbdog`, and `diskcache`) installed in your Python environment to run this script properly.

fix code import requests
from parsel import Selector
from lib.dbdog import DBdog
from diskcache import Cache
import time
import json
from urllib.parse import urljoin

UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36'

dbd = DBdog()  # Initialize DBdog instance


cache = Cache(directory='cache')

@cache.memoize()
def fetch_url(url):
    response = requests.get(url, headers={'user-agent': UA}, timeout=10)
    response.raise_for_status()
    
    return response.text

def get_results(url):
    html_content = fetch_url(url)
        # Use parsel to select and extract data
    # Parse the HTML content
    selector = Selector(text=html_content)

    # Initialize a dictionary to hold the results
    cities = []

    # Extract headings and corresponding cities
    for section in selector.css(".row.colbtm"):
        # Get the province name (section heading)
        province = section.css("h4.page-head-line::text").get()
        
        # Get cities under this province
        
        for li in section.css("li"):
            city_name = li.css("a::text").get()
            city_link = li.css("a::attr(href)").get()
            city_link = urljoin(url, city_link.strip())
            cities.append({"provice": province, "city": city_name, "isp_name", isp_name, "link": city_link})
        

    # Convert the results to JSON
    json_output = json.dumps(cities, ensure_ascii=False, indent=4)

    # Print JSON output
    print(json_output)


urls = 'http://mob.yinhangkahao.com/yidong/index.html', 移动
'http://mob.yinhangkahao.com/liantong/index.html'， 联通
 'http://mob.yinhangkahao.com/dianxin/index.html' 电信
for url in urls
a = get_results(url, isp_name)

It looks like there are several issues in your provided code snippet, such as syntax errors, incorrect dictionary key-value pairs, and missing constructs in the `for` loop. Below is the corrected version of your script:

```python
import requests
from parsel import Selector
from lib.dbdog import DBdog
from diskcache import Cache
import json
from urllib.parse import urljoin

UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36'

dbd = DBdog()  # Initialize DBdog instance
cache = Cache(directory='cache')

@cache.memoize()
def fetch_url(url):
    response = requests.get(url, headers={'user-agent': UA}, timeout=10)
    response.raise_for_status()
    return response.text

def get_results(url, isp_name):
    html_content = fetch_url(url)
    selector = Selector(text=html_content)

    # Initialize a list to hold the results
    cities = []

    # Extract headings and corresponding cities
    for section in selector.css(".row.colbtm"):
        # Get the province name (section heading)
        province = section.css("h4.page-head-line::text").get()
        
        # Get cities under this province
        for li in section.css("li"):
            city_name = li.css("a::text").get()
            city_link = li.css("a::attr(href)").get()
            if city_link:  # Ensure city_link is not None
                city_link = urljoin(url, city_link.strip())
            cities.append({"province": province, "city": city_name, "isp_name": isp_name, "link": city_link})
    
    # Convert the results to JSON
    json_output = json.dumps(cities, ensure_ascii=False, indent=4)

    # Print JSON output
    print(json_output)

# Define the URLs and their corresponding ISP names
urls = [
    ('http://mob.yinhangkahao.com/yidong/index.html', '移动'),
    ('http://mob.yinhangkahao.com/liantong/index.html', '联通'),
    ('http://mob.yinhangkahao.com/dianxin/index.html', '电信')
]

# Call get_results for each URL and its corresponding ISP name
for url, isp_name in urls:
    get_results(url, isp_name)
```

### Corrections and Improvements Made:

1. **Fixed Dictionary Key-Value Pair**: Changed `"isp_name"` in the append statement to `"isp_name": isp_name` to provide the correct key-value format.

2. **Corrected `for` Loop Syntax**: The `for` loop was incorrectly formatted; I've corrected it to unpack both `url` and `isp_name`.

3. **Corrected `province` Spelling**: Fixed the spelling of `"provice"` to `"province"` in the append statement.

4. **Ensured `city_link` is Not None**: Added a check for `city_link` to ensure it is not `None` before processing it.

5. **Formatted the `urls` List**: Changed how the URLs were originally defined to a list of tuples for better readability and manageability.

You can now run the updated code, and it should work correctly, extracting city information for each ISP.

who did eat dinner? or who ate dinner?  

"Who ate dinner?" is the correct form. This is because "ate" is the simple past tense of "eat," and it is appropriate to use it when referring to a completed action.

with who you ate the cake? or who you ate the cake with?

The more grammatically formal way to phrase the question is, "With whom did you eat the cake?" However, in everyday conversation, it's common and acceptable to say, "Who did you eat the cake with?" The second version is widely used in informal contexts.

but why using auxiliary verb did since we are using who already 

The use of "did" in the question "Who did you eat the cake with?" is necessary for forming the interrogative in English when "who" is the object of the verb. Here's why:

1. **Subject vs. Object:** "Who" can serve as either a subject or an object. In "Who ate the cake?" "who" is the subject, so no auxiliary verb is needed. However, in "Who did you eat the cake with?" "who" is the object of the verb "eat," and "you" is the subject.

2. **Question Formation:** In English, when forming questions where the question word is the object (not the subject), we typically use an auxiliary verb like "did" to help form the question. This is part of standard English grammar to maintain correct sentence structure.

So, using "Who did you eat the cake with?" is necessary to construct the question correctly when "who" refers to the object.

can we say multinational  person?

The term "multinational" is typically used to describe organizations or companies that operate in multiple countries. When referring to a person, it's more common to use terms like "multicultural," "multinational background," or "of multiple nationalities." For example:

- "She has a multicultural background."
- "He is of multiple nationalities."
- "She comes from a multinational family."

These phrases capture the idea that the person has connections to, experiences in, or heritage from more than one country.