Abstract: Provided is a method for manufacturing a polarizing plate, comprising a step of irradiating an optical laminate with ultraviolet rays having an emission wavelength band of 380 nm to 410 nm. The optical laminate sequentially comprises a first base film, a first adhesive layer, a linear polarizer, a second adhesive layer, a second base film and a reverse dispersion liquid crystal layer. The first adhesive layer and the second adhesive layer each comprise a photosensitizer for initiating a curing reaction in a wavelength band of 350 nm to 410 nm, and the ultraviolet rays are irradiated on the first base film side of the optical laminate.

Abstract: Disclosed are novel compounds of Chemical Formula 1, optical isomers of the compounds, and pharmaceutically acceptable salts of the compounds or the optical isomers. The compounds, isomers, and salts exhibit excellent activity as GLP-1 receptor agonists. In particular, they, as GLP-1 receptor agonists, exhibit excellent glucose tolerance, thus having a great potential to be used as therapeutic agents for metabolic diseases. Moreover, they exhibit excellent pharmacological safety for cardiovascular systems.

Abstract: The prevent invention relates a method and apparatus based on neural network for pre-diagnosing defect and fire in battery cell. A method based on neural network for pre-diagnosing defect and fire in battery cell in an apparatus for pre-diagnosing defect and fire in battery cell is proposed, the method including collecting, by the battery cell defect and fire pre-diagnosis apparatus, data including at least one of chemical composition, current, voltage, and temperature data measured for each predetermined time interval within each charge and discharge cycle while charging and discharging a plurality of batteries; training, by the battery to cell defect and fire pre-diagnosis apparatus, the neural network by inputting the collected data to the neural network; and predicting, by the battery cell defect and fire pre-diagnosis apparatus, a battery deviated from a main cluster of the neural network as defective.

Abstract: The prevent invention relates a method and apparatus based on neural network for pre-diagnosing defect and fire in battery cell. A method based on neural network for pre-diagnosing defect and fire in battery cell in an apparatus for pre-diagnosing defect and fire in battery cell is proposed, the method including collecting, by the battery cell defect and fire pre-diagnosis apparatus, data including at least one of chemical composition, current, voltage, and temperature data measured for each predetermined time interval within each charge and discharge cycle while charging and discharging a plurality of batteries; training, by the battery to cell defect and fire pre-diagnosis apparatus, the neural network by inputting the collected data to the neural network; and predicting, by the battery cell defect and fire pre-diagnosis apparatus, a battery deviated from a main cluster of the neural network as defective.