Bacterial enzymes and multi-enzymatic systems for cleaning-up dyes from the environment

Synthetic dyes are xenobiotic compounds that are being increasingly used in several industries, with special emphasis in the paper, textile and leather industries. Over 100,000 commercial dyes exist today and more than 7 × 105 tons of dyestuff is produced annually, of which 1–1.5 × 105 tons is released into the wastewaters (Rai et al in Crit Rev Environ Sci Tecnhol 35:219–238, 2005). Among these, azo dyes, characterized by the presence of one or more azo groups (–N=N–), and anthraquinonic dyes represent the largest and most versatile groups.

Calibração automática para sistema de localização Multi-câmara

Mestrado em Engenharia Electrotécnica e de Computadores - Sistemas Autónomos

Aplicação do multi-layer stream mapping para a gestão da eficiência de recursos e produtividade de um sistema de produção

Mestrado em Engenharia Mecânica – Especialização Gestão Industrial

Bacterial agents isolated from cerebrospinal fluid of patients with Acquired Immunodeficiency Syndrome (AIDS) and neurological complications

Cerebrospinal fluid (CSF) samples from 2083 patients with acquired immunodeficiency syndrome (AIDS) and neurological complications were bacteriologically examined during a period of 7 years (1984-1990). The percentage of patients who had at least one bacterial agent cultured from the CSF was 6.2%. Mycobacterium tuberculosis was the most frequently isolated agent (4.3%), followed by Mycobacterium avium complex or MAC (0.7%), Pseudomonas spp (0.5%), Enterobacter spp (0.4%), and Staphylococcus aureus (0.3%). Among 130 culture positive patients, 89 (68.5%) had M. tuberculosis and 15 (11.6%) had MAC. The frequency of bacterial isolations increased from 1988 (5.2%) to 1990 (7.2%), partly due to the increase in MAC isolations. Bacterial agents were more frequently isolated from patients in the age group 21-30 years and from women (p<0.05).

Investigar em educação: desafios da construção de conhecimento e da formação de investigadores num campo multi-referenciado

O livro que agora se apresenta decorre do trabalho desenvolvido num ciclo de seminários realizados no ano lectivio de 2008/09 e pretende explicitar e sistematizar alguns elementos em torno das principais questões organizadoras: o quê investigar em educação? Como se investiga em educação? Para quê investigar em educação? Porém, não são respostas definitivas ou acabadas que se pretendem alcançar, nem tão pouco podemos escamotear que se tratam de respostas situadas em função dos percursos formativos e profissionais dos investigadores, mestrandos e doutorandos envolvidos nos seminários. Procuramos, acima de tudo, explicitar o posicionamento que, no nosso caso, tem vindo a ser privilegiado em termos de trabalho científico no campo da investigação em educação

Species and serovars of enteropathogenic agents associated with acute diarrheal disease in Rosario, Argentina

We report the most frequent species and serovars of enteropathogenic organisms in Rosario from 1985 to 1993. Enteropathogenic Escherichia coli was the most prevalent agent affecting 144/570 (25.2%) children; 0111 represented 41.8%, 055: 13.6%, 0119: 12.7%. Among enterotoxigenic E. coli (ETEC) the most frequent were ETEC-ST 0128:H21 and 0153:H45. Shigella spp were isolated in 8.8%; S.flexneri: 7%, principally type 2 (59.5%); S. sonnei: 1.6%, and S. dysenteriae type 2: 0.2%. Campylobacter spp were found in 6.1% of patients; C.jejuni: 4.6%; C. coli: 1.4% and C. lari: 0.2%; except groups 0 13,50 and 0 4 (2 cases each), no predominant serogroups were found. Salmonella was isolated in 2.8% of cases, being the predominant serovar S. typhimurium until 1986, but a dramatically increase of cases due to S. enteritidis was observed since 1987. There was 1.9% of Aeromonas spp and 2 cases due to Vibrio cholerae non 0-1. No Yersinia was found. In patients with gastroenteritis due to Shigella, Campylobacter, Salmonella, or EPEC as the unique pathogen, leukocytes were observed in the faeces in 70%, 50%, 20%, and 10% of cases respectively.

Aerobic bacteria, Chlamydia trachomatis, Pneumocystis carinii and Cytomegalovirus as agents of severe peneumonia in small infants

The authors studied 58 infants hospitalized for pneumonia in a semi-intensive care unit. Age ranged from 1 complete to 6 incomplete months. The infants were sent from another hospital in 20 cases and from home in a further 38. Pulmonary involvement, which was alveolar in 46 cases and interstitial in 12, was bilateral in 31 children. The investigation was carried out prospectively on the etiological agents associated with respiratory infection to look for evidence of aerobic bacteria (blood cultures), Chlamydia trachomatis and Cytomegalovirus (serology), and Pneumocystis carinii (direct microscopy of tracheal aspirated material). The following infectious agents were diagnosed in 21 children (36.2%): Aerobic bacteria (8), Chlamydia trachomatis (5), Pneumocystis carinii (3), Cytomegalovirus (3), Cytomegalovirus and Chlamydia trachomatis (1), Aerobic bacteria and Cytomegalovirus (1). Seven cases of infection by Chlamydia trachomatis and/or Cytomegalovirus were diagnosed out of the 12 cases with pulmonary interstitial involvement.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Intelligent Remuneration and Tariffs for Virtual Power Players

Power systems have been through deep changes in recent years, namely due to the operation of competitive electricity markets in the scope the increasingly intensive use of renewable energy sources and distributed generation. This requires new business models able to cope with the new opportunities that have emerged. Virtual Power Players (VPPs) are a new type of player that allows aggregating a diversity of players (Distributed Generation (DG), Storage Agents (SA), Electrical Vehicles (V2G) and consumers) to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players’ benefits. A major task of VPPs is the remuneration of generation and services (maintenance, market operation costs and energy reserves), as well as charging energy consumption. This paper proposes a model to implement fair and strategic remuneration and tariff methodologies, able to allow efficient VPP operation and VPP goals accomplishment in the scope of electricity markets.

Virtual Power Players Internal Negotiation and Management in MASCEM

Electricity Markets are not only a new reality but an evolving one as the involved players and rules change at a relatively high rate. Multi-agent simulation combined with Artificial Intelligence techniques may result in very helpful sophisticated tools. This paper presents a new methodology for the management of coalitions in electricity markets. This approach is tested using the multi-agent market simulator MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), taking advantage of its ability to provide the means to model and simulate Virtual Power Players (VPP). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market and internally, with their members in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. A case study using real data from the Iberian Electricity Market is performed to validate and illustrate the proposed approach.

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.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.

Demonstration of the Multi-Agent Simulator of Competitive Electricity Markets

Electricity markets are complex environments with very particular characteristics. A critical issue concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, performed so that the competitiveness could be increased, but with exponential implications in the increase of the complexity and unpredictability in those markets’ scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behavior. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This paper presents the Multi-Agent System for Competitive Electricity Markets (MASCEM) – a simulator based on multi-agent technology that provides a realistic platform to simulate electricity markets, the numerous negotiation opportunities and the participating entities.

Automatic Electricity Markets Data Extraction for Realistic Multi-agent Simulations

Electricity markets worldwide suffered profound transformations. The privatization of previously nationally owned systems; the deregulation of privately owned systems that were regulated; and the strong interconnection of national systems, are some examples of such transformations [1, 2]. In general, competitive environments, as is the case of electricity markets, require good decision-support tools to assist players in their decisions. Relevant research is being undertaken in this field, namely concerning player modeling and simulation, strategic bidding and decision-support.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.