• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.258-267
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• 2020

The agrivoltaic can produce electricity and grow crops on fields at the same time. It is necessary to analyze the cultivation environment and evaluate the crop productivity under agrivoltaic because the shading point changes according to structure of agrivoltaic and sun's position. Two types of "fixing" and "tracing" agrivoltaic were installed, and a rice cultivation experiment was conducted in the fields under each agrivoltaic and without shading (control). "Hyunpoombyeo" was transplanted on June 7, 2019, and grown with fertilization of 9.0-4.5-5.7 kg/10a (N-P-K). Fifteen weather stations were installed under each agrivoltaic to measure solar radiation and temperature, and yield and yield-related elements were investigated by points. The accumulated solar radiation during the rice growing season in fixing was no much difference between points, and that in tracing was much difference between points. However, the average solar radiations of two agrivoltaics were similar. The mean temperature, yield, and yield-related elements showed a significant difference for the shading rate, and decreased with increasing the shading rate except ripening grain rate and 1000 grain weight of fixing agrivoltaic. In the relationship between shading rate and yield, fixing and tracing were fitted to a logistic equation and a simple linear equation, respectively, and showed a high correlation (tracing: R2 = 0.62, fixing: R2 = 0.73). The shading rate variation by point for two types was large despite similar yield variation. Thus, it needs to be more closely examined the relationship of the shading rate for a specific period rather than the shading rate during the whole growing season.

• Journal of Wetlands Research
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• v.24 no.4
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• pp.345-353
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• 2022

A wastewater treatment plant (WWTP) is a major gateway for the engineered nano-particles (ENPs) entering the water bodies. However existing studies have reported that many WWTPs exceed the No Observed Effective Concentration (NOEC) for ENPs in the effluent and thus they need to be designed or operated to more effectively control ENPs. Understanding and predicting ENPs behaviors in the unit and \the whole process of a WWTP should be the key first step to develop strategies for controlling ENPs using a WWTP. This study aims to provide a modeling tool for predicting behaviors and removal efficiencies of ENPs in a WWTP associated with process characteristics and major operating conditions. In the developed model, four unit processes for water treatment (primary clarifier, bioreactor, secondary clarifier, and tertiary treatment unit) were considered. Additionally the model simulates the sludge treatment system as a single process that integrates multiple unit processes including thickeners, digesters, and dewatering units. The simulated ENP was nano-sized TiO₂, (nano-TiO₂) assuming that its behavior in a WWTP is dominated by the attachment with suspendid solids (SS), while dissolution and transformation are insignificant. The attachment mechanism of nano-TiO₂ to SS was incorporated into the model equations using the apparent solid-liquid partition coefficient (Kd) under the equilibrium assumption between solid and liquid phase, and a steady state condition of nano-TiO₂ was assumed. Furthermore, an MS Excel-based user interface was developed to provide user-friendly environment for the nano-TiO₂ removal efficiency calculations. Using the developed model, a preliminary simulation was conducted to examine how the solid retention time (SRT), a major operating variable affects the removal efficiency of nano-TiO₂ particles in a WWTP.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.239-249
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• 2020

Rice flour varieties have been developed to replace wheat, and consumption of rice flour has been encouraged. damage related to pre-harvest sprouting was occurring due to a weather disaster during the ripening period. Thus, it is necessary to develop pre-harvest sprouting rate prediction system to minimize damage for pre-harvest sprouting. Rice cultivation experiments from 20 17 to 20 19 were conducted with three rice flour varieties at six regions in Gangwon-do, Chungcheongbuk-do, and Gyeongsangbuk-do. Survey components were the heading date and pre-harvest sprouting at the harvest date. The weather data were collected daily mean temperature, relative humidity, and rainfall using Automated Synoptic Observing System (ASOS) with the same region name. Gradient Boosting Machine (GBM) which is a machine learning model, was used to predict the pre-harvest sprouting rate, and the training input variables were mean temperature, relative humidity, and total rainfall. Also, the experiment for the period from days after the heading date (DAH) to the subsequent period (DA2H) was conducted to establish the period related to pre-harvest sprouting. The data were divided into training-set and vali-set for calibration of period related to pre-harvest sprouting, and test-set for validation. The result for training-set and vali-set showed the highest score for a period of 22 DAH and 24 DA2H. The result for test-set tended to overpredict pre-harvest sprouting rate on a section smaller than 3.0 %. However, the result showed a high prediction performance (R2=0.76). Therefore, it is expected that the pre-harvest sprouting rate could be able to easily predict with weather components for a specific period using machine learning.

• Korean Journal of Environment and Ecology
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• v.35 no.1
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• pp.68-80
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• 2021

This study aimed to provide basic data such as the structure of the Carpinus turczaninowii community and characteristics of the habitat environment for ex situ conservation by analyzing the plant community structure of Carpinus turczaninowii, an island plant resource. For the community structure analysis, this study established 29 plots, sized 100㎡ each, in Seokmo, Yeongjong, Yeongheung, and Daebu islands. TWINSPAN was used for the classification of communities. The classification identified six communities. Group I was the C. turczaninowii-Quercus serrata community, group II was the C. turczaninowii-Pinus densiflora community, group III was the C. turczaninowii-Quercus mongolica community, group IV was the C. turczaninowii-Sorbus alnifolia community, group V was the C. turczaninowii typical community, and group VI was the C. turczaninowii-Quercus variabilis community. The species diversity was 0.90008~1.12868, the dominance was 0.17536~0.25665, and the similarity index was 17.1429~38.2979%. The result of correlation analysis of 7 environmental factors for 6 communities by RDA ordination showed a positive correlation between the crown density and litter layer and a negative correlation between the bare rock, soil hardness, and altitude on the 1st axis. On the 2nd axis, the bare rock and crown density showed a positive correlation, and the slope showed a negative correlation. In the C. turczaninowii-Quercus serrata community (I), the crown density and the litter layer were the environmental factors affecting the vegetation distribution. In the C. turczaninowii-Pinus densiflora (II) and C. turczaninowii-Quercus mongolica (III) communities, the slope was the factor affecting vegetation distribution. In the C. turczaninowii-Sorbus alnifolia (IV), C. turczaninowii typical (V), and C. turczaninowii-Quercus variabilis (VI) communities, the bare rock, altitude, and soil hardness were the factors affecting vegetation distribution.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.

• The Korean Society of Law and Medicine
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• v.23 no.3
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• pp.139-207
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• 2022

As various state restrictions on individual freedom were imposed during the COVID-19 pandemic, concerns have been raised that excessive infringements on fundamental rights were indiscriminately permitted based on the public interest of preventing infectious diseases. Therefore, the question of how to set acceptable limits of liberty restrictions on individuals has emerged. However, since the phenomenon of infections spreading to the population is only predicted statistically, how to deal with the risk of the infected individual as a subject of legal analysis has become a problem. In the absence of a theoretical framework of legal analysis of risk, the risk of infected individuals during the pandemic was not analyzed strictly, and proportionality review of infection prevention measures was often only an abstract comparison of the importance of public interest and individual rights. Therefore, this research aims to conduct a theoretical review on how risk can be conceptualized legally in a public health crisis, and to develop a theoretical framework for proportionality review of the risk of liberty-limiting measures during a pandemic. Chapter 2 analyzes the legal philosophical concepts of risk, which are the basis for liberty restrictions during a public health crisis, and applies and extends them to the pandemic. Chapter 3 reviews previous studies related to liberty restriction measures in the context of the COVID-19 pandemic, and points out they have a limitation that specific criteria for the proportionality review of public health measures in the pandemic have not been presented. Accordingly, Chapter 3 specifies the methodological framework for proportionality review, referring to the theoretical discussion on risks in Chapter 2. Chapter 4 reviews the legitimacy of gathering restriction orders, applying the theoretical discussion in Chapter 2 and the criteria for proportionality review established in Chapter 3. In particular, Section 4 examines logic of proportionality review in judicial precedents over the ban on gathering restrictions implemented in the COVID-19 pandemic. In analyzing the precedents, the logic of proportionality review in each case is critically reviewed and reconstructed based on the theoretical framework presented in this research.

• Journal of Popular Narrative
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• v.26 no.2
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• pp.251-276
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• 2020

The purpose of this article is to critically read Globalizing Borderlands Studies in Europe and North America to examine trends in border studies conducted so far in Europe and North America and to discuss the expandability and limitations of the narrative. It introduces a variety of case studies covering the borderlands of Europe and North America from ancient to modern times. It consists of a total of 10 chapters, in addition to the introduction chapter to clarify the purpose and definition of the collaboration and the short conclusion chapter on the prospects for the future of borderlands studies. This volume has some important implications for current borderland research in two main respects. First, it can introduce us we the areas and targets that the leading researchers from European and North American academia (usually the United States') have paid attention to. It also examines the current status of borderland research and predicts whether it will be possible to study various border areas where exist in other regions (especially in Asia) based on accumulating academic achievements, as well as the possibility of expansion of so-called 'globalization'. Second, it introduces the borderland as a conceptual space, beyond the border area as a physical space that is commonly thought of when it comes to 'border'. Cases of "conceptual borderlands" can be applied to a number of topics ranging from an individual's identities to the methods of governance, religions, economies, social institutions, families, labor issues, public health services and gender issues. There are, however, also some questions to be noted in the volume: the lack of consistent use of terminology, which can be considered general problems of collaboration studies; the fact that the authors still tend to understand borderlands within the imperialist discourse, perhaps because of their academic background is situated mainly in Europe and North America; the borderlands cases described here as the areas of conflict and struggle only. Nevertheless, the book is of significance in that it suggests a possibility of various borderlands studies and helps us to have better understanding of the current geopolitical situation imposed on the Korean Peninsula, which is located on the borderland between the continental and maritime powers.

• Journal of Life Science
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• v.32 no.11
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• pp.865-871
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• 2022

Tenebrio molitor larvae is well known as edible insect. Then, although it has been widely studied that Tenebrio molitor larvae has various bioactive functions such as antioxidant, anti-wrinkle, and anticancer. Nevertheless, antioxidant effects of Tenebrio molitor larvae water extract (TMH) has not been well described in Adult Retina Pigment Epithelial cell line (ARPE-19). In this study, we demonstrated that antioxidant effects of TMH against H₂O₂-induced oxidative stress in ARPE-19. Thus, we selected for our studies and performed a series of dose-response assay to determine the working concentration that lead to a consistent and high degree of cytotoxicity, which we defined as the level of H₂O₂ that killed 40% of the ARPE-19 cells. ARPE-19 cells were pre-treated with various concentrations of TMH (0.1 up to 2 mg/ml) before exposure to 300 µM H₂O₂. As we expected, TMH effectively prevented ARPE-19 cells from 300 µM H₂O₂-induced cell death in a dose-dependent manner. Furthermore, TMH inhibited the phosphorylation of mitogen-activated protein kinases (MAPKs) such as extracellular signal regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38. Overall, the inhibitory effects of TMH on H₂O₂-induced apoptosis and oxidative stress were associated with the protection cleaved caspase-3, Bax, Bcl-2, and HO-1. The TMH suppressed H₂O₂-induced cell membrane leakage and oxidative stress in ARPE-19 cells. Thus, these results suggest that the TMH plays an important role in antioxidant effect in ARPE-19.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of the Korean Orthopaedic Association
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• v.54 no.1
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• pp.30-36
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• 2019

Purpose: This study examined the effects of gender, age, underlying disease, duration after onset of symptoms, preoperative invasive procedures, bacterial culture of joint fluid, and stage of infection by the Gachter classification on the prognosis of patients with infectious knee arthritis who underwent arthroscopic surgery. Materials and Methods: From June 2014 to December 2016, 51 patients who underwent arthroscopic surgery for infective knee arthritis were enrolled in this study. The average follow-up period was 14.2±2.1 months (range, 12-20 months). The subjects were 27 men (52.9%) and 24 women (47.1%), with an average age of 55.1±17.6 years (range, 13-84 years). A preoperative evaluation of the joint aspiration with a count of more than 50,000 leukocytes and a polymorphonuclear leukocyte count of 95% or more was performed. All patients underwent arthroscopic surgery and postoperative continuous joint irrigation. Results: The initial mean value of the C-reactive protein decreased from 9.55±6.76 mg/dl (range, 1.51-31.06 mg/dl) to a final mean of 0.74±1.26 mg/dl (range, 0.08-6.77 mg/dl); the mean duration of C-reactive protein normalization was 27.6±18.9 days (range, 8-93 days). Among the 51 patients who received arthroscopic surgery and antibiotics, 44 patients (86.3%) with infectious knee arthritis completed treatment with improved clinical symptoms, such as fever, pain, and edema, and the C-reactive protein decreased to less than 0.5 mg/dl. Finally, 5 cases were treated with two or more arthroscopic operations, and 2 cases were converted to arthroplasty after prosthesis of antibiotic-loaded acrylic cement. Conclusion: The duration of surgery after the onset of symptoms and the stage according to the Gächter classification are important prognostic factors for predicting the successful treatment of infectious knee arthritis. On the other hand, the other factors were not statistically significant. Nevertheless, patients with bacteria cultured from the joint fluids appear to reflect the treatment period because the period of normalization of the C-reactive protein is shorter than that of the control group.