• Korean Journal of Occupational Health Nursing
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• v.28 no.4
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• pp.293-299
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• 2019

Purpose: This study aimed to identify the factors that affect the health promotion behaviors of shift workers. Methods: Using self-administered questionnaires, data were collected between August 13th and 31st, 2018. Descriptive statistics were computed, and t-test, analysis of variance, and correlational and multiple stepwise regression analyses were conducted using International Business Machines Corporation (IBM) Statistical Package for the Social Sciences (SPSS) version 22. Results: Health promotion behaviors were significantly and positively correlated with psychological well-being (r=.491, p<.001), fatigue (r=.170, p=.030), and self-efficacy (r=.520, p<.001). Psychological well-being (${\beta}=.249$, p=.014), fatigue (${\beta}=.179$, p=.007), and self-efficacy (${\beta}=.335$, p=.001) had significant effects on health promotion behaviors and together explained 31.7% of the variance. Conclusion: A systematic educational program that enhances psychological well-being should be developed and implemented to nurture health promotion behaviors among shift workers. Additionally, an intervention program that can enhance health promotion behaviors should be implemented to improve self-efficacy and prevent fatigue among shift workers.

• Journal of Climate Change Research
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• v.9 no.2
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• pp.171-179
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• 2018

The causal relationship between El Nino-Southern Oscillation (ENSO) and winter climate variability in Korea is tested by analyzing Korea Meteorological Administration Automatic Synoptic Observing System datasets for the past 59 years. Consistent with previous studies, positive phase of ENSO (El Nino) tends to cause warmer temperature and heavier precipitation in Korea in early winter with three-week lead time. This causality is quantified by performing Granger causality test. It turns out that ENSO explains an additional 9.25% of the variance of early-winter temperature anomalies in Korea, beyond that already provided by temperature itself. Likewise, 22.18% additional information is gained to explain early-winter precipitation variance by considering ENSO. This result, which differs from simple lead-lag correlation analysis, suggests that ENSO needs to be considered in predicting early-winter surface climate variability in Korea.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• Journal of Korean Society of Transportation
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• v.20 no.7
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• pp.79-85
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• 2002

In this paper, multilevel models are adopted to identify interactions among household members in trip making behaviors. The multilevel approach is a proper methodology to handle samples, which are extracted from a hierarchical structure universe. PSTP dataset is used in developing models and understand proportion of variations among individuals and household. The results of this study show that for activity participation and travel behavior household level variance is more than 1/4 of person level variance and therefore not negligible. The results confirm the importance of multilevel model in travel behavior analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1342-1359
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• 2021

The data deduplication technique identifies the duplicates and minimizes the redundant storage data in the backup server. The chunk level deduplication plays a significant role in detecting the appropriate chunk boundaries, which solves the challenges such as minimum throughput and maximum chunk size variance in the data stream. To provide the solution, we propose a new chunking algorithm called Dynamic Prime Chunking (DPC). The main goal of DPC is to dynamically change the window size within the prime value based on the minimum and maximum chunk size. According to the result, DPC provides high throughput and avoid significant chunk variance in the deduplication system. The implementation and experimental evaluation have been performed on the multimedia and operating system datasets. DPC has been compared with existing algorithms such as Rabin, TTTD, MAXP, and AE. Chunk Count, Chunking time, throughput, processing time, Bytes Saved per Second (BSPS) and Deduplication Elimination Ratio (DER) are the performance metrics analyzed in our work. Based on the analysis of the results, it is found that throughput and BSPS have improved. Firstly, DPC quantitatively improves throughput performance by more than 21% than AE. Secondly, BSPS increases a maximum of 11% than the existing AE algorithm. Due to the above reason, our algorithm minimizes the total processing time and achieves higher deduplication efficiency compared with the existing Content Defined Chunking (CDC) algorithms.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.435-443
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• 2015

Two phase sampling design is useful to increase estimation efficiency using deep stratification, improved non-response adjustment and reduced coverage bias. The same sampling units are commonly used for the first and the second phases in complex two-phase sampling design. In this paper we consider a sampling scheme where the first phase sampling units are clusters and the second phase sampling units are list samples. Using selected clusters in first phase requires that we list up elements in the selected clusters from the first phase and then use the list as a secondary sampling frame for the second phase sampling design. Then we select second phase samples from the listed sampling frame. We suggest an estimator based on the complex two-phase sampling design with different sampling units of each phase. Also the estimated variances of the estimator obtained by using classic and replication variance methods are considered and compared using simulation studies. For real data analysis, 2010 Korea Farm Household Economy Survey (KFHES) and 2011 Korea Agriculture Survey (KAS) are used.

• Journal of Environmental Impact Assessment
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• v.6 no.2
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• pp.165-180
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• 1997

Mitigation measures in EIS are the important factors in the effectiveness of EIS. This paper analyzed the content of mitigation measures described in the 30 EISs selected, using 7 analysis items in order to discover the degree of effectiveness of mitigation measures. 30 EISs used in this study were selected through variance maximization strategy, and the 7 analysis items were; 1) thoroughness of mitigation content, 2) quantification of mitigation content, 3) explicit description of mitigation effect, 4) likelihood of mitigation effect, 5) impacted area of mitigation effect, 6) time frame of mitigation effect, and 7) environmental impact of mitigation measures. The results showed that the effectiveness of mitigation measures in the analyzed EISs was relatively low both in the appropriateness and in the specificity. It was suggested that in order to improve the appropriateness of EIS as a decision making tool, the effect of mitigation measures, as well as the mitigation measures themselves, should be studied and described more thoroughly and specifically.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.12
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• pp.1757-1762
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• 2011

Aflatoxins are natural contaminants of poultry feeds and feed ingredients and cause liver damage, immunosuppression, reduction in performance and mortality in broilers. A number of studies have been carried out to study the effects of aflatoxin on feed conversion ratio in broilers. The results on feed conversion ratio of 10 research articles in broilers fed with aflatoxin from first day of age to six weeks of age were compiled and were subjected to meta-analysis. Chi-square test and $Tau^2$ (heterogeneity co-efficient) were applied to test for significance of heterogeneity of studies. To integrate results, fixed effect model by Inverse Variance method (IV method) was used when heterogeneity was insignificant and otherwise random effect model by DerSimonian and Laird Method (DL method) was used. The results of meta-analysis showed that the adverse effect of aflatoxin on feed conversion ratio at the end of first week was negligible, second week was medium and third to six weeks was very large.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1521-1521
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• 2001

The objective of the research being presented was to construct and calibrate a spectrometer for the analysis of single kernels of corn. In light of the difficulties associated with capturing the spatial variability in composition of corn kernels by single-beam spectrometry, a hyperspectral imaging spectrometer was constructed with the intention that it would be used to analyze single kernels of corn for the prediction of moisture and oil content. The spectrometer operated in the range of 750- 1090 nanometers. After evaluating four methods of standardizing the output from the spectrometer, calibrations were made to predict whole-kernel moisture and oil content from the hyperspectral image data. A genetic algorithm was employed to reduce the number of wavelengths imaged and to optimize the calibrations. The final standard errors of prediction during cross-validation (SEPCV) were 1.22% and 1.25% for moisture and oil content, respectively. It was determined, by analysis of variance, that the accuracy and precision of single-kernel corn analysis by hyperspectral imaging is superior to the single kernel reference chemistry method (as tested).

• Journal of Korean Society of Rural Planning
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• v.13 no.2
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• pp.1-5
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• 2007

Shannon and Simpson indexes are used for species diversity analysis of ecosystem. In species diversity analysis of ecosystem, not only frequency of each species but also survey size have to be considered. In this study, total information composed with knowledge and ignorance was suggested as a species diversity analysis method for ecosystem survey. To apply developed method, flora in the Sangachun river valley was sampled with 19 sites and 198 species. In applying results, Shannon index shows more reasonable results than Simpson index by the variance of sample size but has difficulties of determining the relation of surveying species number and sample site number. Suggested total information can overcome this difficulty by the relation of knowledge and ignorance.