Plants social network analysis, which combines plants society and social network analyses, is a new research method for understanding plants society. This study was conducted to investigate the relationship between species, using plant social network analysis targeting Taejongdae in Busan, and build basic data for management. Taejongdae, located in the warm temperate forest in Korea, is a representative coastal forest of Busan Metropolitan City, and the Pinus thunbergii-Eurya japonicacommunity is widely distributed. This study set up 100 quadrats (size of 100m2each) in Taejongdae to investigate the species that emerged and analyzed the interspecies association focusing on major species. Based on the results, a sociogram was created using the Gephi 0.9.2, and the network centrality and structure were analyzed. The results showed that the frequency of appearance was high in the order of P. thunbergii, E. japonica, Quercus serrata, Sorbus alnifolia, Ligustrum japonicum, and Styrax japonicusand that many evergreen broad-leaved trees appeared due to the environmental characteristics of the site. The plants social network of Taejongdae was composed of a small-scale network with 50 nodes and 172 links and was divided into 4 groups through modularization. The succession sere identified through a sociogram confirmed that the group that include P. thunbergiiand E. japonicawould progress to a deciduous broadleaf community dominated by Q. serrataand Carpinus tschonoskii, using hub nodes such as Prunus serrulataf. spontaneaand Toxicodendron trichocarpum. Another succession sere was highly likely to progress to an evergreen broad-leaved community dominated by Machilus thunbergiiand Neolitsea sericea, using M. thunbergiias a medium. In some areas, a transition to a deciduous broad-leaved community dominated by Celtis sinensis, Q. variabilisand Zelkova serratausing Lindera obtusilobaand C. sinensisas hub nodes was expected.
Park, Woo-Jin ;Park, Dong-Su;Shin, Mun-Beom;Seo, Young-Kyo
Journal of the Korean Geotechnical Society
/
v.39
no.5
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pp.51-63
/
2023
Numerical analysis was conducted to determine the effect of soil behavior by thawing and freezing of seasonal frozen soil on pile foundations. The analysis was performed using the finite element method (FEM) to simulate soil-pile interaction based on the atmosphere temperature change. Thermomechanical coupled modeling using FEM was applied with the temperature-dependent nonlinear properties of the frozen soil. The analysis model cases were applied to the MCR and HDP models to simulate the elastoplastic behavior of soil. The numerical analysis results were analyzed and compared with various conditions having different length and width sizes of the pile. The results of the numerical analysis showed t hat t he HDP model was relat ively passive, and t he aspect and magnit ude of t he bearing capacit y and displacement of the pile head were similar depending on the length and width of the pile conditions. The vertical displacement of the pile head by thawing and freezing of the ground showed a large variation in displacement for shorter length conditions. In the MCR model, the vertical displacement appeared in the maximum thaw settlement and frost heaving of 0.0387 and 0.0277 m, respectively. In the HDP model, the vertical displacement appeared in the maximum thaw settlement and frost heaving of 0.0367 and 0.0264 m, respectively. The results of the pile bearing capacity for the two elastoplastic models showed a larger difference in the width condition than the length condition of the pile, with a maximum of about 14.7% for the width L condition, a maximum of about 5.4% for M condition, and a maximum of about 5.3% for S condition. The significance of the effect on the displacement of the pile head and the bearing capacity depended on the pile-soil contact area, and the difference depended on the presence or absence of an active layer in the soil and its thickness.
In this study, growth changes of the diameter at breast height (DBH), height, basal area, volume, and biomass of Korean white pine (Pinus koraiensis Siebold & Zucc.) on a plantation were examined via long-term monitoring. In addition, this study was performed to provide the basic data for timber production in line with DBH class by comparing the growth of the relative DBH size. Growth characteristics according to DBH class were analyzed by categorizing trees into five classes based on sorted DBH rankings: class I (1%-20%; upper 20%), class II (21%-40%), class III (41%-60%), class IV (61%-80%), class V (81%-100%; lower 20%). A total class (0%-100%) was also used. Total increment and mean annual increment (MAI) were calculated using data from nine measurements taken over 39 years. Tree characteristics based on average values and stand characteristics based on unit area per hectare were examined. According to the total increments of variables, the differences in DBH, basal area, volume, and biomass among classes I-V increased over time, whereas the height difference did not continually increase. According to MAI, the maximum DBH value was 0.92 cm·yr-1 at age 23 in class I, whereas the maximum value in all trees was 0.69 cm·yr-1 at age 17. The maximum value of height MAI for class I was 0.52 m·yr-1 at age 23, whereas that for all trees was 0.49 m·yr-1 at age 20. In terms of basal area, volume, and biomass growth at tree-and stand-level, the maximum MAI of class I and all trees was not observed during the measurement period. Therefore, additional long-term monitoring data are required to determine the maximum MAI of the variables.
This purpose of this study is to examine how emotional intelligence has a moderating effect on the relationship between emotional labor and job burnout. To test the hypotheses, moderation analysis, mediation analysis and moderated mediation analysis were performed. Of the 1,500 self-report questionnaires distributed to franchise employees, 1,412 usable responses and utilized in the final analyses. The resoults show that emotional labor significantly affected job burnout, and job burnout completely mediated the effect of emotional labor on turnover intention. And emotional intelligence moderated the relationship between emotional labor and job burnout, suggesting that the emotional dissonance-job burnout relationship was stronger in the low emotional intelligence group than in the high emotional intelligence group. The results of this study provide some implications for Korean companies entering the Chinese franchise field. First, the mediating effects of "emotional labor → job burnout → turnover intention" and the effect of emotional labor on job burnout were mitigated by self-efficacy in Chinese franchise workers. And the self-efficacy of the employees was an important factor. The implication of this study is that self-efficacy, which is a controlling variable affecting emotional labor, job burnout, and turnover intention, is important, so Korean companies that enter the Chinese franchise employ employees with high self-efficacy and to develop and implement an employee education program that can enhance self-efficacy.
Direct catalytic decomposition is a promising method for controlling the emission of nitrous oxide (N2O) from the semiconductor and display industries. In this study, a γ-Al2O3 catalyst was developed to reduce N2O emissions by a catalytic decomposition reaction. The γ-Al2O3 catalyst was prepared by an extrusion method using boehmite powder, and a N2O decomposition test was performed using a catalyst reactor that was approximately 25.4 mm (1 in) in diameter packed with approximately 5 mm of catalysts. The N2O decomposition tests were carried out with approximately 1% N2O at 550 to 750 ℃, an ambient pressure, and a GHSV=1800-2000 h-1. To confirm the N2O decomposition properties and the effect of O2 and steam on the N2O decomposition, nitrogen, air, and air and steam were used as atmospheric gases. The catalytic decomposition tests showed that the 1% N2O had almost completely disappeared at 700 ℃ in an N2 atmosphere. However, air and steam decreased the conversion rate drastically. The long term stability test carried out under an N2 atmosphere at 700 ℃ for 350 h showed that the N2O conversion rate remained very stable, confirming no catalytic activity changes. From the results of the N2O decomposition tests and long-term stability test, it is expected that the prepared γ-Al2O3 catalyst can be used to reduce N2O emissions from several industries including the semiconductor, display, and nitric acid manufacturing industry.
Purpose. The purpose of this study was to evaluate the effect of surface treatments on the shear bond strength of two types of zirconia (3-TZP and 5Y-PSZ) with resin cement. Materials and methods. Two different types of zirconia specimens with a fully sintered size of 14.0×14.0×2.0 mm3 were prepared, polished with 400, 600, and 800 grit silicon carbide paper, and buried in epoxy resin. They were classified into four groups each control, sandblasting, primer, and sandblasting & primer. Cylindrical resin adhered to the surface-treated zirconia with resin cement. It was stored in distilled water (37℃) for 24 hours, and a shear bond strength test was performed. The normality of the experimental group was confirmed with the Kolmogorov-Smirnov & Shapiro-Wilk test. The interaction and statistical difference were analyzed using a two-way ANOVA. A post-hoc analysis was performed using Dunnett T3. Results. As a result of two-way ANOVA, there was no significant difference in shear bonding strength between zirconia types (P > .05), but there was a significant correlation in the sandblasting, primer, and alumina sandblasting & primer group (P < .05). Dunnett T3 post-test showed that, regardless of the type of zirconia, shear bonding strength was sandblasting & primer > Primer > sandblasting > control group (P < .05). Conclusion. There was no difference in shear bond strength between the types of zirconia. The highest shear bond strength was shown when the mechanical and chemical treatments of the zirconia surface was performed simultaneously.
The West Philippine Basin, an oceanic basin half the size of the Philippine Sea Plate, lies in the western part of the plate and south of the Korean Peninsula on the Eurasian Plate. It subducts beneath the Eurasian Plate and the Philippine Islands bordering the Ryukyu Trench and the Philippine Trench with 25-50% of this basin already consumed. However, the history of the opening of the basin's southern region has been a topic of debate. The non-transform discontinuity formed during the seafloor spreading is similar to the transform fault boundaries normally perpendicular to mid-ocean ridge axes; however, it was created irregularly due to ridge propagations caused by variations of mantle convection attributable to magma supply changes. By analyzing high-resolution multi-beam echo-sounding data, we confirmed that the non-transform discontinuity due to the propagating rift evolved in the entire basin and that the abyssal hill strike direction changed from E-W to NNW-SSE from the fossil spreading center. In the early stage of basin extension, the Amami-Sankaku Basin was rotated 90 degrees clockwise from its current orientation, and it bordered the Palau Basin along the Mindanao Fracture Zone. The Amami-Sankaku Basin separated from the Palau Basin while the spreading of the West Philippine Basin began with a counter-clockwise rotation. This indicates that the non-transform discontinuities formed by a sudden change in magma supply due to the drift of the Philippine Sea Plate and simultaneously with the rapid changes in the spreading direction from ENE-WSW to N-S. The Palau Basin was considered to be the sub-south of the West Philippine Basin, but recent studies have shown that it extends into an independent system. Evidence from sediment layers and crustal thickness hints at the possibility of its existence before the West Philippine Basin opened, although its evolution continues to be debated. We performed a combined analysis using high-resolution multi-beam bathymetry and satellite gravity data to uncover new insights into the evolution of the West Philippine Basin. This information illuminates the complex plate interactions and provides a crucial contribution toward understanding the opening history of the basin and the Philippine Sea Plate.
The giant freshwater prawn (Macrobrachium rosenbergii) suffers economic losses in aquaculture facilities due to problems such as poor body color and carapace weakness. While this species is farmed on an animal-based diet, in the wild it consumes a detritus diet with a high proportion of plant matter. The plant matter they consume in the wild is not only a source of basic nutrients, but also carotenoids, which are responsible for body color. Korean goldenbell (Forsythia koreana) is a flowering tree widely distributed in Korea, and its leaves contain carotenoids comparable to yellow carrots. In this study, we investigated the effects of feeding Forsythia leaves to M. rosenbergii on their body color and health. The experimental diets were 100% commercial feed (control), CON, 80% commercial feed plus 20% Forsythia leaf powder, FP, and 80% commercial feed plus 20% unprocessed Forsythia leaves, FL, and each diet was fed to juvenile prawn with an average weight of 1.1 ± 0.2 g for 10 weeks. In terms of body color, the CON prawn were consistently clear ivory in color, while the FP and FL prawn changed from blue to dark brown as the days of rearing progressed. Survival and growth were not significantly different between CON and FP or FL. Histological comparison of the hepatopancreas revealed that the vacuole size of B cells, the constituent cells of the hepatopancreatic tubule, was significantly larger in FP and FL compared to CON. The vacuoles of B cells are responsible for the absorption and digestion of nutrients, suggesting that the supply of Forsythia leaves may have had a positive effect on the health of the M. rosenbergii. The above results show that the use of Forsythia leaves in the aquaculture of M. rosenbergii can be expected to improve body color and health without inhibiting growth.
This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.
In this study, we propose a method to monitor the surface area of agricultural reservoirs in South Korea using Sentinel-1 synthetic aperture radar images and the deep learning model, Swin Transformer. Utilizing the Google Earth Engine platform, datasets from 2017 to 2021 were constructed for seven agricultural reservoirs, categorized into 700 K-ton, 900 K-ton, and 1.5 M-ton capacities. For four of the reservoirs, a total of 1,283 images were used for model training through shuffling and 5-fold cross-validation techniques. Upon evaluation, the Swin Transformer Large model, configured with a window size of 12, demonstrated superior semantic segmentation performance, showing an average accuracy of 99.54% and a mean intersection over union (mIoU) of 95.15% for all folds. When the best-performing model was applied to the datasets of the remaining three reservoirsfor validation, it achieved an accuracy of over 99% and mIoU of over 94% for all reservoirs. These results indicate that the Swin Transformer model can effectively monitor the surface area of agricultural reservoirs in South Korea.
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