Electroanalytical study of the pesticide asulam

The electrochemical behaviour of the herbicide Asulam was studied by cyclic and square wave voltammetry. Asulam may be irreversibly oxidised at a glassy carbon electrode. Maximum currents were obtained at pH=1.9 in aqueous electrolyte solution. Based on the electrochemical behaviour of Asulam, two analytical methodologies were developed for its determination in water samples, using square wave voltammetry (SWV) and flow injection analysis (FIA) coupled with an amperometric detector. Limits of detection of 7.1x10-6 mol L-1 and 1.2x10-8 mol L-1 for SWV and FIA respectively, were achieved. Repeatability was calculated by assessing the relative standard deviation (%) for 10 consecutive determinations of one sample. The found values were 2.1% for SWV and 5.0% for FIA. Validation of the results provided by SWV and FIA methodologies was performed by comparison with results from an HPLC-DAD technique. Good relative deviations were found (<5%). Recovery trials were performed to assess the accuracy of the results and the obtained values were between 84% and 107% for both methods.

Caracterização molecular da resistência a antimicrobianos em Escherichia coli produtoras de β-lactamases de espectro alargado isoladas em cães e estudo da partilha de clones bacterianos entre animais de companhia e coabitantes humanos

A pressão seletiva originada pelo uso excessivo de antimicrobianos na medicina humana e veterinária tem contribuído para a emergência de estirpes bacterianas multirresistentes, sendo os estudos mais escassos relativamente à sua presença nos animais de companhia. Porque os animais e os seus proprietários partilham o mesmo espaço habitacional, apresentando comportamentos de contacto próximo, existe uma hipótese elevada de transferência microbiana inter-espécie. Ante esta possibilidade é importante escrutinar o papel dos animais de companhia enquanto reservatórios de estirpes e de genes de resistência, bem como a sua envolvência na disseminação de estirpes bacterianas multirresistentes. Importa também, investigar o papel das superfícies e objetos domésticos partilhados por ambos, como potenciadores deste fenómeno. O objetivo deste trabalho foi, identificar o filogrupo e fazer a caracterização molecular dos genes que conferem resistência aos β-lactâmicos e às quinolonas, em quarenta isolados de Escherichia coli produtoras de β-lactamases de espectro alargado (ESBL), obtidas em zaragatoas fecais de cães consultados no Hospital Veterinário do ICBAS-UP. Complementarmente pretendeu-se inferir sobre a partilha de clones de Escherichia coli e Enterococcus spp. com elevadas resistências, em cinco agregados familiares (humanos e seus animais de companhia) assim como avaliar a potencial disseminação de estirpes multirresistentes no ambiente doméstico. Previamente foram recolhidas zaragatoas de fezes, pelo e mucosa oral dos animais e em alguns casos, dos seus proprietários, e ainda do ambiente doméstico. As zaragatoas foram processadas e as estirpes isoladas com base em meios seletivos. Foram realizados testes de suscetibilidade antimicrobiana de modo a estabelecer o fenótipo de resistência de cada isolado. O DNA foi extraído por varias metodologias e técnicas de PCR foram utilizadas para caracterização de filogrupos (Escherichia coli) e identificação da espécie (Enterococcus spp.). A avaliação da proximidade filogenética entre isolados foi efetuada por ERIC PCR e PFGE. No conjunto de quarenta isolados produtores de ESBL e/ou resistentes a quinolonas verificou-se que 47,5% pertenciam ao filogrupo A, havendo uma menor prevalência do filogrupo D (25,0%), B1 (17,5%), e B2 (10,0%).A frequência de resistência nestes isolados é factualmente elevada, sendo reveladora de uma elevada pressão seletiva. Com exceção de dois isolados, os fenótipos foram justificados pela presença de β-lactamases. A frequência da presença de genes foi: 47% blaTEM, 34% blaSHV, 24% blaOXA , 18% blaCTX-M-15, 8% blaCTX-M-2, 3% blaCTX-M-9. Nos isolados resistentes às quinolonas verificou-se maioritariamente a presença de mutações nos genes cromossomais gyrA e parC, e em alguns casos a presença de um determinante de resistência mediado por plasmídeo – qnrS. Nos cinco “agregados familiares” (humanos e animais) estudados foi observada uma partilha frequente de clones de E. coli e Enterococcus faecalis com múltiplas resistências, isolados em fezes e mucosa oral de cães e gatos e fezes e mãos dos respetivos proprietários, evidenciando-se assim uma possível transferência direta entre coabitantes (agregados A, C, D, E). Ficou também comprovado com percentagens de similaridade genotípica superiores a 94% que essa disseminação também ocorre para o ambiente doméstico, envolvendo objetos dos animais e de uso comum (agregados A, E). Os resultados obtidos reforçam a necessidade de um uso prudente dos antimicrobianos, pois elevados padrões de resistências terão um impacto não só na qualidade de vida dos animais mas também na saúde humana. Adicionalmente importa sensibilizar os proprietários para a necessidade de uma maior vigilância relativamente às formas de interação com os animais, bem como para a adoção de medidas higiénicas cautelares após essa mesma interação.

A comparative analysis of fault injection methods via enhanced on-chip debug infrastructures

On-chip debug (OCD) features are frequently available in modern microprocessors. Their contribution to shorten the time-to-market justifies the industry investment in this area, where a number of competing or complementary proposals are available or under development, e.g. NEXUS, CJTAG, IJTAG. The controllability and observability features provided by OCD infrastructures provide a valuable toolbox that can be used well beyond the debugging arena, improving the return on investment rate by diluting its cost across a wider spectrum of application areas. This paper discusses the use of OCD features for validating fault tolerant architectures, and in particular the efficiency of various fault injection methods provided by enhanced OCD infrastructures. The reference data for our comparative study was captured on a workbench comprising the 32-bit Freescale MPC-565 microprocessor, an iSYSTEM IC3000 debugger (iTracePro version) and the Winidea 2005 debugging package. All enhanced OCD infrastructures were implemented in VHDL and the results were obtained by simulation within the same fault injection environment. The focus of this paper is on the comparative analysis of the experimental results obtained for various OCD configurations and debugging scenarios.

Electricity Markets Simulation: MASCEM Contributions to the Challenging Reality

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 sophisticated tools very helpful under this context. Some simulation tools have already been developed, some of them very interesting. However, at the present state it is important to go a step forward in Electricity Markets simulators as this is crucial for facing changes in Power Systems. This paper explains the context and needs of electricity market simulation, describing the most important characteristics of available simulators. We present our work concerning MASCEM simulator, presenting its features as well as the improvements being made to accomplish the change and challenging reality of Electricity Markets.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had 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 behaviour. 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 dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Citrate selective electrodes for the flow injection analysis of soft drinks, beers and pharmaceutical products

Aiming the establishment of simple and accurate readings of citric acid (CA) in complex samples, citrate (CIT) selective electrodes with tubular configuration and polymeric membranes plus a quaternary ammonium ion exchanger were constructed. Several selective membranes were prepared for this purpose, having distinct mediator solvents (with quite different polarities) and, in some cases, p-tert-octylphenol (TOP) as additive. The latter was used regarding a possible increase in selectivity. The general working characteristics of all prepared electrodes were evaluated in a low dispersion flow injection analysis (FIA) manifold by injecting 500µl of citrate standard solutions into an ionic strength (IS) adjuster carrier (10−2 mol l−1) flowing at 3ml min−1. Good potentiometric response, with an average slope and a repeatability of 61.9mV per decade and ±0.8%, respectively, resulted from selective membranes comprising additive and bis(2-ethylhexyl)sebacate (bEHS) as mediator solvent. The same membranes conducted as well to the best selectivity characteristics, assessed by the separated solutions method and for several chemical species, such as chloride, nitrate, ascorbate, glucose, fructose and sucrose. Pharmaceutical preparations, soft drinks and beers were analyzed under conditions that enabled simultaneous pH and ionic strength adjustment (pH = 3.2; ionic strength = 10−2 mol l−1), and the attained results agreed well with the used reference method (relative error < 4%). The above experimental conditions promoted a significant increase in sensitivity of the potentiometric response, with a supra-Nernstian slope of 80.2mV per decade, and allowed the analysis of about 90 samples per hour, with a relative standard deviation <1.0%.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

Distributed Intelligent Management of Microgrids using a Multi-Agent Simulation Platform

Multi-agent approaches have been widely used to model complex systems of distributed nature with a large amount of interactions between the involved entities. Power systems are a reference case, mainly due to the increasing use of distributed energy sources, largely based on renewable sources, which have potentiated huge changes in the power systems’ sector. Dealing with such a large scale integration of intermittent generation sources led to the emergence of several new players, as well as the development of new paradigms, such as the microgrid concept, and the evolution of demand response programs, which potentiate the active participation of consumers. This paper presents a multi-agent based simulation platform which models a microgrid environment, considering several different types of simulated players. These players interact with real physical installations, creating a realistic simulation environment with results that can be observed directly in the reality. A case study is presented considering players’ responses to a demand response event, resulting in an intelligent increase of consumption in order to face the wind generation surplus.