The recent publications in the field highlight a significant shift towards integrating data-driven methodologies with traditional analytical approaches, particularly in areas such as parameter identifiability, fault diagnosis, and error detection in numerical programs. This trend underscores the growing recognition of the complementary strengths of both approaches, especially in complex systems where traditional methods may fall short. For instance, the comparison of analytical and data-driven methods for parameter identifiability in power systems demonstrates the potential of data-driven techniques to extend the scope of traditional analysis, offering comparable accuracy without the need for explicit models. Similarly, the exploration of multi-condition fault diagnosis reflects an increasing focus on developing robust methods capable of handling the dynamic and complex nature of modern industrial systems. Moreover, the introduction of novel algorithms and approaches, such as the DELA method for detecting errors in numerical programs, showcases the field's ongoing innovation in addressing critical challenges with efficiency and precision.

Noteworthy Papers

• Comparing analytic and data-driven approaches to parameter identifiability: A power systems case study: Demonstrates the complementary nature of analytical and data-driven methods in achieving accurate parameter identifiability and reduction, with both approaches yielding comparable results.
• Multi-Condition Fault Diagnosis of Dynamic Systems: A Survey, Insights, and Prospects: Provides a comprehensive review of multi-condition fault diagnosis, highlighting the challenges and prospects in developing robust diagnostic methods for complex systems.
• A Novel Supervisory Control Algorithm to Avoid Deadlock in a Manufacturing System Based on Petri Net in Presence of Resource Failure: Introduces an innovative algorithm for deadlock avoidance in manufacturing systems, ensuring system liveness and performance continuity even in the event of resource failures.
• DELA: A Novel Approach for Detecting Errors Induced by Large Atomic Condition Numbers: Presents a groundbreaking method for detecting significant errors in numerical programs, offering a highly efficient alternative to traditional high-precision approaches.