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.

Quality-of-service aware routing for static and mobile IPv6-based low-power and lossy sensor networks using RPL

The Internet of Things (IoT) has emerged as a paradigm over the last few years as a result of the tight integration of the computing and the physical world. The requirement of remote sensing makes low-power wireless sensor networks one of the key enabling technologies of IoT. These networks encompass several challenges, especially in communication and networking, due to their inherent constraints of low-power features, deployment in harsh and lossy environments, and limited computing and storage resources. The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) [1] was proposed by the IETF ROLL (Routing Over Low-power Lossy links) working group and is currently adopted as an IETF standard in the RFC 6550 since March 2012. Although RPL greatly satisfied the requirements of low-power and lossy sensor networks, several issues remain open for improvement and specification, in particular with respect to Quality of Service (QoS) guarantees and support for mobility. In this paper, we focus mainly on the RPL routing protocol. We propose some enhancements to the standard specification in order to provide QoS guarantees for static as well as mobile LLNs. For this purpose, we propose OF-FL (Objective Function based on Fuzzy Logic), a new objective function that overcomes the limitations of the standardized objective functions that were designed for RPL by considering important link and node metrics, namely end-to-end delay, number of hops, ETX (Expected transmission count) and LQL (Link Quality Level). In addition, we present the design of Co-RPL, an extension to RPL based on the corona mechanism that supports mobility in order to overcome the problem of slow reactivity to frequent topology changes and thus providing a better quality of service mainly in dynamic networks application. Performance evaluation results show that both OF-FL and Co-RPL allow a great improvement when compared to the standard specification, mainly in terms of packet loss ratio and average network latency. 2015 Elsevier B.V. Al

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.

Scalable and interference aware wi-fi mesh networks using cots devices

A crescente tendencia no acesso móvel tem sido potenciada pela tecnologia IEEE 802.11. Contudo, estas redes têm alcance rádio limitado. Para a extensão da sua cobertura é possível recorrer a redes emalhadas sem fios baseadas na tecnologia IEEE 802.11, com vantagem do ponto de vista do custo e da flexibilidade de instalação, face a soluções cabladas. Redes emalhadas sem fios constituídas por nós com apenas uma interface têm escalabilidade reduzida. A principal razão dessa limitação deve-se ao uso do mecanismo de acesso ao meio partilhado Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) em topologias multi-hop. Especificamente, o CSMA/CA não evita o problema do nó escondido levando ao aumento do número de colisões e correspondente degradação de desempenho com impacto direto no throughput e na latência. Com a redução da tecnologia rádio torna-se viável a utilização de múltiplos rádios por nó, sem com isso aumentar significativamente o custo da solução final de comunicações. A utilização de mais do que um rádio por nó de comuniações permite superar os problemas de desempenho inerentes ás redes formadas por nós com apenas um rádio. O objetivo desta tese, passa por desenvolver uma nova solução para redes emalhadas multi-cana, duar-radio, utilizando para isso novos mecanismos que complementam os mecanismos definidos no IEEE 802.11 para o estabelecimento de um Basic Service Set (BSS). A solução é baseada na solução WiFIX, um protocolo de routing para redes emalhadas de interface única e reutiliza os mecanismos já implementados nas redes IEEE 802.11 para difundir métricas que permitam à rede escalar de forma eficaz minimizando o impacto na performance. A rede multi-hop é formada por nós equipados com duas interfaces, organizados numa topologia hierárquica sobre múltiplas relações Access Point (AP) – Station (STA). Os resultados experimentais obtidos mostram a eficácia e o bom desempenho da solução proposta face à solução WiFIX original.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Implementation of an IoT-based Sensor Network Integrating IoT-based Sensor Networks with Large-Scale Message Distribution

As technology advances not only do new standards and programming styles appear but also some of the previously established ones gain relevance. In a new Internet paradigm where interconnection between small devices is key to the development of new businesses and scientific advancement there is the need to find simple solutions that anyone can implement in order to allow ideas to become more than that, ideas. Open-source software is still alive and well, especially in the area of the Internet of Things. This opens windows for many low capital entrepreneurs to experiment with their ideas and actually develop prototypes, which can help identify problems with a project or shine light on possible new features and interactions. As programming becomes more and more popular between people of fields not related to software there is the need for guidance in developing something other than basic algorithms, which is where this thesis comes in: A comprehensive document explaining the challenges and available choices of developing a sensor data and message delivery system, which scales well and implements the delivery of critical messages. Modularity and extensibility were also given much importance, making this an affordable tool for anyone that wants to build a sensor network of the kind.

QUANTITATIVE VS. CONVENTIONAL PCR FOR DETECTION OF HUMAN ADENOVIRUSES IN WATER AND SEDIMENT SAMPLES

SUMMARY Human Adenoviruses (HAdV) are notably resistant in the environment. These agents may serve as effective indicators of fecal contamination, and may act as causative agents of a number of different diseases in human beings. Conventional polymerase chain reaction (PCR) and, more recently, quantitative PCR (qPCR) are widely used for detection of viral agents in environmental matrices. In the present study PCR and SYBR(r)Green qPCR assays were compared for detection of HAdV in water (55) and sediments (20) samples of spring and artesian wells, ponds and streams, collected from dairy farms. By the quantitative methodology HAdV were detected in 87.3% of the water samples and 80% of the sediments, while by the conventional PCR 47.3% and 35% were detected in water samples and sediments, respectively.

Sustainability of systems interoperability in dynamic business networks

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

IDENTIFICATION OF CANINE VISCERAL LEISHMANIASIS IN A PREVIOUSLY UNAFFECTED AREA BY CONVENTIONAL DIAGNOSTIC TECHNIQUES AND CELL-BLOCK FIXATION

After the report of a second case of canine visceral leishmaniasis (CVL) in São Bento da Lagoa, Itaipuaçu, in the municipality of Maricá, Rio de Janeiro State, an epidemiological survey was carried out, through active search, totaling 145 dogs. Indirect immunofluorescence assay (IFA), enzyme-linked immunosorbent assay (ELISA), and rapid chromatographic immunoassay based on dual-path platform (DPP(r)) were used to perform the serological examinations. The parasitological diagnosis of cutaneous fragments was performed by parasitological culture, histopathology, and immunohistochemistry. In the serological assessment, 21 dogs were seropositive by IFA, 17 by ELISA, and 11 by DPP(r), with sensitivity of 66.7%, 66.7% and 50%, and specificity of 87.2%, 90.2% and 94%, respectively for each technique. The immunohistochemistry of bone marrow using the cell-block technique presented the best results, with six positive dogs found, three of which tested negative by the other parasitological techniques. Leishmania sp. was isolated by parasitological culture in three dogs. The detection of autochthonous Leishmania infantum in Itaipuaçu, and the high prevalence of seropositive dogs confirm the circulation of this parasite in the study area and alert for the risk of expansion in the State of Rio de Janeiro.

HUMAN T-LYMPHOTROPIC VIRUS 1 (HTLV-1) AND HUMAN T-LYMPHOTROPIC VIRUS 2 (HTLV-2): GEOGRAPHICAL RESEARCH TRENDS AND COLLABORATION NETWORKS (1989-2012)

Publications are often used as a measure of research work success. Human T-lymphotropic virus (HTLV) type 1 and 2 are human retroviruses, which were discovered in the early 1980s, and it is estimated that 15-20 million people are infected worldwide. This article describes a bibliometric review and a coauthorship network analysis of literature on HTLV indexed in PubMed in a 24-year period. A total of 7,564 documents were retrieved, showing a decrease in the number of documents from 1996 to 2007. HTLV manuscripts were published in 1,074 journals. Japan and USA were the countries with the highest contribution in this field (61%) followed by France (8%). Production ranking changed when the number of publications was normalized by population (Dominican Republic and Japan), by gross domestic product (Guinea-Bissau and Gambia), and by gross national income per capita (Brazil and Japan). The present study has shed light on some of the defining features of scientific collaboration performed by HTLV research community, such as the existence of core researchers responsible for articulating the development of research in the area, facilitating wider collaborative relationships and the integration of new authors in the research groups.

PERFORMANCE OF CONVENTIONAL PCRs BASED ON PRIMERS DIRECTED TO NUCLEAR AND MITOCHONDRIAL GENES FOR THE DETECTION AND IDENTIFICATION OF Leishmania spp.

In visceral leishmaniasis, the detection of the agent is of paramount importance to identify reservoirs of infection. Here, we evaluated the diagnostic attributes of PCRs based on primers directed to cytochrome-B (cytB), cytochrome-oxidase-subunit II (coxII), cytochrome-C (cytC), and the minicircle-kDNA. Although PCRs directed to cytB, coxII, cytC were able to detect different species of Leishmania, and the nucleotide sequence of their amplicons allowed the unequivocal differentiation of species, the analytical and diagnostic sensitivity of these PCRs were much lower than the analytical and diagnostic sensitivity of the kDNA-PCR. Among the 73 seropositive animals, the asymptomatic dogs had spleen and bone marrow samples collected and tested; only two animals were positive by PCRs based on cytB, coxII, and cytC, whereas 18 were positive by the kDNA-PCR. Considering the kDNA-PCR results, six dogs had positive spleen and bone marrow samples, eight dogs had positive bone marrow results but negative results in spleen samples and, in four dogs, the reverse situation occurred. We concluded that PCRs based on cytB, coxII, and cytC can be useful tools to identify Leishmania species when used in combination with automated sequencing. The discordance between the results of the kDNA-PCR in bone marrow and spleen samples may indicate that conventional PCR lacks sensitivity for the detection of infected dogs. Thus, primers based on the kDNA should be preferred for the screening of infected dogs.

Learning networks and moodle use in online courses: a social network analysis study

Dissertação para obtenção do Grau de Doutor em Ciências da Educação Especialidade em Tecnologias, Redes e Multimédia na Educação e Formação

Social BPM – The role of social networks in business process management

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Estatística e Gestão de Informação

Corporate networks in Poland: Interlocking directorates and business systems

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Synthesis, characterization and applications of new schiff base fluorescent chemosensors for metal and DNA interactions: conventional and "green" approaches

Dissertação apresentada para a obtenção do Grau de Doutor em Química Sustentável, especialidade de Química-Física Inorgânica, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia