• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.25-34
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• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• Genomics & Informatics
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• v.12 no.4
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• pp.254-260
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• 2014

Best linear unbiased prediction (BLUP) has been used to estimate the fixed effects and random effects of complex traits. Traditionally, genomic relationship matrix-based (GRM) and random marker-based BLUP analyses are prevalent to estimate the genetic values of complex traits. We used three methods: GRM-based prediction (G-BLUP), random marker-based prediction using an identity matrix (so-called single-nucleotide polymorphism [SNP]-BLUP), and SNP-SNP variance-covariance matrix (so-called SNP-GBLUP). We used 35,675 SNPs and R package "rrBLUP" for the BLUP analysis. The SNP-SNP relationship matrix was calculated using the GRM and Sherman-Morrison-Woodbury lemma. The SNP-GBLUP result was very similar to G-BLUP in the prediction of genetic values. However, there were many discrepancies between SNP-BLUP and the other two BLUPs. SNP-GBLUP has the merit to be able to predict genetic values through SNP effects.

• Archives of Pharmacal Research
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• v.23 no.6
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• pp.535-547
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• 2000

Recent findings indicate that mechanical strain (deformation) exerted by the extracellular matrix modulates activation of airway smooth muscle cells. Furthermore, cytoskeletal organization in airway smooth muscle appears to be dynamic, and subject to modulation by receptor activation and mechanical strain. Mechanosensitive modulation of crossbridge activation and cytoskeletal organization may represent intracellular feedback mechanisms that limit the shortening of airway smooth muscle during bronchoconstriction. Recent findings suggest that receptor-mediated signal transduction is the primary target of mechanosensitive modulation. Mechanical strain appears to regulate the number of functional G-proteins and/or phospholipase C enzymes in the cell membrane possibly by membrane trafficking and/or protein translocation. Dense plaques, membrane structures analogous to focal adhesions, appear to be the primary target of cytoskeletal regulation. Mechanical strain and receptor-binding appear to regulate the assembly and phosphorylation of dense plaque proteins in airway smooth muscle cells. Understanding these mechanisms may reveal new pharmacological targets for control1ing airway resistance in airway diseases.

• Convergence Security Journal
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• v.16 no.4
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• pp.3-15
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• 2016

Business data means data of all the documents and electronically generated on / off-line form, storage, use, and transfer the company work process. Business, organization, sales, marketing, means any data related to shipping. Many companies are investing in privacy. But not so for business data. In most companies, secret, confidential rating already exists, the basis is insufficient to establish that decisions can be analyzed in detail to reflect the actual business data in use. In this paper we want to present the criteria that can offer ways to design your business data decision matrix to establish the qualitative and quantitative criteria (evaluation indicators) that can be classified business data and protected by each class.

• Genomics & Informatics
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• v.5 no.1
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• pp.19-23
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• 2007

In this paper, we provide an overview of statistical models for haplotype-based association studies, and summarize their features based on the design matrix. We classify the design matrix into the two types: direct and indirect. For these two kinds of matrices, we present and compare characteristics using a simple hypothetical example, and a real data set. The motivation behind this study was to provide practitioners with an improved understanding, to facilitate the informed selection of the appropriate haplotype-based model and to improve the interpretability of the models.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.241-246
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• 2013

A classic document clustering technique may incorrectly classify documents into different clusters when documents that should belong to the same cluster do not have any shared terms. Recently, to overcome this problem, internal and external knowledge-based approaches have been used for text document clustering. However, the clustering results of these approaches are influenced by the inherent structure and the topical composition of the documents. Further, the organization of knowledge into an ontology is expensive. In this paper, we propose a new enhanced text document clustering method using non-negative matrix factorization (NMF) and WordNet. The semantic terms extracted as cluster labels by NMF can represent the inherent structure of a document cluster well. The proposed method can also improve the quality of document clustering that uses cluster labels and term weights based on term mutual information of WordNet. The experimental results demonstrate that the proposed method achieves better performance than the other text clustering methods.

• Genomics & Informatics
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• v.17 no.2
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• pp.18.1-18.10
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• 2019

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.8.2-8.2
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• 2011

This talk demonstrates diverse patterned structures utilizing in-situ self-organization and phase separation of the materials into an ordered fashion. The patterned structures in this talk include electrospun nanofibers and electrosprayed microparticles embedding small particles. The positions of the small particles are in-situ controlled during the electrohydrodynamic process by the interaction with the polymer matrix. Another topic of the talk includes selective deposition of spin-coated materials on a corrugated surface that was prepared by buckling of polymer thin films. Solution are strong tendency to be positioned in the trench area of the surface, which facilitates the fabrication of micropatterns of diverse materials.

• Educational Technology International
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• v.9 no.1
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• pp.1-23
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• 2008

Despite considerable commitment to the application of action learning as an organization development intervention, no identified systematic investigation of action learning practices has been reported. Based on a systematic literature review, the purpose of this paper is to identify whether researchers strike a balance between action and learning in their studies of action learning. Research findings in this study included: (1) only 32 empirical studies were found from the electronic database search; (2) based on the hypothesized continuum of Revans' original proposition of balancing action and learning, the author categorized 32 studies into three groups: action-oriented, learning-oriented, and balanced action learning; (3) there were only nine studies on balanced action learning among 32 empirical studies, whose insights included an effective use of project teams, applications of action learning for organization development, and key success factors such as time, reflection, and management support; (4) case study was among the most frequently used research method and only six quality studies met key methodological traits; and (5) therefore, more rigorous empirical research employing quantitative methods as well as case studies is needed to determine whether researchers strike a balance between action and learning in studies on action learning.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.10a
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• pp.42-46
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• 1999

Knowledge management(KM) is emerging as a robust management mechanism with which an organization can remain highly intelligent and competitive in a turbulent market. Organization memory(or knowledge) is at the heart of KM success. How to create organizational memory has been debated among researchers. In literature, a wide variety of methods for creating organizational memory have been proposed only to prove that its applicability is limited to decision-making problems which require shallow or non-causal knowledge type. However, organizational memory with a sense of causal knowledge is highly required in solving complicated decision-making problems in which complex dynamics exist between various factors and influence each other with cause and effect relationship among them. In this respect, we propose a new approach to creating a causal-typed organizational memory (CATOM), which has a form of causal knowledge and is represented in a matrix form, by using an inference diagram. An algorithm for CATOM creation is suggested and applied to an illustrative example. Results show that our proposed KM approach can effectively equip an organization with semi-automated CATOM creation and inference process which is deemed useful in a highly competitive business environment.