• Korean Journal of Fisheries and Aquatic Sciences
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• v.26 no.1
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• pp.1-7
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• 1993

To clarify the effect of storage temperature on the physicochemical and rheological properties of plaice, Paralichthys olivaceus muscle, the changes in IMP accumulation rates, rigor index, breaking strength and K-value of muscle during storage at $0^{\circ},\;5^{\circ}C$ and $10^{\circ}C$ were studied. The maximum speed and content of IMP accumulation were showed in sample stored at $0^{\circ}C$ among the all samples. The content and time reached around the maximum IMP accumulation for each samples were $6.94{\mu}mole/g$ and 30hrs for samples stored at $0^{\circ}C,\;6.30{\mu}mole/g$ and 31.5hrs for $5^{\circ}C$ samples and $5.40{\mu}mole/g$ and 45hrs for $10^{\circ}C$ samples, respectively. Values of breaking strength in sample stored at $5^{\circ}C$ increased rapidly than samples at $0^{\circ}C$ showed maximum values after 10hrs. However, In case of fresh flesh stored at $10^{\circ}C$, there was no increase of breaking strength during storage. The breaking strength was not closely related with rigor index. The times reached around $5\%$ K-value which is representative excellent freshness index of sashimi were 14hrs for samples stored at $0^{\circ}C$, 13hrs for $5^{\circ}C$ samples and 4hrs for $10^{\circ}C$ samples. From the results above, it was suggested that the storage at low temperature($10^{\circ}C{\sim}5^{\circ}C$) was effective in increase the IMP contents and breaking strength, and maintenance the high-freshness of fresh plaice flesh at early periods after death.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.2
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• pp.158-164
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• 2006

Offensive odor from CAFO(concentrated animal feeding operation) and its control have become a significant issue in Korea. Control of odors from the CAFO requires to identify major odorant and their generation mechanisms. In this study, an easy method to collect gas sample and to quantify its odorants is proposed. The method involves on-site odorant extraction with solid-phase microextraction and quantitation with GC/MSD or GC/FID. Analytes of the current study include: trimethylamine(TMA), carbon disulfide($CS_2$), dimethyl sulfide(DMS), dimethyl disulfide(DMDS), acetic acid(AA), propionic acid(PA) and n-butyric acid(BA). The resulting linearity($R^2$) of calibration curve for each analyte was good over the range from several ppbv to ppmv; 0.984 for TMA(0.056-1.437), 0.996 for $CS_2$(0.039-0.999), 0.994 for DMS(0.029-0.756), 0.995 for DMDS(0.024-0.623), 0.992 for AA(0.068-1.314), 0.955 for PA(0.047-0.940), and 0.976 for BA(0.036-0.712). Method detection limits were 5.67, 6.39, 5.78, 25.2, 0.098, 0.363 and 0.099 ppbv for AA, PA, BA, TMA, DMS, $CS_2$, and DMDS, respectively. With the developed method, odorants from poultry, swine, and cattle barns were analysed. All the compounds but DMDS were detected from the sample collected in the poultry barn, and their levels exceeded the representative published human olfactory threshold.

• Geophysics and Geophysical Exploration
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• v.9 no.1
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• pp.60-66
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• 2006

Three differing sandstones, two synthetic and one field sample, have been tested ultrasonically under a range of confining pressures and pore pressures representative of in-situ reservoir pressures. These sandstones include: a synthetic sandstone with calcite intergranular cement produced using the CSIRO Calcite In-situ Precipitation Process (CIPS); a synthetic sandstone with silica intergranular cement; and a core sample from the Otway Basin Waarre Formation, Boggy Creek 1 well, from the target lithology for a trial $CO_2$ pilot project. Initial testing was carried on the cores at "room-dried" conditions, with confining pressures up to 65 MPa in steps of 5 MPa. All cores were then flooded with $CO_2$, initially in the gas phase at 6 MPa, $22^{\circ}C$, then with liquid-phase $CO_2$ at a temperature of $22^{\circ}C$ and pressures from 7 MPa to 17 MPa in steps of 5 MPa. Confining pressures varied from 10 MPa to 65 MPa. Ultrasonic waveforms for both P- and S-waves were recorded at each effective pressure increment. Velocity versus effective pressure responses were calculated from the experimental data for both P- and S-waves. Attenuations $(1/Q_p)$ were calculated from the waveform data using spectral ratio methods. Theoretical calculations of velocity as a function of effective pressure for each sandstone were made using the $CO_2$ pressure-density and $CO_2$ bulk modulus-pressure phase diagrams and Gassmann effective medium theory. Flooding the cores with gaseous phase $CO_2$ produced negligible change in velocity-effective stress relationships compared to the dry state (air saturated). Flooding with liquid-phase $CO_2$ at various pore pressures lowered velocities by approximately 8% on average compared to the air-saturated state. Attenuations increased with liquid-phase $CO_2$ flooding compared to the air-saturated case. Experimental data agreed with the Gassmann calculations at high effective pressures. The "critical" effective pressure, at which agreement with theory occurred, varied with sandstone type. Discrepancies are thought to be due to differing micro-crack populations in the microstructure of each sandstone type. The agreement with theory at high effective pressures is significant and gives some confidence in predicting seismic behaviour under field conditions when $CO_2$ is injected.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.3
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• pp.65-74
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• 2017

There have been questions how entrepreneurs think, act and why individuals become entrepreneurs. The trait-based explanation of entrepreneurial activities has been main stream. However, the trait-based theory has been criticized because it assumes that entrepreneurial traits are inherited, stable and enduring over time. This research accepts the cognitive theory to see how entrepreneurs learn or accept others' values, how entrepreneurial perceptions of opportunity impact entrepreneurial actions and how individuals acquire the social legitimation of the formation of entrepreneurial activities. In order to capture the attitudes, activities and motivations of people who are involved in entrepreneurial activities, the author uses the GEM Korea 2016 data. The data from the Global Entrepreneurship Monitor(GEM) has been well known for the data to capture individuals early-stage entrepreneurial activities. This paper used the sample from the APS(Adult Population Survey) of the GEM which was completed by a representative sample of two thousand adults in Korea by the qualified survey vendor, with strict procedures and oversight by the GEM central data team. The hypotheses are tested with logit regression analysis to estimate the probability of the influence of perceptual variables such as individual perception in social learning, the opportunity recognition in the environment, and social legitimation in the entrepreneurial activities. Based on the results, individuals tend to have high entrepreneurial activities if individuals have high self-efficacy. Also, the existence of role models around the entrepreneurs encourages the individuals involve in entrepreneurial activities more however the perception of opportunity in the environment is not strongly associated with entrepreneurial activities. The media exposure of successful entrepreneurs is more important than others' perception of entrepreneurs on the desirable career option or respect from communities. This paper can contribute to the cognitive processes, particular perception about oneself, as well as perception which is impacted by a community or a society.

• Asia pacific journal of information systems
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• v.19 no.3
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• pp.125-150
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• 2009

RFID(Radio Frequency IDentification) is a wireless frequency of recognition technology that can be used to recognize, trace, and identify people, things, and animals using radio frequency(RF). RFID will bring about many changes in manufacturing and distributions, among other areas. In accordance with the increasing importance of RFID techniques, great advancement has been made in RFID studies. Initially, the RFID research started as a research literature or case study. Recently, empirical research has floated on the surface for announcement. But most of the existing researches on RFID adoption have been restricted to a dichotomous measure of 'adoption vs. non-adoption' or adoption intention. In short, RFID research is still at an initial stage, mainly focusing on the research of the RFID performance, integration, and its usage has been considered dismissive. The purpose of this study is to investigate which factors are important for the RFID adoption and implementation with organizational resources. In this study, the organizational resources are classified into either finance resources or IT knowledge resources. A research model and four hypotheses are set up to identify the relationships among these variables based on the investigations of such theories as technological innovations, adoption stage, and organizational resources. In order to conduct this study, a survey was carried out from September 27, 2008 until October 23, 2008. The questionnaire was completed by 143 managers and workers from physical distribution and manufacturing companies related to the RFID in South Korea. 37 out of 180 surveys, which turned out unfit for the study, were discarded and the remaining 143(adoption stage 89, implementation stage 54) were used for the empirical study. The statistics were analyzed using Excel 2003 and SPSS 12.0. The results of the analysis are as follows. First, the adoption stage shows that perceived benefits, standardization, perceived cost savings, environmental uncertainty, and pressures from rival firms have significant effects on the intent of the RFID adoption. Further, the implementation stage shows that perceived benefits, standardization, environmental uncertainty, pressures from rival firms, inter-organizational cooperation, and inter-organizational trust have significant effects on the extent of the RFID use. In contrast, inter-organizational cooperation and inter-organizational trust did not show much impact on the intent of RFID adoption while perceived cost savings did not significantly affect the extent of RFID use. Second, in the adoption stage, financial issues had adverse effect on both inter-organizational cooperation and the intent against the RFID adoption. IT knowledge resources also had a deterring effect on both perceived cost savings and the extent of the RFID adoption. Third, in the implementation stage, finance resources had a moderate effect on environmental uncertainty and extent of RFID use while IT knowledge resources had also a moderate effect on perceived cost savings and the extent of the RFID use. Limitations and future research issues can be summarized as follows. First, it is difficult to say that the sample is large enough to be representative of the population. Second, because the sample of this study was conducted among manufacturers only, it may be limited in analyzing fully the effect on the industry as a whole. Third, in consideration of the fact that the organizational resources in the RFID study require a great deal of researches, this research may deem insufficient to fulfill the purpose that it initially set out to achieve. Future studies using performance research are, therefore, needed to help better understand the organizational level of the RFID adoption and implementation.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.35-39
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• 2010

Purpose: The aim of this study was to investigate distribution of particle size in phytate kit and compare filtered method with non-filtered method using 200 nm filter for sentinel lymphoscintigraphy (SLS). Materials and Methods: Five phytate kit of having the same available period was measured by particle size analyzer. For in-vivo experiment, $^{99m}Tc$-phytate was injected intradermally at both foot to perform lymphoscintigraphy. Imaging was acquired at 1hour after injection. Region of interest (ROI) was drawn in inguinal and background area for analysis. RAW 264.7 cells (Murine macrophage cell) were prepared for measurement of celluar uptake as a representative of macrophages. Paired t-test was performed using SPSS (SPSS Inc, USA) for statistical analysis. Results: The size of most particle in Techne phytate kit was distributed in 130~650 nm(90.5 %). In-vivo study, the ROI analysis showed similar result between filtered and non-filtered sample, and the numerical value of count/pixel were $58.3{\pm}5.97$ and $60.2{\pm}4.88$. In-vitro study, cellular uptake study also showed no difference between filtered and non-filtered sample by gamma counting. Conclusion: The present study demonstrates that there was no meaning of 200 nm filtered method for SLS using $^{99m}Tc$-phytate.

• Korean Journal of Environment and Ecology
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• v.34 no.6
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• pp.589-600
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• 2020

Effective conservation and management of protected areas require monitoring the settlement of invasive alien species and reducing their dispersion capacity. We simulated the potential distribution of invasive alien plant species (IAPS) using three representative species distribution models (Bioclim, GLM, and MaxEnt) based on the IAPS distribution in the forest genetic resource reserve (2,274ha) in Uljin-gun, Korea. We then selected the realistic and suitable species distribution model that reflects the local region and ecological management characteristics based on the simulation results. The simulation predicted the tendency of the IAPS distributed along the linear landscape elements, such as roads, and including some forest harvested area. The statistical comparison of the prediction and accuracy of each model tested in this study showed that the GLM and MaxEnt models generally had high performance and accuracy compared to the Bioclim model. The Bioclim model calculated the largest potential distribution area, followed by GLM and MaxEnt in that order. The Phenomenological review of the simulation results showed that the sample size more significantly affected the GLM and Bioclim models, while the MaxEnt model was the most consistent regardless of the sample size. The optimal model overall for predicting the distribution of IAPS among the three models was the MaxEnt model. The model selection approach based on detailed flora distribution data presented in this study is expected to be useful for efficiently managing the conservation areas and identifying the realistic and precise species distribution model reflecting local characteristics.

• Journal of Distribution Research
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• v.17 no.1
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• pp.37-63
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• 2012

In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.

• 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.

• Journal of Food Hygiene and Safety
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• v.22 no.3
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• pp.173-178
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• 2007

Children may respond differently to the caffeine from adults because they have different physiologic makeup and are functionally immature in terms of hepatic and renal function; this leads to the slower clearance of caffeine in early life. Therefore, children are often assumed to be more susceptible to caffeine effects. Alarge number of food supplements may interfere with these processes, and therefore caffeine exposure may have more serious consequences for children than for adults, irrespective of sensitivity. However, there has never been a national dietary survey on caffeine intakes in children. The purpose of our study was to identify caffeine intakes and beverage sources of caffeine in a representative sample of children in Busan, Korea. Caffeine intakes were based only on beverages included in the Continuing Surveys of Food Intakes by individuals. The caffeine content of the beverages ranged from 2.8 to 65.2mg/100ml for cola, soft drinks, and teas. Caffeine was not completely absent from caffeine-free colas, juice, and milk. In this study, cola-type beverages were an important dietary source of caffeine in the children. Daily caffeine intake for children was estimated to range from 12.5 to 250 mg/day. In general, the acceptable daily intake (ADI) of caffeine should cover the entire population including children. Therefore, special considerations should be needed regarding the consumption of soft drinks containing caffeine to children below the 12 years of age.