• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.234-244
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• 2023

Background: Effective treatment of fluoroquinolone-resistant multidrug-resistant tuberculosis (FQr-MDR-TB) is difficult because of the limited number of available core anti-TB drugs and high rates of resistance to anti-TB drugs other than FQs. However, few studies have examined anti-TB drugs that are effective in treating patients with FQr-MDR-TB in a real-world setting. Methods: The impact of anti-TB drug use on treatment outcomes in patients with pulmonary FQr-MDR-TB was retrospectively evaluated using a nationwide integrated TB database (Korean Tuberculosis and Post-Tuberculosis). Data from 2011 to 2017 were included. Results: The study population consisted of 1,082 patients with FQr-MDR-TB. The overall treatment outcomes were as follows: treatment success (69.7%), death (13.7%), lost to follow-up or not evaluated (12.8%), and treatment failure (3.9%). On a propensity-score-matched multivariate logistic regression analysis, the use of bedaquiline (BDQ), linezolid (LZD), levofloxacin (LFX), cycloserine (CS), ethambutol (EMB), pyrazinamide, kanamycin (KM), prothionamide (PTO), and para-aminosalicylic acid against susceptible strains increased the treatment success rate (vs. unfavorable outcomes). The use of LFX, CS, EMB, and PTO against susceptible strains decreased the mortality (vs. treatment success). Conclusion: A therapeutic regimen guided by drug-susceptibility testing can improve the treatment of patients with pulmonary FQr-MDR-TB. In addition to core anti-TB drugs, such as BDQ and LZD, treatment of susceptible strains with later-generation FQs and KM may be beneficial for FQr-MDR-TB patients with limited treatment options.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.97-106
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• 2023

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Korean Journal of Construction Engineering and Management
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• v.15 no.1
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• pp.111-121
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• 2014

The Clean Development Mechanism (CDM) allows New & Renewable Energy projects to make additional income by selling CER's, which represent the amount of Green House Gases(GHG) that is reduced in the project. However, forward contracts used to hedge fluctuating market prices does not allow projects to sell CER's at a premium. As an alternate approach to maximize CER revenue, CER's are modeled as a 'real option', in which CER's are sold only above the desired sales price. Using the Binomial Option Pricing model, the resultant lattices are used to determine whether to sell, defer or abandon the option at individual nodes. Overlaying Pascal's Triangle on the lattices also enabled the calculation of the annual probabilities for deferring CER sales without incurring downside losses. Application to an actual Landfill Gas project showed increased overall NPV, and that CER sales could be deferred at a maximum of 2 years. The proposed framework allows transparency in the analysis and provides valuable and strategical information when making investment decisions related to CER sales of CDM projects.

• Asia Marketing Journal
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• v.15 no.3
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• pp.159-172
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• 2013

In the present study, we apply the multiple cue probability learning (MCPL) paradigm to examine consumer learning from feedback in repeated trials. This paradigm is useful in investigating consumer learning, especially learning the relationships between the overall quality and attributes. With this paradigm, we can analyze what people learn from repeated trials by using the lens model, i.e., whether it is knowledge or consistency. In addition to introducing this paradigm, we aim to demonstrate that knowledge people gain from repeated trials with feedback is robust enough to weaken one of the most often examined contextual effects, the asymmetric dominance effect. The experiment consists of learning session and a choice task and stimuli are sport rafting boats with motor engines. During the learning session, the participants are shown an option with three attributes and are asked to evaluate its overall quality and type in a number between 0 and 100. Then an expert's evaluation, a number between 0 and 100, is provided as feedback. This trial is repeated fifteen times with different sets of attributes, which comprises one learning session. Depending on the conditions, the participants do one (low) or three (high) learning sessions or do not go through any learning session (no learning). After learning session, the participants then are provided with either a core or an extended choice set to make a choice to examine if learning from feedback would weaken the asymmetric dominance effect. The experiment uses a between-subjects experimental design (2 × 3; core set vs. extended set; no vs. low vs. high learning). The results show that the participants evaluate the overall qualities more accurately with learning. They learn the true trade-off rule between attributes (increase in knowledge) and become more consistent in their evaluations. Regarding the choice task, there is a significant decrease in the percentage of choosing the target option in the extended sets with learning, which clearly demonstrates that learning decreases the magnitude of the asymmetric dominance effect. However, these results are significant only when no learning condition is compared either to low or high learning condition. There is no significant result between low and high learning conditions, which may be due to fatigue or reflect the characteristics of learning curve. The present study introduces the MCPL paradigm in examining consumer learning and demonstrates that learning from feedback increases both knowledge and consistency and weakens the asymmetric dominance effect. The latter result may suggest that the previous demonstrations of the asymmetric dominance effect are somewhat exaggerated. In a single choice setting, people do not have enough information or experience about the stimuli, which may lead them to depend mostly on the contextual structure among options. In the future, more realistic stimuli and real experts' judgments can be used to increase the external validity of study results. In addition, consumers often learn through repeated choices in real consumer settings. Therefore, what consumers learn from feedback in repeated choices would be an interesting topic to investigate.

• Journal of Arbitration Studies
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• v.12 no.1
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• pp.271-321
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• 2002

Every year, many million of business transactions take place. Ocassionally, disagreements develop over these business transactions. Many of these disputes are resolved by mediation, arbitration and out-of-court settlement options. The American Arbitration Association(AAA) helps resolve a wide range of disputes through mediation, arbitration, elections and other out-of-court settlement procedures. The AAA offers a broad range of dispute resolution services to business executives, attorneys, individuals, trade associations, unions, management, consumers, families, communities, and all level of governments. The 198,491 cases composed of the 194,303 arbitration cases and the 4,188 mediation cases, were filed with the AAA in 2000. These case filings represent a full range of matters, including commercial finance, construction, labor and employment, environmental, health care, insurance, real state, securities, and technology disputes. The Korean Commercial Arbitration Board (KCAB) does more than render arbitration services. It helps facilitate settlements and guarantee implementation thereof between trading partners at home and abroad involving disputes related to such areas as the sale of commodities, construction, joint venture agreements, technical assistance, agency agreements, and maritime transport. The 643 cases composed of the the 197 arbitration cases and the 446 mediation cases, were filed with the KCAB in 2001. There are some differences between the AAA and the KCAB regarding the number and the area of mediation and arbitration case filings, the breath of service offerings, the scope of alternative dispute resolution, and the education and training. In order to apply to the proceedings of the commercial mediation and arbitration, the AAA has the Commercial Mediation Rules, the Commercial Arbitration Rules, the Expedited Procedures, the Optional Procedures for Large, Complex Commerical Dispute, and the Optional Rules for Emergency Measures of Protection as amended and effective on September 1, 2000. In order to apply to the proceedings of commercial arbitration, the KCAB has the Arbitration Rules as amended by the Supreme Court on April 27, 2000, which have been changed to incorporate the revisions of the Arbitration Act that went into effect on December 31, 1999. There are some differences between the AAA's commercial Arbitration Rules and the KCAB's Arbitration Rules regarding the clauses of jurisdiction and administrative conference, number of arbitrators, communication with arbitrator, vacancies, preliminary hearing, exchange of information, oaths, evidence by affidavit and posthearing filing of documents or others, interim measures, serving of notice, form of award, scope of award, delivery of award to parties, modification of award, release of liability, administrative fees, neutral arbitrator's compensation, and expedited procedures. In conclusion, for the vitalization of KCAB and its ADR system, the following measures should be taken : the effective case management, the development of on0-line ADR, the establishment of ADR system of electronic commerce disputes, and the variety of dispute resolution rules in each expert field.

• Environmental and Resource Economics Review
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• v.13 no.4
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• pp.735-761
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• 2004

As a possible alternative to Traditional Discounted Cash Flow Method, "Option Pricing Model" has drawn academic attentions for the last a few decades. However, it has failed to replace traditional DCF method practically due to its mathematical complexity. This paper introduces an option pricing valuation model specifically adjusted for the natural resource development projects. We add market information and industry-specific features into the model so that the model remains objective as well as realistic after the adjustment. The following two features of natural resource development projects take central parts in model construction; product price is a unique source of cash flow's uncertainty, and the projects have cost structure from capital-intense industry, in which initial capital cost takes most part of total cost during the projects. To improve the adaptability of Option Pricing Model specifically to the natural resource development projects, we use Two-Factor Model and Long-term Asset Model for the analysis. Although the model introduced in this paper is still simple and reflects limited reality, we expect an improvement in applicability of option pricing method for the evaluation of natural resource development projects can be made through the process taken in this paper.

• Archives of design research
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• v.18 no.2 s.60
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• pp.219-232
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• 2005

Moderns are living within flood of web contents, animation, reflex data etc. as well as sight, product, environment design. There fore, modern consumer has much options. Designer must provide various result for consumer for this reason. And must invent new sensitivity and propose to consumer. As purpose of this MCC sensitivity palette research takes advantage of the most sensitive color, do. Because applying correct sensitivity more than when design with matter already settled, rid private prejudice, and is thing to convey design intention exactly to user. Excellent culture contents must be able to equip international color design sensitivity. MCC sensitivity palette research studies and carries on the head emotion and sensitivity language that is nationality first, and collect End arranged sensitivity adjective through data analysis and picture data analysis that is the next time research leader Munheonjeok. And distributed collected adjective equally, and arrange distributed adjective by field of each sensitivity and collect system. Do 3 colors, 4 colors color scheme in selected sensitivity adjective and completed Simheom version. Result of beta version research to color specialist and designer last digital palette through question and inquiry compose. Through this process, completed more real and correct digital color sensitivity palette. Completed color scheme is operated in www.mcdri.net on web, and also programs to windows base and developed to software. MCC color scheme palette that research result is made includes sensitivity data database. This database can use directly in industry and continuous development is available. Software can search color scheme in language and idea development through classification search that use 3 attributes of color is available there is cough data of each output device different color error.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.172-172
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• 2011

The interaction between land surface and atmosphere is essentially affected by hydrometeorological variables including soil moisture. Accurate estimation of soil moisture at spatial and temporal scales is crucial to better understand its roles to the weather systems. The KLDAS(Korea Land Data Assimilation System) is a regional, specifically Korea peninsula land surface information systems. As other prior land data assimilation systems, this can provide initial soil field information which can be used in atmospheric simulations. For this study, as an enabling high-resolution tool, weather research and forecasting(WRF-ARW) model is applied to produce precipitation data using GFS(Global Forecast System) with GFS embedded and KLDAS soil moisture information as initialization data. WRF-ARW generates precipitation data for a specific region using different parameters in physics options. The produced precipitation data will be employed for simulations of Hydrological Models such as HEC(Hydrologic Engineering Center) - HMS(Hydrologic Modeling System) as predefined input data for selected regional water responses. The purpose of this study is to show the impact of a hydrometeorological variable such as soil moisture in KLDAS on hydrological consequences in Korea peninsula. The study region, Chongmi River Basin, is located in the center of Korea Peninsular. This has 60.8Km river length and 17.01% slope. This region mostly consists of farming field however the chosen study area placed in mountainous area. The length of river basin perimeter is 185Km and the average width of river is 9.53 meter with 676 meter highest elevation in this region. We have four different observation locations : Sulsung, Taepyung, Samjook, and Sangkeug observatoriesn, This watershed is selected as a tentative research location and continuously studied for getting hydrological effects from land surface information. Simulations for a real regional storm case(June 17~ June 25, 2006) are executed. WRF-ARW for this case study used WSM6 as a micro physics, Kain-Fritcsch Scheme for cumulus scheme, and YSU scheme for planetary boundary layer. The results of WRF simulations generate excellent precipitation data in terms of peak precipitation and date, and the pattern of daily precipitation for four locations. For Sankeug observatory, WRF overestimated precipitation approximately 100 mm/day on July 17, 2006. Taepyung and Samjook display that WRF produced either with KLDAS or with GFS embedded initial soil moisture data higher precipitation amounts compared to observation. Results and discussions in detail on accuracy of prediction using formerly mentioned manners are going to be presented in 2011 Annual Conference of the Korean Society of Hazard Mitigation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.12
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• pp.1069-1075
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• 2017

With the recent development of space technology, the satellite market, especially the small satellite market, is growing globally. As the satellite market continues to grow, the launch vehicle market is also growing, and demand for low-cost launches is increasing. There are a number of options for low-cost launches, including development of engine that uses low-cost propellants, product and transportation cost savings, but the most effective way to reduce launch costs is to reuse the used launch vehicles. USA's Space Shuttle, a famous rocket as manned spacecraft, could be referred as the start of reusable launch vehicle. However, Space Shuttle had limited reusable parts and it was very expensive even though it is a reusable launch vehicle because of its low efficiency. In recent years, aiming at a real reusable launch vehicle, reusable launch vehicle for commercial purposes have been developed around USA's SpaceX and Blue Origin, and re-landing tests were successfully accomplished. In addition, SpaceX successfully did the re-using of first-stage launch vehicle that had been succeeded in re-landing already. In accordance with this trend, countries such as Europe and India are also concentrating on the study of reusable launch vehicles. Including Blue Origin, companies like Virgin Galactic and XCOR in the United States, are also trying to commercialize the same reusable technology as the private manned space tourism. Confirmation of these technology trends is essential, because the re-use technology could change the landscape of the global launch vehicle market.