Motivation: The human immune system evolved a multi-layered control mechanism to eliminate self-reactive cells. Of these so-called tolerance induction mechanisms, lymphocytes T education in the thymus gland represents the very first one. This complicated process is not fully understood and quantitative models able to help in this endeavor are lacking.

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In this paper we propose a multiperiod optimization model based on the maximal covering location problem in order to support safety policies within urban areas. In particular, we focus on the field of car accidents control, by considering the problem of the optimal location of intersection safety cameras (ISC) on an urban traffic network to maximize road control and reduce the number and the impact of car accidents. The effectiveness of accidents prevention programs can be increased by changing periodically the position of the available ISCs on a given time horizon.

Variational models for image segmentation, e.g. Mumford-Shah variational model [47] and Chan-Vese model [21,59], generally involve a regularization term that penalizes the length of the boundaries of the segmentation. In practice often the length term is replaced by a weighted length, i.e., some portions of the set of boundaries are penalized more than other portions, thus unbalancing the geometric term of the segmentation functional. In the present paper we consider a class of variational models in the framework of ?-convergence theory.

To study naive and memory CD8 T cell turnover, we performed BrdU incorporation experiments in adult thymectomized C57BL/6 mice and analyzed data in a mathematical framework.

This paper focuses on a new method to compute fitness function (ff) values in genetic algorithms for bus network optimization. In the proposed methodology, a genetic algorithm is used to generate iteratively new populations (sets of bus networks). Each member of the population is evaluated by computing a number of performance indicators obtained by the analysis of the assignment of the O/D demand associated to the considered networks. Thus, ff values are computed by means of a multicriteria analysis executed on the performance indicators so found.