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Panama prepares to evacuate first island in face of rising sea levels<br/><br/>On a tiny island off Panama’s Caribbean coast, about 300 families are packing their belongings in preparation for a dramatic change. Generations of Gunas who have grown up on Gardi Sugdub in a life dedicated to the sea and tourism will trade that next week for the mainland’s solid ground.<br/><br/>They go voluntarily — sort of.<br/><br/>The Gunas of Gardi Sugdub are the first of 63 communities along Panama’s Caribbean and Pacific coasts that government officials and scientists expect to be forced to relocate by rising sea levels in the coming decades. <br/><br/>Photos by AP<br/><br/>Subscribe to The Manila Times Channel - https://tmt.ph/YTSubscribe <br/>Visit our website at https://www.manilatimes.net <br/> <br/>Follow us: <br/>Facebook - https://tmt.ph/facebook <br/>Instagram - https://tmt.ph/instagram <br/>Twitter - https://tmt.ph/twitter <br/>DailyMotion - https://tmt.ph/dailymotion <br/> <br/>Subscribe to our Digital Edition - https://tmt.ph/digital <br/> <br/>Check out our Podcasts: <br/>Spotify - https://tmt.ph/spotify <br/>Apple Podcasts - https://tmt.ph/applepodcasts <br/>Amazon Music - https://tmt.ph/amazonmusic <br/>Deezer: https://tmt.ph/deezer <br/>Tune In: https://tmt.ph/tunein<br/> <br/>#themanilatimes<br/>#worldnews <br/>#panama<br/>#sea
⏲ 0:44 👁 40.3M
Gentleman's Gazette
⏲ 13 minutes 14 seconds 👁 150.4K
Laptop Seniors
⏲ 4 minutes 56 seconds 👁 697
3 Easy Steps , to Becoming More Self-Aware.<br/>According to Personal Development Master blog, self-awareness is a person's ability to pay attention to the way they think, behave and feel.<br/>In being self-aware, one has to examine patterns in <br/>the way they perceive themselves, the world, other <br/>people and even their own moods and emotions. .<br/>Being self-aware can help people determine <br/>whether they are making good choices that serve <br/>their interests, or if they are headed in a bad direction.<br/>Here are three tips for better self-awareness <br/>from Personal Development Master's blog. .<br/>1. Think about what bothers you about <br/>other people. Think about how you <br/>might show those characteristics yourself. .<br/>The opposite helps, too. What do you like about other people? How could you be more like that?.<br/>2. Be aware of automatic responses.<br/>In times of stress, many people act according <br/>to ingrained responses developed over time. .<br/>Being aware of these responses can make <br/>you aware of what triggers those behaviors <br/>and help you take control of your actions. .<br/>3. Ask people in your life for feedback. <br/>Insights from people can help you see your <br/>actions and feelings from a new perspective. .<br/>Try not to look at negative comments <br/>as a personal attack. Instead, think of it <br/>as a chance to develop and grow. .<br/>Taking steps to know yourself better <br/>can change the way you perceive the world <br/>and how the world perceives you.
⏲ 1:31 👁 5.3M
Stephanie Perry
⏲ 1 hour 36 minutes 50 seconds 👁 3.4K
Goorin Bros.
⏲ 7 minutes 46 seconds 👁 7.5K
Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \
⏲ 4:9 👁 75K
Panama Relocation Tours
⏲ 7 minutes 21 seconds 👁 8.3K
South Florida PBS
⏲ 58 minutes 22 seconds 👁 20.1K
Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \
⏲ 4:28 👁 40K
Rigval Reza
⏲ 5 minutes 27 seconds 👁 5.7K
NAĐa Vu
⏲ 7 minutes 39 seconds 👁 1.3K
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