• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.5
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• pp.659-666
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• 2023

Characterized by boom-and-bust cycles, low entry barriers, and an almost perfectly competitive structure, the construction industry presents a unique challenge for the survival and growth of its constituent companies. A crucial aspect of this challenge is the ongoing monitoring of their financial health and business performance. To understand the typical financial and operational status of construction companies, this study analyzes the financial statements of 6,252 such companies, all of which have undergone at least one external audit between 2000 and 2019. These statements were used to develop combined financial profiles and derive industry averages. The findings indicate that the construction industry experiences limited growth in sales and profitability. High leverage ratios can jeopardize financial stability, pushing companies to seek production efficiency, such as enhancing gross asset turnover. This tendency has been particularly noticeable in the aftermath of the global financial crisis in 2008.

• Journal of Veterinary Science
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• v.24 no.2
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• pp.20.1-20.13
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• 2023

Background: As Actinobacillus pleuropneumonniae (APP) infection causes considerable losses in the pig industry, there is a growing need to develop effective therapeutic interventions that leverage host immune defense mechanisms to combat these pathogens. Objectives: To demonstrate the role of microRNA (miR)-127 in controlling bacterial infection against APP. Moreover, to investigate a signaling pathway in macrophages that controls the production of anti-microbial peptides. Methods: Firstly, we evaluated the effect of miR-127 on APP-infected pigs by cell count/enzyme-linked immunosorbent assay (ELISA). Then the impact of miR-127 on immune cells was detected. The cytokines tumor necrosis factor (TNF)-α and interleukin (IL)-6 were evaluated by ELISA. The expression of cytokines (anti-microbial peptides [AMPs]) was assessed using quantitative polymerase chain reaction. The expression level of IL-6, TNF-α and p-P65 were analyzed by western blot. The expression of p65 in the immune cells was investigated by immunofluorescence. Results: miR-127 showed a protective effect on APP-infected macrophage. Moreover, the protective effect might depend on its regulation of macrophage bactericidal activity and the generation of IL-22, IL-17 and AMPs by targeting sphingosine-1-phosphate receptor3 (SIPR3), the element involved in the Toll-like receptor (TLR) cascades. Conclusions: Together, we identify that miR-127 is a regulator of S1PR3 and then regulates TLR/nuclear factor-κB signaling in macrophages with anti-bacterial acticity, and it might be a potential target for treating inflammatory diseases caused by APP.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.71-79
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• 2017

In this paper, we implemented the exchange rate forecasting neural network using heterogeneous computing. Exchange rate forecasting requires a large amount of data. We used a neural network that could leverage this data accordingly. Neural networks are largely divided into two processes: learning and verification. Learning took advantage of the CPU. For verification, RTL written in Verilog HDL was run on FPGA. The structure of the neural network has four input neurons, four hidden neurons, and one output neuron. The input neurons used the US $ 1, Japanese 100 Yen, EU 1 Euro, and UK £ 1. The input neurons predicted a Canadian dollar value of $ 1. The order of predicting the exchange rate is input, normalization, fixed-point conversion, neural network forward, floating-point conversion, denormalization, and outputting. As a result of forecasting the exchange rate in November 2016, there was an error amount between 0.9 won and 9.13 won. If we increase the number of neurons by adding data other than the exchange rate, it is expected that more precise exchange rate prediction will be possible.

• Korea Trade Review
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• v.42 no.2
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• pp.77-96
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• 2017

This paper attempts to analyze the influences of Korean global retailer's service marketing mix on local customers' satisfaction and their loyalty behaviors. Based on a literature review, three hypotheses are putting forward. The data from 139 customers in Weihai, China were used to test these hypotheses. This paper used Structural Equation Modeling to identify the relationship among the service marketing mix, the customer satisfaction and the customer loyalty behaviors. According to the empirical analysis, this study showed satisfactory data-fit of the proposed model and supported two of the three hypotheses. The empirical results indicated that the service marketing mix factors except the promotion factor take significant effect on the local customer satisfaction, and this in turn have influence on the customer loyalty behaviors. The result shows that Korean global retailers will need to leverage service marketing mix strategically when entering China. Practical implications of these findings needs to be considered for the global retailer to establish an effective marketing strategy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

• Knowledge Management Research
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• v.24 no.2
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• pp.1-25
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• 2023

While organizational learning (OL) is vital for ventures to build knowledge bases necessary for successful innovation, less attention has been paid to how learning organizations leverage it for performance improvement. We investigate entrepreneurial orientation's (EO) role in performance-by-learning mechanisms underpinning ventures' innovative initiatives, adopting dyadic performance indicators: technological competitiveness and business performance. Analyzing 218 Korean ventures, our study shows that firms valuing OL, characterized by acquisitive and experimental learning, exhibit high EO, facilitating productive use of knowledge-based resources and enhancing performance. Importantly, EO fully mediates the performance implications of OL. Our findings suggest that a comprehensive learning approach for knowledge acquisition and experimentation provides ventures, often facing smallness and newness liabilities, with a fertile entrepreneurial ground for increased innovation returns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.388-399
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• 2023

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

• Journal of English Language & Literature
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• v.55 no.1
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• pp.177-198
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• 2009

How are we approach the fourteenth chapter of Ulysses known as 'The Oxen of the Sun' in this globalized age of hyper-theorization? My paper argues that examining the wide reverberations set off by Derrida's comment in "Ulysses Gramophone"-"Everything has already happened to us with Ulysses"-in relation to the central textual theme of cosmopolitanism may provide a reading that not only pays due respect to the critical legacy of the early structuralist interpretations but equally takes into account the political sensibilities of our time. The neologism 'chaosmopolitanism,'in fact, serves as that very critical measure designed to bridge the gap separating the long tradition of Western Eurocentric discourse on cosmopolitanism on the one hand and the geopolitical background conditioning its discursive possibility, namely, the chaotic condition of international colonialism on the other, whose exemplary, and exemplarily creative, fusion bears none other name than Ulysses. But the idea of chaosmopolitanism gains its conceptual leverage on yet another, no less pivotal register, for, just as with Derrida's first-person plural pronoun, the trope leads us to reflect on our own situatedness in the East Asian region in light of Joyce's unabashedly universalist vision, whose over-arching textual purview nonetheless leaves the space called the Far East in the singular position of virtual exclusion. What does it then mean to enjoy Joyce's "chaffering allincluding most farraginous chronicle" in light of our East Asian perspective? To this second question, my inquiry turns to the dual theme of enjoyment and debt as they are problematized by Stephen Dedalus' telegram to Mulligan, which reads, "the sentimentalist is he who would enjoy without incurring the immense debtorship for a thing done." Itself a quotation from George Meredith's novel The Ordeal of Richard Feverel, the transcribed message invites us to reconsider the scrupulous endeavor underwriting Joyce's signatory gusto, but at the same time forcing us to confront and reassess our own debt to the problematic heritage known as Western literature or, to borrow Derrida's expression, Abrahamic language.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.