RESUMEN
Interfaces between dissimilar correlated oxides can offer devices with versatile functionalities, and great efforts have been made to manipulate interfacial electronic phases. However, realizing such phases is often hampered by the inability to directly access the electronic structure information; most correlated interfacial phenomena appear within a few atomic layers from the interface. Here, atomic-scale epitaxy and photoemission spectroscopy are utilized to realize the interface control of correlated electronic phases in atomic-scale ruthenate-titanate heterostructures. While bulk SrRuO3 is a ferromagnetic metal, the heterointerfaces exclusively generate three distinct correlated phases in the single-atomic-layer limit. The theoretical analysis reveals that atomic-scale structural proximity effects yield Fermi liquid, Hund metal, and Mott insulator phases in the quantum-confined SrRuO3 . These results highlight the extensive interfacial tunability of electronic phases, hitherto hidden in the atomically thin correlated heterostructure. Moreover, this experimental platform suggests a way to control interfacial electronic phases of various correlated materials.
RESUMEN
BACKGROUND: Recent attempts on adopting artificial intelligence algorithm on coronary diagnosis had limitations on data quantity and quality. While most of previous studies only used vessel image as input data, flow features and biometric features should be also considered. Moreover, the accuracy should be optimized within gray zone as the purpose is to decide stent insertion with estimated fractional flow reserve. OBJECTIVES: The main purpose of this study is to develop an artificial intelligence-based coronary vascular diagnosis system focused on performance in the gray zone, from CT image extraction to FFR estimation. Three main issues should be considered for an algorithm to be used for pre-screening: algorithm optimization in the gray zone, minimization of labor during image processing, and consideration of flow and biometric features. This paper introduces a full FFR pre-screening system from automatic image extraction to an algorithm for estimating the FFR value. METHOD: The main techniques used in this study are an automatic image extraction algorithm, lattice Boltzmann method based computational fluid dynamics analysis of a synthetic model and patient data, and an AI algorithm optimization. For feature extraction, this study focused on an automatic process to reduce manual labor. The algorithm consisted of two steps: the first algorithm calculates flow features from geometrical features, and the second algorithm estimates the FFR value from flow features and patient biometric features. Algorithm selection, outlier elimination, and k-fold selection were included to optimize the algorithm. CONCLUSION: Eight types of algorithms including two neural network models and six machine learning models were optimized and tested. The random forest model shows the highest performance before optimization, whereas the multilayer perceptron regressor shows the highest gray zone accuracy after optimization.
