• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.411-434
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• 2022

In deep geological disposal concept for spent nuclear fuel, it is well-known that rock mass at near-field experiences the thermal-hydraulic-mechanical (THM) coupled behavior. The mechanical properties of rock changes during the coupled process, and it is important to consider the changes into the analysis of numerical simulation and in-situ tests for long-term stability evaluation of nuclear waste disposal repository. This report collected the previous studies on indirect coupled behaviors of rock. The effects of water saturation and temperature on some mechanical properties of rock was considered, while the change in hydraulic conductivity of rock due to stress was included in the indirect coupled behavior.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.558-567
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• 2022

The evolution of temperature and thermal stress in a granite specimen is studied via heating experiment in the context of a high-level radioactive waste repository. A heating condition based on the decay-induced heat is applied to a cubic granite specimen to measure the temperature and stress distributions and their evolution over time. The temperature increases quickly due to heat conduction along the heated surfaces, but a significant amount of thermal energy is also lost through other surfaces due to air convection and conduction into the loading machine. A three-dimensional finite element-based model is used to numerically reproduce the experiment, and the thermo-mechanical coupling behavior and modeling conditions are validated with the comparison to the experimental results. The most crucial factors influencing the heating experiment are analyzed and summarized in this paper for future works.

• Tunnel and Underground Space
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• v.33 no.1
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• pp.10-28
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• 2023

Long-term shear behavior of the rock discontinuities should be analyzed and its stability should be evaluated to ensure the long-term stability of a high-level radioactive waste disposal repository. The long-term shear behavior of the discontinuities can be modeled with creep and RSF models. The shear creep test, velocity step test, and slide-hold-slide test can be performed to determine their model parameters or analyze the shear behavior by experiments under various conditions. Testing apparatuses for direct shear, triaxial compression, and biaxial shear were mainly used and improved to reproduce the thermo-hydro-mechanical conditions of local bedrock, and it was confirmed that the shear behavior could vary. In order to design a high-level radioactive waste disposal site in Korea, the long-term behavior of rock discontinuities should be investigated in consideration of rock types, thermo-hydro-mechanical conditions, metamorphism, and restoration of shear resistance.

• Nuclear Engineering and Technology
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• v.26 no.2
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• pp.285-293
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• 1994

The transport of radionuclides in a compacted bentonite is dominated by diffusion. Through-diffusion tests for iodide were performed to investigate the diffusion characteristics of anionic radionuclides in a domestic bentonite. The bentonite used was sampled from the southeastern area of Korea and the solution was synthetic groundwater spiked with a tracer of I -125(as Na$^{125}$ I). The dry densities of compacted bentonite were 1.2, 1.4, and 1.7 Mg/㎥. The apparent diffusion coefficients and the effective diffusion coefficients of the iodide decrease with increasing dry density. The values were from 3.80 to 7.12$\times$10$^{-11}$ $m^2$/s for the apparent diffusion coefficients and from 1.25 to 7.97$\times$10$^{-12}$ $m^2$/s for the effective diffusion coefficient, respectively. The experimental results also showed that the apparent diffusion coefficients depended on the pore structure of compacted bentonite and the effective diffusion coefficients were attributed to the pore structure and the effective porosity that represents the available pathway for the diffusional transport of iodide. The results obtained will be used as basic data for the safety assessment of a repository.

• Restorative Dentistry and Endodontics
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• v.46 no.4
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• pp.55.1-55.9
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• 2021

Objectives: The purpose of this systematic review was to collect and discuss the technique of adhesive systems application on dentin substrate under electric current. Materials and Methods: The first search strategy was based on data available at PubMed, LILACS, Scielo, Scopus, and Cochrane Library, using a combination of descriptors such as "dentin bond agents OR adhesive system AND electric current OR electrobond" or "dentin bonding agents OR dentin bonding agent application OR adhesive system AND electric current OR electrobond", with no limit regarding the publication year. The second search strategy was based on the articles' references found previously. An additional search strategy was applied that concerned the proposed theme in the SBU-UNICAMP (Unicamp's Library System Institutional Repository). Results: Twelve studies published between 2006 and 2020 were found. The analyses of the selected studies showed that the use of electric current during adhesive systems application on dentin, whether conventional or self-conditioning, increases resinous monomer infiltration in the dentin substrate, which improves the hybridization processes and the bond strength of the restorative material to dentin. Conclusions: Despite the favorable results related to the use of this technique, there is still no specific protocol for the application of adhesive systems under electric current.

• Analytical Science and Technology
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• v.21 no.2
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• pp.143-147
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• 2008

U(IV) ion, the valance state of uranium presumed at in a deep-depth disposal of a spent fuel, was prepared and separated from U(VI) ion. In order to prepare U(IV) ion, tests were performed by adding several reducing agents into a uranyl solution or by dissolution of uranium oxide in a mixed acid added with a reducing agent. The valance states of the uranium in the prepared solutions were identified by separating two ions with a Dowex AG 50W-X8 cation exchange resins and measuring the solutions using a laser-induced fluorescence spectroscopy. However, U(IV) and U(VI) were not separated by a Lichroprep Si60 exchange resin in the same separation condition of Pu(IV) and Pu(VI).

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3831-3843
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• 2023

The adsorption behaviour of Se(-II) onto granite and MX-80 bentonite in Ca-Na-Cl solutions has been studied utilizing adsorption experiments and surface complexation modelling. Adsorption kinetic experiments allude to steady-state adsorption periods after 7 days for granite and 14 days for MX-80 bentonite. Batch adsorption experiments were carried out to determine the influence that the physicochemical solution properties would have on Se(-II) adsorption behaviour. Adsorption of Se(-II) onto granite and MX-80 bentonite follows the trend of anionic adsorption, with a decrease in R_d values as the solution pH increased. There is also an ionic strength influence on the adsorption of Se(-II) onto granite with a decrease in the R_d value as the ionic strength increased. This effect is not found when observing Se(-II) adsorption onto MX-80 bentonite. Final experiments with a representative groundwater, determined that the adsorption of Se(-II) onto granite and MX-80 bentonite returned R_d values of (1.80 ± 0.10) m³·kg^-1 and (0.47 ± 0.38) m³·kg^-1, respectively. In support of the experiments, a surface complexation modelling approach has been employed to simulate the adsorption of Se(-II) onto granite and MX-80 bentonite, where it was determined that two different surface complexes, ≡S_Se^- and ≡SOH₂⁺_H₂ were capable of simulating Se(-II) adsorption behaviour.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.547-555
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• 2023

Due to enhanced precision in uranium isotope measurements with MC-ICP-MS, there has been a surge in studies concerning the naturally occurring uranium isotope ratio (²³⁸U/²³⁵U) and its associated fractionation processes. Several researchers have highlighted that the ²³⁸U/²³⁵U ratio, previously assumed to be constant, can vary by several per mil depending on different natural fractionation processes. This review paper outlines the uranium isotope values (δ²³⁸U) for major terrestrial reservoirs and their variations. It discusses the range of δ238U values and uranium isotope fractionation seen in uranium ore deposits, based on deposit type and ore-forming conditions. In conclusion, this paper emphasizes the importance of studies on uranium ore deposits. Such deposits serve as natural simulation models vital for designing high-level radioactive waste repository sites.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.