A new model for the analysis of operating conditions of micro-cogenerative SOFC units

High Temperature Solid Oxide Fuel Cells (HT-SOFCs) represent a very promising technology for efficient energy conversion, and offer the possibility of distributed micro-cogeneration. Efficient modelling of actual SOFC operating conditions, after proper validation, is very important for the control and management of future commercial units, and very useful for the design of experiments in prototype systems, like the one considered in this work. The paper presents a new zero dimensional (0D) model for the simulation of a SOFCs based micro-cogenerative power system for residential use, fed by natural gas. The novelty of the proposed model consists in the ability to accurately reproduce the logic of the on-board control system. The model is validated against the data collected during an experimental campaign recently conducted by the authors on a field unit. The validated model is used to investigate the actual performance of the generator under different operating conditions, and to design new experiments. The results shown in this paper highlight the importance of the thermal control strategy on the actual performance of the cogenerative power system.     

A new platform for Real-Time PCR detection of Salmonella spp., Listeria monocytogenes and Escherichia coli O157 in milk

Intoxications and infections caused by food-borne pathogens represent an increasing public health problem, and diagnostic tests in multiplex format are needed for the rapid identification of food contaminations caused by more than one microbial species. We have developed a multiple PCR-based platform for the simultaneous detection of the widespread milk-associated pathogens Salmonella spp., Listeria monocytogenes and Escherichia coli O157. The assay combines an enrichment step in a medium properly formulated for the simultaneous growth of target pathogens, a DNA isolation method, and a multiplex Real-Time PCR detection system based either on dual-labelled probes (mRT-PCR), or on melting curve analysis (mHRM). The second, producing a distinct peak for each amplification product, allows the qualitative assessment of pathogen presence. Moreover, the internal amplification control (IAC) included in the reaction, ensuring the reliability of results, complies with quality management programmes. Inclusivity and exclusivity were 100% each, with a detection limit of 1 CFU for each pathogen in a total of five 25 ml-aliquots of raw milk, and a duration of two working days.The assay represents an alternative approach for the qualitative detection of the cited bacterial species, suitable for a relatively inexpensive screening of several milk samples, reducing the turnaround time and the workload.     

Robocast zirconia-toughened alumina scaffolds: Processing,structural characterisation and interaction with human primary osteoblasts

Zirconia-toughened alumina (ZTA) is the gold-standard ceramic in hip arthroplasty, but still lacks direct osseointegration and a metal shell, often coated with a bioactive layer, is currently required. The latter could potentially be replaced by a thinner, architectured ZTA layer, thereby allowing for larger acetabular components, with larger range of motion and lower dislocation risk. Robocasting may be an adequate technique to fabricate the architectured layer. Therefore, as a first step, this study aimed to produce ZTA scaffolds (3D-ZTA) by robocasting and assess their in vitro response. Shape retention was achieved by using a stable, well-dispersed, high solid loading ink injected in acid pH waterbath. 3D-ZTA exhibit regularly spaced microporous, rough struts and fully interconnected macroporosity. Human primary osteoblasts were homogenously distributed inside 3D-ZTA and showed increased osteogenic marker expression compared to 2D-ZTA control. Further work will focus on optimizing scaffold design to improve cell retention and extracellular matrix maturation.     

M‐matrices and global convergence of discontinuous neural networks

The paper considers a general class of neural networks possessing discontinuous neuron activations and neuron interconnection matrices belonging to the class of M‐matrices or H‐matrices. A number of results are established on global exponential convergence of the state and output solutions towards a unique equilibrium point. Moreover, by exploiting the presence of sliding modes, conditions are given under which convergence in finite time is guaranteed. In all cases, the exponential convergence rate, or the finite convergence time, can be quantitatively estimated on the basis of the parameters defining the neural network. As a by‐product, it is proved that the considered neural networks, although they are described by a system of differential equations with discontinuous right‐hand side, enjoy the property of uniqueness of the solution starting at a given initial condition. The results are proved by a generalized Lyapunov‐like approach and by using tools from the theory of differential equations with discontinuous right‐hand side. At the core of the approach is a basic lemma, which holds under the assumption of M‐matrices or H‐matrices, and enables to study the limiting behaviour of a suitably defined distance between any pair of solutions to the neural network. Copyright © 2006 John Wiley & Sons, Ltd.     

A procedure for the computation of accurate PWL approximations of non‐linear dynamical systems

In this paper we propose a variational method to find out piecewise‐linear (PWL) approximations of non‐linear dynamical systems in view of their circuit implementations. The method is based on some significant trajectories of the dynamical system and provides reasonably accurate PWL approximations with a relatively low number of parameters. The effectiveness of the method is validated by applying it to the approximation of limit cycles (both stable and unstable) in the Bautin system. Copyright © 2006 John Wiley & Sons, Ltd.     

Complete stability of feedback CNNs with dynamic memristors and second‐order cells

The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that is CNNs using a dynamic (D) memristor (M). After giving a foundation to the DM‐CNN model, the paper establishes a fundamental result on complete stability, that is convergence of solutions toward equilibrium points, when the DM‐CNN has symmetric interconnections. Because of the presence of dynamic memristors, a DM‐CNN displays peculiar and basically different dynamic properties with respect to standard CNNs. First of all a DM‐CNN computes during the time evolution of the memristor fluxes, instead of the capacitor voltages as for a standard CNN. Furthermore, when a steady state is reached, the memristors keep in memory the result of the computation, that is the limiting values of the fluxes, while all memristor currents and voltages, as well as all currents, voltages, and power in the DM‐CNN vanish. Instead, for standard CNNs, currents, voltages, and power do not drop off when a steady state is reached. Copyright © 2016 John Wiley & Sons, Ltd.     

Modeling secondary electron images for linewidth measurement by critical dimension scanning electron microscopy

Modeling of critical dimensions scanning electron microscopy with sub-nanometer uncertainty is required to provide a metrics and to avoid yield loss in the processing of advanced CMOS technologies. In this paper, a new approach is proposed, which includes a new Monte Carlo scheme, a new Monte Carlo code, as well as the coupling with electrostatic fields to take into account self-charging effects.