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Online social network services (SNS) such as Twitter and Facebook are currently representative means of disseminating information on the Web. It is therefore crucial to internet marketing to understand the dynamics of information diffusion in online social networks. To this end, we present a probabilistic approach to process discovery, based on an extended hidden Markov model, considering the log data extracted from online SNS. Specifically, we first group users based on their interactions using SNS log data and three community detection algorithms. The process discovery algorithm is an extension of the hidden Markov model and applies to the user communities to reflect probabilistic dissemination among user communities. We illustrate the proposed method with real SNS data gathered from a Facebook fan page. We expect that our method can promote comprehension of the information dynamics in online social networks by visualizing probabilistic information diffusion through user groups. [ABSTRACT FROM AUTHOR]

Effective performance measures must be developed in order to effectively maintain successful collaboration. This paper presents a methodology of collaborative performance measures to evaluate the overall performance of a collaboration process between multiple manufacturing partners. The partners first define collaborative key performance indicators (cKPI), and they then measure the cKPIs and calculate the synthetic performance from the cKPI values to evaluate the result of the collaboration case. To measure different scales of cKPI, we develop a two-folded desirability function based on the logistic sigmoid functions. The proposed methodology provides a quantitative way to measure collaborative performance in order to effectively manage collaboration among partners, continuously improving collaboration performance. [ABSTRACT FROM AUTHOR]

A series of 3,4-dihydroquinazoline derivatives with anti-cancer activities against human colon cancer HT-29 cell were subjected to three-dimensional quantitative structure-activity relationship (3D-QSAR) studies using the comparative molecular similarity indices analysis (CoMSIA) approaches. The most potent compound, BK10001 was used to align the molecules. As a result, the best prediction was obtained with CoMSIA combined electrostatic, hydrophobic, and hydrogen-bond acceptor fields (q² = 0.648, r² = 0.882). This model was validated by an external test set of six compounds giving satisfactory predictive r² values of 0.879. This model would guide the design of potent 3,4-dihydroquinazoline derivatives as anti-cancer agent for the treatment of human colon cancer. [ABSTRACT FROM AUTHOR]