Abstract: A display device includes: a display layer including a plurality of non-folding areas and a folding area disposed between the plurality of non-folding areas; a barrier layer disposed under the display layer; and a support plate disposed under the barrier layer, where the barrier layer includes: a first part having a first modulus, and a second part adjacent to the first part and having a second modulus different from the first modulus.

Abstract: Provided is a display apparatus including a display layer including scan lines, data lines, and pixels, and defined by a first area exposed to an outside in both a closed state and an open state, and a second area, which extends from the first area in a first direction, is exposed to an outside in the open state, and in which at least a portion thereof is opposite to the first area in the closed state, a scan-driving circuit connected to the scan lines, deformation sensors arranged in the first direction and having resistance values corresponding to respective shapes of the display layer, deformation sensor drivers arranged in the first direction, respectively connected to the deformation sensors, and configured to drive the deformation sensors along the first direction, and a driving circuit configured to measure the resistance values of the deformation sensors.

Abstract: Disclosed are a method and apparatus for estimating lithofacies by learning well logs. The method includes a model formation step of forming lithofacies estimation model to output lithofacies corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having lithofacies corresponding to measured depth as answers, and lithofacies estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which lithofacies are to be estimated, the values being arranged corresponding to measured depth, to the lithofacies estimation model to estimate lithofacies corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Abstract: Disclosed are a method and apparatus for estimating lithofacies by learning well logs. The method includes a model formation step of forming lithofacies estimation model to output lithofacies corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having lithofacies corresponding to measured depth as answers, and lithofacies estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which lithofacies are to be estimated, the values being arranged corresponding to measured depth, to the lithofacies estimation model to estimate lithofacies corresponding to measured depth.

Abstract: The present invention relates to a composition containing beta-glucan as an effective ingredient for prevention and treatment of osteoporosis. The above captioned beta-glucan is preferably the beta-1.3/1.6 glucan having a lactic acid as a substituent and this beta-1.3/1.6 glucan is preferably produced from Aureobasidium pullulans SM2001(KCCM 10307).

Abstract: The present invention relates to a composition containing beta-glucan as an effective ingredient for prevention and treatment of osteoporosis. The above captioned beta-glucan is preferably the beta-1.3/1.6 glucan having a lactic acid as a substituent and this beta-1.3/1.6 glucan is preferably produced from Auerobasidium pullulans SM2001(KCCM 10307).