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The absence of accurate point classification limits the effective use of airborne bathymetric LiDAR (ABL) data for coastal zone mapping. In this study, we propose a classification approach using a custom waveform decomposition technique with the pseudo-waveform generated from ABL point cloud data. Initially, the input point clouds were organized into a 2D grid. Next, the points that fall into a grid cell were organized into a histogram using Z-values to generate the pseudo-waveform. Subsequently, the pseudo-waveform was decomposed into water bottom, column, surface, and noise components using a custom multiple Gaussian curve fitting method. The proposed approach was evaluated with datasets acquired in Florida, USA, using a Riegl VQ-880-G ABL system. With an optimized parameter set, the proposed approach achieved F1 score of 98.944% for the classification of water bottom and an overall accuracy of 91.234% for all the classes. Further, the proposed approach was evaluated with datasets acquired in South Korea using a Seahawk system and compared against MBES data, demonstrating that the water bottom was successfully classified with a vertical error of 0.049 ± 0.167 m. [ABSTRACT FROM AUTHOR]

• A framework for solar site selection using digital numerical maps is proposed. • A high-resolution solar map is created considering shade effects and tilted surfaces. • A clustering approach to identify suitable sites on highway fill slopes is presented. • A case study is provided to identify the most suitable site in Gongju, South Korea. Photovoltaic systems are promising replacements for fossil fuels at places where high solar energy is available. The estimation of available solar energy is the key to maximizing energy generation because sites with high available solar energy must be selected. Previous approaches to site selection required several experts to avoid subjective biases, often relying on rough estimations where the topography was not fully considered. Therefore, this study proposes a computational method that estimates the potential of solar energy for prioritizing and selecting sites for photovoltaic solar panels using publicly available digital numerical maps. These maps provide various kinds of spatial data to support proper land utilization. We focus on the use of national highway slopes as potential installation sites because they are typically unused public areas with high accessibility and few restrictions. For the estimation of annual solar irradiation, elevation contours and highway networks are first separated from the digital numerical maps. The extracted contours are subsequently used to generate a set of topographic data for the estimation. The extracted highway network is used to produce a binary mask representing fill slopes within which grid cells of high solar energy are identified and clustered to locate suitable sites. The proposed method is applied to a test site near Gongju, South Korea, where top 10 potential installation sites are clustered and ranked based on their estimated annual solar irradiation. The most suitable site is identified in eastern Gongju with an annual solar energy of approximately 49,623.7 MWh. [ABSTRACT FROM AUTHOR]

The mechanisms by which hypoglycemia increases cardiovascular mortality remain unclear. The aim of the study is to investigate changes in serum electrolytes, norepinephrine concentrations, electrocardiography, and baroreflex sensitivity (BRS) and associations between corrected QT (QTc) intervals and the changes in serum electrolytes during combined pituitary stimulation test (CPST). We recruited the subjects who were admitted to the Gyeongsang National University Hospital to undergo CPST between September 2013 and December 2014. Participants were 12 patients suspected of having hypopituitarism. Among 12 patients, cardiac arrhythmia in two patients occurred during hypoglycemia. There were significant differences in serum levels of potassium (P<0.001), sodium (P=0.003), chloride (P=0.002), and calcium (P=0.017) at baseline, hypoglycemia, and 30 and 120 minutes after hypoglycemia. Also, there was a significant increase in heart rate (P=0.004), corrected QT (QTc) interval (P=0.008), QRS duration (P=0.021), and BRS (P=0.005) at hypoglycemia, compared to other time points during CPST. There was a positive association between QTc intervals and serum sodium levels (P<0.001) in 10 patients who did not develop arrhythmia during CPST. This study showed that there were significant changes in serum levels of potassium, sodium, chloride, and calcium, as well as heart rate, QTc interval, QRSd, and BRS during CPST. It was revealed that QTc intervals had a significant association with concentrations of sodium. [ABSTRACT FROM AUTHOR]

The sites selected for solar PV facilities significantly affect the amount of electric power that can be generated over the long term. Therefore, predicting the power output of a specific PV plant is important when evaluating potential PV sites. However, whether prediction models built with data from existing PV plants can be applied to other plants for long-term power forecasting remains poorly understood. In this case, topographical and meteorological conditions, which differ among sites and change over time, make it challenging to accurately estimate the potential for energy generation at a new site. This study proposes a monthly PV power forecasting model to predict the amount of PV solar power that could be generated at a new site. The forecasting model is trained with time series datasets collected over 63 months from 164 PV sites with data such as the power plant capacity and electricity trading data, weather conditions, and estimated solar irradiation. Specifically, a recurrent neural network (RNN) model with long short-term memory was built to recognize the temporal patterns in the time series data and tested to evaluate the forecasting performance for PV facilities not used in the training process. The results show that the proposed model achieves the normalized root-mean-square-error of 7.416% and the mean absolute-percentage-error (MAPE) of 10.805% for the testing data (i.e., new plants). Furthermore, when the previous 10 months' data were used, the temporal patterns were well captured for forecasting, with a MAPE of 11.535%. Thus, the proposed RNN approach successfully captures the temporal patterns in monthly data and can estimate the potential for power generation at any new site for which weather information and terrain data are available. Consequently, this work will allow planning officials to search for and evaluate suitable locations for PV plants in a wide area. • An LSTM-RNN-based forecasting model is presented for investigation of PV sites. • Time series data of spatial and meteorological conditions depict input variables. • Monthly solar photovoltaic power generation at any specific site can be predicted. • nRMSE of 7.416% is achieved for long-term power prediction of new candidate sites. [ABSTRACT FROM AUTHOR]

The economics of photovoltaic systems are closely associated with the geographical locations of the sites. To identify appropriate land for photovoltaic systems, previous studies have mainly used geographic-information-system-based solar energy models capable of estimating spatial and temporal availability of solar energy. The models often require handling large and computationally challenging datasets to cover vast areas. In this study, we proposed and assessed the concept of a two-stage approach that sequentially searches and prioritizes suitable sites of photovoltaic panels using low- and high-resolution maps to reduce computational burdens and increase estimation accuracy. Resolutions at raster-cell sizes of 30 × 30 m and 5 × 5 m were selected to study the South Korean national highway network. Selected candidate sites in the 10th-percentile for low- and high-resolution cases produced 1,097 and 721 clusters with calculated solar energy potentials of 19,510,115 and 4,186,935 MWh, respectively. Approximately 90% of the edge-based distances between two adjacent potential sites for photovoltaic panels from the low- and high-resolution maps were found to be within 240 m. This result shows that although the low-resolution maps may not provide precise locations for prospective solar photovoltaic sites, they may help to identify potentially suitable areas for further investigation. • A two-stage approach was proposed using two solar irradiation maps. • The approach sequentially searches and prioritizes solar PV sites. • Raster-cell sizes of 30 × 30 m and 5 × 5 m were selected to study the Korean highways. • 1,097 and 721 sites were identified from low- and high-resolution maps. • 90% of two adjacent sites from the two maps were found to be within 240 m. [ABSTRACT FROM AUTHOR]

• The purpose of this study was to examine stability postulate of the self-control theory. • The current study used six-wave panel data of the Korean Children & Youth Panel Survey (KCYPS), which measured self-control of elementary school students at one year each of time points. • The test of relative stability hypothesis comprises four analyses of data: correlations, individual change scores, SGM (Semi-parametric Group-based Modeling), and Multinomial Logistic Regression. • Results revealed that the hypothesis of self-control stability was not strongly supported by the longitudinal data. • Based on prior empirical research and this study's finding, more research is needed to determine the stability of self-control. Gottfredson and Hirschi argued the self-control theory in A General Theory of Crime (1990). Since then, many studies have examined the central proposition of low self-control as the cause of criminal behavior with cross-sectional data. However, few research studies have tested other stability propositions that low self-control is stable across the life-course and that once it is established at age 8 to 10, it remains relatively stable regardless of life changes. The purpose of this study was to examine stability postulate of the self-control theory. The current study used six-wave panel data of the Korean Children & Youth Panel Survey (KCYPS), which measured self-control of elementary school students at one year each of time points. The test of relative stability hypothesis comprises four analyses of data: correlations, individual change scores, SGM (Semi-parametric Group-based Modeling), and Multinomial Logistic Regression. Results revealed that the hypothesis of self-control stability was not strongly supported by the longitudinal data. Based on prior empirical research and this study's finding, more research is needed to determine the stability of self-control. [ABSTRACT FROM AUTHOR]

This article aimed to compare the level of suicidal ideation in the religiously affiliated and nonaffiliated groups and identify the moderating effect of religion variables on the relationship between depression and suicidal ideation. The sample in this study was 1180 residents who were older than 60 years and who resided in Gangwon Province, South Korea. We studied the cross-sectional relationships among religions and studied variables including organizational religious activity (ORA), non-ORA (NORA), intrinsic religiosity (IR), depression, well-being, and suicidal ideation. After controlling for sociodemographic variables, the depression, well-being, and suicidal ideation level in the religiously affiliated group were not significantly different from that of the religiously nonaffiliated group and the variables also were not significantly different between Christians and Buddhists. The effect of depression on suicidal ideation was significantly moderated by ORA and IR, and the effect showed different patterns by religion; ORA was a moderator only among the Buddhist group, and IR only moderated the relationships among the Christians. [ABSTRACT FROM AUTHOR]

Photovoltaic systems require large swaths of land that are currently being used for other purposes, such as farming. One option for developing large photovoltaic systems is converting farms that are currently economically unviable into commercial photovoltaic systems. However, this may not always be an economically rational decision as crop prices have the potential to increase over time. Fluctuations in farm income due to changes in crop prices can alter the optimal choice of whether to continue farming or to convert farmland into commercial photovoltaic systems. This study attempts to resolve this issue by proposing a real options framework to value farm production when crop prices are uncertain. By integrating uncertainty into the decision-making process, the value of keeping unprofitable farms operating prior to developing the area into a commercial photovoltaic system is assessed. This helps decision makers understand the extent to which potential income from developing a photovoltaic system should be greater than potential income from farming when deciding on investing in a photovoltaic system. A case study is conducted to examine this framework and to calculate the net present value of a farm in South Korea. The results indicate that although the money lost from continuing to farm is substantial, farmers should defer conversion to a commercial photovoltaic system until a sufficient drop in crop prices occurs. When applying this strategy, the farmer can gain an additional 100% of expected revenue simply by deferring the development decision until having better information on the market prices of crops. • This paper evaluates the economic value of maintaining farm operations prior to developing a lot into a photovoltaic system. • A real options framework is presented to model crop prices to support decision-making processes in farmland development. • A case study is conducted to examine the timing of farmland development for photovoltaic systems in South Korea. • Farmers should defer farmland development until a sufficient drop in crop prices. [ABSTRACT FROM AUTHOR]