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Under a flexible mass-production system, a manufacturer may need to provide highly customized products to meet customer satisfaction. It is likely that components in a customized product are correlated in such a way that the demands of some components depend on those of others. In order to cope with dependence in the demands, we proposed a continuous review multi-item inventory (Q, r) model that included a general form of correlation and dependence in demands among components. We represented the proposed model by using a probabilistic graphical model under the assumption that the demands of all components and their correlations were represented by a multivariate Gaussian probability distribution. By taking an advantage of a directed acyclic graph and its topological order, we demonstrated that the correlated demands among components in the proposed model could be solved without any approximation and assumption. As an illustration of the proposed method, we solved an inventory (Q, r) model of eight correlated components and discussed the experimental results in terms of correlation and dependence in demand. [ABSTRACT FROM AUTHOR]

This paper presents an application of the Independent Component Analysis (ICA) method to genomic data. In particular, experimentally produced perturbation effects over the E.coli bacterium are monitored through the changes of gene expression values observed at regular times, and until steady state has been reached. The aim is to control the response of the SOS system to DNA damage. We might assume that only part of the genetic regulatory network is affected directly by the perturbation conditions, as indirect cascade effects might also be present, and some genes may change just because of randomness. ICA decomposes the gene matrix and identifies groups of genes belonging to a certain estimated component by virtue of co-expression; it is of course of interest to establish co-regulation dynamics, which might underlie the captured correlation. Stronger forms of dependence, like Mutual Information, are thus computed and compared with linear correlation in order to validate the results and establish the role of the identified components in determining the network dynamics. [ABSTRACT FROM AUTHOR]

This study's objective was to identify, in a statistically valid and efficient manner, the risk factors associated with dental implant failure. We hypothesize that factors exist which can be modified by clinicians to enhance outcome. A retrospective cohort study design was used. Cohort members had ⩾ one implant placed. Risk factors were classified as demographic, health status, implant-, anatomic-, or prosthetic-specific, and reconstructive variables. The outcome variable was implant failure. The cohort was composed of 677 patients who had 2349 implants placed. Based on the adjusted multivariate model, factors associated with implant failure were tobacco use, implant length, staging, well size, and immediate implants (p ⩽ 0.05). In the setting of correlated survival observations, we recommend adjusting for the correlation of the observations to provide statistically valid and efficient results. Three of the identified factors—tobacco use, immediate implants, and implant staging—potentially may be modified to enhance implant survival. [ABSTRACT FROM AUTHOR]