• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Chest Surgery
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• v.36 no.7
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• pp.463-471
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• 2003

The long-term results of combined mitral valve repair and aortic valve replacement (AVR) have not been well evaluated. This study was performed to investigate the early and long-term results of mitral valve repair with AVR. Material and Method: We retrospectively reviewed 45 patients who underwent mitral valve repair and AVR between September 1990 and April 2002. The average age was 47 years: 28 were men and 17 women. Twelve patients had atrial fibrillation and three had a previous cardiac operation. The mitral valve disease consisted of pure insufficiency (MR) in 34 patients, mitral stenosis (MS) in 3, and mixed lesion in 8. Mitral valve disease was due to rheumatic origin in 24 patients, degenerative in 11, annular dilatation in 8, and ischemia or endocarditis in 2. The functional anatomy of mitral valve was annular dilatation in 31 patients, chordal elongation in 19, leaflet thickening in 19, commissural fusion in 13, chordal fusion in 10, chordal rupture in 6, and so on. Aortic prostheses used included mechanical valve in 32 patients, tissue valve in 12, and pulmonary autograft in one. The techniques of mitral valve repair included annuloplasty in 32 patients and various valvuloplasty of 54 techniques in 29 patients. Total cardiopulmonary bypass and aortic cross clamp time were 204$\pm$62 minute and 153$\pm$57 minutes, respectively. Result: Early death was in one patient due to low output syndrome (2.2%). After follow up of 57$\pm$37 months, late death was in one patient and the actuarial survival at 10 years was 96$\pm$4%. Recurrent MR developed grade II or III in 11 patients and moderate MS in 3. Three patients required reoperation for valve-related complications. The actuarial freedom from recurrent MR, MS, and reoperation were 64$\pm$11%, 86$\pm$8%, and 89$\pm$7% respectively. Conclusion: Combined mitral valve repair with AVR offers good early and long-term survival, and adequate techniques and selection of indication of mitral valve repair, especially in rheumatic disease, are prerequisites for better long-term results.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.2
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• pp.74-80
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• 2015

Purpose Breast lymphoscintigraphy is an important technique to present for body surface precisely, which shows a lymph node metastasis of malignant tumors at an early stage and is performed before and after surgery in patients with breast cancer. In this study, we evaluated several methods of body outline imaging to present exact location of lesions, as well as compared respective exposure doses. Materials and Methods RANDO phantom and SYMBIA T-16 were used for obtaining imaging. A lesion and an injection site were created by inserting a point source of 0.11 MBq on the axillary sentinel lymph node and 37 MBq on the right breast, respectively. The first method for acquiring the image was used by drawing the body surface of phantom for 30 sec using $Na^{99m}TcO_4$ as a point source. The second, the image was acquired with $^{57}Co$ flood source for 30 seconds on the rear side and the left side of the phantom, the image as the third method was obtained using a syringe filled with 37 MBq of $Na^{99m}TcO_4$ in 10 ml of saline, and as the fourth, we used a photon energy and scatter energy of $^{99m}Tc$ emitting from phantom without any addition radiation exposure. Finally, the image was fused the scout image and the basal image of SPECT/CT using MATLAB$^{(R)}$ program. Anterior and lateral images were acquired for 3 min, and radiation exposure was measured by the personal exposure dosimeter. We conducted preference of 10 images from nuclear medicine doctors by the survey. Results TBR values of anterior and right image in the first to fifth method were 334.9 and 117.2 ($1^{st}$), 266.1 and 124.4 ($2^{nd}$), 117.4 and 99.6 ($3^{rd}$), 3.2 and 7.6 ($4^{th}$), and 565.6 and 141.8 ($5^{th}$). And also exposure doses of these method were 2, 2, 2, 0, and $30{\mu}Sv$, respectively. Among five methods, the fifth method showed the highest TBR value as well as exposure dose, where as the fourth method showed the lowest TBR value and exposure dose. As a result, the last method ($5^{th}$) is the best method and the fourth method is the worst method in this study. Conclusion Scout method of SPECT/CT can be useful that provides the best values of TBR and the best score of survey result. Even though personal exposure dose when patients take scout of SPECT/CT was higher than another scan, it was slight level comparison to 1 mSv as the dose limit to non-radiation workers. If the scout is possible to less than 80 kV, exposure dose can be reduced, and also useful lesion localization provided.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.3
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• pp.177-191
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• 2021

The Zhenzigou Pb-Zn deposit, one of the largest Pb-Zn deposit in the northeast of China, is located at the Qingchengzi mineral field in Jiao Liao Ji belt. The geology of this deposit consists of Archean granulite, Paleoproterozoinc migmatitic granite, Paleo-Mesoproterozoic sodic granite, Paleoproterozoic Liaohe group, Mesozoic diorite and monzoritic granite. The Zhenzigou deposit which is a strata bound SEDEX or SEDEX type deposit occurs as layer ore and vein ore in Langzishan formation and Dashiqiao formation of the Paleoproterozoic Liaohe group. Based on mineral petrography and paragenesis, dolomites from this deposit are classified three type (1. dolomite (D₀) as hostrock, 2. dolomite (D₁) in layer ore associated with white mica, quartz, K-feldspar, sphalerite, galena, pyrite, arsenopyrite from greenschist facies, 3. dolomite (D₂) in vein ore associated with quartz, apatite and pyrite from quartz vein). The structural formulars of dolomites are determined to be Ca_1.00-1.03Mg_0.94-0.98Fe_0.00-0.06As_0.00-0.01(CO₃)₂(D₀), Ca_0.97-1.16Mg_0.32-0.83Fe_0.10-0.50Mn_0.01-0.12Zn_0.00-0.01Pb_0.00-0.03As_0.00-0.01(CO₃)₂(D₁), Ca_1.00-1.01Mg_0.85-0.92Fe_0.06-0.11 Mn_0.01-0.03As_0.01(CO₃)₂(D₂), respectively. It means that dolomites from the Zhenzigou deposit have higher content of trace elements compared to the theoretical composition of dolomite. Feo and MnO contents of these dolomites (D₀, D₁ and D₂) contain 0.05-2.06 wt.%, 0.00-0.08 wt.% (D₀), 3.53-17.22 wt.%, 0.49-3.71 wt.% (D₁) and 2.32-3.91 wt.%, 0.43-0.95 wt.% (D₂), respectively. The dolomite (D₁) from layer ore has higher content of these trace elements (FeO, MnO, ZnO and PbO) than dolomite (D₀) from hostrock and dolomite (D₂) from quartz vein. Dolomites correspond to Ferroan dolomite (D₀ and D₂), and ankerite and Ferroan dolomite (D₁), respectively. Therefore, 1) dolomite (D₀) from hostrock is a Ferroan dolomite formed by marine evaporative lagoon environment in Paleoproterozoic Jiao Liao Ji basin. 2) Dolomite (D₁) from layer ore is a ankerite and Ferroan dolomite formed by hydrothermal metasomatism origined metamorphism (greenschist facies) associated with Paleoproterozoic intrusion. 3) Dolomte (D₂) from quartz vein is a Ferroan dolomite formed by hydrothermal fluid origined Mesozoic intrusion.