Evaluation of the respiratory motion effect in small animal PET images with GATE Monte Carlo simulations

The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to increased interest in in vivo small animal imaging. Small animal imaging has been applied frequently to the imaging of small animals (mice and rats), which are ubiquitous in modeling human diseases and testing treatments. The use of PET in small animals allows the use of subjects as their own control, reducing the interanimal variability. This allows performing longitudinal studies on the same animal and improves the accuracy of biological models. However, small animal PET still suffers from several limitations. The amounts of radiotracers needed, limited scanner sensitivity, image resolution and image quantification issues, all could clearly benefit from additional research. Because nuclear medicine imaging deals with radioactive decay, the emission of radiation energy through photons and particles alongside with the detection of these quanta and particles in different materials make Monte Carlo method an important simulation tool in both nuclear medicine research and clinical practice. In order to optimize the quantitative use of PET in clinical practice, data- and image-processing methods are also a field of intense interest and development. The evaluation of such methods often relies on the use of simulated data and images since these offer control of the ground truth. Monte Carlo simulations are widely used for PET simulation since they take into account all the random processes involved in PET imaging, from the emission of the positron to the detection of the photons by the detectors. Simulation techniques have become an importance and indispensable complement to a wide range of problems that could not be addressed by experimental or analytical approaches.

Technology for creativity and innovation: tools, techniques and applications

It is widely accepted that organizations and individuals must be innovative and continually create new knowledge and ideas to deal with rapid change. Innovation plays an important role in not only the development of new business, process and products, but also in competitiveness and success of any organization. Technology for Creativity and Innovation: Tools, Techniques and Applications provides empirical research findings and best practices on creativity and innovation in business, organizational, and social environments. It is written for educators, academics and professionals who want to improve their understanding of creativity and innovation as well as the role technology has in shaping this discipline.

Copper Bis(oxazoline) Encapsulated in Zeolites and Its Application as Heterogeneous Catalysts for the Cyclopropanation of Styrene

A copper C(2)-symmetric bis(oxazoline), CuBox, was introduced in two forms of commercial Y zeolite: a sodium form (NaY) and an ultrastable form (NaUSY). CuBox was introduced by first partially exchanging the sodium cations of both zeolites for copper and then by refluxing the obtained materials with a solution of bis(oxazoline) (Box). Two different loadings were prepared for each form of zeolite. The materials were characterized by copper ICP-AES, elemental analysis, XPS, FTIR, TG, and nitrogen adsorption isotherms at -196 degrees C. Evidence for Box ligand location in the supercages of NaY and NaUSY zeolites and its coordination to the exchanged copper(II) was obtained by the several techniques used. The materials were all active in the cyclopropanation of styrene with ethyldiazoacetate at room temperature and diastereoselective toward trans cydopropanes. Although the materials containing Box showed low enantioselectivities, their catalytic activities were higher than the parent copper exchanged zeolites, and did not decrease with reuse, at least during three consecutive cycles.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Incorporation of niobium in SAPO-11 materials: Synthesis and characterization

The present work concerns a new synthesis approach to prepare niobium based SAPO materials with AEL structure and the characterization ofNb species incorporated within the inorganic matrixes. The SAPO-11 materials were synthesized with or without the help of a small amine, methylamine (MA) as co-template, while Nb was added directly during the preparation of the initial gel. Structural, textural and acidic properties of the different supports were evaluated by XRD, TPR, UV-Vis spectroscopy, pyridine adsorption followed by IR spectroscopy and thermal analyses. Pure and well crystalline Nb based SAPO-11 materials were obtained, either with or without MA, using in the initial gel a low Si content of about 0.6. Increasing the Si content of the gel up to 0.9 led to an important decrease of the samples crystallinity. Niobium was found to incorporate the AEL pores support as small Nb2O5 oxide particles and also as extra framework cationic species (Nb5+), compensating the negative charges from the matrix and generating new Lewis acid sites. (C) 2011 Elsevier Inc. All rights reserved.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Natural gas system operation: lagrangean optimization techniques

To comply with natural gas demand growth patterns and Europe´s import dependency, the gas industry needs to organize an efficient upstream infrastructure. The best location of Gas Supply Units – GSUs and the alternative transportation mode – by phisical or virtual pipelines, are the key of a successful industry. In this work we study the optimal location of GSUs, as well as determining the most efficient allocation from gas loads to sources, selecting the best transportation mode, observing specific technical restrictions and minimizing system total costs. For the location of GSUs on system we use the P-median problem, for assigning gas demands nodes to source facilities we use the classical transportation problem. The developed model is an optimisation-based approach, based on a Lagrangean heuristic, using Lagrangean relaxation for P-median problems – Simple Lagrangean Heuristic. The solution of this heuristic can be improved by adding a local search procedure - the Lagrangean Reallocation Heuristic. These two heuristics, Simple Lagrangean and Lagrangean Reallocation, were tested on a realistic network - the primary Iberian natural gas network, organized with 65 nodes, connected by physical and virtual pipelines. Computational results are presented for both approaches, showing the location gas sources and allocation loads arrangement, system total costs and gas transportation mode.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

Ressonância magnética de corpo inteiro/whole-body MRI

A introdução de Ressonância Magnética de Corpo Inteiro (RM-CI) de alta resolução é baseada no desenvolvimento rápido e intensivo dos equipamentos de RM. Estes avanços associados ao aparecimento de novos métodos de aquisição de imagem, como as técnicas de Multistation ou imagem paralela impulsionam a RM-CI. Associado ao desenvolvimento tecnológico, a RM-CI apresenta vantagens clínicas essencialmente para patologias oncológicas como é o caso das metástases ósseas, e para patologias do foro cardiovascular. Muitas destas situações ainda se encontram em investigação mas os primeiros resultados têm superado todas as expectativas nomeadamente a RM-CI com a aplicação da técnica de Difusão.

Optimization techniques for power distribution planning with uncertainties: a comparative study

Important research effort has been devoted to the topic of optimal planning of distribution systems. The non linear nature of the system, the need to consider a large number of scenarios and the increasing necessity to deal with uncertainties make optimal planning in distribution systems a difficult task. Heuristic techniques approaches have been proposed to deal with these issues, overcoming some of the inherent difficulties of classic methodologies. This paper considers several methodologies used to address planning problems of electrical power distribution networks, namely mixedinteger linear programming (MILP), ant colony algorithms (AC), genetic algorithms (GA), tabu search (TS), branch exchange (BE), simulated annealing (SA) and the Bender´s decomposition deterministic non-linear optimization technique (BD). Adequacy of theses techniques to deal with uncertainties is discussed. The behaviour of each optimization technique is compared from the point of view of the obtained solution and of the methodology performance. The paper presents results of the application of these optimization techniques to a real case of a 10-kV electrical distribution system with 201 nodes that feeds an urban area.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Information and communication technologies in education – a web-based intelligent tutoring system on teaching and learning electrical project

Electrical activity is extremely broad and distinct, requiring by one hand, a deep knowledge on rules, regulations, materials, equipments, technical solutions and technologies and assistance in several areas, as electrical equipment, telecommunications, security and efficiency and rational use of energy, on the other hand, also requires other skills, depending on the specific projects to be implemented, being this knowledge a characteristic that belongs to the professionals with relevant experience, in terms of complexity and specific projects that were made.

Combining artificial intelligence techniques for the training of power system control centre operators

Control Centre operators are essential to assure a good performance of Power Systems. Operators’ actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.

Estudo e desenvolvimento de um sensor de chuva piezoelétrico para automóveis

A presença de sistemas eletrónicos nos veículos automóveis tem vindo a aumentar de forma considerável nos últimos 30 anos, tornando possível o aumento dos padrões de eficiência, segurança e conforto dos mesmos. Os sistemas de acionamento automático dos limpa-para-brisas, baseados em sensores de chuva óticos, têm registado um crescimento quase exponencial nos últimos 10 a 15 anos; no ano 2000, apenas 5 % dos veículos novos produzidos na Europa estavam equipados com este sistema, hoje é um equipamento amplamente difundido na oferta automóvel existente. O presente trabalho consistiu no estudo de uma solução para deteção de chuva em veículos automóveis com a aplicação de um sensor piezoelétrico, tendo em vista a obtenção de uma solução mais versátil e aplicável em vários pontos do veículo. As reduzidas dimensões, a elevada sensibilidade do sensor e a facilidade de aplicação nas superfícies de ensaio foram fatores que motivaram a escolha deste tipo de equipamento como elemento sensorial. As hipóteses definidas para o procedimento laboratorial basearam-se nas conclusões obtidas em estudos anteriormente desenvolvidos no campo dos sensores de chuva para automóveis e nas capacidades dos materiais piezoelétricos para medição de pluviosidade. Os sensores foram instalados sob as superfícies do veículo que apresentavam, simultaneamente, uma maior exposição à pluviosidade, quando este está em movimento, e um menor risco de sofrer danos. Os resultados obtidos permitiram concluir que a utilização deste tipo de sensores permite detetar elevados níveis de pluviosidade e em superfícies com considerável capacidade de deformação elástica. A sua implementação futura em veículos automóveis exige mais algum trabalho de melhoria dos processos de fixação dos sensores e do condicionamento de sinal utilizados.