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.

Abstract: A cycle propelling mechanism includes a rail disposed on a cycle frame, two cranks rotatably coupled to the cycle frame with a main drive shaft, two levers each include one end pivotally coupled to the crank and the other end slidably coupled to the rail for allowing the levers to be moved cyclically relative to the cycle frame, and two foot pedals pivotally attached to the levers for being stepped and operated by user's feet and for allowing the cranks and the lever to be rotated relative to the cycle frame in an elliptical moving stroke and for allowing the foot pedals to be stepped and moved for a longer stepping or moving stroke by the user, and for effectively exercising the more or bigger muscle groups of the user, and for applying bigger power to the cycle propelling mechanism.

Abstract: An assemblable eating utensil is disclosed that provides an eating utensil that is portable, easy to assemble and disassemble, and convenient to use.