Titlebook: IAENG Transactions on Engineering Technologies; Special Volume of th Gi-Chul Yang,Sio-long Ao,Len Gelman Book 2013 Springer Science+Busines

混沌

Adding Relation Between Two Levels of a Linking Pin Organization Structure Maximizing Communicationt . is adjacent such that the communication of information in the organization becomes the most efficient. For a model of adding an edge between a node with a depth . and its descendant with a depth ., we formulated the total shortening distance which is the sum of shortening lengths of shortest pat

Intervention

Bayesian Inference for the Parameters of Two-Parameter Exponential Lifetime Models Based on Type-I ype-II censored samples. Bayes point estimates and credible intervals of the unknown parameters are proposed under the assumption of suitable priors on the unknown parameters and under the assumption of the squared error loss function. Illustrative example is provided to motivate the proposed Bayes

EXULT

Analysing Metric Data Structures Thinking of an Efficient GPU Implementation,large volumes of data are processing, query response time can be quite high. In this case, it is necessary to apply mechanisms to significantly reduce the average query response time. For that purpose, modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, the au

rectum

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Least Squares Data Fitting Subject to Decreasing Marginal Returns, measuring process, then the least sum of squares change to the data that provides nonnegative third divided differences is proposed. The method is highly suitable for estimating points on a sigmoid curve of unspecified parametric form subject to increasing marginal returns or subject to diminishing

Euthyroid

The Further Development of Stem Taper and Volume Models Defined by Stochastic Differential Equation process can be adequately modeled by parametric stochastic differential equations (SDEs). We focus on the segmented stem taper model defined by the Gompertz, geometric Brownian motion and Ornstein-Uhlenbeck stochastic processes. This class of models enables the representation of randomness in the t

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