jxSensor: a sensor network integration layer for JXTA

Nowadays, Wireless Sensor Networks (WSN) arealready a very important data source to obtain data about the environment. Thus, they are key to the creation of Cyber-Physical Systems (CPS). Given the popularity of P2P middlewares as ameans to efficiently process information and distribute services, being able to integrate them to WSN¿s is an interesting proposal. JXTA is a widely used P2P middleware that allows peers to easily exchange information, heavily relying on its main architectural highlight, the capability to organize peers with common interests into peer groups. However, right now, approaches to integrate WSNs to a JXTA network seldom take advantage of peer groups. For this reason, in this paper we present jxSensor, an integrationlayer for sensor motes which facilitates the deployment of CPS¿s under this architecture. This integration has been done taking into account JXTA¿s idiosyncrasies and proposing novel ideas,such as the Virtual Peer, a group of sensors that acts as a single entity within the peer group context.

Universality of the 1/3 shot-noise suppression factor in nondegenerate diffusive conductors

Shot-noise suppression is investigated in nondegenerate diffusive conductors by means of an ensemble Monte Carlo simulator. The universal 1/3 suppression value is obtained when transport occurs under elastic collision regime provided the following conditions are satisfied: (i) The applied voltage is much larger than the thermal value; (ii) the length of the device is much greater than both the elastic mean free path and the Debye length. By fully suppressing carrier-number fluctuations, long-range Coulomb interaction is essential to obtain the 1/3 value in the low-frequency limit.

DWI and complex brain network analysis predicts vascular cognitive impairment in spontaneous hypertensive rats undergoing executive function tests

The identification of biomarkers of vascular cognitive impairment is urgent for its early diagnosis. The aim of this study was to detect and monitor changes in brain structure and connectivity, and to correlate them with the decline in executive function. We examined the feasibility of early diagnostic magnetic resonance imaging (MRI) to predict cognitive impairment before onset in an animal model of chronic hypertension: Spontaneously Hypertensive Rats. Cognitive performance was tested in an operant conditioning paradigm that evaluated learning, memory, and behavioral flexibility skills. Behavioral tests were coupled with longitudinal diffusion weighted imaging acquired with 126 diffusion gradient directions and 0.3 mm(3) isometric resolution at 10, 14, 18, 22, 26, and 40 weeks after birth. Diffusion weighted imaging was analyzed in two different ways, by regional characterization of diffusion tensor imaging (DTI) indices, and by assessing changes in structural brain network organization based on Q-Ball tractography. Already at the first evaluated times, DTI scalar maps revealed significant differences in many regions, suggesting loss of integrity in white and gray matter of spontaneously hypertensive rats when compared to normotensive control rats. In addition, graph theory analysis of the structural brain network demonstrated a significant decrease of hierarchical modularity, global and local efficacy, with predictive value as shown by regional three-fold cross validation study. Moreover, these decreases were significantly correlated with the behavioral performance deficits observed at subsequent time points, suggesting that the diffusion weighted imaging and connectivity studies can unravel neuroimaging alterations even overt signs of cognitive impairment become apparent.