Three species of subfamily Cultrinae currently live in Korea, but Erythroculter erythropterus has been introduced into the Nakdonggang River and has taken over the habitat, reducing the habitat of Culter brevicauda. Only the endangered species C. brevicauda still lives in the Yeongsangang River, and it is necessary to be careful not to introduce E. erythropterus in the future. And Hemiculter eigenmanni is also found throughout the country. In order to effectively manage and conserve the species in its various habitats and against invasions, this study was initiated. The ultrastructure of the egg envelopes of three species of Cultrinae— E. erythropterus, C. brevicauda, and H. eigenmanni, inhabiting the Geumgang and Yeongsangang Rivers were observed. It was found that the zona radiata of the egg envelopes of all three species were divided into two layers–an outer and inner layer, with the outer surface having a non-structural form. This form is characteristic of fishes with muddy, stagnant habitats or spawning grounds. The number of pore canals on the surface of the egg envelopes was 83 for E. erythropterus, 75 for C. brevicauda, and 58 for H. eigenmanni per 10 ㎛2, and the thickness was 7.89±0.34 ㎛, 12.27±0.46 ㎛, and 7.42±0.24 ㎛, respectively. The shape of the micropyle demonstrated a funnel shape narrowing toward the inner diameter in all three species, and the size of the inner diameter was 6.62±0.29 (6.09~7.10) ㎛ in E. erythropterus, 4.19±0.39 (3.39~4.58) ㎛in C. brevicauda, and 3.98±0.46 (3.47~4.93) ㎛in H. eigenmanni. The differences between species were identified in the number of pore canals, thickness, and micropyle inner diameter of egg envelopes, which were species-specific. Our study revealed a morphological mechanism in the egg envelope that prevents the formation of interspecific hybrids, and these features can be taxonomic traits that clarify species names. It also provided useful data for the production (breeding) of the second generation in aquaculture.

As renewable energy generation prediction systems have been introduced into the energy trading market, accurate prediction of solar photovoltaic (PV) energy generation has become a crucial challenge for ensuring stable trade of variable energy. Therefore, it is essential to quantitatively investigate and analyze current prediction technology and develop more advanced prediction systems. In this study, three models for predicting PV energy generation were investigated. Multiple regression, random forest, and gradient boosting machine (GBM) models were con-structed for 15 utility-scale power plants throughout South Korea. Model performance was evaluated in terms of root mean square error (RMSE) and mean absolute error (MAE). The multiple regression-based model had an RMSE of 12.00% and MAE of 9.06%. The random forest model had an RMSE of 11.69% and MAE of 8.61%. The GBM model had an RMSE of 11.34% and MAE of 8.18%, indicating its superior accuracy among the three models in predicting PV energy generation. These findings provide valuable insights that may contribute to the development of advanced PV energy generation models and their application in grid operations.

As renewable energy generation prediction system has been introduced into the energy trading market, making a model to accurately predict the quantity of solar photovoltaic (PV) energy generation has become a significant problem. Moreover, to encourage an accurate prediction of the quantity of energy generation, an incentive system has been implemented for those who predict the quantity of solar PV energy under the error rate of 8%. Therefore, it has become more important to investigate and analyze current prediction technology numerically and develop more advanced prediction system. In this study, we tried to develop a better model to improve the accuracy of solar PV energy generation quantity by comparing three models made with gradient boosting machine (GBM), Model 1, Model 2, Model 3 respectively. Model 1 was built with the whole training data set without any additional preprocessing. After conducting some additional preprocessing procedure to predict solar energy generation more accurately, we made Model 2 with the whole training data set and Model 3 with only upper 10% of energy generation capacity. To compare the accuracy of three models, normalized mean absolute error (nMAE) was used as an evaluation index. The nMAE of Model 1 was 9.64% while the Model 2 showed 8.41%. Also, Model 3, which was constructed with the training set of upper 10% energy generation capacity, outperformed with the nMAE of 8.08%. For further study, to check the effectiveness of models constructed with GBM, a time series model, autoregressive integrated moving average (ARIMA), was also built and the nMAE was compared.

The grain size of CVD (Chemical Vapor Deposition) graphene was controlled by changing the precursor gas flow rates, operation temperature, and chamber pressure. Graphene of average grain sizes of 4.1 µm, 2.2 µm, and 0.5 µm were synthesized in high quality and full coverage. The possibility to tailor the thermoelectric conversion characteristics of graphene has been exhibited by examining the grain size effect on the three elementary thermal and electrical properties of σ, S, and k. Electrical conductivity (σ) and Seebeck coefficients (S) were measured in a vacuum for supported graphene on SiO₂/Si FET (Field Effect Transistor) substrates so that the charge carrier density could be changed by applying a gate voltage (V_G). Mobility (µ) values of 529~1042, 459~745, and 314~490 cm²/V·s for the three grain sizes of 4.1 µm, 2.2 µm, and 0.5 µm, respectively, were obtained from the slopes of the measured σ vs. V_G graphs. The power factor (PF), the electrical portion of the thermoelectric figure of merit (ZT), decreased by about one half as the grain size was decreased, while the thermal conductivity (k) decreased by one quarter for the same grain decrease. Finally, the resulting ZT increased more than two times when the grain size was reduced from 4.1 µm to 0.5 µm.