Abstract: Methods and systems for managing the operation of data processing systems are disclosed. A data processing system may include a computing device that may provide computer implemented services. To provide the computer implemented services, hardware components of the data processing system may need to operate in predetermined manners. To manage the operation of the hardware components, the data processing system may cool them when their temperatures fall outside of thermal operating ranges. To facilitate cooling, fans may be densely packed and arranged in a manner the occupies a majority of the space in a stack up. At least one side of the fans may be exposed and may not be covered.

Abstract: Methods, systems, and devices for providing computer implemented services are disclosed. To provide the computer implemented services, the quantity of hardware resources available for providing the computer implemented services may be modified. The quantity of hardware resources may be modified by adding removable cards to a host system. The host system may, while the added removable cards are cold, selectively warm the removable cards through conduction heating to retain their temperatures within operating temperature ranges.

Abstract: Methods, systems, and devices for managing the operation of data processing systems are disclosed. A data processing system may include a computing device that may provide computer-implemented services. To provide the computer-implemented services, hardware components of the data processing system may need to operate within certain thermal dissipation requirements. To regulate the temperature of the hardware components, a fan may circulate air through the data processing system when the temperatures fall outside the thermal dissipation requirements. To regulate the temperature of the hardware components more efficiently, higher air flow rates may be desired. To increase air flow rates, a three-dimensional ventilation port may be implemented to de-constrict air flow when air enters or exits the data processing system.

Abstract: An electronic device includes a substrate, a first silicon transistor, a second silicon transistor and a first oxide semiconductor transistor. The first silicon transistor, the second silicon transistor and the first oxide semiconductor transistor are disposed on the substrate. The first silicon transistor has a first terminal electrically connected to a first voltage level, a second terminal and a control terminal. The second silicon transistor has a first terminal electrically connected to the second terminal of the first silicon transistor, a second terminal electrically connected to a second voltage level, and a control terminal electrically connected to the control terminal of the first silicon transistor. The first oxide semiconductor transistor has a first terminal electrically connected to the first terminal of the second silicon transistor. Wherein, a voltage value of the first voltage level is greater than a voltage value of the second voltage level.

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: 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.