62 resultados para Israeli Organised Crime
Resumo:
A number of recent papers have brought suggestive evidence for an active role of Chlamydiales in the establishment of the plastid. Chlamydiales define a very ancient group of obligate intracellular bacterial pathogens that multiply in vesicles within eukaryotic phagotrophic host cells such as animals, amoebae or other protists, possibly including the hypothetical phagotroph that internalized the cyanobacterial ancestor of the plastid over a billion years ago. We briefly survey the case for an active role of these ancient pathogens in plastid endosymbiosis. We argue that a good understanding of the Chlamydiales infection cycle and diversity may help to shed light on the process of metabolic integration of the evolving plastid.
Resumo:
The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.