In this study, the effect of particle size of genistein-loaded solid lipid particulate systems on drug dissolution behavior and oral bioavailability was investigated. Genistein-loaded solid lipid microparticles and nanoparticles were prepared with glyceryl palmitostearate. Except for the particle size, other properties of genistein-loaded solid lipid microparticles and nanoparticles such as particle composition and drug loading efficiency and amount were similarly controlled to mainly evaluate the effect of different particle sizes of the solid lipid particulate systems on drug dissolution behavior and oral bioavailability. The results showed that genistein-loaded solid lipid microparticles and nanoparticles exhibited a considerably increased drug dissolution rate compared to that of genistein bulk powder and suspension. The microparticles gradually released genistein as a function of time while the nanoparticles exhibited a biphasic drug release pattern, showing an initial burst drug release, followed by a sustained release. The oral bioavailability of genistein loaded in solid lipid microparticles and nanoparticles in rats was also significantly enhanced compared to that in bulk powders and the suspension. However, the bioavailability from the microparticles increased more than that from the nanoparticles mainly because the rapid drug dissolution rate and rapid absorption of genistein because of the large surface area of the genistein-solid lipid nanoparticles cleared the drug to a greater extent than the genistein-solid lipid microparticles did. Therefore, the findings of this study suggest that controlling the particle size of solid-lipid particulate systems at a micro-scale would be a promising strategy to increase the oral bioavailability of genistein.

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.