• The Journal of Industrial Distribution & Business
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• v.11 no.7
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• pp.51-60
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• 2020

Purpose: Recently, Korean cosmetics distribution market has been reorganized with the H&B store. In the domestic cosmetics distribution market, existing brand road shops are decreasing, and multi-shops are leading the H & B stores, which have greatly improved their experience and content. In these environmental changes, the offline distribution channels are turning into the multi-editing shops that have introduced products of various brands and greatly enhanced experiences and contents. Nevertheless, most studies of factors and measurement items for measuring customer experience in the H&B store use Schmitt (1999)'s Strategic Experience Modules (SEMs). Therefore, the purpose of this study is to propose a measure that is practicable through consideration of the in-store customer experience components of the H&B store. Research design, data and methodology: Based on Schmitt's Strategic Experience Modules (SEMs), which are widely used in customer experience marketing, the metric pool was constructed through customer and literature research on H & B store managers. Since then, 101 preliminary surveys and 211 main surveys have been conducted in order to propose a dimension of customer experience and refine the metrics. Results: As a result of the research, H&B store's customer experience was derived from a measurement model consisting of 19 measurement items in total of five dimensions: environmental experience, intellectual experience, behavioral experience, tech experience, and relationship experience. This study analyzed that compared to the existing Schmitt's Strategic Experience Modules (SEMs), (1) emotional experience expanded to environmental experience, (2) Cognitive and relationship experiences are maintained (3) behavioral experience was subdivided into physical and technical experiences. In particular, the environmental experience has been proposed as a major component is an important point because the H&B store recently opened a large flagship store and is competitive in constructing a differentiated space. Conclusions: Related experience was seen as an important component of customer experience in the offline store, but in the process of refining the scale, interaction items with employees of the H&B store were removed, and rather, participation in the APP or SNS channel of the company, event Participation, interaction with other customers, etc. appear to be important, while suggesting the practical implications.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.6
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• pp.656-661
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• 2017

To develop a highly value-added product from extract from small and damaged sea mussels Mytilus edulis, we prepared two types of sea mussel sauce (MS): bottled (BMS) and retort pouched (RMS). We investigated the processing conditions, quality metrics and flavor compounds in each type of sauce. We found that the most appropriate base formulation for both BMS and RMS consisted of 40.0% SME (Brix $30^{\circ}$), 15.0% sugar, 6.0% salt, 4.0% monosodium glutamate, 4.0% soy sauce, 3.5% starch, 3.0% yeast extract, 3.5% wheat flour and 21.0% water. The crude protein, salinity, volatile basic nitrogen and amino-nitrogen content of the BMS and RMS were 8.7% and 8.8%, 9.3% and 9.2%, 24.9 and 31.4 mg/100 g, and 468.5 and 455.1 mg/100 g, respectively. For comparison, the ranges of these values in commercial oyster sauces (COS) are 4.7-7.5%, 10.7-12.0%, 8.2-12.5 mg/100 g, and 225.7-448.2 mg/100 g, respectively. The total free amino acid content of RMS and Premium COS was 7,215.7 and 6,160.7 mg/100 g, respectively, and the main free amino acids were glutamic acid, taurine, glycine, alanine, arginine, proline and lysine. These results demonstrate that BMS and RMS have favorable organoleptic qualities and good storage stability compared to COS, and are suitable for commercialization as high-flavor seasoning sauces.

• Korean Journal of Radiology
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• v.24 no.7
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• pp.647-659
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• 2023

Objective: The study was conducted to investigate the effect of correct occlusion of the left atrial appendage (LAA) on intracardiac blood flow and thrombus formation in patients with atrial fibrillation (AF) using four-dimensional (4D) flow magnetic resonance imaging (MRI) and three-dimensional (3D)-printed phantoms. Materials and Methods: Three life-sized 3D-printed left atrium (LA) phantoms, including a pre-occlusion (i.e., before the occlusion procedure) model and correctly and incorrectly occluded post-procedural models, were constructed based on cardiac computed tomography images from an 86-year-old male with long-standing persistent AF. A custom-made closed-loop flow circuit was set up, and pulsatile simulated pulmonary venous flow was delivered by a pump. 4D flow MRI was performed using a 3T scanner, and the images were analyzed using MATLAB-based software (R2020b; Mathworks). Flow metrics associated with blood stasis and thrombogenicity, such as the volume of stasis defined by the velocity threshold ($\left|\vec{V}\right|$ < 3 cm/s), surface-and-time-averaged wall shear stress (WSS), and endothelial cell activation potential (ECAP), were analyzed and compared among the three LA phantom models. Results: Different spatial distributions, orientations, and magnitudes of LA flow were directly visualized within the three LA phantoms using 4D flow MRI. The time-averaged volume and its ratio to the corresponding entire volume of LA flow stasis were consistently reduced in the correctly occluded model (70.82 mL and 39.0%, respectively), followed by the incorrectly occluded (73.17 mL and 39.0%, respectively) and pre-occlusion (79.11 mL and 39.7%, respectively) models. The surfaceand-time-averaged WSS and ECAP were also lowest in the correctly occluded model (0.048 Pa and 4.004 Pa^-1, respectively), followed by the incorrectly occluded (0.059 Pa and 4.792 Pa^-1, respectively) and pre-occlusion (0.072 Pa and 5.861 Pa^-1, respectively) models. Conclusion: These findings suggest that a correctly occluded LAA leads to the greatest reduction in LA flow stasis and thrombogenicity, presenting a tentative procedural goal to maximize clinical benefits in patients with AF.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.807-820
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• 2023

Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.