Atharv Gyan

Artificial Intelligence (AI) has emerged as a powerful tool with the potential to revolutionize various aspects of society, including sustainable development. As the world grapples with pressing environmental and social challenges, AI presents a unique opportunity to address these issues in innovative ways. From optimizing resource management to enhancing decision-making processes, AI can play a significant role in advancing sustainability goals across multiple sectors. One of the key areas where AI can contribute to sustainable development is in environmental conservation and management. AI-powered algorithms can analyze vast amounts of data from sources such as satellite imagery, sensors, and weather forecasts to monitor environmental changes and identify patterns. This data-driven approach enables more accurate predictions of phenomena like deforestation, habitat loss, and climate change impacts, allowing policymakers and conservationists to develop targeted interventions and mitiga...

In the realm of database management, there exists a category of data that is inherently dynamic and time sensitive. This type of data evolves over time, capturing changes, updates, and historical snapshots. Managing such time-varying data presents unique challenges and requires specialized techniques to ensure accurate storage, retrieval, and analysis. This is where the concept of temporal databases comes into play. Temporal databases are designed to handle time-varying data within the framework of Relational Database Management Systems (RDBMS). They enable the storage and manipulation of data while maintaining temporal aspects such as valid time (the time period during which data is considered valid) and transaction time (the time at which data is recorded or modified). There are several key components involved in managing time-varying data within an RDBMS: Temporal Data Model : Temporal databases typically extend the traditional relational data model to incorporate temporal dimension...