5 kWe + 5 kWt reformer-PEMFC energy generator from bioethanol first data on the fuel processor from a demonstrative project

A power unit constituted by a reformer section, a H₂ purification section and a fuel cell stack is being tested c/o the Dept. of Physical Chemistry and Electrochemistry of Università degli Studi di Milano, on the basis of a collaboration with HELBIO S.A. Hydrogen and Energy Production Systems, Patras (Greece), supplier of the unit, and some sponsors (Linea Energia S.p.A., Parco Tecnologico Padano and Provincia di Lodi, Italy). The system size allows to co-generate 5 kW_e (220 V, 50 Hz a.c.) + 5 kW_t (hot water at 65 °C) as peak output. Bioethanol, obtainable by different non-food-competitive biomass, is transformed into syngas by a pre-reforming and reforming reactors couple and the reformate is purified from CO to a concentration below 20 ppmv, suitable to feed a proton exchange membrane fuel cell (PEMFC) stack that will be integrated in the fuel processor in a second step of the experimentation. This result is achieved by feeding the reformate to two water gas shift reactors, connected in series and operating at high and low temperature, respectively. CO concentration in the outcoming gas is ca. 0.4 vol% and the final CO removal to meet the specifications is accomplished by two methanation reactors in series. The second methanation step acts merely as a guard, since ca. 15 ppmv of CO are obtained already after the first reactor.     

Scale detection via keypoint density maps in regular or near-regular textures

In this paper we propose a new method to detect the global scale of images with regular, near regular, or homogenous textures. We define texture “scale” as the size of the basic elements (texels or textons) that most frequently occur into the image. We study the distribution of the interest points into the image, at different scale, by using our Keypoint Density Maps (KDMs) tool. A “mode” vector is built computing the most frequent values (modes) of the KDMs, at different scales. We observed that the mode vector is quasi linear with the scale. The mode vector is properly subsampled, depending on the scale of observation, and compared with a linear model. Texture scale is estimated as the one which minimizes an error function between the related subsampled vector and the linear model. Results, compared with a state of the art method, are very encouraging.     

Electrochemical behavior of hydrophobic silane–zeolite coatings for corrosion protection of aluminum substrate

The anticorrosive properties of a silane–zeolite composite coating applied on a 6061 aluminum substrate was investigated. The composite film, deposited by dip-coating technique, was prepared with different contents of SAPO34 powder (60–90 wt%) with the purpose of evaluating the protective action offered by the zeolite-filled silane matrix. Corrosion protection performance, during immersion in 3.5% NaCl solution, was evaluated by means of a potentiodynamic polarization test and electrochemical impedance spectroscopy (EIS). The coating evidenced good barrier properties and high hydrophobicity. The addition of zeolite in the silane matrix induced, as expected, a reduction of cathodic and anodic current. The zeolite improved the barrier properties of the hybrid sol–gel films, enhancing the resistance to localized corrosion attacks. Better results were observed for the sample with 80 wt% of zeolite filler that evidenced still acceptable protective action after 3 days of immersion in the sodium chloride solution.     

In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins

Peroxisomes are ubiquitous membrane-bound organelles, and aberrant localisation of peroxisomal proteins contributes to the pathogenesis of several disorders. Many computational methods focus on assigning protein sequences to subcellular compartments, but there are no specific tools tailored for the sub-localisation (matrix vs. membrane) of peroxisome proteins. We present here In-Pero, a new method for predicting protein sub-peroxisomal cellular localisation. In-Pero combines standard machine learning approaches with recently proposed multi-dimensional deep-learning representations of the protein amino-acid sequence. It showed a classification accuracy above 0.9 in predicting peroxisomal matrix and membrane proteins. The method is trained and tested using a double cross-validation approach on a curated data set comprising 160 peroxisomal proteins with experimental evidence for sub-peroxisomal localisation. We further show that the proposed approach can be easily adapted (In-Mito) to the prediction of mitochondrial protein localisation obtaining performances for certain classes of proteins (matrix and inner-membrane) superior to existing tools.     

Symbolic compaction with analogue constraints

A tool named SPARCS-A for compaction of integrated circuits with analogue constraints is presented. the approach is structured in two steps. First a robust and efficient constraint graph compaction algorithm produces a compacted layout quickly, where parasitics are controlled so as to guarantee that the performance constraints are met. Next the layout produced by the first step is fed into a linear programming (LP) solver which enforces symmetries and performs global interconnect length minimization. the computational cost of the iterative LP solver is modest, because its initial state is the configuration found by the constraint graph algorithm and only symmetry constraints need to be enforced. With considerable computational efficiency this algorithm produces a compacted layout which satisfies the high-level performance constraints and is feasible for practical use within industrial-strength analogue synthesis systems. the use of such a compactor allows one to relax the requirements on parasitic control during placement and routing, thus improving the efficiency of the entire layout design process.     

Issues About Retinex Theory and Contrast Enhancement

We present an interpretation of Land’s Retinex theory that we show to be consistent with the original formulation. The proposed model relies on the computation of the expectation value of a suitable random variable weighted with a kernel function, thus the name Kernel-Based Retinex (KBR) for the corresponding algorithm. KBR shares the same intrinsic characteristics of the original Retinex: it can reduce the effect of a color cast and enhance details in low-key images but, since it can only increase pixel intensities, it is not able to enhance over-exposed pictures. Comparing the analytical structure of KBR with that of a recent variational model of color image enhancement, we are able to perform an analysis of the action of KBR on contrast, showing the need to anti-symmetrize its equation in order to produce a two-sided contrast modification, able to enhance both under and over-exposed pictures. The anti-symmetrized KBR equations show clear correspondences with other existing color correction models, in particular ACE, whose relationship with Retinex has always been difficult to clarify. Finally, from an image processing point of view, we mention that both KBR and its antisymmetric version are free from the chromatic noise due to the use of paths in the original Retinex implementation and that they can be suitably approximated in order to reduce their computational complexity from to , being N the number of input pixels.     

A robust fault detection and isolation filter design under sensitivity constraint: An LMI approach

This paper deals with the design of a residual generator (RG) for linear time‐invariant systems subject to simultaneous different faults, disturbances and measurement noises. The objective is to design an RG filter that maximizes the transmission from a potential fault to a related residual, while minimizing the ones from nuisances (disturbances, measurement noises and other faults). The isolation of each fault is carried out by designing a bank of RG filters, each one insensitive, as much as possible, to nuisances and capable of detecting the occurrence of its related fault. The design is carried out through ℋ︁_∞ filtering techniques under an eigenstructure assignment constraint. Under mild assumptions, the RG filter can be obtained by solving a λ‐parameterized linear matrix inequality optimization problem. A comparison with existing fault detection and isolation (FDI) methods is considered in order to exhibit the relative merits of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.