Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face limitations due to high deployment costs, sparse sensor coverage, and environmental interferences. To address these challenges, this paper proposes a framework for high-resolution spatiotemporal Air Quality Index (AQI) mapping using sparse sensor data, satellite imagery, and various spatiotemporal factors. By leveraging Graph Neural Networks (GNNs), we estimate AQI values at unmonitored locations based on both spatial and temporal dependencies. The framework incorporates a wide range of environmental features, including meteorological data, road networks, points of interest (PoIs), population density, and urban green spaces, which enhance prediction accuracy. We illustrate the use of our approach through a case study in Lahore, Pakistan, where multi-resolution data is used to generate the air quality index map at a fine spatiotemporal scale.

This study is about the implementation of a reinforcement learning algorithm in the trajectory planning of manipulators. We have a 7-DOF robotic arm to pick and place the randomly placed block at a random target point in an unknown environment. The obstacle is randomly moving which creates a hurdle in picking the object. The objective of the robot is to avoid the obstacle and pick the block with constraints to a fixed timestamp. In this literature, we have applied a deep deterministic policy gradient (DDPG) algorithm and compared the model's efficiency with dense and sparse rewards.

In recent work, [1] introduced the concept of using a Block Adjacency Matrix (BA) for the representation of spatio-temporal data. While their method successfully concatenated adjacency matrices to encapsulate spatio-temporal relationships in a single graph, it formed a disconnected graph. This limitation hampered the ability of Graph Convolutional Networks (GCNs) to perform message passing across nodes belonging to different time steps, as no temporal links were present. To overcome this challenge, we introduce an encoder block specifically designed to learn these missing temporal links. The encoder block processes the BA and predicts connections between previously unconnected subgraphs, resulting in a Spatio-Temporal Block Adjacency Matrix (STBAM). This enriched matrix is then fed into a Graph Neural Network (GNN) to capture the complex spatio-temporal topology of the network. Our evaluations on benchmark datasets, surgVisDom and C2D2, demonstrate that our method, with slightly higher complexity, achieves superior results compared to state-of-the-art results. Our approach's computational overhead remains significantly lower than conventional non-graph-based methodologies for spatio-temporal data.