• Safety and Health at Work
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• v.9 no.3
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• pp.334-338
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• 2018

Background: With global changes in the current state of work and employment, the role of health-adverse psychosocial work environments has received increasing attention in developed as well as in rapidly developing countries. Thus, there is a need to apply valid measurement tools for monitoring and preventive purposes. This study aims to examine the factorial structure and psychometric properties of the Persian version of the effort-reward imbalance (ERI) questionnaire, assessing one of the internationally leading concepts of stressful work. Methods: This descriptive cross-sectional study of a random sample of 202 white collar employees in an industrial company in Iran analyzes the ERI scales by exploratory and confirmatory factor analysis. Moreover, aspects of construct and criterion validity are tested. To this end, correlations of ERI scales with subscales of organizational injustice, a complementary work stress model, and also the correlations of ERI scales with a questionnaire assessing psychosomatic symptoms are performed. Results: Internal consistency of the three ERI scales was satisfactoryy (Cronbach ${\alpha}$ effort: 0.76, reward: 0.79, overcommitment: 0.75). Fit indices of confirmatory factor analsis pointed to an adequate representation of the theoretical construct (e.g., adjusted goodness of fit index (AGFI): 0.73, goodness of fit index (GFI): 0.78). Negative correlations with subscales of organizational injustice supported the notion of construct validity of the ERI scales, and positive correlations of ERI scales with psychosomatic symptoms indicated preliminary criterion validity. Conclusion: The Persian version of the ERI questionnaire has acceptable psychometric properties and can be used as a valid instrument in research on this topic.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.537-547
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• 2010

The importance of vegetation in studies of global climate and biogeochemical cycles is well recognized. Especially. the FPAR (fraction of photosynthetically active radiation) is one of the important parameters in ecosystem productivity and carbon budget models. Therefore, accurate estimates of vegetation parameters are increasingly important in environmental impact assessment studies. In this study, optical FPAR using the Terra MODIS (MODerate resolution Imaging Spectroradiometer), SPOT VEGETATION and ECOCLIMAP data reproduced on the Korean peninsula. We applied the empirical method which is usually estimated as a linear or nonlinear function of vegetation indices. As results, we estimated the accurate expression which is 0.9039 of $R^2$ in cropland and 0.7901 of $R^2$ in forest. Finally, this study could be demonstrated to calibrate that produced FPAR while the overall pattern and random noise through the comparative analysis of FPAR on the reference data. Optimal use of input parameter on the Korean peninsula should be helping the accuracy of output as well as the improved quality of research.

• Journal of Forest and Environmental Science
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• v.38 no.2
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• pp.90-101
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• 2022

The study aims to assess composition, diversity and population indices of natural regeneration of woody species in Jebel El Gerrie forest reserve, Blue Nile State, Sudan. We conducted field work between December 2018 and January 2019. We used random sampling to collect vegetation data in the forest where we made a total of 90 circular sample plots (radius 17.84 m) and distributed them proportionally to the area of each of the four density-based vegetation classes of the forest i.e. high density (C1), medium density (C2), low density (C3) and crop land (C4). In each sample plot we identified all regenerating tree species and counted their regeneration frequencies. We calculated ecological metrics of regeneration frequency, density, abundance, richness, evenness, diversity and importance value index (IVI) and drew abundance rank curve. Results revealed that out of fifteen mature tree species present, natural regeneration of 8 species, which belong to 6 families, was observed. The relatively most frequently naturally regenerating and abundant species were Anogeissus leiocarpa and Combretum hartmannianum. Richness, evenness and diversity of regenerating species were 1.33, 0.82 and 1.7, respectively. One-way ANOVA (α=0.05) of mean regeneration densities disclosed that there were significant differences (F_3,86=16.77, p=0.000) between C2 & C3 (p=0.000) and C2 & C4 (p=0.000). While regeneration of seven tree species were absent, two, two and four species were of good, poor and fair regeneration status, respectively. A comparison of mean density of natural regeneration with that of parent trees reflects a poor regeneration status of the forest. The study provides empirical results on the regeneration status of species and signifies the need for management interventions for species conservation and restoration, maintenance of biodiversity and sustainable production.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.7-14
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• 2022

This study aimed to test the factor structure of the measure of student participation in distance education. The study population consisted of all teachers in public education and faculty members in higher education in the Kingdom of Saudi Arabia by applying it to a sample of bachelor's and graduate students at the college of Education at umm al-Qura University. The (ESE) was applied to a random sample representing the study population consisting of (216) respondents. The results of the study showed that the scale consists of three main factors, with showed a high degree of construct validity through fit indices of the confirmatory factor analysis. The results have shown a gradual consistency of the measure's invariance that reaches the high level of the Measurement Invariance across the gender and study groups variables.

• Journal of Korean Neurosurgical Society
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• v.64 no.3
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• pp.418-426
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• 2021

Objective : A role of diffusion-weighted imaging (DWI) in the diagnosis of cerebral venous thrombosis (CVT) is not well-understood. This study evaluates the effectiveness of DWI in the diagnosis of CVT. Methods : Literature search was conducted in electronic databases for the identification of studies which reported the outcomes of patients subjected to DWI for CVT diagnosis. Random-effects meta-analyses were performed to achieve overall estimates of important diagnostic efficiency indices including hyperintense signal rate, the sensitivity and specificity of DWI in diagnosing CVT, and the apparent diffusion coefficient (ADC) of DWI signal areas and surrounding tissue. Results : Nineteen studies (443 patients with 856 CVTs; age 40 years [95% confidence interval (CI), 33 to 43]; 28% males [95% CI, 18 to 38]; symptom onset to DWI time 4.6 days [95% CI, 2.3 to 6.9]) were included. Hyperintense signals on DWI were detected in 40% (95% CI, 26 to 55) of the cases. The sensitivity of DWI for detecting CVT was 22% (95% CI, 11 to 34) but specificity was 98% (95% CI, 95 to 100). ADC values were quite heterogenous in DWI signal areas. However, generally the ADC values were lower in DWI signal areas than in surrounding normal areas (mean difference-0.33×10-3 ㎟/s [95% CI, -0.44 to -0.23]; p<0.00001). Conclusion : DWI has a low sensitivity in detecting CVT and thus has a high risk of missing many CVT cases. However, because of its high specificity, it may have supporting and exploratory roles in CVT diagnosis.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3A
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• pp.539-546
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• 2006

The reliability analysis of simply-supported composite plate girder and box girder bridges under positive flexure is performed. The bridges are designed based on the AASHTO-LRFD specification. A performance function for flexural failure is expressed as a function of such random variables as flexural resistance of composite section and design moments due to permanent load and live load. For the flexural resistance, the statistical parameters obtained by analyzing over 16,000 samples of domestic structural steel products are used. Several different values of statistical parameters with the bias factor in the range of 0.95-1.05 and the coefficient of variation in the range of 0.15-0.25 are used for the live-load moment. Due to the lack of available domestic measured data on the dead load moment, the same values of statistical properties used in the calibration of AASHTO-LRFD are applied. The reliability indices for the composite plate girder and box girder bridges with various span lengths are calculated by applying the Rackwitz-Fiessler technique.

• Current Optics and Photonics
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• v.8 no.3
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• pp.230-238
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• 2024

The refractive index is a key material-design parameter, especially for high-refractive-index glasses, which are used for precision optics and devices. Increased demand for high-precision optical lenses produced by the glass-mold-press (GMP) process has spurred extensive studies of proper glass materials. B₂O₃, SiO₂, and multiple heavy-metal oxides such as Ta₂O₅, Nb₂O₅, La₂O₃, and Gd₂O₃ mostly compose the high-refractive-index glasses for GMP. However, due to many oxides including up to 10 components, it is hard to predict the refractivity solely from the composition of the glass. In this study, the refractive index of optical glasses based on the B₂O₃-La₂O₃-Ta₂O₅-SiO₂ system is predicted using machine learning (ML) and compared to experimental data. A dataset comprising up to 271 glasses with 10 components is collected and used for training. Various ML algorithms (linear-regression, Bayesian-ridge-regression, nearest-neighbor, and random-forest models) are employed to train the data. Along with composition, the polarizability and density of the glasses are also considered independent parameters to predict the refractive index. After obtaining the best-fitting model by R² value, the trained model is examined alongside the experimentally obtained refractive indices of B₂O₃-La₂O₃-Ta₂O₅-SiO₂ quaternary glasses.

• Animal Bioscience
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• v.37 no.8
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• pp.1428-1439
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• 2024

Objective: Compared to Mimas pigeons, Shiqi pigeons exhibit greater tolerance to coarse feeding because of their abundant gut microbiota. Here, to investigate the potential of utilizing intestinal flora derived from Shiqi pigeons, the intestinal flora and body indices of Mimas squabs were evaluated after fecal microbiota transplantation (FMT) from donors. Methods: A total of 90 one-day-old squabs were randomly divided into the control group (CON), the low-concentration group (LC) and the high-concentration group (HC): gavaged with 200 μL of bacterial solution at concentrations of 0, 0.1, and 0.2 g/15 mL, respectively. Results: The results suggested that FMT improved the body weight of Mimas squabs in the HC and LC groups (p<0.01), and 0.1 g/15 mL was the optimal dose during FMT. After 16S rRNA sequencing was performed, compared to those in the CON group, the abundance levels of microflora, especially Lactobacillus, Muribaculaceae, and Megasphaera (p<0.05), in the FMT-treated groups were markedly greater. Random forest analysis indicated that the main functions of key microbes involve pathways associated with metabolism, further illustrating their important role in the host body. Conclusion: FMT has been determined to be a viable method for augmenting the weight and intestinal microbiota of squabs, representing a unique avenue for enhancing the economic feasibility of squab breeding.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.4
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• pp.81-99
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• 2015

The acceleration of global warming has required better understanding of carbon cycles over local and regional areas such as the Korean peninsula. Since forests serve as a carbon sink, which stores a large amount of terrestrial carbon, there has been a demand to accurately estimate such forest carbon sequestration. In Korea, the National Forest Inventory(NFI) has been used to estimate the forest carbon stocks based on the amount of growing stocks per hectare measured at sampled location. However, as such data are based on point(i.e., plot) measurements, it is difficult to identify spatial distribution of forest carbon stocks. This study focuses on urban areas, which have limited number of NFI samples and have shown rapid land cover change, to estimate grid-based forest carbon stocks based on UNFCCC Approach 3 and Tier 3. Land cover change and forest carbon stocks were estimated using Landsat 5 TM data acquired in 1991, 1992, 2010, and 2011, high resolution airborne images, and the 3rd, 5th~6th NFI data. Machine learning techniques(i.e., random forest and support vector machines/regression) were used for land cover change classification and forest carbon stock estimation. Forest carbon stocks were estimated using reflectance, band ratios, vegetation indices, and topographical indices. Results showed that 33.23tonC/ha of carbon was sequestrated on the unchanged forest areas between 1991 and 2010, while 36.83 tonC/ha of carbon was sequestrated on the areas changed from other land-use types to forests. A total of 7.35 tonC/ha of carbon was released on the areas changed from forests to other land-use types. This study was a good chance to understand the quantitative forest carbon stock change according to the land cover change. Moreover the result of this study can contribute to the effective forest management.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.