Abstract: Embodiments include systems and methods for product scrapping and processing. Methods may include transmitting a list of stock keeping units (SKUs) to an interface; causing an external system to determine the location identifier associated with each SKU, command at least one automated guided vehicle (AGV) to retrieve each SKU, and transport each SKU to at least one terminal; compare, by a user device associated with the at least one terminal, the registered SKU identifier with a physical SKU identifier; when the registered SKU identifier is consistent with the physical SKU identifier: transport the corresponding SKU to undergo a scrapping process or a conversion process; when the registered SKU identifier is inconsistent with the physical SKU identifier: perform logic steps to assess the corresponding SKU, and modify the registered SKU identifier according to results of the performed logic steps.

Abstract: A compound of Chemical Formula 1: wherein X1 is O or S; any one of R1 to R5 is a moiety bonded to L1 of the following Chemical Formula A at **: Ar1 is a substituted or unsubstituted aryl group, Chemical Formula 1 comprises at least one deuterium, and the other substituents are as defined in the specification; and an organic light-emitting device including the same. When a compound of Chemical Formula 1 is included in an organic light emitting device, the device exhibits lower driving voltage, improved light efficiency, and improved service life characteristics.

Abstract: The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.

Abstract: A neural network data quantizing method includes: obtaining local quantization data by firstly quantizing, based on a local maximum value for each output channel of a current layer of a neural network, global recovery data obtained by recovering output data of an operation of the current layer based on a global maximum value corresponding to a previous layer of the neural network; storing the local quantization data in a memory to perform an operation of a next layer of the neural network; obtaining global quantization data by secondarily quantizing, based on a global maximum value corresponding to the current layer, local recovery data obtained by recovering the local quantization data based on the local maximum value for each output channel of the current layer; and providing the global quantization data as input data for the operation of the next layer.

Abstract: A light emitting device includes: a first electrode that is a reflective electrode; a first electron transport layer disposed on the first electrode; a second electron transport layer disposed on the first electron transport layer; an emission layer disposed on the second electron transport layer, and a second electrode positioned on the emission layer and that is a transflective electrode, wherein the first electron transport layer and the second electron transport layer include different metal oxides, and a thickness of the first electron transport layer is greater than a thickness of the second electron transport layer.

Abstract: A compound of Chemical Formula 1 and an organic light-emitting device including the same: where X1 is O or S; R3 is a polycyclic heterocyclic group comprising at least one selected from N, O and S, or is a substituted or unsubstituted monocyclic heterocyclic group comprising at least one selected from O and S; Ar is a substituted or unsubstituted aryl group; and the other substituents are as described in the specification. When included in an organic light emitting device, the compound of Chemical Formula 1 can adjust the role of smoothly injecting holes into the light emitting layer, and can balance the movement of holes and electrons in the host, resulting in the device exhibiting lower driving voltage and improved efficiency and stability.

Abstract: An image sensor includes a substrate having a first surface and a second surface, which are opposite to each other, the substrate including a unit pixel region including a device isolation pattern adjacent to the first surface and a photoelectric conversion region adjacent to the second surface, a pixel isolation pattern provided in the substrate to define the unit pixel regions, an impurity region in the unit pixel region and being adjacent to a side surface of the device isolation pattern, a gate electrode provided on the first surface, and an auxiliary isolation pattern provided between a first side surface of the gate electrode and the impurity region, when the image sensor is viewed in a plan view. A bottom surface of the auxiliary isolation pattern may be located at a level different from a bottom surface of the device isolation pattern.

Abstract: An image-based jig inspection system for inspecting a state of a sensorless jig managed in a vehicle body factory includes: a jig configured to fix a vehicle body component mounted by a plurality of clamping units; a camera module configured to photograph an inspection image of the jig; a vision system configured to compare the inspection image with a predetermined image analysis reference to determine a forward moving state or a backward moving state of a respective one of the clamping units; a welding robot configured to weld the component; and a main controller configured to control the welding robot according to a predetermined process based on the state information of the respective one of the clamping units.

Abstract: A semiconductor device may include a substrate having a first surface and a second surface opposite to the first surface, a protection layer on the first surface of the substrate, metal layers in the substrate, extending in a first direction parallel to the first surface, and spaced apart from each other in a second direction perpendicular to the first surface, a via structure vertically penetrating the metal layers and the substrate, a circuit layer on the second surface of the substrate, and a connection terminal on a bottom surface of the circuit layer. Each of the metal layers may have a tetragonal or circular shape, when viewed in a plan view.

Abstract: The present disclosure provides a compound of a specific chemical structure binding to MLKL or having an MLKL inhibiting or degrading activity, or pharmaceutically acceptable salts thereof. The present disclosure provides a composition comprising such compound or pharmaceutically acceptable salts thereof. The present disclosure provides the pharmaceutical use of the compound according to the present invention, salts thereof, and a composition comprising same for treating or preventing MLKL-related diseases. The present disclosure also provides a method for treating or preventing MLKL-related diseases, comprising administering to a subject in need of treatment an effective amount of a compound according to the present invention, salts thereof, or a composition comprising same.

Abstract: Provided is an organic electroluminescent device, the organic electroluminescent device comprising a cathode, an anode, and one or more organic material layers provided between the cathode and the anode, the one or more organic material layers comprising a light emitting layer, an electron transport region and a hole transport region, wherein all of the organic materials, other than a dopant, contained in the one or more organic material layers have a triplet state energy of 2.5 eV or higher, at least three types of the organic materials having a triplet state energy of 2.5 eV or higher have a triplet state energy of 2.7 eV or higher, and at least two types of the organic materials having a triplet state energy of 2.5 eV or higher include a spiro compound.

Abstract: The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.

Abstract: The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.