Effects of irradiation on superconducting properties of small-grained MgB_(2) thin films

We investigate the effect of ion irradiation on MgB_(2) thin films with small grains of approximately 122 nm and 140 nm.The flux pinning by grain boundaries is insignificant in the pristine MgB_(2) films due to good inter-grain connectivity,but is significantly improved after 120-keV Mn-ion irradiation.The scaling behavior of the flux pinning force density for the ion-irradiated MgB_(2) thin films with nanoscale grains demonstrates the predominance of pinning by grain boundaries,in contrast to the single-crystalline MgB_(2) films where normal point pinning was dominant after low-energy ion irradiation.These results suggest that irradiation-induced defects can accumulate near the grain boundaries in metallic MgB_(2) superconductors.

Analysis of AI-based techniques for forecasting water level according to rainfall

Water level forecasting according to rainfall is important for water resource management and disaster prevention.Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area.Recently,with the improvement of AI(Artificial Intelligence)technology,a research using AI technology in the water resource field is being conducted.In this research,water level forecasting was performed using an AI-based technique that can capture the relationship between data.As the watershed for the study,the Seolmacheon catchment which has the rich historical hydrological data,was selected.SVM(Support Vector Machine)and a gradient boosting technique were used for AI machine learning.For AI deep learning,water level forecasting was performed using a Long Short-Term Memory(LSTM)network among Recurrent Neural Networks(RNNs)used for time series analysis.The correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are mainly used forhydrological analysis,were used as performance indicators.As a result of the analysis,all three techniques performed excellently in water level forecasting.Among them,the LSTM network showed higher performance as the correction period using historical data increased.When there is a concern about an emergency disaster such as torrential rainfall in Korea,water level forecasting requires quick judgment.It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.

Comparison of the performance of a hydrologic model and a deep learning technique for rainfall-runoff analysis

Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning.Conventional rainfall-runoff analysis methods generally have used hydrologic models.Rainfall-runoff analysis should consider complex interactions in the water cycle process,including precipitation and evapotranspiration.In this study,rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data itself.The study was conducted in the Yeongsan River basin,which forms a large-scale agricultural area even after industrialization,as the study area.As the hydrology model,SWAT(Soil and Water Assessment Tool)was used,and for the deep learning method,a Long Short-Term Memory(LSTM)network was used among RNNs(Recurrent Neural Networks)mainly used in time series analysis.As a result of the analysis,the correlation coefficient and NSE(Nash-Sutcliffe Efficiency),which are performance indicators of the hydrological model,showed higher performance in the LSTM network.In general,the LSTM network performs better with a longer calibration period.In other words,it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.