• Animal Bioscience
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• v.34 no.11
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• pp.1811-1821
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• 2021

Objective: A trial was conducted to investigate the effects of supplemental levels of Mn provided by organic and inorganic trace mineral supplements on growth, tissue mineralization, mineral balance, and antioxidant status of growing broiler chicks. Methods: A total of 500 male chicks (8-d-old) were used in 10-day feeding trial, with 10 treatments and 10 replicates of 5 chicks per treatment. A 2×5 factorial design was used where supplemental Mn levels (0, 25, 50, 75, and 100 mg Mn/kg diet) were provided as MnSO₄·H₂O or MnPro. When Mn was supplied as MnPro, supplements of zinc, copper, iron, and selenium were supplied as organic minerals, whereas in MnSO₄·H₂O supplemented diets, inorganic salts were used as sources of other trace minerals. Performance data were fitted to a linearbroken line regression model to estimate the optimal supplemental Mn levels. Results: Manganese supplementation improved body weight, average daily gain (ADG) and feed conversion ratio (FCR) compared with chicks fed diets not supplemented with Mn. Manganese in liver, breast muscle, and tibia were greatest at 50, 75, and 100 mg supplemental Mn/kg diet, respectively. Higher activities of glutathione peroxidase and superoxide dismutase (total-SOD) were found in both liver and breast muscle of chicks fed diets supplemented with inorganic minerals. In chicks fed MnSO₄·H₂O, ADG, FCR, Mn balance, and concentration in liver were optimized at 59.8, 74.3, 20.6, and 43.1 mg supplemental Mn/kg diet, respectively. In MnPro fed chicks, ADG, FCR, Mn balance, and concentration in liver and breast were optimized at 20.6, 38.0, 16.6, 33.5, and 62.3 mg supplemental Mn/kg, respectively. Conclusion: Lower levels of organic Mn were required by growing chicks for performance optimization compared to inorganic Mn. Based on the FCR, the ideal supplemental levels of organic and inorganic Mn in chick feeds were 38.0 and 74.3 mg Mn/kg diet, respectively.

• The Korean Journal of Franchise Management
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• v.13 no.2
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• pp.1-15
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• 2022

Purpose: The globalization of the Korean restaurant franchise industry differs from the business performance of enhancing the brand image and customers' intention to revisit depending on the degree of localization marketing. Therefore, it is necessary to consider the extent to which the localization marketing activities of overseas Korean restaurant franchise companies affect the customer's perception. This study aims to investigate the effects of localization marketing (Localized Menu, Localized Price, Localized Service Experience, Localized Promotion, Localized Physical Environment) of Korean restaurant franchise companies on customer revisit intention. Research design, data, and methodology: For this study, 150 questionnaires using local Korean restaurants in Beijing, China, were analyzed using SPSS Ver.21 and AMOS Ver.22. Result: It was confirmed that the localized menu, localized service experience, and localized physical environment all affect the intention to revisit customers. Based on these verification results, if overseas franchises fully recognize localization marketing, which is an important factor for local business success, and establish localization strategies, they can gain an edge in competition with local Korean restaurants or restaurant franchises founded by locals. There may be a higher probability that However, it was found that localization price and localization promotion had no mediating effect of brand image between revisit intention and revisit intention. It was found that it had no effect on the degree of inquiry and had a negative effect. Conclusions: Due to the impact of the COVID-19 pandemic, there have been many changes in the domestic and overseas food service industry over the past two years. Therefore, in future research, it is necessary to study the localization of overseas Korean restaurant franchise companies that are more multidimensionally subdivided. Various measures of customized localization marketing for optimal regional characteristics should be developed and applied to enhance customer revisiting and brand image of Korean restaurant franchise companies entering overseas. In the future, this study will be meaningful data for the establishment of localization marketing (Localized Menu, Localized Price, Localized Service Experience, Localized Promotion, Localized Physical Environment) strategies for Korean restaurant franchise companies that consider overseas expansion or have already entered.

• Herbal Formula Science
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• v.31 no.1
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• pp.53-65
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• 2023

Objectives : Present study investigated the hepatoprotective effects and the optimal mixing ratio of fermented Schizandrae Fructus Pomace (fSFP) and Hoveniae Semen Cum Fructus (HSCF) extract combination in carbon tetrachloride (CCl₄)-induced acute liver injury mice. Methods : ICR mice were orally administered with 200 mg/kg of fSFP, HSCF and mixtures of fSFP and HSCF [MSH (w:w); 1:1, 1:2, 1:4, 1:6, 2:1, 4:1, 6:1, and 8:1] for 7 consecutive days. Silymarin (100 mg/kg) was administered as a reference drug. 0.5 mL/kg of CCl₄ was injected intraperitoneally to induce acute liver injury. Body weight gain, relative liver weight, serum chemistry, histopathological analysis, and hepatic endogenous antioxidants capacities were observed. Results : All diverse combinations of MSH significantly reduced relative liver weight increase by CCl₄. In addition, MSH administrations significantly decreased the elevation of serum alanine aminotransferase and aspartate aminotransferase activities by CCl₄. Histopathological observation indicated that all MSH treatments significantly reduced the increase of degenerated hepatocytes, inflammatory cell infiltration, and histological activity index score by CCl₄. Moreover, all MSH administrations reduced the elevation of malondialdehyde contents, and ameliorated the reduction of hepatic glutathione contents, superoxide dismutase activity, and catalase activity. Among the various mixing ratio of MSH combinations, MSH 1:1 and 2:1 showed the most potent anti-oxidative stress, and hepatoprotective effect. Conclusion : Present results suggest that 1:1 and 2:1 combinations of MSH is promising herbal formulation with the hepatoprotective effect against oxidative stress.

• Convergence Security Journal
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• v.22 no.4
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• pp.161-168
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• 2022

After the corona virus, as non-face-to-face services are activated, domain services that guarantee integrity by embedding sensing information of the Internet of Things (IoT) with block chain technology are expanding. For example, in areas such as safety and security using CCTV, a process is required to safely update firmware in real time and to confirm that there is no malicious intrusion. In the existing safe security processing procedures, in many cases, the person in charge performing official duties carried a USB device and directly updated the firmware. However, when private blockchain technology such as Hyperledger is used, the convenience and work efficiency of the Internet of Things environment can be expected to increase. This article describes scenarios in how to prevent vulnerabilities in the operating environment of various customers such as firmware updates and device changes in a non-face-to-face environment. In particular, we introduced the optimal blockchain technique for the Internet of Things (IoT), which is easily exposed to malicious security risks such as hacking and information leakage. In this article, we tried to present the necessity and implications of security management that guarantees integrity through operation applying block chain technology in the increasingly expanding Internet of Things environment. If this is used, it is expected to gain insight into how to apply the blockchain technique to guidelines for strengthening the security of the IoT environment in the future.

• Animal Bioscience
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• v.35 no.10
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• pp.1545-1555
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• 2022

Objective: Our study aimed to investigate the effects of a 2% increase in dietary total digestible nutrients (TDN) value during the growing (7 to 12 mo of age) and fattening (13 to 30 mo of age) period of Hanwoo steers. Methods: Two hundred and twenty Hanwoo steers were assigned to one of two treatments: i) a control group (basal TDN, BTDN, n = 111 steers, growing = 70.5%, early fattening = 71.0%, late fattening = 74.0%) or high TDN (HTDN, n = 109 steers, growing = 72.6%, early = 73.1%, late = 76.2%). Growth performance, carcass traits, blood parameters, and gene expression of longissimus dorsi (LD) (7, 18, and 30 mo) were quantified. Results: Steers on the BTDN diets had increased (p≤0.02) DMI throughout the feeding trial compared to HTDN, but gain did not differ appreciably. A greater proportion of cattle in HTDN received Korean quality grade 1 (82%) or greater compared to BTDN (77%), while HTDN had a greater yield grade (29%) than BTDN (20%). Redness (a^*) of LD muscle was improved (p = 0.021) in steers fed HTDN. Feeding the HTDN diet did not alter blood parameters. Steers fed HTDN diet increased (p = 0.015) the proportion of stearic acid and tended to alter linoleic acid. Overall, saturated, unsaturated, monounsaturated, and polyunsaturated fatty acids of LD muscle were not impacted by the HTDN treatment. A treatment by age interaction was noted for mRNA expression of myosin heavy chain (MHC) IIA, IIX, and stearoyl CoA desaturase (SCD) (p≤0.026). No treatment effect was detected on gene expression from LD muscle biopsies at 7, 18, and 30 mo of age; however, an age effect was detected for all variables measured (p≤0.001). Conclusion: Our results indicated that feeding HTDN diet could improve overall quality grade while minimum effects were noted in gene expression, blood parameters, and growing performance. Cattle performance prediction in the feedlot is a critical decision-making tool for optimal planning of cattle fattening and these data provide both benchmark physiological parameters and growth performance measures for Hanwoo cattle feeding enterprises.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Information Systems Review
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• v.20 no.4
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• pp.59-78
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• 2018

With the advent of the various device types and marketing communication, customer's search and purchase behavior have become more complex and segmented. However, extant research on multichannel marketing effects of the purchase funnel has not reflected the specific features of device User Interface (UI) and User Experience (UX). In this study, we analyzed the marketing channel effects of multi-device shoppers using a unique click stream dataset from global online retailers. We examined device types that activate online shopping and compared the differences between marketing channels that promote visits. In addition, we estimated the direct and indirect effects on visits and purchase revenue through customer's accumulated experience and channel conversions. The findings indicate that the same customer selects a different marketing channel according to the device selection. These results can help retailers gain a better understanding of customers' decision-making process in multi-marketing channel environment and devise the optimal strategy taking into account various device types. Our empirical analyses yield business implications based on the significant results from global big data analytics and contribute academically meaningful theoretical framework using an economic model. We also provide strategic insights attributed to the practical value of an online marketing manager.

• Journal of Animal Science and Technology
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• v.66 no.3
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• pp.514-522
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• 2024

This study mainly evaluated the responses in growth performance of growing pigs to different energy systems and energy levels in diets. Subsequently, we compared the nutrient digestibility and digestible nutrient concentrations of each energy level diet. In experiment 1, a total of 144 growing pigs with an average initial body weight (BW) of 26.69 ± 7.39 kg were randomly allotted to six dietary treatments (four pigs/pen; six replicates/treatment) according to a 2 × 3 factorial arrangement resulting from two energy systems (metabolizable energy [ME] and net energy [NE]) and three energy levels (low [LE], recommended [C], and high energy [HE]). Pigs were fed the experimental diets for 6 weeks and were allowed free access to feed and water during the experimental period. In experiment 2, 12 growing pigs with an average initial BW of 27.0 ± 1.8 kg were randomly allotted to individual metabolism crates and fed the six diets in a replicated 6 × 6 Latin square design. The six dietary treatments were identical to those used in the growth trial. Pigs were fed their respective diets at 2.5 times the estimated energy requirement for maintenance per day, and this was divided into two equal meals provided twice per day during the experimental period. Differences in energy systems and energy levels had no significant effect on the growth performance or nutrient digestibility (except acid-hydrolyzed ether extract [AEE]) of growing pigs in the current study. However, the digestible concentrations of ether extract, AEE, and acid detergent fiber (g/kg dry matter [DM]) in diets significantly increased (p < 0.05) with increasing energy levels. Additionally, there was a tendency (p = 0.09) for an increase in the digestible crude protein content (g/kg DM) as the energy content of the diet increased. Consequently, differences in energy systems and levels did not affect the BW, average daily gain, and average daily feed intake of growing pigs. This implies that a higher variation in dietary energy levels may be required to significantly affect growth performance and nutrient digestibility when considering digestible nutrient concentrations.

• Journal of Service Research and Studies
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• v.14 no.3
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• pp.188-205
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• 2024

The food truck business, which involves selling various types of food from mobile vehicles, has gained significant popularity in urban centers and at events. These food trucks have rapidly expanded due to their relatively low initial investment and high flexibility, attracting customers with unique menus and personalized services. However, as competition increases, the need to manage service quality to boost customer satisfaction and encourage repeat visits has become more critical. Despite this growing importance, there has been limited empirical research on the topic. This study aims to analyze customer experiences with food truck services to gain strategic insights for improving service quality. By applying structural topic modeling to customer review data, the study identified 50 key topics. The process included a comprehensive evaluation of model diagnostics and interpretability to determine the optimal number of topics, ultimately selecting the most relevant ones related to service experiences. The impact of these identified topics on overall customer satisfaction was empirically tested using regression analysis. The results showed that aspects such as "Food Taste," "Friendly Staff," and "Positive Emotion" had a positive influence on customer satisfaction, whereas "Delayed Service," "Negative Emotion," and "Beverage Service" had a negative impact. Based on this analysis, the study proposes concrete methods for food truck operators to systematically analyze customer feedback and use it to drive service improvements and innovation. This research highlights the importance of data-driven decision-making in small business environments like food trucks and contributes to expanding the application of topic modeling in the service industry.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.