• Journal of KIISE:Databases
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• v.32 no.2
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• pp.111-119
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• 2005

Electronic catalog contains ich semantics associated with products, and serves as a challenging practical domain for ontology application. Ontology is concerned with the nature and relations of being. It can play a crucial role in e-commerce as a formalization of e-Catalogs. Description Logics provide a theoretical core for most of the current ontology languages. In this paper, we present an ontological model of e-Catalogs in DL. We take an Extended Entity Relationship approach for conceptual modeling method, and present the fundamental set of modeling constructs and corresponding description language representation for each construct. Additional semantic knowledge can be represented directly in DL. Our modeling language stands within SHIQ(d) which is known reasonably practical with regard to its expressiveness and complexity. We illustrate sample scenarios to show how our approach may be utilized in modeling e-Catalogs, and also implement the scenarios through a DL inference tool to see the practical feasibility.

• Journal of Microbiology and Biotechnology
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• v.21 no.7
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• pp.719-727
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• 2011

Botulism is a neuroparalytic disease caused by Clostridium botulinum, which produces seven (A-G) antigenically diverse neurotoxins (BoNTs). BoNTs are the most poisonous substances known to humans, with a median lethal dose ($LD_{50}$) of approximately 1 ng/kg of body weight. Owing to their extreme potency and lethality, they have the potential to be used as a bioterrorism agent. The mouse bioassay is the gold standard for the detection of botulinum neurotoxins; however, it requires at least 3-4 days for completion. Attempts have been made to develop an ELISA-based detection system, which is potentially an easier and more rapid method of botulinum neurotoxin detection. The present study was designed using a synthetic gene approach. The synthetic gene encoding the catalytic domain of BoNT serotype B from amino acids 1-450 was constructed with PCR overlapping primers (BoNT/B LC), cloned in a pQE30 UA vector, and expressed in an E. coli M15 host system. Recombinant protein production was optimized at 0.5 mM IPTG final concentration, 4 h post induction, resulting in a maximum yield of recombinant proteins. The immunogenic nature of the recombinant BoNT/B LC protein was evaluated by ELISA. Antibodies were raised in BALB/c mice using various adjuvants. A significant rise in antibody titer (p<0.05) was observed in the Alum group, followed by the Titermax Classic group, Freund's adjuvant, and the Titermax Gold group. These developed high-titer antibodies may prove useful for the detection of botulinum neurotoxins in food and clinical samples.

• Safety and Health at Work
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• v.9 no.3
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• pp.265-276
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• 2018

Background: Resilience engineering is a paradigm for safety management that focuses on coping with complexity to achieve success, even considering several conflicting goals. Modern sociotechnical systems have to be resilient to comply with the variability of everyday activities, the tight-coupled and under-specified nature of work, and the nonlinear interactions among agents. At organizational level, resilience can be described as a combination of four cornerstones: monitoring, responding, learning, and anticipating. Methods: Starting from these four categories, this article aims at defining a semiquantitative analytic framework to measure organizational resilience in complex sociotechnical systems, combining the resilience analysis grid and the analytic hierarchy process. Results: This article presents an approach for defining resilience abilities of an organization, creating a structured domain-dependent framework to define a resilience profile at different levels of abstraction, and identifying weaknesses and strengths of the system and potential actions to increase system's adaptive capacity. An illustrative example in an anesthesia department clarifies the outcomes of the approach. Conclusion: The outcome of the resilience analysis grid, i.e., a weighed set of probing questions, can be used in different domains, as a support tool in a wider Safety-II oriented managerial action to bring safety management into the core business of the organization.

• The KIPS Transactions:PartD
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• v.16D no.4
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• pp.561-570
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• 2009

In order to manage repair and maintenance efficiently in the mobile environment, the system structure to manage service as a policy and the policy description language are needed. This research defined the structure of PEP, which is the executioner of policy in the IETF policy framework, and proposed the policy description language which can be carried out under the PEP structure. The proposed policy description language derived demand matters based on documentary data and the characteristics of mobile and the policy information model was designed with the three stage approaches and was defined as policy description language. The three stage approaches are made up of the policy domain that decides the scope to which the policy applies, the policy rules which distinguish the kinds of policy application and control, and policy grammar which contextualizes the policy structure. In order to verify the efficiency of the policy description language, scenarios are defined with the policy description language and verified it by using policy tool and proved the expansive nature by comparing and analyzing other policy description language.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4191-4211
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• 2019

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies. Therefore, any form of evolution can lead to a more complex situation than a single system. On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment. SPL development organizations need to consider software evolution in a systematic way due to their complexity and size. Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL. Thus, the addition of new requirements or the rapid context change is common in SPL products. To cope with rapid change several researchers have discussed the evolution of software product lines. However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software. Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution. We used classification, topic modeling, and sentiment extraction to elicit user requirements. Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach. Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products. Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.1-8
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• 2009

Learning user's preference is a key issue in intelligent system such as personalized service. The study on user preference model has adapted simple user preference model, which determines a set of preferred keywords or topic, and weights to each target. In this paper, we recommend multi-perspective user preference model that factors sentiment information in the model. Based on the topicality and sentimental information processed using natural language processing techniques, it learns a user's preference. To handle timc-variant nature of user preference, user preference is calculated by session, short-term and long term. User evaluation is used to validate the effect of user preference teaming and it shows 86.52%, 86.28%, 87.22% of accuracy for topic interest, keyword interest, and keyword favorableness.

• Journal of the Korea Institute of Building Construction
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• v.24 no.3
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• pp.355-362
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• 2024

The sustained upkeep of apartment buildings necessitates ongoing maintenance and timely repairs, particularly given their complex nature due to extensive areas, common facilities, and multiple residential and service structures. Additionally, the need for cost-effective maintenance is paramount for ensuring safety, preserving value, and maintaining economic efficiency. However, the multitude of external variables influencing apartment complex maintenance, coupled with the challenges in data collection, have resulted in limited research in this domain. To address this gap, the current study aims to develop a framework for predicting maintenance costs utilizing deep learning techniques, grounded in real-world apartment complex maintenance cost data. This study intends to provide a practical and valuable contribution to the field of apartment complex management, empowering stakeholders with enhanced predictive capabilities for optimizing maintenance strategies and resource allocation.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.4
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• pp.345-359
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• 2024

Deposition of extracellular matrix (ECM) in the trabecular meshwork (TM) increases aqueous humour outflow resistance leading to elevation of intraocular pressure (IOP) in primary open-angle glaucoma, which remains the only modifiable risk factor. Resveratrol has been shown to counteract the steroid-induced increase in IOP and increase the TM expression of ECM proteolytic enzymes; however, its effects on the deposition of ECM components by TM and its associated pathways, such as TGF-β-SMAD signalling remain uncertain. This study, therefore, explored the effects of trans-resveratrol on the expression of ECM components, SMAD signalling molecules, plasminogen activator inhibitor-1 and tissue plasminogen activator in dexamethasone-treated human TM cells (HTMCs). We also studied the nature of molecular interaction of trans-resveratrol with SMAD4 domains using ensemble docking. Treatment of HTMCs with 12.5 µM trans-resveratrol downregulated the dexamethasone-induced increase in collagen, fibronectin and α-smooth muscle actin at gene and protein levels through downregulation of TGF-β1, SMAD4, and upregulation of SMAD7. Downregulation of TGF-β1 signalling by trans-resveratrol could be attributed to its effect on the transcriptional activity due to high affinity for the MH2 domain of SMAD4. These effects may contribute to resveratrol's IOP-lowering properties by reducing ECM deposition and enhancing aqueous humour outflow in the TM.

• Journal of Korean Elementary Science Education
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• v.43 no.3
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• pp.433-445
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• 2024

This study aims to compare and analyze the educational contents of the material area in the elementary science curriculums of North and South Korea. The research subjects are materials and motion and energy (partial) areas of the revised science curriculum of South Korea in 2022 and materials around us and science in daily life (partial) areas of the nature and education program of North Korea in 2013. This study compared the elements of the educational content of the material domain between North and South Korea according to the grade. Furthermore, the reflection of the material domain goals of North and South Korea at the international level was analyzed using the evaluation framework of the Trends in International Mathematics and Science Study (TIMSS) 2023 for the material content domains for fourth-grade elementary schools. Four teachers who majored in elementary science education and one expert in science education participated in the analysis. The results are as follows. First, in terms of the properties of matter, the content covered in the curriculum of North and South Korea differed in application period by grade and in the scope and level of content. Second, regarding material change, North Korea did not cover acids and bases but included methods for speeding up dissolution. Third, North Korea reflected the goal of the TIMSS 2023 properties of materials more highly than South Korea. Fourth, similar to the results for the analysis on the properties of materials, North Korea reflected the goal of the TIMSS 2023 for changes of materials more highly than did South Korea. In conclusion, the elements and timing of application of the material contents differed between North and South Korea, and the degree of reflection of goals at the international level was found to be higher for North Korea. In the future, this study hopes that cooperation and research on the development of integrated science and curriculum will occur along with the revitalization of educational exchange between North and South Korea from the perspective of the preparation for unification beyond the ideological conflict between them.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.