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Validity of the Happiness-Enhancing Activities and Positive Practices Inventory(HAPPI) Scale in Physical Activities Participation in Korean Old Adults (신체활동참여 한국 노인을 위한 행복증진활동(HAPPI)척도의 구인타당도)

  • Kim, Woo-Kyung
    • Journal of Korea Entertainment Industry Association
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    • v.13 no.7
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    • pp.285-294
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    • 2019
  • The purpose of this study is to assess construct validity and verify the concept of the Happiness-enhancing Activities and Positive Practices Inventory(HAPPI) developed by Henricksen & Stephens(2013), for Korean old adults who participating physical activities with measuring happiness-related propensity. In this study, the research model was confirmed by evidenced based on the content validity, EFA, construct validity of the latent structure analysis with CFA, reliability as internal consistency. Using self-reported questionnaire conducted among 370 participants who physical activities. Total of 344 data were selected. As a result, internal consistency α was acceptable. Evidence-based on convergent and discriminant of the CFA as GFI=.925, CFI .962, TLI .953, and RMSEA .062 appeared significantly. Model goodness-of-fit, C.R. ratio(Critical ratio: estimates/SE) and Squared Multiple Correlations(SMC), and average variance extracted(AVE) was verified with the hypothesis of the model. Therefore, HAPPI validity evidence for the model fit was confirmed. In conclusion, the HAPPI 4 factors and 16 items(Other-focused, Personal recreation and interests, Achievement, Self-Concordant Work, Spiritual and thought-related) has reliable evidence to apply for Korean old adults and applicable assessment of happiness.

Determinants of Variance Risk Premium (경제지표를 활용한 분산프리미엄의 결정요인 추정과 수익률 예측)

  • Yoon, Sun-Joong
    • Economic Analysis
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    • v.25 no.1
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    • pp.1-33
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    • 2019
  • This paper examines the economic factors that are related to the dynamics of the variance risk premium, and specially, which economic factors are related to the forecasting power of the variance premium regarding future index returns. Eleven general economic variables, eight interest rate variables, and eleven sentiment-associated variables are used to figure out the relevant economic variables that affect the variance risk premium. According to our empirical results, the won-dollar exchange rates, foreign reserves, the historical/implied volatility, and interest rate variables all have significant coefficients. The highest adjusted R-squared is more than 65 percent, indicating their significant explanatory power of the variance risk premium. Next, to verify the economic variables associated with the predictability of the variance risk premium, we conduct forecasting regressions to predict future stock returns and volatilities for one to six months. Our empirical analysis shows that only the won-dollar exchange rate, among the many variables associated with the dynamics of the variance risk premium, has a significant forecasting ability regarding future index returns. These results are consistent with results found in previous studies, including Londono (2012) and Bollerslev et al. (2014), which show that the variance risk premium is related to global risk factors.

Predicting the splitting tensile strength of manufactured-sand concrete containing stone nano-powder through advanced machine learning techniques

  • Manish Kewalramani;Hanan Samadi;Adil Hussein Mohammed;Arsalan Mahmoodzadeh;Ibrahim Albaijan;Hawkar Hashim Ibrahim;Saleh Alsulamy
    • Advances in nano research
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    • v.16 no.4
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    • pp.375-394
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    • 2024
  • The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

Application of Machine Learning Algorithm and Remote-sensed Data to Estimate Forest Gross Primary Production at Multi-sites Level (산림 총일차생산량 예측의 공간적 확장을 위한 인공위성 자료와 기계학습 알고리즘의 활용)

  • Lee, Bora;Kim, Eunsook;Lim, Jong-Hwan;Kang, Minseok;Kim, Joon
    • Korean Journal of Remote Sensing
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    • v.35 no.6_2
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    • pp.1117-1132
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    • 2019
  • Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

Stand Volume Estimation of Pinus Koraiensis Using Landsat TM and Forest Inventory (Landsat TM 영상과 현장조사를 이용한 잣나무림 재적 추정)

  • Park, Jin-Woo;Lee, Jung-Soo
    • Journal of the Korean Association of Geographic Information Studies
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    • v.17 no.1
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    • pp.80-90
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    • 2014
  • The objective of this research is to estimate the stand volume of Pinus koraiensis, by using the investigated volume and the information of remote sensing(RS), in the research forest of Kangwon National University. The average volume of the research forest per hectare was $307.7m^3/ha$ and standard deviation was $168.4m^3/ha$. Before and after carrying out 3 by 3 majority filtering on TM image, eleven indices were extracted each time. Independent variables needed for linear regression equation were selected using mean pixel values by indices. The number of indices were eleven: six Bands(except for thermal Band), NDVI, Band Ratio(BR1:Band4/Band3, BR2:Band5/Band4, BR3:Band7/Band4), Tasseled Cap-Greeness. As a result, NDVI and TC G were chosen as the most suitable indices for regression before and after filtering, and R-squared was high: 0.736 before filtering, 0.753 after filtering. As a result of error verification for an exact comparison, RMSE before and after filtering was about $69.1m^3/ha$, $67.5m^3/ha$, respectively, and bias was $-12.8m^3/ha$, $9.7m^3/ha$, respectively. Therefore, the regression conducted with filtering was selected as an appropriate model because of low RMSE and bias. The estimated stand volume applying the regression was $160,758m^3$, and the average volume was $314m^3/ha$. This estimation was 1.2 times higher than the actual stand volume of Pinus koraiensis.

Impact of Awareness and Educational Experiences on Cardiopulmonary Resuscitation in the Ability to Execute of Cardiopulmonary Resuscitation among Korean Adults (한국 성인에서 심폐소생술에 대한 인지, 교육경험이 그 시행능력에 미치는 영향)

  • Lee, Jae-Kwang;Kim, Jeongwoo;Kim, Kunil;Kim, Keunhyung;Kim, Dongphil;Kim, Yuri;Moon, Seonggeun;Min, Byungju;Yu, Hwayoung;Lee, Chealim;Jeong, Wonyoung;Han, Changhun;Huh, Inho;Park, Jung Hee;Lee, Moo-Sik
    • Journal of agricultural medicine and community health
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    • v.43 no.4
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    • pp.234-249
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    • 2018
  • This study was performed to identify the impact of awareness and educational experiences on cardiopulmonary resuscitation in the ability to execute of cardiopulmonary resuscitation among Korean adults. This study used original data of 2014 Community Health Data Survey. 228,712 participants in this survey were resident in South Korea who is aged 19 or older on July 2014. Participants in this survey were sampled an average of 900 residents(target error ${\pm}3percent$) per community health center of Korea. Data were analyzed by using R 3.1.3 employing chi-squared test, fisher's exact analysis, and logistic regression analysis. Ability to execute CPR was significantly higher in males(3.34 time), higher the education level (1.61 times), the white color occupation (1.14 times), the higher the income level (1.07 times), the higher the education level (0.91 times), non-hypertensive patients (1.12 times), non-diabetic patients (1.16 times), non-dyslipidemic patients (0.86 times), non-stroke patients (0.30 times), CPR education experience group (3.25 times), CPR experience group with manikin-based training (4.30 times), higher subjective health status (1.08 times, 1.16 times) respectively. This study identified that awareness, educational experience, and mannequin-based learning experience of CPR impacted on the ability to execute CPR. Responding to education-related factors could contribute to reducing the rate of out-of-hospital acute cardiac arrest by improving the ability to execute CPR of the general public.

Modeling and mapping fuel moisture content using equilibrium moisture content computed from weather data of the automatic mountain meteorology observation system (AMOS) (산악기상자료와 목재평형함수율에 기반한 산림연료습도 추정식 개발)

  • Lee, HoonTaek;WON, Myoung-Soo;YOON, Suk-Hee;JANG, Keun-Chang
    • Journal of the Korean Association of Geographic Information Studies
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    • v.22 no.3
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    • pp.21-36
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    • 2019
  • Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

Effects of Boliing, Steaming, and Chemical Treatment on Solid Wood Bending of Quercus acutissima Carr. and Pinus densiflora S. et. Z. (자비(煮沸), 증자(蒸煮) 및 약제처리(藥劑處理)가 상수리나무와 소나무의 휨가공성(加工性)에 미치는 영향(影響))

  • So, Won-Tek
    • Journal of the Korean Wood Science and Technology
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    • v.13 no.1
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    • pp.19-62
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    • 1985
  • This study was performed to investigate: (i) the bending processing properties of silk worm oak (Quercus acutissima Carr.) and Korean red pine (Pinus densiflora S. et Z.) by boiling and steaming treatments; (ii) the effects of interrelated factors - sapwood and heartwood, annual ring placement, softening temperature and time, moisture content. and wood defects on bending processing properties; (iii) the changing rates of bending radii after release from a tension strap, and (iv) the improving methods of bending process by treatment with chemicals. The size of specimens tested was $15{\times}15{\times}350mm$ for boiling and steaming treatments and $5{\times}10{\times}200mm$ for treatments with chemicals. The specimens were green for boiling treatments and dried to 15 percent for steaming treatments. The specimens for treatments with chemicals were soaked in saturated urea solution, 35 percent formaldehyde solution, 25 percent polyethylene glycol -400 solution, and 25 percent ammonium hydroxide solution for 5 days and immediately followed the bending process, respectively. The results obtained were as follows: 1. The internal temperature of silk worm oak and Korean red pine by boiling and steaming time was raised slowly to $30^{\circ}C$ but rapidly from $30^{\circ}C$ to $80-90^{\circ}C$ and then slowly from $80-90^{\circ}C$ to $100^{\circ}C$. 2. The softening time required to the final temperature was directly proportional to the thickness of specimen. The time required from $25^{\circ}C$ to $100^{\circ}C$ for 15mm-squared specimen was 9.6-11.2 minutes in silk worm oak and 7.6-8.1 minutes in Korean red pine. 3. The moisture content (M.C.) of specimen by steaming time was increased rapidly first 4 minutes in the both species, and moderately from 4 to 20 minutes and then slowly and constantly in silk worm oak, and moderately from 4 to 15 minutes and then slowly and constantly in Korean red pine. The M.C. of 15mm-squared specimen in 50 minutes of steaming was increased to 18.0 percent in the oak and 22.4 percent in the pine from the initial conditioned M.C. of 15 percent The rate of moisture adsorption measured was therefore faster in the pine than in the oak. 4. The mechanical properties of the both species were decreased significantly with the increase of boiling rime. The decrement by the boiling treatment for 60 minutes was measured to 36.6-45.0 percent in compressive strength, 12.5-17.5 percent in tensile strength, 31.6-40.9 percent in modulus of rupture, and 23.3-34.6 percent in modulus of elasticity. 5. The minimum bending radius (M.B.R.) of sapwood and heartwood was 60-80 mm and 90 mm in silk worm oak, and 260 - 300 mm and 280 - 300 mm in Korean red pine, respectively. Therefore, the both species showed better bending processing properties in sapwood than in heartwood. 6. The M.B.R. of edge-grained and flat-grained specimen in suk worm oak was 60-80 mm, but the M.B.R. in Korean red pine was 240-280 mm and 260-360 mm, respectively. Comparing the M.B.R. of edge-grained with flat-grained specimen, in the pine the edge-grained showed better bending processing property than the flat-grained. 7. The bending processing properties of the both species were improved by the rising of softening temperature from $40^{\circ}C$ to $100^{\circ}C$. The minimum softening temperature for bending was $90^{\circ}C$ in silk worm oak and $80^{\circ}C$ in Korean red pine, and the dependency of softening temperature for bending was therefore higher in the oak than in the pine. 8. The bending processing properties of the both species were improved by the increase of softening time as well as temperature, but even after the internal temperature of specimen reaching to the final temperature, somewhat prolonged softening was required to obtain the best plastic conditions. The minimum softening time for bending of 15 mm-squared silk worm oak and Korean red pine specimen was 15 and 10 minutes in the boiling treatment, and 30 and 20 minutes in the steaming treatment, respectively. 9. The optimum M.C. for bending of silk worm oak was 20 percent, and the M.C. above fiber saturation point rather degraded the bending processing property, whereas the optimum M.C. of Korean red pine needed to be above 30 percent. 10. The bending works in the optimum conditions obtained as seen in Table 24 showed that the M.B.R. of silk worm oak and Korean red pine was 80 mm and 240 mm in the boiling treatment, and 50 mm and 280 mm in the steaming treatment, respectively. Therefore, the bending processing property of the oak was better in the steaming than in the boiling treatment, but that of the pine better in the boiling than in the steaming treatment. 11. In the bending without a tension strap, the radio r/t of the minimum bending radius t to the thickness t of silk worm oak and Korean red pine specimen amounted to 16.0 and 21.3 in the boiling treatment, and 17.3 and 24.0 in the steaming treatment, respectively. But in the bending with a tension strap, the r/t of the oak and the pine specimen decreased to 5.3 and 16.0 in t he boiling treatment, and 3.3 and 18.7 in the steaming treatment, respectively. Therefore, the bending processing properties of the both species were significantly improved by the strap. 12. The effect of pin knot on the degradation of bending processing property was very severe in silk worm oak by side, e.g. 90 percent of the oak specimens with pin knot on the concave side were ruptured when bent to a 100 mm radius but only 10 percent of the other specimens with pin knot on the convex side were ruptured. 13. The changing rate in the bending radius of specimen bent to a 300 mm radius after 30 days of exposure to room temperature conditions was measured to 4.0-10.3 percent in the boiling treatment and 13,0-15.0 percent in the steaming treatment. Therefore, the degree of spring back after release was higher in the steaming than in the boiling treatment. And the changing rate of moisture-proofing treated specimen by expoxy resin coating was only -1.0.0 percent. 14. Formaldehyde, 35 percent solution, and 25 percent polyethylene glycol-400 solution found no effect on the plasticization of the both species, but saturated urea solution and 25 percent ammonium hydroxide solution found significant effect in comparison to non-treated specimen. But the effect of the treatment with chemicals alone was inferior to that of the steaming treatment, and the steaming treatment after the treatment with chemicals improved 10-24 percent over the bending processing property of steam-bent specimen. 15. Three plasticity coefficients - load-strain coefficient, strain coefficient, and energy coefficient - were evaluated to be appropriate for the index of bending processing property because the coefficients had highly significant correlation with the bending radius. The fitness of the coefficients as the index was good at load-strain coefficient, energy coefficient, and strain coefficient, in order.

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Monitoring Ground-level SO2 Concentrations Based on a Stacking Ensemble Approach Using Satellite Data and Numerical Models (위성 자료와 수치모델 자료를 활용한 스태킹 앙상블 기반 SO2 지상농도 추정)

  • Choi, Hyunyoung;Kang, Yoojin;Im, Jungho;Shin, Minso;Park, Seohui;Kim, Sang-Min
    • Korean Journal of Remote Sensing
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    • v.36 no.5_3
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    • pp.1053-1066
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    • 2020
  • Sulfur dioxide (SO2) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO2 can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO2 crucial. In South Korea, SO2 monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO2 concentrations. Thus, this research estimated spatially continuous ground-level SO2 concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO2 estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO2 concentrations based on the proposed model agreed with the general seasonality of SO2 and the temporal patterns of emission sources.

Development of Yóukè Mining System with Yóukè's Travel Demand and Insight Based on Web Search Traffic Information (웹검색 트래픽 정보를 활용한 유커 인바운드 여행 수요 예측 모형 및 유커마이닝 시스템 개발)

  • Choi, Youji;Park, Do-Hyung
    • Journal of Intelligence and Information Systems
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    • v.23 no.3
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    • pp.155-175
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    • 2017
  • As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.