• Journal of Forest and Environmental Science
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• v.40 no.2
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• pp.99-110
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• 2024

According to the REDD+ program, it is necessary to monitor, quantify, and report forest conditions in protected land areas. The objectives of this work were to quantify the average monthly aerial net primary productivity (ANPP_MONTH) of semi-arid Chaco Forest at Copo National Park (CNP), Santiago del Estero, Argentina, during the period 2000-2023, as well as its spatial distribution and relationship, and its use efficiency (RUE) of average monthly rainfall (AMR). The ANPP_MONTH model accounted for 90% of the seasonal variability from October to May, the average seasonal ANPP_MONTH was 145 tons of dry matter per hectare (t dm/ha), being the maximum in January with 192 t dm/ha and the minimum in May with 91 t dm/ha. The surface area covered by ANPP_MONTH exhibited a consistent positive trend from October to May (t test=15.65, p<0.01). Strong and significant direct relationships were found between ANPP_MONTH and AMRs, linear models explaining 90% and 96% of the variability, respectively. The results obtained become reference values for assessing the capacity of the forest systems to stock carbon for global warming mitigation and for monitoring and controlling their response to extreme climatic adversities. The average ANPP_MONTH reduces uncertainty when defining the thresholds to monitor and quantify ANPP and forest area, thus facilitating the detection of negative changes in land use in CNP. Such results evidence the National Parks Administration's high effectiveness for the maintenance of protected area and for the high ANPP of the FCHS of CNP in the period 2000-2023.

• Journal of Technologic Dentistry
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• v.46 no.1
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• pp.8-14
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• 2024

Purpose: This study compares the deformation of traditional resin dentures to resin dentures printed with digital light processing (DLP). Methods: Eleven edentulous research models were developed. Ten of them were made with traditional resin dentures. The remaining one was prepared for scanning and 3D (three-dimensional) printing. Ten traditional resin dentures were made, with the remaining artificial teeth created using 3D software and a DLP printer. Traditional resin dentures, 3D printed resin denture artificial teeth, and a denture base with artificial teeth were all cleaned simultaneously in an ultrasonic cleaner for 3 minutes. Three groups were assigned four artificial tooth measurement points, which were then measured with digital calipers. The measured data was analyzed using descriptive statistics. The significance test was conducted using a nonparametric test Kruskal-Wallis test due to the small number of specimens (α=0.05). Results: The traditional resin dentures had the lowest strain rate at -0.04%, while the group that manufactured only artificial teeth had the highest strain rate at -0.09%. However, no statistically significant difference was observed between the 3 groups (p>0.05). Conclusion: During ultraviolet-type ultrasonic cleaning, traditional resin dentures (TD group) and denture base with artificial teeth made of DLP (DD group) demonstrated stable durability, whereas the artificial teeth made of DLP (AD group) with only artificial teeth did not show a good deformation rate.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.453-465
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• 2024

This paper presents a logarithmic shear deformation theory to study the thermal buckling response of power-law FG one-dimensional structures in thermal conditions with different boundary conditions. It is assumed that the functionally graded material and thermal properties are supposed to vary smoothly according to a contentious function across the vertical direction of the beams. A P-FG type function is employed to describe the volume fraction of material and thermal properties of the graded (1D) beam. The Ritz model is employed to solve the thermal buckling problems in immovable boundary conditions. The outcomes of the stability analysis of FG beams with temperature-dependent and independent properties are presented. The effects of the thermal loading are considered with three forms of rising: nonlinear, linear and uniform. Numerical results are obtained employing the present logarithmic theory and are verified by comparisons with the other models to check the accuracy of the developed theory. A parametric study was conducted to investigate the effects of various parameters on the critical thermal stability of P-FG beams. These parameters included support type, temperature fields, material distributions, side-to-thickness ratios, and temperature dependency.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.230-236
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• 2024

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.

• The Korean Journal of Applied Statistics
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• v.37 no.5
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• pp.691-702
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• 2024

In this paper, we propose mixed projection forest (MPF), a new classification ensemble method that can be effectively applied in the field of big data analysis. When training individual classifiers within an ensemble, MPF uses oblique hyperplanes using combined rotation matrix derived from data projection techniques of principal component analysis (PCA) and canonical linear discriminant analysis (CLDA), thereby improving the accuracy of each classifier. Additionally, the diversity of individual classifiers is improved by generating various rotation matrices through random partitioning of the input variable set. This approach ultimately enhances classification performance and proves to be highly effective in big data analysis that demands precision. We conducted a performance comparison of MPF with existing classification ensemble models using 30 real or simulated datasets. The results indicate that MPF achieves competitive performance in terms of classification accuracy and classifier diversity.

• The Korean Journal of Quaternary Research
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• v.33 no.1_2
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• pp.25-35
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• 2021

In this study, we identified characteristics of heatwaves on the Korean Peninsula and related atmospheric circulation patterns using data on the daily maximum temperature (TMX) and reanalysis data for the past 42 years (1979-2020) and analyzed their connection to the Arctic oscillation (AO). The heatwave on the Korean Peninsula showed to be stronger and more frequent in the 2000s. The recent strong and frequent heatwaves on the Korean Peninsula are mainly affected by abnormal high-pressure over the Korean Peninsula on the middle/upper-level atmosphere and the strengthening of the North Pacific high pressure. Interestingly, composite difference of sea level pressure showed very similar results to the positive AO pattern. The correlation coefficients between the summertime AO and the TMX and HWD of the Korean Peninsula were 0.407 and 0.437, respectively, which showed a statistical significance in 1%, and showed a clear relationship with the abnormal high-pressure over the Korean Peninsula and the strengthening of the North Pacific high pressure. In addition, in the positive AO phase, the TMX and HWD of the Korean peninsula were approximately 30.1 ℃ and 14.6 days, which were about 1.2 ℃ and 8.8 days higher than in the negative AO phase, respectively. As a result of the 15-year moving average correlation analysis, the relationship between the heatwave and AO on the Korean Peninsula has increased significantly since 2003, and the linear relationship between them has become more apparent. Moreover, after the 2000s, when the relationship developed, AO had more strongly induced the atmospheric circulation pattern to be more favorable to the occurrence of heatwaves in the Korean Peninsula. This study implies that understanding the AO, which is the large-scale variability in the Northern Hemisphere, and the Arctic-mid latitude teleconnection, can improve the performance of global climate models and help predict the seasonality of the summer heatwave on the Korean Peninsula.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.1-11
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• 2024

Geotechnical parameter estimation is critical to the design, performance, safety, and cost and schedule management in Tunnel Boring Machine projects. Since these parameters vary within a certain range, relying on mean values for evaluation introduces significant risks to the project. Due to the non-homogeneous characteristics of geological formation, data may not exhibit a normal distribution and the presence of outliers might be deceptive. Therefore, the use of reliable analyses and simulation models is inevitable in the course of the data evaluation process. Advanced modeling techniques enable comprehensive analysis of the project data and allowing to model the uncertainty in geotechnical parameters. This study involves using Monte Carlo Simulation method to predict probabilistic distributions of field data, and therefore, establish a basis for designs and in turn to minimize project risks. In the study, 166 sets of geotechnical data Obtained from 35 boreholes including Standard Penetration Test, Limit Pressure, Liquid Limit, and Plastic Limit values, which are mostly utilized parameters in estimating project requirements, were used to estimate the geotechnical data distribution of the study field. In this context, firstly, the data was subjected to multi-parameter linear regression and variance analysis. Then, the obtained equations were implemented into a Monte Carlo Simulation, and probabilistic distributions of the geotechnical data of the field were simulated and corresponding to the 90% probability range, along with the minimum and maximum values at the 5% probability levels presented. Accordingly, while the average SPT N30 value is 42.86, but the highest occurrence rate is 50.81. For Net Limit Pressure, the average field data is 17.07 kg/cm², with the maximum occurrence between 9.6 kg/cm² and 13.7 kg/cm². Similarly, the average Plastic Limit value is 22.32, while the most probable value is 20.6. The average Liquid Limit value is 56.73, with the highest probability at 54.48, as indicated in the statistical data distribution. Understanding the percentage distribution of data likely to be encountered in the project allows for accurate forecasting of both high and low probability scenarios, offering a significant advantage, particularly in ordering TBM requirements.

• Clinical Nutrition Research
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• v.11 no.2
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• pp.110-119
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• 2022

Musculoskeletal pains (MPs), defined as persistent or recurrent pain, is a complex health problem. High overall calorie and fat intake have been related to obesity and MPs. Dietary energy density (DED), defined as energy content of food and beverages (in kcal) per unit total weight, has been associated with chronic muscle, cartilage, bone damage and pain. Thus, the purpose of this study is to investigate the association between DED and MPs in adult men and women. A total of 175 men and women (> 18 years) with MP participated in the study. A validated short form physical activity (PA) questionnaire, demographic, and McGill Pain Questionnaire were used. Anthropometric measurements were evaluated via standard protocols. Furthermore, a seven-day 24-hour recall of diet was used to determine the dietary intake. Total DED was calculated and divided into quartiles. Linear regression was used to discern the association between DED and MPs in adults. Participants assigned in the highest category of DED were characterized by lower intake of potassium, magnesium, vitamin C, folate, and fiber. However, results showed displayed higher intake of sodium, vitamin E, vitamin B3, fat, protein, cholesterol, saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids (p < 0.001). Finally, after adjustment for confounders such as age, gender, PA, body mass index, waist circumference, education, job, marital status, history of some chronic diseases and vitamin C supplementation, a significant positive association was detected between DED and pain intensity. There was no significant association between DED and pain frequency in all models.

• Safety and Health at Work
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• v.15 no.3
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• pp.300-309
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• 2024

Background: Risk perception plays a crucial role in workers' unsafe behaviors. However, little research has explored why workers generate biased risk perceptions, namely underestimating or overestimating the risks of unsafe actions. Cognitive biases in risk perception arise from uncertainties about the dangers of unsafe behaviors. As a typical heuristic strategy, the anchoring effect is critical in decision-making under uncertain conditions. Consequently, this study empirically analyzed the influence of anchoring effects on workers' risk perception. Methods: In 2022, a survey was conducted with 1,418 coal mine workers from Shanxi Province, China. The survey instruments assessed workers' risk perception of unsafe behavior, anchoring effects, need for cognition, and safety knowledge. Multivariable linear regression models were employed to analyze the associations among these variables. Results: The findings verified the proposed anchoring effects. Specifically, experimenter-provided high-risk anchors led workers to overestimate unsafe behavior risks, thus reducing their tendency to engage in such behavior. In contrast, experimenter-provided low-risk anchors and accident-injury experiences (self-generated anchors) decreased workers' risk perception, increasing their propensity to engage in unsafe behavior. Additionally, workers' safety knowledge and need for cognition significantly affected anchoring effects. Conclusion: This research enhances workplace safety studies by applying the anchoring effect from psychology to risk perception research. Suggestions for improving risk perception encompass implementing hazard warnings, fostering safety education, and providing training. Furthermore, managers should give special attention to workers with accident-injury experience and promptly correct their accident fluke mentality, thereby improving overall risk awareness.

• Journal of agricultural medicine and community health
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• v.39 no.3
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• pp.176-186
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• 2014

Objectives: As a sequel to the former analysis of the quality of life (QoL) among young-old and old-old in Korea, this research was aimed to identify factors related to the quality of life and the gender difference after controlling for the related factors among Korean elderly. Methods: Selected elderly data of 1,339 subjects from the 5th Korea National Health and Nutrition Examination Survey conducted in 2010 was analyzed. In this survey, QoL was measured using Euro Quality of Life (EQ-5D) instrument. Data were analyzed using complex survey data analysis on IBM-SPSS 20.0. The related factors were identified using general linear models with backward elimination. The gender difference was tested also using general linear models. Results: The distributions of educational level, family income level, and presence of cohabitant were different between male and female elderly in both young-old and old-old age group. So were the health behaviors and perceived health, and experience of stress, depression, and suicidal thoughts. QoL and its subscales- mobility, self care, daily living, pain and discomfort, and anxiety and depression- were consistently better among male elderly regardless of age group. Among the variables considered, education, family income level, presence of cohabitant, perceived health, age group and BMI were found to be related to the QoL at p=.05, and presence of chronic diseases at p=.10. The difference in QoL between male and female elderly after controlling for the variables was statistically significant. Conclusion: Improving QoL is particularly important for the elderly. In order to improve QoL of the elderly, age- and gender- differences need to be considered when developing services and programs for the elderly.