Detectives with pimples: how teen noir is crossing the frontiers of the traditional noir films

In the last ten years, teen noir movies and series — such as Donnie Darko (2001), Brick (2005), or Veronica Mars (2004-2007) — have become increasingly popular among audiences, both in the USA and in Europe, and aroused the curiosity of critics. These teen noir adventures present darker themes and technical features that distinguish them from numerous productions aiming at young adults. Their narrative and aesthetic characteristics reinvent and subvert the tradition of classic noir movies of the forties and fifties, thus generating a sense of novelty. In this article, I focus my attention on Veronica Mars, a famous teen noir series, created by Rob Thomas, to examine: a) the teen noir themes; b) the new profile and role of the private investigator; c) the empowerment of girls/young women; d) razor-sharp dialogues; e) intertextual references to old- school noir movies. In order to do so, resort to the research of specialists in the field of neo noir, such as Mark Conrad, Foster Hirsch, or Roz Kaveney. My main goal is to prove that a new (sub)genre is slowly emerging and revivifying teen cinema.

Can ERASMUS mobility really help crossing borders? the in and out of a case-study

As lectures, but above all, as Erasmus....

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.