Conceptual design of a mechatronic system for supporting basic quality of life of bedridden elderly people

Ambient Assisted Living is an important subject to be explored and developed, especially in developed countries, due to the increasing number of aged people. In this context the development of mechatronic support systems for bedridden elderly people (BEP) living in their homes is essential in order to support independence, autonomy and improve their quality of life. Some basic tasks as eating, taking a bath and/or hygiene cares become difficult to execute, regarding that often the main caregiver is the other element of the aged couple (husband or wife). This paper presents the conceptual design of a mechanical system especially devoted to assist the caregiver in the handling and repositioning of the BEP. Issues as reducing the number of caregivers, to only one, and reducing the system's handling complexity (because most of the time it will be used by an aged person) are considered. The expertise obtained from the visits to rehabilitation centers and hospitals, and from working meetings, are considered in the development of the proposed mechatronic system.

Student stress and quality of education

Today, managers are increasingly interested in knowing how the work in organizations aftects employees' health. Less common is the interest in stress erupting in the academic community - among students, faculty and administrators. The authors present a reflection paper focused on student stress. In this paper, they first examine McLean 's model of context, vulnerability and stressors. This model provides the framework for the student surveys and for the entire paper. Based on the students surveys, an assessment is made of how asma" group of students are coping with stress. The paper fina"y suggests what can be done by students, faculty, and administrators to insta" and/or improve social support systems that might reduce the harmful eftects of stress on students and thus impact the quality of education.

Modelo de gestão municipal baseado na utilização estratégica de recursos da tecnologia da informação para a gestão governamental: formatação do modelo e avaliação em um município

São desafios constantes da gestão efetiva dos municípios a estruturação e disponibilização de informações confiáveis, oportunas e personalizadas para apoiar as decisões da administração pública municipal e para elaborar e controlar o planejamento estratégico municipal alinhado aos anseios dos cidadãos. A adaptação de modelos de gestão da iniciativa privada para o ambiente público é uma alternativa para enfrentar esses desafios. Este artigo propõe e avalia um modelo para a gestão governamental. O modelo é baseado na utilização estratégica da tecnologia da informação, que proporcione ao gestor público monitoração e controle da execução estratégica, informações executivas para a tomada de decisão, gestão dos relacionamentos com os cidadãos e o domínio sobre os processos da gestão municipal. A metodologia da pesquisa enfatizou o estudo de caso no município de Curitiba, utilizando um protocolo de pesquisa elaborado a partir da pesquisa bibliográfica exploratória. A seguir, são analisados diferenças, similaridades e resultados da aplicação de elementos que compõem o modelo proposto no município estudado. A conclusão evidencia que a utilização e adaptação do modelo proposto nas gestões municipais podem contribuir significativamente na evolução de seus modelos de gestão.

Optimização de recursos na construção de linhas de caminhos de ferro

Os Caminhos-de-ferro representam um conjunto de abordagens quase ilimitadas, nestes termos, o tema proposto – “A optimização de recursos na construção de linhas de Caminhos de Ferro”, incidirá particularmente sobre a optimização dos recursos: i) materiais; ii) mão-de-obra; iii) equipamentos, afectos a construção da via e da catenária. O presente estudo pretende traçar um encadeamento lógico e intuitivo que permita manter um fio condutor ao longo do todo o seu desenvolvimento, razão pela qual, a sequência dos objectivos apresentados constitui um caminho que permitira abrir sucessivas janelas de conhecimento. O conhecimento da via e da catenária, a compreensão da forma como os trabalhos interagem com os factores externos e a experiência na utilização das ferramentas de planeamento e gestão, são qualidades que conduzem certamente a bons resultados quando nos referimos a necessidade de optimizar os recursos na construção da via e da catenária. A transmissão e reciprocidade da informação, entre as fases de elaboração de propostas e de execução da obra, representam um recurso que pode conduzir a ganhos de produtividade. A coordenação e outro factor determinante na concretização dos objectivos de optimização dos recursos, que se efectua, quer internamente, quer exteriormente. A optimização de recursos na construção da via e da catenária representa o desafio permanente das empresas de construção do sector ferroviário. E neste pressuposto que investem na formação e especialização da sua mão-de-obra e na renovação tecnológica dos seus equipamentos. A optimização dos materiais requer aproximações distintas para o caso da via e para o caso da catenária, assim como, os equipamentos e a mão-de-obra não podem ser desligados, pois não funcionam autonomamente, no entanto a respectiva optimização obedece a pressupostos diferentes.

Planificação de um sistema de informação para a unidade de formação contínua e avançada da Escola Superior de Educação do Instituto Politécnico do Porto

Projecto apresentado ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do Grau de Mestre em Assessoria de Administração

VPPs information needs for effective operation in competitive electricity markets

The large increase of renewable energy sources and Distributed Generation (DG) of electricity gives place to the Virtual Power Producer (VPP) concept. VPPs may turn electricity generation by renewable sources valuable in electricity markets. Information availability and adequate decision-support tools are crucial for achieving VPPs’ goals. This involves information concerning associated producers and market operation. This paper presents ViProd, a simulation tool that allows simulating VPPs operation, focusing mainly in the information requirements for adequate decision making.

Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

A simulation tool for demand response programs implementation

The design and development of simulation models and tools for Demand Response (DR) programs are becoming more and more important for adequately taking the maximum advantages of DR programs use. Moreover, a more active consumers’ participation in DR programs can help improving the system reliability and decrease or defer the required investments. DemSi, a DR simulator, designed and implemented by the authors of this paper, allows studying DR actions and schemes in distribution networks. It undertakes the technical validation of the solution using realistic network simulation based on PSCAD. DemSi considers the players involved in DR actions, and the results can be analyzed from each specific player point of view.

Energy and reserve provision dispatch considering distributed generation and demand response

In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot market´s efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers.

Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach

This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G user´s profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.