Short note space

Food is a fundamental necessity for human survival, leading to the expansion of crop cultivation beyond traditional rural areas into urban environments. Urban farming presents several advantages, including access to fresh, organic produce and the creation of new job opportunities. However, despite these benefits, urban agriculture faces notable challenges. Organic foods are often more expensive, and the success of urban farming initiatives can be hampered by insufficient space for plant growth, insufficient knowledge, inadequate conditions for plant growth, and significant environmental changes driven by global development. To address these challenges, this research focuses on leveraging Artificial Intelligence (AI) techniques to enhance urban farming practices. By applying AI, it is possible to optimize growing conditions, predict and manage plant needs, and adapt to changing environmental factors. AI can also help streamline operations and reduce costs, making urban farming more viab...

Abstract Multiprotocol Label Switching (MPLS) is a versatile network technology that enhances the performance, reliability, and scalability of data traffic management. This article explores a case study of MPLS, detailing its architecture and key components, such as Provider Edge (PE) Routers and Provider (P) Routers. It highlights the benefits of MPLS including simplified connectivity deployment, protocol efficiency, and improved performance. Through practical analysis and examples, this content provides a comprehensive understanding of MPLS networks, their implementation, and their role in modern telecom infrastructure. Introduction Multi-Protocol Label Switching (MPLS) is a pivotal routing technique primarily utilized in telecommunication networks. In this case study, all telecom network nodes are located in specialized core rooms that maintain strict environmental conditions and uninterrupted power to ensure reliable performance. These core rooms are geographically distributed to m...

Here, I used a different method to represent hourly data across various dates. I converted specific dates to common dates and then plotted the time series data on a 24-hour time frame along the x-axis, with traffic patterns on the y-axis.  import os import pandas as pd import numpy as np import matplotlib.pyplot as plt from datetime import datetime import matplotlib.dates import matplotlib.dates as md path = 'datasheet.csv' data = pd.read_csv(path) data.head() # Convert data to DataFrame df = pd.DataFrame({'Date&Time': data['Date'] , 'DL': data['DL']/1000000000, 'UL': data['UL']/1000000000}) # Replace all Date&Time with '02/02/2024' df['Date&Time'] = '02/02/2024 ' + df['Date&Time'].str.split(' ', expand=True)[1] # Convert 'Date&Time' column to datetime format df['Formated_Date'] = pd.to_datetime(df['Date&Time'], format='%m/%d/%Y %H:%M...

First, we must identify the various activities that must be carried out in constructing a two-story house and group them into suitable work breakdown structures (WBS). The following figure indicates the WBS of this sample project and appropriate implementation periods for each activity. Note that the project has to complete within 12 months as a condition. Therefore, we have to be very careful when scheduling the duration of activities in order to complete the project on time. The next step is to build a project using a computer program. Here I used MS Project software and the following figure shows the Project plan and the status of activities completed. The red color activities and arrows indicate the critical path of the project. Path activities have to be completed between the exact period in order to hand over the project on time.