• Fisheries and Aquatic Sciences
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• v.3 no.3_4
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• pp.180-187
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• 2000

Reproductive effort and spawning frequency of the two palaemonid prawns, Exopalaemon modestus and Palaemon gravieri, were investigated. In both embryos of the two species, egg size was larger in E. modestus than in P. gravien but for a given size, number of eggs (EN) was fewer in E. modestus. The statistical results revealed that there were significant differences in egg size and EN between the two species. E. modestus living in freshwater environments had larger and fewer offspring than P. gravieri inhabiting marine environments. These findings are consistent with predictions from r- and K-selections models. Reproductive effort (RE) also was higher in E. modestus, suggesting the possibility for E. modestus to invest larger amount of energy per individual offspring. In the two prawns the ovarian dry weight of females with eyed eggs was significantly higher than those with non-eyed eggs. This indicates that the ovarian maturation occurs during the period between the two embryonic stages, suggesting females being potentially of continuous breeding within a single reproductive period. In the both species brood loss did not occur during the incubation period.

• Ocean and Polar Research
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• v.34 no.3
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• pp.275-286
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• 2012

Over the last 20 years there have been more than 3000 peer-reviewed papers relating to climate change and biodiversity published, and still the numbers are increasing. However, most studies focused on the impacts of climate change at population or community levels, and the results invariably reveal that there has been, or will be, a negative effect on the structure and pattern of biodiversity. Moreover, the climate change models and statistical analyses used to test the impacts are only newly developed, and the analyses or predictions can often be misled. In this review, I ask why an individual's life history is considered in the study how climate change affects biodiversity, and what ecological factors are impacted by climate change. Using evidence from a range of species, I demonstrate that diverse life history traits, such as early growth rate, migration/foraging behaviour and lifespan, can be shifted by climate change at individual level. Particularly I discuss that the optimal decision under unknown circumstance (climate change) would be the reduction of the ecological fitness at individual level, and hence, a shift in the balance of the ecosystem could be affected without having a critical impact on any one species. To conclude, I summarize the links between climate changes, ecological decision in life history, the revised consequence at individual level, and discuss how the finely-balanced relationship affects biodiversity and population structure.

• Transactions of Materials Processing
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• v.25 no.4
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• pp.235-241
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• 2016

Dimensional accuracy of hot coil is improved by precise control of thickness profiles, flatness, width and winding profile. Especially, precise width control is important because yield could be increased significantly. Precise width control can be improved by predicting the amount of width spread. The purpose of this study is to develop the advanced prediction model for width spread in hot finish rolling for controlling width precisely. FE-simulations were performed to investigate the effect of process variables on width spread such as reduction ratio, forward and backward tension and initial width at each stand. From the statistical analysis of simulated data, advanced model was developed based on the existing models for strip width spread. The experimental hot rolling trials showed that newly developed model provided fairly accurate predictions on the strip width spread during the whole hot finishing rolling process.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.5
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• pp.701-711
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• 2006

Sewage treatment plants located near to large cities emit extremely higher concentration of odorous materials. This study evaluated flux profiles of ammonia emitted from the water surface of sewage treatment plants using a dynamic flux chamber. Also, an ammonia overall mass transfer coefficient and a mass transfer model was developed in order to estimate fluxes of ammonia using environment parameters and the flux from the sewage treatment plants. The developed mass transfer model was evaluated through a fitness analysis. Comparison modeled flux applying empirical overall mass transfer coefficients of ammonia and measured ammonia flux show a high linearity with 0.977. The flux ratio of 1.282 demonstrated highly statistical fitness, also. Modeled flux using the mass transfer model was compared with measured flux. In result, it indicated that empirical overall mass transfer coefficients were similar to measured flux. The mass transfer model using the empirical overall mass transfer coefficient developed in this study was proved to be an easy and effective method to make accurate and precise predictions for ammonia flux discharged from sewage treatment plants.

• Korean Management Science Review
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• v.24 no.2
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• pp.177-196
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• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. Integrated models consist of four models: ASFM model which combines Association Rule(A) and Frequency Matrix(B), ASRI model which combines Association Rule(A) and Rule Induction(C), FMRI model which combines Frequency Matrix(B) and Rule Induction(C), and ASFMRI model which combines Association Rule(A), Frequency Matrix(B), and Rule Induction(C). The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set. it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.

• Steel and Composite Structures
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• v.12 no.4
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• pp.325-350
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• 2012

Concrete filled steel hollow structural sections (CFSs) are an efficient, sustainable, and attractive option for both ambient temperature and fire resistance design of columns in multi-storey buildings and are becoming increasingly common in modern construction practice around the world. Whilst the design of these sections at ambient temperatures is reasonably well understood, and models to predict the strength and failure modes of these elements at ambient temperatures correlate well with observations from tests, this appears not to be true in the case of fire resistant design. This paper reviews available data from furnace tests on CFS columns and assesses the statistical confidence in available fire resistance design models/approaches used in North America and Europe. This is done using a meta-analysis comparing the available experimental data from large-scale standard fire tests performed around the world against fire resistance predictions from design codes. It is shown that available design approaches carry a very large uncertainty of prediction, suggesting that they fail to properly account for fundamental aspects of the underlying thermal response and/or structural mechanics during fire. Current North American fire resistance design approaches for CFS columns are shown to be considerably less conservative, on average, than those used in Europe.

• Journal of Preventive Medicine and Public Health
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• v.42 no.6
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• pp.371-376
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• 2009

In this article, we reviewed the literature on risk assessment (RA) models with and without molecular genomic markers and the current utility of the markers in the pharmacogenetic field. Epidemiological risk assessment is applied using statistical models and equations established from current scientific knowledge of risk and disease. Several papers have reported that traditional RA tools have significant limitations in decision-making in management strategies for individuals as predictions of diseases and disease progression are inaccurate. Recently, the model added information on the genetic susceptibility factors that are expected to be most responsible for differences in individual risk. On the continuum of health care, from diagnosis to treatment, pharmacogenetics has been developed based on the accumulated knowledge of human genomic variation involving drug distribution and metabolism and the target of action, which has the potential to facilitate personalized medicine that can avoid therapeutic failure and serious side effects. There are many challenges for the applicability of genomic information in a clinical setting. Current uses of genetic markers for managing drug therapy and issues in the development of a valid biomarker in pharmacogenetics are discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Steel and Composite Structures
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• v.18 no.2
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• pp.519-546
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• 2015

This study addresses the load capacity prediction of circular concrete-filled steel tube (CFST) columns under axial compression using current design codes. Design methods given in the Chinese code CECS 28:2012 (2012), American code AISC 360-10 (2010) and EC4 (2004) are presented and described briefly. A wide range of experimental data of 353 CFST columns is used to evaluate the applicability of CECS 28:2012 in calculating the strength of circular CFST columns. AISC 360-10 and EC4 (2004) are also compared with the test results. The comparisons indicate that all three codes give conservative predictions for both short and long CFST columns. The effects of concrete strength, steel strength and diameter-to-thickness ratio on the accuracy of prediction according to CECS 28:2012 are discussed, which indicate a possibility of extending the limitations on the material strengths and diameter-to-thickness ratio to higher values. A revised equation for slenderness reduction factor in CECS 28:2012 is given.

• Nuclear Engineering and Technology
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• v.53 no.11
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• pp.3563-3579
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• 2021

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector machines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions.