Efficient container handling and early damage detection are critical for minimizing operational delays, reducing costs, and ensuring safety in global maritime logistics.This work presents a deep learning-based methodology for real-time container tracking and automated damage detection during crane unloading operations at container terminals.We develop and deploy two specialized YOLOv12-based object detection models: one for identifying containers in motion and another for detecting structural damages such as bents, dents, and holes.Our models are trained and evaluated on a real-world dataset curated from video feeds captured at the EUROGATE Container Terminal in Limassol, Cyprus.The system is designed for robust performance under realistic terminal conditions, including variable lighting and motion.Our models achieve high detection accuracy, with a mAP50 of 0.99 for container detection and 0.75 for damage detection, substantially outperforming existing benchmarks.These results highlight the practical potential of our method for improving efficiency and safety in automated maritime logistics.

Modern data storage systems are characterized by a distributed architecture as well as the presence of multiple storage tiers and caches. Both system developers and operators are challenged with the complexity of such systems as it is hard to evaluate how a configuration change will impact the workload or system performance and identify the best configuration to satisfy some performance objective. DITIS is a new simulator that models the end-to-end execution of file requests on distributed tiered storage systems that addresses the aforementioned challenges efficiently without any costly system redeployments. The demonstration will showcase the key functionalities and benefits offered by DITIS, including (i) analyzing workload traces to understand their characteristics and the behavior of the underlying storage system; (ii) running simulations with different configurations to evaluate their impact on performance; and (iii) running optimizations over custom search spaces to find the best configuration that satisfies a given objective.