• Korean Journal of Materials Research
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• v.4 no.4
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• pp.483-490
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• 1994

The effect of vanadium additions on the thermal stability of Al-TI alloy \vas investigated. Al- 8wt.%Ti and Al-8wt.%(Ti+V) alloys wirh different Ti to V atomic ratios of 3 : 1 and 1 : 1 were pre- pared by mechanical alloying. The steady states wwe obtalncd after mechanical alloy~ng for ltihours for all the alloy compositions. The mechanically alloyed powders were consolidaicd by vacuum hot pressing and thermal st.ability was investigated by hardness testing afrcr aging thc specimens at $400^{\circ}C$, $480^{\circ}C$, $550^{\circ}C$ for up to 1000hrs. It was confirmed that addit~on of V- increased the thermal stability of Al-Ti alloy by reducing coarsening rate of $Ai_{3}Ti$ intermetallic compound.

• Reproductive and Developmental Biology
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• v.37 no.2
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• pp.75-77
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• 2013

This study was conducted to investigate optimal time of artificial insemination (AI) to Hanwoo female after natural estrus. AI was occurred 12 and 24 hours after natural estrus in both heifer and multiparous recipient then pregnancy and parturition rates were estimated. Results indicated that AI performed at 24 hours after natural estrus showed significant (p<0.05) higher pregnancy rate in both heifer and multiparous recipient groups with significantly (p<0.05) higher abortion rate. However, there are no significant differences of parturition rate, twin birth and sex ratio in both heifer and multiparous recipient groups. Therefore, our results may suggest that performance of AI at 24 hours after natural estrus promise higher pregnancy rate than AI at 12 hours after natural estrus in both heifer and multiparous recipient.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.75-80
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• 2021

Do happy applicants achieve more? Although it is well established that happiness predicts desirable work-related outcomes, previous findings were primarily obtained in social settings. In this study, we extended the scope of the "happiness premium" effect to the artificial intelligence (AI) context. Specifically, we examined whether an applicant's happiness signal captured using an AI system effectively predicts his/her objective performance. Data from 3,609 job applicants showed that verbally expressed happiness (frequency of positive words) during an AI interview predicts cognitive task scores, and this tendency was more pronounced among women than men. However, facially expressed happiness (frequency of smiling) recorded using AI could not predict the performance. Thus, when AI is involved in a hiring process, verbal rather than the facial cues of happiness provide a more valid marker for applicants' hiring chances.

• Journal of Information Technology Services
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• v.20 no.4
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• pp.129-140
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• 2021

Recently, interest in AI tutors is rising as a way to bridge the educational gap in school settings. However, research confirming the effectiveness of AI tutors is lacking. The purpose of this study is to explore how effective learner characteristics and recommended item learning activities are in predicting learner's academic performance in an adaptive online learning environment. This study proposed the hypothesis that learner characteristics (prior knowledge, midterm evaluation) and recommended item learning activities (learning time, correct answer check, incorrect answer correction, satisfaction, correct answer rate) predict academic achievement. In order to verify the hypothesis, the data of 362 learners were analyzed by collecting data from the learning management system (LMS) from the perspective of learning analytics. For data analysis, regression analysis was performed using the regsubset function provided by the leaps package of the R program. The results of analyses showed that prior knowledge, midterm evaluation, correct answer confirmation, incorrect answer correction, and satisfaction had a positive effect on academic performance, but learning time had a negative effect on academic performance. On the other hand, the percentage of correct answers did not have a significant effect on academic performance. The results of this study suggest that recommended item learning activities, which mean behavioral indicators of interaction with AI tutors, are important in the learning process stage to increase academic performance in an adaptive online learning environment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.173-182
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• 2020

The purpose of this paper is to study the influence of new service methods of "artificial intelligence (AI) + medical cosmetology", a new service means, on customers' purchase intentions. To AI medical beauty APP sales as an empirical study. This paper designed Likert seven scale to investigate, using SPSS 24.0 statistical analysis software and AMOS24.0 structural equation software to analyze the survey data. The analysis method uses reliability analysis, validity analysis, and construct equation model analysis. Through empirical research, the following results can be found, 1. The system quality of AI medical beauty app will have a positive impact on perceived usefulness and perceived ease of use. 2. The information quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness. 3. The service quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness 4. Consumers' perceived ease of use has a positive impact on perceived usefulness and purchase intention. 5. The usefulness of consumers' notification has a positive effect on purchase intention.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• Korean Journal of Weed Science
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• v.18 no.2
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• pp.106-115
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• 1998

This study was conducted to investigate the effect of oxadiazon, molinate, thiobencarb before seeding on control of red rice and barnyardgrass in water-seeded rice. High application rate plot among oxadiazon treatment plots could observe phytotoxicity symptoms depending on field conditions, but these injury recovered gradually with time. Molinate and thiobencarb application plots at the concentration of 225~400, 210~420g ai/l0a respectively were not observed phytotoxicity. Control of red rice was different according to kinds of herbicides and application rates. Oxadiazon showed higher control performance at the concentration of more than 60g ai/10a. Control effect of molinate and thiobencarb against red rice was enhanced with the increase of application rate, and both herbicides showed satisfactory effect at more than 300g ai/10a. Control of barnyardgrass showed up to 90~100% in all tested herbicides. There was no significant yield reduction by oxadiazon, molinate, and thiobencarb application before seeding in all tested field. In the pot experiment, crop injury, seedling stand, and early growth were more advantageous at time of drainge after one day after seeding than flooding until rooting.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.93-107
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• 2020

Recently, artificial intelligence (AI) technologies has been widely used in various fields such as finance, and distribution. Accordingly, Korea has also announced its AI R&D strategy for the realization of i-Korea 4.0 in May 2018. However, Korea's AI technology is inferior to major competitors such as the US, Canada, and Japan Therefore, in order to cope with the 4th industrial revolution, it is necessary to allocate AI R&D budgets efficiently through selection and concentration so as to gain competitive advantage under a limited budget. In this study, the importance of each AI technology was evaluated in multi-dimensional way through the questionnaire of expert group using the evaluation index derived from the literature review From the results of this study, we draw the following implication. In order to successfully establish the AI technology development strategies, it is necessary to prioritize the cognitive computing technology that has great market growth potential, ripple effect of technology development, and the urgency of technology development according to the principle of selection and concentration. To this end, it is necessary to find creative ideas, manage assessments, converge multidisciplinary systems and strengthen core competencies. In addition, since AI technology has a large impact on socioeconomic development, it is necessary to comprehensively grasp and manage scientific and technological regulations in order to systematically promote AI technology development.

• The Korea Journal of Herbology
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• v.33 no.3
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• pp.55-61
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• 2018

Objectives : Artemisia capillaris Thunberg (AC) and Artemisia iwayomogi Kitamura (AI) have been used without distinguishment since ancient times due to similar appearance. In this study, we compared the inhibitory effects of AC and AI on the expression of inflammatory cytokines induced by lipopolysaccharide (LPS) in murine macrophages. Methods : AC and AI were extracted by reflux with distilled water (DW) and 70% ethanol (EtOH). We investigated the inhibitory effects of AC and AI on the expression of nitric oxide (NO), inducible NO synthase (iNOS) and tumor necrosis $factor-{\alpha}$($TNF-{\alpha}$) induced by LPS in macrophages. Results : Firstly, yield of the samples was higher in order of Artemisia iwayomogi DW Extract (AID), Artemisia iwayomogi 70% EtOH Extract (AIE), Artemisia capillaris DW Extract (ACD) and Artemisia capillaris 70% EtOH Extract (ACE). All of the samples were not toxic in macrophages. The inhibitory effect of the samples on LPS-induced NO expression was stronger in the order of AIE, ACE, AID and ACD. The inhibitory effect of the samples on LPS-induced inducible iNOS expression was stronger in the order of AIE, ACE and AID. Effect of ACD was same with that of AID. In addition, inhibitory effect of the samples on LPS induced $TNF-{\alpha}$expression wes stronger in the order of AIE, ACE, AID and ACD. Conclusion: These results showed that AI would be more effective than AC and 70% EtOH would be more effective than DW as an extraction solvent in inflammatory diseases.

• Knowledge Management Research
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• v.25 no.2
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• pp.155-176
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• 2024

This study explores variations in preferences for human and AI agents within the medical and financial services. Study 1 investigates whether there are preferential disparities between human and AI agents across these service domains. It finds that human agents are favored over AI agents in medical services, while AI agents receive greater preference in the financial services. Study 2 delves into the underlying reasons for the preference differentials between human and AI agents by assessing the significance of certain capabilities as perceived by users in each domain. The findings reveal a mediating role of perceived empathy importance in the effect of service domains on human-AI preference. Furthermore, perceived empathy is deemed a more critical capability by users for preferring human over AI agents across both service domains compared to other capabilities such as experience and agency. This research is noteworthy for elucidating the variances in preferences for human and AI agents across medical and financial services and the rationale behind these differences. It enhances our theoretical comprehension of the pivotal factors influencing preferences for human and AI agents, underscoring the significance of human experiential capabilities like empathy.