1 Basic aspects of continuous time Markov chains 3 1.1 Markov property . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Regular jump chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Holding times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Poisson process . . . . . . . . . . . . . . ....

We present a unified view to models for text databases, proving new relations between empirical and theoretical models. A particular case that we cover is the Web. We also introduce a simple model for random queries and the size of their answers, giving experimental results that support them. As an example of the importance of text modeling, we analyze time and space overhead of inverted

the purpose of this article is to present the development of a wind farm, with a condition monitoring system (cms) based on control charts as the algorithm, centred on a new index, to prevent soil pollution by oil spills in wind farms. to this end, temperature sensors can be considered as one of the more significant sensors to be employed in this study, because the information obtained with reg...