A Novel Micro- and Nano-Scale Positioning Sensor Based on Radio Frequency Resonant Cavities

In many micro- and nano-scale technological applications high sensitivity displacement sensors are needed, especially in ultraprecision metrology and manufacturing. In this work a new way of sensing displacement based on radio frequency resonant cavities is presented and experimentally demonstrated using a first laboratory prototype. The principle of operation of the new transducer is summarized and tested. Furthermore, an electronic interface that can be used together with the displacement transducer is designed and proved. It has been experimentally demonstrated that very high and linear sensitivity characteristic curves, in the range of some kHz/nm; are easily obtainable using this kind of transducer when it is combined with a laboratory network analyzer. In order to replace a network analyzer and provide a more affordable, self-contained, compact solution, an electronic interface has been designed, preserving as much as possible the excellent performance of the transducer, and turning it into a true standalone positioning sensor. The results obtained using the transducer together with a first prototype of the electronic interface built with cheap discrete elements show that positioning accuracies in the micrometer range are obtainable using this cost-effective solution. Better accuracies would also be attainable but using more involved and costly electronics interfaces.

Altruism in the (Social) Network

This paper explores the role of social integration on altruistic behavior. To this aim, we develop a two-stage experimental protocol based on the classic Dictator Game. In the first stage, we ask a group of 77 undergraduate students in Economics to elicit their social network; in the second stage, each of them has to unilaterally decide over the division of a fixed amount of money to be shared with another anonymous member in the group. Our experimental design allows to control for other variables known to be relevant for altruistic behavior: framing and friendship/acquaintance relations. Consistently with previous research, we find that subjects favor their friends and that framing enhances altruistic behavior. Once we control for these effects, social integration (measured by betweenness, a standard centrality measure in network theory) has a positive effect on giving: the larger social isolation within the group, the more likely it is the emergence of selfish behavior. These results suggest that information on the network structure in which subjects are embedded is crucial to account for their behavior.

Using network mesures to test evolved NK-landscapes

In this paper we empirically investigate which are the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms. To this end, we evolve instances that maximize the estimation of distribution algorithm complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.