The field of power system optimization and cybersecurity is rapidly evolving, with a focus on developing innovative solutions to address the complexities of modern power systems. Recent research has centered on improving the efficiency and resilience of power systems, with a particular emphasis on optimizing power flow, enhancing cybersecurity, and developing advanced algorithms for real-time decision-making. Notably, homotopy-guided self-supervised learning methods have shown promise in solving optimal power flow problems, while neural network-based approaches have demonstrated significant improvements in solving day-ahead offering problems. Additionally, researchers have made strides in developing cyber-resilient fault diagnosis methodologies and scalable iterative algorithms for solving optimal transmission switching problems. The development of tri-level stochastic-robust co-planning frameworks for distribution networks and renewable charging stations has also emerged as a key area of research. Some noteworthy papers in this area include: The paper on Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flow, which introduces a novel method for solving optimal power flow problems. The paper on DER Day-Ahead Offering: A Neural Network Column-and-Constraint Generation Approach, which proposes a neural network-accelerated column-and-constraint generation method for solving day-ahead offering problems.