1 

Hellenic Naval Academy

Zannis, T. C., Katsanis, J. S., Christopoulos, G. P., Yfantis, E. A., Papagiannakis, R. G., Pariotis, E. G., ... & Vallis, A. G. (2022). Marine exhaust gas treatment systems for compliance with the IMO 2020 global sulfur cap and tier III NOx limits: a review.Energies, 15(10), 3638. https://doi.org/10.3390/en15103638

2

Hellenic Naval Academy

Vallis, A. G., Zannis, T. C., Hristoforou, E. V., Yfantis, E. A., Pariotis, E. G., Hountalas, D. T., & Katsanis, J. S. (2022). Design of Container Ship Main Engine Waste Heat Recovery Supercritical CO2 Cycles, Optimum Cycle Selection through Thermo-Economic Optimization with Genetic Algorithm and Its Exergo-Economic and Exergo-Environmental Analysis. Energies, 15(15), 5398. https://doi.org/10.3390/en15155398

3

Hellenic Naval Academy

Alexiou, K., Pariotis, E. G., Leligou, H. C., & Zannis, T. C. (2022). Towards data-driven models in the prediction of ship performance (speed—power) in actual seas: A comparative study between modern approaches. Energies, 15(16), 6094. https://doi.org/10.3390/en15166094

4

Hellenic Naval Academy

Alexiou, K., Pariotis, E. G., & Leligou, H. C. (2023). Sensor Data Quality in Ships: A Time Series Forecasting Approach to Compensate for Missing Data and Drift in Measurements of Speed through Water Sensors. Designs, 7(2), 46. https://doi.org/10.3390/designs7020046

5

PRISMA ELECTRONICS

Spandonidis, C., & Paraskevopoulos, D. (2023). Evaluation of a Deep Learning-Based Index for Prognosis of a Vessel’s Propeller-Hull Degradation. Sensors, 23(21), 8956. https://doi.org/10.3390/s23218956