An Encryption and Error-Control Coding scheme based on Non binary LDPC codes

In this paper we present a combined error-control coding and encryption scheme that provides to a given system with both high levels of reliability of the transmission and security. These two aims are usually present in wireless data transmission systems. The scheme is based on efficient Non Binary Low Density Parity Check codes which were selected for this design because they outer perform their binary counterparts. By means of a set of operations over the parity check matrix of the code, encryption capabilities are added to the scheme, without producing any degradation in the corresponding Bit Error Rate performance, as usually happens when encryption and error control coding are applied separately.Sociedad Argentina de Informática e Investigación Operativ

Proteomics Characterization of Outer Membrane Vesicles from the Extraintestinal Pathogenic Escherichia coli ΔtolR IHE3034 Mutant

Extraintestinal pathogenic Escherichia coli are the cause of a diverse spectrum of invasive infections in humans and animals, leading to urinary tract infections, meningitis, or septicemia. In this study, we focused our attention on the identification of the outer membrane proteins of the pathogen in consideration of their important biological role and of their use as potential targets for prophylactic and therapeutic interventions. To this aim, we generated a DeltatolR mutant of the pathogenic IHE3034 strain that spontaneously released a large quantity of outer membrane vesicles in the culture supernatant. The vesicles were analyzed by two-dimensional electrophoresis coupled to mass spectrometry. The analysis led to the identification of 100 proteins, most of which are localized to the outer membrane and periplasmic compartments. Interestingly based on the genome sequences available in the current public database, seven of the identified proteins appear to be specific for pathogenic E. coli and enteric bacteria and therefore are potential targets for vaccine and drug development. Finally we demonstrated that the cytolethal distending toxin, a toxin exclusively produced by pathogenic bacteria, is released in association with the vesicles, supporting the recently proposed role of bacterial vesicles in toxin delivery to host cells. Overall, our data demonstrated that outer membrane vesicles represent an ideal tool to study Gram-negative periplasm and outer membrane compartments and to shed light on new mechanisms of bacterial pathogenesis

Exploring host-pathogen interactions through genome wide protein microarray analysis

During bacterial pathogenesis extensive contacts between the human and the bacterial extracellular proteomes take place. The identification of novel host-pathogen interactions by standard methods using a case-by-case approach is laborious and time consuming. To overcome this limitation, we took advantage of large libraries of human and bacterial recombinant proteins. We applied a large-scale protein microarray-based screening on two important human pathogens using two different approaches: (I) 75 human extracellular proteins were tested on 159 spotted Staphylococcus aureus recombinant proteins and (II) Neisseria meningitidis adhesin (NadA), an important vaccine component against serogroup B meningococcus, was screened against ∼2300 spotted human recombinant proteins. The approach presented here allowed the identification of the interaction between the S. aureus immune evasion protein FLIPr (formyl-peptide receptor like-1 inhibitory protein) and the human complement component C1q, key players of the offense-defense fighting; and of the interaction between meningococcal NadA and human LOX-1 (low-density oxidized lipoprotein receptor), an endothelial receptor. The novel interactions between bacterial and human extracellular proteins here presented might provide a better understanding of the molecular events underlying S. aureus and N. meningitidis pathogenesis

Trellis-hopping Turbo Coding

We present a turbo coding scheme whose constituent codes are designed using convolutional encoders with time-varying coefficients. These encoders are finite state sequence machines that operate over the Galois field GF(q). The scheme includes an encryption polynomial whose coefficients are changed periodically by means of a user key. The trellis coding procedure thus hops from one trellis to another, following a random sequence taken over a set of subtrellises which correspond to different convolutional encoders. The proposed scheme is introduced as a turbo code with encryption properties, and is presented in two forms, the systematic and the nonsystematic schemes

A Novel Multi-Fidelity Framework for Conventional and Alternative Jet Fuel Combustion Characterization

The endorsement of mid- and long-term climate neutrality policies by governments and other institutions in the last decade strengthens the ambition to identify sustainable alternatives to fossil fuels for any category of transportation systems. In this context, the aviation industry is one of the most impacted sectors, with precise climate neutrality targets to be achieved by mid-century. One potential solution for transitioning towards a low-carbon future is blending conventional and alternative jet fuels. Among the latter sustainable aviation fuels (SAFs) constitute the most practical way forward, given their “drop-in” nature, with their use being expected to increase in the coming years. However, the impact of unusual physical and chemical properties of SAFs may deeply impact the performance and operability of engines, e.g., in terms of altitude relight, lean blow-out, and cold start. In this respect, adequate characterization of alternative jet fuels is a necessary step toward the application of large-scale computational fluid dynamics (CFD) aimed at assessing the performance of SAF-fueled combustion devices. Thus, several approaches have been proposed to formulate surrogate fuels that emulate the physicochemical properties of real hydrocarbon mixtures. These strategies typically hinge on genetic optimization algorithms, which address objective functions combining specific fuel properties, e.g., the liquid-phase viscosity and the cetane number, and provide a single optimal surrogate composition. In the present work, we illustrate a novel strategy that resorts to the Bayesian inference framework to statistically characterize the possible surrogate fuel composition based on available experimental data, the intrinsic uncertainty of which is naturally considered. Moreover, the Bayesian framework fosters employing polynomial chaos expansion (PCE) representations of the major chemical observables, e.g., the ignition delay time, characterizing the real fuel, which could not be easily incorporated into standard optimization algorithms. This way, a comprehensive probability description of the surrogate composition is returned instead of a single set of optimal components’ proportions and paves the way toward the cost-effective use of ad-hoc surrogate mixtures in CFD codes

Uncertainty quantification in RANS prediction of LOX cross-flow injection in methane

This work presents the numerical characterization under uncertainty of a pintle-injector liquid rocket engine thrust chamber, fueled with LOX-CH4 and operated at subcritical pressure. Being the design optimization the ultimate goal of this effort, the numerical characterization is carried out employing a Eulerian-Lagrangian Reynolds-averaged Navier Stokes equations approach. The numerical model of choice, as well as the rich variety of physical phenomena taking place in such a device, require the knowledge of a large number of model parameters, many of which are challenging to be calibrated under the severe thermophysical conditions of interest. A possible way to overcome this lack of knowledge is to resort to the Uncertainty Quantification (UQ) framework to estimate the effects of model and parameter uncertainties on the solution accuracy. In particular, this research aims at propagating the uncertainty associated with the most probable diameter which characterizes the injection Rosin-Rammler distribution for the liquid droplets, employing a Polynomial Chaos Expansion (PCE) representation of the uncertainty. The pintle configuration consists of a horizontal gaseous methane inflow and a vertical LOX spray injection. A set of RANS are conducted to generate the PCEs surrogate model for the estimation of the probability distribution of the quantities of interest, as well as the visualization of their credibility intervals. Lastly, to assess whether the uncertainty on the droplet diameter can overshadow the sensitivity to the pintle design, the same uncertainty quantification analysis is performed for two geometries, which differ in the distance between the annulus final section and the fuel-oxidizer impingement location

Use of piezoelectric patches in Health Usage and Monitoring Systems: A preliminary assessment

Macro Fiber CompositeTM (MFC) piezoelectric devices have relevant potentialities as sensors, and a strong interest is currently growing in the aerospace field for their application in Structural Health Monitoring (SHM) or Health Usage and Monitoring Systems (HUMS) of equipment’s, with special attention to the self-powered characteristics of these systems. In the first part of a research activity carried out in Pisa at the Department of Civil and Industrial Engineering (DICI), as part of an existing collaboration with the R&D Division of the company MBDA Italia S.p.A., both the experimental and numerical characterization of simple structural elements instrumented with piezoelectric patches were executed. The studied structural element, a cantilevered carbon epoxy coupon with integrated MFC patches, was dynamically loaded with a shaker. The results of this preliminary characterization are shown and discussed. Finally, a single degree of freedom model is proposed that reproduces, as a first approximation, the behavior of each single vibration mode of the three-dimensional hybrid specimen