• Safety and Health at Work
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• 제13권4호
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• pp.469-474
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• 2022

Background: Nowadays, counter-productive work behaviors (CWBs) have turned into a common and costly position for many organizations and especially health centers. Therefore, the study was carried out to examine and compare the demand-control-support (DCS) and effort-reward imbalance (ERI) models as predictors of CWBs. Methods: The study was cross-sectional. The population was all nurses working in public hospitals in Hamadan, Iran of whom 320 were selected as the sample based on simple random sampling method. The instruments used were Job Content Questionnaire, Effort-Reward Imbalance Questionnaire, and Counterproductivity Work Behavior Questionnaire. Data were analyzed using correlation and regression analysis in SPSS18. Results: The findings indicated that both ERI and DCS models could predict CWB (p ≤ 0.05); however, the DCS model variables can explain the variance of CWB-I and CWB-O approximately 8% more than the ERI model variables and have more power in predicting these behaviors in the nursing community. Conclusion: According to the results, job stress is a key factor in the incidence of CWBs among nurses. Considering the importance and impact of each component of ERI and DCS models in the occurrence of CWBs, corrective actions can be taken to reduce their incidence in nurses.

• Smart Structures and Systems
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• 제32권1호
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• 원예과학기술지
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• 제30권3호
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• pp.270-277
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• 2012

Greenhouse industry has been growing in many countries due to both the advantage of stable year-round crop production and increased demand for fresh vegetables. In greenhouse cultivation, $CO_2$ concentration plays an essential role in the photosynthesis process of crops. Continuous and accurate monitoring of $CO_2$ level in the greenhouse would improve profitability and reduce environmental impact, through optimum control of greenhouse $CO_2$ enrichment and efficient crop production, as compared with the conventional management practices without monitoring and control of $CO_2$ level. In this study, a mathematical model was developed to estimate the $CO_2$ emission from soil as affected by environmental factors in greenhouses. Among various model types evaluated, a linear regression model provided the best coefficient of determination. Selected predictor variables were solar radiation and relative humidity and exponential transformation of both. As a response variable in the model, the difference between $CO_2$ concentrations at the soil surface and 5-cm depth showed are latively strong relationship with the predictor variables. Segmented regression analysis showed that better models were obtained when the entire daily dataset was divided into segments of shorter time ranges, and best models were obtained for segmented data where more variability in solar radiation and humidity were present (i.e., after sun-rise, before sun-set) than other segments. To consider time delay in the response of $CO_2$ concentration, concept of time lag was implemented in the regression analysis. As a result, there was an improvement in the performance of the models as the coefficients of determination were 0.93 and 0.87 with segmented time frames for sun-rise and sun-set periods, respectively. Validation tests of the models to predict $CO_2$ emission from soil showed that the developed empirical model would be applicable to real-time monitoring and diagnosis of significant factors for $CO_2$ enrichment in a soil-based greenhouse.

• Safety and Health at Work
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• 제10권3호
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• pp.370-376
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• 2019

Background: Respirator fit testing is a method to assess if the respirator provides an adequate face seal for the worker. Methods: Workers from four Norwegian smelters were invited to participate in the study, and 701 respirator fit tests were performed on 127 workers. Fourteen respirator models were included: one FFABE1P3 and 11 FFP3 respirator models produced in one size and two silicone half masks with P3 filters available in three sizes. The workers performed a quantitative fit test according to Health and Safety Executive 282/28 with 5-6 different respirator models, and they rated the respirators based on comfort. Predictors of overall fit factors were explored. Results: The pass rate for all fit tests was 62%, 56% for women, and 63% for men. The silicone respirators had the highest percentage of passed tests (92-100%). The pass rate for the FFP3 models varied from 19-89%, whereas the FFABE1P3 respirator had a pass rate of 36%. Five workers did not pass with any respirators, and 14 passed with all the respirators tested. Only 63% passed the test with the respirator they normally used. The mean comfort score on the scale from 1 to 5 was 3.2. The respirator model was the strongest predictor of the overall fit factor. The other predictors (age, sex, and comfort score) did not improve the fit of the model. Conclusion: There were large differences in how well the different respirator models fitted the Norwegian smelter workers. The results can be useful when choosing which respirators to include in respirator fit testing programs in similar populations.

• Journal of Forest and Environmental Science
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• 제30권4호
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• pp.351-361
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• 2014

Leaf area ($A_0$) and leaf biomass ($M_0$) estimation are significant prerequisites to studying tree physiological processes and modeling in the forest ecosystem. The objective of this study was to develop allometric models for estimating $A_0$ and $M_0$ of Swietenia mahagoni L. from different tree parameters such as DBH and tree height of mahogany plantations in the northeastern region of Bangladesh. A total of 850 healthy and well formed trees were selected randomly for sampling in the five study sites. Then, twenty two models were developed based on different statistical criteria that propose reliable and accurate models for estimating the $A_0$ and $M_0$ using non-destructive measurements. The results exposed that model iv and xv were selected on a single predictor of DBH and showed more statistically accuracy than other models. The selected models were also validated with an additional test data set on the basis of linear regression and t-test for mean difference between observed and predicted values. After that, a comparison between the best logarithmic and non-linear allometric model shows that the non-linear model produces systematic biases and underestimates $A_0$ and $M_0$ for larger trees. As a result, it showed that the bias-corrected logarithmic model iv and xv can be used to help quantify forest structure and functions, particularly valuable in future research for estimating $A_0$ and $M_0$ of S. mahagoni in this region.

• 지반과기술
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• 제3권3호
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• pp.15-22
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• 2006

The optimum blasting pattern to excavate a quarry efficiently and economically can be determined based on the minimum production cost, which is generally estimated according to rock fragmentation. Therefore, it is a critical problem to predict fragment size distribution of blasted rocks over an entire quarry. By comparing various prediction models, it can be ascertained that the result obtained from Kuz-Ram model relatively coincides with that of field measurements. Kuz-Ram model uses the concept of rock factor to signify conditions of rock mass such as block size, rock jointing, strength and others. For the evaluation of total production cost, it is imperative to estimate 3-D spatial distribution of rock factor for the entire quarry. In this study, a sequential indicator simulation technique is adopted for estimation of spatial distribution of rock factor due to its higher reproducibility of spatial variability and distribution models than Kriging methods. Further, this can reduce the uncertainty of predictor using distribution information of sample data. The entire quarry is classified into three types of rock mass and optimum blasting pattern is proposed for each type based on 3-D spatial distribution of rock factor. In addition, plane maps of rock factor distribution for each ground level are provided to estimate production costs for each process and to make a plan for an optimum blasting pattern.

• Journal of Chest Surgery
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• 제26권1호
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• pp.24-31
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• 1993

The long term clinical results following valve replacement with Hancock and Carpentier-Edwards bioprostheses were compared between tow valve models and between tow groups totaling 249 patients who were discharged after valve replacement from 1976 to 1986. The two groups of patients were treated with nonrandomized fashion. Follow-up was 87% complete. Cummulative duration of follow-up was 1909 patient-years, with maximum follow-up duration of 15 years. The actuarial survival for 122 patients with Hancock valves was 95.2%[\ulcornerstandard deviation] and 84.4% after 5 and 10 years of follow-up, respectively. Comparable figures for 127 patients undergoing valve replacement with Carpentier-Edwards valves were 87.3% and 76.4%, respectively[p=NS]. The probability of freedom from structural valve deterioration after 5 and 10 years of follow-up was 97.2% and 60.6%, respectively, with Hancock valves and 97.2% and 55.7%, respectively, with Carpentier-Edwards valves[p=NS]. Considering all 249 patients, multivariate [Cox model] regression revealed that ejection fraction was only significant predictor of structural valve deterioration. The probability of freedom from thromboembolism after 5 and 10 years of follow-up was 91.3% and 86.4%, respectively, with Hancock valves and 94.2% and 82.5%, respectively, with Carpentier-Edwards valves[p=NS]. Hence more strict control of anticoagulation should be done on patients with left atrial factors. In summary, there were no significant differences in actuarial survival rate and major valve related complications between tow valve models. These results suggests that its use should be confined to older patients or patients with a contraindication of anticoagulation.

• 산업경영시스템학회지
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• 제38권1호
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• pp.118-123
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• 2015

There have been various studies on measurements of flood risk and forecasting models. For river and dam region, PDF and FVI has been proposed for measurement of flood risk and regression models have been applied for forecasting model. For Bo region unlikely river or dam region, flood risk would unexpectedly increase due to outgoing water to keep water amount under the designated risk level even the drain system could hardly manage the water amount. GFI and general linear model was proposed for flood risk measurement and forecasting model. In this paper, FVI with the consideration of duration on GFI was proposed for flood risk measurement at Bo region. General linear model was applied to the empirical data from Bo region of Nadong river to derive the forecasting model of FVI at three different values of Base High Level, 2m, 2.5m and 3m. The significant predictor variables on the target variable, FVI were as follows: ground water level based on sea level with negative effect, difference between ground altitude of ground water and river level with negative effect, and difference between ground water level and river level after Bo water being filled with positive sign for quantitative variables. And for qualitative variable, effective soil depth and ground soil type were significant for FVI.

• 한국도로학회논문집
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• 제10권1호
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• pp.19-29
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• 2008

사고예측모형은 도로에서 발생한 교통사고자료를 통계적으로 모형화한 것으로 종속변수는 과거의 사고건수가 되고 설명 변수로는 주로 사고가 일어난 장소의 도로 기하구조 조건, 교통조건, 운영조건 등 도료의 속성자료가 이용된다. 기존의 사고예측모형의 한계를 극복하고자 새로운 방안인 Hauer의 연구를 구체적으로 소개하고 이를 국내 고속도로 사망사고자료를 통해 적용하였다. Hauer의 방법론에 의한 사고예측모형을 구축한 결과 AADT와 종단구배를 통해 사고예측모형의 적합도를 상당히 높일 수 있었으나, 곡선반경은 사고건수와 직접적 인 관련이 있는 것으로 파악되지 않았다. 이러한 사고예측모형은 기존의 모형과 비교 시 여러 설명변수 중 어떤 변수가 모형에 도입되어야 하는지를 결정할 때 분명한 근거를 지니기 때문에 중요한 변수가 누락되거나 혹은 중요하지 않는 변수가 도입될 가능성 이 낮아지는 장점을 지니고 있다.

• 지질공학
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• 제34권1호
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.