The fields of nonlinear control, structural health monitoring, and signal processing are experiencing significant growth, driven by advancements in robust and offset-free tracking, Bayesian inverse problems, and innovative control strategies. A common theme among these areas is the emphasis on improving accuracy, efficiency, and robustness in various applications, including robotics, mechanical systems, and process control. Notable developments include the introduction of kernelized data-driven predictive control and velocity form formulations for recurrent neural networks, which have shown promise in achieving robust and offset-free tracking of nonlinear systems. Additionally, researchers are exploring new approaches to quantify uncertainty and identifiability in Bayesian inverse problems, leading to the development of frameworks that rigorously quantify the limits of resolution and uncertainty of inferred states. The integration of machine learning and data-driven techniques with traditional control and estimation methods is also gaining traction, with applications in areas such as motion control systems and analog-to-digital converters. In the field of robotics, advancements in coverage and control are optimizing spatial coverage and improving task efficiency, with the integration of optimal transport theory with multi-agent coverage control showing promise in achieving non-uniform area coverage. Overall, these emerging trends and innovations have the potential to significantly impact various applications and industries, enabling more informed decision-making, improved performance, and increased efficiency.