• Information Systems Review
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• v.25 no.3
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• pp.99-120
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• 2023

Courier services users' experience of violating privacy affects psychology and behavior of protecting personal privacy. Depending on what privacy infringement experience (PIE) of courier services users, learning about perceived privacy infringement incidents is made, recognition is formed, affection is formed, and behavior is appeared. This paradigm of changing in privacy psychologies of courier services users has an important impact on predicting responses of privacy protective action (PPA). In this study, a theoretical research framework are developed to explain the privacy protective action (PPA) of courier services users by applying attitude theory. Based on this framework, the relationships among past privacy infringement experience (PIE), perceived privacy risk (PPR), privacy concerns (i.e., concerns in unlicensed secondary use (CIUSU), concerns in information error (CIE), concerns in improper access (CIA), and concern in information collection (CIC), and privacy protective action (PPA) are analyzed. In this study, the proposed research model was surveyed by people with experience in using courier services and was analyzed for finding relationships among research variables using structured an equation modeling software, SMART-PLS. The empirical results show the causal relationships among PIE, PPR, privacy concerns (CIUSU, CIE, CIA, and CIC), and PPA. The results of this study provide useful theoretical implications for privacy management research in courier services, and practical implications for the development of courier services business model.

• Journal of Industrial Convergence
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• v.22 no.3
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• pp.51-65
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• 2024

This study aims to understand positive changes in balance and gait function and difficulties in the instructor's guidance process through repetitive basic motor skill-based physical activities targeting people with cerebellar ataxia. For this purpose, five adults with cerebellar ataxia were selected as research subjects, and their three instructors participated as research participants. To collect quantitative data, the average and standard deviation were examined through pre-and post-evaluation of the research participants' physical activity classes for 16 weeks. The mean and standard deviation of the collected data were calculated using the Shapiro-Wilk test in the SAS 9.1 statistical program (p<.05). As a qualitative data collection method, the cultural description method of developmental research(DSR) proposed by Spradley(1980) was adopted, and the collected data were analyzed inductively according to the analysis method of Mertens(1990). Through this, 31 concepts, 10 subcategories, and 4 categories were discovered. As a result, the difficulties experienced by the research participants included insufficient guidance environment, dissatisfaction of consumers, difficulty in guidance, and non-cooperation of colleagues. Based on these research results, it was found that institutional, legal, and policy support should be provided not only to public institutions but also to private physical activity institutions that can use vouchers in order to maintain the balance of adults with cerebellar ataxia as well as to guide their physical activities.

• Journal of Life Science
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• v.34 no.3
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• pp.179-190
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• 2024

Cannabis sativa is a plant widely cultivated worldwide and has been used as a material for food, medicine, building materials and cosmetics. In this study, we assessed the functional effects of C. sativa stem and root extracts using network pharmacology and confirmed their novel functions. The components in stem and root ethanol extracts were identified by gas chromatography-mass spectrometry analysis, and networks between the components and proteins were constructed using the STICHI database. Functional annotation of the proteins was performed using the KEGG pathway. The effects of the extracts were confirmed in lysophosphatidylcholine-induced THP-1 cells using real-time PCR. A total of 21 and 32 components were identified in stem and root extracts, respectively, and 147 and 184 proteins were linked to stem and root components, respectively. KEGG pathway analysis showed that 69 pathways, including the MAPK signaling pathway, were commonly affected by the extracts. Further investigation using pathway networks revealed that terpenoid backbone biosynthesis was likely affected by the extracts, and the expression of the MVK and MVD genes, key proteins in terpenoid backbone biosynthesis, was decreased in LPC-induced THP-1 cells. Therefore, this study determined the diverse function of C. sativa extracts, providing information for predicting and researching the effects of C. sativa.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.4
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• pp.267-274
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• 2024

Hydrogen embrittlement fracture poses a challenge in ensuring the structural integrity of materials exposed to hydrogen-rich environments. This study advances our comprehension of hydrogen-induced fracture through an integrated numerical modeling approach. In addition, it employs a ductile fracture model named the Gurson-cohesive model (GCM) and hydrogen diffusion analysis. GCM is employed as a fracture model that combines the Gurson model to illustrate the continuum damage evolution and the cohesive zone model to describe crack surface discontinuity and softening behavior. Moreover, porosity and stress triaxiality are considered as crack initiation criteria . A hydrogen diffusion analysis is also integrated with the GCM to account for hydrogen enhanced decohesion (HEDE) mechanisms and their subsequent impacts on crack initiation and propagation. This framework considers the influence of hydrogen on the softening behavior of the traction-separation relationship on the discontinuous crack surface. Parametric studies explore the sensitivity to diffusion properties and hydrogen-induced fracture properties. By combining numerical models of hydrogen diffusion and the ductile fracture model, this study provides an understanding of hydrogen-induced fracture and thereby contributes significantly to the ongoing efforts to design materials that are resilient to hydrogen embrittlement in practical engineering applications.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Archives of design research
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• v.16 no.3
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• pp.201-210
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• 2003

The world is preparing another unseen war, that is, the cultural war of digital economy which will dominate the new millenium. As the “contents”, which are composed of various ingredients of media, gain vitality, the developed nations are in preparation of the war with the “cultural industry” weapons. The digital economic experts say that the left out nations will become economic colony in the new millenium age. The most important characteristics of cultural industry is the unity of creativity and culture which is all the more improved on the basis of the culture created upon knowledge. This leads to competition between nations or regions, and to survive one has to develop the industrial structure through cognition of its own cultural value. Furthermore, it is not a short-term development and investment of cultural products but a study on the method to graft the cultural value to the industry itself. The multi-media period does not accept an independent medium, and the contents products are becoming the leading industry since il is proved that they last semi-permanently in the digital world. The victory lies in the quality and quantity of the contents as the high ability and variety of the technology of media advance in accordance to the market principles. Since the culture, science and economy are becoming one complex structure, all nations of the world are trying the evolve a unique design of their on culture on the basis of the global universality. In consequence, we should excavate a uniqueness from our cultural heritage and develop into a korean design which will be recognized in the world market. The value of our cultural property should not only be used as academic and research purposes but should be re-evaluated with modem view, recognized as the core element that decides the quality of life and developed into exclusive designs. The korean designs represent the mould concept of our people which evolves from the mould or shape alphabet of Korea To meet the requirements of the changing world and in preparation of the cultural competitive age, it is never too early to make a data on the korean designs through their analysis and evaluation.

• Clean Technology
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• v.27 no.4
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• pp.359-366
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• 2021

Halloysite nanotubes (HNTs), the multiwalled clay mineral with the composition of Al₂Si₂O₅(OH)₄·nH₂O, have been highlighted as a low-cost adsorbent for the removal of dyes from wastewater. Although a powder of halloysite presents a high specific surface area, forming media are significantly considered due to sludge-clogging induced by the water-bound agglomeration. However, higher firing temperature to achieve the structural durability of the media and lower utilization rate due to longer penetration depth into the media act as hurdles to increase the dye-adsorption capacity. In this work, the retention of the adsorption capacity of halloysite was evaluated with methylene blue solution after the heat treatment at 750 ℃. In order to improve the utilization rate, tubular media were fabricated by extrusion. The images taken by transmission electron microscopy show that HNTs present excellent structural stability under heat treatment. The HNTs also provide superb capacity retention for MB adsorption (93%, 18.5 mg g^-1), while the diatomite and Magnesol^® XL show 22% (7.65 mg g^-1) and 6% (11.7 mg g^-1), respectively. Additionally, compositing with lignin enhances adsorption capacity, and the heat treatment under the hydrogen atmosphere accelerates the adsorption in the early stage. Compared to the rod-type, the tubular halloysite media rapidly increases methylene blue adsorption capacity.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Journal of Life Science
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• v.27 no.11
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• pp.1331-1339
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• 2017

DNA barcoding is the identification of a species based on the DNA sequence of a fragment of the cytochrome C oxidase subunit I (COI) gene in the mitochondrial genome. It is widely applied to assist with the sustainable development of fishery-product resources and the protection of fish biodiversity. This study attempted to verify horse-head fish (Branchiostegus japonicus) and fake horse-head fish (Branchiostegus albus) species, which are commonly consumed in Korea. For the validation of the two species, a real-time PCR method was developed based on the species' mitochondrial DNA genome. Inter-species variations in mitochondrial DNA were observed in a bioinformatics analysis of the mitochondrial genomic DNA sequences of the two species. Some highly conserved regions and a few other regions were identified in the mitochondrial COI of the species. In order to test whether variations in the sequences were definitive, primers that targeted the varied regions of COI were designed and applied to amplify the DNA using the real-time PCR system. Threshold-cycle (Ct) range results confirmed that the Ct ranges of the real-time PCR were identical to the expected species of origin. Efficiency, specificity and cross-reactivity assays showed statistically significant differences between the average Ct of B. japonicus DNA ($21.85{\pm}3.599$) and the average Ct of B. albus DNA ($33.49{\pm}1.183$) for confirming B. japonicus. The assays also showed statistically significant differences between the average Ct of B. albus DNA ($22.49{\pm}0.908$) and the average Ct of B. japonicus DNA ($33.93{\pm}0.479$) for confirming B. albus. The methodology was validated by using ten commercial samples. The genomic DNA-based molecular technique that used the real-time PCR was a reliable method for the taxonomic classification of animal tissues.