• Journal of Applied Tourism Food and Beverage Management and Research
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• v.8
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• pp.89-121
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• 1997

Many of the measures and policies advocated by supply-siders, such as lower taxation, less government intervention, more freedom from restrictive legislation and regulation, and the need for increased productivity can be found in writing the classical economist. Nor is supply-side economics a complete divorcement from Keynesian analysis. In both camps the objectives are the same-high level employment, stable prices and healthy economic growth, the means or suggestions for attaining the objectives, however, differ. Consequently, recommended economic policies and measures are different. keynesians rely primarily on the manipulation of effective demand to increase output and employment and to combat inflation. They assume ample resources to be available in order that supply will respond to demand. The supply-siders emphasize the need to increase savings, investment, productivity and output as a means of increasing income. Supply-siders assume that the increase in income will lead to an increase in effective demand. Keynesians suggest that savings, particularly those not invested, dampen economic activity. Supply-siders hold that savings, or at least an increase in after-tax income, stimulates work effort and provides funds for investment. Perhaps keynesians are guilty of assuming that most savings are not going to be invested, whereas supply-siders may erroneously assume that almost all savings will flow into investment and/ or stimulate work effort. In reality, a middle ground is possible. The supply-siders stress the need to increase supply, but Keynes did not preclude the possibility of increasing economic activity by working through the supply side. According to Keynes' aggregate demand-aggregate supply framework, a decrease in supply will increase output and employment. It must be remembered, however, that Keynes' aggregate supply is really a price. Lowering the price or cost of supply would there by result in higher profit and/ or higher output. This coincides with the viewpoint of supply-siders who want to lower the cost of production via various means for the purpose of increasing supply. Then, too, some of the means, such as tax cuts, tax credits and accelerated depreciation, recommended by suply-siders to increase productivity and output would be favored by Keynesians also as a means of increasing investment, curbing costs, and increasing effective demand. In fact, these very measures were used in the early 1960s in the United State during the years when nagging unemployment was plaguing the economy. Keynesians disagree with the supply-siders' proposals to reduce transfer payments and slow down the process of income redistribution, except in full employment inflationary periods. Keynesians likewise disagree with tax measures that favored business as opposed to individuals and the notion of shifting the base of personal taxation away from income and toward spending. A frequent criticism levied at supply-side economics is that it lacks adequate models and thus far has not been quantified to any great extent. But, it should be remembered that Keynesian economics originally was lacking in models and based on a number of unproved assumptions, such as, the stability of the consumption function with its declining marginal propensity to consume. Just as the economic catastrophe of the great depression of the 1930s paved the way for the application of Keynesian or demand-side policies, perhaps the frustrating and restless conditions of the 1970s and 1980s is an open invitation for the application of supply-side policies. If so, the 1980s and 1990s may prove to be the testing era for the supply-side theories. By the end of 1990s we should have better supply-side models and know much more about the effectiveness of supply-side policies. By that time, also, supply-side thinking may be more crystallized and we will learn whether it is something temporary that will fade away, be widely accepted as the new economics replacing Keynesian demand analysis, or something to be continued but melded or fused with demand management.

• Korean Journal of Ecology and Environment
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• v.46 no.2
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• pp.145-153
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• 2013

It is a global trend that the water management policy is shifting from a water quality-oriented assessment to the aquatic ecosystem-based assessment. The majority of aquatic ecosystem assessment systems were developed solely based on physicochemical factors (e.g., water quality and bed structure) and a limited number of organisms (e.g., plankton and benthic organisms). Only a few systems use plants for a health assessment, although plants are sensitive indicators reflecting long-term disturbances and alterations in water regimes. The development of an assessment system is underway to evaluate and manage lakes as ecosystem units in the Korean Ministry of Environment. We reviewed the existing multivariate health assessment methods of other leading countries, and discussed their applicability to Korean lakes. The application of multivariate assessment methods is costly and time consuming, in addition to the correlation problem among variables. However, a single variable is not available at this moment, and the multivariate method is an appropriate system due to its multidimensional evaluation and cumulative data generation. We, therefore, discussed multivariate assessment methods in three steps: selecting metrics, scoring metrics and assessing indices. In the step of selecting metrics, the best available metrics are species-related variables, such as composition and abundance, as well as richness and diversity. Indicator species, such as sensitive species, are the most frequently used in other countries, but their system of classification in Korea is not yet complete. In terms of scoring metrics, the lack of reference lakes with little anthropogenic impact make this step difficult, and therefore, the use of relative scores among the investigated lakes is a suitable alternative. Overall, in spite of several limitations, the development of a plant-based multivariate assessment method in Korea is possible using mostly field research data. Later, it could be improved based on qualitative metrics on plant species, and with the emergence of further survey data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.50 no.spc1
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• pp.69-76
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• 2005

This study was carried out to improve the rice grain quality of Gyeoungbuk Province from 2002 to 2004. In variation of grain quality characteristics as the cultivation years were changed, the coefficient of variation (CV) of palatability and amylose content were relatively low as $3.9\~4.3\%$ and those of protein content and head rice rate were high as $7.9\~12.2\%$. Among the varieties, the tendency of variation was similar with cultivation years changing, CV of amylose content and palatability also low as $2.6\~3.6\%$ and those of head rice rate and protein content were high as $5.4\~7.2\%$. In variation as affected by shifting of transplanting times, the CV of protein content was low as 2.2, it was also relatively low in amylose content and head rice rate as $3.1\~3.7\%$, but it was high in palatability as $5.8\%$. As the nitrogen application levels were different the CV of amylase content was $1.8\%$ that it was not affected by the N levels. But in case of protein content, the CV was $4.4\%$ that the variation was somewhat increased, it suggested that as the N levels were increased the protein content was also increased. From these results, the rice quality characteristics showed the higher variation in the change of cultivation years than that in rice varieties, transplanting times or nitrogen levels.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.3
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• pp.63-75
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• 2015

As forest therapy is getting more attention than ever, it is important to organize time for activity and location based on spatio-temporal distribution of weather condition in forest. This study aimed to analyze precise spatio-temporal distribution of weather condition by installing long-term weather monitoring device in Yonghyun national natural recreation forest and using acquired weather data in order to support forest recreation and therapy activity. First, we statistically compared 4 models of semi-variogram and the results were all similar. We selected and analyzed the circular model for this study because it was presumed to be the best model for this case. We derived 128 results from the circular model and through semi-variogram, we identified seasonal and temporal distributions of temperature and humidity. Then, we used boxplot, made of partial sill level, to identify significant differences in seasonal and temporal distributions. As a result, in spring and early morning, both temperature and humidity showed equalized result. On the other hand, in summer and early afternoon, both temperature and humidity showed uneven result. In spring and early morning, changes in weather condition are shown little from spatial shifting, it is ideal to perform recreational activities and forest therapy but in summer and early afternoon, it is unadvisable to do so as the changes in weather condition could be harmful unless any other means of preparations are made. This study proposes its significance by analyzing seasonal micro-weather of single recreation forest and presenting seasonal and temporal outcomes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.1-14
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• 2021

Innovation and Scale-up of Start-up companies are becoming important national tasks. In the past, it was spread the start-up policy paradigm such as 'Start-up America', 'Start-up Chile', 'Start-up Britain' to overcome the recession globally. However as the economic recovery has become more visible recently in advanced economies, it is shifting from a start-up support policy to a scale-up oriented policy paradigm such as 'Scale-up America', Scale-up UK', 'Scale-up Denmark'. It is necessary to enter the scale-up phase beyond the start-up phase to increase the number of high-quality jobs and to continue economic growth. Therefore, it is necessary to grow the start-up into a strong medium-sized company and to lay the foundation for survival. Therefore, the purpose of this study is to consider the antecedent factors that influence the scale-up aspiration for the start-up firm to grow into a scale-up company, and empirically identifies the differences between the stages of economic development and entrepreneurs in the country. In order to accomplish the purpose, this study predicted scale-up by aspiration which is a predictor of scale-up behavior because it is difficult to achieve visible growth in a short period of time due to the characteristics of start-up companies. In order to empirically explore these relationships, the data were collected from nascent entrepreneurs who have less than 3.5 years of the Adult Population Survey(APS) among the subjects surveyed by the Global Entrepreneurship Monitor(GEM) and the national economic development stage are divided into Innovation-driven, Efficiency-driven, Factor-driven type economies. For the test hypotheses, this study adopted the multi-level model analysis for comparison between national economic development stages and using the R 3.5.0 program. The results of this study are as follows. There is difference between the national economic development and the entrepreneur in the relationship between innovation orientation of entrepreneurs and scale-up aspirations. As the economy of the country develops, the innovation activity of the entrepreneur becomes more active. Since start-ups are heavily influenced by entrepreneurs, there is a difference in the degree of aspiration depending on how innovative an entrepreneur is in the same environment. In terms of the relationship between innovation orientation and scale-up aspiration, the fear of failure was found to differ between national economic development and entrepreneurs. The fear of failure differ from country to country, and this is one of the important factors affecting entrepreneurial activities. It is expected that the factors influencing the growth of the start-up companies which are identified through the results of these studies, will be used to create a suitable scale-up ecosystem according to the national economic development stage.

• Proceedings of the Korean Society of Crop Science Conference
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• 2019.09a
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• pp.5-5
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• 2019

The objectives of this paper are (i) to analyze past trends and future directions of rice production, consumption and trade across the world and (ii) to discuss emerging challenges and future directions in the global rice industry. Rice is a staple food of over half of the world's 7.7 billion people. It is an important economic, social, political, and cultural commodity in most Asian countries. Rice is the $1^{st}$ most widely consumed, $2^{nd}$ largely produced, and $3^{rd}$ most widely grown food crop in the world. It was cultivated by 144 million farms in over 100 countries with harvested area of over 163 million ha producing about 745 million tons paddy in 2018. About 90% of the total rice is produced in Asia. China and India, the biggest rice producers, account for over half of the world's rice production. Between 1960 and 2018, world rice production increased over threefold from 221 to 745 million tons (2.1% per year) due to area expansion from 120 to 163 million ha (0.5% per year) and paddy yield increase from 1.8 to 4.6 t/ha (1.6% per year). The Green Revolution led massive increase in rice production prevented famines, provided food for millions of people, reduced poverty and hunger, and improved livelihoods of millions of Asians. The future increase in rice production must come from yield increase as the scope for area expansion is limited. Rice is the most widely consumed food crop. The world's average per capita milled rice consumption is 64 kilograms providing 19% of daily calories. Asia accounted for 84% of global consumption followed by Africa (7%), South America (3%), and the Middle East (2%). Asia's per capita rice consumption is 100 kilograms per year providing 28% of daily calories. The global and Asian per capita consumption increased from the 1960s to the 1990s but stable afterward. The per capita rice consumption is expected to decline in Asia but increase outside Asia especially in Africa in the future. The total milled rice consumption was about 490 million tons in 2018 and projected to reach 550 million tons by 2030 and 590 million tons by 2040. Rice is thinly traded in international market because it is a highly protected commodity. Only about 9% of the total production is traded in global rice market. However, the volume of global rice trade has increased over six-fold from 7.5 to 46.5 million tons between the 1960s and 2018. A relatively small number of exporting countries interact with a large number of importing countries. The top five rice exporting countries are India, Thailand, Vietnam, Pakistan, and China accounting for 74% of the global rice export. The top five rice importing countries are China, Philippines, Nigeria, European Union and Saudi Arabia accounting for 26% of the global rice import. Within rice varieties, Japonica rice accounts for the highest share of the global rice trade (about 12%) followed by Basmati rice (about 10%). The high concentration of exports to a few countries makes international rice market vulnerable to supply disruptions in exporting countries, leading to higher world prices of rice. The export price of Thai 5% broken rice increased from 198 US$/ton in 2000 to 421 US$/ton in 2018. The volumes of trade and rice prices in the global market are expected to increase in the future. The major future challenges of the rice industry are increasing demand due to population growth, rising demand in Africa, economic growth and diet diversification, competition for natural resources (land and water), labor scarcity, climate change and natural hazards, poverty and inequality, hunger and malnutrition, urbanization, low income in rice farming, yield saturation, aging of farmers, feminization of agriculture, health and environmental concerns, improving value chains, and shifting donor priorities away from agriculture. At the same time, new opportunities are available due to access to new technologies, increased investment by the private sector, and increased global partnership. More investment in rice research and development is needed to develop and disseminate innovative technologies and practices to overcome problems and ensure food and nutrition security of the future population.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.329-341
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• 2020

As COVID-19 pandemic sweeps across the world, more than 45 million confirmed cases and over 1,000,000 deaths have occurred till now, and this situation is expected to continue for some time. In particular, more than half of the infections in European countries such as Italy and Spain occurred in nursing homes, and it is reported that over 4,000 people died in nursing homes for older adults in the United States. Therefore, the issues that need to be addressed after the COVID-19 crisis include finding a fundamental solution to group care and shifting to family-centered care. More specifically, it is expected that there will be ever more lively discussion on establishing and expanding hyper-technology based community care, that is, family-centered care integrated with ICT and other Industry 4.0 technologies. This poses a challenge of how to combine social security and social welfare with Industry 4.0 in concrete ways that go beyond the abstract suggestions made in the past. A case in point is the proposal involving smart welfare cities. Given this background, the present paper examined the concept, scope, and content of non-face-to-face care in the context of previous literature on the function and scope of the social security platform, and the concept and expandability of the smart welfare city. Implementing a smart city to realize the kind of social security and welfare that our society seeks to provide has significant bearing on the implementation of community care or aging in place. One limitation of this paper, however, is that it does not address concrete measures for implementing non-face-to-face care from the policy and legal/institutional perspectives, and further studies are needed to explore such measures in the future. It is expected that the findings of this paper will provide the future course and vision not only for the smart welfare city but also for the social security and welfare system in administrative, practical, and legislative aspects, and ultimately contribute to improving the quality of human life.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.172-176
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• 2010

Purpose: The patients' moves occurred at PET/CT scan will cause the decline of correctness in results by resulting in inconsistency of Attenuation Correction (AC) and effecting on quantitative evaluation. This study has evaluated the utility of reconstruction method using AC position changing method when having inconsistency of AC depending on the position change of emission scan after transmission scan in obtaining PET/CT 3D image. Materials and Methods: We created 1 mL syringe injection space up to ${\pm}2$, 6, 10 cm toward x and y axis based on central point of polystyrene ($20{\times}20110$ cm) into GE Discovery STE16 equipment. After projection of syringe with $^{18}F$-FDG 5 kBq/mL, made an emission by changing the position and obtained the image by using AC depending on the position change. Reconstruction method is an iteration reconstruction method and is applied two times of iteration and 20 of subset, and for every emission data, decay correction depending on time pass is applied. Also, after setting ROI to the position of syringe, compared %Difference (%D) at each position to radioactivity concentrations (kBq/mL) and central point. Results: Radioactivity concentrations of central point of emission scan is 2.30 kBq/mL and is indicated as 1.95, 1.82 and 1.75 kBq/mL, relatively for +x axis, as 2.07, 1.75 and 1.65 kBq/mL for -x axis, as 2.07, 1.87 and 1.90 kBq/mL for +y axis and as 2.17, 1.85 and 1.67 kBq/mL for -y axis. Also, %D is yield as 15, 20, 23% for +x axis, as 9, 23, 28% for -x axis, as 12, 21, 20% for +y axis and as 8, 22, 29% for -y axis. When using AC position changing method, it is indicated as 2.00, 1.95 and 1.80 kBq/mL, relatively for +x axis, as 2.25, 2.15 and 1.90 kBq/mL for -x axis, as 2.07, 1.90 and 1.90 kBq/mL for +y axis, and as 2.10, 2.02, and 1.72 kBq/mL for -y axis. Also, %D is yield as 13, 15, 21% for +x axis, as 2, 6, 17% for -x axis, as 9, 17, 17% for +y axis, and as 8, 12, 25% for -y axis. Conclusion: When in inconsistency of AC, radioactivity concentrations for using AC position changing method increased average of 0.14, 0.03 kBq/mL at x, y axis and %D was improved 6.1, 4.2%. Also, it is indicated that the more far from the central point and the further position from the central point under the features that spatial resolution is lowered, the higher in lowering of radioactivity concentrations. However, since in actual clinic, attenuation degree increases more, it is considered that when in inconsistency, such tolerance will be increased. Therefore, at the lesion of the part where AC is not inconsistent, the tolerance of radioactivity concentrations will be reduced by applying AC position changing method.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.