• Development and Reproduction
• /
• v.13 no.2
• /
• pp.63-77
• /
• 2009

The neuronal ceroid-lipofuscinoses (NCLs) are a kind of neurodegenerative storage disorders. The NCLs are charecterizated by accumulation of autofluorescent lipofuscin or lipopigment in the brain. All NCL group belongs to in lysosomal storage disorders (LSDs), except Northern epilepsy. NCLs are the most common group of progressive neurodegenerative disorders in childhood, with an incidence as high as I in 12,500 live births. Four main clinical types have been described based on the onset age : infantile, late infantile, juvenile and adult types. Clinical symptoms of NCLs include loss of vision, seizures, epilepsy, progressive mental retardation and a premature death. Although mutation causes neurodegeneration in NCLs, the molecular mechanism by which mutation leads to neurodegeneration remains unclear. In this paper, we review the characteristics of these NCLs.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.34 no.1
• /
• pp.11-18
• /
• 2022

In order to improve the accuracy of particle tracking prediction techniques near the Korean Strait, this study compared and analyzed a particle tracking model based on a seawater flow numerical model and a machine learning based on a particle tracking model using field observation data. The data used in the study were the surface drifter buoy movement trajectory data observed in the Korea Strait, prediction data by machine learning (linear regression, decision tree) using the tide and wind data from three observation stations (Gageo Island, Geoje Island, Gyoboncho), and prediciton data by numerical models (ROMS, MOHID). The above three data were compared through three error evaluation methods (Correlation Coefficient (CC), Root Mean Square Errors (RMSE), and Normalized Cumulative Lagrangian Separation (NCLS)). As a final result, the decision tree model had the best prediction accuracy in CC and RMSE, and the MOHID model had the best prediction results in NCLS.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.34 no.3
• /
• pp.72-81
• /
• 2022

Positions of five drifting buoys deployed on August 2020 near southwestern area of Jeju Island and numerically predicted velocities were used to develop five Artificial Intelligence-based models (AI models) for the prediction of particle tracks. Five AI models consisted of three machine learning models (Extra Trees, LightGBM, and Support Vector Machine) and two deep learning models (DNN and RBFN). To evaluate the prediction accuracy for six models, the predicted positions from five AI models and one numerical model were compared with the observed positions from five drifting buoys. Three skills (MAE, RMSE, and NCLS) for the five buoys and their averaged values were calculated. DNN model showed the best prediction accuracy in MAE, RMSE, and NCLS.

• Elastomers and Composites
• /
• v.46 no.4
• /
• pp.335-342
• /
• 2011

The virgin and recycled polyethylene composites with various ratio of fly ash were manufactured by using a fully intermeshing co-rotating twin screw extruder for the reuse of fly ash from power plant and post-consumed polyethylene. Fly ash were blended with virgin HDPE and recycled polyethylene at the weight fraction of 0 to 40 wt.%. Mechanical properties such as yield strength, abrasion resistance, and slow crack resistance were measured with ISO and ASTM standards. The experimental results for the various composites showed that the elongation at break and the yield stress of the composites decreased with increasing fly ash contents. Generally, the abrasion resistance of PEs decreased with increasing sandpaper grits but the abrasion resistance of the composites increased with fly ash content at finer abrasive surface. The slow crack growth resistance of virgin HDPE, recycled JRPE and the JRPE composite showed higher slow crack growth resistance up to 50% of load at notch depth of 20% and 30%, but KRPE and the KRPE composite showed much lower resistance than virgin HDPE, JRPE and the JRPE composite. Time to break, measured with NCLS test method, of all PEs and the composites satisfies the regulation of Korean Industrial Specification for sewer pipe and support application.