Development of an operational tool for oil spill forecast: application to oil exposed regions

Tese de doutoramento, Ciências do Mar, da Terra e do Ambiente (Modelação), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014

Face recogniton based on the optimal combination of neural networks, eigenfaces and least squares matching methods

Report of a research project of the Fachhochschule Hannover, University of Applied Sciences and Arts, Department of Information Technologies. Automatic face recognition increases the security standards at public places and border checkpoints. The picture inside the identification documents could widely differ from the face, that is scanned under random lighting conditions and for unknown poses. The paper describes an optimal combination of three key algorithms of object recognition, that are able to perform in real time. The camera scan is processed by a recurrent neural network, by a Eigenfaces (PCA) method and by a least squares matching algorithm. Several examples demonstrate the achieved robustness and high recognition rate.

Selection and combination of biomarkers to control and prevent the risk of occupational exposure to the mixture of lead, arsenic and manganese

Tese de doutoramento, Farmácia (Toxicologia), Universidade de Lisboa, Faculdade de Farmácia, 2014

A 30-Day-Ahead Forecast Model for Grass Pollen in North London, United Kingdom

A 30-day ahead forecast method has been developed for grass pollen at north London. The total period of the grass pollen season is covered by eight multiple regression models, each covering a 10-day period running consecutively from 21st May to 8th August. This means that three models were used for each 30-day forecast. The forecast models were produced using grass pollen and environmental data from 1961-1999 and tested on data from 2000 and 2002. Model accuracy was judged in two ways: the number of times the forecast model was able to successfully predict the severity (relative to the 1961-1999 dataset as a whole) of grass pollen counts in each of the eight forecast periods on a scale of one to four; and the number of times the forecast model was able to predict whether grass pollen counts were higher or lower than the mean. The models achieved 62.5% accuracy in both assessment years when predicting the relative severity of grass pollen counts on a scale of one to four, which equates to six of the eight 10-day periods being forecast correctly. The models attained 87.5% and 100% accuracy in 2000 and 2002 respectively when predicting whether grass pollen counts would be higher or lower than the mean. Attempting to predict pollen counts during distinct 10-day periods throughout the grass pollen season is a novel approach. The models also employed original methodology in the use of winter averages of the North Atlantic Oscillation to forecast 10-day means of allergenic pollen counts.

Constructing a 7-day Ahead Forecast Model for Grass Pollen at North London, United Kingdom

A number of media outlets now issue medium-range (~7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts. The objective of this study is to construct a medium-range (< 7 day) forecast model for grass pollen at north London. The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990-1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post peak periods of grass pollen release. The forecast consisted of five regression models. Two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods. Overall the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis. This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.

Long-term and Short-term Forecast Models for Poaceae (Grass) Pollen in Poznan, Poland, Constructed Using Regression Analysis

Airborne concentrations of Poaceae pollen have been monitored in Poznań for more than ten years and the length of the dataset is now considered sufficient for statistical analysis. The objective of this paper is to produce long-range forecasts that predict certain characteristics of the grass pollen season (such as the start, peak and end dates of the grass pollen season) as well as short-term forecasts that predict daily variations in grass pollen counts for the next day or next few days throughout the main grass pollen season. The method of forecasting was regression analysis. Correlation analysis was used to examine the relationship between grass pollen counts and the factors that affect its production, release and dispersal. The models were constructed with data from 1994-2004 and tested on data from 2005 and 2006. The forecast models predicted the start of the grass pollen season to within 2 days and achieved 61% and 70% accuracy on a scale of 1-4 when forecasting variations in daily grass pollen counts in 2005 and 2006 respectively. This study has emphasised how important the weather during the few weeks or months preceding pollination is to grass pollen production, and draws attention to the importance of considering large-scale patterns of climate variability (indices of the North Atlantic Oscillation) when constructing forecast models for allergenic pollen.

Klebsiella pneumoniae subsp. pneumoniae–bacteriophage combination from the caecal effluent of a healthy woman

A recent study characterizing bacteriophage populations within human caecal effluent demonstrated the presence of numerous Podoviridae, Siphoviridae and Myoviridae within this material (Hoyles et al., 2014, Res Microbiol 165, 803–812). Further to this work, anaerobic bacteria were isolated on fastidious anaerobe agar from the caecal effluent of a healthy 31-year-old woman. Ten colonies were selected at random, streaked to purity and screened against the remaining caecal effluent (filter-sterilized, 0.45 μm pore size) in an attempt to isolate lytic bacteriophages. Bacteriophages within the effluent [2×105 ± 2.65×103 (n=3) pfu/ml] were active against five of the isolates, all identified by 16S rRNA gene sequence analysis as Klebsiella pneumoniae. One of the five isolates, L4-FAA5, was characterized further and found to be K. pneumoniae subsp. pneumoniae capsule type K2 rmpA+, and was used to propagate a bacteriophage (which we named KLPN1) to purity. Bacteriophage KLPN1 was a member of the Siphoviridae with a rosette-like tail tip and exhibited depolymerase activity, demonstrated by the formation of plaque-surrounding haloes that increased in size over the course of incubation. When screened against a panel of 21 clinical strains representing unknown K. pneumoniae subsp. pneumoniae capsule types and types K1, K2, K5, K20, K54 and K57, KLPN1 infected only K2 strains, but did not exhibit depolymerase activity against these. Whole-genome sequence analysis of KLPN1 showed the bacteriophage to have a genome of 49,037 bp (50.53 GC mol%) comprising 73 predicted ORFs, of which 22 encoded genes associated with structure, host recognition, packaging, DNA replication and cell lysis. The host recognition-associated gene was a potential depolymerase. This is the first report of the isolation of a bacterium–bacteriophage combination from the human caecum, and only the third member of the Siphoviridae known to infect K. pneumoniae subsp. pneumoniae.

Klebsiella pneumoniae subsp. pneumoniae–bacteriophage combination from the caecal effluent of a healthy woman

A sample of caecal effluent was obtained from a female patient who had undergone a routine colonoscopic examination. Bacteria were isolated anaerobically from the sample, and screened against the remaining filtered caecal effluent in an attempt to isolate bacteriophages (phages). A lytic phage, named KLPN1, was isolated on a strain identified as Klebsiella pneumoniae subsp. pneumoniae (capsular type K2, rmpA+). This Siphoviridae phage presents a rosette-like tail tip and exhibits depolymerase activity, as demonstrated by the formation of plaque-surrounding haloes that increased in size over the course of incubation. When screened against a panel of clinical isolates of K. pneumoniae subsp. pneumoniae, phage KLPN1 was shown to infect and lyse capsular type K2 strains, though it did not exhibit depolymerase activity on such hosts. The genome of KLPN1 was determined to be 49,037 bp (50.53 %GC) in length, encompassing 73 predicted ORFs, of which 23 represented genes associated with structure, host recognition, packaging, DNA replication and cell lysis. On the basis of sequence analyses, phages KLPN1 (GenBank: KR262148) and 1513 (a member of the family Siphoviridae, GenBank: KP658157) were found to be two new members of the genus “Kp36likevirus”.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Real-time energy resources scheduling considering short-term and very short-term wind forecast

This paper proposes an energy resources management methodology based on three distinct time horizons: day-ahead scheduling, hour-ahead scheduling, and real-time scheduling. In each scheduling process it is necessary the update of generation and consumption operation and of the storage and electric vehicles storage status. Besides the new operation condition, it is important more accurate forecast values of wind generation and of consumption using results of in short-term and very short-term methods. A case study considering a distribution network with intensive use of distributed generation and electric vehicles is presented.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Self-adaptive combination of global tabu search and local search for nonlinear equations

Solving systems of nonlinear equations is a very important task since the problems emerge mostly through the mathematical modelling of real problems that arise naturally in many branches of engineering and in the physical sciences. The problem can be naturally reformulated as a global optimization problem. In this paper, we show that a self-adaptive combination of a metaheuristic with a classical local search method is able to converge to some difficult problems that are not solved by Newton-type methods.

Distributed energy resources scheduling considering real-time resources forecast

Energy resource scheduling is becoming increasingly important, such as the use of more distributed generators and electric vehicles connected to the distribution network. This paper proposes a methodology to be used by Virtual Power Players (VPPs), regarding the energy resource scheduling in smart grids and considering day-ahead, hour-ahead and realtime time horizons. This method considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. In this paper, distribution function errors are used to simulate variations between time horizons, and to measure the performance of the proposed methodology. A 33-bus distribution network with large number of distributed resources is used.