Abstract: A light-emitting device comprises a substrate; a first light-emitting unit and a second light-emitting unit formed on the substrate, each of the first light-emitting unit and the second light-emitting unit comprises a first semiconductor layer, a second semiconductor layer, and an active layer between the first semiconductor layer and the second semiconductor layer, wherein the first light-emitting unit comprises a first semiconductor mesa and a first surrounding part surrounding the first semiconductor mesa, and the second light-emitting unit comprises a second semiconductor mesa and a second surrounding part surrounding the second semiconductor mesa; a trench formed between the first light-emitting unit and the second light-emitting unit and exposing the substrate; a first insulating layer comprising a first opening on the first surrounding part and a second opening on the second semiconductor layer of the second light-emitting unit; and a connecting electrode comprising a first connecting part on the first

Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes an original physiological parameter inputting step, an original chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the original chest images, segmenting images of a left lung, a right lung and a heart from each of the original chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training two respiratory status classifiers using a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.

Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

Abstract: A respiratory status classifying method is for classifying as one of at least two respiratory statuses and includes a training's physiological parameter inputting step, a training's chest image inputting step, a characteristic physiological parameter generating step, a characteristic chest image generating step, a training step and a classifier generating step. The characteristic chest image generating step includes processing at least a part of the training's chest images, segmenting images of a left lung, a right lung and a heart from each of the training's chest images that are processed, and enhancing image data of the images being segmented, so as to generate a plurality of characteristic chest images. The training step includes training a plurality of characteristic physiological parameters and the characteristic chest images by at least one machine learning algorithm.