• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.23-35
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• 2024

This study analyzed the correlation between each of the 12 influencing factors in FAR 16.04 and the decision-making process for construction contract types, using data from a total of 2,406 DoD Construction Acquisitions spanning from 2008 to 2022. The study considered 12 independent variables, grouped into 4 Characteristics with 3 factors each. Meanwhile, all other contract types were categorized into two types: Firm-Fixed-Price (FFP) and Cost-Reimbursement Contract (CRC), which served as the dependent variables. The findings revealed that FFP contracts significantly dominated in terms of acquisition volume. In line with prevailing beliefs, logistic data analysis and Analytical Hierarchy Process (AHP) analysis of Relative Weights from Experts' Survey demonstrated that independent variables like Uncertainty of the Scope of Work and Complexity found out to be increasing the likelihood of selecting CRC. The number of contractors in the market does indeed influence the possibilities of contract decision-making between CRC and FFP. Meanwhile, the p-values of the top 3 influencing factors on CRC from the AHP analysis-namely, Appropriateness of CAS, Project Urgency, and Cost Analysis-exceeded 0.05 in the binominal regression results, rendering it inconclusive whether they significantly influenced the construction contract type decision, particularly with respect to payment methods. This outcome partly results from the fact that a majority of respondents possessed specific experiences related to the USFK relocation project. Furthermore, influencing factors in construction projects behave differently than common beliefs suggest. As a result, it is imperative to consider the 12 influencing factors categorized into 4 Characteristics areas before establishing acquisition strategies for targeted construction projects.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.1-12
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• 2008

The purpose of this study is to characterize the hydrogeochemical characteristics of hot spring waters and to interpret the source of noble gases and the geochemical environment of the hot spring waters distributed along the eastern area of the Korean peninsula. For this purpose, We carried out the chemical, stable isotopic and noble gas isotopic analyses for eleven hot spring water and fourteen hot spring gas samples collected from six hot spring sites. The hot spring waters except the Osaek hot spring water show the pH range of 7.0 to 9.1. However, the Osaek $CO_2$-rich hot spring water shows a weak acid of pH 5.7. The temperature of hot spring waters in the study area ranges from $25.7^{\circ}C$ to $68.3^{\circ}C$. Electrical conductivity of hot spring waters varies widely from 202 to $7,130{\mu}S/cm$. High electrical conductivity (av., $3,890{\mu}S/sm$) by high Na and Cl contents of the Haeundae and the Dongrae hot spring waters indicates that the hot spring waters were mixed with seawater in the subsurface thermal system. The type of hot springs in the viewpoint of dissolved components can be grouped into three types: (1) alkaline Na-$HCO_3$ type including sulfur gas of the Osaek, Baekam, Dukgu and Chuksan hot springs, and (2) saline Na-Cl type of the Haeundae and Dongrae hot springs, and (3) weak acid $CO_2$-rich Na-$HCO_3$ type of Osaek hot spring. Tritium ratios of the Haeundae and the Dongrae hot springs indicate different residence time in their aquifers of older water of $0.0{\sim}0.3$ TU and younger water of $5.9{\sim}8.8$ TU. The ${\delta}^{18}O$ and ${\delta}D$ values of hot spring waters indicate that they originate from the meteoric water, and that the values also reflect a latitude effect according to their locations. $^3He/^4He$ ratios of the hot spring waters except Osaek $CO_2$-rich hot spring water range from $0.1{\times}10^{-6}$ to $1.1{\times}10^{-6}$ which are plotted above the mixing line between air and crustal components. It means that the He gas in hot spring waters was originated mainly from atmosphere and crust sources, and partly from mantle sources. The Osaek $CO_2$-rich hot spring water shows $3.3{\times}10^{-6}$ in $^3He/^4He$ ratio that is 2.4 times higher than those of atmosphere. It provides clearly a helium source from the deep mantle. $^{40}Ar/^{36}Ar$ ratios of hot spring water are in the range of an atmosphere source.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.63-77
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• 2011

The World Wide Web is a very large distributed digital information space. From its origins in 1991, the web has grown to encompass diverse information resources as personal home pasges, online digital libraries and virtual museums. Some estimates suggest that the web currently includes over 500 billion pages in the deep web. The ability to search and retrieve information from the web efficiently and effectively is an enabling technology for realizing its full potential. With powerful workstations and parallel processing technology, efficiency is not a bottleneck. In fact, some existing search tools sift through gigabyte.syze precompiled web indexes in a fraction of a second. But retrieval effectiveness is a different matter. Current search tools retrieve too many documents, of which only a small fraction are relevant to the user query. Furthermore, the most relevant documents do not nessarily appear at the top of the query output order. Also, current search tools can not retrieve the documents related with retrieved document from gigantic amount of documents. The most important problem for lots of current searching systems is to increase the quality of search. It means to provide related documents or decrease the number of unrelated documents as low as possible in the results of search. For this problem, CiteSeer proposed the ACI (Autonomous Citation Indexing) of the articles on the World Wide Web. A "citation index" indexes the links between articles that researchers make when they cite other articles. Citation indexes are very useful for a number of purposes, including literature search and analysis of the academic literature. For details of this work, references contained in academic articles are used to give credit to previous work in the literature and provide a link between the "citing" and "cited" articles. A citation index indexes the citations that an article makes, linking the articleswith the cited works. Citation indexes were originally designed mainly for information retrieval. The citation links allow navigating the literature in unique ways. Papers can be located independent of language, and words in thetitle, keywords or document. A citation index allows navigation backward in time (the list of cited articles) and forwardin time (which subsequent articles cite the current article?) But CiteSeer can not indexes the links between articles that researchers doesn't make. Because it indexes the links between articles that only researchers make when they cite other articles. Also, CiteSeer is not easy to scalability. Because CiteSeer can not indexes the links between articles that researchers doesn't make. All these problems make us orient for designing more effective search system. This paper shows a method that extracts subject and predicate per each sentence in documents. A document will be changed into the tabular form that extracted predicate checked value of possible subject and object. We make a hierarchical graph of a document using the table and then integrate graphs of documents. The graph of entire documents calculates the area of document as compared with integrated documents. We mark relation among the documents as compared with the area of documents. Also it proposes a method for structural integration of documents that retrieves documents from the graph. It makes that the user can find information easier. We compared the performance of the proposed approaches with lucene search engine using the formulas for ranking. As a result, the F.measure is about 60% and it is better as about 15%.

• Journal of Technology Innovation
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• v.17 no.1
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• pp.1-48
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• 2009

Recently we have found some symptoms that R&D fiscal incentives might not work well what it has intended through the analysis of current statistics of firm's R&D data. Firstly, we found that the growth rate of R&D investment in private sector during the recent decade has been slowdown. The average of growth rate (real value) of R&D investment is 7.1% from 1998 to 2005, while it was 13.9% from 1980 to 1997. Secondly, the relative share of R&D investment of SME has been decreased to 21%('05) from 29%('01), even though the tax credit for SME has been more beneficial than large size firm, Thirdly, The R&D expenditure of large size firms (besides 3 leading firms) has not been increased since late of 1990s. We need to find some evidence whether fiscal incentives are effective in increasing firm's R&D investment. To analyse econometric model we use firm level unbalanced panel data for 4 years (from 2002 to 2005) derived from MOST database compiled from the annual survey, "Report on the Survey of Research and Development in Science and Technology". Also we use fixed effect model (Hausman test results accept fixed effect model with 1% of significant level) and estimate the model for all firms, large firms and SME respectively. We have following results from the analysis of econometric model. For large firm: i ) R&D investment responds elastically (1.20) to sales volume. ii) government R&D subsidy induces R&D investment (0.03) not so effectively. iii) Tax price elasticity is almost unity (-0.99). iv) For large firm tax incentive is more effective than R&D subsidy For SME: i ) Sales volume increase R&D investment of SME (0.043) not so effectively. ii ) government R&D subsidy is crowding out R&D investment of SME not seriously (-0.0079) iii) Tax price elasticity is very inelastic (-0.054) To compare with other studies, Koga(2003) has a similar result of tax price elasticity for Japanese firm (-1.0036), Hall((l992) has a unit tax price elasticity, Bloom et al. (2002) has $-0.354{\sim}-0.124$ in the short run. From the results of our analysis we recommend that government R&D subsidy has to focus on such an areas like basic research and public sector (defense, energy, health etc.) not overlapped private R&D sector. For SME government has to focus on establishing R&D infrastructure. To promote tax incentive policy, we need to strengthen the tax incentive scheme for large size firm's R&D investment. We recommend tax credit for large size film be extended to total volume of R&D investment.

• The Journal of the Petrological Society of Korea
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• v.21 no.2
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• pp.129-150
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• 2012

The tectonic evolution of the Central Ogcheon Belt has been newly analyzed in this paper from the detailed geological maps by lithofacies classification, the development processes of geological structures, microstructures, and the time-relationship between deformation and metamorphism in the Ogcheon, Cheongsan, Mungyeong Buunnyeong, Busan areas, Korea and the fossil and radiometric age data of the Ogcheon Supergroup(OSG). The 1st tectonic phase($D^*$) is marked by the rifting of the original Gyeonggi Massif into North Gyeonggi Massif(present Gyeonggi Massif) and South Gyeonggi Massif (Bakdallyeong and Busan gneiss complexes). The Joseon Supergroup(JSG) and the lower unit(quartzose psammitic, pelitic, calcareous and basic rocks) of OSG were deposited in the Ogcheon rift basin during Early Paleozoic time, and the Pyeongan Supergroup(PSG) and its upper unit(conglomerate and pelitic rocks and acidic rocks) appeared in Late Paleozoic time. The 2nd tectonic phase(Ogcheon-Cheongsan phase/Songnim orogeny: D1), which occurred during Late Permian-Middle Triassic age, is characterized by the closing of Ogcheon rift basin(= the coupling of the North and South Gyeonggi Massifs) in the earlier phase(Ogcheon subphase: D1a), and by the coupling of South China block(Gyeonggi Massif and Ogcheon Zone) and North China block(Yeongnam Massif and Taebaksan Zone) in the later phase(Cheongsan subphase: D1b). At the earlier stage of D1a occurred the M1 medium-pressure type metamorphism of OSG related to the growth of coarse biotites, garnets, staurolites. At its later stage, the medium-pressure type metamorphic rocks were exhumed as some nappes with SE-vergence, and the giant-scale sheath fold, regional foliation, stretching lineation were formed in the OSG. At the D1b subphase which occurs under (N)NE-(S)SW compression, the thrusts with NNE- or/and SSW-vergence were formed in the front and rear parts of couple, and the NNE-trending Cheongsan shear zone of dextral strike-slip and the NNE-trending upright folds of the JSG and PSG were also formed in its flank part, and Daedong basin was built in Korean Peninsula. After that, Daedong Group(DG) of the Late Triassic-Early Jurassic was deposited. The 3rd tectonic phase(Honam phase/Daebo orogeny: D2) occurred by the transpression tectonics of NNE-trending Honam dextral strike-slip shearing in Early~Late Jurassic time, and formed the asymmetric crenulated fold in the OSG and the NNE-trending recumbent folds in the JSG and PSG and the thrust faults with ESE-vergence in which pre-Late Triassic Supergroups override DG. The M2 contact metamorphism of andalusite-sillimanite type by the intrusion of Daebo granitoids occurred at the D2 intertectonic phase of Middle Jurassic age. The 4th tectonic phase(Cheongmari phase: D3) occurred under the N-S compression at Early Cretaceous time, and formed the pull-apart Cretaceous sedimentary basins accompanying the NNE-trending sinistral strike-slip shearing. The M3 retrograde metamorphism of OSG associated with the crystallization of chlorite porphyroblasts mainly occurred after the D2. After the D3, the sinistral displacement(Geumgang phase: D4) occurred along the Geumgang fault accompanied with the giant-scale Geumgang drag fold with its parasitic kink folds in the Ogcheon area. These folds are intruded by acidic dykes of Late Cretaceous age.

• Progress in Medical Physics
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• v.26 no.3
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• pp.168-177
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• 2015

For evaluating the treatment planning accurately, the quality assurance for treatment planning is recommended when patients were treated with IMRT which is complex and delicate. To realize this purpose, treatment plan quality assurance software can be used to verify the delivered dose accurately before and after of treatment. The purpose of this study is to evaluate the accuracy of treatment plan quality assurance software for each IMRT plan according to MLC DLG (dosimetric leaf gap). Novalis Tx with a built-in HD120 MLC was used in this study to acquire the MLC dynalog file be imported in MobiusFx. To establish IMRT plan, Eclipse RTP system was used and target and organ structures (multi-target, mock prostate, mock head/neck, C-shape case) were contoured in I'mRT phantom. To verify the difference of dose distribution according to DLG, MLC dynalog files were imported to MobiusFx software and changed the DLG (0.5, 0.7, 1.0, 1.3, 1.6 mm) values in MobiusFx. For evaluation dose, dose distribution was evaluated by using 3D gamma index for the gamma criteria 3% and distance to agreement 3 mm, and the point dose was acquired by using the CC13 ionization chamber in isocenter of I'mRT phantom. In the result for point dose, the mock head/neck and multi-target had difference about 4% and 3% in DLG 0.5 and 0.7 mm respectively, and the other DLGs had difference less than 3%. The gamma index passing-rate of mock head/neck were below 81% for PTV and cord, and multi-target were below 30% for center and superior target in DLGs 0.5, 0.7 mm, however, inferior target of multi-target case and parotid of mock head/neck case had 100.0% passing rate in all DLGs. The point dose of mock prostate showed difference below 3.0% in all DLGs, however, the passing rate of PTV were below 95% in 0.5, 0.7 mm DLGs, and the other DLGs were above 98%. The rectum and bladder had 100.0% passing rate in all DLGs. As the difference of point dose in C-shape were 3~9% except for 1.3 mm DLG, the passing rate of PTV in 1.0 1.3 mm were 96.7, 93.0% respectively. However, passing rate of the other DLGs were below 86% and core was 100.0% passing rate in all DLGs. In this study, we verified that the accuracy of treatment planning QA system can be affected by DLG values. For precise quality assurance for treatment technique using the MLC motion like IMRT and VMAT, we should use appropriate DLG value in linear accelerator and RTP system.

• Journal of agricultural medicine and community health
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• v.24 no.2
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• pp.245-268
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• 1999

It is necessary that the old should have the physical and social ability to perform their daily life. This study is to grasp their degree of disability and problems and suggest their solutions. It surveyed the 87 old people over 65 years old from September 1st until September 30th, in 1997. The findings are as follows. 1) The activities of daily living(ADL) to find their degree of physical disability shows that their average performance ability is 75.9% of all the action while 24.1% of all the old people needs the others' help. As they get older and older, the aged drop off in their physical ability, which is related to a statistical sense (p<0.001). 2) The social disability shows that the aged have their great difference from 9.2% to 85.1% in their instrumental activities of daily living(IADL), intellectual ability and social role. 3) A simple analysis shows that the activities of daily living are, in a statistical sense, related to age(p<0.001), the use of elder's hall(p<0.001), the understanding degree of health(p<0.01) and so forth. 4) A simple analysis shows that the instrumental activities of daily living are, in a statistical sense, related to age(p<0.001), the degree of education(p<0.05), the life of leisure(p<0.001), the understanding degree of health and so forth. 5) A multivariate logistic regression analysis shows that the disability of daily living is related to age, the visit of elder's hall, the period of solitary living, instrumental activities of daily living is age and the visit of elder's hall, and social role is the visit of elder's hall and the decree of education, while intellectual activity has no related variables in a statistical sense.

• Tuberculosis and Respiratory Diseases
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• v.49 no.3
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• pp.332-342
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• 2000

Background : The importance of pro-inflammatory cytokines, especially tumor necrosis factor $\alpha$ (INF-$\alpha$) and interleukin-1$\beta$ (IL-1$\beta$), have been extensively documented in the generation of inflammatory lung disease. Lung epithelial cells are also actively involved in initiating and maintaining inflammation by producing pro-inflammatory mediators. Understanding the mechanism of pro-inflammatory cytokine expression in lung epithelial cells is crucial to the development of new therapeutic modalities for inflammatory lung disease. Transcription of most pro-inflammatory cytokines is dependent on the activation of NF-${\kappa}B$. However, the relationship between pro-inflammatory cytokine expression and NF-${\kappa}B/I{\kappa}B$ pathway in lung epithelial cells is not clear. Methods : BEAS-2B, A549, Na-H157, NCI-H719 cells were stimulated with IL-$1{\beta}$ or TNF-$\alpha$ at various times, and then IL-8 and TNF-$\alpha$mRNA expressions were assayed by Northern blot analysis. IL-$1{\beta}$ or TNF-$\alpha$-induced NF-${\kappa}B$ activation was assessed by the nuclear translocation of p65 NF-${\kappa}B$ subunit. The degradation of $I{\kappa}B{\alpha}$ and $I{\kappa}B{\beta}$ by IL-$1{\beta}$ or TNF-$\alpha$stimulation was assayed by Western blot analysis. The phosphorylation of $I{\kappa}B{\alpha}$ was evaluated by Western blot analysis after pre-treating cells with proteasome inhibitor followed by IL-$1{\beta}$ or TNF-$\alpha$ stimulation. The basal level of IKK $\alpha$ expression was evaluated by Western blot analysis. Results: $I{\kappa}B{\alpha}$ and $I{\kappa}B{\alpha}$ was rapidly degraded after 5 minutes of incubation with IL-$1{\beta}$ or TNF-$\alpha$ in BEAS-2B, A549, and NCI-H157 cells. The activation of NF-${\kappa}B{\alpha}$ and the induction of IL-8 and TNF-$\alpha$ mRNA expression were observed by IL-$1{\beta}$ or TNF-$\alpha$ stimulation in these cells. In contrast, neither the changes in NF-${\kappa}B/I{\kappa}B$ pathway nor IL-8 and TNF-$\alpha$mRNA expression was induced by IL-$1{\beta}$ or TNF-$\alpha$ stimulation in NCI-H719 cells. IL-$1{\beta}$ and TNF-$\alpha$-induced $I{\kappa}B$ phosphorylation was observed in BEAS-2B, A549, and NCI-H157 cells, but not in NCI-H719 cells. The basal level of IKK$\alpha$ expression was not different between cell. Conclusion : NF-${\kappa}B/I{\kappa}B$ pathway plays an important role in the expression of pro-inflammatory cytokine in most lung epithelial cells. The absence of the effect on NF-${\kappa}B/I{\kappa}B$ pathway in NCI-H719 cells sæms to be due to the defect in the intracellular signal transduction pathway upstream to IKK.