• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.74-74
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• 2011

최근 새로운 형태의 디스플레이에 관한 관심이 집중되고 있다. 이들 중 특히 투명 산화물 반도체는 기존의 실리콘 기반의 반도체에 비해 가시광 영역에서 높은 투과도를 보이며, 또한 기존의 비정질 실리콘 소자에 비해서 10 cm2/Vs이상의 높은 전하 이동도 값을 가진다. 본 연구에서는 투명 산화물 반도체 소재 중 InGaZnO4를 사용하여 펄스 레이저 방법으로 Al2O3 (0001)기판 위에 비정질 상태인 a-InGaZnO4 박막을 성장 시켰다. 박막의 증착 온도를 변화(RT, $50^{\circ}C$, $150^{\circ}C$, $250^{\circ}C$, $450^{\circ}C$, $550^{\circ}C$)시켜 성장된 박막의 구조적, 화학적, 전기적 그리고 광학적 특성을 조사하였다. 증착 온도가 $450{\sim}550^{\circ}C$ 사이에서 박막의 상태가 비정질(amorphous)에서 polycrystalline으로 성장되는 것을 X-Ray Diffraction과 Field Emission-Scanning Electron Microscope를 이용하여 확인하였고 이는 InGaZnO4 박막의 결정화 온도가 $450^{\circ}C$ 이상임을 알 수 있었다. X-ray Photoelectron Spectroscopy를 통해서 target 물질과 성장된 박막의 조성 및 화학적 상태를 고찰한 결과, 박막의 결정성 변화가 화학적 상태 변화와는 무관하다는 사실을 알 수 있었다. 온도 의존 비저항 측정을 통해 박막이 반도체 성향을 가지는 것을 확인 하였다. 또한 Hall 측정 결과 증착 온도가 올라 갈수록 전하 밀도는 증가 하지만, 전하 이동도는 다결정 박막($550^{\circ}C$)에서 급격히 감소하고, 이로 인해 비저항 값이 크게 증가함을 알 수 있었다. 이는 다결정 박막 내 존재하는 grain boundary들이 이동도 값에 영향을 준다는 것으로 추측할 수 있다. Ultra violet-Visible-Near Infrared 측정을 통해 가시광 영역에서 80%이상의 투과율을 나타내며 증착 온도가 증가함에 따라 에너지 밴드갭(Eg)이 커지는 것을 확인 할 수 있는데 이는 Hall 측정 결과에서 확인한 전하 밀도의 증가로 인해 에너지 밴드갭이 커지는 Burstein-Moss 효과로 설명할 수 있다.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.1
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• pp.32-38
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• 2005

A long-term study initiated in 1989 at San-born Field, Columbia, Missouri, was designed to evaluate the affect of environmental factors, nitrogen application, and crop rotation on soybean (Glycine max [L.] Merr.) seed composition. Soybeans were grown as part of a four- year rotation which included corn (Zea maize L.), wheat (Triticum aestivum L.), and red clover (Trifolium pratense L.). Results from soil tests made prior to initiation of the study and subsequently every five years, were used to calculate application rates of nitrogen, phosphorus, and potassium necessary for target yield of pursuant crops. In the experimental design, nitrogen was applied to one-half of the plot on which the non-leguminous crop, either corn or wheat was grown. Analysis of soybean seed by near infrared reflectance spectroscopy collected over an 11-year period revealed a linear increase in protein and decrease in oil content. Application of nitrogen fertilizer to non-leguminous crops did not have an apparent effect on total protein or oil content of subsequent soybean crop. Analysis of soybean seed proteins by sodium dodecyl sulfate polyacrylamide gel electrophoresis in conjunction with computer­assisted densitometry revealed subtle changes in the accumulation of seed proteins. Immunoblot analysis using antibodies raised against the $\beta-subunit$ of $\beta-conglycinin$ showed a gradual increase in the accumulation of the 7S components during successive years of the experiment. A linear increase in temperature and decrease in rainfall was observed from the onset of data· collection. Higher temperatures during the growing season have been linked to increased protein and diminished oil content of soybean, thus changes observed in this study are possibly related to climatic conditions. However, crop rotation and subsequent changes in soil ecology may contribute to these observed changes in the seed composition.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2018.04a
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• pp.31-31
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• 2018

The purpose of this study was to statistically analyze amylose and protein content of rice variety resources collected from China (1,542), Japan (1,409), Korea (413), and India (287). The statistical analysis was conducted using ANOVA and DMRT based on the data obtained from NIRS analysis. The average amylose contents were 18.85% in Japanese, 19.99% in Korean, 20.27% in Chinese, and 25.46% in Indian resources. The average protein contents were 7.23% in Korean, 7.73% in Japanese, 8.01% in Chinese, and 8.17% in Indian resources. The amylose and protein content using ANOVA showed significant differences at the level of 0.01. The F-test for amylose content was 158.34, and for protein content was 53.95 compared to critical value 3.78. The amylose and protein content using DMRT (p<0.01) showed significant difference between countries. The value of statistical treatment was divided into three groups such as $China^a$, $Korea^a$, $Japan^b$, $India^c$ in amylose and $China^a$, $India^a$, $Japan^b$, $Korea^c$ in protein. Japanese resources had the lowest level of amylose contents, whereas, the lowest level of protein content was found in Korean resources compared to other origins. Indian resources showed the highest level of amylose and protein contents. It is recommended that these results could be helpful to future breeding experiments.

• Korean Journal of Materials Research
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• v.29 no.5
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• pp.282-287
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• 2019

The photovoltaic properties of $TiO_2$ used for the electron transport layer in perovskite solar cells(PSCs) are compared according to the particle size. The PSCs are fabricated and prepared by employing 20 nm and 30 nm $TiO_2$ as well as a 1:1 mixture of these particles. To analyze the microstructure and pores of each $TiO_2$ layer, a field emission scanning electron microscope and the Brunauer-Emmett-Teller(BET) method are used. The absorbance and photovoltaic characteristic of the PSC device are examined over time using ultraviolet-visible-near-infrared spectroscopy and a solar simulator. The microstructural analysis shows that the $TiO_2$ shape and layer thicknesses are all similar, and the BET analysis results demonstrate that the size of $TiO_2$ and in surface pore size is very small. The results of the photovoltaic characterization show that the mean absorbance is similar, in a range of about 400-800 nm. However, the device employing 30 nm $TiO_2$ demonstrates the highest energy conversion efficiency(ECE) of 15.07 %. Furthermore, it is determined that all the ECEs decrease over time for the devices employing the respective types of $TiO_2$. Such differences in ECE based on particle size are due to differences in fill factor, which changes because of changes in interfacial resistance during electron movement owing to differences in the $TiO_2$ particle size, which is explained by a one-dimensional model of the electron path through various $TiO_2$ particles.

• Journal of agriculture & life science
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• v.53 no.3
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• pp.147-157
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• 2019

In this study, we tried to find out the most appropriate pre-processing method and to verify the feasibility of developing a low-price sensing system for predicting the hardy kiwis sugar content based on VNIRS and subsequent spectral analysis. A total of 495 hardy kiwi samples were collected from three farms in Muju, Jeollabukdo, South Korea. The samples were scanned with a spectrophotometer in the range of 730-2300 nm with 1 nm spectral sampling interval. The measured data were arbitrarily separated into calibration and validation data for sugar content prediction. Partial least squares (PLS) regression was performed using various combinations of pre-processing methods. When the latent variable (LV) was 8 with the pre-processing combination of standard normal variate (SNV) and orthogonal signal correction (OSC), the highest R2 values of calibration and validation were 0.78 and 0.84, respectively. The possibility of predicting the sugar content of hardy kiwi was also examined at spectral sampling intervals of 6 and 10 nm in the narrower spectral range from 730 nm to 1200 nm for a low-price optical sensing system. The prediction performance had promising results with R2 values of 0.84 and 0.80 for 6 and 10 nm, respectively. Future studies will aim to develop a low-price optical sensing system with a combination of optical components such as photodiodes, light-emitting diodes (LEDs) and/or lamps, and to locate a more reliable prediction model by including meteorological data, soil data, and different varieties of hardy kiwi plants.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.268-276
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• 2021

Functional near-infrared spectroscopy-based brain-computer interface (fNIRS-based BCI) has been receiving much attention. However, we are practically constrained to obtain a lot of fNIRS data by inherent hemodynamic delay. For this reason, when employing machine learning techniques, a problem due to the high-dimensional feature vector may be encountered, such as deteriorated classification accuracy. In this study, we employ an elastic net-based feature selection which is one of the embedded methods and demonstrate the utility of which by analyzing the results. Using the fNIRS dataset obtained from 18 participants for classifying brain activation induced by mental arithmetic and idle state, we calculated classification accuracies after performing feature selection while changing the parameter α (weight of lasso vs. ridge regularization). Grand averages of classification accuracy are 80.0 ± 9.4%, 79.3 ± 9.6%, 79.0 ± 9.2%, 79.7 ± 10.1%, 77.6 ± 10.3%, 79.2 ± 8.9%, and 80.0 ± 7.8% for the various values of α = 0.001, 0.005, 0.01, 0.05, 0.1, 0.2, and 0.5, respectively, and are not statistically different from the grand average of classification accuracy estimated with all features (80.1 ± 9.5%). As a result, no difference in classification accuracy is revealed for all considered parameter α values. Especially for α = 0.5, we are able to achieve the statistically same level of classification accuracy with even 16.4% features of the total features. Since elastic net-based feature selection can be easily applied to other cases without complicated initialization and parameter fine-tuning, we can be looking forward to seeing that the elastic-based feature selection can be actively applied to fNIRS data.

• The Journal of Internal Korean Medicine
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• v.43 no.5
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• pp.1018-1028
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• 2022

Objectives: Parkinson's disease is characterized by progressive, irreversible damage to dopamine neurons in the substantia nigra pars compacta, as well as motor and non-motor symptoms. This disease currently has no dependable disease-modifying treatment. In this paper, we describe the treatment of a 67-year-old female with Parkinson's disease using acupuncture and exercise therapy. Case Presentation: Clinical symptoms and the United Kingdom Parkinson's Disease Society Brain Bank Diagnostic Criteria were used to diagnose the patient with Parkinson's disease. Over a 12-week period, the patient visited a Korean medicine hospital 18 times and was treated with acupuncture and exercise therapy in addition to anti-Parkinson's drugs. Before and after treatment, clinical examinations were performed using tools such as the Unified Parkinson's Disease Rating Scale, Fall Efficacy Scale, Parkinson's Disease Questionnaire, Berg Balance Scale, and Non-Motor Symptoms Scale. Furthermore, functional near-infrared spectroscopy was used to assess cortical hemodynamics. All clinical examination results improved after 12 weeks of intervention. In particular, improvements on the Total Unified Parkinson's Disease Rating Scale and Part III of this scale demonstrated large, clinically important differences. Conclusion: This case suggests that combining acupuncture and exercise therapy could produce an effective treatment for Parkinson's disease patients.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.1-7
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• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• Journal of Environmental Science International
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• v.33 no.1
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• pp.1-8
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• 2024

When situated in green landscapes, water bodies play a crucial role in positively influencing mood and mental health, yet research on the cognitive mechanisms underlying these therapeutic effects is lacking. This study is intended to examine differences in brain function among adult males exposed to forest landscapes with or without water bodies. The wooded landscapes included views of a waterfall, a valley, and a forest without water. The control group was exposed to a local urban landscape. Twelve adult males participated in a field experiment in which prefrontal cortex (PFC) activity was measured using near-infrared spectroscopy (NIRS). In the experiment, participants engaged in low-intensity walking in three forested areas with similar vegetation and climatic conditions. Participants showed significant differences in left PFC activity depending on whether they were in the three forested landscapes or in the control landscape (P < 0.01). An analysis of variance (ANOVA) confirmed that significantly lower left PFC activity was recorded in the wooded landscape containing a water view . Notably, the lowest PFC values recorded in the landscape with a waterfall view suggest that landscapes with dynamic water flow may be associated with greater therapeutic benefits in terms of PFC activity than static landscapes. Our results underscore that water is a critical aspect of a landscape due to its therapeutic benefits and should be incorporated in the planning and design of green spaces for health promotion.

• Journal of Plant Biotechnology
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• v.50
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• pp.97-107
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• 2023

Malaria remains to be one of the most severe global public health concerns. Traditionally, Aspilia Africana and Warburgia ugandensis have been used to treat malaria in several African countries for millennia. In the current study, A. africana calli (AaC), A. africana in vitro roots (AaIR), A. africana wild leaf (AaWL), and W. ugandensis stem bark (WuSB) were dried and pulverized. Fourier transform near-infrared spectroscopy was used to analyze the powdered samples, while 80% ethanolic extracts of each sample were assayed for antiplasmodial activity (against Plasmodium falciparum strains DD2 (chloroquine-resistant) and 3D7 (chloroquine-sensitive)) and cytotoxicity. WuSB showed the highest antiplasmodial activity (IC₅₀ = 1.57 ± 0.210 ㎍/ml and 8.92 ± 0.365 ㎍/ml against P. falciparum 3D7 and DD2, respectively) and selectivity indices (43.90 ± 7.914 and 7.543 ± 0.051 for P. falciparum 3D7 and DD2, respectively). The highest total polyphenolic contents (total phenolic and flavonoid contents of 367.9 ± 3.55 mg GAE/g and 203.9 ± 1.43 mg RUE/g, respectively) were recorded for WuSB and the lowest were recorded for AaC. The antiplasmodial activities of the tested plant tissues correlated positively with total polyphenolic content. The high selectivity indices of WuSB justify its traditional applications in treating malaria and present it as a good candidate for discovering new antimalarial compounds. We recommend elicitation treatment for AaIR, which showed moderate antiplasmodial activity against P. falciparum DD2, to increase its secondary metabolite production for optimal antimalarial activity.