A few what-ifs on using statistical analysis of stochastic simulation runs to extract timeliness properties

Modern real-time systems, with a more flexible and adaptive nature, demand approaches for timeliness evaluation based on probabilistic measures of meeting deadlines. In this context, simulation can emerge as an adequate solution to understand and analyze the timing behaviour of actual systems. However, care must be taken with the obtained outputs under the penalty of obtaining results with lack of credibility. Particularly important is to consider that we are more interested in values from the tail of a probability distribution (near worst-case probabilities), instead of deriving confidence on mean values. We approach this subject by considering the random nature of simulation output data. We will start by discussing well known approaches for estimating distributions out of simulation output, and the confidence which can be applied to its mean values. This is the basis for a discussion on the applicability of such approaches to derive confidence on the tail of distributions, where the worst-case is expected to be.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

Characterization of an antioxidant surfactant-free topical formulation containing Castanea sativa leaf extract

Context: Inclusion of antioxidants in topical formulations can contribute to minimize oxidative stress in the skin, which has been associated with photoaging, several dermatosis and cancer. Objective: A Castanea sativa leaf extract with established antioxidant activity was incorporated into a semisolid surfactant-free formulation. The objective of this study was to perform a comprehensive characterization of this formulation. Materials and methods: Physical, microbiological and functional stability were evaluated during 6 months storage at 20 °C and 40 °C. Microstructure elucidation (cryo-SEM), in vitro release and in vivo moisturizing effect (Corneometer® CM 825) were also assessed. Results and discussion: Minor changes were observed in the textural and rheological properties of the formulation when stored at 20 °C for 6 months and the antioxidant activity of the plant extract remained constant throughout the storage period. Microbiological quality was confirmed at the end of the study. Under accelerated conditions, higher modifications of the evaluated parameters were observed. Cryo-SEM analysis revealed the presence of oil droplets dispersed into a gelified external phase. The release rate of the antioxidant compounds (610 ± 70 µgh−0.5) followed Higuchi model. A significant in vivo moisturizing effect was demonstrated, that lasted at least 4 h after product’s application. Conclusion: The physical, functional and microbiological stability of the antioxidant formulation was established. Specific storage conditions should be recommended considering the influence of temperature on the stability. A skin hydration effect and good skin tolerance were also found which suggests that this preparation can be useful in the prevention or treatment of oxidative stress-mediated dysfunctions.

Protective effect of C. sativa leaf extract against UV mediated-DNA damage in a human keratinocyte cell line

Toxic effects of ultraviolet (UV) radiation on skin include protein and lipid oxidation, and DNA damage. The latter is known to play a major role in photocarcinogenesis and photoaging. Many plant extracts and natural compounds are emerging as photoprotective agents. Castanea sativa leaf extract is able to scavenge several reactive species that have been associated to UV-induced oxidative stress. The aim of this work was to analyze the protective effect of C. sativa extract (ECS) at different concentrations (0.001, 0.01, 0.05 and 0.1 μg/mL) against the UV mediated-DNA damage in a human keratinocyte cell line (HaCaT). For this purpose, the cytokinesis-block micronucleus assay was used. Elucidation of the protective mechanism was undertaken regarding UV absorption, influence on 1O2 mediated effects or NRF2 activation. ECS presented a concentration-dependent protective effect against UV-mediated DNA damage in HaCaT cells. The maximum protection afforded (66.4%) was achieved with the concentration of 0.1 μg/mL. This effect was found to be related to a direct antioxidant effect (involving 1O2) rather than activation of the endogenous antioxidant response coordinated by NRF2. Electrochemical studies showed that the good antioxidant capacity of the ECS can be ascribed to the presence of a pool of different phenolic antioxidants. No genotoxic or phototoxic effects were observed after incubation of HaCaT cells with ECS (up to 0.1 μg/mL). Taken together these results reinforce the putative application of this plant extract in the prevention/minimization of UV deleterious effects on skin.

Learning business strategies for competitive electronic marketplaces

This paper presents a Multi-Agent Market simulator designed for developing new agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. This tool studies negotiations based on different market mechanisms and, time and behavior dependent strategies. The results of the negotiations between agents are analyzed by data mining algorithms in order to extract rules that give agents feedback to improve their strategies. The system also includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agent reactions.

Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

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.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

O conceito da qualidade nos produtos à base de Ginkgo biloba comercializados em Portugal

O extracto de Ginkgo biloba é o produto fitoterápico mais vendido na Europa. Em Portugal e muitos países, a maioria dos produtos à base de plantas são comercializados como suplementos alimentares, não estando garantidos, parâmetros de qualidade, segurança e eficácia. Realizouse um estudo, com recolha de informações, tendo por base uma amostra de 50 produtos à base de ginkgo. Da análise, verificou-se que 94% podiam ser encontrados à venda na internet, e desse total, 89% possuíam informação on-line quanto à composição. Apenas 40% referem a utilização do extracto padronizado de ginkgo e muitos recomendam doses superiores às referidas como terapêuticas.