Abstract: An embodiment solid electrolyte includes a first compound and a second compound. The first compound is represented by a first chemical formula Li7-aPS6-a(X11-bX2b)a, wherein X1 and X2 are the same or different and each represents F, Cl, Br, or I, and wherein 0<a?2 and 0<b<1, and the second compound is represented by a second chemical formula Li7-cP1-2dMdS6-c-3d(X11-eX2e)c, wherein X1 and X2 are the same or different and each represents F, Cl, Br, or I, wherein M represents Ge, Si, Sn, or any combination thereof, and wherein 0<c?2, 0<d<0.5, and 0<e<1.

Abstract: A semiconductor manufacturing equipment may include a process chamber for treating a substrate; a front-end module including a first transfer robot, wherein the first transfer robot may be configured to transport the substrate received in a container; a transfer chamber between the front-end module and the process chamber, wherein the transfer chamber may be configured to load or unload the substrate into or out of the process chamber; and a cassette capable of receiving a replaceable component capable of being used in the process chamber. The front-end module may include a seat plate configured to move in a sliding manner so as to retract or extend into or from the front-end module. The cassette may be configured to be loaded into the front-end module while the cassette is seated on the seat plate.

Abstract: In an embodiment an apparatus includes a processor configured to generate a feature extraction module using a dataset in which an attribute for each object is defined, receive an image obtained by a camera, extract an attribute of an object of interest from the image using the learned feature extraction module, identify an object re-identification candidate group based on the extracted attribute of the object of interest and re-identify the object of interest based on the identified object re-identification candidate group.

Abstract: An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.

Abstract: An apparatus for predicting an injury level of a user of a vehicle may include: a communication circuit configured to communicate with an external device; a memory configured to store a genetic algorithm and a machine learning model; and a processor electrically connected with the communication circuit and the memory. The processor may be configured to: obtain, via the communication circuit, traffic accident data associated with a traffic accident; select input data, which includes at least a part of the traffic accident data, for training of the machine learning model, the input data selected using the genetic algorithm; train the machine learning model using the input data; and predict an injury level of the user of the vehicle using the trained machine learning model when the training of the machine learning model is completed.