Nutritional value and influence of the thermal processing on a traditional Portuguese fermented sausage (alheira)

Alheiras are a traditional, smoked, fermented meat sausage, produced in Portugal, with an undeniable cultural and gastronomic legacy. In this study, we assessed the nutritional value of this product, as well as the influence of different types of thermal processing. Alheiras from Mirandela were submitted to six different procedures: microwave, skillet, oven, charcoal grill, electric fryer and electric grill. Protein, fat, carbohydrate, minerals, NaCl, and cholesterol contents, as well as fatty acid profile were evaluated. The results show that alheiras are not hypercaloric but an unbalanced foodstuff (high levels of proteins and lipids) and the type of processing has a major impact on their nutritional value. Charcoal grill is the healthiest option: less fat (12.5 g/100 g) and cholesterol (29.3 mg/100 g), corresponding to a lower caloric intake (231.8 kcal, less 13% than the raw ones). Inversely, fried alheiras presented the worst nutritional profile, with the highest levels of fat (18.1 g/100 g) and cholesterol (76.0 g/100 g).

Wear resistance of TiAlSiN thin coatings

In the last decades TiAlN coatings deposited by PVD techniques have been extensively investigated but, nowadays, their potential development for tribological applications is relatively low. However, new coatings are emerging based on them, trying to improve wear behavior. TiAlSiN thin coatings are now investigated, analyzing if Si introduction increases the wear resistance of PVD films. Attending to the application, several wear test configurations has been recently used by some researchers. In this work, TiAlSiN thin coatings were produced by PVD Unbalanced Magnetron Sputtering technique and they were conveniently characterized using Scanning Electron Microscopy (SEM) provided with Energy Dispersive Spectroscopy (EDS), Atomic Force Microscopy (AFM), Electron Probe Micro-Analyzer (EPMA), Micro Hardness (MH) and Scratch Test Analysis. Properties as morphology, thickness, roughness, chemical composition and structure, hardness and film adhesion to the substrate were investigated. Concerning to wear characterization, two very different ways were chosen: micro-abrasion with ball-on-flat configuration and industrial non-standardized tests based on samples inserted in a feed channel of a selected plastic injection mould working with 30% (wt.) glass fiber reinforced polypropylene. TiAlSiN coatings with a small amount of about 5% (wt.) Si showed a similar wear behavior when compared with TiAlN reported performances, denoting that Si addition does not improve the wear performance of the TiAlN coatings in these wear test conditions.

Mechanical and tribological characterization of TiB2 thin films

Titanium Diboride (TiB2) presents high mechanical and physical properties. Some wear studies were also carried out in order to evaluate its tribological properties. One of the most popular wear tests for thin films is the ball-cratering configuration. This work was focused on the study of the tribological properties of TiB2 thin films using micro-abrasion tests and following the BS EN 1071-6: 2007 standard. Due to high hardness usually patented by these films, diamond was selected as abrasive on micro-abrasion tests. Micro-abrasion wear tests were performed under five different durations, using the same normal load, speed rotation and ball. Films were deposited by unbalanced magnetron sputtering Physical Vapour Deposition (PVD) technique using TiB2 targets. TiB2 films were characterized using different methods as Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), Atomic Force Microscopy (AFM), X-ray Diffraction (XRD), Electron Probe Micro-Analyser (EPMA), Ultra Micro Hardness and Scratch-test Analysis, allowing to confirm that TiB2 presents adequate mechanical and physical properties. Ratio between hardness (coating and abrasive particles), wear resistance and wear coefficient were studied, showing that TiB2 films shows excellent properties for tribological applications.

TiB2 nanostructured coating for GFRP injection moulds

In the injection moulding of polypropylene reinforced with hard glass fibres, die materials are commonly subjected to severe abrasive wear. In order to improve its wear resistance, an unbalanced magnetron sputtering PVD compositional monolayered coating has been produced. The film was composed by a nanostructured TiB2 monolayer. Microstructure characterization and thickness evaluation were conducted by scanning electron microscopy (SEM). Film topography and roughness were accessed by SEM and Atomic Force Microscopy (AFM). The phase analyse was investigated by X-ray diffraction (XRD), using Cu Kalpha radiation. Scratch tests were conducted in order to study the film adhesion to the substrate. Load-Displacement curves (nanoindentation analysis) allowed measuring the film hardness and Young's modulus. A ball-cratering tribometer was used to determine the micro-abrasion laboratorial wear resistance, under different tests conditions, using SiC particles in distilled water slurry. At the end of these tests, the worn surfaces were analyzed by SEM and Energy Dispersive X-ray Spectroscopy (EDS) in order to compare these results with some other coatings already tested in the same conditions. To test the practical wear resistance, 135000 injection cycles were done in a plastic injection industrial mould. Coated samples were put on the plastic feed canal, after a turbulent zone. In these tests, a 30% (wt) glass fibres reinforced polypropylene was used. Worn sample surfaces were analyzed by SEM after 45.000 and 90.000 cycles. Image analyses were made in order to evaluate the damage increases and to observe the wear mechanisms involved.

Increasing the wear resistance of molds for injection of glass fiber reinforced plastics

Abrasion by glass fibers during injection molding of fiber reinforced plastics raises new challenges to the wear performance of the molds. In the last few decades, a large number of PVD and CVD coatings have been developed with the aim of minimizing abrasion problems. In this work, two different coatings were tested in order to increase the wear resistance of the surface of a mold used for glass fiber reinforced plastics: TiAlSiN and CrN/CrCN/DLC. TiAlSiN was deposited as a graded monolayer coating while CrN/CrCN/DLC was a nanostructured coating consisting of three distinct layers. Both coatings were produced by PVD unbalanced magnetron sputtering and were characterized using scanning electron microscopy (SEM) provided with energy dispersive spectroscopy (EDS), atomic force microscopy (AFM), micro hardness (MH) and scratch test analysis. Coating morphology, thickness, roughness, chemical composition and structure, hardness and adhesion to the substrate were investigated. Wear resistance was characterized through industrial tests with coated samples and an uncoated reference sample inserted in a feed channel of a plastic injection mold working with 30 wt.% glass fiber reinforced polypropylene. Results after 45,000 injection cycles indicate that the wear resistance of the mold was increased by a factor of 25 and 58, by the TiAlSiN and CrN/CrCN/DLC coatings, respectively, over the uncoated mold steel.

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

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. 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. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Adapting Meeting Tools to Agent Decision

Electricity markets are complex environments comprising several negotiation mechanisms. MASCEM (Multi- Agent System for Competitive Electricity Markets) is a simulator developed to allow deep studies of the interactions between the players that take part in the electricity market negotiations. ALBidS (Adaptive Learning Strategic Bidding System) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM it considers several different methodologies based on very distinct approaches. The Six Thinking Hats is a powerful technique used to look at decisions from different perspectives. This paper aims to complement ALBidS strategies usage by MASCEM players, providing, through the Six Thinking Hats group decision technique, a means to combine them and take advantages from their different perspectives. The combination of the different proposals resulting from ALBidS’ strategies is performed through the application of a Genetic Algorithm, resulting in an evolutionary learning approach.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

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 a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

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.

Stochastic Short-term Incentive-based Demand Response Scheduling of Load-serving Entities

In competitive electricity markets it is necessary for a profit-seeking load-serving entity (LSE) to optimally adjust the financial incentives offering the end users that buy electricity at regulated rates to reduce the consumption during high market prices. The LSE in this model manages the demand response (DR) by offering financial incentives to retail customers, in order to maximize its expected profit and reduce the risk of market power experience. The stochastic formulation is implemented into a test system where a number of loads are supplied through LSEs.

MASCEM Restructuring: Ontologies For Scenarios Generation in Power Systems Simulators

The electricity market restructuring, along with the increasing necessity for an adequate integration of renewable energy sources, is resulting in an rising complexity in power systems operation. Various power system simulators have been introduced in recent years with the purpose of helping operators, regulators, and involved players to understand and deal with this complex environment. This paper focuses on the development of an upper ontology which integrates the essential concepts necessary to interpret all the available information. The restructuring of MASCEM (Multi-Agent System for Competitive Electricity Markets), and this system’s integration with MASGriP (Multi-Agent Smart Grid Platform), and ALBidS (Adaptive Learning Strategic Bidding System) provide the means for the exemplification of the usefulness of this ontology. A practical example is presented, showing how common simulation scenarios for different simulators, directed to very distinct environments, can be created departing from the proposed ontology.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

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 a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Tolerância a falhas em sistemas de tempo-real distribuídos e embebidos

Este documento descreve um modelo de tolerância a falhas para sistemas de tempo-real distribuídos. A sugestão deste modelo tem como propósito a apresentação de uma solu-ção fiável, flexível e adaptável às necessidades dos sistemas de tempo-real distribuídos. A tolerância a falhas é um aspeto extremamente importante na construção de sistemas de tempo-real e a sua aplicação traz inúmeros benefícios. Um design orientado para a to-lerância a falhas contribui para um melhor desempenho do sistema através do melhora-mento de aspetos chave como a segurança, a confiabilidade e a disponibilidade dos sis-temas. O trabalho desenvolvido centra-se na prevenção, deteção e tolerância a falhas de tipo ló-gicas (software) e físicas (hardware) e assenta numa arquitetura maioritariamente basea-da no tempo, conjugada com técnicas de redundância. O modelo preocupa-se com a efi-ciência e os custos de execução. Para isso utilizam-se também técnicas tradicionais de to-lerância a falhas, como a redundância e a migração, no sentido de não prejudicar o tempo de execução do serviço, ou seja, diminuindo o tempo de recuperação das réplicas, em ca-so de ocorrência de falhas. Neste trabalho são propostas heurísticas de baixa complexida-de para tempo-de-execução, a fim de se determinar para onde replicar os componentes que constituem o software de tempo-real e de negociá-los num mecanismo de coordena-ção por licitações. Este trabalho adapta e estende alguns algoritmos que fornecem solu-ções ainda que interrompidos. Estes algoritmos são referidos em trabalhos de investiga-ção relacionados, e são utilizados para formação de coligações entre nós coadjuvantes. O modelo proposto colmata as falhas através de técnicas de replicação ativa, tanto virtual como física, com blocos de execução concorrentes. Tenta-se melhorar ou manter a sua qualidade produzida, praticamente sem introduzir overhead de informação significativo no sistema. O modelo certifica-se que as máquinas escolhidas, para as quais os agentes migrarão, melhoram iterativamente os níveis de qualidade de serviço fornecida aos com-ponentes, em função das disponibilidades das respetivas máquinas. Caso a nova configu-ração de qualidade seja rentável para a qualidade geral do serviço, é feito um esforço no sentido de receber novos componentes em detrimento da qualidade dos já hospedados localmente. Os nós que cooperam na coligação maximizam o número de execuções para-lelas entre componentes paralelos que compõem o serviço, com o intuito de reduzir atra-sos de execução. O desenvolvimento desta tese conduziu ao modelo proposto e aos resultados apresenta-dos e foi genuinamente suportado por levantamentos bibliográficos de trabalhos de in-vestigação e desenvolvimento, literaturas e preliminares matemáticos. O trabalho tem também como base uma lista de referências bibliográficas.

Negotiation context analysis in electricity markets

Contextualization is critical in every decision making process. Adequate responses to problems depend not only on the variables with direct influence on the outcomes, but also on a correct contextualization of the problem regarding the surrounding environment. Electricity markets are dynamic environments with increasing complexity, potentiated by the last decades' restructuring process. Dealing with the growing complexity and competitiveness in this sector brought the need for using decision support tools. A solid example is MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), whose players' decisions are supported by another multiagent system – ALBidS (Adaptive Learning strategic Bidding System). ALBidS uses artificial intelligence techniques to endow market players with adaptive learning capabilities that allow them to achieve the best possible results in market negotiations. This paper studies the influence of context awareness in the decision making process of agents acting in electricity markets. A context analysis mechanism is proposed, considering important characteristics of each negotiation period, so that negotiating agents can adapt their acting strategies to different contexts. The main conclusion is that context-dependant responses improve the decision making process. Suiting actions to different contexts allows adapting the behaviour of negotiating entities to different circumstances, resulting in profitable outcomes.