Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

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 is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Devolução de doses unitárias: análise de uma prática diária

A Distribuição Individual Diária em Dose Unitária deve assegurar o maior rigor no Processo de Distribuição de Medicamentos, o que nem sempre se verificou no Hospital Pedro Hispano, dado que as Doses Unitárias devolvidas pelos Serviços de Internamento, nem sempre seguiam os Parâmetros de Qualidade. Assim, desenvolveu-se um estudo quasi-experimental, longitudinal, prospetivo e analítico, entre 1 de Março a 30 de Junho de 2012, no qual se analisaram as Doses Unitárias, devolvidas pelos Serviços de Internamento da Unidade Local de Saúde de Matosinhos a funcionam com Distribuição Individual Diária em Dose Unitária, tendo-se recolhido as não conformes relativamente aos Parâmetros de Qualidade instituídos. Na 1.ª fase do estudo (Recolha de amostras), foram contabilizadas 337 amostras, correspondentes a uma perda de 471 €. Na 2.ª fase do estudo (Implementação de acções correctivas às amostras previamente recolhidas), foram rejeitadas definitivamente 129 amostras, correspondentes a uma perda de 198 €. Na 4.ª fase do estudo (Segunda recolha de amostras), foram contabilizadas 228 amostras, correspondentes a uma perda de 199 €. A 3.ª fase do estudo incluiu a divulgação dos resultados às enfermeiras chefes dos serviços envolvidos no estudo e a 5.ª fase a comparação dos resultados obtidos na 1.ª, 2.ª e 4.ª fase do estudo. As amostras foram recolhidas em 16 serviços de internamento, sendo os valores monetários mais relevantes associados ao Espessante para alimentos líquidos e ao Imipeno + Cilastatina. A prática observada e sujeita a estudo aumenta os riscos associados ao consumo do medicamento e os custos relativos ao Processo, sendo de eliminar. A sua monitorização deve constituir uma rotina, uma vez que houve mudança de comportamentos. A redução/eliminação desta prática não conforme conduz à integração de novas tarefas com valor acrescentado, com aumento da fiabilidade do Processo.

Bit error models

The use of bit error models in communication simulation has been widely studied. In this technical report we present three models: the Independent Channel Model; the Gilbert-Elliot Model and the Burst-Error Periodic Model.

On sampling collection procedure effectiveness for forest soil characterization

One of the most important measures to prevent wild forest fires is the use of prescribed and controlled burning actions as it reduce the fuel mass availability. The impact of these management activities on soil physical and chemical properties varies according to the type of both soil and vegetation. Decisions in forest management plans are often based on the results obtained from soil-monitoring campaigns. Those campaigns are often man-labor intensive and expensive. In this paper we have successfully used the multivariate statistical technique Robust Principal Analysis Compounds (ROBPCA) to investigate on the sampling procedure effectiveness for two different methodologies, in order to reflect on the possibility of simplifying and reduce the sampling collection process and its auxiliary laboratory analysis work towards a cost-effective and competent forest soil characterization.

Soil sampling design and sampling techniques for soil properties monitoring – common difficulties related to forest soils

Mathematical models and statistical analysis are key instruments in soil science scientific research as they can describe and/or predict the current state of a soil system. These tools allow us to explore the behavior of soil related processes and properties as well as to generate new hypotheses for future experimentation. A good model and analysis of soil properties variations, that permit us to extract suitable conclusions and estimating spatially correlated variables at unsampled locations, is clearly dependent on the amount and quality of data and of the robustness techniques and estimators. On the other hand, the quality of data is obviously dependent from a competent data collection procedure and from a capable laboratory analytical work. Following the standard soil sampling protocols available, soil samples should be collected according to key points such as a convenient spatial scale, landscape homogeneity (or non-homogeneity), land color, soil texture, land slope, land solar exposition. Obtaining good quality data from forest soils is predictably expensive as it is labor intensive and demands many manpower and equipment both in field work and in laboratory analysis. Also, the sampling collection scheme that should be used on a data collection procedure in forest field is not simple to design as the sampling strategies chosen are strongly dependent on soil taxonomy. In fact, a sampling grid will not be able to be followed if rocks at the predicted collecting depth are found, or no soil at all is found, or large trees bar the soil collection. Considering this, a proficient design of a soil data sampling campaign in forest field is not always a simple process and sometimes represents a truly huge challenge. In this work, we present some difficulties that have occurred during two experiments on forest soil that were conducted in order to study the spatial variation of some soil physical-chemical properties. Two different sampling protocols were considered for monitoring two types of forest soils located in NW Portugal: umbric regosol and lithosol. Two different equipments for sampling collection were also used: a manual auger and a shovel. Both scenarios were analyzed and the results achieved have allowed us to consider that monitoring forest soil in order to do some mathematical and statistical investigations needs a sampling procedure to data collection compatible to established protocols but a pre-defined grid assumption often fail when the variability of the soil property is not uniform in space. In this case, sampling grid should be conveniently adapted from one part of the landscape to another and this fact should be taken into consideration of a mathematical procedure.