• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.26-46
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• 2020

An irrigated-maize agroecosystem is viewed as an open thermodynamic system upon which solar radiation impresses a large gradient that moves the system away from equilibrium. Following the imperative of the second law of thermodynamics, such agroecosystem resists and reduces the externally applied gradient by using all means of this nature-human coupled system acting together as a nonequilibrium dissipative process. The ultimate purpose of our study is to test this hypothesis by examining the energetics of agroecosystem growth and development. As a first step toward this test, we employed the eddy covariance flux data from 2003 to 2014 at the AmeriFlux NE1 irrigated-maize site at Mead, Nebraska, USA, and analyzed the energetics of this agroecosystem by scrutinizing its radiation, energy and entropy exchange. Our results showed: (1) more energy capture during growing season than non-growing season, and increasing energy capture through growing season until senescence; (2) more energy flow activity within and through the system, providing greater potential for degradation; (3) higher efficiency in terms of carbon uptake and water use through growing season until senescence; and (4) the resulting energy degradation occurred at the expense of increasing net entropy accumulation within the system as well as net entropy transfer out to the surrounding environment. Under the drought conditions in 2012, the increased entropy production within the system was accompanied by the enhanced entropy transfer out of the system, resulting in insignificant net entropy change. Drought mitigation with more frequent irrigation shifted the main route of entropy transfer from sensible to latent heat fluxes, yielding the production and carbon uptake exceeding the 12-year mean values at the cost of less efficient use of water and light.

• Korean journal of applied entomology
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• v.47 no.2
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• pp.163-168
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• 2008

Temperature dependent development of Tetranychus. urticae Koch was studied on the leaf of eggplant at 17, 22, 27, 32 and $37^{\circ}C$. T. urticae showed a minimum mortality at $27^{\circ}C$ and it increased at higher or lower temperatures than $27^{\circ}C$. The hatchability was low at 17 and $37^{\circ}C$. The duration of development decreased with increasing temperatures i.e., 5.3d at $37^{\circ}C$ and 25.8d at $17^{\circ}C$. Linear regression analysis of temperature vs. rate of development yielded the higher $r^2{\geq}0.88$ resulting in a good fit of the estimated line in the range of $17{\sim}37^{\circ}C$. Developmental zero temperature was $12.5^{\circ}C$ for the entire immature stage of female and $12.8^{\circ}C$ for that of male. Thermal constants were 80.5 and 74.7 degree days for those of female and male, respectively. Adult life span and oviposition period decreased with increasing temperatures. The number of eggs laid per female peaked at 141.0 eggs at $27^{\circ}C$, while that was a minimum 78.0 eggs at $37^{\circ}C$. Rate of hatchability, ratio of female, and $R_o$ were increased up to $27^{\circ}C$, and than declined thereafter. Intrinsic rate of natural increase (Rm) increased with rising temperatures and showed a maximum 0.5652 at $37^{\circ}C$. Also, ${\lambda}$ increased with increasing temperature. Doubling time (Dt) and generation time (T) decreased with increasing temperature.

• Journal of Chest Surgery
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• v.29 no.1
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• pp.1-6
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• 1996

We developed an experimental model of brain death using dogs. Brain death was induced by increasing the intracranial pressure (ICP) gradually by continuous Infusion of saline through an epidural Foley catheter in 5 mongrel dogs (weight, 18~22kg). Hemodynamic and electrocardiographic changes were evaluated continuously during the process of brain death and obtained the following results. 1. The average volume and time required to induce brain death was 4.8$\pm$1.0ml and 143.0$\pm$30.9minutes respectively. 2. There was a steady rise of the ICP after starting the constant infusion of saline, and ICP rised continuously until the brain death (122.0$\pm$62.5mmHg). After reaching to the maximal value (125.0$\pm$47.7mmHg) at 30 minutes after brain death, the ICP dropped and remained approximately constant at the slightly higher level than the mean arterial pressure (MAP). 3. MAP showed no change until the establishment of brain death and it declined gradually. The peak heart rate reached to 172.6$\pm$35.3/min at 30 minutes after the brain death. 4. Even though the body temperature and all hemodynamic variables, such as cardiac output, mean pulmonary arterial pressure, left ventricular (LV) end-diastolic pressure and LV maximum + dp/dt, were slightly greater than those of basal state, at the point of brain death, there was no statistically significant change during t e process of brain death. 5. There was no remarkable arrhythmias during the experiment except ventricular premature beats which was observed transiently in one dog at the time of brain death. Hemodynamic changes in the brain death model induced by gradual ICP increment were inconspicuous, and arrhythmias were rarely seen. Hyperdynamic state, which was observed at the point of brain death in another brain death model caused by abrupt ICP increase, was not observed.

• Ocean and Polar Research
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• v.26 no.2
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• pp.199-205
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• 2004

$SiO_2$ and CaO are added to decrease the smelting temperature in the reduction-smelting method for manganese nodule processing. These elements are components of the manganese nodules and might be very important controlling factors in the processing due to the locally variable content. The 707 chemical data of manganese nodules acquired from 1994 to 2001 in KODOS(Korea Deep Ocean Survey) area were used for the hierarchical cluster analysis. The chemical data were classified by the morphological types, and the averages of the chemical data for each station were classified by the facies groups and the localities. All data are plotted on the $SiO_2-CaO-MnO$ phase diagram at $1773^{\circ}K$ to compare with the best compositional area in the nodule smelting. Variations and distributions of $SiO_2$ and CaO in KODOS nodules were also reviewed. The mineral phases assigned by the cluster analysis are CFA(Carbonate Fluorapatite), Fe-oxide, Al-silicate, and Mn-oxide. MnO contents are generally higher than $SiO_2$ contents in most of the morphological types except for the Is- and It-type. The Dt- and Tt-type show wider range and the E-types show high anomaly in their CaO contents. The stations which belong to facies group A and B show generally higher MnO contents than $SiO_2$ contents, however, the stations of facies group C and D show wide range in their MnO and $SiO_2$ contents. It seems to be very important to control the $SiO_2$ contents in the processing because of the wide range in the northern area. The additions of approximately 10 wt.% CaO and 10 wt.% $SiO_2$ are recommended for the northern area, whereas, the additions of approximately 10 wt.% CaO and 20 wt.% $SiO_2$ are recommended for the southern area.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.

• The Korean Journal of Physiology
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• v.17 no.1
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• pp.45-54
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• 1983

Contractile responses of myocardium and vascular smooth muscle to angiotensin II were studied in isolated rabbit papillary muscles and aortic helical strips, with respect to the sensitivity and the mechanism of action. All experiments were performed in $HCO-_3\;-buffered Tyrode solution which was aerated with $3%\;CO_2-97%\;O_2$ and kept pH 7.35 at $35^{\circ}C$. Action potentials were measured by conventional microelectrode technique in the papillary muscles. Helical strips of vascular smooth muscle were prepared from the descending thoracic aorta of the rabbit. Angiotensin II elicited a positive inotropic effect in doses from $10^{-8}$ to $10^{-6}\;M$, and this effect was dose-dependent and characterized by a symmetrical increase of maximum dP/dt during contraction and relaxation phase. Slow responses (or slow action potentials) were induced by A. II $(10^{-6}\;M)$ in the papillary muscle hypopolarized by 27 mM $K^+$. These A. II-induced slow action potentials were eliminated by verapamil (2 mg/l), but not affected by propranolol $(10^{-5}\;M)$. In aortic helical strips, contractile force was increased dose-dependently in the range of $10^{-10}{\sim}10^{-7}\;M$ A. II. $ED_{50}$ in aorta was $3{\times}10^{-9}\;M$ A. II, whereas that in paillary muscle was $2.5{\times}10^{-7}\;M$ A. II. A. II contracted vascular smooth muscle in depolarizing concentration of $K^+$ (100 mM $K^+$), and also produced a sustained contraction even in the presence of verapamil and regitine. The results of this experiment suggest that the primarily important physiological role of A. II is the action on the blood vessel, and the positive inotropic effect of A. II in papillary muscle results from the increase of slow inward $Ca^{++}$ current, and that A. II-induced contraction of aorta is independent of transmembrane potential and associated with promoting bet transmembrane $Ca^{++}\;-influx$ and the mobilization of cellular $Ca^{++}$.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.91-107
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• 2018

It is required to monitor and assess the desertification in Tunisia, where the Sahara Desert, which is located in the southern part of Tunisia, is recently expanding northward. In this study, by using remote sensed data, land cover changes were examined, and the Normalized Difference Vegetation Index (NDVI), Topsoil Grain Size Index (TGSI) and Albedo are used to monitor and assess desertification in Tunisia. Decision Tree was constructed, and the frequencies and trends of each assessment indicator, desertification degree and land cover were identified. In addition, we analyzed the correlation between assessment indicators and precipitation. As a result, desertification is generally intensifying northward, especially in areas with high levels of desertification. Also, bivariate correlation analysis showed that Albedo, NDVI and TGSI were all highly correlated with precipitation. It indicates that changes in precipitation have also been shown to affect Tunisian desertification. In conclusion, this study has improved the usability of various methodologies considering the assessment indicators based on satellite imagery, Decision Tree, which is a method of evaluating them complexly, and trends of land cover change.

• Korean journal of applied entomology
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• v.37 no.2
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• pp.171-178
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• 1998

This study was conducted to investigate the effect of temperature on the development of amushroom-infesting sciarid fly, Bradysia sp. (Diptera: Sciaridae). Egg period was 12.1, 7.0, 4.4, 3.4, and3.2 days, larval period was 38.3, 26.5, 13.4, 13.2, 12.7 days and pupal period was 10.4, 7.1, 4.4, 3.3, 3.2days, and total development period from egg to adult emergence was 60.8,40.6, 22.2, 19.9, and 19.1 daysat 10, 15, 20,25, 28"C, respectively. Development threshold temperature (DT) and effective accumulativetemperatures (ET) were 3.8"C, 74.8DD in eggs, 1.2"C, 321.8DD in larva, and 3.1$^{\circ}$C, 76.5DD in pupa,respectively. The number of eggs laid per female was 107.9, 129.7, 131.8, 86.9, and 82.7 at respectivetemperatures. Preoviposition period was 6.6, 4.4, 2.2, 1.3, 1.8 days, oviposition period 1.5, 1.5, 1.1, 1.1,1.1 days, postoviposition period 2.0, 1.1, 0.9, 0.6, and 0.3 days at th'e temperature of 10, 15, 20, 25 and 28"C, respectively. The longevity of male and female at the temperature was 13.3, 7.8, 5.9,4.1, 3.4 days and10.4, 7.0, 4.2, 3.0, 3.3days, respectively. The optimum temperature for hatchability was estimated to the20$^{\circ}$C and adult emergence was highest at 20$^{\circ}$C. Pupation rate was 50.7, 68.4, 84.3, 86.5, 45.4% at 10, 15,20, 25, and 28"C, respectively. at 10, 15,20, 25, and 28"C, respectively.tively.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.127-137
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• 2019

Recidivism prediction has been a subject of constant research by experts since the early 1970s. But it has become more important as committed crimes by recidivist steadily increase. Especially, in the 1990s, after the US and Canada adopted the 'Recidivism Risk Assessment Report' as a decisive criterion during trial and parole screening, research on recidivism prediction became more active. And in the same period, empirical studies on 'Recidivism Factors' were started even at Korea. Even though most recidivism prediction studies have so far focused on factors of recidivism or the accuracy of recidivism prediction, it is important to minimize the prediction misclassification cost, because recidivism prediction has an asymmetric error cost structure. In general, the cost of misrecognizing people who do not cause recidivism to cause recidivism is lower than the cost of incorrectly classifying people who would cause recidivism. Because the former increases only the additional monitoring costs, while the latter increases the amount of social, and economic costs. Therefore, in this paper, we propose an XGBoost(eXtream Gradient Boosting; XGB) based recidivism prediction model considering asymmetric error cost. In the first step of the model, XGB, being recognized as high performance ensemble method in the field of data mining, was applied. And the results of XGB were compared with various prediction models such as LOGIT(logistic regression analysis), DT(decision trees), ANN(artificial neural networks), and SVM(support vector machines). In the next step, the threshold is optimized to minimize the total misclassification cost, which is the weighted average of FNE(False Negative Error) and FPE(False Positive Error). To verify the usefulness of the model, the model was applied to a real recidivism prediction dataset. As a result, it was confirmed that the XGB model not only showed better prediction accuracy than other prediction models but also reduced the cost of misclassification most effectively.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.