Browsing by Author "Martins, I."
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- Cloning, characterization and in vitro and in planta expression of a glucanase inhibitor protein (GIP) of Phytophthora cinnamomiPublication . Martins, I.; Martins, F.; Belo, H.; Vaz, M.; Carvalho, M.; Cravador, A.; Choupina, A.Oomycetes from the genus Phytophthora are fungus-like plant pathogens that are devastating for agriculture and natural ecosystems. They are able to secrete a glucanase inhibitor protein (GIP) that inhibits the activity of endoglucanases (EGases) involved in defense responses against infection. One of the most widely distributed and aggressive Phytophthora species, with more than 1,000 host plants is P. cinnamomi. In this work we report the sequencing and characterization of a class of GIPs secreted by Phytophthora cinnamomi. The gip gene from P. cinnamomi has a 937 bp ORF encoding a putative peptide of 312 deduced amino acids. The expression of this gene was studied during growth in different carbon sources (glucose, cellulose and sawdust), by RT-qPCR and its level of expression was evaluated at five time points. The highest expression of gip gene occurred in sawdust at 8 h of induction. In vivo infection of C. sativa revealed an increase in gip expression from 12 to 24 h. At 36 h its expression decreased suggesting that a compensatory mechanism must occur in plant.
- Cloud and clear sky pixel classification in ground-based all-sky hemispherical digital imagesPublication . Ferreira, P. M.; Martins, I.; Ruano, AntonioCloudiness is the non-predictable factor most a ecting the solar radiation reaching a particular location on the Earth surface. Therefore it has great impact on the performance of predictive solar radiation models for that location. This work represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate the fraction of visible sky corresponding to clouds and to clear sky. The general approach, common to many image processing applications, consists in finding one threshold on a given pixel intensity scale that segments the image pixels into clear sky and cloud. In order to allow the evaluation and comparison of image thresholding methods, the pixels of 410 images were manually classified as clear sky or cloud, establishing a reference database. Two well known image thresholding algorithms are tested and a neural network approach is presented. For the latter, a number of statistical measures is extracted from the images constituting a feature space of potential inputs for the neural network. The actual inputs and number of neurons to be employed are selected by means of a multi-objective genetic algorithm.
- Correction: Ferreira, P.M., et al. A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature. Sensors 2012, 12, 15750–15777Publication . Ferreira, P. M.; Gomes, João M.; Martins, I.; Ruano, AntonioAccurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.
- Estimation and prediction of cloudiness from ground-based all-sky hemispherical digital imagesPublication . Martins, I.; Ferreira, P. M.; Ruano, AntonioCloudiness is the environmental factor most affecting the solar radiation reaching a particular location on the Earth surface. Therefore it has great impact on the performance of predictive solar radiation models for that location. This work aims contributing to the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate the fraction of visible sky corresponding to clouds and to clear sky, and by using radial basis function neural networks to model and predict the estimated cloudiness time-series. The general approach for cloudiness estimation, common to many image processing applications, consists in finding one threshold on a given pixel intensity scale that segments the image pixels into clear sky and cloud. The neural network models, selected by means of multiobjective genetic algorithms, are trained as one-step-ahead predictors and used iteratively in order to predict the cloudiness time-series up to a required prediction horizon. In order to allow the evaluation and comparison of image thresholding methods as well as forming a timeseries suitable to train and evaluate neural network models, the pixels of 410 images were manually classified as clear sky or cloud, establishing a reference database. Two well known image thresholding algorithms are tested and a neural network approach is presented. For the latter, a number of statistical measures are extracted from the images constituting a feature space of potential inputs. As well as for the cloudiness predictive models, the actual inputs and number of neurons to be employed are selected by means of a multi-objective genetic algorithm.
- Estimation and prediction of cloudiness from ground-based all-sky hemispherical digital imagesPublication . Martins, I.; Ferreira, P. M.; Ruano, AntonioCloudiness is the environmental factor most a ecting the solar radiation reaching a particular location on the Earth surface. Therefore it has great impact on the performance of predictive solar radiation models for that location. This work aims contributing to the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate the fraction of visible sky corresponding to clouds and to clear sky, and by using radial basis function neural networks to model and predict the estimated cloudiness time-series. The general approach for cloudiness estimation, common to many image processing applications, consists in nding one threshold on a given pixel intensity scale that segments the image pixels into clear sky and cloud. The neural network models, selected by means of multiobjective genetic algorithms, are trained as one-step-ahead predictors and used iteratively in order to predict the cloudiness time-series up to a required prediction horizon. In order to allow the evaluation and comparison of image thresholding methods as well as forming a timeseries suitable to train and evaluate neural network models, the pixels of 410 images were manually classified as clear sky or cloud, establishing a reference database. Two well known image thresholding algorithms are tested and a neural network approach is presented. For the latter, a number of statistical measures are extracted from the images constituting a feature space of potential inputs. As well as for the cloudiness predictive models, the actual inputs and number of neurons to be employed are selected by means of a multi-objective genetic algorithm.
- Metalloproteinase activity in the hemolymph of hydrothermal vent mussel Bathymodiolus azoricusPublication . Martins, I.; Company, Rui; Cerqueira, T.; Bebianno, Maria João; Santos, R.S.; Bettencourt, R.The extreme conditions present at the hydrothermal vent ecosystems such as, high temperature and pressure, high concentrations of trace metals, toxic gases such as methane (CH4), carbon dioxide (CO2) and hydrogen sulfide (H2S) could apparently be deleterious to the aerobic organisms. However, B. azoricus developed physiological strategies to cope with such inhospitable environment being the most successful and widespread species in the Mid Atlantic Ridge hydrothermal vents. Such remarkable adaptive response to environmental stressors must represent readjustments on normal biochemical reactions in order to maintain the integrity of cell function and metabolism. In bivalve molluscs, hemolymph and the circulating cells hemocytes, forms a primary line of defense against infectious agents and cellular stressful factors.
- Neural models project for solar radiation and atmospheric temperature forecastPublication . Martins, I.; Ruano, A. E.; Ferreira, P. M.This work arises from the necessity of temperature and solar radiation forecast, to improve the Heating, Ventilating, and Air Conditioning (HVAC) systems e ciency. To do so, it was necessary to determine neural models capable of such forecast. The chosen characteristics were solar radiation and temperature because these two characteristics directly a ect the room temperature inside a building. This forecast system will be implemented on a portable computational device, so it must be built with low computational complexity. During this dissertation the various research phases are described with some detail. The applications were developed on Python programming language due to it library collection. In this task several algorithms were developed to determine the cloudiness index. The results of these algorithms were compared with the results obtained using neural models for the same purpose. In solar radiation and temperature forecast only neural models were used. The cloudiness index forecast was not implemented as this is only an intermediate step; instead measured values of cloudiness index were used for the solar radiation forecast. Regarding the solar radiation forecast two neural models were implemented and compared, one of the models has an exogenous input, the cloudiness index forecast, and the other one is simply a time series. This models were compared to determine if the inclusion of the cloudiness index forecast improves solar radiation forecast. In temperature forecast only one model will be presented, a Nonlinear AutoRegressive with eXogenous input (NARX) model, with solar radiation forecast as exogenous input. All the neural models are radial Basis Function (RBF) and there structure was determined using a Multi-Objective Genetic Algorithm (MOGA). The models were used to determine cloudiness index, forecast solar radiation and temperature.
- A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperaturePublication . Ferreira, P. M.; Gomes, João M.; Martins, I.; Ruano, AntonioAccurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.