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In this work, we consider a duty-cycled wireless sensor network with the assumption that the on/off schedules are uncoordinated. In such networks, as all nodes may not be awake during the transmission of time synchronization messages, nodes will require to re-transmit the synchronization messages. Ideally a node should re-transmit for the maximum sleep duration to ensure that all nodes are synchronized. However, such a proposition will immensely increase the energy consumption of the nodes. Such a situation demands that there is an upper bound of the number of retransmissions. We refer to the time a node spends in re-transmission of the control message as broadcast duration. We ask the question, what should be the broadcast duration to ensure that a certain percentage of the available nodes are synchronized. The problem to estimate the broadcast duration is formulated so as to capture the probability threshold of the nodes being synchronized. Results show the proposed analytical model can predict the broadcast duration with a given lower error margin under real world conditions, thus demonstrating the efficiency of our solution.
A prototype multiwavelength sensor able to characterise soot emissions in Diesel exhaust in terms of size and concentration has been tested against other methods for diesel particle measurements like electrical mobility sizing (SMPS) and raw exhaust gravimetric sampling (RES). Measurements carried out with the prototype sensor were correlated with the SMPS by assuming spherical and/or fractal aggregate morphology of the particles. Correlation of RES gravimetric data against the sensor and the SMPS led to the calculation of the solid density for soot particles to be 2.3 gr/cm3.
In the brain-cell microenvironment, diffusion plays an important role: apart from delivering glucose and oxygen from the vascular system to brain cells, it also moves informational substances between cells. The brain is an extremely complex structure of interwoven, intercommunicating cells, but recent theoretical and experimental works showed that the classical laws of diffusion, cast in the framework of porous media theory, can deliver an accurate quantitative description of the way molecules are transported through this tissue. The mathematical modeling and the numerical simulations are successfully applied in the investigation of diffusion processes in tissues, replacing the costly laboratory investigations. Nevertheless, modeling must rely on highly accurate information regarding the main parameters (tortuosity, volume fraction) which characterize the tissue, obtained by structural and functional imaging. The usual techniques to measure the diffusion mechanism in brain tissue are the radiotracer method, the real time iontophoretic method and integrative optical imaging using fluorescence microscopy. A promising technique for obtaining the values for characteristic parameters of the transport equation is the direct optical investigation using optical fibers. The analysis of these parameters also reveals how the local geometry of the brain changes with time or under pathological conditions. This paper presents a set of computations concerning the mass transport inside the brain tissue, for different types of cells. By measuring the time evolution of the concentration profile of an injected substance and using suitable fitting procedures, the main parameters characterizing the tissue can be determined. This type of analysis could be an important tool in understanding the functional mechanisms of effective drug delivery in complex structures such as the brain tissue. It also offers possibilities to realize optical imaging methods for in vitro and in vivo measurements using optical fibers. The model also may help in radiotracer biomarker models for the understanding of the mechanism of action of new chemical entities.