Abstract: The present invention relates to a biodegradable polyester resin, in which the first repeat unit comprising a first diol residue and an aromatic dicarboxylic acid residue and the second repeat unit comprising a second diol residue and an aliphatic dicarboxylic acid residue satisfy a ratio of the number of repeat units in a specific range, and the softness index of the resin satisfies a specific range, and to a process for preparing the same. Since the biodegradable polyester resin can provide a biodegradable polyester sheet or film that can be simultaneously enhanced in productivity, processability, and moldability and is excellent in tensile strength, tear strength, and friction coefficient and excellent in biodegradability and water degradability, it can be utilized in more diverse fields.

Abstract: Embodiments relate to a biodegradable and polymerizable polyester composition, to a biodegradable polyester resin prepared therefrom, and to a process for preparing the same. As the biodegradable and polymerizable polyester composition comprises an inorganic filler in a certain content and satisfies a certain range of viscosity, it has excellent dispersibility during polycondensation; thus, it is possible to control the content of metals in a biodegradable polyester resin prepared from the biodegradable polyester polymerization composition and the dispersion index within optimal ranges. In addition, as the biodegradable and polymerizable polyester composition comprising an inorganic filler is subjected to a polycondensation reaction, the dispersibility during the polycondensation reaction is very excellent, which can suppress the aggregation of inorganic filler particles and prevent filter clogging, resulting in a decrease in defects such as voids in the processing process.

Abstract: The present disclosure according to at least one embodiment provides a method performed by a computing device for generating a task model based on meta-learning, including calculating a task-adaptation loss of the task model, the calculating the task-adaptation loss being based on a result of training the task model by using a training data set, updating the task model based on the task-adaptation loss, calculating a meta-optimization loss of the updated task model by using a validation data set, and further updating the updated task model based on the meta-optimization loss.

Abstract: A surface light source includes a plate type light source body having a sealed discharging space formed therein, a plate type electrode unit having a plurality of regions adjacent to at least one major surface of the light source body, and a multiple voltage applying unit operable to apply voltages independently to each of the plurality of regions. In this way, brightness of the surface light source can be controlled independently in each of the plurality of regions and a local dimming for a surface light source can be realized.