• Nuclear Engineering and Technology
• /
• v.25 no.4
• /
• pp.539-547
• /
• 1993

A stochastic approach using continuous time Markov process is presented to model the one-dimensional nuclide transport in fractured rock media as a further extension for previous works［1-3］. Nuclide transport of decay chain of arbitrary length in the single planar fractured rock media in the vicinity of the radioactive waste repository is modeled using a continuous time Markov process. While most of analytical solutions for nuclide transport of decay chain deal with the limited length of decay chain, do not consider the case of having rock matrix diffusion, and have very complicated solution form, the present model offers rather a simplified solution in the form of expectance and its variance resulted from a stochastic modeling. As another deterministic way, even numerical models of decay chain transport, in most cases, show very complicated procedure to get the solution and large discrepancy for the exact solution as opposed to the stochastic model developed in this study. To demonstrate the use of the present model and to verify the model by comparing with the deterministic model, a specific illustration was made for the transport of a chain of three member in single fractured rock medium with constant groundwater flow rate in the fracture, which ignores the rock matrix diffusion and shows good capability to model the fractured media around the repository.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.1-15
• /
• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of computational fluids engineering
• /
• v.19 no.3
• /
• pp.91-97
• /
• 2014

A high resolution numerical method aimed at solving cavitating flow was proposed and applied to gas-liquid two-phase shock tube problem with arbitrary void fraction. The present method with compressibility effects employs a finite-difference 4th-order Runge-Kutta method and Roe's flux difference splitting approximation with the MUSCL TVD scheme. The Jacobian matrix from the inviscid flux of constitute equation is diagonalized analytically and the speed of sound for the two-phase media is derived by eigenvalues. So that the present method is appropriate for the extension of high order upwind schemes based on the characteristic theory. By this method, a Riemann problem for Euler equations of one dimensional shock tube was computed. Numerical results of high speed flow phenomena such as detailed observations of shock and expansion wave propagations through the gas-liquid two-phase media and some data related to computational efficiency are made. Comparisons of predicted results and solutions at isothermal condition are provided and discussed.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.1
• /
• pp.41-48
• /
• 2012

Introduction: Breast cancer is the second leading cancer in Korean women. To assess potential genetic associations between the prostate stem cell antigen (PSCA) gene in the chromosome 8q24 locus and breast cancer risk in Korean women, 13 SNPs were selected and associations with breast cancer risk were analyzed with reference to hormone receptor (HR) and menopausal status. Methods:We analyzed DNA extracted from buffy coat from 456 patients and 461 control samples, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) based upon region-specific PCR followed by allelespecific single base primer extension reactions. Risks associated with PSCA genotypes and haplotypes were estimated with chi-square test (${\chi}^2$-test), and polytomous logistic regression models using odds ratios (OR) and 95% confidence intervals (CIs), by HR and menopausal status. Results: In case-control analysis, odds ratios (OR) of rs2294009, rs2294008, rs2978981, rs2920298, rs2976395, and rs2976396 were statistically significant only among women with estrogen receptor (ER) negative cancers, and those of rs2294008, rs2978981, rs2294010, rs2920298, rs2976394, rs10216533, and rs2976396 were statistically significant only in pre-menopausal women, and not in postmenopausal women. Risk with the TTGGCAA haplotype was significantly elevated in ER (-) status (OR= 1.48, 95% CI= 1.03~2.12, p<0.05). Especially risk of allele T of rs2294008 is significantly low in pre-menopausal breast cancer patients and AA genotype of rs2976395 in ER (-) status represents the increase of OR value. Conclusion: This report indicated for the first time that associations exist between PSCA SNPs and breast cancer susceptibility in Korean women, particularly those who are pre-menopausal with an estrogen receptor negative tumor status.

• Plant Biotechnology Reports
• /
• v.4 no.3
• /
• pp.201-211
• /
• 2010

We present a highly effective T-DNA inserted gene screening method as part of a reverse genetics model system using the Chinese cabbage (Brassica rapa L. spp. pekinensis). Three-step two-dimensional (2D) matrix strategies are potentially accurate and useful for the identification of specific T-DNA inserted mutants from a large population. To construct our Chinese cabbage model, we utilized a forward genetics screening approach for the abnormal phenotypes that were obtained from transgenic plants of Brassica rapa generated with Agrobacteria tumefaciens containing the pRCV2 vector. From one transgenic plant with an abnormal phenotype, we observed that the st1 gene (which is related to senescence-associated process proteins) contained a T-DNA fragment, and that its expression level was decreased. This T-DNA insert was then used as a control to construct an effective screening pool. As a result, the optimum template concentration was found to be 0.1-1 ng in our PCR strategy. For other conditions, positive changes to the Gibbs free energy prevented the formation of oligo dimers and hairpin loop structures, and autosegment extension gave better results for long fragment amplification. Using this effective reverse genetics screening method, only 23 PCR reactions were necessary to select a target gene from a pool of 100 individual DNAs. Finally, we also confirmed that the sequence we obtained from the above method was identical to the flanking sequence isolated by rescue cloning.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.32 no.4
• /
• pp.1-11
• /
• 2004

Numerical analyses on the aerodynamic characteristics of a counter rotating axial flow fan were conducted for the development of an axial type in-line duct fan. The counter rotating fan has a front rotor and a rear rotor which are counter rotating each other. Blade design of the counter rotating fan was done by extension of design method for axial flow fan which consists of rotor and stator blades. Through flow analysis was performed using matrix method which is applied for flow fields prediction of compressors or turbines. Aerodynamic characteristics and characteristic curves of the counter rotating fan were analyzed by expansion of the frequency domain panel method with duct modeling. Pressure losses were higher at leading edge and hub region of rotor blades. Characteristic curve of the counter rotating fan was overpredicted without consideration of viscous effect.

• KIISE Transactions on Computing Practices
• /
• v.23 no.8
• /
• pp.466-472
• /
• 2017

Hardware Transactional Memory (HTM) has greatly changed the parallel programming paradigm for transaction processing. Since Intel has recently proposed Transactional Synchronization Extension (TSX), a number of studies based on HTM have been conducted. However, the existing studies support conflict prediction for a single cause of the transaction processing and provide a standardized TSX environment for all workloads. To solve the problems, we propose an efficient hardware transactional memory scheme for processing transactions in multi-core in-memory environment. First, the proposed scheme determines whether to use Software Transactional Memory (STM) or the serial execution as a fallback path of HTM by using a prediction matrix to collect the information of previously executed transactions. Second, the proposed scheme performs efficient transaction processing according to the characteristic of a given workload by providing a retry policy based on machine learning algorithms. Finally, through the experimental performance evaluation using Stanford transactional applications for multi-processing (STAMP), the proposed scheme shows 10~20% better performance than the existing schemes.

• Korean Journal of Soil Science and Fertilizer
• /
• v.43 no.4
• /
• pp.430-437
• /
• 2010

The continuous release of nutrient sources into natural water resource can be a continuing problem in eutrophication, as well as severe reductions in water quality. However, any desirable measure is not developed yet even though so many researches and efforts have been done to solve this problem. Forest as one of troublesome nonpoint sources may contributes most to nutrient loading, but the loading of N and P from forest in order to grasp the eutrophication potential of nonpoint sources has not been evaluated. The nutrient sources from the organic litter accumulated on the surface of forest soils can be a critical factor in continuity of eutrophication of a lake. The decomposition rate of litter can be estimated to predict release of N and P from the forest stand. The loss rate of nitrogen is complicated but depends in part upon the physical matrix of the element. Therefore, long-term nutrient budget and flux estimates at stand would be useful tools in calculating potential nutrient fluxes into the watercourses in a sustainable way. The present investigation can give insight to the actual situation of the eutrophication potentials of forest as the practical nonpoint sources.

• Archives of Plastic Surgery
• /
• v.38 no.1
• /
• pp.109-112
• /
• 2011

Purpose: Neurofibromas may present as multiple or solitary lesions. Although there is no predilection site for solitary lesions, they are rare on the hand. In addition, solitary intramuscular neurofibromas are a very rare pathological type. Here, we report a rare solitary intramuscular neurofibroma in the hand. This paper examines the clinical characteristics of intramuscular neurofibroma arising from the lumbricalis in order to enable a correct diagnosis and treatment. Methods: A 32-year-old male presented with a painless mass on the palm. The physical examination revealed a $3{\times}2$ cm protruding mass that was non-tender to palpation. The vascular and sensory examinations were unremarkable, while the motor examination showed mild difficulty with flexion and extension. Magnetic resonance imaging demonstrated an enhancing solid mass between the thenar eminence and second metacarpophalangeal joint. The diagnosis of an intramuscular neurofibroma was confirmed following surgical excision and histological evaluation. Results: The pathological examination was consistent with a neurofibroma, with delicate fascicles and loose fusiform cells in a fibrous stroma, with oval or spindle-shaped nuclei and scant cytoplasm. The background matrix was pale staining and had focal myxoid stroma. There was no significant nuclear pleomorphism and no mitoses. Immunohistochemistry with S-100 was slightly positive. At the 6-month follow-up, motor and sensory function were intact and the range of motion was full. Conclusion: A neurofibroma is a rare tumor of the hand, especially the intramuscular type. Hand surgeons should consider the diagnosis of this tumor based on the examination and imaging.

• Korean Journal of Food Science and Technology
• /
• v.12 no.3
• /
• pp.176-181
• /
• 1980

Yeast invertase was immobilized on the 2 kinds of matrices : one is an indirectly coupled enzyme to the cyanogen bromide activated Sepharose by using ${\omega}-aminohexyl$ group as an extension arm, and the other is a tightly adsorbed enzyme on the modified hydrophobic cellulose derivative which has a phenoxyacetyl group as a linkage. The enzyme preparation coupled on Sepharose retained 26.0% of the original activity against sucrose as a substrate, while the preparation immobilized on phenoxyacetyl cellulose retained 72.9% . The immobilized invertase preparation on ${\omega}-aminohexyl$ Sepharose showed the optimal pH 4.5, optimal temperature $60^{\circ}C$, activation energy $5,941\;cal/mole{\cdot}deg$ and Km' 22.2 mM against sucrose, while the preparation adsorbed on phenoxyacetyl cellulose showed the optimal pH 4.0, optimal temperature $60^{\circ}C$, activation energy $7,769\;cal/mole{\cdot}deg$ and Km' 69.9 mM.