• Journal of Astronomy and Space Sciences
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• v.36 no.1
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• pp.21-33
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• 2019

This paper focuses on the interpretation of radiation fluxes from active galactic nuclei. The advantage of positron annihilation spectroscopy over other methods of spectral diagnostics of active galactic nuclei (therefore AGN) is demonstrated. A relationship between regular and random components in both bolometric and spectral composition of fluxes of quanta and particles generated in AGN is found. We consider their diffuse component separately and also detect radiative feedback after the passage of high-velocity cosmic rays and hard quanta through gas-and-dust aggregates surrounding massive black holes in AGN. The motion of relativistic positrons and electrons in such complex systems produces secondary radiation throughout the whole investigated region of active galactic nuclei in form of cylinder with radius R= 400-1000 pc and height H=200-400 pc, thus causing their visible luminescence across all spectral bands. We obtain radiation and electron energy distribution functions depending on the spatial distribution of the investigated bulk of matter in AGN. Radiation luminescence of the non-central part of AGN is a response to the effects of particles and quanta falling from its center created by atoms, molecules and dust of its diffuse component. The cross-sections for the single-photon annihilation of positrons of different energies with atoms in these active galactic nuclei are determined. For the first time we use the data on the change in chemical composition due to spallation reactions induced by high-energy particles. We establish or define more accurately how the energies of the incident positron, emitted ${\gamma}-quantum$ and recoiling nucleus correlate with the atomic number and weight of the target nucleus. For light elements, we provide detailed tables of all indicated parameters. A new criterion is proposed, based on the use of the ratio of the fluxes of ${\gamma}-quanta$ formed in one- and two-photon annihilation of positrons in a diffuse medium. It is concluded that, as is the case in young supernova remnants, the two-photon annihilation tends to occur in solid-state grains as a result of active loss of kinetic energy of positrons due to ionisation down to thermal energy of free electrons. The single-photon annihilation of positrons manifests itself in the gas component of active galactic nuclei. Such annihilation occurs as interaction between positrons and K-shell electrons; hence, it is suitable for identification of the chemical state of substances comprising the gas component of the investigated media. Specific physical media producing high fluxes of positrons are discussed; it allowed a significant reduction in the number of reaction channels generating positrons. We estimate the brightness distribution in the ${\gamma}-ray$ spectra of the gas-and-dust media through which positron fluxes travel with the energy range similar to that recorded by the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics (PAMELA) research module. Based on the results of our calculations, we analyse the reasons for such a high power of positrons to penetrate through gas-and-dust aggregates. The energy loss of positrons by ionisation is compared to the production of secondary positrons by high-energy cosmic rays in order to determine the depth of their penetration into gas-and-dust aggregations clustered in active galactic nuclei. The relationship between the energy of ${\gamma}-quanta$ emitted upon the single-photon annihilation and the energy of incident electrons is established. The obtained cross sections for positron interactions with bound electrons of the diffuse component of the non-central, peripheral AGN regions allowed us to obtain new spectroscopic characteristics of the atoms involved in single-photon annihilation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.2
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• pp.79-93
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• 2021

The question of why do you want to start a "start?" Is the most basic step in trying to do something. In other words, previous studies have shown that the degree of confidence in an individual's decision affects the setting of a specific purpose. Based on this, this study aims to provide basic data for deriving the direction of entrepreneurship education in college students by analyzing the effects of goal-oriented behavioral model on college students' self-determination and intention to start a business through the 5 factor model. To achieve the purpose of the study, a self-report questionnaire was conducted from October 01 to November 11, 2019 for university students attending located in Gyeonggi-do, Seoul. A total of 150 questionnaires were distributed, and 125 parts were used for the final analysis, except 25 parts with insincere responses or errors. Data were analyzed using SPSS Win 24, and reliability, validity analysis, frequency analysis, One-way ANOVA and regression analysis were performed, and three-step regression analysis and Sobel verification were performed for mediating effects. The summary of the study is as follows. First, the influence of university students' goal-oriented behavioral model on self-determination showed that attitudes, subjective norms, and perceived behavioral controls had statistically significant positive effects, and positive and negative expectations were statistically significant. Did not affect. Therefore, the higher the attitude, subjective norms, and perceived behavioral control, the higher the university students' self-determination. Second, the influence of college students' goal-oriented behavioral model on the intention to start a business was as follows.). As a result, the higher the perceived behavioral control and positive expectation, the higher the intention to start up. Third, regression model 1 showed that the behavioral control and positive expectation sentiment among the goal-oriented behavioral model had a significant positive influence on the college students' intention to start a business. Affected. Regression model II added the parameters of the 5 factor model, which increased 2.5% of explanatory power than the first regression model. Perceived behavioral control and positive expectations had a statistically significant positive effect, negative expectations had a statistically significant negative effect, and among the 5 factor model, openness had a statistically significant positive (+) Affected. From these results, it can be seen that the Big Five personality factors have a mediating effect on the relationship between goal-oriented behavior model and intention to start up. This study confirmed that the goal-oriented behavioral model of college students is an important variable in implementing self-determination and intention to start a business. In addition, by using his Big 5 personality factors as positive feedback, he has proved to play an important role by identifying the mediation role that can be set, planned and utilized to plan and achieve his life. The result of this study is that college students are interested in the intention of individual start-ups, so they are not freed from difficult employment difficulties. It is intended to provide basic data useful in the age of creation of government.

• Journal of the Korean Institute of Gas
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• v.26 no.3
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• pp.45-53
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• 2022

In order to improve the emission of diesel engines, natural gas-diesel dual fuel combustion compression ignition engines are in the spotlight. In particular, a reactivity controlled compression ignition (RCCI) combustion strategy is investigated comprehensively due to its possibility to improve both efficiency and emissions. With advanced diesel direct injection timing earlier than TDC, it achieves spontaneous reaction with overall lean mixture from a homogeneous mixture in the entire cylinder area, reducing nitrogen oxides (NOx) and particulate matter (PM) and improving braking heat efficiency at the same time. However, there is a disadvantage in that the amount of incomplete combustion increases in a low load region with a relatively small amount of fuel-air. To solve this, sensitive control according to the diesel injection timing and fuel ratio is required. In this study, experiments were conducted to improve efficiency and exhaust emissions of the natural gas-diesel dual fuel engine at low load, and evaluate combustion stability according to the diesel injection timing at the operation point for power generation. A 6 L-class commercial diesel engine was used for the experiment which was conducted under a 50% load range (~50 kW) at 1,800 rpm. Two injectors with different spray patterns were applied to the experiment, and the fraction of natural gas and diesel injection timing were selected as main parameters. Based on the experimental results, it was confirmed that the brake thermal efficiency increased by up to 1.3%p in the modified injector with the narrow-angle injection added. In addition, the spray pattern of the modified injector was suitable for premixed combustion, increasing operable range in consideration of combustion instability, torque reduction, and emissions level under Tier-V level (0.4 g/kWh for NOx).

• Korean Journal of Plant Resources
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• v.36 no.1
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• pp.15-25
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• 2023

Myriophyllum spicatum L. has been used as an ornamental in ponds and aquariums, and as a folk remedy for inflammation and pus. Nevertheless, the biological activity and underlying mechanisms of anti-inflammatory effects are unclear. This study is aimed at investigating the antioxidative and anti-inflammatory activities of ethanol extract of Myriophyllum spicatum L. (EMS) in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Antioxidant activity of EMS was assessed by radical-scavenging effects on ferric reducing antioxidant power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals. As inflammatory response parameters produced by LPS-stimulated RAW 264.7 cells were quantified to assess the anti-inflammatory activity of EMS. Our results showed that EMS increased FRAP and DPPH radical-scavenging activity. In EMS-treated RAW 264.7 cells, the production of NO, PGE₂, TNF-α and IL-1β was significantly inhibited at the non-cytotoxic concentration. In addition, EMS significantly attenuated LPS-stimulated the toll-like receptor (TLR) 4/myeloid differentiation protein (MyD) 88 signaling pathway, and inhibited nuclear translocation of nuclear factor-kappa B(NF-κB). Positive correlations were noted between anti-inflammatory activity and antioxidant activity. In conclusion, it was indicated that EMS suppresses the transcription of inflammatory factors by inhibiting the TLR4/MyD88/NF-κB signaling pathway, thereby suppressing LPS-stimulated inflammation in RAW 264.7 cells. This study highlights the potential role of EMS against inflammation and associated diseases.

• Radiation Oncology Journal
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• v.25 no.2
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• pp.79-92
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• 2007

[ $\underline{Purpose}$ ]: For the first time, a nationwide survey in the Republic of Korea was conducted to determine the basic parameters for the treatment of esophageal cancer and to offer a solid cooperative system for the Korean Pattern of Care Study database. $\underline{Materials\;and\;Methods}$: During $1998{\sim}1999$, biopsy-confirmed 246 esophageal cancer patients that received radiotherapy were enrolled from 23 different institutions in South Korea. Random sampling was based on power allocation method. Patient parameters and specific information regarding tumor characteristics and treatment methods were collected and registered through the web based PCS system. The data was analyzed by the use of the Chi-squared test. $\underline{Results}$: The median age of the collected patients was 62 years. The male to female ratio was about 91 to 9 with an absolute male predominance. The performance status ranged from ECOG 0 to 1 in 82.5% of the patients. Diagnostic procedures included an esophagogram (228 patients, 92.7%), endoscopy (226 patients, 91.9%), and a chest CT scan (238 patients, 96.7%). Squamous cell carcinoma was diagnosed in 96.3% of the patients; mid-thoracic esophageal cancer was most prevalent (110 patients, 44.7%) and 135 patients presented with clinical stage III disease. Fifty seven patients received radiotherapy alone and 37 patients received surgery with adjuvant postoperative radiotherapy. Half of the patients (123 patients) received chemotherapy together with RT and 70 patients (56.9%) received it as concurrent chemoradiotherapy. The most frequently used chemotherapeutic agent was a combination of cisplatin and 5-FU. Most patients received radiotherapy either with 6 MV (116 patients, 47.2%) or with 10 MV photons (87 patients, 35.4%). Radiotherapy was delivered through a conventional AP-PA field for 206 patients (83.7%) without using a CT plan and the median delivered dose was 3,600 cGy. The median total dose of postoperative radiotherapy was 5,040 cGy while for the non-operative patients the median total dose was 5,970 cGy. Thirty-four patients received intraluminal brachytherapy with high dose rate Iridium-192. Brachytherapy was delivered with a median dose of 300 cGy in each fraction and was typically delivered $3{\sim}4\;times$. The most frequently encountered complication during the radiotherapy treatment was esophagitis in 155 patients (63.0%). $\underline{Conclusion}$: For the evaluation and treatment of esophageal cancer patients at radiation facilities in Korea, this study will provide guidelines and benchmark data for the solid cooperative systems of the Korean PCS. Although some differences were noted between institutions, there was no major difference in the treatment modalities and RT techniques.

• Asia Marketing Journal
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• v.14 no.1
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• pp.83-98
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• 2012

In a market where new and used cars are competing with each other, we would run the risk of obtaining biased estimates of cross elasticity between them if we focus on only new cars or on only used cars. Unfortunately, most of previous studies on the automobile industry have focused on only new car models without taking into account the effect of used cars' pricing policy on new cars' market shares and vice versa, resulting in inadequate prediction of reactive pricing in response to competitors' rebate or price discount. However, there are some exceptions. Purohit (1992) and Sullivan (1990) looked into both new and used car markets at the same time to examine the effect of new car model launching on the used car prices. But their studies have some limitations in that they employed the average used car prices reported in NADA Used Car Guide instead of actual transaction prices. Some of the conflicting results may be due to this problem in the data. Park (1998) recognized this problem and used the actual prices in his study. His work is notable in that he investigated the qualitative effect of new car model launching on the pricing policy of the used car in terms of reinforcement of brand equity. The current work also used the actual price like Park (1998) but the quantitative aspect of competitive price promotion between new and used cars of the same model was explored. In this study, I develop a model that assumes that the cross elasticity between new and used cars of the same model is higher than those amongst new cars and used cars of the different model. Specifically, I apply the nested logit model that assumes the car model choice at the first stage and the choice between new and used cars at the second stage. This proposed model is compared to the IIA (Independence of Irrelevant Alternatives) model that assumes that there is no decision hierarchy but that new and used cars of the different model are all substitutable at the first stage. The data for this study are drawn from Power Information Network (PIN), an affiliate of J.D. Power and Associates. PIN collects sales transaction data from a sample of dealerships in the major metropolitan areas in the U.S. These are retail transactions, i.e., sales or leases to final consumers, excluding fleet sales and including both new car and used car sales. Each observation in the PIN database contains the transaction date, the manufacturer, model year, make, model, trim and other car information, the transaction price, consumer rebates, the interest rate, term, amount financed (when the vehicle is financed or leased), etc. I used data for the compact cars sold during the period January 2009- June 2009. The new and used cars of the top nine selling models are included in the study: Mazda 3, Honda Civic, Chevrolet Cobalt, Toyota Corolla, Hyundai Elantra, Ford Focus, Volkswagen Jetta, Nissan Sentra, and Kia Spectra. These models in the study accounted for 87% of category unit sales. Empirical application of the nested logit model showed that the proposed model outperformed the IIA (Independence of Irrelevant Alternatives) model in both calibration and holdout samples. The other comparison model that assumes choice between new and used cars at the first stage and car model choice at the second stage turned out to be mis-specfied since the dissimilarity parameter (i.e., inclusive or categroy value parameter) was estimated to be greater than 1. Post hoc analysis based on estimated parameters was conducted employing the modified Lanczo's iterative method. This method is intuitively appealing. For example, suppose a new car offers a certain amount of rebate and gains market share at first. In response to this rebate, a used car of the same model keeps decreasing price until it regains the lost market share to maintain the status quo. The new car settle down to a lowered market share due to the used car's reaction. The method enables us to find the amount of price discount to main the status quo and equilibrium market shares of the new and used cars. In the first simulation, I used Jetta as a focal brand to see how its new and used cars set prices, rebates or APR interactively assuming that reactive cars respond to price promotion to maintain the status quo. The simulation results showed that the IIA model underestimates cross elasticities, resulting in suggesting less aggressive used car price discount in response to new cars' rebate than the proposed nested logit model. In the second simulation, I used Elantra to reconfirm the result for Jetta and came to the same conclusion. In the third simulation, I had Corolla offer $1,000 rebate to see what could be the best response for Elantra's new and used cars. Interestingly, Elantra's used car could maintain the status quo by offering lower price discount ($160) than the new car ($205). In the future research, we might want to explore the plausibility of the alternative nested logit model. For example, the NUB model that assumes choice between new and used cars at the first stage and brand choice at the second stage could be a possibility even though it was rejected in the current study because of mis-specification (A dissimilarity parameter turned out to be higher than 1). The NUB model may have been rejected due to true mis-specification or data structure transmitted from a typical car dealership. In a typical car dealership, both new and used cars of the same model are displayed. Because of this fact, the BNU model that assumes brand choice at the first stage and choice between new and used cars at the second stage may have been favored in the current study since customers first choose a dealership (brand) then choose between new and used cars given this market environment. However, suppose there are dealerships that carry both new and used cars of various models, then the NUB model might fit the data as well as the BNU model. Which model is a better description of the data is an empirical question. In addition, it would be interesting to test a probabilistic mixture model of the BNU and NUB on a new data set.

• Radiation Oncology Journal
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• v.19 no.2
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• pp.127-135
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• 2001

Purpose : This study is a prospective randomized clinical trial comparing the efficacy and complication of anti-emetic drugs for prevention of nausea and vomiting after radiotherapy which has moderate emetogenic potential. The aim of this study was to investigate whether the anti-emetic efficacy of ondansetron $(Zofran^{\circledR})$ 8 mg bid dose (Group O) is better than the efficacy of metoclopramide 5 mg lid dose (Group M) in patients undergoing fractionated radiotherapy to the abdominal region. Materials and Methods : Study entry was restricted to those patients who met the following eligibility criteria: histologically confirmed malignant disease; no distant metastasis; performance status of not more than ECOG grade 2; no previous chemotherapy and radiotherapy. Between March 1997 and February 1998, 60 patients enrolled in this study. All patients signed a written statement of informed consent prior to enrollment. Blinding was maintained by dosing identical number of tablets including one dose of matching placebo for Group O. The extent of nausea, appetite loss, and the number of emetic episodes were recorded everyday using diary card. The mean score of nausea, appetite loss and the mean number of emetic episodes were obtained in a weekly interval. Results : Prescription error occurred in one patient. And diary cards have not returned in 3 patients due to premature refusal of treatment. Card from one patient was excluded from the analysis because she had a history of treatment for neurosis. As a result, the analysis consisted of 55 patients. Patient characteristics and radiotherapy characteristics were similar except mean age was $52.9{\pm}11.2$ in group M, $46.5{\pm}9.5$ in group O. The difference of age was statistically significant. The mean score of nausea, appetite loss and emetic episodes in a weekly interval was higher in group M than O. In group M, the symptoms were most significant at 5th week. In a panel data analysis using mixed procedure, treatment group was only significant factor detecting the difference of weekly score for all three symptoms. Ondansetron $(Zofran^{\circledR})$ 8 mg bid dose and metoclopramide 5 mg lid dose were well tolerated without significant side effects. There were no clinically important changes In vital signs or clinical laboratory parameters with either drug. Conclusion : Concerning the fact that patients with younger age have higher emetogenic potential, there are possibilities that age difference between two treatment groups lowered the statistical power of analysis. There were significant difference favoring ondansetron group with respect to the severity of nausea, vomiting and loss of appetite. We concluded that ondansetron is more effective anti-emetic agents in the control of radiotherapy-induced nausea, vomiting, loss of appetite without significant toxicity, compared with commonly used drug, i.e., metoclopramide. However, there were patients suffering emesis despite the administration of ondansetron. The possible strategies to improve the prevention and the treatment of radiotherapy-induced emesis must be further studied.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.