• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.93-105
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• 2024

Car-sharing is a representative model of the sharing economy, and it is a service that rents or uses a car for the necessary time without owning a car. This industry is growing due to various factors such as technological advances, increasing awareness of environmental protection, and increasing demand for solving traffic congestion problems in cities. Accordingly, there is a need for a strategic approach for companies providing car-sharing services to respond quickly to market changes in order to expand market share and differentiate services. Accordingly, this study conducted a case study on open innovation activities between Gongcar and existing rental car companies, focusing on the research question "What effects do open innovation activities between car-sharing companies and existing rental car companies cause?" As a result of the study, it was confirmed that Gongcar have (1) the ability to actively respond to market fluctuations by establishing a flexible vehicle supply chain based on demand, (2) have significantly reduced growth capital expenditure (Growth Capex), and both cafe and rental car companies have (3) performed successful open innovation by improving key KPI indicators and recording financial performance. This study reveals how open innovation acts as a key business growth engine in the car-sharing industry, and its significance is found in that it empirically confirmed the successful implementation conditions of open innovation based on resource dependence theory.

• Korean Journal of Radiology
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• v.21 no.7
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• pp.859-868
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• 2020

Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cut-off was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. Conclusion: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.77-85
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• 2024

In this dissertation, a we designed and implemented a patrol robot that integrates a thermal imaging camera, speed dome camera, PTZ camera, radar, lidar sensor, and smartphone. This robot has the ability to monitor and respond efficiently even in complex environments, and is especially designed to demonstrate high performance even at night or in low visibility conditions. An orbital movement system was selected for the robot's mobility, and a smartphone-based control system was developed for real-time data processing and decision-making. The combination of various sensors allows the robot to comprehensively perceive the environment and quickly detect hazards. Thermal imaging cameras are used for night surveillance, speed domes and PTZ cameras are used for wide-area monitoring, and radar and LIDAR are used for obstacle detection and avoidance. The smartphone-based control system provides a user-friendly interface. The proposed robot system can be used in various fields such as security, surveillance, and disaster response. Future research should include improving the robot's autonomous patrol algorithm, developing a multi-robot collaboration system, and long-term testing in a real environment. This study is expected to contribute to the development of the field of intelligent surveillance robots.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.467-476
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• 2023

Schisandra chinensis (S. chinensis) is a deciduous broad-leaved cave plant belonging to the Schisandraceae family and is widely distributed in East Asia including Korea, Japan, China, and Taiwan. It has been reported that the main components contained in S. chinensis include lignan compounds and triterpenoid compounds. To distinguish the characteristics of S. chinensis by production region of Korea, a discriminant was established by performing metabolite profiling and principal component analysis, a multivariate statistical analysis technique. As a result, 16 types of triterpenoids, 9 types of lignan, and 1 type each of flavonoid, phenylpropanoid, and fatty acid were identified. In addition, through multivariate statistical analysis, it was confirmed that the four groups in Danyang, Moongyeong, Geochang, and Pyeongchang were divided, by applying the s-plot model of orthogonal partial least squares discriminant analysis. Biomarkers were identified: lanostane, cycloartane, schiartane triterpenoid, and dibenzocyclo-octadiene lignan were identified as chemical markers, respectively.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4274-4281
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• 2023

Blanket is of vital importance for engineering application of the fusion reactor. Nuclear heat deposition in materials is the main heat source in blanket structure. In this paper, the three-dimensional method for thermal-hydraulics/neutronics coupling analysis is developed and applied for the full-scale module of the helium-cooled ceramic breeder tritium breeding blanket (HCCB TBB) designed for China Fusion Engineering Test Reactor (CFETR). The explicit coupling scheme is used to support data transfer for coupling analysis based on cell-to-cell mapping method. The coupling algorithm is realized by the user-defined function compiled in Fluent. The three-dimensional model is established, and then the coupling analysis is performed using the paralleled Coupling Analysis of Thermal-hydraulics and Neutronics Interface Code (CATNIC). The results reveal the relatively small influence of the coupling analysis compared to the traditional method using the radial fitting function of internal heat source. However, the coupling analysis method is quite important considering the nonuniform distribution of the neutron wall loading (NWL) along the poloidal direction. Finally, the structure optimization of the blanket is carried out using the coupling method to satisfy the thermal requirement of all materials. The nonlinear effect between thermal-hydraulics and neutronics is found during the blanket structure optimization, and the tritium production performance is slightly reduced after optimization. Such an adverse effect should be thoroughly evaluated in the future work.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.531-550
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• 2023

Purpose: The unique nature of life-and-death healthcare services sets them apart from other service industries. While many studies exist on the relationship between healthcare services and customer satisfaction, most of them focus on mildly ill patients, ignoring the differences between critically ill and non-seriously ill patients. This study discusses the actual quality of healthcare services for patients who are facing life-threatening illnesses and are on life support, as well as their right to protection and dignity. Methods: The survey conducted to 149 patients with the four major illnesses: cancer, heart disease, brain disease and rare and incurable disease, those who have experiences with senior general hospitals. Results: The basic statistics of this study are adequate to represent the four major critical illnesses, and the reliability and validity of this study's hypotheses, which were measured by multiple items, were analyzed, and the internal consistency was judged to be high. In addition, it was found that the convergent validity was good and the discriminant validity was also secured. When examining the goodness of fit of the hypotheses, the SRMR, which is the standardized root mean square of residuals that measures the difference between the covariance matrix of the data variables and the theoretical covariance matrix structure of the model, met the optimal criteria. Conclusion: The academic implications of this study are differentiated from other studies by moving away from evaluating the quality of healthcare services for mildly ill patients and focusing on the rights and dignity of patients with life-threatening illnesses in four senior general hospitals. In terms of academic implications, this study enriches the depth of related studies by demonstrating the right to protection and dignity as a factor of patient-centeredness based on physical environment quality, interaction quality, and outcome quality, which are presented as sub-factors of healthcare quality. We found that the three quality factors classified by Brady and Cronin (2001) are optimized for healthcare quality assessment and management, and that the results of patients' interaction quality assessment can be used to provide a comprehensive quality rating for hospitals. Health and human rights are inextricably linked, so assessing the degree to which rights and dignity are protected can be a superior and more comprehensive measurement tool than traditional health level measures for healthcare organizations. Practical implications: Improving the quality of the physical environment and the quality of outcomes is an important challenge for hospital managers who attract patients with life and death conditions, but given the scale and economics of time, money, and human inputs, improving the quality of interactions and defining them as performance indicators in hospital quality management is an efficient way to create maximum value in the short term.

• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.69-84
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• 2024

Objective : This study aimed to categorize the lifestyles of older adults into two types - healthy and unhealthy, and use machine learning to identify the personal values that influence these lifestyles. Methods : This cross-sectional study targeting middle-aged and older adults (55 years and above) living in local communities in South Korea. Data were collected from 300 participants through online surveys. Lifestyle types were dichotomized by the Yonsei Lifestyle Profile (YLP)-Active, Balanced, Connected, and Diverse (ABCD) responses using latent profile analysis. Personal value information was collected using YLP-Values (YLP-V) and analyzed using machine learning to identify the relative importance of personal values on lifestyle types. Results : The lifestyle of older adults was categorized into healthy (48.87%) and unhealthy (51.13%). These two types showed the most significant difference in social relationship characteristics. Among the machine learning models used in this study, the support vector machine showed the highest classification performance, achieving 96% accuracy and 95% area under the receiver operating characteristic (ROC) curve. The model indicated that individuals who prioritized a healthy diet, sought health information, and engaged in hobbies or cultural activities were more likely to have a healthy lifestyle. Conclusion : This study suggests the need to encourage the expansion of social networks among older adults. Furthermore, it highlights the necessity to comprehensively intervene in individuals' perceptions and values that primarily influence lifestyle adherence.

• Resources Recycling
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• v.33 no.3
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• pp.38-47
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• 2024

Molybdenite is the primary molybdenum resource and is extracted via flotation due to its unique hydrophobic surface. Meanwhile, the grade and crystal size of mined molybdenite are decreasing. As a result, the size of the molybdenum ore required for liberation is decreasing, and the flotation process's feed size input is also decreasing. Therefore, in order to secure molybdenum, it is necessary to perform research on the flotation for the fine molybdenite. In this study, we developed a method to enhance the flotation efficiency of fine molybdenite particles in the range of 5-30 ㎛. The methodology involved implementing bubble size reduction and particle aggregation. Through simulations of bubble-particle collision probability and flotation experiments, we were able to find the ranges of bubble size and particle aggregate size that make fine particles float more effectively. This range provided the conditions for effective flotation of fine molybdenite particles. Therefore, we will implement the flotation conditions established in this study for fine molybdenum ore to improve the flotation process in molybdenum mineral processing plants in the future.

• The Journal of the Acoustical Society of Korea
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• v.43 no.4
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• pp.400-405
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• 2024

When cavitation noise occurs in ship propellers, the level of underwater radiated noise abruptly increases, which can be a critical threat factor as it increases the probability of detection, particularly in the case of naval vessels. Therefore, accurately and promptly assessing cavitation signals is crucial for improving the survivability of submarines. Traditionally, techniques for determining cavitation occurrence have mainly relied on assessing acoustic/vibration levels measured by sensors above a certain threshold, or using the Detection of Envelop Modulation On Noise (DEMON) method. However, technologies related to this rely on a physical understanding of cavitation phenomena and subjective criteria based on user experience, involving multiple procedures, thus necessitating the development of techniques for early automatic recognition of cavitation signals. In this paper, we propose an algorithm that automatically detects cavitation occurrence based on simple statistical features reflecting cavitation characteristics extracted from acoustic signals measured by sensors attached to the hull. The performance of the proposed technique is evaluated depending on the number of sensors and model test conditions. It was confirmed that by sufficiently training the characteristics of cavitation reflected in signals measured by a single sensor, the occurrence of cavitation signals can be determined.