Chronic disease prevention and health promotion. Public policies and social network analysis of the hospitals services and oyhers health and social organizations to support joint action for children with diabetes

Comunicação apresentada na 18th Conference International of Health Promotion Hospitals & Health Services "Tackling causes and consequences of inequalities in health: contributions of health services and the HPH network", em Manchester de 14-16 de april de 2010

Service-oriented mobility of java code in web services-based architectures

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

Estudo e análise das redes centradas em conteúdos

A Internet, conforme a conhecemos, foi projetada com base na pilha de protocolos TCP/IP, que foi desenvolvida nos anos 60 e 70 utilizando um paradigma centrado nos endereços individuais de cada máquina (denominado host-centric). Este paradigma foi extremamente bem-sucedido em interligar máquinas através de encaminhamento baseado no endereço IP. Estudos recentes demonstram que, parte significativa do tráfego atual da Internet centra-se na transferência de conteúdos, em vez das tradicionais aplicações de rede, conforme foi originalmente concebido. Surgiram então novos modelos de comunicação, entre eles, protocolos de rede ponto-a-ponto, onde cada máquina da rede pode efetuar distribuição de conteúdo (denominadas de redes peer-to-peer), para melhorar a distribuição e a troca de conteúdos na Internet. Por conseguinte, nos últimos anos o paradigma host-centric começou a ser posto em causa e apareceu uma nova abordagem de Redes Centradas na Informação (ICN - information-centric networking). Tendo em conta que a Internet, hoje em dia, basicamente é uma rede de transferência de conteúdos e informações, porque não centrar a sua evolução neste sentido, ao invés de comunicações host-to-host? O paradigma de Rede Centrada no Conteúdo (CCN - Content Centric Networking) simplifica a solução de determinados problemas de segurança relacionados com a arquitetura TCP/IP e é uma das principais propostas da nova abordagem de Redes Centradas na Informação. Um dos principais problemas do modelo TCP/IP é a proteção do conteúdo. Atualmente, para garantirmos a autenticidade e a integridade dos dados partilhados na rede, é necessário garantir a segurança do repositório e do caminho que os dados devem percorrer até ao seu destino final. No entanto, a contínua ineficácia perante os ataques de negação de serviço praticados na Internet, sugere a necessidade de que seja a própria infraestrutura da rede a fornecer mecanismos para os mitigar. Um dos principais pilares do paradigma de comunicação da CCN é focalizar-se no próprio conteúdo e não na sua localização física. Desde o seu aparecimento em 2009 e como consequência da evolução e adaptação a sua designação mudou atualmente para Redes de Conteúdos com Nome (NNC – Named Network Content). Nesta dissertação, efetuaremos um estudo de uma visão geral da arquitetura CCN, apresentando as suas principais características, quais os componentes que a compõem e como os seus mecanismos mitigam os tradicionais problemas de comunicação e de segurança. Serão efetuadas experiências com o CCNx, que é um protótipo composto por um conjunto de funcionalidades e ferramentas, que possibilitam a implementação deste paradigma. O objetivo é analisar criticamente algumas das propostas existentes, determinar oportunidades, desafios e perspectivas para investigação futura.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Multi-Agent Based Smart Grid Management and Simulation: Situation Awareness and Learning in a Test Bed with Simulated and Real Installations and Players

The rising usage of distributed energy resources has been creating several problems in power systems operation. Virtual Power Players arise as a solution for the management of such resources. Additionally, approaching the main network as a series of subsystems gives birth to the concepts of smart grid and micro grid. Simulation, particularly based on multi-agent technology is suitable to model all these new and evolving concepts. MASGriP (Multi-Agent Smart Grid simulation Platform) is a system that was developed to allow deep studies of the mentioned concepts. This paper focuses on a laboratorial test bed which represents a house managed by a MASGriP player. This player is able to control a real installation, responding to requests sent by the system operators and reacting to observed events depending on the context.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Incentive-based demand response programs designed by asset-light retailers for day-ahead market

Following the deregulation experience of retail electricity markets in most countries, the majority of the new entrants of the liberalized retail market were pure REP (retail electricity providers). These entities were subject to financial risks because of the unexpected price variations, price spikes, volatile loads and the potential for market power exertion by GENCO (generation companies). A REP can manage the market risks by employing the DR (demand response) programs and using its' generation and storage assets at the distribution network to serve the customers. The proposed model suggests how a REP with light physical assets, such as DG (distributed generation) units and ESS (energy storage systems), can survive in a competitive retail market. The paper discusses the effective risk management strategies for the REPs to deal with the uncertainties of the DAM (day-ahead market) and how to hedge the financial losses in the market. A two-stage stochastic programming problem is formulated. It aims to establish the financial incentive-based DR programs and the optimal dispatch of the DG units and ESSs. The uncertainty of the forecasted day-ahead load demand and electricity price is also taken into account with a scenario-based approach. The principal advantage of this model for REPs is reducing the risk of financial losses in DAMs, and the main benefit for the whole system is market power mitigation by virtually increasing the price elasticity of demand and reducing the peak demand.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

A Green Approach for Selfish Misbehavior Detection in 802.11-Based Wireless Networks

IEEE 802.11 is one of the most well-established and widely used standard for wireless LAN. Its Medium Access control (MAC) layer assumes that the devices adhere to the standard’s rules and timers to assure fair access and sharing of the medium. However, wireless cards driver flexibility and configurability make it possible for selfish misbehaving nodes to take advantages over the other well-behaving nodes. The existence of selfish nodes degrades the QoS for the other devices in the network and may increase their energy consumption. In this paper we propose a green solution for selfish misbehavior detection in IEEE 802.11-based wireless networks. The proposed scheme works in two phases: Global phase which detects whether the network contains selfish nodes or not, and Local phase which identifies which node or nodes within the network are selfish. Usually, the network must be frequently examined for selfish nodes during its operation since any node may act selfishly. Our solution is green in the sense that it saves the network resources as it avoids wasting the nodes energy by examining all the individual nodes of being selfish when it is not necessary. The proposed detection algorithm is evaluated using extensive OPNET simulations. The results show that the Global network metric clearly indicates the existence of a selfish node while the Local nodes metric successfully identified the selfish node(s). We also provide mathematical analysis for the selfish misbehaving and derived formulas for the successful channel access probability.

Graphene-based biomimetic materials targeting urine metabolite as potential cancer biomarker: Application over different conductive materials for potentiometric transduction

This work presents a novel surface Smart Polymer Antibody Material (SPAM) for Carnitine (CRT, a potential biomarker of ovarian cancer), tested for the first time as ionophore in potentiometric electrodes of unconventional configuration. The SPAM material consisted of a 3D polymeric network created by surface imprinting on graphene layers. The polymer was obtained by radical polymerization of (vinylbenzyl) trimethylammonium chloride and 4-styrenesulfonic acid (signaling the binding sites), and vinyl pivalate and ethylene glycol dimethacrylate (surroundings). Non-imprinted material (NIM) was prepared as control, by excluding the template from the procedure. These materials were then used to produce several plasticized PVC membranes, testing the relevance of including the SPAM as ionophore, and the need for a charged lipophilic additive. The membranes were casted over solid conductive supports of graphite or ITO/FTO. The effect of pH upon the potentiometric response was evaluated for different pHs (2-9) with different buffer compositions. Overall, the best performance was achieved for membranes with SPAM ionophore, having a cationic lipophilic additive and tested in HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid) buffer, pH 5.1. Better slopes were achieved when the membrane was casted on conductive glass (-57.4 mV/decade), while the best detection limits were obtained for graphite-based conductive supports (3.6 × 10−5mol/L). Good selectivity was observed against BSA, ascorbic acid, glucose, creatinine and urea, tested for concentrations up to their normal physiologic levels in urine. The application of the devices to the analysis of spiked samples showed recoveries ranging from 91% (± 6.8%) to 118% (± 11.2%). Overall, the combination of the SPAM sensory material with a suitable selective membrane composition and electrode design has lead to a promising tool for point-of-care applications.

Partitioning the Network-on-Chip to Enable Virtualization on Many-Core Processors

6th International Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.

Carnitine tailored Sensors on Surface Molecular Imprinting based on Graphene layers

III Jornadas de Electroquímica e Inovação (Electroquímica e Nanomateriais), na Universidade de Trás-os-Montes e Alto Douro, Vila Real, 16 a 17 de Setembro de 2013

Host-Tailored Sensors for Carnitine Potentiometric Measurements based on Surface Molecular Imprinting

Graduate Student Symposium on Molecular Imprinting 2013, na Queen’s University, Belfast, United Kingdom, 15 a 17 de Agosto de 2013

Molecular analysis of the dengue virus type 1 and 2 in Brazil based on sequences of the genomic envelope-nonstructural protein 1 junction region

The genomic sequences of the Envelope-Non-Structural protein 1 junction region (E/NS1) of 84 DEN-1 and 22 DEN-2 isolates from Brazil were determined. Most of these strains were isolated in the period from 1995 to 2001 in endemic and regions of recent dengue transmission in São Paulo State. Sequence data for DEN-1 and DEN-2 utilized in phylogenetic and split decomposition analyses also include sequences deposited in GenBank from different regions of Brazil and of the world. Phylogenetic analyses were done using both maximum likelihood and Bayesian approaches. Results for both DEN-1 and DEN-2 data are ambiguous, and support for most tree bipartitions are generally poor, suggesting that E/NS1 region does not contain enough information for recovering phylogenetic relationships among DEN-1 and DEN-2 sequences used in this study. The network graph generated in the split decomposition analysis of DEN-1 does not show evidence of grouping sequences according to country, region and clades. While the network for DEN-2 also shows ambiguities among DEN-2 sequences, it suggests that Brazilian sequences may belong to distinct subtypes of genotype III.