The field of fuzzy logic and belief revision is moving towards more expressive and robust frameworks for handling uncertainty and incomplete information. Recent developments have focused on improving the representation of rules in Learning Fuzzy-Classifier Systems, allowing for self-adaptive rule representation mechanisms that can optimize performance in the presence of unknown data characteristics. Additionally, there is a growing interest in developing more principled and predictable methods for belief revision, including the use of preference relations and belief algebras to characterize iterated revision rules. Noteworthy papers in this area include: Fuzzy-UCS Revisited, which proposes a novel self-adaptive rule representation mechanism for Michigan-style Learning Fuzzy-Classifier Systems, allowing for improved classification performance and robustness in the presence of noisy inputs and real-world problems with inherent uncertainty. On Definite Iterated Belief Revision with Belief Algebras, which introduces a novel framework for iterated belief revision using preference relations and belief algebras, providing a more predictable and principled method for belief revision. A New Tractable Description Logic under Categorical Semantics, which proposes a new extension of the Description Logic EL with a weakened negation, allowing for the representation of negative knowledge while retaining tractability. Non-expansive Fuzzy ALC, which proposes a novel fuzzy description logic that balances expressiveness and complexity, allowing for the representation of vague knowledge with preferable logical properties.