• Journal of Veterinary Science
• /
• v.23 no.1
• /
• pp.7.1-7.14
• /
• 2022

Background: Enterotoxigenic Escherichia coli (ETEC) infection is a primary cause of livestock diarrhea. Therefore, effective vaccines are needed to reduce the incidence of ETEC infection. Objectives: Our study aimed to develop a multivalent ETEC vaccine targeting major virulence factors of ETEC, including enterotoxins and fimbriae. Methods: SLS (STa-LTB-STb) recombinant enterotoxin and fimbriae proteins (F4, F5, F6, F18, and F41) were prepared to develop a multivalent vaccine. A total of 65 mice were immunized subcutaneously by vaccines and phosphate-buffered saline (PBS). The levels of specific immunoglobulin G (IgG) and pro-inflammatory cytokines were determined at 0, 7, 14 and 21 days post-vaccination (dpv). A challenge test with a lethal dose of ETEC was performed, and the survival rate of the mice in each group was recorded. Feces and intestine washes were collected to measure the concentrations of secretory immunoglobulin A (sIgA). Results: Anti-SLS and anti-fimbriae-specific IgG in serums of antigen-vaccinated mice were significantly higher than those of the control group. Immunization with the SLS enterotoxin and multivalent vaccine increased interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) concentrations. Compared to diarrheal symptoms and 100% death of mice in the control group, mice inoculated with the multivalent vaccine showed an 80% survival rate without any symptom of diarrhea, while SLS and fimbriae vaccinated groups showed 60 and 70% survival rates, respectively. Conclusions: Both SLS and fimbriae proteins can serve as vaccine antigens, and the combination of these two antigens can elicit stronger immune responses. The results suggest that the multivalent vaccine can be successfully used for preventing ETEC in important livestock.

• Nuclear Engineering and Technology
• /
• v.55 no.6
• /
• pp.2112-2124
• /
• 2023

With the rise of economic and safety standards for nuclear reactors, new concepts of Gen-IV reactors and modular reactors showed more complex designs that challenge current tools for reactor physics analysis. A Monte Carlo (MC) two-step method was proposed in this work. This calculation scheme uses the continuous-energy MC method to generate multi-group cross-sections from heterogeneous models. The multi-group MC method, which can adapt locally-heterogeneous models, is used in the core calculation step. This calculation scheme is verified using a Gen-IV modular lead-based fast reactor (LFR) benchmark case. The influence of homogenized patterns, scatter approximations, flux separable approximation, and local heterogeneity in core calculation on simulation results are investigated. Results showed that the cross-sections generated using the 3D assembly model with a locally heterogeneous representation of control rods lead to an accurate estimation with less than 270 pcm bias in core reactivity, 0.5% bias in control rod worth, and 1.5% bias on power distribution. The study verified the applicability of multi-group cross-sections generated with the MC method for LFR analysis. The study also proved the feasibility of multi-group MC in core calculation with local heterogeneity, which saves 85% time compared to the continuous-energy MC.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.72-81
• /
• 2023

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

• Smart Structures and Systems
• /
• v.32 no.1
• /
• pp.1-7
• /
• 2023

In modal analysis, the mode shape reflects the vibration characteristics of the structure, and thus it is widely performed for finite element model updating and structural health monitoring. Generally, the acceleration-based mode shape is suitable to express the characteristics of structures for the translational vibration; however, it is difficult to represent the rotational mode at boundary conditions. A tilt sensor and gyroscope capable of measuring rotational mode are used to analyze the overall behavior of the structure, but extracting its mode shape is the major challenge under the small vibration always. Herein, we conducted a feasibility study on a multi-mode shape estimating approach utilizing a single physical quantity signal. The basic concept of the proposed method is to receive multi-metric dynamic responses from two sensors and obtain mode shapes through bridge loading test with relatively large deformation. In addition, the linear transformation matrix for estimating two mode shapes is derived, and the mode shape based on the gyro sensor data is obtained by acceleration response using ambient vibration. Because the structure's behavior with respect to translational and rotational mode can be confirmed, the proposed method can obtain the total response of the structure considering boundary conditions. To verify the feasibility of the proposed method, we pre-measured dynamic data acquired from five accelerometers and five gyro sensors in a lab-scale test considering bridge structures, and obtained a linear transformation matrix for estimating the multi-mode shapes. In addition, the mode shapes for two physical quantities could be extracted by using only the acceleration data. Finally, the mode shapes estimated by the proposed method were compared with the mode shapes obtained from the two sensors. This study confirmed the applicability of the multi-mode shape estimation approach for accurate damage assessment using multi-dimensional mode shapes of bridge structures, and can be used to evaluate the behavior of structures under ambient vibration.

• Advances in aircraft and spacecraft science
• /
• v.10 no.3
• /
• pp.223-243
• /
• 2023

There is a burgeoning demand for minimizing the mass of satellites because of its direct impact on reducing launch-to-orbit cost. This must be done without compromising the structure's efficiency. The present paper introduces a relatively low-cost and easily implementable approach for optimizing structural mass to a maximum natural frequency. The natural frequencies of the satellite are of utmost pertinence to the application requirements, as the sensitive electronic instrumentation and onboard computers should not be affected by the vibrations of the satellite structure. This methodology is applied to a realistic model of Al-Azhar University micro-satellite in partnership with the Egyptian Space Agency. The procedure used in structural design can be summarized in two steps. The first step is to select the most favorable primary structural configuration among several different candidate variants. The nominated variant is selected as the one scoring maximum relative dynamic stiffness. The second step is to use perforation patterns reduce the overall mass of structural elements in the selected variant without changing the weight. The results of the presented procedure demonstrate that the mass reduction percentage was found to be 39% when compared to the unperforated configuration that had the same plate thickness. The findings of this study challenge the commonly accepted notion that isogrid perforations are the most effective means of achieving the goal of reducing mass while maintaining stiffness. Rather, the study highlights the potential benefits of exploring a wider range of perforation unit cells during the design process. The study revealed that rectangular perforation patterns had the lowest efficiency in terms of modal stiffness, while triangular patterns resulted in the highest efficiency. These results suggest that there may be significant gains to be made by considering a broader range of perforation shapes and configurations in the design of lightweight structures.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.3
• /
• pp.1-6
• /
• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.361-369
• /
• 2023

Achieving realistic visual quality while maintaining optimal real-time rendering performance is a major challenge in evolving computer graphics and interactive 3D applications. Normal mapping, as a core technology in 3D, has matured through continuous optimization and iteration. Hybrid normal mapping as a new mapping model has also made significant progress and has been applied in the 3D asset production pipeline. This study comprehensively explores the hybrid normal techniques, analyzing Linear Blending, Overlay Blending, Whiteout Blending, UDN Blending, and Reoriented Normal Mapping, and focuses on how the various hybrid normal techniques can be used to achieve rendering performance and visual fidelity. performance and visual fidelity. Under the consideration of computational efficiency, visual coherence, and adaptability in different 3D production scenes, we design comparative experiments to explore the optimal solutions of the hybrid normal techniques by analyzing and researching the code, the performance of different hybrid normal mapping in the engine, and analyzing and comparing the data. The purpose of the research and summary of the hybrid normal technology is to find out the most suitable choice for the mainstream workflow based on the objective reality. Provide an understanding of the hybrid normal mapping technique, so that practitioners can choose how to apply different hybrid normal techniques to the corresponding projects. The purpose of our research and summary of mixed normal technology is to find the most suitable choice for mainstream workflows based on objective reality. We summarized the hybrid normal mapping technology and experimentally obtained the advantages and disadvantages of different technologies, so that practitioners can choose to apply different hybrid normal mapping technologies to corresponding projects in a reasonable manner.

• Ocean Systems Engineering
• /
• v.14 no.1
• /
• pp.85-99
• /
• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• Journal of Nutrition and Health
• /
• v.57 no.2
• /
• pp.228-243
• /
• 2024

Purpose: This study aimed to identify the distinctions in dietary and health-related behaviors among Indonesian women who marry Koreans or into multicultural families (MF) and those who marry Indonesians living in Korea (IK) and in Indonesia (II). Methods: The study was performed with 192 subjects using an online questionnaire regarding food choice, dietary and health behavior, and nutrition quotient (NQ). The analysis used Pearson's chi-squared test, the Fisher's exact test, multinomial logistic regression, and the general linear model. Results: The MF group consumed Korean food more than once a day and Indonesian food 1-2 times monthly (p < 0.001). The main challenge for the IK and II groups in consuming Korean food was the presence of pork and the different food flavors (p < 0.001). The MF group tended to have normal body mass index, consumed more vitamin and mineral supplements (p = 0.014), and exercised regularly ≥150 min/week compared to the IK and II groups (p < 0.001). However, the MF group had the highest rate of skipping breakfast (p = 0.040). When evaluating the NQ of the participants, the MF group consumed more vegetables (p = 0.026), mixed grains (p = 0.031), and spicy and salt soups (p = 0.006). The II group consumed more fish (p = 0.005), beans (p = 0.009), and nuts (p = 0.003). The IK group checked the nutrition labels the most (p = 0.005), while their consumption of vegetables, fish, beans, and nuts was lowest. The MF group had a higher balance score, which resulted in a substantially more nutritious food intake compared to the other two groups (p = 0.037). Conclusion: The MF group consumed more vegetables and mixed grains, adequate fish, beans, and nuts, and engaged in longer daily physical activity. However, the IK group had a relatively low-quality diet and nutritional intake status compared to the other two groups, and this needs to be improved in the future.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.2
• /
• pp.389-396
• /
• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.