The recent developments in the field of process modeling and analysis have seen significant advancements in the integration of data-awareness and reactive synthesis within business process models. Researchers are increasingly focusing on ensuring the soundness and adaptability of these models, particularly in complex, data-driven environments. The introduction of algorithms for soundness correction in data Petri nets and extensions to BPMN that incorporate probabilistic decision-making and resource impact analysis are indicative of this trend. Additionally, there is a growing interest in converting BPMN diagrams into formal privacy calculus to enhance privacy preservation in software development. The field is also witnessing advancements in the semantics and performance analysis of process calculi, with a particular emphasis on modular and rewritable Petri nets, which promise more efficient and adaptive modeling of concurrent systems. These innovations collectively push the boundaries of process modeling towards more robust, adaptable, and privacy-conscious frameworks.