• 대한금속재료학회지
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• 제50권7호
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• pp.495-502
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• 2012

The aim of this study was to analyze high temperature deformation stability and properties of duplex stainless steels(DSS) according to annealing temperature. In order to analyze high temperature deformation stability, a number of compression tests were carried out with a stain rate of $10^{-2}s^{-1}{\sim}10s^{-1}$ up to a compression ratio of 50% in a temperature range of $950^{\circ}C-1300^{\circ}C$. The analysis of high temperature deformation stability of DSS was performed based on the Ziegler model. In order to analyze the high temperature properties of DSS, annealing treatments were conducted by isothermal holding for 1 hr at $950^{\circ}C$ to $1300^{\circ}C$ with $50^{\circ}C$ intervals followed by water cooling. The hardness and tensile tests were performed on specimens with different volume fractions of constituent phases, such as austenite, ferrite and sigma. The hardness and tensile strength of 2507 according to the annealing temperature are better than those of 2205. The strain rate sensitivity and Ziegler parameter are higher in 2205 than in 2507 as a whole, which implies that 2205 is better than 2507 in terms of forgeability at high temperature.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of Microbiology and Biotechnology
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• 제29권10호
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• pp.1522-1542
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• 2019

To adapt to environmental changes and to maintain cellular homeostasis, microorganisms adjust the intracellular concentrations of biochemical compounds, including metal ions; these are essential for the catalytic function of many enzymes in cells, but excessive amounts of essential metals and heavy metals cause cellular damage. Metal-responsive transcriptional regulators play pivotal roles in metal uptake, pumping out, sequestration, and oxidation or reduction to a less toxic status via regulating the expression of the detoxification-related genes. The sensory and regulatory functions of the metalloregulators have made them as attractive biological parts for synthetic biology, and the exceptional sensitivity and selectivity of metalloregulators toward metal ions have been used in heavy metal biosensors to cope with prevalent heavy metal contamination. Due to their importance, substantial efforts have been made to characterize heavy metal-responsive transcriptional regulators and to develop heavy metal-sensing biosensors. In this review, we summarize the biochemical data for the two major metalloregulator families, SmtB/ArsR and MerR, to describe their metal-binding sites, specific chelating chemistry, and conformational changes. Based on our understanding of the regulatory mechanisms, previously developed metal biosensors are examined to point out their limitations, such as high background noise and a lack of well-characterized biological parts. We discuss several strategies to improve the functionality of the metal biosensors, such as reducing the background noise and amplifying the output signal. From the perspective of making heavy metal biosensors, we suggest that the characterization of novel metalloregulators and the fabrication of exquisitely designed genetic circuits will be required.

• 대한방사성의약품학회지
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• 제3권2호
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• pp.103-112
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• 2017

The combination of nanoparticle with radioisotope could give the in vivo information with high sensitivity and specificity. However, radioisotope labeling of nanoparticle is very difficult and radioisotopes have different physicochemical properties, so the radioisotope selection of nanoparticle should be carefully considered. $^{18}F$ was first option to be considered for labeling of nanoparticle. For the labeling of $^{18}F$ with nanoparticle, Prosthetic group is widely used. Iodine, another radioactive halogen, is often used. Since radioiodine isotopes are various, they can be used for different imaging technique or therapy in the same labeling procedures. $^{99m}Tc$ can easily be obtained as pertechnatate ($^{99m}{TcO_4}^-$) by commercial generator. Ionic $^{68}Ga$ (III) in dilute HCl solution is also obtained by generator system, but $^{68}Ga$ can be substituted for $^{67}Ga$ because of the short half-life (67.8 min). $^{64}Cu$ emits not only positron but also ${\beta}-particle$. Therefore $^{64}Cu$ can be used for imaging and therapy at the same time. These radioactive metals can be labeled with nanoparticle using the bifunctional chelator. $^{89}Zr$ has longer half-life (78.4 h) and is used for the longer imaging time. Unlike different metals, $^{89}Zr$ should use the other chelate such as DFO, 3,4,3-(LI-1,2-HOPO) or DFOB.

• 대한금속재료학회지
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• 제56권11호
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• pp.813-821
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• 2018

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature.

• 분석과학
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• 제15권1호
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• pp.7-14
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• 2002

본 연구에서는 레이저 애블레이션 이온 트랩 질량분석법을 이용하여 금속 및 세라믹 시료들에 대한 원소분석을 수행하였고, 이때 이온화 장치로는 XeCI 엑시머 레이저를 사용하였고 검출장치로서 이온트랩 질량 분석기를 사용하였다. 시료는 트랩의 바깥에 장착하여 시료의 교환이 매우 쉽도록 하였고 고체시료의 분석에 있어서 매우 효과적임을 밝혔다. 헬륨기체의 압력이나 이온저장시간, 초기질량제한 RF 전압 등에 대한 기초 실험을 통하여 실험의 최적 조건을 구하였고 (헬륨 기체압력 $1{\times}10^{-4}$ Torr 이온 저장시간 100 ms 초기 질량 제한전압 $1150V_{p-p}$), 이 결과를 토대로 금속시료(구리, 몰리브데늄)와 세라믹 시료(알루미나 세라믹, 지르코니아 세라믹) 들에 대한 원소분석을 수행하였다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제20권6호
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• pp.19-27
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• 2015

Permissible soil concentrations for explosives (i.e., TNT and RDX) and heavy metals (i.e., Cu, Zn, Pb, and As) heve been derived from human risk and ecotoxicity, respectively. For TNT and RDX, human risk based-permissible soil concentrations were determined as 460 mg-TNT/kg-soil and 260 mg-RDX/kg-soil. Ecotoxicity based-permissible soil concentrations for Cu and Zn were determined from species sensitivity distribution (SSD) and uncertainty factor of 1 to 5, yielding 18.0-40.0 mg-Cu/kg-soil and 46.0-100 mg-Zn/kg-soil. For Pb and As, ecotoxicity data were not enough to establish SSD so that a deterministic method was used, generating 13.8-30.8 mg-Pb/kg-soil and 2.10-4.60 mg-As/kg-soil. It is worth noting that the methodology used to derive permissible concentrations in soil can differ depending on ecotoxicity data availability and socio-economic situations, which results in different permissible concentrations. The permissible concentrations presented in this study have been derived from conservative assumptions for exposure parameters, and thus should be considered as soil standards. In the light of remediation and pollution management of a site of interest, the site-specific and receptor-specific permissible soil concentrations should be derived considering potential receptors, current and future land use, background concentrations, and socio-economic consultation.

• Environmental Analysis Health and Toxicology
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• 제23권4호
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• pp.337-343
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• 2008

따개비류는 암반기질에 무리를 이루어 비교적 단단히 부착생활을 하며 전 세계적으로 분포하는 생물이다. 본 생물은 형태가 다른 두 단계의 플랑크톤 유생시기를 거쳐 기질에 부착하여 생활한다. 따개비류에 관한 국내연구는 순수 분류학적으로 몇 편의 연구가 이루어졌을 뿐이며 실험실 내 사육조건 및 유생의 민감도 등에 관한 연구는 제대로 수행되어 있지 않은 실정이다. 본 연구에서는 첫 번째 단계 유생을 대상으로 몇몇 중금속(Cd, Cr, Cu 그리고 Zn)에 대한 민감도를 조사하였고 타 연구결과와의 비교로 생태 독성학적 적용을 시도하였다. 네 가지 금속물질, 카드뮴, 크롬, 구리 그리고 아연에 대한 따개비 유생의 반수사망농도는 각각 0.43, 24.4, 0.22 그리고 1.74mg/L로 산정되었다. 외국의 생물검정용 따개비와 본 연구에 사용된 고랑따개비의 민감도는 아주 유사하게 나타났다. 따라서 국내산 고랑따개비의 유생은 해양 환경 평가에 일반적으로 이용되는 해양생물과 동반 검정의 수행에 자격을 갖추었다고 할 수 있다. 본 연구의 결과는 해양환경의 평가는 물론 방오물질개발 등에의 기초자료를 제공할 수 있을 것으로 사료된다.

• Korean Chemical Engineering Research
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• 제50권5호
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• pp.933-937
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• 2012

본 연구에서는 환경오염을 발생시키는 주요한 중금속 물질의 하나인 구리를 상대적으로 쉽게 검출하기 위해, CNT 전극 및 벗김전압전류법을 이용하여 구리 금속의 감도 향상을 위한 최적조건 및 민감도를 평가하였다. 또한 구리의 벗김반응이 발생될 때의 반응 메카니즘에 대한 연구도 수행하였다. 이를 위해, 네모파 벗김전압전류법 및 선형주사 전압 전류법등의 전기화학적 분석법이 이용되었다. 평가 결과, 네모파 벗김전압전류법의 최적조건으로, 15 mV의 네모파증폭율, 60 Hz의 주파수, -1.0V vs. Ag/AgCl의 석출전위 및 200초의 석출시간이 결정되었다. 구리 금속의 민감도를 측정한 결과 $1.824{\mu}A/{\mu}M$의 민감도를 얻을 수 있었다. 선형주사 전압전류법을 이용하여 구리의 벗김반응에 영향을 끼치는 인자를 평가하였을 때, 확산반응 보다는 표면반응이 구리의 벗김반응 성능에 영향을 끼치는 것으로 측정되었다. 이러한 전기화학적 분석 결과가 다른 참고문헌들과 비교되어졌고, 구리금속의 민감도 측면에서 본 연구에서 제안한 CNT 전극의 우수함이 입증되었다.

• Radiation Oncology Journal
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• 제17권3호
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• pp.256-260
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• 1999

목적 : 얇은 박막을 위에 얹은 TLD 선량계의 반응 값에 영향을 미치는 인자를 조사하였고, 특히, 금속박막을 얹은 TLD 선량계의 광자에너지와 표면 흡수선량에 대한 의존성을 조사하였다. 방법 및 재료 : 본 연구에서는 TLD-100과 TLD-100 위에 얹은 Slt은 물질로는 주석, 금, 그리고 TE 플라스틱 판을 사용하였다. 각 금속 박막의 두께는 0.1 mm, TE 플라스틱 판의 두께는 1 mm였고 각 박막의 면적은 TLD-100의 면적과 같이 하였다. 방사선치료에 많이 산이는 6 MV에서 15 MV사이의 광자에너지에 대한 TLD-100의 반응감도와 금박막을 얹은 TLD-100의 반응감도, 그리고 주석 박막을 얹은 TLD-100의 반응감도를 비교해 보았다. 결과 : 금속 박막을 얹은 TLD의 경우 표면 흡수선량의 증가가 명백히 나타나고, 금 박막을 얹은 TLD의 경우 10MV에서 정상 TLD보다 약 1.83배 정도 과잉 반응하는 것으로 관측되었으며, 흡수선량에 따른 반응감도의 변화는 주석을 얹은 TLD의 경우가 가장 작았다 금속 박막을 얹은 TLD의 일반 TLD에 대한 상대 반응감도는 에너지에 대한 의존성을 거의 나타내지 않았다. 그리고 311은 박막의 조직에 대한 등가 두께에 따라 반응감도가 증가하였다. 결론 : 금속박막을 얹은 TLD 선량계의 반응값이 고 에너지(6-15 MV)에 대한 의존성을 거의 나타내지 않았으며, 흡수선량에 대한 선형성도 뛰어난 것으로 관측되었다. 따라서 금속박막을 얹은 TLD 선량계는 매우 작은 크기의 광자빔과 표면흡수선량의 측정에 매우 적합한 것으로 사료된다.