• Preventive Nutrition and Food Science
• /
• v.8 no.1
• /
• pp.24-28
• /
• 2003

There is evidence to suggest that cranberry juice supplements improve the health of the urinary tract by inhibiting the binding of fimbriated uropathogenic E. coli to the bladder mucosa. In patients with neurogenic bladders, urinary tract infections (UTI) are particularly common and often poorly managed by antibiotic treatment. A double-blind, randomized, placebo-controlled trial was undertaken on 29 geriatric and spinal cord injured patients with dysfunctional bladders. They received three times daily at mealtimes a 4 oz bottle of cranberry juice (Ocean Spray Cranberries, USA) or a specially prepared synthetic placebo drink. Two episodes of UTI arose in week one of cranberry intake and none thereafter, compared to four episodes of UTI in 4 placebo patients in weeks four, six and 10. Mean bacterial adhesion counts on bladder cells of the patients rose during the first month of treatment in 71 % of the placebo patients compared to only 31 % of cranberry patients (p < 0.001). The difference persisted to some extent for the second and third months. Bacterial adhesion levels correlated with culture findings (higher adhesion and higher viable counts in urine) (p < 0.001), positive leukocyte nitrite tests (136$\pm$131 bacteria per cell versus 52$\pm$86 in negative tests) (p < 0.001), and higher white blood cell counts (> 10) per high power field (126$\pm$125 versus 48$\pm$85 bacteria per cell) (p<0.001). E. coli was the most frequently isolated organism (40% samples) followed by K. pneumoniae (17%) and a number of other uropathogens. Group B Streptococci, and coagulase negative Staphylococcus were recovered from urine in 4 samples but were not associated with any red blood cell presence. The daily intake of cranberry juice, in amounts which are not detrimental to long term compliance, appeared to have a role in reducing the risk of bladder colonization and infection in a highly susceptible patient population.

• The Journal of Engineering Geology
• /
• v.6 no.3
• /
• pp.137-153
• /
• 1996

The purpose of this study is to investigate the possibilities of identifying and detecting underground cavities using seismic waves recorded by the fixed and mobile stations. During 18 months of field work we recorded chemical explosions near the Bongdarn station. Seismic Stations were installed on the free surface and underground inside the Samba mine. The seismograms at the fixed(lorg-term) seismic station show abrupt change of polarization characteristics which can he associated with the appearance of P-to-S converted phase(PS) at 150 ~ 200 msec after the first P arrival. This result indicates that converted phases are generated very near to the Bongdarn station at a depth of 190m. Shear-wave splitting phenomena have also been observeci The time delay between fast shear(fS) and slow shear(sS) waves ranges between 30 and 60 msec(average is 42 msec). However, exact time delay between the fast and the slow shear waves can not be accurately measured because of the very short time delay and limitation of sampling rate. Chemical explosion experiments were recorded at stations along various paths to contrast the seismic response of areas with and without cavities. The seismograms recorded at the stations installed at cavity areas show an abrupt change of polarization characteristics but not on the other stations. Seismic waves propagating through the cavity are characterized by the attenuation of high frequency waves and predominantly low frequency seismic waves after the S wave arrivals.

• The Journal of Society for e-Business Studies
• /
• v.26 no.2
• /
• pp.123-141
• /
• 2021

As the dependence of technology on the economic structure increases, the importance of National Core Technology is increasing. However, due to the nature of the technology itself, it is difficult to determine the scope of the technology to be protected because the scope of the relation is abstract and information disclosure is limited due to the nature of the National Core Technology. To solve this problem, we propose the most appropriate literature type and method of analysis to distinguish important technologies related to National Core Technology. We conducted a pilot test to apply TF-IDF, and LDA topic modeling, two techniques of text mining analysis for big data analysis, to four types of literature (news, papers, reports, patents) collected with National Core Technology keywords in the field of Display industry. As a result, applying LDA theme modeling to patent data are highly relevant to National Core Technology. Important technologies related to the front and rear industries of displays, including OLEDs and microLEDs, were identified, and the results were visualized as networks to clarify the scope of important technologies associated with National Core Technology. Throughout this study, we have clarified the ambiguity of the scope of association of technologies and overcome the limited information disclosure characteristics of national core technologies.

• Journal of the Korean Society of Floral Art and Design
• /
• no.41
• /
• pp.93-114
• /
• 2019

The purpose of this study is to analyze the characteristics and problems of the content of flower design education videos on YouTube and to search for improvement direction. The subjects of analysis were 129 pieces of videos uploaded in the last one year including 'craftman floral design' as a search term. The result shows that contents covered were practical lectures, theory lectures, test related tips, job and character introduction, test work, educational guidance and publicity. The production format could be divided into studio lecture, classroom lecture, video feature, interview, Vlog, and television program. The hub-type programming strategy that periodically uploads the videos satisfying the target audiences' interests is mostly applied. The type of lecture covered 'practical skill test' got a good response from the users. Overall, content diversity, interaction between creators and users, and harmonious programming strategies are lacking. In order to improve this, it is necessary for emotional and expressive creators to pioneer differentiated fields and practice based on actual field. The introduction of interactive elements such as games and quizzes and the application of new media technologies such as VR and AR are worth trying. Three strategic types of 'hero', 'hub', and 'how to' should be applied complementary. As the demand for education content related to flower design is expected to expand in the future, it is required to develop content that can be used in various platforms, foster professional creators, and develop associated business models.

• Journal of agricultural medicine and community health
• /
• v.29 no.2
• /
• pp.265-285
• /
• 2004

Objectives: The acute toxic effects of pesticide are well known. Concern has also been expressed that long-term exposure may result in damage to the central nervous system. This study was conducted to test the hypothesis that central nervous system symptoms might occur due to pesticide exposure. Methods: In a cross-sectional study, first, cumulative exposure index (CEI) was estimated. Neurologic symptoms (Q-16 questionnaire) for 541 farmers (exposed to pesticides) were compared with 119 non-exposed persons in spraying season nine rural areas in Korea. Results: The pesticides poisoning rates for last 3 months were 67.2% for orchard farmers, 55.3%for dry field farmers, and 20.5% for husbandry farmers, respectively, showing significant difference (p<0.001). Compared with non-exposure group, exposure groups (especially, orchard farmers) reported significantly more neurologic symptoms and had a higher overall neurological symptoms score (p<0.001). Factors related to the positive neurological symptoms (answers "yes" to six or more of Q-16 questionnaire) adjusted for age, sex, education level, smoking and alcohol drinking were type of farming (OR 3.08, 95% CI 1.50-6.30 in orchard farmers vs non-exposure group), CEI (OR 2.75, 95% CI 1.12-6.78 in Q3 vs Q1), past poisoning (OR 1.97, 95% CI 1.21-3.20 vs normal), current mild poisoning (OR 3.03, 9500 CI 1.47-6.22 vs normal) and current moderate poisoning (OR 6.34, 95% CI 3.03-13.25 vs normal), respectively. Conclusions: These results suggest that long-term exposure to pesticides appears to be associated with subtle changes in the central nervous system.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.33-56
• /
• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Applied Biological Chemistry
• /
• v.48 no.1
• /
• pp.23-33
• /
• 2005

Agricultural sciences field in South Korea has many strong points such as numerous researchers, establishment of research infra-structure, excellence in research competitiveness and high technological level. However, there are also many weaknesses including insufficient leadership at related societies and institutes, deficiency of the next generation research group, and insufficiency in research productivity. There are many opportunities including increasing the importance of the biotechnological industry, activating international cooperation researches, and exploring the multitude of possible research areas to be studied. However, some threats still exist, such as pressure from the government of developed countries to open the agricultural market, the decrease of specialized farms, and intensification for researches to gratify economic and social demands. To encourage research activities in the agricultural sciences field in Korea, the following actions and systems are required: 1) formulation of a mid- and a long-term research master plan, 2) development of a database on the man power in related fields, 3) activation of top-down research topics, and associated increase of individual research grants, 4) development of special national programs for basic researches in agricultural sciences, 5) organization of a committee for policy and planning within the related societies, and 6) system development for the fair evaluation of the research results.

• Journal of Chest Surgery
• /
• v.32 no.5
• /
• pp.448-455
• /
• 1999

Background: Recent developments in techniques of video-assisted thoracic surgery(VATS) and endoscopic equipment has expanded the application of video-assisted surgical procedures in the field of thoracic surgery. Especially, it will probably become the treatment of choice of spontaneous pneumothorax(SP). There is, however, a high recurrence rate, high cost, and paucity of long-term results. We report the results of postoperative follow-up and analyze perioperative parameters affected to recurrence, retrospectively. Material and Method: From march 1992 to march 1997, 276 patients with spontaneous pneumothorax underwent 292 VATS procedures. Conversion to open thoracotomy was necessitated in eight patients, and this patients excluded from the study. Result: The sex distribution was 249 males and 31 females. The mean age was 28.1 12.2 years(range, 15 to 69 years). Primary SP was 237cases(83.5%) and secondary SP was 47cases(16.5%). The major underlying lung diseases associated with secondary SP were tuberculosis 27cases(57.4%) and emphysema 8cases (38.3%). Operative indications included Ipsilateral recurrence 123(43.9%), persistent air-leak 53(18.9%), x-ray visible bleb 40(14.3%), tension 30(10.7%), contralateral recurrence 21(7.5%), uncomplicated first episode 8(2.9%), bilateral 3(1.1%), complicated episode 2(0.7%). Blebs were visualized in 247cases(87%) and 244cases(85.9%) performed stapled blebectomy. Early postoperative complications occurred in 33 cases(11.6%): 16 prolonged air-leak more than 5 days(four of them were required a second operation and found missed blebs); 5 bleeding; 5 empyema; 2 atelectasis; 1 wound infection. No deaths occured. The mean operative time was 52.8 23.1 minutes(range, 20 to 165 minutes). The mean d ration of chest tube drainage was 5.0 4.5 days(range, 2 to 37 days). The mean duration ofhospital stay was 8.2 5.5 days (range, 3 to 43days). At a mean follow-up 22.3 18.4 months(range, 1 to 65 months), 12 patients(4.2%) were lost to follow-up. There were 24 recurrences and seven patients underwent second operation and 6 patients(85.7%) were found the missed blebs. 12 perioperative parameters(age, sex, site, underlying disease, extent of collapse, operative indication, size of bleb, number of bleb, location of bleb, bleb management, pleural procedure, prolonged postoperative air-leak) were analyzed statistically to identify significant predictors of recurrence. The significant predictors of recurrence was the underlying disease[17.0%(8/47): 6.8%(16/237), p=0.038], prolonged postoperative air-leakage[37.5%(6/16): 6.7%(18/268), p=0.001], and pleural procedure [11.4%(19/167): 4.3%(5/117), p=0.034]. Blebectomy has less recurrence rate then non-blebectomy [8.2%(20/244) : 10.0%(4/40), p>0. 5]. However, this difference was not statistically significant(p=0.758). Conclusion: We conclude that it is important that we shoud careful finding of bleb during VATS due to reducing of recurrnece, and cases of no bleb identified and secondary spontaneous pneumothorax were indicated of pleurodectomy. VATS is a valid alternative to open procedure for the treatment of spontaneous pneumothorax with less pain, shorter hospital stay, more rapid return to work, high patient acceptance, less scar and exellent cosmetics. But, there is high recurrence rate and high cost, and than it is necessary to evaluate of long-term results for recurrence and to observate carefully during VATS.

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

• Clean Technology
• /
• v.25 no.1
• /
• pp.81-90
• /
• 2019

A dielectric barrier discharge (DBD) plasma reactor packed with $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst was used for the dry ($CO_2$) reforming of propane (DRP) to improve the production of syngas (a mixture of $H_2$ and CO) and the catalyst stability. The plasma-catalytic DRP was carried out with either thermally or plasma-reduced $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst at a $C_3H_8/CO_2$ ratio of 1/3 and a total feed gas flow rate of $300mL\;min^{-1}$. The catalytic activities associated with the DRP were evaluated in the range of $500{\sim}600^{\circ}C$. Following the calcination in ambient air, the ${\gamma}-Al_2O_3$ impregnated with the precursor solution ($Ni(NO_3)_2$ and $Ce(NO_3)_2$) was subjected to reduction in an $H_2/Ar$ atmosphere to prepare $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst. The characteristics of the catalysts were examined using X-ray diffraction (XRD), transmission electron microscopy (TEM), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectrometry (EDS), temperature programmed reduction ($H_2-TPR$), temperature programmed desorption ($H_2-TPD$, $CO_2-TPD$), temperature programmed oxidation (TPO), and Raman spectroscopy. The investigation revealed that the plasma-reduced $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst exhibited superior catalytic activity for the production of syngas, compared to the thermally reduced catalyst. Besides, the plasma-reduced $Ni-CeO_2/{\gamma}-Al_2O_3$ catalyst was found to show long-term catalytic stability with respect to coke resistance that is main concern regarding the DRP process.