The field of simulation and process mining is undergoing significant developments, driven by the increasing need for efficient and accurate analysis of complex systems. One of the key trends is the integration of advanced technologies, such as artificial intelligence and machine learning, to improve the modeling and simulation processes. This has led to the creation of new tools and techniques, such as automated test model generation and conformance checking methods, which enable the analysis of large and complex datasets. Another area of focus is the development of approaches that can adapt to changing process dynamics, such as streaming process simulation discovery techniques. These advancements have the potential to significantly impact various industries, including manufacturing, healthcare, and software engineering. Noteworthy papers include: Simulation Everywhere, which evaluates the evolutionary expansion of discrete-event simulation and its relevance in diverse fields. FoldA, which proposes a new technique for computing partial-order alignments using directed Petri net unfoldings, providing a more accurate representation of concurrency. Logic Mining from Process Logs, which presents an approach for generating logical specifications from process models discovered via workflow mining, enabling automated specification and verification. Online Discovery of Simulation Models for Evolving Business Processes, which proposes a streaming process simulation discovery technique that adapts to real-time operational changes, ensuring more stable simulations and robustness in handling concept drift.