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Mediation analysis of dietary habits, nutrient intakes, daily life in the relationship between working hours of Korean shift workers and metabolic syndrome : the sixth (2013 ~ 2015) Korea National Health and Nutrition Examination Survey (교대근무자의 근무시간과 대사증후군의 관계에서 식습관, 영양섭취상태, 일상생활의 매개효과 분석 : 6기 국민건강영양조사 (2013 ~ 2015) 데이터 이용)

  • Kim, Yoona;Kim, Hyeon Hee;Lim, Dong Hoon
    • Journal of Nutrition and Health
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    • v.51 no.6
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    • pp.567-579
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    • 2018
  • Purpose: This study examined the mediation effects of dietary habits, nutrient intake, daily life in the relationship between the working hours of Korean shift workers and metabolic syndrome. Methods: Data were collected from the sixth (2013-2015) Korea National Health and Nutrition Examination Survey (KNHANES). The stochastic regression imputation was used to fill missing data. Statistical analysis was performed in Korean shift workers with metabolic syndrome using the SPSS 24 program for Windows and a structural equation model (SEM) using an analysis of moment structure (AMOS) 21.0 package. Results: The model fitted the data well in terms of the goodness of fit index (GFI) = 0.939, root mean square error of approximation (RMSEA) = 0.025, normed fit index (NFI) = 0.917, Tucker-Lewis index (TLI) = 0.984, comparative fit index (CFI) = 0.987, and adjusted goodness of fit index (AGFI) = 0.915. Specific mediation effect of dietary habits (p = 0.023) was statistically significant in the impact of the working hours of shift workers on nutrient intake, and specific mediation effect of daily life (p = 0.019) was statistically significant in the impact of the working hours of shift workers on metabolic syndrome. On the other hand, the dietary habits, nutrient intake and daily life had no significant multiple mediator effects on the working hours of shift workers with metabolic syndrome. Conclusion: The appropriate model suggests that working hours have direct effect on the daily life, which has the mediation effect on the risk of metabolic syndrome in shift workers.

Estimation of Soil Moisture Using Sentinel-1 SAR Images and Multiple Linear Regression Model Considering Antecedent Precipitations (선행 강우를 고려한 Sentinel-1 SAR 위성영상과 다중선형회귀모형을 활용한 토양수분 산정)

  • Chung, Jeehun;Son, Moobeen;Lee, Yonggwan;Kim, Seongjoon
    • Korean Journal of Remote Sensing
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    • v.37 no.3
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    • pp.515-530
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    • 2021
  • This study is to estimate soil moisture (SM) using Sentinel-1A/B C-band SAR (synthetic aperture radar) images and Multiple Linear Regression Model(MLRM) in the Yongdam-Dam watershed of South Korea. Both the Sentinel-1A and -1B images (6 days interval and 10 m resolution) were collected for 5 years from 2015 to 2019. The geometric, radiometric, and noise corrections were performed using the SNAP (SentiNel Application Platform) software and converted to backscattering coefficient of VV and VH polarization. The in-situ SM data measured at 6 locations using TDR were used to validate the estimated SM results. The 5 days antecedent precipitation data were also collected to overcome the estimation difficulty for the vegetated area not reaching the ground. The MLRM modeling was performed using yearly data and seasonal data set, and correlation analysis was performed according to the number of the independent variable. The estimated SM was verified with observed SM using the coefficient of determination (R2) and the root mean square error (RMSE). As a result of SM modeling using only BSC in the grass area, R2 was 0.13 and RMSE was 4.83%. When 5 days of antecedent precipitation data was used, R2 was 0.37 and RMSE was 4.11%. With the use of dry days and seasonal regression equation to reflect the decrease pattern and seasonal variability of SM, the correlation increased significantly with R2 of 0.69 and RMSE of 2.88%.

Evaluation of stream flow and water quality changes of Yeongsan river basin by inter-basin water transfer using SWAT (SWAT을 이용한 유역간 물이동량에 따른 영산강유역의 하천 유량 및 수질 변동 분석)

  • Kim, Yong Won;Lee, Ji Wan;Woo, So Young;Kim, Seong Joon
    • Journal of Korea Water Resources Association
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    • v.53 no.12
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    • pp.1081-1095
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    • 2020
  • This study is to evaluate stream flow and water quality changes of Yeongsan river basin (3,371.4 km2) by inter-basin water transfer (IBWT) from Juam dam of Seomjin river basin using SWAT (Soil and Water Assessment Tool). The SWAT was established using inlet function for IBWT between donor and receiving basins. The SWAT was calibrated and validated with 14 years (2005 ~ 2018) data of 1 stream (MR) and 2 multi-functional weir (SCW, JSW) water level gauging stations, and 3 water quality stations (GJ2, NJ, and HP) including data of IBWT and effluent from wastewater treatment plants of Yeongsan river basin. For streamflow and weir inflows (MR, SCW, and JSW), the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS) were 0.69 ~ 0.81, 0.61 ~ 0.70, 1.34 ~ 2.60 mm/day, and -8.3% ~ +7.6% respectively. In case of water quality, the R2 of SS, T-N, and T-P were 0.69 ~ 0.81, 0.61 ~ 0.70, and 0.54 ~ 0.63 respectively. The Yeongsan river basin average streamflow was 12.0 m3/sec and the average SS, T-N, and T-P were 110.5 mg/L, 4.4 mg/L, 0.18 mg/L respectively. Under the 130% scenario of IBWT amount, the streamflow, SS increased to 12.94 m3/sec (+7.8%), 111.26 mg/L (+0.7%) and the T-N, T-P decreased to 4.17 mg/L (-5.2%), 0.165 mg/L (-8.3%) respectively. Under the 70% scenario of IBWT amount, the streamflow, SS decreased to 11.07 m3/sec (-7.8%), 109.74 mg/L (-0.7%) and the T-N, T-P increased to 4.68 mg/L (+6.4%), 0.199 mg/L (+10.6%) respectively.

Estimation of the Lodging Area in Rice Using Deep Learning (딥러닝을 이용한 벼 도복 면적 추정)

  • Ban, Ho-Young;Baek, Jae-Kyeong;Sang, Wan-Gyu;Kim, Jun-Hwan;Seo, Myung-Chul
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.66 no.2
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    • pp.105-111
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    • 2021
  • Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.

Physical Offset of UAVs Calibration Method for Multi-sensor Fusion (다중 센서 융합을 위한 무인항공기 물리 오프셋 검보정 방법)

  • Kim, Cheolwook;Lim, Pyeong-chae;Chi, Junhwa;Kim, Taejung;Rhee, Sooahm
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1125-1139
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    • 2022
  • In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

Generation of Daily High-resolution Sea Surface Temperature for the Seas around the Korean Peninsula Using Multi-satellite Data and Artificial Intelligence (다종 위성자료와 인공지능 기법을 이용한 한반도 주변 해역의 고해상도 해수면온도 자료 생산)

  • Jung, Sihun;Choo, Minki;Im, Jungho;Cho, Dongjin
    • Korean Journal of Remote Sensing
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    • v.38 no.5_2
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    • pp.707-723
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    • 2022
  • Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R2 of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

Estimation of High Resolution Sea Surface Salinity Using Multi Satellite Data and Machine Learning (다종 위성자료와 기계학습을 이용한 고해상도 표층 염분 추정)

  • Sung, Taejun;Sim, Seongmun;Jang, Eunna;Im, Jungho
    • Korean Journal of Remote Sensing
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    • v.38 no.5_2
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    • pp.747-763
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    • 2022
  • Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

Performance evaluation of hyperspectral bathymetry method for morphological mapping in a large river confluence (초분광수심법 기반 대하천 합류부 하상측정 성능 평가)

  • Kim, Dongsu;Seo, Youngcheol;You, Hojun;Gwon, Yeonghwa
    • Journal of Korea Water Resources Association
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    • v.56 no.3
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    • pp.195-210
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    • 2023
  • Additional deposition and erosion in large rivers in South Korea have continued to occur toward morphological stabilization after massive dredging through the four major river restoration project, subsequently requiring precise bathymetry monitoring. Hyperspectral bathymetry method has increasingly been highlighted as an alternative way to estimate bathymetry with high spatial resolution in shallow depth for replacing classical intrusive direct measurement techniques. This study introduced the conventional Optimal Band Ratio Analysis (OBRA) of hyperspectral bathymetry method, and evaluated the performance in a domestic large river in normal turbid and flow condition. Maximum measurable depth was estimated by applying correlation coefficient and root mean square error (RMSE) produced during OBRA with cascadedly applying cut-off depth, where the consequent hyperspectral bathymetry map excluded the region over the derived maximum measurable depth. Also non-linearity was considered in building relation between optimal band and depth. We applied the method to the Nakdong and Hwang River confluence as a large river case and obtained the following features. First, the hyperspectal method showed acceptable performance in morphological mapping for shallow regions, where the maximum measurable depth was 2.5 m and 1.25 m in the Nakdong and Hwang river, respectively. Second, RMSE was more feasible to derive the maximum measurable depth rather than the conventional correlation coefficient whereby considering various scenario of excluding range of in situ depths for OBRA. Third, highly turbid region in Hwang River did not allow hyperspectral bathymetry mapping compared with the case of adjacent Nakdong River, where maximum measurable depth was down to half in Hwang River.

The Effect of Corporate Association on the Perceived Risk of the Product (소비자의 제품 지각 위험에 대한 기업연상과 효과: 지식과 관여의 조절적 역활을 중심으로)

  • Cho, Hyun-Chul;Kang, Suk-Hou;Kim, Jin-Yong
    • Journal of Global Scholars of Marketing Science
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    • v.18 no.4
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    • pp.1-32
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    • 2008
  • Brown and Dacin (1997) have investigated the relationship between corporate associations and product evaluations. Their study focused on the effects of associations with a company's corporate ability (CA) and its corporate social responsibility (CSR) on consumers' product evaluations. Their study has found that both of CA and CSR influenced product evaluation but CA association has a stronger effect than CSR associations. Brown and Dacin (1997) have, however, claimed that there are few researches on how corporate association impacts product responses. Accordingly, some of researchers have found the variables to moderate or to mediate the relationship between the corporate association and the product responses. In particular, there has been existed a few of studies that tested the influence of the reputation on the product-relevant perceived risk, but the effects of two types of the corporate association on the product-relevant perceived risk were not identified so far. The primary goal of this article is to identify and empirically examine some variables to moderate the effects of CA association and CSR association on the perceived risk of the product. In this articles, we take the concept of the corporate associations that Brown and Dacin (1997) had proposed. CA association is those association related to the company's expertise in producing and delivering its outputs and CSR association reflected the organization's status and activities with respect to its perceived societal obligations. Also, this study defines the risk, which is the uncertainty or loss of the product and corporate that consumers have taken in a particular purchase decision or after having purchased. The risk is classified into product-relevant performance risk and financial risk. Performance risk is the possibility or the consequence of a product not functioning at some expected level and financial risk is the monetary loss one perceives to be incurring if a product does not function at some expected level. In relation to consumer's knowledge, expert consumers have much of the experiences or knowledge of the product in consumer position and novice consumers does not. The model tested in this article are shown in Figure 1. The model indicates that both of CA association and CSR association influence on performance risk and financial risk. In addition, the effects of CA and CSR are moderated by product category knowledge (product knowledge) and product category involvement (product involvement). In this study, the relationships between the corporate association and product-relevant perceived risk are hypothesized as the following form. For example, Hypothesis 1a($H_{1a}$) is represented that CA association has a positive influence on the performance risk of consumer. Also, the hypotheses that identified some variables to moderate the effects of two types of corporate association on the perceived risk of the product are laid down. One of the hypotheses of the interaction effect is Hypothesis 3a($H_{3a}$), it is described that consumer's knowledges of the product moderates the negative relationship between CA association and product-relevant performance risk. A field experiment was conducted in order to examine our model. The company tested was not real but imagined to meet the internal validity. Water purifiers were used for our study. Four scenarios have been developed and described as the imaginary company: Type A with both of superior CA and CSR, Type B with superior CSR and inferior CA, Type C with superior CA and inferior CSR, and Type D with both inferior of CA and CSR. The respondents of this study were classified into four groups. One type of four scenarios (Type A, B, C, or D) in its questionnaire was given to the respondent who filled out questions. Data were collected by means of a self-administered questionnaire to the respondents, chosen in convenience. A total of 300 respondents filled out the questionnaire but 207 were used for further analysis. Table 1 indicates that the scales in this study are reliable because the range of coefficients of Cronbach's $\alpha$ are from 0.85 to 0.92. The composite reliability is in the range of 0,85 to 0,92 and average variance extracted is in 0.72-0.98 range that is higher than the base level of 0.6. As shown in Table 2, the values for CFI, NNFI, root-mean-square error approximation (RMSEA), and standardized root-mean-square residual (SRMR) are acceptably close to the standards suggested by Hu and Bentler (1999):.95 for CFI and NNFI,.06 for RMSEA, and.08 for SRMR. We also tested discriminant validity provided by Fornell and Larcker (1981). As shown in Table 2, we found strong evidence for discriminant validity between each possible pair of latent constructs in all samples. Given that these batteries of overall goodness-of-fit indices were accurate and that the model was developed on theoretical bases, and given the high level of consistency across samples, this enables us to proceed the previously defined scales. We used the moderated hierarchical regression analysis to test the influence of the corporate association(CA and CSR associations) on product-relevant perceived risk(performance and financial risks) and to identify the variables moderating the relationship between the corporate association and product-relevant performance risk. In this study, dependent variables are performance and financial risk. CA and CSR associations are described the independent variables. The moderating variables are product category knowledge and product category involvement. The results are, as expected, found that CA association has statistically a significant influence on the perceived risk of the product, but CSR association does not. Product category knowledge and involvement moderate the relationship between the CA association and the perceived risk of the product. However, the effect of CSR association on the perceived risk of the product is not moderated by the consumers' knowledge and involvement. For this result, it is necessary for a corporate to inform its customers CA association more than CSR association so that they could be felt to be the reduction of the perceived risk. The important theoretical contribution of this research is the meanings that two types of corporate association that Brown and Dacin(1997), and Brown(1998) have proposed replicated the difference of the effects on product evaluation. According to Hunter(2001), it was an important affair to accomplish the validity of a particular study and we had to take about ten studies to deduce a strict study. Next, there is the contribution of the this study to find that the effects of corporate association on the perceived risk of the product are varied by the moderator variables. In particular, the moderating effect of knowledge on the relationship between corporate association and product-relevant perceived risk has not been tested in Korea. In the managerial implications of this research, we suggest the necessity to stress the ability that corporate manufactures the product well(CA association) than the accomplishment of corporate's social obligation(CSR association). This study suffers from various limitations that imply future research directions. The moderating effects of product category knowledge and involvement on the relationship between corporate association and perceived risk need to be replicated. Next, future research could explore whether the mediated effects of the perceived risk has the relationship between corporate association and consumer's product purchase. In addition, to ensure the external validity of the study will be needed to use realistic company, not artificial.

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