• Korean Journal of Metals and Materials
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• v.50 no.7
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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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• v.25 no.1
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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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• v.29 no.10
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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.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.3 no.2
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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.

• Korean Journal of Metals and Materials
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• v.56 no.11
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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.

• Analytical Science and Technology
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• v.15 no.1
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• pp.7-14
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• 2002

Laser ablation ion trap mass spectrometry (LAITMS) was developed for the analysis of metal and ceramic samples. For this study, XeCl excimer laser (308 nm) was used for ablating the samples and ITMS was used as a detector. Samples were introduced from outside of a ring electrode and this way of sample introduction was very effective for solid samples when laser ablation was employed. Helium gas was used as a buffer gas, and its effect on sensitivity and some parameters (buffer gas pressure, ion storage time, and cut-off RF voltage) were studied. The optimized conditions were $1{\times}10^{-4}$ Torr of buffer gas pressure, 100 ms of ion storage time and $1150V_{p-p}$ of cut-off RF voltage. From that results, copper (Cu) and molybdenum (Mo) metals were tested with LAITMS and the mass spectra of these pure metals were compared with the natural abundance of isotope ratio. We also examined ceramic samples ($Al_2O_3$, $ZrO_2$) and represented the result of elemental analysis.

• Journal of Soil and Groundwater Environment
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• v.20 no.6
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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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• v.23 no.4
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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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• v.50 no.5
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• pp.933-937
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• 2012

In the present study, we evaluate the sensitivity and optimal stripping voltammetry (SV) conditions of copper (Cu), which is one of the main trace heavy metals inducing the environmental contamination, using carbon nanotube (CNT) electrode. In addition, the reaction mechanism of stripping reaction of Cu is investigated. The electrochemical analyses such as squarewave stripping voltammetry (SWSV) and linear scan voltammetry (LSV) are used for the evaluations. As a result of that, the best SWSV conditions like squarewave amplitude of 15 mV, frequency of 60 Hz, deposition potential of -1.0V vs. Ag/AgCl and deposition time of 200s are determined with the measured Cu sensitivity of $1.824{\mu}A/{\mu}M$. As a driving force affecting the stripping reaction of Cu, surface reaction is more dominant one than diffusion. These results are compared with other reference results and it is confirmed that our suggested CNT electrode gives rise to better Cu sensitivity result than other references.

• Radiation Oncology Journal
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• v.17 no.3
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• pp.256-260
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• 1999

Purpose : An investigation has been carried out on the factors which affect the response reading of thermoluminescent dosimeters (TLD-100) loaded with thin material in high energy Photon. The aim of the study was to assess the energy response of TLD-100 to the therapeutic ranges of photon beam. Materials and Methods : In this technique, TLD-100 (abbreviated as TLD) chips and three different thin material (Tin, Gold, and Tissue equivalent plastic plate) which mounted on the TLD chip were used in the clinical photon beam. The thickness of each metal plates was 0.1 mm and TE plastic plate was 1 mm thick. These compared with the photon energy dependence of the sensitivities of TLD (normal chip), TLD loaded with Tin or Gold plate, for the photon energy range 6 MV to 15 MV, which was of interest in radiotherapy. Results : The enhancement of surface dose in the TLD with metal plate was clearly detected. The TLD chips with a Gold plate was found to larger response by a factor of 1.83 in 10 MV photon beam with respect to normal chip. The sensitivity of TLD loaded with Tin was less than that for normal TLD and TLD loaded with Gold. The relative sensitivity of TLD loaded with metal has little energy dependence. Conclusion : The good stability and linearity with respect to monitor units of TLD loaded with metal were demonstrated by relative measurements in high energy Photon ($6\~15$ MV) beams. The TLD laminated with metals embedded system in solid water phantom is a suitable detector for relative dose measurements in a small beam size and surface dose.