Multi-wavelength variability properties of Fermi blazar S5 0716+714

S5 0716+714 is a typical BL Lacertae object. In this paper we present the analysis and results of long term simultaneous observations in the radio, near-infrared, optical, X-ray and $\gamma$-ray bands, together with our own photometric observations for this source. The light curves show that the variability amplitudes in $\gamma$-ray and optical bands are larger than those in the hard X-ray and radio bands and that the spectral energy distribution (SED) peaks move to shorter wavelengths when the source becomes brighter, which are similar to other blazars, i.e., more variable at wavelengths shorter than the SED peak frequencies. Analysis shows that the characteristic variability timescales in the 14.5 GHz, the optical, the X-ray, and the $\gamma$-ray bands are comparable to each other. The variations of the hard X-ray and 14.5 GHz emissions are correlated with zero-lag, so are the V band and $\gamma$-ray variations, which are consistent with the leptonic models. Coincidences of $\gamma$-ray and optical flares with a dramatic change of the optical polarization are detected. Hadronic models do not have the same nature explanation for these observations as the leptonic models. A strong optical flare correlating a $\gamma$-ray flare whose peak flux is lower than the average flux is detected. Leptonic model can explain this variability phenomenon through simultaneous SED modeling. Different leptonic models are distinguished by average SED modeling. The synchrotron plus synchrotron self-Compton (SSC) model is ruled out due to the extreme input parameters. Scattering of external seed photons, such as the hot dust or broad line region emission, and the SSC process are probably both needed to explain the $\gamma$-ray emission of S5 0716+714.Comment: 43 pages, 13 figures, 3 tables, to be appeared in Ap

A rough association rule is applicable for knowledge

[[abstract]]The traditional association rule which should be fixed in order to avoid both that only trivial rules are retained and also that interesting rules are not discarded. In fact, the situations which use the relative comparison to express are more complete than to use the absolute comparison. Through relative comparison we proposes a new approach for mining association rule, which has the ability to handle the uncertainty in the classing process, so that we can reduce information loss and enhance the result of data mining. In this paper, the new approach can be applied in find association rules, which has the ability to handle the uncertainty in the classing process and suitable for all data types.[[conferencetype]]國際[[iscallforpapers]]Y[[conferencelocation]]ShangHai, Chin

Mining Customer Knowledge for a Recommendation System in Convenience Stores

[[abstract]]Taiwan's rapid economic growth with increasing personal income leads increasing numbers of young unmarried people to eat out, and shopping at convenience stores for food is indispensable to the lives of these people. Thus, it is an essential issue for convenience store owners to know how to accurately market appropriate products and to choose effective endorsers for brands or products in order to attract target consumers. Data mining is a business intelligence analysis approach with great potential to help businesses focus on the most important business information contained in a database. Therefore, this study uses the Apriori algorithm as an association rules approach, and clustering analysis for data mining. The authors divide consumers into three groups by their consumer profiles and then find each group's product preference mixes, product endorsers, and product/brand line extensions for new product development. These are developed as a recommendation system for 7-11 convenience stores in Taiwan.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙

Developing a Scale Measurement of Market Uncertainty: A Cluster Analysis on Taiwan’s Financial Services

[[abstract]]he way to ensure a company's long-term advantages for survival is to completely know market uncertainty. Though the financial services have been made great contributions to Taiwan's economic development, past research pays little attention on them without a scale development of market uncertainty. Building on extensive literature, a 53-item survey questionnaire was developed and 323 respondents from 28 domestic financial services were selected as the sample of this study. Using an exploratory factor analysis (EFA), we would retrieve four dimensions of market uncertainty, including market situation, market forecasting, market innovation and competitor's threats. Meanwhile, we would divide market uncertainty into three groups by cluster analysis and further verify them with business performance as well as project efficiency.[[conferencetype]]國際[[conferencedate]]20091208~20091211[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

Knowledge Management and Innovation: The mediating effects of organizational learning

[[abstract]]The relationship between knowledge management and innovation is very critical now. However, without good capability of organizational learning, one organizational can't retain some important knowledge management practices and increase innovation. This study tries to set a model figuring out the moderating effects of organizational learning between knowledge management and innovation based on Common Wealth Magazine's Top 1000 manufacturers and Top 100 financial firms in Taiwan 2007. The result reveals that the relationship among knowledge management, as well as organizational learning and organizational innovation utilizing structural equation modeling. The results show that organizational learning is the mediating variable between knowledge management and organizational innovation. Therefore, knowledge management is an important input, and organizational learning is a key process, then organizational innovation is a critical output.[[conferencetype]]國際[[conferencedate]]20091208~20091211[[iscallforpapers]]Y[[conferencelocation]]Hong Kong, Chin

The Relationships among Brand Image, Brand Trust, and Online Word-of-Mouth: an Example of Online Gaming

[[abstract]]This study mainly investigates the relationships among brand image, brand trust, and online word-of-mouth. The present study collects 317 players of the online game ¿World of Warcraft¿ to empirically investigate the relationships among brand image, brand trust, and online word-of-mouth, and examines the moderating effect of experience. By manipulating structural equation modeling (SEM), the research results indicate that brand trust serves as a partial mediator between brand image and online word-of-mouth. The other findings specify the substantial moderating effect of experience in brand image, brand trust, and online word-of-mouth.[[conferencetype]]國際[[conferencedate]]20091208~20091211[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

The impacts of brand trust, customer satisfaction, and brand loyalty on word-of-mouth

[[abstract]]The study mainly investigates the relationships among brand trust, customer satisfaction, brand loyalty, and word-of-mouth. Nowadays, the automotive industry is facing the competitive environment. Whether the industry can enhance brand trust, increase customer satisfaction, and then improve brand loyalty and word-of-mouth, which is the key issue of this study. Toyota was selected as the object of this study. 375 questionnaires were provided and 258 valid replies were received. This study uses the structural equation model to empirically explore the relationships among brand trust, customer satisfaction, brand loyalty, and word-of-mouth. The results indicate the best model is causal chain, that is, brand trust must affect brand loyalty through customer satisfaction initially, and then impact word-of-mouth through brand loyalty.[[conferencetype]]國際[[iscallforpapers]]Y[[conferencelocation]]Maca