• Journal of Life Science
• /
• v.30 no.6
• /
• pp.522-531
• /
• 2020

The aim of this study was to establish the optimal medium composition for enhancing L(+)-lactic acid (LLA) production using response surface methodology (RSM). Lactobacillus paracasei SRCM201474 was selected as the LLA producer by productivity analysis from nine candidates isolated from kimchi and identified by 16S rRNA gene sequencing. Plackett-Burman design was used to assess the effect of eleven media components on LLA production, including carbon (glucose, sucrose, molasses), nitrogen (yeast extract, peptone, tryptone, beef extract), and mineral (NaCl, K₂HPO₄, MgSO₄, MnSO₄) materials. Glucose, sucrose, molasses, and peptone were subsequently chosen as promising media for further optimization studies, and a hybrid design experiment was used to establish their optimal concentrations as glucose 15.48 g/l, sucrose 16.73 g/l, molasses 39.09 g/l, and peptone 34.91 g/l. The coefficient of determination of the equation derived from RSM regression for LLA production was mathematically reliable at 0.9969. At optimum parameters, 33.38 g/l of maximum LLA increased by 193% when compared with MRS broth as unoptimized medium (17.66 g/l). Our statistical model was confirmed by subsequent validation experiments. Increasing the performance of LLA-producing microorganisms and establishing an effective LLA fermentation process can be of particular benefit for bioplastic technologies and industrial applications.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.4
• /
• pp.137-144
• /
• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.34 no.4
• /
• pp.567-571
• /
• 2005

In this study, the effects of varying the amount of water, soaking time at $25^{\circ}C$ and drying time after soaking at $45^{\circ}C$ on the induction of resveratrol were evaluated to optimize the soaking treatment by response surface methodology (RSM). After response surface regression (RSREG), the second-order polynomial equation was fitted to the experimental data. The analysis of variance showed that the model appeared to be adequate $(R^2=0.9547)$ with no significant lack of fit (p>0.1). From statistical analysis, amount of water and soaking time were found to be significant factors. On the other hand, drying time was not significant. Ridge analysis showed that the optimized parameters were $67.15\%$ for amount of water, 19.58 hr for soaking time, 65.56 hr for drying time. It was confirmed that resveratrol content was increased from $0.1\;{\mu}g/g$ to $4.55\;{\mu}g/g$ under the optimized conditions. In addition, the experimental values at the optimized condition agreed with values predicted by ridge analysis. The analytical method validation parameters such as accuracy, precision, and specificity were calculated to ensure the method's validity.

• Journal of Korea Water Resources Association
• /
• v.53 no.11
• /
• pp.929-938
• /
• 2020

In this study, we estimated missing evapotranspiration (ET) data at a eddy-covariance flux tower in the Cheongmicheon farmland site using the Artificial Neural Network (ANN). The ANN showed excellent performance in numerical analysis and is expanding in various fields. To evaluate the performance the ANN-based gap-filling, ET was calculated using the existing gap-filling methods of Mean Diagnostic Variation (MDV) and Food and Aggregation Organization Penman-Monteith (FAO-PM). Then ET was evaluated by time series method and statistical analysis (coefficient of determination, index of agreement (IOA), root mean squared error (RMSE) and mean absolute error (MAE). For the validation of each gap-filling model, we used 30 minutes of data in 2015. Of the 121 missing values, the ANN method showed the best performance by supplementing 70, 53 and 84 missing values, respectively, in the order of MDV, FAO-PM, and ANN methods. Analysis of the coefficient of determination (MDV, FAO-PM, and ANN methods followed by 0.673, 0.784, and 0.841, respectively.) and the IOA (The MDV, FAO-PM, and ANN methods followed by 0.899, 0.890, and 0.951 respectively.) indicated that, all three methods were highly correlated and considered to be fully utilized, and among them, ANN models showed the highest performance and suitability. Based on this study, it could be used more appropriately in the study of gap-filling method of flux tower data using machine learning method.

• Wind and Structures
• /
• v.34 no.1
• /
• pp.127-136
• /
• 2022

In this work a multi-fidelity non-intrusive polynomial chaos (MF-NIPC) has been applied to a structural wind engineering problem in architectural design for the first time. In architectural design it is important to design structures that are safe in a range of wind directions and speeds. For this reason, the computational models used to design buildings and bridges must account for the uncertainties associated with the interaction between the structure and wind. In order to use the numerical simulations for the design, the numerical models must be validated by experi-mental data, and uncertainties contained in the experiments should also be taken into account. Uncertainty Quantifi-cation has been increasingly used for CFD simulations to consider such uncertainties. Typically, CFD simulations are computationally expensive, motivating the increased interest in multi-fidelity methods due to their ability to lev-erage limited data sets of high-fidelity data with evaluations of more computationally inexpensive models. Previous-ly, the multi-fidelity framework has been applied to CFD simulations for the purposes of optimization, rather than for the statistical assessment of candidate design. In this paper MF-NIPC method is applied to flow around a rectan-gular 5:1 cylinder, which has been thoroughly investigated for architectural design. The purpose of UQ is validation of numerical simulation results with experimental data, therefore the radius of curvature of the rectangular cylinder corners and the angle of attack are considered to be random variables, which are known to contain uncertainties when wind tunnel tests are carried out. Computational Fluid Dynamics (CFD) simulations are solved by a solver that employs the Finite Element Method (FEM) for two turbulence modeling approaches of the incompressible Navier-Stokes equations: Unsteady Reynolds Averaged Navier Stokes (URANS) and the Large Eddy simulation (LES). The results of the uncertainty analysis with CFD are compared to experimental data in terms of time-averaged pressure coefficients and bulk parameters. In addition, the accuracy and efficiency of the multi-fidelity framework is demonstrated through a comparison with the results of the high-fidelity model.

• The Korean Journal of Franchise Management
• /
• v.8 no.2
• /
• pp.37-47
• /
• 2017

Purpose - Recently, the food service franchise market is experiencing rapid growth and competition is intensifying. Therefore, consumer choice has expanded, and reputation management has become important as a strategy for survival of corporations. Based on previous studies, this research proposed the theoretical framework about the structural relationships among reputation, trust(cognitive trust, affective trust), and loyalty. Research design, data, and methodology - This study examined the structural relationship between reputation, trust, and loyalty from the customer's perspective. Based on comprehensive validation procedures across nine food service Franchise firm types, This study found support for a five-dimensional scale with the following dimensions: Customer Orientation, Employer Brand, Reliable and Financially Strong Company, Product and Service Quality, and Social and Environmental Responsibility. In order to verify the research purposes, research model and hypotheses were developed. The data were collected from 227 food service franchise consumers through online survey. The data was analyzed with SPSS 24.0 and Amos 23.0 statistical program. Result - The results of the study are as follows. First, customer orientation, reliable·financially strong company and product·service quality have significant impact on corporate cognitive trust. And employer brand, product/service quality and social·environmental responsibility have significant impact on corporate affective trust. Second, cognitive trust and affective trust have significant impacts on consumer loyalty. Conclusions - The implications of this study are following as: From the theoretical perspective, this study considers trust as two dimensions such as cognitive and affective, not a single dimension, and identify what dimensions of franchise firms affect consumers' reputation perception and in turn lead cognitive and affective trust, and loyalty. This study also provides several managerial implications. In the franchise market where competition is intensifying, it is very important to analyze the attitudes of consumers in order to gain an advantage in competition with other competitors. In this study, it is meaningful that the study was conducted on consumers who have experience using a restaurant franchise company. Also, reputation is necessary to pay attention to the company because it is an important variable that strengthens with customer through confidence in food service franchise business, and leads loyalty and consumer consumption. Therefore, marketers should develop marketing strategies considering various reputation factors.

• Journal of Soil and Groundwater Environment
• /
• v.28 no.6
• /
• pp.71-89
• /
• 2023

A groundwater potential map (GPM) was built for the Nakdonggang River Basin based on ten variables, including hydrogeologic unit, fault-line density, depth to groundwater, distance to surface water, lineament density, slope, stream drainage density, soil drainage, land cover, and annual rainfall. To integrate the thematic layers for GPM, the criteria were first weighted using the Analytic Hierarchical Process (AHP) and then overlaid using the Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) model. Finally, the groundwater potential was categorized into five classes (very high (VH), high (H), moderate (M), low (L), very low (VL)) and verified by examining the specific capacity of individual wells on each class. The wells in the area categorized as VH showed the highest median specific capacity (5.2 m³/day/m), while the wells with specific capacity < 1.39 m³/day/m were distributed in the areas categorized as L or VL. The accuracy of GPM generated in the work looked acceptable, although the specific capacity data were not enough to verify GPM in the studied large watershed. To create GPMs for the determination of high-yield well locations, the resolution and reliability of thematic maps should be improved. Criterion values for groundwater potential should be established when machine learning or statistical models are used in the GPM evaluation process.

• Journal of Environmental Health Sciences
• /
• v.49 no.6
• /
• pp.334-343
• /
• 2023

Background: South Korea's Act on Registration and Evaluation, etc. of Chemicals (known as K-REACH) was established to protect public health and the environment from hazardous chemicals. 4,4'-Methylenedianiline (MDA), which is used as a major intermediate in industrial polymer production and as a vulcanizing agent in South Korea, is classified as a toxic substance under the K-REACH act. Although MDA poses potential ecological risks due to industrial emissions and hazards to aquatic ecosystems, no ecological risk assessment has been conducted. Objectives: The aim of this study is to assess the ecological risk of MDA by identifying the actual exposure status based on the K-REACH act. Methods: Various toxicity data were collected to establish predicted no effect concentrations (PNECs) for water, sediment, and soil. Using the SimpleBox Korea v2.0 model with domestic release statistical data and EU emission factors, predicted environmental concentrations (PECs) were derived for ten sites, each referring to an MDA-using company. Hazard quotient (HQ) was calculated by ratio of the PECs and PNECs to characterize the ecological risk posed by MDA. To validate the results of modeling-based assessment, concentration of MDA was measured using in-site freshwater samples (two to three samples per site). Results: PNECs for water, sediment, and soil were 0.000525 mg/L, 4.36 mg/kg dw, and 0.1 mg/kg dw, respectively. HQ for surface water and sediment at several company sites exceeded 1 due to modeling data showing markedly high PEC in each environmental compartment. However, in the results of validation using in-site surface water samples, MDA was not detected. Conclusions: Through an ecological risk assessment conducted in accordance with the K-REACH act, the risk level of MDA emitted into the environmental compartments in South Korea was found to be low.

• The korean journal of orthodontics
• /
• v.54 no.1
• /
• pp.48-58
• /
• 2024

Objective: To quantify the effects of midline-related landmark identification on midline deviation measurements in posteroanterior (PA) cephalograms using a cascaded convolutional neural network (CNN). Methods: A total of 2,903 PA cephalogram images obtained from 9 university hospitals were divided into training, internal validation, and test sets (n = 2,150, 376, and 377). As the gold standard, 2 orthodontic professors marked the bilateral landmarks, including the frontozygomatic suture point and latero-orbitale (LO), and the midline landmarks, including the crista galli, anterior nasal spine (ANS), upper dental midpoint (UDM), lower dental midpoint (LDM), and menton (Me). For the test, Examiner-1 and Examiner-2 (3-year and 1-year orthodontic residents) and the Cascaded-CNN models marked the landmarks. After point-to-point errors of landmark identification, the successful detection rate (SDR) and distance and direction of the midline landmark deviation from the midsagittal line (ANS-mid, UDM-mid, LDM-mid, and Me-mid) were measured, and statistical analysis was performed. Results: The cascaded-CNN algorithm showed a clinically acceptable level of point-to-point error (1.26 mm vs. 1.57 mm in Examiner-1 and 1.75 mm in Examiner-2). The average SDR within the 2 mm range was 83.2%, with high accuracy at the LO (right, 96.9%; left, 97.1%), and UDM (96.9%). The absolute measurement errors were less than 1 mm for ANS-mid, UDM-mid, and LDM-mid compared with the gold standard. Conclusions: The cascaded-CNN model may be considered an effective tool for the auto-identification of midline landmarks and quantification of midline deviation in PA cephalograms of adult patients, regardless of variations in the image acquisition method.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.85-109
• /
• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.