Abstract: One aspect of the present disclosure relates to a sol-gel composition enabling reversible sol-gel transition and, more specifically, to a sol-gel composition of which the viscosity changes by external physical force so that reversible sol-gel transition is performed.

Abstract: A display apparatus includes a base substrate, an active pattern disposed on the base substrate, a gate insulation layer disposed on the active pattern, a gate electrode disposed on the gate insulation layer and overlapping the active pattern, a first insulation layer disposed on the gate electrode and having a total amount of hydrogen of about 5 atomic percent (at. %) to about 30 at. %, and a source electrode and a drain electrode which are disposed on the first insulation layer and are electrically connected to the active pattern.

Abstract: The present invention provides zeolite hollow spheres in which zeolite crystals grow to form a framework of macropore through a hydrothermal crystallization process using the hydrophilic surface of a carbon sphere as a hard template, wherein the zeolite framework is an ordered, porous crystalline zeolite material with a number of channels or pores interconnected, which has pore structures including mesopores and micropores. The zeolite hollow spheres of the present invention can be used for various purposes such as catalysts and adsorbents.

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: Provided is an organic light-emitting device including: an anode; a cathode provided to face the anode; and organic material layers including a light emitting layer disposed between the anode and the cathode, wherein the light emitting layer, one or more layers of the organic material layers disposed between the anode and the light emitting layer, and one or more layers from among the organic material layers disposed between the cathode and the light emitting layer, each include one or more compounds each composed of sp3 carbon as a center, the light emitting layer includes a host including one or more anthracene-based compounds, and among organic materials included in the organic material layers, the bandgap energy (Ebg) of each of the organic materials except for a dopant compound is 3 eV or more.

Abstract: A chip-on-film package may include a film substrate including a chip region and an edge region, a semiconductor chip provided on the chip region and mounted on a top surface of the film substrate, the semiconductor chip including a chip pad adjacent to a bottom surface thereof, an input line and an output line provided on the edge region and disposed on the top surface of the film substrate, a connection terminal interposed between the film substrate and the semiconductor chip, and a redistribution pattern disposed between the semiconductor chip and the connection terminal.

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.