CEERMA and PRH 38.1 program researchers participated in the 34th European Safety and Reliability Conference (ESREL 2024) held from June 23 to 27, 2024, at the Jagiellonian University in Krakow, Poland. The event brought together experts worldwide to discuss the latest safety and reliability engineering advancements, providing an invaluable platform for knowledge exchange and collaboration.

Representing the PRH 38.1 program, the delegation included Professor and coordinator Márcio Moura; Professor and technical committee member Isis Lins; Professor and advisor Caio Soutor Maior; doctoral fellow in Production Engineering Lavínia Araújo; and CEERMA members, post-doctoral researcher July Bias and Eduardo Menezes, Professor from the Department of Mechanical Engineering. Their presence underscored the program's commitment to advancing research and development in critical areas of engineering by organizing special sessions at the conference. These sessions included "Advances in Reliability Engineering and Risk Management In Oil And Gas Industries," organized by Márcio Moura, Isis Lins, Enrique López Droguett (Full Professor at the University of California, Los Angeles - UCLA), and Manoel Feliciano Silva (CENPES, PETROBRAS). Other sessions included "Quantum-Based Optimization and Inference for Risk and Reliability Applications" and "Quantum Machine Learning For Risk and Reliability Applications," both organized by Enrique López Droguett, Márcio Moura, and Isis Lins.

The team made significant contributions to the conference by submitting and presenting a total of 10 papers. These works, which were met with great interest, highlight the innovative approaches being developed within the program and their practical applications in industrial processes. These contributions initiated productive discussions and fostered international collaboration, furthering the advancement of safety and reliability engineering. The presented works can be accessed below:

• Quantum Noise Assessment in Quantum Machine Learning Models for Equipment Failure Modes Diagnosis
• Quantum Approaches for Reliability Estimation: A Systematic Literature Review
• Exploring Initialization Strategies for Quantum Optimization Algorithms to Solve the Redundancy Allocation Problem
• Quantum Machine Learning for Failure Diagnosis: Analysis of Different Quantum Encoding Methods
• Quantum Natural Language Processing to Human Factor Identification in Accident Reports
• Subsea Equipment Reliability: A Critical Analysis of Existing Literature
• Integrating RESNET50 and LSTM for CV-based Drowsiness Detection in Environments with Monitoring Tasks
• Method to Evaluate Similarity of Completion Equipment Using Deep Learning
• Deep Learning for Fault Classification Using Digital Twin Centrifugal Pump
• Modeling Optimal Reliability Test Planning Problem for Completion Technologies Under Development