Scale-up of sulphur resistant promoted-vanadium oxide catalysts for self-regenerating catalytic filters in off-road diesel engines and domestic apparatus

Wall-flow catalytic filters were prepared by impregnation of porous cordierite honeycombs with Cs-Fe-V catalysts. The catalytic filters were tested at the exhaust of a gas-oil burner. Results reported indicate that the trapped soot begin to burn at a temperature around 300–320 °C allowing the self-regeneration of the filter. Performances were found to remain stable also under high SO₂ concentrations, making these catalytic filters suitable for soot removal in off-road diesel engines and domestic apparatus.     

Usefulness of Leifson staining method in diagnosis of Helicobacter pylori infection

The Leifson staining method was used to diagnose Helicobacter pylori infection and was compared to histology, culture, and the rapid urease test (RUT). Histology gave the best sensitivity (98%), compared to Leifson staining (97%), culture (92%), and RUT (85%) (P < 0.005). Leifson staining is a sensitive, rapid, economical method for diagnosis of H. pylori infection in dyspeptic patients.     

Video texture modelling and synthesis using fractal processes

The authors propose a new approach for the synthesis of natural video textures using a fractal- based approach. Specifically, a video texture is modelled according to the three-dimensional (3D) extended self-similar (ESS) model introduced, which generalises the fractional Brownian motion process. The analysis of original video textures is based on the estimation of the autocorrelation functions (ACFs) of the textures' increments. The 3D-ESS model is then used to synthesise a process whose increments have the same ACFs of the given prototype. The synthesis is accomplished by generalising to the 3D case the incremental Fourier synthesis algorithm. Experimental results for the analysis and synthesis of natural video textures are eventually provided.     

Unsteady Flow Visualization by Animating Evenly-Spaced Streamlines

In recent years the work on vector field visualization has been concentrated on LIC-based methods. In this paper we propose an alternative solution for the visualization of unsteady flow fields. Our approach is based on the computation of temporal series of correlated images. While other methods are based on pathlines and try to correlate successive images at the pixel level, our approach consists in correlating instantaneous visualizations of the vector field at the streamline level. For each frame a feed forward algorithm computes a set of evenly-spaced streamlines as a function of the streamlines generated for the previous frame. This is achieved by establishing a correspondence between streamlines at successive time steps. A cyclical texture is mapped onto every streamline and textures of corresponding streamlines at different time steps are correlated together so that, during the animation, they move along the streamlines, giving the illusion that the flow is moving in the direction defined by the streamline. Our method gives full control on the image density so that we are able to produce smooth animations of arbitrary density, covering the field of representations from sparse, that is classical streamline-based images, to dense, that is texture-like images.     

Learning in the “Real World”

In this paper we define and characterize the process of developing a real-world Machine Learning application, with its difficulties and relevant issues, distinguishing it from the popular practice of exploiting ready-to-use data sets. To this aim, we analyze and summarize the lessons learned from applying Machine Learning techniques to a variety of problems. We believe that these lessons, though primarily based on our personal experience, can be generalized to a wider range of situations and are supported by the reported experiences of other researchers.     

Quantifying the impact of cloud obscuration on remote sensing of active fires in the Brazilian Amazon

Vegetation fires remain as one of the most important processes governing land use and land cover change in tropical areas. The large area extent of fire prone areas associated with human activities makes satellite remote sensing of active fires a valuable tool to help monitor biomass burning in those regions. However, identification of active fire fronts under optically thick clouds is not possible through passive remote sensing, often resulting in omission errors. Previous analyses of fire activity either ignored the cloud obscuration problem or applied corrections based on the assumption that fire occurrence is not impacted by the presence of clouds. In this study we addressed the cloud obscuration problem in the Brazilian Amazon region using a pixel based probabilistic approach, using information on previous fire occurrence, precipitation and land use. We implemented the methodology using data from the geostationary GOES imager, covering the entire diurnal cycle of fire activity and cloud occurrence. Our assessment of the method indicated that the cloud adjustment reproduced the number of potential fires missed within 1.5% and 5% of the true fire counts on annual and monthly bases respectively. Spatially explicit comparison with high resolution burn scar maps in Acre state showed a reduction of omission error (from 58.3% to 43.7%) and only slight increase of commission error (from 6.4% to 8.8%) compared to uncorrected fire counts. A basin-wide analysis of corrected GOES fire counts during 2005 showed a mean cloud adjustment factor of approximately 11%, ranging from negligible adjustment in the central and western part of the Brazilian Amazon to as high as 50% in parts of Roraima, Para and Mato Grosso.     

Output stabilization of time-varying input delay systems using interval observation technique

The output stabilization problem for a linear system with an unknown bounded time-varying input delay is considered. The interval observation technique is applied in order to obtain guaranteed interval estimate of the system state. The procedure of the interval observer synthesis uses lower and upper estimates of the unknown delay and requires to solve a special Silvester’s equation. The interval predictor is introduced in order to design a linear stabilizing feedback. The control design procedure is based on Linear Matrix Inequalities (LMI). The theoretical results are supported by numerical simulations and compared with a control design scheme based on a Luenberger-like observer.     

PCSK9 Induces Tissue Factor Expression by Activation of TLR4/NFkB Signaling

Proprotein convertase subtilisin kexin 9 (PCSK9) increases LDL cholesterol (C) concentration by accelerating the hepatic degradation of the LDL receptor (R) thus promoting atherogenesis. The molecule, however, also exerts proinflammatory effects independent of circulating LDL-C by enhancing local cytokine production and activation of NFkB, a process that might involve Toll-like receptor 4 (TLR4), a crucial component of the innate immunity system. Tissue factor (TF), a glycoprotein which plays an essential role in coagulation and inflammation, is rapidly induced by circulating monocytes stimulated by proinflammatory agents through NFkB-dependent mechanisms. The aims of our study were (1) to assess whether PCSK9 may induce monocytic TF expression and (2) to evaluate whether the TLR4/NFkB signaling pathway may contribute to that effect. Experiments were carried out in peripheral blood mononuclear cells (PBMCs), THP-1 cells, and HEK293 cells transfected with plasmids encoding the human TLR4 complex. PCSK9 increased procoagulant activity (PCA), mRNA and TF protein expression in both PBMCs and THP-1 cultures. Pre-treatment with inhibitors of TLR4/NFkB signaling such as LPS-RS, CLI-095, and BAY 11-7082, downregulated PCSK9-induced TF expression. A similar effect was obtained by incubating cell cultures with anti-PCSK9 human monoclonal antibody. In TLR4-HEK293 cells, PCSK9 activated the TLR4/NFkB signaling pathway to an extent comparable to LPS, the specific agonist of TLR4s and quantitative confocal microscopy documented the colocalization of PCSK9 and TLR4s. In conclusion, PCSK9 induces TF expression through activation of TLR4/NFkB signaling.     

Constrained optimization of noncoherent serial acquisition of spread-spectrum code by exploiting the generalized Q-functions

Initial code acquisition of direct-sequence spread-spectrum signals is typically based on serial multidwell hypothesis tests to limit the costs of mobile terminals. The procedure that searches for the correct code and its actual time offset usually adopts a user-oriented quality criterion based on constant error rates. The false-alarm and miss detection probabilities can be theoretically evaluated by means of the class of generalized Q (GQ) functions. We show that the GQ functions constitute a useful tool not only for analytic performance analysis, but also for the optimized design of initial code synchronization systems. Some examples of application of two-dwell (search/verification) procedures are provided. The mathematical problem consists of the minimization of an objective function (i.e., the mean acquisition time) depending on four parameters (two testing durations and two thresholds) with two constraints (the probabilities of miss detection and false alarm). In particular, we have implemented and analyzed the convergence of the steepest descent and the Newton-Raphson numerical algorithms. The computational cost of the method and the effect of multipath Rayleigh channels are also discussed. The optimized acquisition procedure has evidenced a significant reduction of the mean duration of serial tests in comparison with (suboptimum) previous attempts.