• Title/Summary/Keyword: Non-interaction

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Effect of fabrication method and surface polishing on the surface roughness and microbial adhesion of provisional restoration (임시 수복물의 제작 방식과 표면 연마가 표면 거칠기와 세균 부착에 미치는 영향)

  • Yeon-Ho Jung;Hyun-Jun Kong;Yu-Lee Kim
    • Journal of Dental Rehabilitation and Applied Science
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    • v.40 no.3
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    • pp.149-158
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    • 2024
  • Purpose: This study aims to investigate the effects of provisional restoration fabrication methods and surface polishing on surface roughness and microbial adhesion through in vitro experiments. Materials and Methods: 120 cylindrical provisional restoration resin blocks (10 × 10 × 2.5 mm) were manufactured according to four fabrication methods, and 30 specimens were assigned to each group. Afterwards, they were divided into non-polishing group, #400 grit SiC polishing group, and #800 grit SiC polishing group and polished to a 10 × 10 × 2 mm specimen size (n = 10). The surface roughness Ra and Ry of the specimen was measured using a Surface Roughness Tester. Three specimens were extracted from each group and were coated with artificial saliva, and then Streptococcus mutans were cultured on the specimens at 37℃ for 4 hours. The cultured specimens were fixed to fixatives and photographed using a scanning electron microscope. For statistical analysis, the two way of ANOVA was performed for surface roughness Ra and Ry, respectively, and the surface roughness was tested post-mortem with a Scheffe test. Results: The fabrication method and the degree of surface polishing of the provisional restorations had a significant effect on both surface roughness Ra and Ry, and had an interaction effect. There was no significant difference in Ra and Ry values in all polishing groups in DLP and LCD groups. Conclusion: The fabrication method and surface polishing of the provisional restoration had a significant effect on surface roughness and showed different adhesion patterns for S. mutans adhesion.

A Study on the Impact of Artificial Intelligence on Decision Making : Focusing on Human-AI Collaboration and Decision-Maker's Personality Trait (인공지능이 의사결정에 미치는 영향에 관한 연구 : 인간과 인공지능의 협업 및 의사결정자의 성격 특성을 중심으로)

  • Lee, JeongSeon;Suh, Bomil;Kwon, YoungOk
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.231-252
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    • 2021
  • Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

Studies on the Productivity of Individual Leaf Blade of Paddy Rice (수도의엽신별 생육효과에 관한 연구)

  • Dong-Sam Cho
    • KOREAN JOURNAL OF CROP SCIENCE
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    • v.18
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    • pp.1-27
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    • 1975
  • Experiment I: A field experiment was conducted in an attempt to find the effect of top-dressing at heading time in different levels of nitrogen application and of different positioned leaf blades formed by the treatment of leaf defoliation at heading time on the ripening and the yield of rice. The results obtained are as follows: 1. Average number of ears per hill and average number of grains per ear in different levels of nitrogen application were increased as the amount of nitrogen applied was increased. while the rate of ripened grains the yield of rough rice and the weight of 1, 000 kernels of brown rice were decreased respectively as the amount of nitrogen applied was increased. 2. The rate of ripened grains and the weight of 1.000 kernels of brown rice in different levels of nitrogen, top-dressing at heading time were larger than those in control and increased. The yield of rough rice although statistically significant differences were not recognized, were numerically increased. 3. The rate of ripened grains, the yield of rough rice, the weight of 1, 000 kernels of brown rice and the rate of hulling in different treatments of leaf defoliation were remarkably decreased as the degree of leaf-defoliation became larger. 4. The rate of ripened grains, the yield of rough rice, the weight of 1, 000 kernels of brown rice and the rate of hulling in different combinations of number of remained leaves positioned differently, formed the order of $L_1(flag leaf)>L_2>L_3>L_4$ when only one leaf blade was remained, and were increased as the positions of leaves were higher when two leaf blades. were, remained. 5. In case of decrease in the number of leaf blades positioned differently, by the treatment of leaf. defoliation, rate of ripened grains, the yield of rough rice, the weight of 1, 000 kernels of brown rice and the rate of hulling were increased as the area of remained leaves became larger and the nitrogen content of a leaf blade was increased. 6. There was a tendency that the increase in the amount of fertilizer application made the rate of ripened grains and the weight of 1, 000 kernels of brown rice reduced in any number of remained leaf blades, but the application of top-dressing at heading. time resulted in the reverse tendency. The yield of rough rice showed a tendency to be increased as the amount of basal dressing and top-dressing increased and for the application of top-dressing at heading time, the yield of rough rice was less at the smaller number of those. 7. The productivity effect of the rate of ripened grains and the yield of brown rice covered by leaf blades was more than 50 per cent and that of the. weight of 1, 000 kernels of brown rice was not more than 1.0 percent. As the amount of nitrogen application increased the. effect of leaf blades on the rate of ripened. grains and the weight of 1, 000 kernels of brown rice was increased. The effect of leaf blades on the weight of brown rice was increased as the amount of basal dressing-application, but the effect was decreased as the amount of top-dressing at heading time increased, 8. The productivity effects of different positioned leaf blades on the rate of ripened grains, the yield of rough rice and the weight of 1, 000 kernels of brown rice were in order of $L_1(flag leaf)>L_2>L_3>L_4$ the productivity effects of $L_1$ and $L_2$ had a tendency to be increased as the amount of nitrogen applied was increased. Experiment II: A field experiment was done in order to disclose the effect of the time of nitrogen application on yield component and the effect of different positioned leaves formed by leaf defoliation at heading time on the rate of ripened grains and the yield of rice. The results obtained are as follows: 1. Average number of ears per hill was increased in the treatment of nitrogen application from basal dressing to 22 days before heading and in the treatment of application distributed weekly. Number of grains was increased in the treatment of nitrogen application from 36 days to 15 days before heading. The rate of ripened grains was, lower in the treatment of nitrogen application from top-dressing to 15 days before heading than in that of non-application, was higher in the treatment of nitrogen application within 8 days before heading, and was the lowest in that of application 29 days before heading. The yield of rough rice was the highest in the treatment of nitrogen application from 29 days to 22 days before heading. The weight of 1, 000 kernels of brown rice was a little high in the treatment of application from 29 days to 8 days before heading. 2. The rate of ripened grains the yield of rough rice, the weight of 1, 000 kernels of brown rice and the rate of hulling in different treatments of leaf defoliation were remarkably decreased as the degree of leaf defoliation got larger and there were highly significant differences among treatments. There was also a recognized interaction between the time of nitrogen application and leaf defoliation. 3. In relation to the rate of ripened grains, the weight of 1. 000 kernels of brown rice and the rate of hulling in different numbers of remained leaves positioned differently and their combinations, the yield components were in order of $L_1(flag leaf)>L_2>L_3>L_4$ when only one leaf was remained, which indicated that the components were increased as the leaf position got higher. When two laves were remained, the rate of ripened grains, the yield of rough rice and rate of hulling were high in case of the combinations of upper positioned leaves, and the increase in the weight of 1, 000 kernels of brown rice appeared to be affected most]y by flag leaf. When three leaf blades were remained similarly the components were increased with the combination of upper positioned leaf blades. 4. In case of decreased different positioned leaf blades by treatment of leaf defoliation, there was a significant positive regression between the leaf area, the dry matter weight of leaf blades and the nitrogen contents of leaf blades, and rate of ripened grains and the yield of rough rice, but there was no constant tendency between the former components and the weight of 1. 000 kernels of brown rice. 5. The closer the time of fertilizer application to heading time, the more the rate of ripened grains and the weight of 1, 000 kernels was decreased by defoliation, and the less were the remained leaf blades, the more remarkable was the tendency. The rate of ripened grains and the weight of 1. 000 kernels was increased by the top-dressing after heading time as the number of remained leaf blades. When the number of remained leaf blades was small the yield of rough rice was increased as the time of fertilizer application was closer to heading time. 6. Discussing the productivity effects of different organs in different times of nitrogen application, the productivity effect of a leaf blade on the rate of ripened grains was higher as the time of nitrogen application got later, and in the treatment of non-fertilization the productivity effect of a leaf blade and that of culm were the same. In the productivity effect on the yield of brown rice, the effect of culm covered more than 50 percent independently on the time of nitrogen application, and the tendency was larger in the treatment of non-fertilizer. The productivity effect of culm on the weight of 1. 000 kernels of brown rice was more than 90 percent, and the productivity effect of a leaf blade was increased as the time of application got later. 7. The productivity effect of a leaf blade in different positions on the rate of ripened grains, the yield of rough rice and the weight of 1, 000 kernels of brown rice had a tendency to be increased as the time of application got later and as the position of leaf blades got higher. In the treatment of weekly application through the entire growing period, the rate of ripened grains and the yield of rough rice were affected by flag leaf and the second leaf at the same level, the but the weight of 1, 000 kernels of brown rice was affected by flag leaf with more than 60 percent of the yield of total leaves.

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Studies on Nutrio-physiological Response of Rice Plant to Root Environment (근부환경(根部環境)에 따른 수도(水稻)의 영양생리적(營養生理的) 반응(反應)에 관(關)한 연구(硏究))

  • Park, J.K.;Kim, Y.S.;Oh, W.K.;Park, H.;Yazawa, F.
    • Korean Journal of Soil Science and Fertilizer
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    • v.2 no.1
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    • pp.53-68
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    • 1969
  • The nutriophysiological response of rice plant to root environment was investigated with eye observation of root development and rhizosphere in situation. The results may be summarized as follows: 1) The quick decomposition of organic matter, added in low yield soil, caused that the origainal organic matter content was reached very quickly, in spite of it low value. In high yield soil the reverse was seen. 2) In low yield soil root development, root activity and T/R value were very low, whereas addition of organic matter lowered them still wore. This might be contributed to gas bubbles around the root by the decomposition of organic matter. 3) Varietal difference in the response to root environment was clear. Suwon 82 was more susceptible to growth-inhibitine conditions on low-yield soil than Norin 25. 4) Potassium uptake was mostly hindered by organic matter, while some factors in soil hindered mostly posphorus uptake. When the organic matter was added to such soil, the effect of them resulted in multiple interaction. 5) The root activity showed a correlation coeffieient of 0.839, 0.834 and 0.948 at 1% level with the number of root, yield of aerial part and root yield, respectively. At 5% level the root-activity showed correlation-coefficient of 0.751, 0.670 and 0.769 with the uptake of the aerial part of respectively. N, P and K and a correlation-coefficient of 0.729, 0.742 and 0.815 with the uptake of the root of respectively N.P. and K. So especially for K-uptake a high correlation with the root-activity was found. 6) The nitrogen content of the roots in low-yield soil was higher than in high-yield soil, while the content in the upper part showed the reverse. It may suggest ammonium toxicity in the root. In low-yield soil Potassium and Phosphorus content was low in both the root and aerial part, and in the latter particularly in the culm and leaf sheath. 7) The content of reducing sugar, non-recuding sugar, starh and eugar, total carbohydrates in the aerial part of plants in low yield soil was higher than in high yield soil. The content of them, especially of reducing sugar in the roots was lower. It may be caused by abnormal metabolic consumption of sugar in the root. 8) Sulfur content was very high in the aerial part, especially in leaf blade of plants on low yield soil and $P_2O_5/S$ value of the leaf blade was one fifth of that in high yield soil. It suggests a possible toxic effect of sulfate ion on photophosphorization. 9) The high value of $Fe/P_2O_5$ of the aerial part of plants in low yield soil suggests the possible formation of solid $Fe/PO_4$ as a mechanical hindrance for the translocation of nutrients. 10) Translocation of nutrients in the plant was very poor and most nutrients were accumulated in the root in low yield soil. That might contributed to the lack of energy sources and mechanical hindrance. 11) The amount of roots in high yield soil, was greater than that in low yield soil. The in high-yield soil was deep, distribution of the roots whereas in the low-yield soil the root-distribution was mainly in the top-layer. Without application of Nitrogen fertilizer the roots were mainly distributed in the upper 7cm. of topsoil. With 120 kg N/ha. root were more concentrated in the layer between 7cm. and 14cm. depth. The amount of roots increased with the amount of fertilizer applied.

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