The field of control systems is moving towards the development of more advanced and robust methods for ensuring safety and stability. One key area of focus is the adaptation of control barrier functions, which are used to enforce state constraints and prevent systems from entering unsafe regions. Researchers are exploring new approaches to adapting these functions online, using techniques such as machine learning and uncertainty-aware verification. Another important area of research is reachability analysis, which involves determining the set of all possible states that a system can reach from a given initial condition. This is crucial for safety verification and control design, and researchers are working on developing more efficient and accurate methods for performing reachability analysis, including data-driven approaches and the use of zonotopes. Notable papers in this area include the development of a learning-based approach for adapting control barrier functions, a data-driven Hamiltonian for constructing safe sets from trajectory data, and a novel approach for reachability analysis of piecewise affine systems using hybrid zonotopes. Additionally, the use of physics-informed neural networks for control barrier function synthesis and the development of libraries such as ZETA for zonotope-based estimation and fault diagnosis are also noteworthy.