Griffin, J.M.;                     Mathew, J.;                     Gasparics, A.;                     Vértesy, G.;                     Uytdenhouwen, I.;                     Chaouadi, R.;                     Fitzpatrick, M.E.    
        Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel. Appl. Sci. 2022, 12, 3721.
    https://doi.org/10.3390/app12083721
    AMA Style
    
                                Griffin JM,                                 Mathew J,                                 Gasparics A,                                 Vértesy G,                                 Uytdenhouwen I,                                 Chaouadi R,                                 Fitzpatrick ME.        
                Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel. Applied Sciences. 2022; 12(8):3721.
        https://doi.org/10.3390/app12083721
    
    Chicago/Turabian Style
    
                                Griffin, James M.,                                 Jino Mathew,                                 Antal Gasparics,                                 Gábor Vértesy,                                 Inge Uytdenhouwen,                                 Rachid Chaouadi,                                 and Michael E. Fitzpatrick.        
                2022. "Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel" Applied Sciences 12, no. 8: 3721.
        https://doi.org/10.3390/app12083721
    
    APA Style
    
                                Griffin, J. M.,                                 Mathew, J.,                                 Gasparics, A.,                                 Vértesy, G.,                                 Uytdenhouwen, I.,                                 Chaouadi, R.,                                 & Fitzpatrick, M. E.        
        
        (2022). Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) Steel. Applied Sciences, 12(8), 3721.
        https://doi.org/10.3390/app12083721