• Asian-Australasian Journal of Animal Sciences
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• v.31 no.8
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• pp.1238-1243
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• 2018

Objective: Two experiments were conducted to determine the effects of feeding level on nutrient digestibility and enteric methane ($CH_4$) emissions in growing goats and Sika deer. Methods: Three growing male goats (initial body weight [BW] of $22.4{\pm}0.9kg$) and three growing male deer (initial BW of $20.2{\pm}4.8kg$) were each allotted to a respiration-metabolism chamber for an adaptation period of 7 d and a data collection period of 3 d. An experimental diet was offered to each animal at one of three feeding levels (1.5%, 2.0%, and 2.5% of BW) in a $3{\times}3$ Latin square design. The chambers were used for measuring enteric $CH_4$ emission. Results: Nutrient digestibility decreased linearly in goats as feeding level increased, whereas Sika deer digestibility was not affected by feeding level. The enteric production of $CH_4$ expressed as g/kg dry matter intake (DMI), g/kg organic matter intake, and % of gross energy intake decreased linearly with increased feeding level in goats; however, that of Sika deer was not affected by feeding level. Six equations were estimated for predicting the enteric $CH_4$ emission from goats and Sika deer. For goat, equation 1 was found to be of the highest accuracy: $CH_4(g/d)=6.2({\pm}14.1)+10.2({\pm}7.01){\times}DMI(kg/d)+0.0048({\pm}0.0275){\times}dry$ matter digestibility (DMD, g/kg)-0.0070 (${\pm}0.0187$)${\times}$neutral detergent fiber digestibility (NDFD; g/kg). For Sika deer, equation 4 was found to be of the highest accuracy: $CH_4(g/d)=-13.0({\pm}30.8)+29.4({\pm}3.93){\times}DMI(kg/d)+0.046(0.094){\times}DMD(g/kg)-0.0363({\pm}0.0636){\times}NDFD(g/kg)$. Conclusion: Increasing the feeding level increased $CH_4$ production in both goats and Sika deer, and predictive models of enteric $CH_4$ production by goats and Sika deer were estimated.

• Food Science and Preservation
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• v.18 no.5
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• pp.721-728
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• 2011

1(3)-palmitoyl-2-oleoyl-3(1)-stearoyl-(POS)-glycerol-enriched reaction products were synthesized from camellia oil, palmitic ethyl ester, and stearic ethyl ester via lipase-catalyzed interesterification. Response surface methodology (RSM) was employed to optimize the production of the POS-enriched reaction product (Y1, %) and the stearicand palmitic-acid contents at the sn-2 position due to acyl migration (Y2, %). The reaction factors were the enzyme amount (X1, 2-6%), reaction time (X2, 60-360 min), and substrate molar ratio of camellia oil to palmitic ethyl ester and stearic ethyl ester (X3, 1-3 mol). The predictive models for Y1 and Y2 were adequate and reproducible as no lack of fit was signified (0.128 and 0.237) and as there were satisfactory levels of R2 (0.968 and 0.990, respectively). The optimal conditions for the reaction product for maximizing Y1 while minimizing Y2 were predicted at the reaction combination of 5.86% enzyme amount, 60 min reaction time, and 1:3 substrate molar ratio (3 moles of palmitic ethyl ester and 3 moles of stearic ethyl ester). Actual reaction was performed under the same conditions as above, and the resulting product contained 20.19% TAG-P/O/S and 12.71% saturated fatty acids at the sn-2 position.

• Safety and Health at Work
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• v.11 no.1
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• pp.118-124
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• 2020

Background: The poultry industry in Pakistan has flourished since the 1960s; however, there are scarce data regarding the impact of occupational exposure on the pulmonary health of farm workers in terms of years working in the industry. The objective of the present study was to assess the effect of poultry environment on the health of occupationally exposed poultry farmers in countries of warm climatic regions, such as Pakistan. This study will also show the effect of exposure to poultry facilities on the health of poultry farmers in the context of low-income countries with a relatively inadequate occupational exposure risk management. Materials and methods: The lung function capacity of 79 poultry workers was measured using a spirometer. Along with spirometry, a structured questionnaire was also administrated to obtain information about age, height, weight, smokers/nonsmokers, years of working experience, and pulmonary health of farm workers. The workers who were directly involved in the care and handling of birds in these intensive facilities were considered and divided into four groups based on their years of working experience: Group I (3-10 months), Group II (1-5 years), Group III (6-10 years), and Group IV (more than 11 years). The forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and the FEV1/FVC ratio were considered to identify lung function abnormalities. Statistical analysis was carried out using independent sample t test, Chi-square test, Pearson's correlation, and linear regression. Results: Based on the performed spirometry, 68 (86 %) of workers were found normal and healthy, whereas 11 (14 %) had a mild obstruction. Of the 11 workers with mild obstruction, the highest number with respect to the total was in Group IV (more than 11 years of working experience) followed by Group III and Group II. Most of the workers were found healthy, which seems to be because of the healthy survivor effect. For the independent sample t test, a significant difference was noticed between healthy and nonhealthy farmers, whereas Chi-square test showed a significant association with height, drugs, and working experience. Linear regression that was stratified by respiratory symptoms showed for workers with symptoms, regression models for all spirometric parameters (FVC, FEV1, and FEV1/FVC) have better predictive power or R square value than those of workers without symptoms. Conclusion: These findings suggest that lung function capacity was directly related to years of working experience. With increasing number of working years, symptoms of various respiratory problems enhanced in the poultry workers. It should be noted that most of the poultry workers were healthy and young, the rationale being that there is a high turnover rate in this profession. The mobility in this job and our finding of 86% of the healthy workers in the present study also proposed healthy worker survivor effect.

• Food Science of Animal Resources
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• v.36 no.6
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• pp.752-759
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• 2016

The objective of this study was to describe the growth patterns of Staphylococcus aureus in combinations of NaCl and $NaNO_2$, using a probabilistic model. A mixture of S. aureus strains (NCCP10826, ATCC13565, ATCC14458, ATCC23235, and ATCC27664) was inoculated into nutrient broth plus NaCl (0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, and 1.75%) and $NaNO_2$ (0, 15, 30, 45, 60, 75, 90, 105, and 120 ppm). The samples were then incubated at 4, 7, 10, 12 and $15^{\circ}C$ for up to 60 d under aerobic or vacuum conditions. Growth responses [growth (1) or no growth (0)] were then determined every 24 h by turbidity, and analyzed to select significant parameters (p<0.05) by a stepwise selection method, resulting in a probabilistic model. The developed models were then validated with observed growth responses. S. aureus growth was observed only under aerobic storage at $10-15^{\circ}C$. At $10-15^{\circ}C$, NaCl and $NaNO_2$ did not inhibit S. aureus growth at less than 1.25% NaCl. Concentration dependency was observed for NaCl at more than 1.25%, but not for $NaNO_2$. The concordance percentage between observed and predicted growth data was approximately 93.86%. This result indicates that S. aureus growth can be inhibited in vacuum packaging and even aerobic storage below $10^{\circ}C$. Furthermore, $NaNO_2$ does not effectively inhibit S. aureus growth.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of health informatics and statistics
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• v.42 no.4
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• pp.309-316
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• 2017

Objectives: Radon and its progeny pose environmental risks as a carcinogen, especially to the lungs. Investigating factors affecting indoor radon concentrations and models thereof are needed to prevent exposure to radon and to reduce indoor radon concentrations. The purpose of this study was to identify factors affecting indoor radon concentration and to construct a comprehensive model thereof. Methods: Questionnaires were administered to obtain data on residential environments, including building materials and life style. Decision tree and structural equation modeling were applied to predict residences at risk for higher radon concentrations and to develop the comprehensive model. Results: Greenery ratio, impermeable layer ratio, residence at ground level, daily ventilation, long-term heating, crack around the measuring device, and bedroom were significantly shown to be predictive factors of higher indoor radon concentrations. Daily ventilation reduced the probability of homes having indoor radon concentrations ${\geq}200Bq/m^3$ by 11.6%. Meanwhile, a greenery ratio ${\geq}65%$ without daily ventilation increased this probability by 15.3% compared to daily ventilation. The constructed model indicated greenery ratio and ventilation rate directly affecting indoor radon concentrations. Conclusions: Our model highlights the combined influences of geographical properties, groundwater, and lifestyle factors of an individual resident on indoor radon concentrations in Korea.

• Korean Journal of Comparative Education
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• v.24 no.5
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• pp.53-70
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• 2014

This study was performed in order to suggest the future model of Asian universities that could be used in the planning of the global competitive strategy. Futurologists forecasted the future of higher education using Harman Fan Scenario as like this. First, most current universities will be 'the satellite university' until 2015. Second, they also will replace 'the bookless university' until 2020. Third, they will be 'no calendar university' until 2025. And then they may be 'all have access university' until 2030. After 2030, futurologists prospected that almost universities based on off-line campus will be disappeared into the history. The analysis method of Harman fan scenario and applied scenarios were also used to "A study on the future scenario of Korean university". The predictive model and the alternative models were explored in a view point of students, enterprise, and government. Individuality with educational excellence are standardized for learner, profit and effectiveness are applied for enterpriser, and equality with welfare are adapted for national leader. Asian universities need to focus on bringing up the practical ability based on conscious and emotional education instead of knowledge based on memory. Also they need to enforce the specialized education that can create new jobs through convergence of interdisciplinary. Especially, Asian nations need to explore, to find the strengthen area of their universities compared with USA. And these area should be specialized. The convergency strategy between oriental medicine and informatics is a meaningful sample. Based on this point, a predicted with 3 alternative scenarios in a view point of equality were suggested for the future of Asian universities.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1317-1328
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• 2021

This study aims to provide a predictive model based on climate models for simulating continuous daily rainfall sequences by combining bias-correction and spatio-temporal downscaling approaches. For these purposes, this study proposes a combined modeling system by applying conditional Copula and Multisite Non-stationary Hidden Markov Model (MNHMM). The GloSea5 system releases the monthly rainfall prediction on the same day every week, however, there are noticeable differences in the updated prediction. It was confirmed that the monthly rainfall forecasts are effectively updated with the use of the Copula-based bias-correction approach. More specifically, the proposed bias-correction approach was validated for the period from 1991 to 2010 under the LOOCV scheme. Several rainfall statistics, such as rainfall amounts, consecutive rainfall frequency, consecutive zero rainfall frequency, and wet days, are well reproduced, which is expected to be highly effective as input data of the hydrological model. The difference in spatial coherence between the observed and simulated rainfall sequences over the entire weather stations was estimated in the range of -0.02~0.10, and the interdependence between rainfall stations in the watershed was effectively reproduced. Therefore, it is expected that the hydrological response of the watershed will be more realistically simulated when used as input data for the hydrological model.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Korean Journal of Food Science and Technology
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• v.50 no.6
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• pp.621-628
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• 2018

We developed a quantitative microbial risk assessment model for determning the effect of seasoning on Clostridium perfringens behavior in beef jerky under aerobic and anaerobic conditions. C. perfringens was not detected (<0.5 log CFU/g) in beef jerky samples (n=275), regardless of storage conditions or the presence of seasoning. Survival models of C. perfringens on beef jerky were developed as a function of temperature (10, 17, 25, and $35^{\circ}C$). Risk of C. perfringens due to the consumption of beef jerky was estimated with @RISK and FDA-iRISK. The probability of foodborne illness due to C. perfringens through consumption of seasoned, vacuum packed beef jerky was estimated to be $2.77{\times}10^{-16}$ per person per day. Overall, the risk of contamination of beef jerky with C. perfringens is very low.