• Asia-Pacific Journal of Business
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• v.10 no.2
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• pp.1-12
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• 2019

Collaborative research and development (R&D) has been encouraged based on the belief that knowledge spill-over is mutually beneficial for partners. Although the benefits are supported by science and technology policy research, the risk of R&D collaboration has not been extensively discussed. Two independent studies suggest that there are risks associated with the overuse of collaborative research frameworks. Two sets of R&D collaboration data were analyzed: between the national bio-technology research program and 1) Data Envelop Analysis (DEA), and 2) between Stochastic Frontier Analysis (SFA). In the case of SFA, output measures were integrated into a single output, with weights extracted from research programme managers' responses to the questionnaire. While the DEA result demonstrated the inefficiency of collaborative research, SFA did not. Unlike previous research highlighting risks associated with disclosing proprietary R&D and potential conflict of interest, our study indicates that the transaction's social cost affects collaborative research efficiency. Therefore, governments promoting R&D collaborations should be carefully managed, and policy makers must reconsider the strict conditions governing compulsory collaborative R&D programs.

• Archives of Pharmacal Research
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• v.27 no.2
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• pp.169-172
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• 2004

Activity-guided fractionation of the EtOAc-soluble extract of the stems of Couepia ulei, using a bioassay based on the induction of quinone reductase (QR) in cultured Hepa 1c1c7 mouse hepatoma cells led to the isolation of two active compounds, a new natural product, erythro-2,3-bis(4-hydroxy-3-methoxyphenyl)-3-ethoxypropan-1-o1 (1), and a known compound, evofolin-B (2), along with five inactive compounds all of known structure, viz., betulinic acid, oleanolic acid, pomolic acid, ($\pm$)-syringaresinol, and ursolic acid. These isolates were identified by analysis of physical and spectral data. Compounds 1 and 2 exhibited QR inducing activity, with observed CD (concentration required to double induction) values of 16.7 and 16.4 $\mu\textrm{M}$, respectively.

• ETRI Journal
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• v.43 no.6
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• pp.1004-1012
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• 2021

The collaboration productively interacting between multi-agents has become an emerging issue in real-world applications. In reinforcement learning, multi-agent environments present challenges beyond tractable issues in single-agent settings. This collaborative environment has the following highly complex attributes: sparse rewards for task completion, limited communications between each other, and only partial observations. In particular, adjustments in an agent's action policy result in a nonstationary environment from the other agent's perspective, which causes high variance in the learned policies and prevents the direct use of reinforcement learning approaches. Unexpected social loafing caused by high dispersion makes it difficult for all agents to succeed in collaborative tasks. Therefore, we address a paradox caused by the social loafing to significantly reduce total returns after a certain timestep of multi-agent reinforcement learning. We further demonstrate that the collaborative paradox in multi-agent environments can be avoided by our proposed effective early stop method leveraging a metric for social loafing.

• Research in Mathematical Education
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• v.25 no.1
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• pp.1-20
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• 2022

Collaborative learning has been highlighted as an effective method of teachers' professional development in various studies. To disclose teachers' discourse threads in the process of collaborative learning for developing their knowledge, this paper adopted two methods including "content analysis" and "time-sequential analysis" of learning analytics. Such analyses were implemented for mining teachers' updated knowledge and the discourse threads in the discussion during collaborative learning. The materials for analysis involved two aspects: one was from the video-taped lesson observation reports written by teachers before and after discussing, and the other was from their discourses during the discussion process. The results proved that teachers' knowledge for teaching the centroid of a triangle was updated in the collaborative learning period, and also revealed the discourse threads of teachers' collaboration contained "requesting information or opinions", "building on ideas", and "providing evidence or reasoning", with the emphasis on "challenging ideas or re-focusing talk"

• Educational Technology International
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• v.16 no.1
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• pp.1-30
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• 2015

The purpose of this research is to propose a novel social LMS developed for group collaborative learning with a think-aloud tool integrated for sharing cognitive processes in order to improve group collaborative learning performance. In this developmental research, the system was designed with three critical elements: the think-aloud element supports learners through shared cognition, the social network element improves the quality of collaborative learning by forming a structured social environment, and the learning management element provides a understructure for collaborative learning for student groups. Moreover, the three critical elements were combined in an educational context and applied in three directions.

• Journal of Smart Tourism
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• v.4 no.1
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• pp.19-29
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• 2024

The objective of this article is to create a model of integrated management from the framework modeling of a digital collaborative network supported by knowledge engineering to make heritage site in the Brazil more effective. It is an exploratory and qualitative research with thematic analysis as technique of data analysis from the collaborative network, digital platform, world heritage, and tourism themes. The snowballing approach was chosen, and the mapping and classification of relevant studies was developed with the use of the spreadsheet tool and the Mendeley® software. The results show that the collaborative network model oriented towards strategic objectives should be supported by a digital platform that provides a technological environment that adds functionalities and digital platform services with the integration of knowledge engineering techniques and tools, enabling the discovery and sharing of knowledge in the collaborative network.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.85-104
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• 2009

Managers have come to realized that a large and increasing amount of sources for a corporate competitive advantage can be found in their company's supply chain. At a fundamental level, there is a similar consensus that building a collaborative supply management is a more effective way of managing firms' supply chains. This paper is an attempt to examine the effects of collaborative supply management by probing a mechanism which shows how competences and/or performance are transferred from suppliers to markets through the supply chains. Research hypotheses regarding the relationships between collaborative supply management, supplier Performance, buyer internal performance (new product development and operational performance), and buyer market performance were empirically validated by utilizing a structural equation modeling analysis. The research result Indicates the positive impacts of a collaborative supply chain on supply performance as well as buyer internal and market performance, which also well illustrate how the improved competence and/or performance of suppliers engendered from the supplier-buyer collaboration flows throughout the entire supply chain.

• The Journal of Korean Medicine
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• v.28 no.1 s.69
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• pp.72-86
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• 2007

Objectives : The purpose of this study was to evaluate and compare the cost-effectiveness of Eastern, Western, and collaborative treatments and suggest a cost-effective approach for patients with frozen shoulder pain. Methods : Using the data of fifty-two patients, treatment effectiveness was measured by CSA, SPABI, and ROM scales and changes from the baseline score were evaluated. Data source for cost estimation was based on the national health insurance (NHI) payment system. Because the price in NHI was differentiated by health care institutions, five collaborative types were considered in assessing costs. Cost-effective ratios were computed for economic evaluation. Results : Compared with Eastern treatment, collaborative and Western treatments showed better effects on CSA scale after 4 weeks' treatment. The collaborative approach was also the most effective treatment on SPADI and ROM scales. The direct cost per patient receiving Eastern treatment was less than other treatments. In general, collaborative treatment dominated Eastern and Western treatments in cost-effectiveness an analysis. However, the cost-effectiveness ratio of Eastern treatment resulted in \9,000 compared to \29,000 of collaborative treatment on SPADI. Four different indicators of ROM scales resulted in different approaches as the cost-effective treatment. Conclusions : Considering cost-effectiveness ratios, collaborative treatment was the best treatment on CSh and SPADI scales after 4 weeks' treatment. As for ROM scales, the recommended alternatives were Eastern treatment for patients with abduction and adduction disabilities, Western treatment for those with flexion disability, and collaborative approach fir those with extension disabiliry.

• Journal of Information Technology Services
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• v.13 no.2
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• pp.173-184
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• 2014

As social network services has become one of the most successful web-based business, recommendation in social network sites that assist people to choose various products and services is also widely adopted. Collaborative Filtering is one of the most widely adopted recommendation approaches, but recommendation technique that use explicit or implicit social network information from social networks has become proposed in recent research works. In this paper, we reviewed and compared research works about recommendation using social network analysis and collaborative filtering in social network sites. As the results of the analysis, we suggested the trends and implications for future research of recommendation in SNSs. It is expected that graph-based analysis on the semantic social network and systematic comparative analysis on the performances of social filtering and collaborative filtering are required.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.343-350
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• 2020

In the era of the Fourth Industrial Revolution in the digital revolution is taking place, various attempts have been made to provide various contents in a digital environment. In this paper, agent-recruitment service system based on collaborative deep learning is proposed for the intelligent head hunting service. The service system is improved from previous research [7] using collaborative deep learning for more reliable recommendation results. The Collaborative deep learning is a hybrid recommendation algorithm using "Recurrent Neural Network(RNN)" specialized for exponential calculation, "collaborative filtering" which is traditional recommendation filtering methods, and "KNN-Clustering" for similar user analysis. The proposed service system can expect more reliable recommendation results than previous research and showed high satisfaction in user survey for verification.