Preliminary study of synthesis gas production from water electrolysis, using the ELECTROFUEL (R) concept

This paper describes preliminary work on the generation of synthesis gas from water electrolysis using graphite electrodes without the separation of the generated gases. This is an innovative process, that has no similar work been done earlier. Preliminary tests allowed to establish correlations between the applied current to the electrolyser and flow rate and composition of the generated syngas, as well as a characterisation of generated carbon nanoparticles. The obtained syngas can further be used to produce synthetic liquid fuels, for example, methane, methanol or DME (dimethyl ether) in a catalytic reactor, in further stages of a present ongoing project, using the ELECTROFUEL® concept. The main competitive advantage of this project lies in the built-in of an innovative technology product, from RE (renewable energy) power in remote locations, for example, islands, villages in mountains as an alternative for energy storage for mobility constraints.

A many-core co-processor for embedded parallel computing on FPGA

Single processor architectures are unable to provide the required performance of high performance embedded systems. Parallel processing based on general-purpose processors can achieve these performances with a considerable increase of required resources. However, in many cases, simplified optimized parallel cores can be used instead of general-purpose processors achieving better performance at lower resource utilization. In this paper, we propose a configurable many-core architecture to serve as a co-processor for high-performance embedded computing on Field-Programmable Gate Arrays. The architecture consists of an array of configurable simple cores with support for floating-point operations interconnected with a configurable interconnection network. For each core it is possible to configure the size of the internal memory, the supported operations and number of interfacing ports. The architecture was tested in a ZYNQ-7020 FPGA in the execution of several parallel algorithms. The results show that the proposed many-core architecture achieves better performance than that achieved with a parallel generalpurpose processor and that up to 32 floating-point cores can be implemented in a ZYNQ-7020 SoC FPGA.

Mobile device-to-device distributed computing using data sets

The rapidly increasing computing power, available storage and communication capabilities of mobile devices makes it possible to start processing and storing data locally, rather than offloading it to remote servers; allowing scenarios of mobile clouds without infrastructure dependency. We can now aim at connecting neighboring mobile devices, creating a local mobile cloud that provides storage and computing services on local generated data. In this paper, we describe an early overview of a distributed mobile system that allows accessing and processing of data distributed across mobile devices without an external communication infrastructure. Copyright © 2015 ICST.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.

Chagas' infection in university students of Santa Cruz de la Sierra, Bolivia. A serologic-electrocardiographic study

In order to learn the prevalence of Chagas' infection among students from Santa Cruz de la Sierra's universities, a random sample of 372 new students was drawn. All participants have had electrocardiograms (EKG) and serologic analysis (IHAT). 64/372 (17.2%) had serologic evidence of Chagas' infection, and from those, 10/64 (15.6%) had some EKG alterations. Among students presenting negative serologic test, 31/308 (10.1%) had EKG alterations. There was no statistical association between Chagas' infection and EKG alterations (X2=1.67, p=0.2). There was a positive association between Chagas' infection and intraventricular conduction defects and this association was higher among the students of 19 years of age or less (O.R. 10.4, p<0.05).

Integrated Management of Cloud Computing Resources

Lunacloud is a cloud service provider with offices in Portugal, Spain, France and UK that focus on delivering reliable, elastic and low cost cloud Infrastructure as a Service (IaaS) solutions. The company currently relies on a proprietary IaaS platform - the Parallels Automation for Cloud Infrastructure (PACI) - and wishes to expand and integrate other IaaS solutions seamlessly, namely open source solutions. This is the challenge addressed in this thesis. This proposal, which was fostered by Eurocloud Portugal Association, contributes to the promotion of interoperability and standardisation in Cloud Computing. The goal is to investigate, propose and develop an interoperable open source solution with standard interfaces for the integrated management of IaaS Cloud Computing resources based on new as well as existing abstraction libraries or frameworks. The solution should provide bothWeb and application programming interfaces. The research conducted consisted of two surveys covering existing open source IaaS platforms and PACI (features and API) and open source IaaS abstraction solutions. The first study was focussed on the characteristics of most popular open source IaaS platforms, namely OpenNebula, OpenStack, CloudStack and Eucalyptus, as well as PACI and included a thorough inventory of the provided Application Programming Interfaces (API), i.e., offered operations, followed by a comparison of these platforms in order to establish their similarities and dissimilarities. The second study on existing open source interoperability solutions included the analysis of existing abstraction libraries and frameworks and their comparison. The approach proposed and adopted, which was supported on the conclusions of the carried surveys, reuses an existing open source abstraction solution – the Apache Deltacloud framework. Deltacloud relies on the development of software driver modules to interface with different IaaS platforms, officially provides and supports drivers to sixteen IaaS platform, including OpenNebula and OpenStack, and allows the development of new provider drivers. The latter functionality was used to develop a new Deltacloud driver for PACI. Furthermore, Deltacloud provides a Web dashboard and REpresentational State Transfer (REST) API interfaces. To evaluate the adopted solution, a test bed integrating OpenNebula, Open- Stack and PACI nodes was assembled and deployed. The tests conducted involved time elapsed and data payload measurements via the Deltacloud framework as well as via the pre-existing IaaS platform API. The Deltacloud framework behaved as expected, i.e., introduced additional delays, but no substantial overheads. Both the Web and the REST interfaces were tested and showed identical measurements. The developed interoperable solution for the seamless integration and provision of IaaS resources from PACI, OpenNebula and OpenStack IaaS platforms fulfils the specified requirements, i.e., provides Lunacloud with the ability to expand the range of adopted IaaS platforms and offers a Web dashboard and REST API for the integrated management. The contributions of this work include the surveys and comparisons made, the selection of the abstraction framework and, last, but not the least, the PACI driver developed.

A middleware for service oriented computing in dynamic environments

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática

Hyperimmune antirabies sera titration by standard mouse neutralization and counterimmunoelectrophoresis tests, comparing results of different laboratories

To determine the rabies antibody level of twenty-four hyperimmune equine sera, Standard Mouse Neutralization (SMN) and Couterimmunoelectrophoresis (CIE) tests were carried out, both at the Instituto Butantan (IB) and Instituto Panamericano de Protección de Alimentos y Zoonosis (INPPAZ). Statistical analysis has shown a correlation (r) of 0.9317 between the SMN and CIE performed at the IB, while at the INPPAZ it scored 0.974. Comparison of CIE data of both laboratories yielded a correlation of 0.845. The CIE technique has shown to be as sensitive and efficient as the SMN in titrating antirabies hyperimmune equine sera. Based on CIE results, a simple, rapid and inexpensive technique, tilers of sera antibody can be reliably estimated in SMN test.

A calculus for modeling and analyzing conversations in service-oriented computing

Dissertação apresentada para a obtenção do Grau de Doutor em Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Exploring distributed computing tools through data mining tasks

Harnessing idle PCs CPU cycles, storage space and other resources of networked computers to collaborative are mainly fixated on for all major grid computing research projects. Most of the university computers labs are occupied with the high puissant desktop PC nowadays. It is plausible to notice that most of the time machines are lying idle or wasting their computing power without utilizing in felicitous ways. However, for intricate quandaries and for analyzing astronomically immense amounts of data, sizably voluminous computational resources are required. For such quandaries, one may run the analysis algorithms in very puissant and expensive computers, which reduces the number of users that can afford such data analysis tasks. Instead of utilizing single expensive machines, distributed computing systems, offers the possibility of utilizing a set of much less expensive machines to do the same task. BOINC and Condor projects have been prosperously utilized for solving authentic scientific research works around the world at a low cost. In this work the main goal is to explore both distributed computing to implement, Condor and BOINC, and utilize their potency to harness the ideal PCs resources for the academic researchers to utilize in their research work. In this thesis, Data mining tasks have been performed in implementation of several machine learning algorithms on the distributed computing environment.

Congenital cytomegalovirus infection in a neonatal intensive care unit in Brazil evaluated by PCR and association with perinatal aspects

Cytomegalovirus (CMV) infection is the most common congenital infection, affecting 0.4% to 2.3% newborns. Most of them are asymptomatic at birth, but later 10% develop handicaps, mainly neurological disturbances. Our aim was to determine the prevalence of CMV shed in urine of newborns from a neonatal intensive care unit using the polymerase chain reaction (PCR) and correlate positive cases to some perinatal aspects. Urine samples obtained at first week of life were processed according to a PCR protocol. Perinatal data were collected retrospectively from medical records. Twenty of the 292 cases (6.8%) were CMV-DNA positive. There was no statistical difference between newborns with and without CMV congenital infection concerning birth weight (p=0.11), gestational age (p=0.11), Apgar scores in the first and fifth minutes of life (p=0.99 and 0.16), mother's age (p=0.67) and gestational history. Moreover, CMV congenital infection was neither related to gender (p=0.55) nor to low weight (<2,500g) at birth (p=0.13). This high prevalence of CMV congenital infection (6.8%) could be due to the high sensitivity of PCR technique, the low socioeconomic level of studied population or the severe clinical status of these newborns.

Sistema para a monitorização ativa com recurso ao cloud computing

O desenvolvimento de aplicações para dispositivos móveis já não é uma área recente, contudo continua a crescer a um ritmo veloz. É notório o avanço tecnológico dos últimos anos e a crescente popularidade destes dispositivos. Este avanço deve-se não só à grande evolução no que diz respeito às características destes dispositivos, mas também à possibilidade de criar aplicações inovadoras, práticas e passíveis de solucionar os problemas dos utilizadores em geral. Nesse sentido, as necessidades do quotidiano obrigam à implementação de soluções que satisfaçam os utilizadores, e nos dias de hoje, essa satisfação muitas vezes passa pelos dispositivos móveis, que já tem um papel fundamental na vida das pessoas. Atendendo ao aumento do número de raptos de crianças e à insegurança que se verifica nos dias de hoje, as quais dificultam a tarefa de todos os pais/cuidadores que procuraram manter as suas crianças a salvo, é relevante criar uma nova ferramenta capaz de os auxiliar nesta árdua tarefa. A partir desta realidade, e com vista a cumprir os aspetos acima mencionados, surge assim esta dissertação de mestrado. Esta aborda o estudo e implementação efetuados no sentido de desenvolver um sistema de monitorização de crianças. Assim, o objetivo deste projeto passa por desenvolver uma aplicação nativa para Android e um back-end, utilizando um servidor de base de dados NoSQL para o armazenamento da informação, aplicando os conceitos estudados e as tecnologias existentes. A solução tem como principais premissas: ser o mais user-friendly possível, a otimização, a escalabilidade para outras situações (outros tipos de monitorizações) e a aplicação das mais recentes tecnologias. Assim sendo, um dos estudos mais aprofundados nesta dissertação de mestrado está relacionado com as bases de dados NoSQL, dada a sua importância no projeto.

The role of ribonuclease R in bacterial adaptation to cold shock

RESUMO:Os microrganismos reagem à súbita descida de temperatura através de uma resposta adaptativa específica que assegura a sua sobrevivência em condições desfavoráveis. Esta adaptação inclui alterações na composição da membrana, na maquinaria de tradução e transcrição. A resposta ao choque térmico pelo frio induz uma repressão da transcrição. No entanto, a descida de temperatura induz a produção de um grupo de proteínas específicas que ajudam a ajustar/re-ajustar o metabolismo celular às novas condições ambientais. Em E. coli o processo de adaptação demora apenas quatro horas, no qual um grupo de proteínas específicas são induzidas. Depois desde período recomeça lentamente a produção de proteínas.A ribonuclease R, uma das proteínas induzidas durante o choque térmico pelo frio, é uma das principais ribonucleases em E. coli envolvidas na degradação do RNA. É uma exoribonuclease que degrada RNA de cadeia dupla, possui funções importantes na maturação e “turnover” do RNA, libertação de ribossomas e controlo de qualidade de proteínas e RNAs. O nível celular desta enzima aumenta até dez vezes após exposição ao frio e estabiliza em células na fase estacionária. A capacidade de degradar RNA de dupla cadeia é importante a baixas temperaturas quando as estruturas de RNA estão mais estáveis. No entanto, este mecanismo é desconhecido. Embora a resposta específica ao “cold shock” tenha sido descoberta há mais de duas décadas e o número de proteínas envolvidas sugerirem que esta adaptação é rápida e simples, continuamos longe de compreender este processo. No nosso trabalho pretendemos descobrir proteínas que interactuem com a RNase R em condições ambientais diferentes através do método “TAP-tag” e espectrometria de massa. A informação obtida pode ser utilizada para deduzir algumas das novas funções da RNase R durante a adaptação bacteriana ao frio e durante a fase estacionária. Mais importante ainda, RNase R poderá ser recrutada para um complexo de proteínas de elevado peso molecular durante o “cold-shock”.------------ABSTRACT:Microorganisms react to the rapid temperature downshift with a specific adaptative response that ensures their survival in unfavorable conditions. Adaptation includes changes in membrane composition, in translation and transcription machinery. Cold shock response leads to overall repression of translation. However, temperature downshift induces production of a set of specific proteins that help to tune cell metabolism and readjust it to the new environmental conditions. For Escherichia coli the adaptation process takes only about four hours with a relatively small set of specifically induced proteins involved. After this time, protein production resumes, although at a slower rate. One of the cold inducible proteins is RNase R, one of the main E. coli ribonucleases involved in RNA degradation. RNase R is an exoribonuclease that digest double stranded RNA, serves important functions in RNA maturation and turnover, release of stalled ribosomes by trans-translation, and RNA and protein quality control. The level of this enzyme increases about ten-fold after cold induction, and it is also stabilised in cells growing in stationary phase. The RNase R ability to digest structured RNA is important at low temperatures where RNA structures are stabilized but the exact role of this mechanism remains unclear. Although specific bacterial cold shock response was discovered over two decades ago and the number of proteins involved suggests that this adaptation is fast and simple, we are still far from understanding this process. In our work we aimed to discover the proteins interacting with RNase R in different environmental conditions using TAP tag method and mass spectrometry analysis. The information obtained can be used to deduce some of the new functions of RNase R during adaptation of bacteria to cold and in stationary growth phase. Most importantly RNase R can be recruited into a high molecular mass complex of protein in cold shock.

An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes.