The effect of ferromagnetic buffer layer on the superconducting properties of relatively thick, high temperature superconductors is investigated. Elaborately designed heterostructure films consisting of epitaxial GdBa 2 Cu 3 O 7-x (GdBCO) on top of two types of La 0.7 Sr 0.3 MnO 3 (LSMO) layer and particles, were fabricated by pulsed laser deposition. Unlike a typical superconducting/ferromagnetic system where the superconducting properties are degraded, the superconducting properties of our GdBCO/LSMO system are enhanced depending on the local structural details of the GdBCO and LSMO layers. The strain state of GdBCO characterized from the bond length of the Cu-O and the Mn-O bonds lengths is observed to vary depending on the formation of LSMO. The phase diagram of T c for the GdBCO/LSMO system established by the micro-strain of the Cu-O bond-length shows that T c reaches its maximum at the critical micro-strain. This result suggests the importance of strain state in improving the superconducting properties of thick-GdBCO/LSMO films.
This study developed a runoff model using a convolution neural network (CNN), which had previously only been used for classification problems, to get away from artificial neural networks (ANNs) that have been extensively used for the development of runoff models, and to secure diversity and demonstrate the suitability of the model. For this model’s input data, photographs typically used in the CNN model could not be used; due to the nature of the study, hydrological images reflecting effects such as watershed conditions and rainfall were required, which posed further difficulties. To address this, the method of a generating hydrological image using the curve number (CN) published by the Soil Conservation Service (SCS) was suggested in this study, and the hydrological images using CN were found to be sufficient as input data for the CNN model. Furthermore, this study was able to present a new application for the CN, which had been used only for estimating runoff. The model was trained and generalized stably overall, and R2, which indicates the relationship between the actual and predicted values, was relatively high at 0.82. The Pearson correlation coefficient, Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), were 0.87, 0.60, and 16.20 m3/s, respectively, demonstrating a good overall model prediction performance.
The effect of magnetic flux pinning is investigated in GdBa2Cu3O7 (GdBCO) thin films with two different types of ferromagnetic La0.7Sr0.3MnO3 (LSMO) buffers (nanoparticles and a layer) deposited on an STO substrate.
This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area.
The existing methods of river evaluation tend to focus exclusively on water quantity; therefore, they do not provide a suitable methodology for integrated water management. In this study, research was carried out to develop an integrated river evaluation system that can simultaneously consider water quantity and water quality to improve the existing river evaluation methods. To this end, specific indicators were established to evaluate water quantity and water quality; moreover, an integrated evaluation formula was developed to express each indicator as an index. The integrated evaluation formula used additive functions and enabled integrated and comprehensive river evaluation through the sum of each indicator’s indices. The research subjects were rivers in the Paldang watershed, which surrounds important water resources in rep. of Korea. The rivers were analyzed using the study’s integrated river evaluation formula to identify the deteriorated grade of the water quality as well as the water quantity. Finally, the results of the integrated river evaluation rating were found to be poor or very poor. Based on this, the study determined that an integrated river management policy is required to simultaneously consider water quantity and water quality to restore the integrity of the rivers in the Special Countermeasures Area. The existing evaluations of rivers, which had been conducted only with a focus on water quantity, could be judged narrow or incomplete results. Based on this finding, it was also possible to identify an urgent need for a basic river management plan that can consider both water quantity and water quality organically. Ultimately, the study demonstrated that its methodology was able to make highly intuitive judgments about rivers’ current conditions; thus, it can be utilized to generate basic data for the establishment of customized river management policies.
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