• Journal of the Korean Society of Food Culture
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• v.36 no.4
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• pp.362-372
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• 2021

The present study was undertaken to investigate the quality characteristics of Korean Woori black pig (KWP) bellies and loins by different slaughter weight (SW) groups. The loin and belly samples collected from KWPs with different body weights (50, 75, 90, 105, and 120 kg) at 24 h post-mortem were used in the present investigation. The samples were analyzed for quality traits, fatty acid profiles, and volatile flavor compounds. Results showed that the fat content of the loin (8.64%) and belly samples (46.78%) was significantly higher in the 120 kg SW group compared to those of other SW groups (p<0.05). However, a lower protein content (12.20-12.67%) was found in the belly cuts of the heavier SW groups (105-120 kg) compared to those of the lighter SW groups (p<0.05). The lowest cooking loss (24.34%) was found in the loin cuts of the 120 kg SW group (p<0.05). Both the loin and belly cuts were observed to be redder in color with increasing SW (p<0.05). Higher oleic acid (C18:1, n9) and total monounsaturated fatty acid content and lower linolenic acid(C18:3, n3) and total polyunsaturated fatty acid content were observed in both cuts of the 120 kg SW group (p<0.05). Out of the flavor compounds identified, 11 and 17 compounds in the loin and belly, respectively, were associated with the SW. An increase in the SW resulted in increased concentrations of C18:1n9- and amino acid-derived flavor compounds. Overall, the meat samples of the heavier SW groups (120 kg) exhibited better quality and higher concentrations of volatile compounds associated with pleasant flavors. However, the meat of the 120 kg SW group also contained a much higher fat level (8.64 and 46.78% in the loin and belly, respectively) that may result in high trimming loss and hence a high rejection risk by consumers.

• Journal of Technology Innovation
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• v.29 no.3
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• pp.33-62
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• 2021

This study compares and analyzes the efficiency of R&D activities of technology-innovative small and medium-sized enterprises(SMEs) between industries and proposes ways to improve efficiency. The research samples are 6,708 technology-innovative SMEs, which have received a guarantee by the KIBO from 2008 to 2011. Input variables are the level of R&D personnel, R&D investment, and output variables are patent applications, prototype. Efficiency is measured by the DEA model, and indirect comparisons that are individually measured by industry are performed. As a result of the analysis, the CCR for determining the optimal returns to scale is 0.19, the BCC for determining the optimal input distribution is 0.70, and the SE for determining the optimal output is 0.30. By industry type, the medium and low-tech industries have high CCR and BCC, while the high-end and high-tech industries have high SE. R&D activities need to be operated on an optimal scale through managing R&D performance because there is the inefficiency of scale across the industry. The contribution of the study is to analyze the R&D efficiency of each industry of technology-innovative SMEs by the technology evaluation data of the KIBO.

• Korean journal of applied entomology
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• v.60 no.3
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• pp.263-268
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• 2021

The venom of honeybees (Apis mellifera L.) is used to treat many diseases because of its anti-inflammatory and analgesic effects. Bee venom consists of several biologically active molecules and exhibits remarkable anti-cancer effects. However, biological amines, which exhibit diverse functionality such as anti-inflammatory and antibacterial effects, have not been previously reported in bee venom. In this study, we determined the content of putrescine in bee venom by using ultra-performance liquid chromatography. The specificity, accuracy, and precision of the assay were assessed, and the assay validated. The linearity of the putrescine assay was r ≥ 0.99, indicating a moderate level of putrescine in the bee venom. The limit of detection and limit of quantification were both 0.9 ㎍/mL, while the rate of recovery was 96.4%-99.9%. The relative standard deviation (RSD) of the intra-day precision and inter-day precision of the putrescine assay were 0.16% - 0.23% and 0.09% - 0.36%, respectively, with the RSD ≤ 5% indicating excellent precision. Thus, the linearity, limit of detection, limit of quantification, and recovery rate of the putrescine assay were satisfactory. The analysis of the bee venom showed that the putrescine content was 3.1 ± 0.09 mg/g. This study provides fundamental data on putrescine content in bee venom, which will prove useful in further studies of its bioactivity.

• Journal of Food Hygiene and Safety
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• v.36 no.5
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• pp.376-381
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• 2021

Red beet (Beta vulgaris L.) is a root vegetable and a popular functional food ingredient of dark red-purple appearance due largely to betacyanins, principally betanine (75-95%) and its isomer, isobetanine (15-45%). This study developed an analytical method for beet red in terms of betanine and isobetanine in processed food products labeled with beet red as a food additive. High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) was used with a C₁₈ column. Linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision and uncertainty in measurement were calculated for method validation. Matrix-matched calibration was applied to the candy, ice cream, and cocoa product, respectively, and R² was ≥0.9998, showing a high level of linearity. The LOD and LOQ were 0.16 to 0.32 and 0.48 to 0.97 mg/L, respectively. As a result of repeated intra-day and interday experiments to validate the accuracy and precision of the analytical method, the recovery rates were 96.0-103.1% and 100.0-102.2%, respectively and the RSD% was 0.5-3.3% and 0.9-3.8%, respectively. Moreover, the measurement uncertainty was estimated to be 1.71-12.43% depending on the matrix and the measured concentration. In this study, betanine and isobetanine were quantified (8.4-3,823.4 mg/kg) by applying the developed analytical method to processed food products (n= 26; e.g., candy, ice cream, and other processed foods) labeled with beet red as a food additive.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.

• Nutrition Research and Practice
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• v.15 no.6
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• pp.715-731
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• 2021

BACKGROUND/OBJECTIVES: Premenstrual syndrome (PMS) is a disorder characterized by repeated emotional, behavioral, and physical symptoms before menstruation, and the exact cause and mechanism are uncertain. Hyperprolactinemia interferes with the normal production of estrogen and progesterone, leading to PMS symptoms. Thus, we judged that the inhibition of prolactin hypersecretion could mitigate PMS symptoms. MATERIALS/METHODS: Hordeum vulgare L. extract (HVE), Chrysanthemum zawadskii var. latilobum extract (CZE), and Lomens-P0 the mixture of these extracts were tested in subsequent experiments. The effect of extracts on prolactin secretion at the in vitro level was measured in GH3 cells. Nitric oxide and pro-inflammatory mediator expression were measured in RAW 264.7 cells to confirm the anti-inflammatory effect. Also, the hyperprolactinemic Institute for Cancer Research (ICR) mice model was used to measure extract effects on prolactin and hormone secretion and uterine inflammation. RESULTS: Anti-inflammatory effects of and prolactin secretion suppress by HVE and CZE were confirmed through in vitro experiments (P < 0.05). Treatment with Lomens-P0 inhibited prolactin secretion (P < 0.05) and restored normal sex hormone secretion in the hyperprolactinemia mice model. In addition, extracts significantly inhibited the expression of pro-inflammatory biomarkers, including interleukin-1𝛽, and -6, tumor necrosis factor-𝛼, inducible nitric oxide synthase, and cyclooxygenase-2 (P < 0.01). We used high-performance liquid chromatography analyses to identify tricin and chlorogenic acid as the respective components of HVE and CZE that inhibit prolactin secretion. The Lomens-P0, which includes tricin and chlorogenic acid, is expected to be effective in improving PMS symptoms in the human body. CONCLUSIONS: The Lomens-P0 suppressed the prolactin secretion in hyperprolactinemia mice, normalized the sex hormone imbalance, and significantly suppressed the expression of inflammatory markers in uterine tissue. This study suggests that Lomens-P0 may have the potential to prevent or remedy materials to PMS symptoms.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.667-680
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• 2021

We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.303-310
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• 2021

This study was conducted to identify the effects of pressure taping between tibia and fibula of High Ankle Sprain athletes on pain, Range of Motion(ROM), and strength and to provide basic data for rehabilitation programs. The subjects of the study were conducted with a total of 10 athletes except for four who gave up who were diagnosed with high ankle sprain, or who were diagnosed with ankle sprain but their physical examinations proved positive. The results showed no significant differences in pain(Visual Analog Scale, VAS). The ROM was significantly increased in inversion(IV) and eversion(EV) in both groups. The Isometric strength was significantly improved in IV(0°, 7°, 14°) and EV(0°) in Taping Group(TG). When taping was applied to athletes with injury to the High Ankle Sprain, ROM and muscle strength improved at the same pain level.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.845-858
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• 2019

The most serious problem company facing in today's business environment is the failure of new product development outcomes. Statistically, almost half of the new products released each year failed. Despite the innovative technological advances, consumers' expectation level become much higher and global competition is intensifying. In addition, the new product life cycle is becoming shorter and shorter. It is difficult for a company to survive without developing long-lived products. The most important issue in a company's success and failure is the successful development and introduction of new products. Previous research has presented many determinants to achieve a successful new product development. This study focuses on dynamic competence as an important determinant, and identifies the constituting elements. Enterprises need to acquire, absorb, integrate and reconfigure their resources to survive and develop continuously. It is necessary to hold a dynamic ability switching resource bases in order to adapt to changing environments. The results of this study are as follows: First, the effect of learning, reconfiguration, and alliance capabilities on the new product development of small and medium-sized manufacturing enterprises seems to be positive. Second, the integrative and reconfiguration capabilities positively affect a new product development under high environmental turbulence.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.4
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• pp.65-74
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• 2021

In order to efficiently inspect rapidly increasing old tunnels in many well-developed countries, many inspection methodologies have been proposed using imaging equipment and image processing. However, most of the existing methodologies evaluated their performance on a clean concrete surface with a limited area where other objects do not exist. Therefore, this paper proposes a 6-step framework for tunnel crack detection deep learning model development. The proposed method is mainly based on negative sample (non-crack object) training and Cascade Mask R-CNN. The proposed framework consists of six steps: searching for cracks in images captured from real tunnels, labeling cracks in pixel level, training a deep learning model, collecting non-crack objects, retraining the deep learning model with the collected non-crack objects, and constructing final training dataset. To implement the proposed framework, Cascade Mask R-CNN, an instance segmentation model, was trained with 1561 general crack images and 206 non-crack images. In order to examine the applicability of the trained model to the real-world tunnel crack detection, field testing is conducted on tunnel spans with a length of about 200m where electric wires and lights are prevalent. In the experimental result, the trained model showed 99% precision and 92% recall, which shows the excellent field applicability of the proposed framework.