• Journal of the Korean Chemical Society
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• v.55 no.3
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• pp.472-477
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• 2011

The concentration of selenium in grain samples was determined using isotope dilution method in ORC-ICPMS. The experimental conditions were optimized to $H_2$ mode and the flow rate was $4.0\;mL\;min^{-1}$. ORC in $H_2$ mode proved to eliminate most of polyatomic interferences except $BrH^+$ when Br is present in sample matrix. Chemical removal of Br was very difficult and the mathematical correction was successfully employed. The fraction of $BrH^+$ generated from Br at the current experimental condition was 14.1%. The signal on m/z 82 was corrected and calculated for isotope dilution. The analytical reliability of the propose method was successfully evaluated by analyzing the certified standard reference material NIST SRM 1566 and 1567. The method was applied to real samples and the results are $0.034{\pm}0.001\;{\mu}g\;g^{-1}$ for white rice, $0.059{\pm}0.002_5\;{\mu}g\;g^{-1}$ for brown rice, $0.029{\pm}0.001_4\;{\mu}g\;g^{-1}$ for black rice, and $0.034{\pm}0.002\;{\mu}g \;g^{-1}$ for barley. The detection limits ($3\sigma$) for Se was $0.012\;ng\;g^{-1}$.

• Economic and Environmental Geology
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• v.34 no.1
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• pp.13-23
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• 2001

Bentonite layers are intercalated within the basal conglomerates in the Tertiary sedimentary basins of Kampo, Janggi and Pohang, southeastern Korea. Eighteen samples of the bentonites went through X-ray diffraction, scanning electron microscopy, heavy mineral analyses, chemical analyses and oxygen, hydrogen stable isotope analyses to define the mineralogical characters of the bentonites. Heavy minerals such as zircons, apatites, amphiboles and biotites separated from bentonites show clean and euhedral surfaces, which are the characteristic features of volcanic origin. But biotites from the Chunbook Conglomerate are found as altered and heavily broken flakes which implies longer transportation of these bentonites. $TiO_{2}/Al_{2}O_{3} ratios of <2 $\mu$m particle fractions (the Chunbook Conglomerate 0.031; Janggi 0.029; Kampo 0.025) suggest that those are originated from volcanic tuffs. That is, the higher the value is, the more mafic in chemical compositions of the original tuffs. Authigenic montmorillonite and zeolite minerals were observed by SEM, which indicates diagenesis origin of bentonites. But the samples from the Chunbook Conglomerate showed only chaotically packed clay flakes in the matrix of sands or conglomerates, which implies detrital influence, not authigenic origin. The structural formulae of montmorillonite from these basins reflects their environment of formation. Fe (Ⅵ) can show the redox condition of its past environment and much lower $Fe^{2+}(Ⅵ)/Fe^{3+}(Ⅵ)$ ratios in montmorillonite of the Chunbook Conglomerate imply the greater oxidizing influence. Calculated burial depths from oxygen stable isotope data of the samples from the Chunbook Conglomerate generally fall to the range of 929~963 m whereas the real burial depth of this area is only 530~580 m. This could be explained as the bentonites of the Chunbook conglomerate had not been formed in situ. Discriminant analyses with the data from chemical analyses and structural formulae of montmorillonites show that bentonites from three different basins could definitely be distinguished with each other. This result arises from the different chemical compositions of original volcanic ashes and the difference of sedimentary environments.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.174-174
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• 2000

A strong antiferromagnetic coupling in Fe/Si multilayered films (MLF) had been recently discovered and much consideration has been given to whether the coupling in the Fe/Si MLF system has the same origin as the metal/metal MLF. Nevertheless, the nature of the interfacial ron silicide is still controversial. On one hand, a metal/ semiconductor structure was suggested with a narrow band-gap semiconducting $\varepsilon$-FeSi spacer that mediates the coupling. However, some features show that the nature of coupling can be well understood in terms of the conventional metal/metal multilayered system. It is well known that both magneto-optical (MO) and optical properties of a metal depend strongly on their electronic structure that is also correlated with the atomic and chemical ordering. In this study, the nature of the interfacial regions is the Fe/Si multilayers has been investigated by the experimental and computer-simulated MO and optical spectroscopies. The Fe/Si MLF were prepared by rf-sputtering onto glass substrates at room temperature with the number of repetition N=50. The thickness of Fe sublayer was fixed at 3.0nm while the Si sublayer thickness was varied from 1.0 to 2.0 nm. The topmost layer of all the Fe/Si MLF is Fe. In order to carry out the computer simulations, the information on the MO and optical parameters of the materials that may constitute a real multilayered structure should be known in advance. For this purpose, we also prepared Fe, Si, FeSi2 and FeSi samples. The structural characterization of Fe/Si MLF was performed by low- and high -angle x-ray diffraction with a Cu-K$\alpha$ radiation and by transmission electron microscopy. A bulk $\varepsilon$-FeSi was also investigated. The MO and optical properties were measured at room temperature in the 1.0-4.7 eV energy range. The theoretical simulations of MO and optical properties for the Fe/Si MLF were performed by solving exactly a multireflection problem using the scattering matrix approach assuming various stoichiometries of a nonmagnetic spacer separating the antiferromagnetically coupled Fe layers. The simulated spectra of a model structure of FeSi2 or $\varepsilon$-FeSi as the spacer turned out to fail in explaining the experimental spectra of the Fe/Si MLF in both intensity and shape. Thus, the decisive disagreement between experimental and simulated MO and optical properties ruled out the hypothesis of FeSi2 and $\varepsilon$-FeSi as the nonmagnetic spacer. By supposing the spontaneous formation of a metallic ζ-FeSi, a reasonable agreement between experimental and simulated MO and optical spectra was obtained.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2000.04a
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• pp.9-10
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• 2000

An important business-field of world-wide steel-industry is the coating of thin metal-sheets with zinc, zinc-aluminum and aluminum based materials. These products mostly go into automotive industry. in particular for the car-body. into building and construction industry as well as household appliances. Due to mass-production, the processing is done in large continuously operating plants where the mostly cold-rolled metal-strip as the substrate is handled in coils up to 40 tons unwind before and rolled up again after passing the processing plant which includes cleaning, annealing, hot-dip galvanizing / aluminizing and chemical treatment. In the liquid Zn, Zn-AI, AI-Zn and AI-Si bathes a combined action of corrosion and wear under high temperature and high stress onto the transfer components (rolls) accounts for major economic losses. Most critical here are the bearing systems of these rolls operating in the liquid system. Rolls in liquid system can not be avoided as they are needed to transfer the steel-strip into and out of the crucible. Since several years, ceramic roller bearings are tested here [1.2], however, in particular due to uncontrollable Slag-impurities within the hot bath [3], slide bearings are still expected to be of a higher potential [4]. The today's state of the art is the application of slide bearings based on Stellite\ulcorneragainst Stellite which is in general a 50-60 wt% Co-matrix with incorporated Cr- and W-carbides and other composites. Indeed Stellite is used as the bearing-material as of it's chemical properties (does not go into solution), the physical properties in particular with poor lubricating properties are not satisfying at all. To increase the Sliding behavior in the bearing system, about 0.15-0.2 wt% of lead has been added into the hot-bath in the past. Due to environmental regulations. this had to be reduced dramatically_ This together with the heavily increasing production rates expressed by increased velocity of the substrate-steel-band up to 200 m/min and increased tractate power up to 10 tons in modern plants. leads to life times of the bearings of a few up to several days only. To improve this situation. the Mechanical Alloying (MA) TeChnique [5.6.7.8] is used to prOduce advanced Stellite-based bearing materials. A lubricating phase is introduced into Stellite-powder-material by MA, the composite-powder-particles are coated by High Energy Milling (HEM) in order to produce bearing-bushes of approximately 12 kg by Sintering, Liquid Phase Sintering (LPS) and Hot Isostatic Pressing (HIP). The chemical and physical behavior of samples as well as the bearing systems in the hot galvanizing / aluminizing plant are discussed. DependenCies like lubricant material and composite, LPS-binder and composite, particle shape and PM-route with respect to achievable density. (temperature--) shock-reSistibility and corrosive-wear behavior will be described. The materials are characterized by particle size analysis (laser diffraction), scanning electron microscopy and X-ray diffraction. corrosive-wear behavior is determined using a special cylinder-in-bush apparatus (CIBA) as well as field-test in real production condition. Part I of this work describes the initial testing phase where different sample materials are produced, characterized, consolidated and tested in the CIBA under a common AI-Zn-system. The results are discussed and the material-system for the large components to be produced for the field test in real production condition is decided. Outlook: Part II of this work will describe the field test in a hot-dip-galvanizing/aluminizing plant of the mechanically alloyed bearing bushes under aluminum-rich liquid metal. Alter testing, the bushes will be characterized and obtained results with respect to wear. expected lifetime, surface roughness and infiltration will be discussed. Part III of this project will describe a second initial testing phase where the won results of part 1+11 will be transferred to the AI-Si system. Part IV of this project will describe the field test in a hot-dip-aluminizing plant of the mechanically alloyed bearing bushes under aluminum liquid metal. After testing. the bushes will be characterized and obtained results with respect to wear. expected lifetime, surface roughness and infiltration will be discussed.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• The Mathematical Education
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• v.63 no.2
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• pp.123-138
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• 2024

This study described the utilization of ChatGPT in teaching and students' learning processes for the course "Introductory Mathematics for Artificial Intelligence (Math4AI)" at 'S' University. We developed a customized ChatGPT and presented a learning model in which students supplement their knowledge of the topic at hand by utilizing this model. More specifically, first, students learn the concepts and questions of the course textbook by themselves. Then, for any question they are unsure of, students may submit any questions (keywords or open problem numbers from the textbook) to our own ChatGPT at https://math4ai.solgitmath.com/ to get help. Notably, we optimized ChatGPT and minimized inaccurate information by fully utilizing various types of data related to the subject, such as textbooks, labs, discussion records, and codes at http://matrix.skku.ac.kr/Math4AI-ChatGPT/. In this model, when students have questions while studying the textbook by themselves, they can ask mathematical concepts, keywords, theorems, examples, and problems in natural language through the ChatGPT interface. Our customized ChatGPT then provides the relevant terms, concepts, and sample answers based on previous students' discussions and/or samples of Python or R code that have been used in the discussion. Furthermore, by providing students with real-time, optimized advice based on their level, we can provide personalized education not only for the Math4AI course, but also for any other courses in college math education. The present study, which incorporates our ChatGPT model into the teaching and learning process in the course, shows promising applicability of AI technology to other college math courses (for instance, calculus, linear algebra, discrete mathematics, engineering mathematics, and basic statistics) and in K-12 math education as well as the Lifespan Learning and Continuing Education.