• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.415-436
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• 2022

This study was conducted by the customer survey of 15 to 65 years old in order to identify the user behavioral effects of perception and characteristics on the common essential applications of smartphones with the United Theory of Acceptance and Use of Technology (UTAUT) and Value-based Acceptance Model (VAM). As a result, it was found that performance expectancy, enjoyment, facilitating conditions, effort expectancy, and social influences, excluding information privacy concern, have a positive effect on use behavior. The moderating effect by age was found that the youth was higher between perceived value and behavioral intention, and the middle-aged was higher between enjoyment and perceived value. This study has significance in providing implications for establishing strategies on designing and pre-loading apps, and increasing usage rate.

• Korean Chemical Engineering Research
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• v.61 no.2
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• pp.226-233
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• 2023

To create an environmentally sustainable fuel with a low sulfur concentration, requires alternative sulfur removal methods. During the course of this study, a high surface gamma alumina-supported ZnO nanocatalyst with a ZnO/-Al₂O₃ ratio of 12% was developed and tested for its ability to improve the activity of the oxidative desulfurization (ODS) process for the desulfurization of kerosene fuel. Scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) were used to characterize the produced nanocatalyst. In a digital batch baffled reactor (20~80 min), the effectiveness of the synthesized nanocatalyst was tested at different initial concentrations of dibenzothiophene (DBT) of 300~600 ppm, oxidation temperatures (25~70 ℃), and oxidation periods (0.5, 1, and 2 hours). The baffles included in the digital baffled batch reactor resist the swirling of the reaction mixture, thus facilitating mixing. The ODS procedure yielded the maximum DBT conversion (95.5%) at 70 ℃ with an 80-minute reaction time and an initial DBT level of 600 ppm. The most precise values of kinetic variables were subsequently determined using a mathematical modelling procedure for the ODS procedure. The average absolute error of the simulation findings was less than 5%, demonstrating a good degree of agreement with the experimental results acquired from all runs. The optimization of the operating conditions revealed that 99.1% of the DBT can be removed in 140 minutes.

• Steel and Composite Structures
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• v.49 no.2
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• pp.143-159
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• 2023

Recently, the design of scaffolding systems has garnered considerable attention due to the increasing number of scaffold collapses. These incidents arise from the underestimation of imposed loads and the site-specific conditions that restrict the application of lateral restraints in scaffold assemblies. The present study is committed to augmenting the buckling resistance of vertical support members, obviating the need for supplementary lateral restraints. To achieve this objective, experimental and computational analyses were performed to assess the axial load buckling capacity of steel props, composed of two hollow steel pipes that slide into each other for a certain length. Three full-scale steel props with various geometric properties were tested to construct and validate the analytical models. The total unsupported length of the steel props is 6 m, while three pins were installed to tighten the outer and inner pipes in the distance they overlapped. Finite Element (FE) modeling is carried out for the three steel props, and the developed models were verified using the experimental results. Also, theoretical analysis is utilized to verify the FE analysis. Using the FE-verified models, a parametric study is conducted to evaluate the effect of different inserted pipe lengths on the steel props' axial load capacity and lateral displacement. Based on the results, the typical failure mode for the studied steel props is global elastic buckling. Also, the prop's elastic buckling strength is sensitive to the inserted length of the smaller pipe. A threshold of minimum inserted length is one-third of the total length, after which the buckling strength increases. The present study offers a prop with enhanced buckling resistance and introduces an equation for calculating an equivalent effective length factor (k), which can be seamlessly incorporated into Euler's buckling equation, thereby facilitating the determination of the buckling capacity of the enhanced props and providing a pragmatic engineering solution.

• Animal Bioscience
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• v.37 no.4
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• pp.576-590
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• 2024

Objective: The objective of this study was to identify genes associated with 305-day milk yield (MY) and fat yield (FY) that also influence the adaptability of the Thai multibreed dairy cattle population to tropical conditions. Methods: A total of 75,776 imputed and actual single nucleotide polymorphisms (SNPs) from 2,661 animals were used to identify genomic regions associated with MY and FY using the single-step genomic best linear unbiased predictions. Fixed effects included herd-year-season, breed regression, heterosis regression and calving age regression effects. Random effects were animal additive genetic and residual. Individual SNPs with a p-value smaller than 0.05 were selected for gene mapping, function analysis, and quantitative trait loci (QTL) annotation analysis. Results: A substantial number of QTLs associated with MY (9,334) and FY (8,977) were identified by integrating SNP genotypes and QTL annotations. Notably, we discovered 17 annotated QTLs within the health and exterior QTL classes, corresponding to nine unique genes. Among these genes, Rho GTPase activating protein 15 (ARHGAP15) and catenin alpha 2 (CTNNA2) have previously been linked to physiological traits associated with tropical adaptation in various cattle breeds. Interestingly, these two genes also showed signs of positive selection, indicating their potential role in conferring tolerance to trypanosomiasis, a prevalent tropical disease. Conclusion: Our findings provide valuable insights into the genetic basis of MY and FY in the Thai multibreed dairy cattle population, shedding light on the underlying mechanisms of tropical adaptation. The identified genes represent promising targets for future breeding strategies aimed at improving milk and fat production while ensuring resilience to tropical challenges. This study significantly contributes to our understanding of the genetic factors influencing milk production and adaptability in dairy cattle, facilitating the development of sustainable genetic selection strategies and breeding programs in tropical environments.

• IMMUNE NETWORK
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• v.22 no.2
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• pp.19.1-19.20
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• 2022

Coxsackievirus B3 (CVB3) infection causes acute pancreatitis and myocarditis. However, its pathophysiological mechanism is unclear. Here, we investigated how lipid metabolism is associated with exacerbation of CVB3 pathology using high-fat diet (HFD)-induced obese mice. Mice were intraperitoneally inoculated with 1×10⁶ pfu/mouse of CVB3 after being fed a control or HFD to induce obesity. Mice were treated with mitoquinone (MitoQ) to reduce the level of mitochondrial ROS (mtROS). In obese mice, lipotoxicity of white adipose tissue-induced inflammation caused increased replication of CVB3 and mortality. The coxsackievirus adenovirus receptor increased under obese conditions, facilitating CVB3 replication in vitro. However, lipid-treated cells with receptor-specific inhibitors did not reduce CVB3 replication. In addition, lipid treatment increased mitochondria-derived vesicle formation and the number of multivesicular bodies. Alternatively, we found that inhibition of lipid-induced mtROS decreased viral replication. Notably, HFD-fed mice were more susceptible to CVB3-induced mortality in association with increased levels of CVB3 replication in adipose tissue, which was ameliorated by administration of the mtROS inhibitor, MitoQ. These results suggest that mtROS inhibitors can be used as potential treatments for CVB3 infection.

• Journal of Forest and Environmental Science
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• v.40 no.2
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• pp.99-110
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• 2024

According to the REDD+ program, it is necessary to monitor, quantify, and report forest conditions in protected land areas. The objectives of this work were to quantify the average monthly aerial net primary productivity (ANPP_MONTH) of semi-arid Chaco Forest at Copo National Park (CNP), Santiago del Estero, Argentina, during the period 2000-2023, as well as its spatial distribution and relationship, and its use efficiency (RUE) of average monthly rainfall (AMR). The ANPP_MONTH model accounted for 90% of the seasonal variability from October to May, the average seasonal ANPP_MONTH was 145 tons of dry matter per hectare (t dm/ha), being the maximum in January with 192 t dm/ha and the minimum in May with 91 t dm/ha. The surface area covered by ANPP_MONTH exhibited a consistent positive trend from October to May (t test=15.65, p<0.01). Strong and significant direct relationships were found between ANPP_MONTH and AMRs, linear models explaining 90% and 96% of the variability, respectively. The results obtained become reference values for assessing the capacity of the forest systems to stock carbon for global warming mitigation and for monitoring and controlling their response to extreme climatic adversities. The average ANPP_MONTH reduces uncertainty when defining the thresholds to monitor and quantify ANPP and forest area, thus facilitating the detection of negative changes in land use in CNP. Such results evidence the National Parks Administration's high effectiveness for the maintenance of protected area and for the high ANPP of the FCHS of CNP in the period 2000-2023.

• Transactions of Materials Processing
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• v.33 no.3
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• pp.200-207
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• 2024

In this study, we developed a semi-analytical approach for the numerical analysis of residual stress in oxide scales formed on hot-rolled steels. The oxide scale, formed during the hot rolling process, experiences complex interactions due to thermal and mechanical influences, significantly affecting the material's integrity and performance. Our research focuses on integrating various stress components such as thermal stress, growth stress, and creep behavior to predict the residual stress within the oxide layer. The semi-analytical method combines analytical expressions for each stress component with numerical integration to account for their cumulative effects. Validation through instrumented indentation tests confirms the reliability of our model, which considers thermal expansion coefficient (CTE) differences, scale growth, and creep-induced stress relaxation. Our findings indicate that thermal stress resulting from CTE differences significantly impacts the overall residual stress, with growth stress contributing a compressive component during cooling, and creep behavior playing a minor role in stress relaxation. This comprehensive approach enhances the accuracy of residual stress prediction, facilitating the optimization of material design and processing conditions for hot-rolled steel products.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.125-139
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• 2021

As the 4th industrial revolution progresses, new technologies and services are being born, growing, and maturing. Now, beyond the mobile era, the metaverse is being discussed as a new paradigm. Therefore, in this study, in preparation for the metaverse era, we tried to analyze what factors have an important influence when consumers want to use new technologies. In particular, the research was conducted focusing on how the context in which consumers use the technology changes depending on whether they are B2C or B2B. For this, augmented reality (AR) was selected in the B2C context by linking the research subject with the metaverse era, and the smart factory was selected in the B2B context. The research model for the analysis was established by deriving and setting common influence variables by reflecting the characteristics of the research target technology based on the modified extended unified theory of acceptance and use of technology. A survey was conducted for empirical analysis, and 150 AR and 150 smart factory subjects were analyzed. The empirical study results are as follows. The relationship between performance expectancy and intention to use, technology readiness and intention to use was found to have a significant positive (+) effect on both AR and smart factory. On the other hand, it was found that effort expectancy, social influence, and trust had a positive (+) effect on intention to use only in AR. Only in smart factory, facilitating conditions had a significant positive (+) effect on intention to use. It was also found that the perceived risk had a significant negative (-) effect on the intention to use only in the smart factory. The results of this study are academically significant in that we empirically test that influencing factors of technology use varies depending on the context in which it is used by consumers. In practice, it provided an implication of what to focus on first is being implemented.

• Journal of Venture Innovation
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• v.4 no.2
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• pp.77-95
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• 2021

In the midst of the progress of the 4th industrial revolution, the Corona19 Pandemic was forming giant double wave. Companies riding this wave can win, but companies that do not will fall into the wave and struggle. In connection with the 4th industrial revolution, various technologies are emerging and commercialized. At this point, consumers, especially digital natives, who have been with digital since birth, tried to find out what factors affect the intention to use these technologies and which factors have the most important influence. For this purpose, data were collected through a survey on factors affecting the intention to use FinTech technology and AR technology for 150 digital natives in their 20s. Based on this, statistical analysis was conducted and the following results were obtained. As a result of the overall analysis regardless of the type of technology, it was found that performance expectancy, effort expectancy, social influence, and habits have a positive (+) effect on digital natives' intention to use the 4th industrial technology. On the other hand, a significant influence relationship between the facilitating conditions, hedonic motivation and intention to use the 4th industrial technology was not tested. It was found that the influence was greatly influenced by social influence and habits. In the case of FinTech and AR, which were further subdivided into this study, different aspects were revealed as a result of separate analysis. In the case of FinTech technology that emphasizes utilitarian value, performance expectancy, effort expectancy, social influence, and habits had a positive (+) effect on intention to use. It was found that the influence was greatly influenced by habits and social influence. In the case of AR, which emphasizes the hedonic value, all the variables adopted in this study had a positive (+) effect on the intention to use the technology. It was found that hedonic motivation and social influence had a great influence. Combining the results of the analysis, social influence was found to be an important influence variable regardless of the type of 4th industrial technology. FinTech technologies such as mobile banking, where services are becoming more common, are habits, and in the case of AR, which has not yet been universalized and is provided mainly for entertainment, hedonic motivation was found to be an important factor. This study was able to present academic and practical implications based on the above confirmation of factors affecting digital natives' acceptance and use of the 4th industry technology.

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2004.06a
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• pp.60-61
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• 2004

Metabolic engineering is now a well established discipline, used extensively to determine and execute rational strategies of strain development to improve the performance of micro-organisms employed in industrial fermentations. The basic principle of this approach is that performance of the microbial catalyst should be adequately characterised metabolically so as to clearlyidentify the metabolic network constraints, thereby identifying the most probable targets for genetic engineering and the extent to which improvements can be realistically achieved. In order to harness correctly this potential, it is clear that the physiological analysis of each strain studied needs to be undertaken under conditions as close as possible to the physico-chemical environment in which the strain evolves within the full-scale process. Furthermore, this analysis needs to be undertaken throughoutthe entire fermentation so as to take into account the changing environment in an essentially dynamic situation in which metabolic stress is accentuated by the microbial activity itself, leading to increasingly important stress response at a metabolic level. All too often these industrial fermentation constraints are overlooked, leading to identification of targets whose validity within the industrial context is at best limited. Thus the conceptual error is linked to experimental design rather than inadequate methodology. New tools are becoming available which open up new possibilities in metabolic engineering and the characterisation of complex metabolic networks. Traditionally metabolic analysis was targeted towards pre-identified genes and their corresponding enzymatic activities within pre-selected metabolic pathways. Those pathways not included at the onset were intrinsically removed from the network giving a fundamentally localised vision of pathway functionality. New tools from genome research extend this reductive approach so as to include the global characteristics of a given biological model which can now be seen as an integrated functional unit rather than a specific sub-group of biochemical reactions, thereby facilitating the resolution of complexnetworks whose exact composition cannot be estimated at the onset. This global overview of whole cell physiology enables new targets to be identified which would classically not have been suspected previously. Of course, as with all powerful analytical tools, post-genomic technology must be used carefully so as to avoid expensive errors. This is not always the case and the data obtained need to be examined carefully to avoid embarking on the study of artefacts due to poor understanding of cell biology. These basic developments and the underlying concepts will be illustrated with examples from the author's laboratory concerning the industrial production of commodity chemicals using a number of industrially important bacteria. The different levels of possibleinvestigation and the extent to which the data can be extrapolated will be highlighted together with the extent to which realistic yield targets can be attained. Genetic engineering strategies and the performance of the resulting strains will be examined within the context of the prevailing experimental conditions encountered in the industrial fermentor. Examples used will include the production of amino acids, vitamins and polysaccharides. In each case metabolic constraints can be identified and the extent to which performance can be enhanced predicted