Identification of factors and groups at risk of infection with Schistosoma mansoni: a strategy for the implementation of control measures?

A fourteen year schistosomiasis control program in Peri-Peri (Capim Branco, MG) reduced prevalence from 43.5 to 4.4%; incidence from 19.0 to 2.9%, the geometric mean of the number of eggs from 281 to 87 and the level of the hepatoesplenic form cases from 5.9 to 0.0%. In 1991, three years after the interruption of the program, the prevalence had risen to 19.6%. The district consists of Barbosa (a rural area) and Peri-Peri itself (an urban area). In 1991, the prevalence in the two areas was 28.4% and 16.0% respectively. A multivariate analysis of risk factors for schistosomiasis indicated the domestic agricultural activity with population attributive risk (PAR) of 29.82%, the distance (< 10 m) from home to water source (PAR = 25.93%) and weekly fishing (PAR = 17.21%) as being responsible for infections in the rural area. The recommended control measures for this area are non-manual irrigation and removal of homes to more than ten meters from irrigation ditches. In the urban area, it was observed that swimming at weekly intervals (PAR = 20.71%), daily domestic agricultural activity (PAR = 4.07%) and the absence of drinking water in the home (PAR=4.29%) were responsible for infections. Thus, in the urban area the recommended control measures are the substitution of manual irrigation with an irrigation method that avoids contact with water, the creation of leisure options of the population and the provision of a domestic water supply. The authors call attention to the need for the efficacy of multivariate analysis of risk factors to be evaluated for schistosomiasis prior to its large scale use as a indicator of the control measures to be implemented.

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

Multy-dimensional composition by objectives in aspect-oriented requirements analysis

Thesis submitted to Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa in partial fulfilment of the requirements for the degree of Master in Computer Science

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.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Intelligent Micro Grid Management using a Multi-Agent approach

Recent changes in electricity markets (EMs) have been potentiating the globalization of distributed generation. With distributed generation the number of players acting in the EMs and connected to the main grid has grown, increasing the market complexity. Multi-agent simulation arises as an interesting way of analysing players’ behaviour and interactions, namely coalitions of players, as well as their effects on the market. MASCEM was developed to allow studying the market operation of several different players and MASGriP is being developed to allow the simulation of the micro and smart grid concepts in very different scenarios This paper presents a methodology based on artificial intelligence techniques (AI) for the management of a micro grid. The use of fuzzy logic is proposed for the analysis of the agent consumption elasticity, while a case based reasoning, used to predict agents’ reaction to price changes, is an interesting tool for the micro grid operator.

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.

Increase of the Delivered Power Probability in Distribution Networks using Pareto DC Programming

A methodology to increase the probability of delivering power to any load point through the identification of new investments in distribution network components is proposed in this paper. The method minimizes the investment cost as well as the cost of energy not supplied in the network. A DC optimization model based on mixed integer non-linear programming is developed considering the Pareto front technique in order to identify the adequate investments in distribution networks components which allow increasing the probability of delivering power for any customer in the distribution system at the minimum possible cost for the system operator, while minimizing the energy not supplied cost. Thus, a multi-objective problem is formulated. To illustrate the application of the proposed methodology, the paper includes a case study which considers a 180 bus distribution network

Study of Proposed Identification of Non-Technical Losses of Electricity in Brazil

The electric utilities have large revenue losses annually due to commercial losses, which are caused mainly by fraud on the part of consumers and faulty meters. Automatic detection of such losses where there is a complex problem, given the large number of consumers and the high cost of each inspection, not to mention the wear of the relationship between company and consumer. Given the above, this paper aims to briefly present some methodologies applied by utilities to identify consumer frauds.

Human cutaneous leishmaniasis caused by Leishmania (Viannia) braziliensis in Santiago del Estero, Argentina: identification of parasites by monoclonal antibodies and isoenzymes

Diagnostic and parasite characterization and identification studies were carried out in human patients with cutaneous leishmaniasis lesions in Santiago del Estero, Northern Province of Argentina. Diagnostic procedures were biopsies of lesions for smears and inoculations in hamster, needle aspirations of material from ulcers for "in vitro" cultures. Immunodiagnostic techniques applied were IFAT-IgG and Montenegro skin test. Primary isolation of eight stocks of leishmanial parasites was achieved from patients with active lesions. All stocks were biologically characterized by their behaviour in hamster, measurements of amastigote and promastigotes and growth "in vitro". Eight stocks were characterized and identified at species level by their reactivity to a cross-panel of sub-genus and specie-specific Monoclonal Antibodies through an Indirect Immunofluorescence technique and a Dot-ELISA. We conclude from the serodeme analysis of Argentina stocks that: stocks MHOM/AR/92/SE-1; SE-2; SE-4; SE-8; SE-8-I; SE-30; SE-34 and SE-36 are Leishmania (Viannia) braziliensis. Three Leishmania stocks (SE-1; SE-2 and SE-30) did not react with one highly specie-specific Monoclonal Antibody (Clone: B-18, Leishmania (Viannia) braziliensis marker) disclosing two serodeme group patterns. Five out of eight soluble extracts of leishmanial promastigotes were electrophoresed on thin-layer starch gels and examined for the enzyme MPI, Mannose Phosphate Isomerase; MDH, Malate Dehydrogenase; 6PGD, 6 Phosphogluconate Dehydrogenase; NH, Nucleoside Hydrolase, 2-deoxyinosinc as substrate; SOD, Superoxide Dismutase; GPI, Glucose Phosphate Isomerase and ES, Esterase. From the isoenzyme studies we concluded that stocks: MHOM/AR/92/SE-1; SE-2; SE-4; SE-8 and SE-8-I are isoenzymatically Leishmania (Viannia) braziliensis. We need to analyze more enzymes before assigning them to a braziliensis zymodeme.

Load Control Timescales Simulation in a Multi-Agent Smart Grid Platform

Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers’ consumption profile, helping to reduce peak demand. Aiming to support small players’ participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques – the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

EVALUATION OF A RAPID SCREENING ASSAY FOR BACTERIAL IDENTIFICATION (DOT-ELISA) IN FECAL SAMPLES FROM CHILDREN

With the objective of standardizing a Dot Enzyme-Linked Immunosorbent Assay (Dot-ELISA) to detect antigens of fecal bacterial enteropathogens, 250 children, aged under 36 months and of both sexes, were studied; of which 162 had acute gastroenteritis. The efficacy of a rapid screening assay for bacterial enteropathogens (enteropathogenic Escherichia coli "EPEC", enteroinvasive Escherichia coli "EIEC", Salmonella spp. and Shigella spp.) was evaluated. The fecal samples were also submitted to a traditional method of stool culture for comparison. The concordance index between the two techniques, calculated using the Kappa (k) index for the above mentioned bacterial strains was 0.8859, 0.9055, 0.7932 and 0.7829 respectively. These values express an almost perfect degree of concordance for the first two and substantial concordance for the latter two, thus enabling this technique to be applied in the early diagnosis of diarrhea in infants. With a view to increasing the sensitivity and specificity of this immunological test, a study was made of the antigenic preparations obtained from two types of treatment: 1) deproteinization by heating; 2) precipitation and concentration of the lipopolysaccharide antigen (LPS) using an ethanol-acetone solution, which was then heated in the presence of sodium EDTA