Abstract: Disclosed is a method of manufacturing a transparent stretchable substrate according to various embodiments of the present disclosure. The method may include generating a substrate part formed of an elastic material, generating an auxetic including a plurality of unit structures on the substrate part, and generating a fixing part on the substrate part on which the auxetic is generated.

Abstract: A positive electrode active material for a secondary battery includes a lithium composite transition metal oxide including nickel (Ni), cobalt (Co), and manganese (Mn), wherein the lithium composite transition metal oxide has a layered crystal structure of space group R3m, includes the nickel (Ni) in an amount of 60 mol % or less based on a total amount of transition metals, includes the cobalt (Co) in an amount greater than an amount of the manganese (Mn), and is composed of single particles.

Abstract: Discussed is a cosmetic dispensing system including a terminal and a cosmetic dispenser in near field communication with the terminal, including a memory storing applications for dispensing the cosmetic, a display receiving a command for inputting a cosmetic provision amount and a command for selecting a cosmetic dispensing icon, and a controller processing the command for inputting the cosmetic provision amount and the command for selecting the cosmetic dispensing icon received form the display. The command for inputting the cosmetic provision amount is data determined based on a number of times of use of cosmetics inputted by a user.

Abstract: A method for predicting preparation times includes: retrieving set of item-level records from a database for preparation menu items for all subscriber restaurants; training and executing a first neural network to generate embeddings for each of the menu items; for a first subset of the set, calculating actual item-level preparation time vectors; for a second subset of the historical set, generating estimated item-level preparation time vectors; retrieving a set of order-level records for preparation of orders from the database; training a second neural network to predict the order-level preparation times, wherein inputs to the second neural network comprise one or more of the item-level preparation time vectors and metadata taken from the order-level records; and following training, executing the second neural network to generate predicted order-level preparation times for current orders within a restaurant.

Abstract: A method for predicting takeout and delivery pickup times includes: retrieving set of item-level records from a database for preparation menu items for all subscriber restaurants; training and executing a first neural network to generate embeddings for each of the menu items; for a first subset of the set, calculating actual item-level preparation time vectors; for a second subset of the historical set, generating estimated item-level preparation time vectors; retrieving a set of order-level records for preparation of orders from the database; training a second neural network to predict the order-level preparation times, wherein inputs to the second neural network comprise one or more of the item-level preparation time vectors and metadata taken from the order-level records; and following training, executing the second neural network to generate predicted order-level preparation times for current orders within a restaurant, and translating the predicted order-level preparation times into pickup times.