Numerical simulation of optical control for a soft particle in a microchannel

Lineshape study of optical force spectra on resonant structures

Understanding the frequency spectrum of the optical force is important for controlling and manipulating micro- and nano-scale objects using light. Spectral resonances of these objects can significantly influence the optical force spectrum. In this paper, we develop a theoretical formalism based on the temporal coupled-mode theory that analytically describes the lineshapes of force spectra and their dependencies on resonant scatterers for arbitrary incident wavefronts. We obtain closed-form formulae and discuss the conditions for achieving symmetric as well as asymmetric lineshapes, pertaining, respectively, to a Lorentzian and Fano resonance. The relevance of formalism as a design tool is exemplified for a conceptual scheme of the size-sorting mechanism of small particles, which plays a role in biomedical diagnosis.

Coronary artery decision algorithm trained by two-step machine learning algorithm

A two-step machine learning (ML) algorithm for estimating both fractional flow reserve (FFR) and decision (DEC) for the coronary artery is introduced in this study. The primary purpose of this model is to suggest the possibility of ML-based FFR to be more accurate than the FFR calculation technique based on a computational fluid dynamics (CFD) method. For this purpose, a two-step ML algorithm that considers the flow characteristics and biometric features as input features of the ML model is designed. The first step of the algorithm is based on the Gaussian progress regression model and is trained by a synthetic model using CFD analysis. The second step of the algorithm is based on a support vector machine with patient data, including flow characteristics and biometric features. Consequently, the accuracy of the FFR estimated from the first step of the algorithm was similar to that of the CFD-based method, while the accuracy of DEC in the second step was improved. This improvement in accuracy was analyzed using flow characteristics and biometric features.

A two-step machine learning (ML) algorithm for coronary artery decision making is introduced, to increase the data quality by providing flow characteristics and biometric features by aid of computational fluid dynamics (CFD).