Slope Stability for Bridge Access Road on Sedimentary Rocks using Geological Cross Sections (지질단면을 이용한 교량 접속도로 퇴적암 비탈면의 안정성 검토 연구)
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- The Journal of the Convergence on Culture Technology
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- v.8 no.1
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- pp.507-512
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- 2022
The subjects of the study are the sedimentary rock slope of the Mesozoic Gyeongsang Supergroup, which has a high risk of failure. The rocks of the slope shall be sandstone, siltstone and dacite, and discontinuities shall develop beddings, shear joints, extension joints, and dacite dyke boundary planes. The type and scale of failure varies depending on the type of rock and the strike/dip of the discontinuities, but the planar failure prevails. Based on the face-mapping data, SMR, physical and mechanical testing of rocks, the critical equilibrium analysis, all representative sections required a countermeasure method because the acceptable safety factor during dry and rainy seasons were far below Fs=1.5 and Fs=1.2. After applying the countermeasure method, both the dry and wet conditions of the slope exceeded the allowable safety factor. In particular, the face-mapping data of the slope-face, the geological cross-sections of several representative sections perpendicular to the slope-face, and the critical equilibrium analysis and the presentation of countermeasure methods that have been reviewed based on them are expected to be reasonable tools for the slope stability.
Background: Helicobacter pylori infection, prevalent in more than half of the global population, is associated with various gastrointestinal diseases, including peptic ulcers and gastric cancer. The effectiveness of early diagnosis and treatment in preventing gastric cancer highlights the need for improved diagnostic methods. This study aimed to develop a simple scoring system based on endoscopic findings to predict H. pylori infection. Methods: A retrospective analysis was conducted on 1,007 patients who underwent upper gastrointestinal endoscopy at Asan Medical Center from January 2019 to December 2021. Exclusion criteria included prior H. pylori treatment, gastric surgery, or gastric malignancies. Diagnostic techniques included rapid urease and 13C-urea breath tests, H. pylori culture, and assessment of endoscopic features following the Kyoto gastritis classification. A new scoring system based on endoscopic findings including regular arrangement of collecting venules (RAC), nodularity, and diffuse or spotty redness was developed for predicting H. pylori infection, utilizing logistic regression analysis in the development set. Results: The scoring system demonstrated high predictive accuracy for H. pylori infection in the validation set. Scores of 2 and 3 were associated with 96% and 99% infection risk, respectively. Additionally, there was a higher prevalence of diffuse redness and sticky mucus in cases where the initial H. pylori eradication treatment failed. Conclusions: Our scoring system showed potential for improving diagnostic accuracy in H. pylori infection. H. pylori testing should be considered upon spotty redness, diffuse redness, nodularity, and RAC absence on endoscopic findings as determined by the predictive scoring system.
Background: Human papillomavirus (HPV) is the most common sexually transmitted infection worldwide and it is responsible for most cases of cervical uterine cancer. Although HPV infections of the cervix do not always progress to cancer, 90% of cervical cancer cases have been found to be associated with high risk HPV (HR-HPV) infection. HPV DNA testing is widely used, along with Papanicolaou (Pap) testing, to screen for cervical abnormalities. However, there are no data on the prevalence of genotype-specific HPV infections assessed by measuring HPV E6/E7 mRNA in women representative of the Chinese population across a broad age range. Materials and Methods: In the present study, we compared the results with the CervicGen HPV RT-qDx assay, which detects 16 HR-HPV genotypes (Alpha-9: HPV 16, 31, 33, 35, 52, and 58; Alpha-7: HPV 18, 39, 45, 51, 59, and 68; and Alpha-5, 6: HPV 53, 56, 66, and 69), and the REBA HPV-ID assay, which detects 32 HPV genotypes based on the reverse blot hybridization assay (REBA) for the detection of oncogenic HPV infection according to cytological diagnosis. We also investigated the prevalence and genotype distribution of HPV infection with a total of 324 liquid-based cytology samples collected in western Shandong province, East China. Results: The overall HPV prevalences determined by HPV DNA and HPV E6/E7 mRNA assays in this study were 79.9% (259/324) and 55.6% (180/324), respectively. Although the positivity of HPV E6/E7 mRNA expression was significantly lower than HPV DNA positivity, the HPV E6/E7 mRNA assay showed greater specificity than the HPV DNA assay (88.6% vs. 48.1%) in normal cytology samples. The prevalence of Alpha-9 (HPV 16, 31, 33, 35, 52, and 58) HPV infection among these women accounted for up to 80.3% and 76.1% of the high-grade lesions detected in the HPV mRNA and DNA tests, respectively. The HR-HPV genotype distribution, based on HPV DNA and E6/E7 mRNA expression by age group in patients with cytologically confirmed lesions, was highest in women aged 40 to 49 years (35.9% for cytologically confirmed cases, Pearson correlation r value=0.993, p<0.001) for high-grade lesions. Among the oncogenic HR-HPV genotypes for all age groups, there was little difference in the distribution of HPV genotypes between the HPV DNA (HPV -16, 53, 18, 58, and 33) and HPV E6/E7 mRNA (HPV -16, 53, 33, 58, and 18) assays. HPV 16 was the most common HPV genotype among women with high-grade lesions. Conclusions: Our results suggest that the HPV E6/E7 mRNA assay can be a sensitive and specific tool for the screening and investigation of cervical cancer. Furthermore, it may provide useful information regarding the necessity for early cervical cancer screenings and the development of additional effective HPV vaccines, such as one for HPV 53 and 58. Additionally, gaining knowledge of HPV distribution may also inform us about ecological changes in HPV after the vaccination.
There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.
Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.
Franchising is one of the fastest growing types of business. It is already popular and well-known in the U.S., and has been growing in many other countries including Korea. Furthermore, many Korean franchising companies have expanded their business overseas actively. According to the data by the Ministry of Industry and Resource, 82 companies out of a sample of 500 franchising companies are already operating in many foreign countries and 48% of them have started their foreign business since 2006. This clearly indicates the fast growing current trend of foreign operation by Korean franchising companies. In spite of the fast growing trend of foreign expansion in the industry, academic research on internationalization of franchising companies is extremely difficult to find. Accordingly, academic research on the issue is necessary and urgent in Korea. Among the various research questions on internationalization of franchising business, this study intends to investigate the difference in organizational factors between the franchising companies doing foreign operation and those doing business only domestically. More specifically, this research has the following purposes. First, considering the lack of theoretical basis of previous studies, resource-based theory and agency theory are employed as the theoretical bases. Second, this study explains the difference in internationalization based on organizational factors such as company size, history and growth rate. Third, the five hypotheses regarding the difference in organizational factors are presented and tested empirically, which is the first attempt in the area of this topic. Finally, the study attempts to clarify the conflicting implications among theories regarding some organizational factos such as growth rate. As the theoretical background, resource-based theory and agency theory are discussed. According to resource-based theory, a firm can grow continuously when it has competence and resource, and also the ability to develop them. The competence and resource can include capital, human resource, management skill, market information, ability to manage risk, etc. Meanwhile, agency theory views the relationship between franchisor and franchisee as an agency relationship. In agency theory, bonding capability and monitoring capability are the two key factors which promote internationalization of franchising companies. Based on the two theories, a conceptual model is designed. The model consists of two groups of variables. One is organizational factors including size, history, growth rate, price bonding and geographic dispersion. The other is whether a franchising company is operating overseas or not. We developed the following five research hypotheses basically describing the relationship between organizational factors and internationalization of franchising companies. H1: The size of franchising companies operating overseas is larger than that of franchising companies operating domestically. H2: The history of franchising companies operating overseas is longer than that of franchising companies operating domestically. H3: The growth rate of franchising companies operating overseas is higher than that of franchising companies operating domestically. H4: The price bonding of franchising companies operating overseas is higher than that of franchising companies operating domestically. H5: The geographic dispersion of franchising companies operating overseas is wider than that of franchising companies operating domestically. Data for the analyses are obtained from 2005 Korea Franchise Survey data co-generated by Ministry of Industry and Resource, GS1 Korea, and Korea Franchise Association. Out of 2,804 population companies, 2,489 companies are excluded for various reasons and 315 companies are selected as the final sample. Prior to hypotheses tests, validity and reliability of the measures of size, history, growth rate and price bonding are examined for further analyses. Geographic dispersion is not validated since it is measured using nominal data. A series of independent sample T-tests is used to find out whether there exists any significant difference between the companies internationalized and those operating only domestically for each organizational factor. Among the five factors, size and geographic dispersion show significant difference, growth rate and price bonding do not reveal any difference and, finally, history factor shows conflicting results in the difference depending on how to measure it.