The present study aims to investigate the impact of tourism growth on human development in Indian economy. For this purpose, the study uses annual data from 1980 to 2018 and utilizes two proxies for tourism growth – tourism receipt and tourist arrivals – and uses human development index calculated by UNDP. The study uses control variables such as government expenditure and trade openness. The study employs auto regressive distributed lag (ARDL) approach to investigate the cointegrating relationship among the variables in the model. Further, the study also explores the causal nexus between tourism sector and human development by using the Toda-Yamamoto Granger non-causality test. The result of ARDL bounds test reveals the existence of cointegrating relationship between human development indicators, government expenditure, trade openness, and tourism sector growth. The cointegating coefficient confirms a positive and significant relationship between tourism sector growth and human development in India. The causality result suggests that economic growth and tourism have a positive impact while trade openness has a negative impact on human development in India. The major findings of this study suggest that tourism plays an important role in the socio-economic development of Indian economy in recent years and the country must develop this sector to achieve sustainable development.

Extreme low temperature has various effects on society. It has negative impacts on health, industry, agriculture, and transportation during winter season. Therefore, extreme low temperature is recognized as main factor that causing traffic accidents. This study analyzed the relationship between low temperature and traffic accidents from 2012 to 2018. This study also compared the differences in vulnerability to low temperature between urban and rural areas. Generalized Additive Model (GAM) and Poisson regression model are used to estimate the thresholds and the level of traffic accidents. The results show that the thresholds tend to be decreased gradually, but the level of traffic accidents are more likely to increase in both urban and rural areas Comparatively, rural areas are more vulnerable to traffic accident caused by drop in temperature than urban areas.

Commercial banks play an important role as the main source of funding in the transition process of Vietnam as a market economy. As a result, enhancing the efficiency and productivity of Vietnamese banks can decrease the lending cost for individuals and enterprises. This study is to measure and analyze the productivity change of Vietnamese banking system in different ownership cohorts and sources of this change during the period of restructuring (2011-2019). The Hicks-Moorsteen total factor productivity index is utilised to measure the productivity change and to identify the sources of this change. For an empirical analysis, the data of 28 Vietnamese commercial banks from 2011 to 2018 is collected from their financial statements including balance sheets and income statements. The results show an increase of Vietnamese bank productivity due to the technological progress. While foreign and joint-venture banks are the most advanced cohort, stateowned banks have the lowest rate of productivity growth. The restructuring program negatively impacts scale efficiency and this measure attributes to a decline in the overall efficiency of the banks. We also found that state-owned commercial banks are the most efficient group in the sense that they can achieve the maximal level of revenue from a given amount of expense.

In this paper, we have developed a Fama - French five factor model (FF5 model) from Fama & French (2015) by using concept of timevarying coefficient. For a data set, we have used monthly data form Kenneth R. French home page, it include Japan portfolios (classified by using size and book-to-market) and 5 factors from July 1990 to April 2020. The first analysis, we used Augmented Dickey-Fuller test (ADF test) for the stationary test, from the result, all Japan portfolios and 5 factors are stationary. Next analysis, we estimated a coefficient of Fama - French five factor model by using a generalized additive model with a thin-plate spline to create the time-varying coefficient Fama - French five factor model (TV-FF5 model). The benefit of this study is TV-FF5 model which can capture a different effect at different times of 5 factors but the traditional FF5 model can’t do it. From the result, we can show a time-varying coefficient in all factors and in all portfolios, for time-varying coefficients of Rm-Rf, SMB, and HML are significant for all Japan portfolios, time-varying coefficients of RMW are positively significant for SM, and SH portfolio and time-varying coefficients of CMA are significant for SM, SH, and BM portfolio.

In this paper, we propose the new time-varying coefficient GARCH-in-Mean model. The benefit of our model is to allow the risk-return parameter in the mean equation to vary over time. At the end of 2019 to the beginning of 2020, the world witnessed two shocking events: COVID-19 pandemic and 2020 oil price war. So, we decide to use the daily data from December 2, 2019 to May 29, 2020, which cover these two major events. The purpose of this study is to find the dynamic movement between risk and return in four major oil markets: Brent, West Texas Intermediate, Dubai, and Singapore Exchange, during COVID-19 pandemic and 2020 oil price war. For the European oil market, our model found a significant and positive risk-return relationship in Brent during March 26-April 21, 2020. For the North America oil market, our model found a significant positive risk return relationship in West Texas Intermediate (WTI) during March 12-May 8, 2020. For the Middle East oil market, we found a significant and positive risk-return relationship in Dubai during March 12-April 14, 2020. Lastly, for the South East Asia oil market, we found a significant positive risk return relationship in Singapore Exchange (SGX) from March 9-May 29, 2020.

This research paper examines the causal relationship between India’s economic growth and sectoral contribution to Gross Domestic Product (GDP) and vice versa, in the short-run and long-run, over a 10 years time period. Johansen’s method of cointegration is used to study the cointegration between the sectoral contributions to Indian GDP vis-à-vis India’s economic growth. Further, the route of interconnection between economic growth and sectoral contribution is tested by using Vector Auto Regression (VAR) model. Special attention was given for investigating impulse responses of economic growth depending on the innovations in sectoral contribution using time-series data from 1960 to 2015. This paper highlighted a dynamic co-relationship among industrial sector contribution and agricultural sector contribution and economic development. In the long run, one percent change in industrial sector contribution causes an increase of 3.42 percent in the economic growth and an increase of 1.12 percent in the primary sector contribution, while in the short run industrial and service sector contributions showed significant impact on economic development and agriculture sector. The changing composition of sector contribution is going to be an important activity for the policymakers to monitor and control where the technology and integration of sectors play a significant role in economic development.

In financial economics studies, the autoregressive model has been a workhorse for a long time. However, the model has a fixed value on every parameter and requires the stationarity assumptions. Time-varying coefficient autoregressive model that we use in this paper offers some desirable benefits over the traditional model such as the parameters are allowed to be varied over-time and can be applies to nonstationary financial data. This paper provides the Monte Carlo simulation studies which show that the model can capture the dynamic movement of parameters very well, even though, there are some sudden changes or jumps. For the daily data from January 1, 2015 to February 12, 2020, our paper provides the empirical studies that Thailand, Taiwan and Tokyo Stock market Index can be explained very well by the time-varying coefficient autoregressive model with lag order one while South Korea’s stock index can be explained by the model with lag order three. We show that the model can unveil the non-linear shape of the estimated mean. We employ GJR-GARCH in the condition variance equation and found the evidences that the negative shocks have more impact on market’s volatility than the positive shock in the case of South Korea and Tokyo.

This study investigates the behavior of foreign investors in the Stock Exchange of Thailand (SET) in the time of coronavirus disease 2019 (COVID-19) as to whether trading is abnormal, what strategy is followed, whether herd behavior is present, and whether the actions destabilize the market. Foreign investors’ trading behavior is measured by net buying volume divided by market capitalization, whereas the stock market behavior is measured by logged return on the SET index portfolio. The data are daily from Tuesday, August 28, 2018, to Monday, May 18, 2020. The study extends the conditional-regression model in an event-study framework and extracts the unobserved abnormal trading behavior using the Kalman filtering technique. It then applies vector autoregressions and impulse responses to test for the investors’ chosen strategy, herd behavior, and market destabilization. The results show that foreign investors’ abnormal trading volume is negative and significant. An analysis of the abnormal trading volume with stock returns reveals that foreign investors are not positive-feedback investors, but rather, they self-herd. Although foreign investors’ abnormal trading does not destabilize the market, it induces stock-return volatility of a similar size to normal trade. The methodology is new; the findings are useful for researchers, local authorities, and investors.

This paper describes the initial operations and preliminary results of the Instrument for the study of Stable/Storm-time Space (ISSS) onboard the microsatellite Next Generation Small Satellite-1 (NEXTSat-1), which was launched on December 4, 2018 into a sun-synchronous orbit at an altitude of 575 km with an orbital inclination angle of 97.7°. The spacecraft and the instruments have been working normally, and the results from the observations are in agreement with those from other satellites. Nevertheless, improvement in both the spacecraft/instrument operation and the analysis is suggested to produce more fruitful scientific results from the satellite operations. It is expected that the ISSS observations will become the main mission of the NEXTSat-1 at the end of 2020, when the technological experiments and astronomical observations terminate after two years of operation.

방사성폐기물의 심층처분에서 생물권에서의 방사성 핵종 이동을 핵종의 이동 경과 시간을 계산하여 평가하는 방법이 제안 되었다. 방사성 핵종은 자연 방벽에서 유출되어 지하 천부의 지하수 흐름을 따라 대규모 지표수체에 도달한다고 가정하였다. 생물권은 기반암 위에 있는 대수층을 포함하는 천부 지하 환경으로 정의하였다. 제안된 방법을 이용하여, 계산 알고리즘을 수립하였고, 알고리즘을 수행하는 컴퓨터 코드를 작성하였다. 작성된 코드는 간단한 사례에서 계산된 모의 결과를 해석해 계산 결과 및 지표 부근에서의 방사성 핵종 이동에 의한 방사선량 평가에 널리 이용되는 공공 프로그램의 계산 결과와 비교하여 검증하였다. 사례 연구 조건을 가상의 심층처분장에서의 방사성 핵종 이동에 대한 이전 연구를 통해 작성하였다. 작성된 코드는 사례 연구에서 생물권의 하천으로 유출되는 핵종의 이동량을 계산하였다. 이전 연구에서는 가상의 처분장에서 모암까지의 방사성핵종의 이동만을 보여주었기 때문에, 이 코드는 모든 경로를 지나가는 방사성 핵종의 전체적인 이동을 파악하는데 도움이 될 수 있었다.

목적: 이 연구는 체육수업에 참여하는 학생들을 대상으로 체육교사와 동료의 관계성지지가 학생들의 심리적 욕구, 학습참여와 여가시간 신체활동에 어떻게 영향을 미치는지를 규명하였다. 방법: 연구 참여자들은 서울 및 경기도 소재 중학교에 재학 중인 1,004명의 학생이다. 연구 참여자들을 대상으로 체육수업 상황에서 지각하고 있는 교사와 동료의 관계성지지, 심리적 욕구, 학습 참여(학습 목표)와 여가시간 신체활동 의도에 관한 설문조사를 진행하였다. 수집된 자료는 통계 프로그램을 이용하여, 기술통계, 신뢰도 분석, 타당도 분석, 상관 분석과 경로 분석을 진행했다. 결과: 체육교사와 동료의 관계성지지는 학생들의 심리적 욕구, 학습참여와 신체활동 의도와 정적 상관 및 영향력을 행사하는 것으로 밝혀졌다. 무엇보다 동료의 관계성지지는 교사의 관계성지지 보다 학생들의 심리적 욕구, 학습참여와 신체활동 의도와 더 높은 상관 및 예측력을 지닌 것으로 나타났다. 결론: 체육수업 상황에서 교사만큼이나 동료의 관계성지지가 학생들의 긍정적인 정서 및 심리적 경험을 유도하는 선행변인이라는 것을 보여준다. 본 연구의 결과는 체육수업 및 스포츠 상황에서 교사와 더불어 동료의 영향력에 관한 정보를 교육 현장에 제공하여 실제적인 도움을 줄 것으로 기대된다.

This study analyzed the quality characteristics of black garlic made from Seosan Yukjok Garlic and elephant garlic in Seosan, Chungnam province. Of the inorganic components, Mg content was the highest in all treatment groups, and the Ca content was high in each of the 15 day treatments. The content of K was high after 10 days aging in Yukjok garlic and after 15 days in the elephant garlic. The Fe, Na, K, and Mg content was high in Yukjok black garlic after 15 days, and Na, K, Ca, and Mg were high in the elephant black garlic aged for 15 days. The crude fat content was high in both Yukjok black garlic and elephant black garlic after 15 days. Vitamin C content was highest in both types of garlic after aging for 15 days. An analysis of four kinds of organic acids showed that citric acid was the only organic acid to appear in raw garlic of Yukjok garlic and elephant garlic. Black Yukjok garlic and elephant black garlic had a greater total amino acid content than the raw garlic of either type. However, among the tested amino acids, 13 kinds of amino acids were at their highest after five days of ripening in Yukjok black garlic, while 15 kinds of amino acids were abundant in elephant garlic after the same period. Eight kinds of amino acids were high after aging for 15 days. Through this study, it was confirmed that, in the process of making black garlic, changes in the main components of the garlic occur through different routes, and these changes vary depending on the garlic species. Therefore, this study provided basic data for the processing of Seosan's Yukjok black garlic and elephant black garlic.

We have developed an algorithm for tracking coronal mass ejection (CME) propagation that allows us to estimate CME speed and its arrival time at Earth. The algorithm may be used either to forecast the CME’s arrival on the day of the forecast or to update the CME tracking information for the next day’s forecast. In our case study, we successfully tracked CME propagation using the algorithm based on g-values of interplanetary scintillation (IPS) observation provided by the Institute for Space- Earth Environmental Research (ISEE). We were able to forecast the arrival time (Δt = 0.30 h) and speed (Δv = 20 km/s) of a CME event on October 2, 2000. From the CME-interplanetary CME (ICME) pairs provided by Cane & Richardson (2003), we selected 50 events to evaluate the algorithm’s forecast capability. Average errors for arrival time and speed were 11.14 h and 310 km/s, respectively. Results demonstrated that g-values obtained continuously from any single station observation were able to be used as a proxy for CME speed. Therefore, our algorithm may give stable daily forecasts of CME position and speed during propagation in the region of 0.2–1 AU using the IPS g-values, even if IPS velocity observations are insufficient. We expect that this algorithm may be widely accepted for use in space weather forecasting in the near future.