• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.847-859
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• 2022

The development and commercialization of industrial genetically modified (GM) organisms is actively progressing worldwide, highlighting an increased need for improved safety management protocols. We sought to establish an environmental monitoring method, using real-time polymerase chain reaction (PCR) and propidium monoazide (PMA) treatment to develop a quantitative detection protocol for living GM microorganisms. We developed a duplex TaqMan quantitative PCR (qPCR) assay to simultaneously detect the selectable antibiotic gene, ampicillin (AmpR), and the single-copy Escherichia coli taxon-specific gene, D-1-deoxyxylulose 5-phosphate synthase (dxs), using a direct cell suspension culture. We identified viable engineered E. coli cells by performing qPCR on PMA-treated cells. The theoretical cell density (true copy numbers) calculated from mean quantification cycle (Cq) values of PMA-qPCR showed a bias of 7.71% from the colony-forming unit (CFU), which was within ±25% of the acceptance criteria of the European Network of GMO Laboratories (ENGL). PMA-qPCR to detect AmpR and dxs was highly sensitive and was able to detect target genes from a 10,000-fold (10^-4) diluted cell suspension, with a limit of detection at 95% confidence (LOD_95%) of 134 viable E. coli cells. Compared to DNA-based qPCR methods, the cell suspension direct PMA-qPCR analysis provides reliable results and is a quick and accurate method to monitor living GM E. coli cells that can potentially be released into the environment.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.25-30
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• 2012

This study investigated whether native Korean listeners could use visual cues for the identification of the English consonants /b/ and /v/. Both auditory and audiovisual tokens of word minimal pairs in which the target phonemes were located in word-initial or word-medial position were used. Participants were instructed to decide which consonant they heard in $2{\times}2$ conditions: cue (audio-only, audiovisual) and location (word-initial, word-medial). Mean identification scores were significantly higher for audiovisual than audio-only condition and for word-initial than word-medial condition. Also, according to signal detection theory, sensitivity, d', and response bias, c were calculated based on both hit rates and false alarm rates. The measures showed that the higher identification rate in the audiovisual condition was related with an increase in sensitivity. There were no significant differences in response bias measures across conditions. This result suggests that native Korean speakers can use visual cues while identifying confusing non-native phonemic contrasts. Visual cues can enhance non-native speech perception.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.517-526
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• 2012

This study investigated the impact of individual differences in motivational reactivity on cognitive effort, memory strength (sensitivity) and decision making (criterion bias) in response to Internet ads with positive and negative content. Individual variation in trait motivational activation was measured using the Motivational Activation Measurement developed by A. Lang and her colleagues (A. Lang, Bradley, Sparks, & Lee, 2007). MAM indexes an individual's tendency to approach pleasant stimuli (ASA, Appetitive System Activation) and avoid unpleasant stimuli (DSA, Defensive System Activation). Results showed that individuals higher in ASA exert more cognitive effort during positive ads than individuals lower in ASA. Individuals higher in DSA exert more cognitive effort during negative ads compared to individuals lower in DSA. ASA did not predict recognition memory. However, individuals higher in DSA recognized ads better than those lower in DSA. The criterion bias data revealed participants higher in ASA had more conservative decision criterion, compared to participants lower in ASA. Individuals higher in DSA also showed more conservative decision criterion compared to individuals lower in DSA. The theoretical and practical implications are discussed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.117-122
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• 2007

LB method is one of the most interesting technique to arrange certain molecular groups at precise position relative to others. Also, the LB deposition technique can fabricate extremely thin organic films with a high degree of control over their thickness and molecular architecture. In this way, new thin film materials can be built up at the molecular level, and the relationship between these artificial structures and the properties of materials can be explored. In this paper, evaluation of physical properties was made for dielectric relaxation phenomena by the detection of the surface pressures and displacements current on the monolayer films of phospolipid monomolecular DLPC. Lipid thin films were manufacture by detecting deposition for the accumulation and the current was measured after the electric bias was applied across the manufactured MIM device. It is found that the phospolipid monolayer of dielectric relaxation takes a little time and depend on the molecular area. When electric bias is applied across the manufactured MIM device by the deposition condition of phospolipid mono-layer, it wasn't breakdown when the higher electric field to impress by increase of deposition layers.

• Proceedings of the Korean Vacuum Society Conference
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• 1999.07a
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• pp.133-133
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• 1999

비정질 탄소막 제조에 있어서 수소가 포함된 반응성 가스를 사용할 경우 제작된 탄소막 내부에는 수소가 포함되게 되며, 이러한 수소원자들은 막의 특성에 중요한 영향을 주는 것으로 알려져 있다. 따라서, 본 연구에서는 비정질 탄소막(a-C:H) 내부에 존재하는 수소가 탄소막의 특성에 미치는 영향을 알아보고, 막 내부에 포함된 수소의 함량과 공정조건 사이의 함수계를 조사함으로써 수소의 함량을 인위적으로 통제할 수 있는 가능성을 제시하고자 한다. 수소가 포함된 비정질 탄소막은 2.45 GHz의 전자기파를 사용하는 electron cyclotron resonance plasma enhanced chemical vapor deposition (ECR-PECVD) 방법과 DC magnetron sputtering 법을 사용하여 제작하였다. 기판으로는 Si(001) wafer를 사용하였으며, 아세톤과 에탄올을 사용하여 표면의 유기성분을 제거하고, 진공챔버속에서 Ar 플라즈마를 발생시켜 sputter etching 방법으로 표면을 세척하였다. ECR-PECVD 방법에서는 반응가스로 메탄(CH4)과 수소(H2)의 혼합가스를 사용하였으며, 혼합가스의 비는 5~50% 범위내에서 변화를 주었다. 수소가스의 유량은 100SCCM으로 고정하였으며, 마이크로웨이브의 power는 360~900W였고, 기판에 가해준 negative DC bias 전압은 0~-500V이었다. DC magnetron sputtering 방법에서는 반응가스로 아세틸린(C2H2) 가스를 사용하였으며, 플라즈마 발생을 용이하게 하기 위해서 Ar 가스와 혼합하여 사용하였다. Ar 가스의 유량은 10SCCM으로 고정하였으며, 아세틸렌 가스의 유량은 5~20SCCM 범위내에서 주입하였다. 이때, 기판에 가해준 negative DC bias 전압은 0~-100V이었다. 제작된 탄소막의 수소 함량을 조사하기 위하여 Fourier Transform Infrared (FTIR) 분광법과 Elastic Recoil Detection Analysis (EFDA) 법을 사용하였으며, 증착율은 SEM 단면촬영과 a-step을 이용하여 측정하였고, 막의 경도는 Micro-Hardness Testing 법을 사용하여 측정하였다.

• ETRI Journal
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• v.37 no.5
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• pp.961-971
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• 2015

This paper proposes a 250 mV supply voltage digital low-dropout (LDO) regulator. The proposed LDO regulator reduces the supply voltage to 250 mV by implementing with all digital circuits in a$0.11{\mu}m$ CMOS process. The fast current tracking scheme achieves the fast settling time of the output voltage by eliminating the ringing problem. The over-voltage and under-voltage detection circuits decrease the overshoot and undershoot voltages by changing the switch array current rapidly. The switch bias circuit reduces the size of the current switch array to 1/3, which applies a forward body bias voltage at low supply voltage. The fabricated LDO regulator worked at 0.25 V to 1.2 V supply voltage. It achieved 250 mV supply voltage and 220 mV output voltage with 99.5% current efficiency and 8 mV ripple voltage at $20{\mu}A$ to $200{\mu}A$ load current.

• The Journal of Churna Manual Medicine for Spine and Nerves
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• v.15 no.1
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• pp.75-87
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• 2020

Objectives : This study aimed to evaluate the effectiveness of manual therapy for wrist pain. Methods : We searched electronic databases (PubMed, Embase, Cochrane, CAJ, KISS, RISS, NDSL, OASIS, and KMBASE) for randomized controlled trials for manual therapy as a treatment for wrist pain. Results : A total of 9 randomized controlled trials were selected and meta-analysis was conducted on 6 studies. Three studies with different design of the intervention/control group were excluded from the meta-analysis. A high risk of bias was observed for both performance bias and detection bias. Conclusions : Our systematic review verified the clinical effect of manual therapy on wrist pain. Based on the results of this study, it is expected that clinical studies on wrist diseases and high-level follow-up studies will be conducted.

• The Journal of the Acoustical Society of Korea
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• v.10 no.1
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• pp.20-29
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• 1991

A fundamental assumption in conventional linear predictive coding (LPC) analysis procedure is that the input to an all-pole vocal tract filter is white process. In the case of periodic inputs, however, a pitch bias error is introduced into the conventional LP coefficient. Multi-pulse (MP) LP analysis can reduce this bias, provided that an estimate of the excitation is available. Since the prediction error of conventional LP analysis can be modeled as the sum of an MP excitation sequence and a random noise sequence, we can view extracting MP sequences from the prediction error as a classical detection and estimation problem. In this paper, we propose an algorithm in which the locations and amplitudes of the MP sequences are first obtained by applying a likelihood ratio test (LRT) to the prediction error, and LP coefficients free of pitch bias are then obtained from the MP sequences. To verify the performance enhancement, we iterate the above procedure with adaptive threshold at each step.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.