Abstract: An image is accepted by one or more processing circuits from a user depicting the user's skin. Machine learning models stored in one or more memory circuits are applied to the image to classify acne characteristics. An acne severity grade is provided by the one or more processing circuits and a user interface displays the acne severity grade.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's skin. Machine learning models stored in one or more memory circuits are applied to the image to classify acne characteristics. An acne severity grade is provided by the one or more processing circuits and a user interface displays the acne severity grade.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: An image is accepted by one or more processing circuits from a user depicting the user's facial skin. Machine learning models stored in one or more memory circuits are applied to the image to classify facial skin characteristics. A regimen recommendation is provided to the user based on the classified facial skin characteristics.

Abstract: The present invention provides a method for preparing the polymer grade low-carbon olefin through separation of the methanol pyrolysis gas, including steps of the compression, impurity removal, and absorption and separation. In the absorption and separation step, the pyrolysis gas is sent to the front-end ethylene removing column, and then is, with the C4 absorbent, further absorbed and separated to produce polymer grade ethylene products, polymer grade propylene products, and C4 and C5 products. The moderate-temperature and moderate-pressure separation without a cold box according to the present invention provides safer production process, less investment in the equipment, as well as easier separation and lower energy consumption as a result of the front-end ethylene removing and C4 absorption and separation process.

Abstract: The present invention provides a method for preparing the polymer grade low-carbon olefin through separation of the methanol pyrolysis gas, including steps of the compression, impurity removal, and absorption and separation. In the absorption and separation step, the pyrolysis gas is sent to the front-end ethylene removing column, and then is, with the C4 absorbent, further absorbed and separated to produce polymer grade ethylene products, polymer grade propylene products, and C4 and C5 products. The moderate-temperature and moderate-pressure separation without a cold box according to the present invention provides safer production process, less investment in the equipment, as well as easier separation and lower energy consumption as a result of the front-end ethylene removing and C4 absorption and separation process.