Abstract: Methods and systems for executing tracking and monitoring manufacturing data of a battery are disclosed. One method includes: receiving, by a server system, sensing data of the battery from a sensing system; generating, by the server system, mapping data based on the sensing data; generating, by the server system, identification data of the battery based on the sensing data; generating, by the server system, monitoring data of the battery based on the sensing data, the identification data, and the mapping data; and generating, by the server system, display data for displaying a simulated electrode of the battery on a graphical user interface based on the monitoring data of the battery.

Abstract: Disclosed is A multiple times available drug concentration measuring apparatus including a sample input unit configured to allow a liquid drug sample to be introduced therethrough, a dilution unit configured to add a solvent to a portion of the liquid drug sample in order to dilute the liquid drug sample, a concentration measurement unit configured to measure the concentration of the liquid drug sample using a sensor, and a control unit configured to decide a dilution ratio of the liquid drug sample based on data received from the concentration measurement unit, to perform control such that the dilution unit dilutes the liquid drug sample according to the dilution ratio, and to calculate the concentration of the liquid drug sample based on the dilution ratio and the data received from the concentration measurement unit.

Abstract: The present disclosure provides a new drug candidate material output apparatus, including: a communication module; a memory in which a new drug candidate material output program is stored; and a processor executing the new drug candidate material output program. The new drug candidate material output program provides a drug learning model in which an embedding vector for a chemical structure of a chemical compound and an embedding vector for change information on an amount of a transcriptome induced by each chemical compound are located in a same vector space, outputs a result of the change information on the amount of the transcriptome that matches the embedding vector for the chemical structure of the new material input to the drug learning model, or outputs information on one or more drugs that match the change information on the amount of the transcriptome that is a target input to the drug learning model.