Robust model-order reduction of complex biological processes

This paper addresses robust model-order reduction of a high dimensional nonlinear partial differential equation (PDE) model of a complex biological process. Based on a nonlinear, distributed parameter model of the same process which was validated against experimental data of an existing, pilot-scale BNR activated sludge plant, we developed a state-space model with 154 state variables in this work. A general algorithm for robustly reducing the nonlinear PDE model is presented and based on an investigation of five state-of-the-art model-order reduction techniques, we are able to reduce the original model to a model with only 30 states without incurring pronounced modelling errors. The Singular perturbation approximation balanced truncating technique is found to give the lowest modelling errors in low frequency ranges and hence is deemed most suitable for controller design and other real-time applications. (C) 2002 Elsevier Science Ltd. All rights reserved.

The variety of variables in automated real-time refinement

The refinement calculus is a well-established theory for deriving program code from specifications. Recent research has extended the theory to handle timing requirements, as well as functional ones, and we have developed an interactive programming tool based on these extensions. Through a number of case studies completed using the tool, this paper explains how the tool helps the programmer by supporting the many forms of variables needed in the theory. These include simple state variables as in the untimed calculus, trace variables that model the evolution of properties over time, auxiliary variables that exist only to support formal reasoning, subroutine parameters, and variables shared between parallel processes.

Expansão urbana e o Programa Minha Casa, Minha Vida na região metropolitana da Grande Vitória

Discute as contribuições do Programa Minha Casa Minha Vida (PMCMV) no processo de formação e expansão do espaço urbano da Região Metropolitana da Grande Vitória (RGMV), analisando especificamente a produção das moradias destinadas às famílias de baixa renda até R$ 1.600,00. Busca compreender as características operacionais do Programa e suas implicações sobre o espaço socialmente construído e na vida cotidiana das pessoas. A metodologia analítica foi estruturada com base em dados quantitativos, obtidos em órgãos públicos, sobre a produção habitacional desde o lançamento do Programa (2009) até janeiro de 2014. Os dados foram distribuídos por território e faixa de rendimento das famílias. Como estudo de caso foram pesquisadas três áreas na RMGV, nos municípios de Cariacica, Vila Velha e Vitória por possuírem projetos relevantes do PMCMV em diferentes fases de execução. A pesquisa abrange projetos distribuídos em cinco fases de execução (previstos, em aprovação, aprovados, em construção e entregues). Foram realizadas entrevistas semi-estruturadas com moradores do conjunto habitacional do PMCMV em Vitória; moradores vizinhos aos empreendimentos do PMCMV em Vila Velha; comerciantes; presidente da associação de moradores de bairros; empregados das construtoras e servidores públicos. Foram feitas pesquisas de campo nas áreas selecionadas e nos territórios do entorno de onde estão sendo implantadas as moradias de interesse social. O Programa tem alcançado resultados expressivos: sendo 3.2 milhões de unidades foram contratadas e 1.5 milhão entregues em 5 anos no Brasil. No mesmo período foram 46.879 e 15.295 no Espírito Santo e na RMGV foram 25.919 e 6.958 unidades contratadas e entregues respectivamente. O PMCMV continua a reproduzir historicamente contradições inerentes às políticas habitacionais antecedentes como submissão às estratégias do mercado capitalista e à reprodução de um modelo de crescimento urbano caracterizado pela segregação socioespacial, além de promover a ocupação de novos espaços periféricos das cidades atuando como vetor de expansão urbana da RMGV.

«Corruption» and social and economic criminal law: criminology, criminal policy, political science and law&economics - a new idea about criminal liability of legal entities

Abstract: If we think there is a significant number of legal offshore in the globalized world, then there is not even a global consensus about what «corruption» is. The «illegal corruption» in a country may be legal in another. Moreover, the great global corruption is above the law or above democratic States. And not all democratic States are «Rule of Law». Therefore, the solution is global earlier in time and space law, democratic, free and true law. While the human being does not reach a consensus of what «corruption» really is, the discussion will not go further than a caricature. One of the other problems about «corruption» is that it is very difficult to establish the imputation of crimes, including «corruption» (v.g. Portugal) on some «companies», corporations. We have a juridical problem in the composition of the art. 11. of the Portuguese Penal Code.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.

Sistemas de informação: message integration bus

O trabalho apresentado por este documento aborda os problemas que advêm da necessidade de integração de aplicações, desenvolvidas em diferentes instantes no tempo, por diferentes equipas de trabalho, que para enriquecer os processos de negócio necessitam de comunicar entre si. A integração das aplicações tem de ser feita de forma opaca para estas, sendo disponibilizada por uma peça de software genérica, robusta e sem custos para as equipas desenvolvimento, na altura da integração. Esta integração tem de permitir que as aplicações comuniquem utilizando os protocolos que desejarem. Este trabalho propõe um middleware orientado a mensagens como solução para o problema identificado. A solução apresentada por este trabalho disponibiliza a comunicação entre aplicações que utilizam diferentes protocolos, permite ainda o desacoplamento temporal, espacial e de sincronismo na comunicação das aplicações. A implementação da solução tem base num sistema publish/subscribe orientado ao conteúdo e tem de lidar com as maiores exigências computacionais que este tipo de sistema acarta, sendo que a utilização deste se justifica com o enriquecimento da semântica de subscrição de eventos. Esta implementação utiliza uma arquitectura semi-distribuída, com o objectivo de aumentar a escalabilidade do sistema. A utilização da arquitectura semi-distribuída implica que a implementação da solução tem de lidar com o encaminhamento de eventos e divulgação das subscrições, pelos vários servidores de eventos. A implementação da solução disponibiliza garantias de persistência, processamento transaccional e tolerância a falhas, assim como transformação de eventos entre os diversos protocolos. A extensibilidade da solução é conseguida à custa de um sistema de pluggins que permite a adição de suporte a novos protocolos de comunicação. Os protocolos suportados pela implementação final do trabalho são RestMS e TCP.

Real-time energy resources scheduling considering intensive wind penetration

The use of distributed energy resources, based on natural intermittent power sources, like wind generation, in power systems imposes the development of new adequate operation management and control methodologies. A short-term Energy Resource Management (ERM) methodology performed in two phases is proposed in this paper. The first one addresses the day-ahead ERM scheduling and the second one deals with the five-minute ahead ERM scheduling. The ERM scheduling is a complex optimization problem due to the high quantity of variables and constraints. In this paper the main goal is to minimize the operation costs from the point of view of a virtual power player that manages the network and the existing resources. The optimization problem is solved by a deterministic mixedinteger non-linear programming approach. A case study considering a distribution network with 33 bus, 66 distributed generation, 32 loads with demand response contracts and 7 storage units and 1000 electric vehicles has been implemented in a simulator developed in the field of the presented work, in order to validate the proposed short-term ERM methodology considering the dynamic power system behavior.

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

Virtual power players dealing with excessive wind power situations using real time pricing

The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

LMP triggered real time demand response events

Recent changes in power systems mainly due to the substantial increase of distributed generation and to the operation in competitive environments has created new challenges to operation and planning. In this context, Virtual Power Players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Demand response market implementation has been done in recent years. Several implementation models have been considered. An important characteristic of a demand response program is the trigger criterion. A program for which the event trigger depends on the Locational Marginal Price (LMP) used by the New England Independent System operator (ISO-NE) inspired the present paper. This paper proposes a methodology to support VPP demand response programs management. The proposed method has been computationally implemented and its application is illustrated using a 32 bus network with intensive use of distributed generation. Results concerning the evaluation of the impact of using demand response events are also presented.

Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.

Avaliação da qualidade do ar interior e estudos CFD em espaços do ISEP

Mestrado em Engenharia Química. Ramo Tecnologias de Protecção Ambiental.