Aplicação de métodos estocásticos na gestão de custos de construção de condutas de saneamento

O trabalho apresentado centra-se na determinação dos custos de construção de condutas de pequenos e médios diâmetros em Polietileno de Alta Densidade (PEAD) para saneamento básico, tendo como base a metodologia descrita no livro Custos de Construção e Exploração – Volume 9 da série Gestão de Sistemas de Saneamento Básico, de Lencastre et al. (1994). Esta metodologia descrita no livro já referenciado, nos procedimentos de gestão de obra, e para tal foram estimados custos unitários de diversos conjuntos de trabalhos. Conforme Lencastre et al (1994), “esses conjuntos são referentes a movimentos de terras, tubagens, acessórios e respetivos órgãos de manobra, pavimentações e estaleiro, estando englobado na parte do estaleiro trabalhos acessórios correspondentes à obra.” Os custos foram obtidos analisando vários orçamentos de obras de saneamento, resultantes de concursos públicos de empreitadas recentemente realizados. Com vista a tornar a utilização desta metodologia numa ferramenta eficaz, foram organizadas folhas de cálculo que possibilitam obter estimativas realistas dos custos de execução de determinada obra em fases anteriores ao desenvolvimento do projeto, designadamente numa fase de preparação do plano diretor de um sistema ou numa fase de elaboração de estudos de viabilidade económico-financeiros, isto é, mesmo antes de existir qualquer pré-dimensionamento dos elementos do sistema. Outra técnica implementada para avaliar os dados de entrada foi a “Análise Robusta de Dados”, Pestana (1992). Esta metodologia permitiu analisar os dados mais detalhadamente antes de se formularem hipóteses para desenvolverem a análise de risco. A ideia principal é o exame bastante flexível dos dados, frequentemente antes mesmo de os comparar a um modelo probabilístico. Assim, e para um largo conjunto de dados, esta técnica possibilitou analisar a disparidade dos valores encontrados para os diversos trabalhos referenciados anteriormente. Com os dados recolhidos, e após o seu tratamento, passou-se à aplicação de uma metodologia de Análise de Risco, através da Simulação de Monte Carlo. Esta análise de risco é feita com recurso a uma ferramenta informática da Palisade, o @Risk, disponível no Departamento de Engenharia Civil. Esta técnica de análise quantitativa de risco permite traduzir a incerteza dos dados de entrada, representada através de distribuições probabilísticas que o software disponibiliza. Assim, para por em prática esta metodologia, recorreu-se às folhas de cálculo que foram realizadas seguindo a abordagem proposta em Lencastre et al (1994). A elaboração e a análise dessas estimativas poderão conduzir à tomada de decisões sobre a viabilidade da ou das obras a realizar, nomeadamente no que diz respeito aos aspetos económicos, permitindo uma análise de decisão fundamentada quanto à realização dos investimentos.

Reliable and Fast Hand-Offs in Low-Power Wireless Networks

Hand-off (or hand-over), the process where mobile nodes select the best access point available to transfer data, has been well studied in wireless networks. The performance of a hand-off process depends on the specific characteristics of the wireless links. In the case of low-power wireless networks, hand-off decisions must be carefully taken by considering the unique properties of inexpensive low-power radios. This paper addresses the design, implementation and evaluation of smart-HOP, a hand-off mechanism tailored for low-power wireless networks. This work has three main contributions. First, it formulates the hard hand-off process for low-power networks (such as typical wireless sensor networks - WSNs) with a probabilistic model, to investigate the impact of the most relevant channel parameters through an analytical approach. Second, it confirms the probabilistic model through simulation and further elaborates on the impact of several hand-off parameters. Third, it fine-tunes the most relevant hand-off parameters via an extended set of experiments, in a realistic experimental scenario. The evaluation shows that smart-HOP performs well in the transitional region while achieving more than 98 percent relative delivery ratio and hand-off delays in the order of a few tens of a milliseconds.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Hybrid fuzzy Monte Carlo and logic programming model for distribution network reconfiguration in the presence of outages

This paper presents a methodology for distribution networks reconfiguration in outage presence in order to choose the reconfiguration that presents the lower power losses. The methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. Once obtained the system states by Monte Carlo simulation, a logical programming algorithm is applied to get all possible reconfigurations for every system state. In order to evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation a distribution power flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology to a practical case, the paper includes a case study that considers a real distribution network.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Fuzzy Monte Carlo model for transmission power systems reliability based decision making

This thesis presents the Fuzzy Monte Carlo Model for Transmission Power Systems Reliability based studies (FMC-TRel) methodology, which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states. This is followed by a remedial action algorithm, based on Optimal Power Flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. For the system states that cause load curtailment, an optimization approach is applied to reduce the probability of occurrence of these states while minimizing the costs to achieve that reduction. This methodology is of most importance for supporting the transmission system operator decision making, namely in the identification of critical components and in the planning of future investments in the transmission power system. A case study based on Reliability Test System (RTS) 1996 IEEE 24 Bus is presented to illustrate with detail the application of the proposed methodology.

Thinking over b‐learning strategy: the MIPO model approach

This article describes the main research results in a new methodology, in which the stages and strategies of the technology integration process are identified and described. A set of principles and recommendations are therefore presented. The MIPO model described in this paper is a result of the effort made regarding the understanding of the main success features of good practices, in the web environment, integrated in the information systems/information technology context. The initial model has been created, based on experiences and literature review. After that, it was tested in the information and technology system units at higher school and also adapted as a result of four cycles of an actionresearch work combined with a case study research. The information, concepts and procedures presented here give support to teachers and instructors, instructional designers and planning teams – anyone who wants to develop effective b‐learning instructions.

Identificação de compostos que modulam a patogénese da doença de Machado-Joseph em C.elegans: autofagia como alvo terapêutico: identification of small compounds that modulate pathogenesis of Machado-Joseph disease in a C.elegans model: targeting autophagy

A doença de Machado-Joseph (DMJ) ou ataxia espinocerebelosa do tipo 3 (SCA3), conhecida por ser a mais comum das ataxias hereditárias dominantes em todo o mundo, é uma doença neurodegenerativa autossómica dominante que leva a uma grande incapacidade motora, embora sem alterar o intelecto, culminando com a morte do doente. Atualmente não existe nenhum tratamento eficaz para esta doença. A DMJ é resultado de uma alteração genética causada pela expansão de uma sequência poliglutamínica (poliQ), na região C-terminal do gene que codifica a proteína ataxina-3 (ATXN3). Os mecanismos celulares das doenças de poliglutaminas que provocam toxicidade, bem como a função da ATXN3, não são ainda totalmente conhecidos. Neste trabalho, usamos, pela sua simplicidade e potencial genético, um pequeno animal invertebrado, o nemátode C. elegans, com o objetivo de identificar fármacos eficazes para o combate contra a patogénese da DMJ, analisando simultaneamente o seu efeito na agregação da ATXN3 mutante nas células neuronais in vivo e o seu impacto no comportamento motor dos animais. Este pequeno invertebrado proporciona grandes vantagens no estudo dos efeitos tóxicos de proteínas poliQ nos neurónios, uma vez que a transparência das suas 959 células (das quais 302 são neurónios) facilita a deteção de proteínas fluorescentes in vivo. Para além disso, esta espécie tem um ciclo de vida curto, é económica e de fácil manutenção. Neste trabalho testámos no nosso modelo transgénico da DMJ com 130Qs em C.elegans dois compostos potencialmente moduladores da agregação da ATXN3 mutante e da resultante disfunção neurológica, atuando pela via da autofagia. De modo a validar a possível importância terapêutica da ativação da autofagia os compostos candidatos escolhidos foram o Litío e o análogo da Rapamicina CCI-779, testados independentemente e em combinação. A neuroproteção conferida pelo Litío e pelo CCI-779 independentemente sugere que o uso destes fármacos possa ser considerado uma boa estratégia como terapia para a DMJ, a testar em organismos evolutivamente mais próximos do humano. A manipulação da autofagia, segundo vários autores, parece ser benéfica e pode ser a chave para o desenvolvimento de novos tratamentos para várias doenças relacionadas com a agregação proteica e o envelhecimento.

Conceptual model for decomposing the value for the customer

Value has been defined in different theoretical contexts as need, desire, interest, standard /criteria, beliefs, attitudes, and preferences. The creation of value is key to any business, and any business activity is about exchanging some tangible and/or intangible good or service and having its value accepted and rewarded by customers or clients, either inside the enterprise or collaborative network or outside. “Perhaps surprising then is that firms often do not know how to define value, or how to measure it” (Anderson and Narus, 1998 cited by [1]). Woodruff echoed that we need “richer customer value theory” for providing an “important tool for locking onto the critical things that managers need to know”. In addition, he emphasized, “we need customer value theory that delves deeply into customer’s world of product use in their situations” [2]. In this sense, we proposed and validated a novel “Conceptual Model for Decomposing the Value for the Customer”. To this end, we were aware that time has a direct impact on customer perceived value, and the suppliers’ and customers’ perceptions change from the pre-purchase to the post-purchase phases, causing some uncertainty and doubts.We wanted to break down value into all its components, as well as every built and used assets (both endogenous and/or exogenous perspectives). This component analysis was then transposed into a mathematical formulation using the Fuzzy Analytic Hierarchy Process (AHP), so that the uncertainty and vagueness of value perceptions could be embedded in this model that relates used and built assets in the tangible and intangible deliverable exchange among the involved parties, with their actual value perceptions.

Optimization model for medium term natural gas commitment

In this paper we study the optimal natural gas commitment for a known demand scenario. This study implies the best location of GSUs to supply all demands and the optimal allocation from sources to gas loads, through an appropriate transportation mode, in order to minimize total system costs. Our emphasis is on the formulation and use of a suitable optimization model, reflecting real-world operations and the constraints of natural gas systems. The mathematical model is based on a Lagrangean heuristic, using the Lagrangean relaxation, an efficient approach to solve the problem. Computational results are presented for Iberian and American natural gas systems, geographically organized in 65 and 88 load nodes, respectively. The location model results, supported by the computational application GasView, show the optimal location and allocation solution, system total costs and suggest a suitable gas transportation mode, presented in both numerical and graphic supports.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

A probabilistic methodology for distributed generation location in isolated electrical service area

Distributed generation unlike centralized electrical generation aims to generate electrical energy on small scale as near as possible to load centers, interchanging electric power with the network. This work presents a probabilistic methodology conceived to assist the electric system planning engineers in the selection of the distributed generation location, taking into account the hourly load changes or the daily load cycle. The hourly load centers, for each of the different hourly load scenarios, are calculated deterministically. These location points, properly weighted according to their load magnitude, are used to calculate the best fit probability distribution. This distribution is used to determine the maximum likelihood perimeter of the area where each source distributed generation point should preferably be located by the planning engineers. This takes into account, for example, the availability and the cost of the land lots, which are factors of special relevance in urban areas, as well as several obstacles important for the final selection of the candidates of the distributed generation points. The proposed methodology has been applied to a real case, assuming three different bivariate probability distributions: the Gaussian distribution, a bivariate version of Freund’s exponential distribution and the Weibull probability distribution. The methodology algorithm has been programmed in MATLAB. Results are presented and discussed for the application of the methodology to a realistic case and demonstrate the ability of the proposed methodology for efficiently handling the determination of the best location of the distributed generation and their corresponding distribution networks.

Adaptation model for PCMAT – Mathematics collaborative learning platform

The aim of this paper is to present an adaptation model for an Adaptive Educational Hypermedia System, PCMAT. The adaptation of the application is based on progressive self-assessment (exercises, tasks, and so on) and applies the constructivist learning theory and the learning styles theory. Our objective is the creation of a better, more adequate adaptation model that takes into account the complexities of different users.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

An emotional and context-aware model for adapting RSS news to users and groups

The exhibition of information does not always attend to the preferences and characteristics of the users, nor the context that involves the user. With the aim of overcoming this gap, we propose an emotional context-aware model for adapting information contents to users and groups. The proposed model is based on OCC and Big Five models to handle emotion and personality respectively. The idea is to adapt the representation of the information in order to maximize the positive emotional valences and minimize the negatives. To evaluate the proposed model it was developed a prototype for adapting RSS news to users and group of users.