A lightweight distributed super peer election algorithm for unstructured dynamic P2P systems

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Domestic Consumption Simulation and Management Using a Continuous Consumption Management and Optimization Algorithm

The recent changes concerning the consumers’ active participation in the efficient management of load devices for one’s own interest and for the interest of the network operator, namely in the context of demand response, leads to the need for improved algorithms and tools. A continuous consumption optimization algorithm has been improved in order to better manage the shifted demand. It has been done in a simulation and user-interaction tool capable of being integrated in a multi-agent smart grid simulator already developed, and also capable of integrating several optimization algorithms to manage real and simulated loads. The case study of this paper enhances the advantages of the proposed algorithm and the benefits of using the developed simulation and user interaction tool.

Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

IgG recognizing 21-24 kDa and 30-33 kDa tachyzoite antigens show maximum avidity maturation during natural and accidental human toxoplasmosis

We describe the avidity maturation of IgGs in human toxoplasmosis using sequential serum samples from accidental and natural infections. In accidental cases, avidity increased continuously throughout infection while naturally infected patients showed a different profile. Twenty-five percent of sera from chronic patients having specific IgM positive results could be appropriately classified using exclusively the avidity test data. To take advantage of the potentiality of this technique, antigens recognized by IgG showing steeper avidity maturation were identified using immunoblot with KSCN elution. Two clusters of antigens, in the ranges of 21-24 kDa and 30-33 kDa, were identified as the ones that fulfill the aforementioned avidity characteristics.

Diagnosis of hepatitis C virus in Brazilian blood donors using a reverse transcriptase nested polymerase chain reaction: comparison with enzyme immunoassay and recombinant protein immunoblot assay

Screening blood donations for anti-HCV antibodies and alanine aminotransferase (ALT) serum levels generally prevents the transmission of hepatitis C virus (HCV) by transfusion. The aim of the present study was to evaluate the efficiency of the enzyme immunoassay (EIA) screening policy in identifying potentially infectious blood donors capable to transmit hepatitis C through blood transfusion. We have used a reverse transcriptase (RT)-nested polymerase chain reaction (PCR) to investigate the presence of HCV-RNA in blood donors. The prevalence of HCV-RNA positive individuals was compared with the recombinant immunoblot assay (RIBA-2) results in order to assess the usefulness of both tests as confirmatory assays. Both tests results were also compared with the EIA-2 OD/C ratio (optical densities of the samples divided by the cut off value). ALT results were expressed as the ALT quotient (qALT), calculated dividing the ALT value of the samples by the maximum normal value (53UI/l) for the method. Donors (n=178) were divided into five groups according to their EIA anti-HCV status and qALT: group A (EIA > or = 3, ALT<1), group B (EIA > or = 3, ALT>1), group C (1<=EIA<3, ALT<1), group D (1<=EIA<3, ALT>1) and group E (EIA<=0.7). HCV sequences were detected by RT-nested PCR, using primers for the most conserved region of viral genome. RIBA-2 was applied to the same samples. In group A (n=6), all samples were positive by RT-nested PCR and RIBA-2. Among 124 samples in group B, 120 (96.8%) were RIBA-2 positive and 4 (3.2%) were RIBA-2 indeterminate but were seropositive for antigen c22.3. In group B, 109 (87.9%) of the RIBA-2 positive samples were also RT-nested PCR positive, as well as were all RIBA-2 indeterminate samples. In group C, all samples (n=9) were RT-nested PCR negative: 4 (44.4%) were also RIBA-2 negative, 4 (44.4%) were RIBA-2 positive and 1 (11.1%) was RIBA-2 indeterminate. HCV-RNA was detected by RT-nested PCR in 3 (37.5%) out of 8 samples in group D. Only one of them was also RIBA-2 positive, all the others were RIBA-2 indeterminate. All of the group E samples (controls) were RT- nested PCR and RIBA-2 negative. Our study suggests a strong relation between anti-HCV EIA-2 ratio > or = 3 and detectable HCV-RNA by RT-nested PCR. We have also noted that blood donors with RIBA-2 indeterminate presented a high degree of detectable HCV-RNA using RT-nested PCR (75%), especially when the c22.3 band was detected.

Trace elements quantification in Portuguese red wines

Dissertation for the Degree of Master in Technology and Food Safety – Food Quality

A Random Access Protocol incorporating Multi-Packet Reception, Retransmission Diversity and Successive Interference Cancellation

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.

Electrophoretic protein patterns and numerical analysis of Candida albicans from the oral cavities of healthy children

The aim of this research was to evaluate the protein polymorphism degree among seventy-five C. albicans strains from healthy children oral cavities of five socioeconomic categories from eight schools (private and public) in Piracicaba city, São Paulo State, in order to identify C. albicans subspecies and their similarities in infantile population groups and to establish their possible dissemination route. Cell cultures were grown in YEPD medium, collected by centrifugation, and washed with cold saline solution. The whole-cell proteins were extracted by cell disruption, using glass beads and submitted to SDS-PAGE technique. After electrophoresis, the protein bands were stained with Coomassie-blue and analyzed by statistics package NTSYS-pc version 1.70 software. Similarity matrix and dendrogram were generated by using the Dice similarity coefficient and UPGMA algorithm, respectively, which made it possible to evaluate the similarity or intra-specific polymorphism degrees, based on whole-cell protein fingerprinting of C. albicans oral isolates. A total of 13 major phenons (clusters) were analyzed, according to their homogeneous (socioeconomic category and/or same school) and heterogeneous (distinct socioeconomic categories and/or schools) characteristics. Regarding to the social epidemiological aspect, the cluster composition showed higher similarities (0.788 < S D < 1.0) among C. albicans strains isolated from healthy children independent of their socioeconomic bases (high, medium, or low). Isolates of high similarity were not found in oral cavities from healthy children of social stratum A and D, B and D, or C and E. This may be explained by an absence of a dissemination route among these children. Geographically, some healthy children among identical and different schools (private and public) also are carriers of similar strains but such similarity was not found among other isolates from children from certain schools. These data may reflect a restricted dissemination route of these microorganisms in some groups of healthy scholars, which may be dependent of either socioeconomic categories or geographic site of each child. In contrast to the higher similarity, the lower similarity or higher polymorphism degree (0.499 < S D < 0.788) of protein profiles was shown in 23 (30.6%) C. albicans oral isolates. Considering the social epidemiological aspect, 42.1%, 41.7%, 26.6%, 23.5%, and 16.7% were isolates from children concerning to socioeconomic categories A, D, C, B, and E, respectively, and geographically, 63.6%, 50%, 33.3%, 33.3%, 30%, 25%, and 14.3% were isolates from children from schools LAE (Liceu Colégio Albert Einstein), MA (E.E.P.S.G. "Prof. Elias de Melo Ayres"), CS (E.E.P.G. "Prof. Carlos Sodero"), AV (Alphaville), HF (E.E.P.S.G. "Honorato Faustino), FMC (E.E.P.G. "Prof. Francisco Mariano da Costa"), and MEP (E.E.P.S.G. "Prof. Manasses Ephraim Pereira), respectively. Such results suggest a higher protein polymorphism degree among some strains isolated from healthy children independent of their socioeconomic strata or geographic sites. Complementary studies, involving healthy students and their families, teachers, servants, hygiene and nutritional habits must be done in order to establish the sources of such colonization patterns in population groups of healthy children. The whole-cell protein profile obtained by SDS-PAGE associated with computer-assisted numerical analysis may provide additional criteria for the taxonomic and epidemiological studies of C. albicans.

Geodupa - a geography-driven partial membership algorithm

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Efficient Legendre spectral tau algorithm for solving two-sided space-time Caputo fractional advection-dispersion equation

In this paper we present the operational matrices of the left Caputo fractional derivative, right Caputo fractional derivative and Riemann–Liouville fractional integral for shifted Legendre polynomials. We develop an accurate numerical algorithm to solve the two-sided space–time fractional advection–dispersion equation (FADE) based on a spectral shifted Legendre tau (SLT) method in combination with the derived shifted Legendre operational matrices. The fractional derivatives are described in the Caputo sense. We propose a spectral SLT method, both in temporal and spatial discretizations for the two-sided space–time FADE. This technique reduces the two-sided space–time FADE to a system of algebraic equations that simplifies the problem. Numerical results carried out to confirm the spectral accuracy and efficiency of the proposed algorithm. By selecting relatively few Legendre polynomial degrees, we are able to get very accurate approximations, demonstrating the utility of the new approach over other numerical methods.

Automated Synthesis Procedure of RF Discrete Tuning Differential Capacitance Circuits

The paper presents a RFDSCA automated synthesis procedure. This algorithm determines several RFDSCA circuits from the top-level system specifications all with the same maximum performance. The genetic synthesis tool optimizes a fitness function proportional to the RFDSCA quality factor and uses the epsiv-concept and maximin sorting scheme to achieve a set of solutions well distributed along a non-dominated front. To confirm the results of the algorithm, three RFDSCAs were simulated in SpectreRF and one of them was implemented and tested. The design used a 0.25 mum BiCMOS process. All the results (synthesized, simulated and measured) are very close, which indicate that the genetic synthesis method is a very useful tool to design optimum performance RFDSCAs.

PV inverters for module level applications

Dissertação para obtenção do Grau de Mestre em Energias Renováveis – Conversão Eléctrica e Utilização Sustentáveis

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.