• Korean Journal of Mineralogy and Petrology
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• v.37 no.1
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• pp.1-11
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• 2024

World stone production in 2021 stood at 162.5 million tons, up by 7.5 million tons, or 4.8 percent, compared to the previous year when the production came in at 155 million tons. Six top countries with the most of stone production were China, India, Turkey, Brazil, Iran and Italy and these six countries accounted for 72.8 percent of total production in the world. Stone exports stood at $21.68 billion in 2021, up by $2.3 billion from the previous year. Exports of raw materials and processed stones stood at 54.4 million tons, up by 2.98 million tons from the previous year. In terms of aggregate exports, exports of natural stones increased by $2.3 billion to $21.7 billion while exports of artificial stones rose $2.6 billion to $13.6 billion in 2021 compared to the previous year. The average price of stone (Code: 68.02) was up by $65.2 per ton to $794.82. The price of board, processed stone, an ingredient for building materials, increased by $3.52 per square meter to $42.96 per square meter. Recycling was always the problem as the volume of the total quarry was 333.5 million tons, of which only 28.8 percent were finished products and the remaining 71.2 percent were waste generated from stone extraction and processing. Korea's stone exports stood at $1.97 million in 2021, down 38.3 percent on year, while imports were up 8.6 percent to $758.9 million. Stone exports are expected to grow to 66.1 million tons in 2025, while usage is expected to reach 108.92 million tons, or 2 billion square meters.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.1
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• pp.9-23
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• 2007

To investigate the degree of pollution using the species composition of benthic community and environments, the present study was conducted in Jinhae Bay, May of 1998. In Jinhae Bay, benthic macrofaunal community was investigated on the base of the samples from 67 stations. The main facies of the surface sediment was silty clay and clay. The total species number and the mean density of macrobenthic animals were 255 species and 984 $ind./m^2$, respectively. There were 90 species and 773 $ind./m^2$ of polychaetes as the most major faunal group in Jinhae Bay. At the region between the eastern mouth of Jinhae Bay and Gadeok Is., the species number and density were higher, while lower at the western area of Jinhae Bay. The most dominant benthic macrofauna in Jinhae Bay was the polychaetes, Lumbrineris longifolia(16.9%), and followed by polychaetes Tharyx sp.(6.7%), Clone teres(4.7%), Glycinde sp.(4.2%), bivalves Theora fragilis(4.0%), crustaceans Corophium sp.(4.0%) and so on. The most of the predominant species appeared mainly on the region between the eastern mouth of Jinhae Bay and Gadeok Is. Cluster analysis based on the macrobenthic faunal composition showed that Jinhae Bay could be divided into three station groups: The western Jinhae Bay(Station group A), the mouth of Jinhae Bay(Station groupe B), and offshore area between Gadeok Is. and Geoje Is.(Station group C). The mouth of Jinhae Bay had the highest mean species number and the mean density, and its important species was Lumbrineris longifolia. The offshore area between Gadeok Is. and Geoje Is. had medium mean species number and the mean density. The western Jinhae Bay had the lowest mean species number and the mean density. The distribution of BPI and BC values, used to assess benthic pollution, showed similar patterns. According to the classification proposed by Borja et al.(2000), the stations of the western inner-bay were heavily polluted sites, the stations between mouth of the bay and the offshore area were slightly polluted sites, and the stations of the other area were meanly polluted sites. Benthic community healthiness of the western Jinhae Bay was classified to 'Transitional to pollution' by BC values. The degree of pollution in Jinhae Bay may have extended gradually from the western Jinhae Bay to the mouth of the bay.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.1
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• pp.37-48
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• 2024

Songla (Usnea diffracta Vain.) is one of the lichens belonging to the genus Usnea, and pharmacological activities such as antioxidant, antimicrobial, anti-inflammatory, anti-tumor and cardiovascular protection have been reported in previous studies, but its efficacy in skin and hair is not well known. In this study, the effect of Usnea diffracta extract (UDE) on anti-aging and dermal papilla cell proliferation was verified in vitro. As a result of the experiment, it was confirmed that the UDE significantly reduced the expression of MMP-1 and the activity of MAPKs (ERK, p38, JNK) and AP-1 (c-Fos, c-Jun), which were increased by UVA in HDFn. In addition, the UDE significantly increased the proliferation of HFDPC and significantly increased the mRNA expression of VEGF and KGF, which are hair growth factors. Accordingly, the phosphorylation of ERK/CREB involved in hair proliferation and expression of growth factors was increased in a concentration-dependent manner. The main component represented by the main peak was separated and purified using Prep LC by concentrating the UDE, which was confirmed as diffractaic acid through NMR and Mess analysis. Isolated diffractaic acid significantly reduced the expression of MMP-1 increased by UVA in HDFn and increased the proliferation of HFDPC in a concentration-dependent manner. The result suggest that UDE proved its usability as a natural cosmetic material with anti-aging and dermal papilla cell activation effects.

• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• 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 Korea Port Economic Association
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• v.40 no.1
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• pp.151-163
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• 2024

This study sought to confirm the impact of analytical methods and behavioral economic theory factors on decision-making when making chartering decisions in the dry bulk shipping market. This study on chartering decision-making model was began to verify why shipping companies do not make rational decision-making and behavior based on analytical methods such as freight prediction and process of alternative selection in the same market situation. To understand the chartering decision-making model, it is necessary to study the impact of behavioral economic theory such as heuristics, loss aversion, and herding behavior on chartering decision-making. Through AHP analysis, the importance of the method factors relied upon in chartering decision-making. The dependence of the top factors in chartering decision-making was in the following order: market factors, heuristics, internal factors, herding behavior, and loss aversion. Market factors, heuristics, and internal factors. As for detailed factors, spot freight index and empirical intuition were confirmed as the most important factors relied on when making decisions. It was confirmed that empirical intuition is more important than internal analysis, which is an analytical method. This study can be said to be meaningful in that it academically researched and proved the bounded rationality of humans, which cannot be fully rational, and sometimes relies on experience or psychological tendencies, by applying it to the chartering decision-making model in the dry bulk shipping market. It also suggests that in the dry bulk shipping market, which is uncertain and has a high risk of loss due to decision-making, the experience and insight of decision makers have a very important impact on the performance and business profits of the operation part of shipping companies. Even though chartering are a decision-making field that requires judgment and intuition based on heuristics, decision-makers need to be aware of this decision-making model in order to reduce repeated mistakes of deciding contrary to market situation. It also suggests that there is a need to internally research analytical methods and procedures that can complement heuristics such as empirical intuition.

• 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%.