• Journal of Distribution Science
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• v.12 no.2
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• pp.59-71
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• 2014

Purpose - In recent years, many firms have attempted various approaches to cope with the continual increase of aviation transportation. The previous research into freight charge forecasting models has focused on regression analyses using a few influence factors to calculate the future price. However, these approaches have limitations that make them difficult to apply into practice: They cannot respond promptly to small price changes and their predictive power is relatively low. Therefore, the current study proposes a freight charge-forecasting model using time series data instead a regression approach. The main purposes of this study can thus be summarized as follows. First, a proper model for freight charge using the autoregressive integrated moving average (ARIMA) model, which is mainly used for time series forecast, is presented. Second, a modified ARIMA model for freight charge prediction and the standard process of determining freight charge based on the model is presented. Third, a straightforward freight charge prediction model for practitioners to apply and utilize is presented. Research design, data, and methodology - To develop a new freight charge model, this study proposes the ARIMAC(p,q) model, which applies time difference constantly to address the correlation coefficient (autocorrelation function and partial autocorrelation function) problem as it appears in the ARIMA(p,q) model and materialize an error-adjusted ARIMAC(p,q). Cargo Account Settlement Systems (CASS) data from the International Air Transport Association (IATA) are used to predict the air freight charge. In the modeling, freight charge data for 72 months (from January 2006 to December 2011) are used for the training set, and a prediction interval of 23 months (from January 2012 to November 2013) is used for the validation set. The freight charge from November 2012 to November 2013 is predicted for three routes - Los Angeles, Miami, and Vienna - and the accuracy of the prediction interval is analyzed using mean absolute percentage error (MAPE). Results - The result of the proposed model shows better accuracy of prediction because the MAPE of the error-adjusted ARIMAC model is 10% and the MAPE of ARIMAC is 11.2% for the L.A. route. For the Miami route, the proposed model also shows slightly better accuracy in that the MAPE of the error-adjusted ARIMAC model is 3.5%, while that of ARIMAC is 3.7%. However, for the Vienna route, the accuracy of ARIMAC is better because the MAPE of ARIMAC is 14.5% and the MAPE of the error-adjusted ARIMAC model is 15.7%. Conclusions - The accuracy of the error-adjusted ARIMAC model appears better when a route's freight charge variance is large, and the accuracy of ARIMA is better when the freight charge variance is small or has a trend of ascent or descent. From the results, it can be concluded that the ARIMAC model, which uses moving averages, has less predictive power for small price changes, while the error-adjusted ARIMAC model, which uses error correction, has the advantage of being able to respond to price changes quickly.

• Journal of Trauma and Injury
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• v.30 no.4
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• pp.158-165
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• 2017

Purpose: The purpose of this study was to evaluate the diagnostic accuracy of X-rays in patients with acute traumatic vertebral fractures visiting the emergency department and to analyze the diagnostic value of X-rays for each spine level. Methods: We retrospectively analyzed basal characteristics by reviewing medical records of 363 patients with adult traumatic vertebral fractures, admitted to the emergency center from March 1, 2014 to February 28, 2017. We analyzed spine X-rays and magnetic resonance imaging (MRI) scans to determine distribution according to the vertebral level, and we evaluated the efficacy of X-rays by comparing discrepancies between X-rays and MRI scans. Results: For a total of 363 patients, the mean age was 56.65 (20-93) and 214 (59%) were males. On the basis of X-rays, 67 cases (15.1%) were of the cervical spine, 133 cases (30.0%) were of the thoracic spine, and 243 cases (54.9%) were of the lumbar spine. In particular, the thoracolumbar region (T11-L2) was the most common, with 260 cases (58.7%). In X-rays, fractures were the least in the upper thoracic region (T1-T3), whereas MRI scans revealed fairly uniform distribution across the thoracic spine. Sensitivity of X-rays was lowest in the upper thoracic spine and specificity was almost always greater than 98%, except for 94.7% in L1. Positive predictive value was lower in the mid-thoracic region (T4-T9) and negative predictive value was slightly lower in C6, T2, and T3 than at other sites. Diagnostic accuracy of X-rays by vertebral body, transverse process, and spinous process according to fractured vertebral structures was significantly different according to vertebral level. Conclusions: Diagnostic accuracy of X-rays was lower in the upper thoracic region than in other parts. Further studies are needed to identify better methods for diagnosis considering cost and neurological prognosis.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.593-601
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• 2017

In this paper, we constructed the model of production/investment, distribution, and screening by using variables that can be considered at each stage according to the value chain stage of the movie industry. To increase the predictive power of the model, a regression analysis was used to derive meaningful variables. Based on the given variables, we compared the difference in predictive power between the artificial neural network, which is a machine learning analysis method, and the decision tree analysis method. As a result, the accuracy of artificial neural network was higher than that of decision trees when all variables were added in production/ investment model and distribution model. However, decision trees were more accurate when selected variables were applied according to regression analysis results. In the screening model, the accuracy of the artificial neural network was higher than the accuracy of the decision tree regardless of whether the regression analysis result was reflected or not. This paper has an implication which we tried to improve the performance of movie prediction model by using machine learning analysis. In addition, we tried to overcome a limitation of linear approach by reflecting the results of regression analysis to ANN and decision tree model.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.277-286
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• 2021

Background: As trade increases, the influx of various alien species and their spread to new regions are prevalent and no longer a special problem. Anthropogenic activities and climate changes have made the distribution of alien species out of their native range common. As a result, alien species can be easily found anywhere, and they have nothing but only a few differences in intensity. The prevalent distribution of alien species adversely affects the ecosystem, and a strategic management plan must be established to control them effectively. To this end, hot spots and cold spots were analyzed according to the degree of distribution of invasive alien plants, and major environmental factors related to hot spots were found. We analyzed the 10,287 distribution points of 126 species of alien plants collected through the national survey of alien species by the hierarchical model of species communities (HMSC) framework. Results: The explanatory and fourfold cross-validation predictive power of the model were 0.91 and 0.75 as AUC values, respectively. The hot spots of invasive plants were found in the Seoul metropolitan area, Daegu metropolitan city, Chungcheongbuk-do Province, southwest shore, and Jeju island. Generally, the hot spots were found where the higher maximum temperature of summer, precipitation of winter, and road density are observed, but temperature seasonality, annual temperature range, precipitation of the summer, and distance to river and sea were negatively related to the hot spots. According to the model, the functional traits accounted for 55% of the variance explained by the environmental factors. The species with higher specific leaf areas were more found where temperature seasonality was low. Taller species preferred the bigger annual temperature range. The heavier seed mass was only preferred when the max temperature of summer exceeded 29 ℃. Conclusions: In this study, hot spots were places where 2.1 times more alien plants were distributed on average than non-hot spots (33.5 vs 15.7 species). The hot spots of invasive plants were expected to appear in less stressful climate conditions, such as low fluctuation of temperature and precipitation. Also, the disturbance by anthropogenic factors or water flow had positive influences on the hot spots. These results were consistent with the previous reports about the ruderal or competitive strategies of invasive plants instead of the stress-tolerant strategy. The functional traits are closely related to the ecological strategies of plants by shaping the response of species to various environmental filters, and our result confirmed this. Therefore, in order to effectively control alien plants, it is judged that the occurrence of disturbed sites in which alien plants can grow in large quantities is minimized, and the river management of waterfronts is required.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.53-63
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• 2024

Biodiversity holds significant importance within the framework of environmental impact assessment, being utilized in site selection for development, understanding the surrounding environment, and assessing the impact on species due to disturbances. The field of environmental impact assessment has seen substantial research exploring new technologies and models to evaluate and predict biodiversity more accurately. While current assessments rely on data from fieldwork and literature surveys to gauge species richness indices, limitations in spatial and temporal coverage underscore the need for high-resolution biodiversity assessments through species richness mapping. In this study, leveraging data from the 4th National Ecosystem Survey and environmental variables, we developed a species distribution model using Random Forest. This model yielded mapping results of 24 mammalian species' distribution, utilizing the species richness index to generate a 100-meter resolution map of species richness. The research findings exhibited a notably high predictive accuracy, with the species distribution model demonstrating an average AUC value of 0.82. In addition, the comparison with National Ecosystem Survey data reveals that the species richness distribution in the high-resolution species richness mapping results conforms to a normal distribution. Hence, it stands as highly reliable foundational data for environmental impact assessment. Such research and analytical outcomes could serve as pivotal new reference materials for future urban development projects, offering insights for biodiversity assessment and habitat preservation endeavors.

• Fisheries and Aquatic Sciences
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• v.25 no.5
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• pp.287-297
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• 2022

The objective of this study was to evaluate the probability of norovirus foodborne illness by raw oyster consumption. One hundred fifty-six oyster samples were collected to examine the norovirus prevalence. The oyster samples were inoculated with murine norovirus and stored at 4℃-25℃. A plaque assay determined norovirus titers. The norovirus titers were fitted with the Baranyi model to calculate shoulder period (h) and death rate (Log PFU/g/h). These kinetic parameters were fitted to a polynomial model as a function of temperature. Distribution temperature and time were surveyed, and consumption data were surveyed. A dose-response model was also searched through literature. The simulation model was prepared with these data in @RISK to estimate the probability of norovirus foodborne. One sample of 156 samples was norovirus positive. Thus, the initial contamination level was estimated by the Beta distribution (2, 156), and the level was -5.3 Log PFU/g. The developed predictive models showed that the norovirus titers decreased in oysters under the storage conditions simulated with the Uniform distribution (0.325, 1.643) for time and the Pert distribution (10, 18, 25) for temperature. Consumption ratio of raw oyster was 0.98%, and average consumption amount was 1.82 g, calculated by the Pert distribution [Pert {1.8200, 1.8200, 335.30, Truncate (0, 236.8)}]. ₁F₁ hypergeometric dose-response model [1 - (1 + 2.55 × 10^-3 × dose)^-0.086] was appropriate to evaluate dose-response. The simulation showed that the probability of norovirus foodborne illness by raw oyster consumption was 5.90 × 10^-10 per person per day. The annual socioeconomic cost of consuming raw oysters contaminated with norovirus was not very high.

• Journal of Soil and Groundwater Environment
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• v.22 no.5
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• pp.30-39
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• 2017

In this study, the predictive toxicity of barley Hordeum vulgare was estimated using a modified terrestrial biotic ligand model (TBLM) to account for the toxic effects of $CuOH^+$ and $CuCO_3(aq)$ generated at pH 7 or higher, and this was compared to that from the original TBLM. At pH values higher than 7, the difference in $EA_{50}\{Cu^{2+}\}$ (half maximal effective activity of $Cu^{2+}$) between the two models increased with increasing pH. As Mg concentration increased from 8.24 to 148 mg/L in the pH range of 5.5 to 8.5, the difference in $EA_{50}\{Cu^{2+}\}$ increased, and it reached its maximum at pH 8. The difference in $EC_{50}[Cu]_T$ (half maximal effective concentration of Cu) between the two models increased as dissolved organic carbon (DOC) concentration increased when pH was above 7. Thus, for soils with alkaline pH, the toxic effect of $CuOH^+$ and $CuCO_3(aq)$ are greater at higher salt and DOC concentrations. The acceptable Cu concentration in soil porewater can be estimated by the modified TBLM through deterministic method at pH levels higher than 7, while combination of TBLM and species sensitivity distribution through the probabilistic method could be utilized at pH levels lower than 7.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.134-143
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• 2011

The selection of meaningful clinical tests and its reference values from a high-dimensional clinical data with imbalanced class distribution, one class is represented by a large number of examples while the other is represented by only a few, is an important issue for differential diagnosis between similar diseases, but difficult. For this purpose, this study introduces methods based on the concepts of both discernibility matrix and function in rough set theory (RST) with two discretization approaches, equal width and frequency discretization. Here these discretization approaches are used to define the reference values for clinical tests, and the discernibility matrix and function are used to extract a subset of significant clinical tests from the translated nominal attribute values. To show its applicability in the differential diagnosis problem, we have applied it to extract the significant clinical tests and its reference values between normal (N = 351) and abnormal group (N = 101) with either cholecystitis or cholelithiasis disease. In addition, we investigated not only the selected significant clinical tests and the variations of its reference values, but also the average predictive accuracies on four evaluation criteria, i.e., accuracy, sensitivity, specificity, and geometric mean, during l0-fold cross validation. From the experimental results, we confirmed that two discretization approaches based rough set approximation methods with relative frequency give better results than those with absolute frequency, in the evaluation criteria (i.e., average geometric mean). Thus it shows that the prediction model using relative frequency can be used effectively in classification and prediction problems of the clinical data with imbalanced class distribution.

• Fisheries and Aquatic Sciences
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• v.24 no.2
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• pp.78-88
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• 2021

The annual consumption of fishery products, particularly sea squirt (Halocynthia roretzi), per person has steadily increased in South Korea. However, the quantitative risk of Vibrio parahaemolyticus following intake of sea squirt has not been analyzed. This study focuses on quantitative predictions of the probability of consuming sea squirt and getting of V. parahaemolyticus foodborne illness. The prevalence of V. parahaemolyticus in sea squirt was evaluated, and the time spent by sea squirt in transportation vehicles, market displays, and home refrigerators, in addition to the temperature of each of these, were recorded. The data were fitted to the @RISK program to obtain a probability distribution. Predictive models were developed to determine the fate of V. parahaemolyticus under distribution conditions. A simulation model was prepared based on experimental data, and a dose-response model for V. parahaemolyticus was prepared using data from literature to estimate infection risk. V. parahaemolyticus contamination was detected in 6 of 35 (17.1%) sea squirt samples. The daily consumption quantity of sea squirt was 62.14 g per person, and the consumption frequency was 0.28%. The average probability of V. parahaemolyticus foodborne illness following sea squirt consumption per person per day was 4.03 × 10^-9. The objective of this study was to evaluate the risk of foodborne illness caused by Vibrio parahaemolyticus following sea squirt consumption in South Korea.