• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1490-1500
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• 2024

Helium-induced defect nucleation and accumulation in polycrystalline W and W0.5 wt%ZrC (W0.5ZrC) were studied in-situ using the transmission electron microscopy (TEM) combined with 40 keV He⁺ irradiation at 800 and 1000℃ at the maximum damage level of 1 dpa. Radiation-induced dislocation loops were not observed in the current study. W0.5ZrC was found to be less susceptible to irradiation damage in terms of helium bubble formation and growth, especially at lower temperature (800 ℃) when vacancies were less mobile. The ZrC particles present in the W matrix pin the forming helium bubbles via interaction between C atom and neighbouring W atom at vacancies. This reduces the capability of helium to trap a vacancy which is required to form the bubble core and, as a consequence, delays, the bubble nucleation. At 1000 ℃, significant bubble growth occurred in both materials and all the present bubbles transitioned from spherical to faceted shape, whereas at 800 ℃, the faceted helium bubble population was dominated in W.

• Journal of Forest and Environmental Science
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• v.40 no.1
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• pp.15-23
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• 2024

This research aimed to assess the possibility of detecting forest degradation using time-series satellite imagery and three different deep learning-based change detection techniques. The dataset used for the deep learning models was composed of two sets, one based on surface reflectance (SR) spectral information from satellite imagery, combined with Texture Information (GLCM; Gray-Level Co-occurrence Matrix) and terrain information. The deep learning models employed for land cover change detection included image differencing using the Unet semantic segmentation model, multi-encoder Unet model, and multi-encoder Unet++ model. The study found that there was no significant difference in accuracy between the deep learning models for forest degradation detection. Both training and validation accuracies were approx-imately 89% and 92%, respectively. Among the three deep learning models, the multi-encoder Unet model showed the most efficient analysis time and comparable accuracy. Moreover, models that incorporated both texture and gradient information in addition to spectral information were found to have a higher classification accuracy compared to models that used only spectral information. Overall, the accuracy of forest degradation extraction was outstanding, achieving 98%.

• Safety and Health at Work
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• v.14 no.4
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• pp.488-491
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• 2023

This study aims to ascertain occupations potentially at greatest risk of exposure to SARS-CoV-2 based on pre-lockdown working conditions in France. We combined two French population-based surveys documenting workplace exposures to infectious agents, face-to-face contact with the public, and working with colleagues just before the pandemic. Then, for each 87-level standard French occupational grouping, we estimated the number and percentage of the French working population reporting these occupational exposure factors, by gender, using survey weights. As much as 40% (11 million) of all workers reported at least two exposure factors. Most of the workers concerned were in the healthcare sector. However, army/police officers, firefighters, hairdressers, teachers, cultural/sports professionals, and some manual workers were also exposed. Women were overrepresented in certain occupations with potentially higher risks of exposure such as home caregivers, childminders, and hairdressers. Our gender-stratified matrix can be used to assign prelockdown work-related exposures to cohorts implemented during the pandemic.

• The Journal of the Korean bone and joint tumor society
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• v.15 no.2
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• pp.111-121
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• 2009

Background: Osteosarcoma is one of the most common primary malignant tumors of bone occurring mainly in children and adolescents. Although surgery combined with chemotherapy has markedly improved patient survival during the last years, the use of anticancer drugs is still associated with serious problem, such as the frequent acquisition of drug-resistant phenotypes and occurrence of "secondary malignancies". Several solid tumors display enhanced expression of matrix metalloproteinases (MMPs), and recently clinical trials have been initiated on MMP-inhibitors. On the other hand, bisphosphonates (BPs) are inhibitors of bone resorption, and widely used to treat osteoclast-mediated bone diseases. Also they appear to possess direct antitumor activity. Methods: One osteosarcoma cell line (U2OS) was treated with ibandronate (0, 0.1, 1, $10{\mu}M$) for 48 hours. Cell viabilities were determined using MTT assay, the mRNA levels of MMP-2 and MT1-MMP were detected by reverse-transcription polymerase chain reaction, the amount of MMP-2 and MT1-MMP protein were measured by Westernblot, the activities of MMP-2 were observed by Gelatin zymography, and Matrigel invasion assays were used to investigate the invasive potential of osteosarcoma cell lines before and after ibandronate treatment. Results: The invasiveness of U2OS cell line was reduced dose-dependently following 48 hour treatment of up to $10{\mu}M$ of the ibandronate at which concentration no cytotoxicity occurred. Furthermore, the gelatinolytic activities and protein and mRNA levels of MMP-2 and MT1-MMP were also suppressed by increasing ibandronate concentrations. Conclusion: Given that MMP-2 is instrumental in tumor cell invasion, it is very likely that the reduction in osteosarcoma cell invasion by ibandronate is a consequence, at least in part, of suppressed expression of both MMP-2 and MT1-MMP. Isolation of a molecule (s) responsible for the bisphosphonate inhibition of tumor cell invasion would pave the way for the development of a new generation of metastasis inhibitors.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2001.11a
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• pp.7-7
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• 2001

The increasing interest in light weight materials coupled to the need for cost -effective processing have combined to create a significant opportunity for aluminum P/M. particularly in the automotive industry in order to reduce fuel emissions and improve fuel economy at affordable prices. Additional potential markets for Al PIM parts include hand tools. Where moving parts against gravity represents a challenge; and office machinery, where reciprocating forces are important. Aluminum PIM adds light weight, high compressibility. low sintering temperatures. easy machinability and good corrosion resistance to all advantages of conventional iron bm;ed P/rv1. Current commercial alloys are pre-mixed of either the AI-Si-Mg or AL-Cu-Mg-Si type and contain 1.5% ethylene bis-stearamide as an internal lubricant. The powder is compacted in closed dies at pressure of 200-500Mpa and sintered in nitrogen at temperatures between $580~630^{\circ}C$ in continuous muffle furnace. For some applications no further processing is required. although most applications require one or more secondary operations such as sizing and finishing. These sccondary operations improve the dimension. properties or appearance of the finished part. Aluminum is often considered difficult to sinter because of the presence of a stable surface oxide film. Removal of the oxide in iron and copper based is usually achieved through the use of reducing atmospheres. such as hydrogen or dissociated ammonia. In aluminum. this occurs in the solid st,lte through the partial reduction of the aluminum by magncsium to form spinel. This exposcs the underlying metal and facilitates sintering. It has recently been shown that < 0.2% Mg is all that is required. It is noteworthy that most aluminum pre-mixes contain at least 0.5% Mg. The sintering of aluminum alloys can be further enhanced by selective microalloying. Just 100ppm pf tin chnnges the liquid phase sintering kinetics of the 2xxx alloys to produce a tensile strength of 375Mpa. an increilse of nearly 20% over the unmodified alloy. The ductility is unnffected. A similar but different effect occurs by the addition of 100 ppm of Pb to 7xxx alloys. The lend changes the wetting characteristics of the sintering liquid which serves to increase the tensile strength to 440 Mpa. a 40% increase over unmodified aIloys. Current research is predominantly aimed at the development of metal matrix composites. which have a high specific modulus. good wear resistance and a tailorable coefficient of thermal expnnsion. By controlling particle clustering and by engineering the ceramic/matrix interface in order to enhance sintering. very attractive properties can be achicved in the ns-sintered state. I\t an ils-sintered density ilpproaching 99%. these new experimental alloys hnve a modulus of 130 Gpa and an ultimate tensile strength of 212 Mpa in the T4 temper. In contest. unreinforcecl aluminum has a modulus of just 70 Gpa.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1615-1621
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• 2013

Background: Matrix metalloproteinase 9 (MMP-9) is related to tumor invasion and metastasis. However, the role of MMP-9 expression in breast cancer survival remains controversial. The purpose of this study was to accomplish a more accurate estimation of the association between MMP-9 expression and survival results in breast cancer patients through meta-analysis. Methods: A meta-analysis of published studies investigating the effects of positive MMP-9 expression on both relapse free survival (RFS) and overall survival (OS) was performed. Relevant literature was confirmed by searching electronic databases including PubMed, Ovid, EMBASE and China National Knowledge Infrastructure (CNKI) before November 1, 2012. Individual hazard ratios (HRs) and 95% confidence intervals (CIs) were extracted and pooled HRs with 95% CIs were used to evaluate the strength of the association between positive MMP-9 expression and survival results of breast cancer patients. Funnel plot and Egger's regression tests were used to evaluate publication bias. Heterogeneity and sensitivity analysis was also conducted. All the work was completed using STATA. Results: A total of 2,344 patients from 15 evaluative studies were finally included. Pooled HRs and 95% CIs suggested that MMP-9 overexpression had an unfavorable impact on both OS (HR: 1.70, 95% CI: 1.41-2.04) and RFS (HR: 1.54, 95% CI: 1.17-2.01) in breast cancer patients. There was no significant heterogeneity observed in the studies reported for OS (P=0.360, $I^2$=8.8%), but not RFS (P=0.002, $I^2$=67%). Publication bias was absent among the studies both in OS and RFS cases (t=-0.54, P=0.605 and t=1.71, P=0.131, respectively). Omission of any single study had little effect on the combined risk estimates on sensitivity analysis. Conclusion: The results of this meta-analysis suggest that positive MMP-9 expression confers a higher risk of relapse and a worse survival in patients with breast cancer. Larger prospective studies are now needed to evaluate the clinical utility of MMP-9 expression.

• Applied Chemistry for Engineering
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• v.29 no.2
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• pp.201-208
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• 2018

The ortho- and iso- type unsaturated polyester resins were synthesized and used as a polymer binder of the polymer mortar composite. Styrene monomer and acrylonitrile were used as a diluent for the unsaturated polyester resin. Methyl ethyl ketone peroxide (MEKPO) and cobalt octoate (CoOc) were used as a curing agent and an accelerator, respectively. Four kinds of unsaturated polyester resins were prepared according to types of the resin and diluent, and used as a polymer binder in the preparation of the specimen. A total of 16 polymer mortar specimens were prepared according to the added amount of the polymer binder and subjected to a hot water resistance test, followed by compressive and flexural strength tests, and pore and SEM analyses. As a result, it was found that the strength of the specimen using the iso-type unsaturated polyester resin as the polymer binder was better than that of using the ortho-type unsaturated polyester resin. The total pore volume and diameter measured after the hot water resistance test were reduced compared to the values before the test. In the micrographs observed before the hot water resistance test, the polymer binder, filler and fine aggregate were firmly combined to the co-matrix, but the polymer binder was mostly decomposed in the micrographs observed after the test.

• Molecules and Cells
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• v.39 no.2
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• pp.103-110
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• 2016

As a degenerative joint disease, osteoarthritis (OA) constitutes a major cause of disability that seriously affects the quality of life of a large population of people worldwide. However, effective treatment that can successfully reverse OA progression is lacking until now. The present study aimed to determine whether two small non-coding RNAs miR-29a and miR-140, which are significantly down-regulated in OA, can be applied together as potential therapeutic targets for OA treatment. MiRNA synergy score was used to screen the miRNA pairs that potentially synergistically regulate OA. An in vitro model of OA was established by treating murine chondrocytes with IL-$1{\beta}$. Transfection of miR-29a and miR-140 via plasmids was investigated on chondrocyte proliferation and expression of nine genes such as ADAMTS4, ADAMTS5, ACAN, COL2A1, COL10A1, MMP1, MMP3, MMP13 and TIMP metallopeptidase inhibitor 1 (TIMP1). Western blotting was used to determine the protein expression level of MMP13 and TIMP1, and ELISA was used to detect the content of type II collagen. Combined use of miR-29a and miR-140 successfully reversed the destructive effect of IL-$1{\beta}$ on chondrocyte proliferation, and notably affected the MMP13 and TIMP1 gene expression that regulates extracellular matrix. Although co-transfection of miR-29a and miR-140 did not show a synergistic effect on MMP13 protein expression and type II collagen release, but both of them can significantly suppress the protein abundance of MMP13 and restore the type II collagen release in IL-$1{\beta}$ treated chondrocytes. Compared with single miRNA transfection, cotransfection of both miRNAs exceedingly abrogated the suppressed the protein production of TIMP1 caused by IL-$1{\beta}$, thereby suggesting potent synergistic action. These results provided1novel insights into the important function of miRNAs' collaboration in OA pathological development. The reduced MMP13, and enhanced TIMP1 protein production and type II collagen release also implies that miR-29a and miR-140 combination treatment may be a possible treatment for OA.