A statistical framework for channel availability modelling in the polarisation domain

Cognitive radio has been proposed as a means of improving the spectrum utilisation and increasing spectrum efficiency of wireless systems. This can be achieved by allowing cognitive radio terminals to monitor their spectral environment and opportunistically access the unoccupied frequency channels. Due to the opportunistic nature of cognitive radio, the overall performance of such networks depends on the spectrum occupancy or availability patterns. Appropriate knowledge on channel availability can optimise the sensing performance in terms of spectrum and energy efficiency. This work proposes a statistical framework for the channel availability in the polarization domain. A Gaussian Normal approximation is used to model real-world occupancy data obtained through a measurement campaign in the cellular frequency bands within a realistic scenario.

Modelling mortality: Are we heading in the right direction?

Predicting life expectancy has become of upmost importance in society. Pension providers, insurance companies, government bodies and individuals in the developed world have a vested interest in understanding how long people will live for. This desire to better understand life expectancy has resulted in an explosion of stochastic mortality models many of which identify linear trends in mortality rates by time. In making use of such models for forecasting purposes we rely on the assumption that the direction of the linear trend (determined from the data used for fitting purposes) will not change in the future, recent literature has started to question this assumption. In this paper we carry out a comprehensive investigation of these types of models using male and female data from 30 countries and using the theory of structural breaks to identify changes in the extracted trends by time. We find that structural breaks are present in a substantial number of cases, that they are more prevalent in male data than in female data, that the introduction of additional period factors into the model reduces their presence, and that allowing for changes in the trend improves the fit and forecast substantially.

Exposure modelling to traffic-related air toxic pollutants

Atualmente, a poluição atmosférica constitui uma das principais causas ambientais de mortalidade. Cerca de 30% da população europeia residente em áreas urbanas encontra-se exposta a níveis de poluição atmosférica superiores aos valores- limite de qualidade do ar legislados para proteção da saúde humana, representando o tráfego rodoviário uma das principais fontes de poluição urbana. Além dos poluentes tradicionais avaliados em áreas urbanas, os poluentes classificados como perigosos para a saúde (Hazard Air Pollutants - HAPs) têm particular relevância devido aos seus conhecidos efeitos tóxicos e cancerígenos. Neste sentido, a avaliação da exposição tornase primordial para a determinação da relação entre a poluição atmosférica urbana e efeitos na saúde. O presente estudo tem como principal objetivo o desenvolvimento e implementação de uma metodologia para avaliação da exposição individual à poluição atmosférica urbana relacionada com o tráfego rodoviário. De modo a atingir este objetivo, foram identificados os parâmetros relevantes para a quantificação de exposição e analisados os atuais e futuros potenciais impactos na saúde associados com a exposição à poluição urbana. Neste âmbito, o modelo ExPOSITION (EXPOSure model to traffIc-relaTed aIr pOllutioN) foi desenvolvido baseado numa abordagem inovadora que envolve a análise da trajetória dos indivíduos recolhidas por telemóveis com tecnologia GPS e processadas através da abordagem de data mining e análise geoespacial. O modelo ExPOSITION considera também uma abordagem probabilística para caracterizar a variabilidade dos parâmetros microambientais e a sua contribuição para exposição individual. Adicionalmente, de forma a atingir os objetivos do estudo foi desenvolvido um novo módulo de cálculo de emissões de HAPs provenientes do transporte rodoviário. Neste estudo, um sistema de modelação, incluindo os modelos de transporteemissões- dispersão-exposição, foi aplicado na área urbana de Leiria para quantificação de exposição individual a PM2.5 e benzeno. Os resultados de modelação foram validados com base em medições obtidas por monitorização pessoal e monitorização biológica verificando-se uma boa concordância entre os resultados do modelo e dados de medições. A metodologia desenvolvida e implementada no âmbito deste trabalho permite analisar e estimar a magnitude, frequência e inter e intra-variabilidade dos níveis de exposição individual, bem como a contribuição de diferentes microambientes, considerando claramente a sequência de eventos de exposição e relação fonte-recetor, que é fundamental para avaliação dos efeitos na saúde e estudos epidemiológicos. O presente trabalho contribui para uma melhor compreensão da exposição individual em áreas urbanas, proporcionando novas perspetivas sobre a exposição individual, essenciais na seleção de estratégias de redução da exposição à poluição atmosférica urbana, e consequentes efeitos na saúde.

Modelling the Minho river plume intrusion into the rias baixas

The Minho River, situated 30 km south of the Rias Baixas is the most important freshwater source flowing into the Western Galician Coast (NW of the Iberian Peninsula). This discharge is important to determine the hydrological patterns adjacent to its mouth, particularly close to the Galician coastal region. The buoyancy generated by the Minho plume can flood the Rias Baixas for long periods, reversing the normal estuarine density gradients. Thus, it becomes important to analyse its dynamics as well as the thermohaline patterns of the areas affected by the freshwater spreading. Thus, the main aim of this work was to study the propagation of the Minho estuarine plume to the Rias Baixas, establishing the conditions in which this plume affects the circulation and hydrographic features of these coastal systems, through the development and application of the numerical model MOHID. For this purpose, the hydrographic features of the Rias Baixas mouths were studied. It was observed that at the northern mouths, due to their shallowness, the heat fluxes between the atmosphere and ocean are the major forcing, influencing the water temperature, while at the southern mouths the influence of the upwelling events and the Minho River discharge were more frequent. The salinity increases from south to north, revealing that the observed low values may be caused by the Minho River freshwater discharge. An assessment of wind data along the Galician coast was carried out, in order to evaluate the applicability of the study to the dispersal of the Minho estuarine plume. Firstly, a comparative analysis between winds obtained from land meteorological stations and offshore QuikSCAT satellite were performed. This comparison revealed that satellite data constitute a good approach to study wind induced coastal phenomena. However, since the numerical model MOHID requires wind data with high spatial and temporal resolution close to the coast, results of the forecasted model WRF were added to the previous study. The analyses revealed that the WRF model data is a consistent tool to obtain representative wind data near the coast, showing good results when comparing with in situ wind observations from oceanographic buoys. To study the influence of the Minho buoyant discharge influence on the Rias Baixas, a set of three one-way nested models was developed and implemented, using the numerical model MOHID. The first model domain is a barotropic model and includes the whole Iberian Peninsula coast. The second and third domains are baroclinic models, where the second domain is a coarse representation of the Rias Baixas and adjacent coastal area, while the third includes the same area with a higher resolution. A bi-dimensional model was also implemented in the Minho estuary, in order to quantify the flow (and its properties) that the estuary injects into the ocean. The chosen period for the Minho estuarine plume propagation validation was the spring of 1998, since a high Minho River discharge was reported, as well as favourable wind patterns to advect the estuarine plume towards the Rias Baixas, and there was field data available to compare with the model predictions. The obtained results show that the adopted nesting methodology was successful implemented. Model predictions reproduce accurately the hydrodynamics and thermohaline patterns on the Minho estuary and Rias Baixas. The importance of the Minho river discharge and the wind forcing in the event of May 1998 was also studied. The model results showed that a continuous moderate Minho River discharge combined with southerly winds is enough to reverse the Rias Baixas circulation pattern, reducing the importance of the occurrence of specific events of high runoff values. The conditions in which the estuarine plume Minho affects circulation and hydrography of the Rias Baixas were evaluated. The numerical results revealed that the Minho estuarine plume responds rapidly to wind variations and is also influenced by the bathymetry and morphology of the coastline. Without wind forcing, the plume expands offshore, creating a bulge in front of the river mouth. When the wind blows southwards, the main feature is the offshore extension of the plume. Otherwise, northward wind spreads the river plume towards the Rias Baixas. The plume is confined close to the coast, reaching the Rias Baixas after 1.5 days. However, for Minho River discharges higher than 800 m3 s-1, the Minho estuarine plume reverses the circulation patterns in the Rias Baixas. It was also observed that the wind stress and Minho River discharge are the most important factors influencing the size and shape of the Minho estuarine plume. Under the same conditions, the water exchange between Rias Baixas was analysed following the trajectories particles released close to the Minho River mouth. Over 5 days, under Minho River discharges higher than 2100 m3 s-1 combined with southerly winds of 6 m s-1, an intense water exchange between Rias was observed. However, only 20% of the particles found in Ria de Pontevedra come directly from the Minho River. In summary, the model application developed in this study contributed to the characterization and understanding of the influence of the Minho River on the Rias Baixas circulation and hydrography, highlighting that this methodology can be replicated to other coastal systems.

Wind energy resource modelling in Portugal and its future large-scale alteration due to anthropogenic induced climate changes

The high dependence of Portugal from foreign energy sources (mainly fossil fuels), together with the international commitments assumed by Portugal and the national strategy in terms of energy policy, as well as resources sustainability and climate change issues, inevitably force Portugal to invest in its energetic self-sufficiency. The 20/20/20 Strategy defined by the European Union defines that in 2020 60% of the total electricity consumption must come from renewable energy sources. Wind energy is currently a major source of electricity generation in Portugal, producing about 23% of the national total electricity consumption in 2013. The National Energy Strategy 2020 (ENE2020), which aims to ensure the national compliance of the European Strategy 20/20/20, states that about half of this 60% target will be provided by wind energy. This work aims to implement and optimise a numerical weather prediction model in the simulation and modelling of the wind energy resource in Portugal, both in offshore and onshore areas. The numerical model optimisation consisted in the determination of which initial and boundary conditions and planetary boundary layer physical parameterizations options provide wind power flux (or energy density), wind speed and direction simulations closest to in situ measured wind data. Specifically for offshore areas, it is also intended to evaluate if the numerical model, once optimised, is able to produce power flux, wind speed and direction simulations more consistent with in situ measured data than wind measurements collected by satellites. This work also aims to study and analyse possible impacts that anthropogenic climate changes may have on the future wind energetic resource in Europe. The results show that the ECMWF reanalysis ERA-Interim are those that, among all the forcing databases currently available to drive numerical weather prediction models, allow wind power flux, wind speed and direction simulations more consistent with in situ wind measurements. It was also found that the Pleim-Xiu and ACM2 planetary boundary layer parameterizations are the ones that showed the best performance in terms of wind power flux, wind speed and direction simulations. This model optimisation allowed a significant reduction of the wind power flux, wind speed and direction simulations errors and, specifically for offshore areas, wind power flux, wind speed and direction simulations more consistent with in situ wind measurements than data obtained from satellites, which is a very valuable and interesting achievement. This work also revealed that future anthropogenic climate changes can negatively impact future European wind energy resource, due to tendencies towards a reduction in future wind speeds especially by the end of the current century and under stronger radiative forcing conditions.

Channel modelling for MIMO systems

Systems equipped with multiple antennas at the transmitter and at the receiver, known as MIMO (Multiple Input Multiple Output) systems, offer higher capacities, allowing an efficient exploitation of the available spectrum and/or the employment of more demanding applications. It is well known that the radio channel is characterized by multipath propagation, a phenomenon deemed problematic and whose mitigation has been achieved through techniques such as diversity, beamforming or adaptive antennas. By exploring conveniently the spatial domain MIMO systems turn the characteristics of the multipath channel into an advantage and allow creating multiple parallel and independent virtual channels. However, the achievable benefits are constrained by the propagation channel’s characteristics, which may not always be ideal. This work focuses on the characterization of the MIMO radio channel. It begins with the presentation of the fundamental results from information theory that triggered the interest on these systems, including the discussion of some of their potential benefits and a review of the existing channel models for MIMO systems. The characterization of the MIMO channel developed in this work is based on experimental measurements of the double-directional channel. The measurement system is based on a vector network analyzer and a two-dimensional positioning platform, both controlled by a computer, allowing the measurement of the channel’s frequency response at the locations of a synthetic array. Data is then processed using the SAGE (Space-Alternating Expectation-Maximization) algorithm to obtain the parameters (delay, direction of arrival and complex amplitude) of the channel’s most relevant multipath components. Afterwards, using a clustering algorithm these data are grouped into clusters. Finally, statistical information is extracted allowing the characterization of the channel’s multipath components. The information about the multipath characteristics of the channel, induced by existing scatterers in the propagation scenario, enables the characterization of MIMO channel and thus to evaluate its performance. The method was finally validated using MIMO measurements.

Combining sampling, profiling, remote sensing and modelling to efficiently monitoring estuarine and coastal water quality

Monitoring of coastal and estuarine water quality has been traditionally performed by sampling with subsequent laboratory analysis. This has the disadvantages of low spatial and temporal resolution and high cost. In the last decades two alternative techniques have emerged to overcome this drawback: profiling and remote sensing. Profiling using multi-parameter sensors is now in a commercial stage. It can be used, tied to a boat, to obtain a quick “picture” of the system. The spatial resolution thus increases from single points to a line coincident with the boat track. The temporal resolution however remains unchanged since campaigns and resources involved are basically the same. The need for laboratory analysis was reduced but not eliminated because parameters like nutrients, microbiology or metals are still difficult to obtain with sensors and validation measurements are still needed. In the last years the improvement in satellite resolution has enabled its use for coastal and estuarine water monitoring. Although spatial coverage and resolution of satellite images in the present is already suitable to coastal and estuarine monitoring, temporal resolution is naturally limited to satellite passages and cloud cover. With this panorama the best approach to water monitoring is to integrate and combine data from all these sources. The natural tools to perform this integration are numerical models. Models benefit from the different sources of data to obtain a better calibration. After calibration they can be used to extend spatially and temporally the methods resolution. In Algarve (South of Portugal) a monitoring effort using this approach is being undertaken. The monitoring effort comprises five different locations including coastal waters, estuaries and coastal lagoons. The objective is to establish the base line situation to evaluate the impact of Waste Water Treatment Plants design and retrofitting. The field campaigns include monthly synoptic profiling, using an YSI 6600 multi-parameter system, laboratory analysis and fixed stations. The remote sensing uses ENVISAT\MERIS Level 2 Full Resolution data. This data is combined and used with the MOHID modelling system to obtain an integrate description of the systems. The results show the limitations of each method and the ability of the modelling system to integrate the results and to produce a comprehensive picture of the system.

Hydroinformatics based decision.making integrating modelling, sampling and profiling: the Ria Formosa case

A Waste Water monitoring program aiming to help decision making is presented. The program includes traditional and inboard sensor sampling, hydrodynamic and water quality modeling and a GIS based database to help the decision making of manager authorities. The focus is in the quality of waters receiving discharges from Waste Water Treatment Plants. Data was used to feed model simulations and produce hydrodynamic, effluent dispersion and ecological results. The system was then used to run different scenarios of discharge flow, concentration and location. The results enable to access the current water quality state of the lagoon and are being used as a decision making tool by the waste water managers in the evaluation phase of the treatment plant project to decide the location and the level of treatment of the discharge.

Modelling Atmospheric Concentrations of Ragweed Pollen From Local and Distant Sources

Pollen grains from the genus ragweed (Ambrosia spp.) are important aeroallergens. In Europe, the largest sources of atmospheric ragweed pollen are the Rhône Valley (France), parts of Northern Italy, the Pannonian Plain and Ukraine. Episodes of Long Distance Transport (LDT) of ragweed pollen from these centres can cover large parts of Europe and are predominantly studied using receptor based models (Smith et al., (2013) and references therein). The clinical impact of allergenic ragweed pollen arriving from distant sources remains unclear (Cecchi et al. 2010). Although a recent study has found the major allergens of ragweed in air samples collected in Poznań, Poland, during episodes of long-distance transport from the Pannonian Plain (Grewling et al. 2013). The source orientated models SILAM, DEHM, COSMO-Art, METRAS and ENVIRO-HIRLAM currently report having the capability of modelling atmospheric concentrations of pollen in Europe. The performance of such source-orientated models is strongly dependent on the quality of the emissions data, which is a focus of current research (e.g. Thibaudon et al. (2014)). The output from these models are important for warning allergy sufferers in areas polluted by ragweed, but could also be used to warn the public of ragweed pollen being transported into areas where the plant is not abundant. Areas outside of the main areas of ragweed infection that contain considerable local populations must, however, also include local scale models. These models can be used to predict local concentrations, even when LDT is not present. This concept of combined LDT and local scale calculations has been shown to be work for air pollutants and is considered usable for urban scale calculations of aeroallergens once urban scale maps of aeroallergen sources have been produced.

Wildfire susceptibility modelling in mainland Portugal

Tese de doutoramento, Geografia (Geografia Física), Universidade de Lisboa, Instituto de Geografia e Ordenamento do Território, 2015

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Mathematical modelling of supercritical CO2 extraction of volatile oils from aromatic plants

The modelling of the experimental data of the extraction of the volatile oil from six aromatic plants (coriander, fennel, savoury, winter savoury, cotton lavender and thyme) was performed using five mathematical models, based on differential mass balances. In all cases the extraction was internal diffusion controlled and the internal mass transfer coefficienty (k(s)) have been found to change with pressure, temperature and particle size. For fennel, savoury and cotton lavender, the external mass transfer and the equilibrium phase also influenced the second extraction period, since k(s) changed with the tested flow rates. In general, the axial dispersion coefficient could be neglected for the conditions studied, since Peclet numbers were high. On the other hand, the solute-matrix interaction had to be considered in order to ensure a satisfactory description of the experimental data.

Adaptive tool for automatic data collection of real electricity markets

The study of electricity markets operation has been gaining an increasing importance in last years, as result of the new challenges that the electricity markets restructuring produced. This restructuring increased the competitiveness of the market, but with it its complexity. The growing complexity and unpredictability of the market’s evolution consequently increases the decision making difficulty. Therefore, the intervenient entities are forced to rethink their behaviour and market strategies. Currently, lots of information concerning electricity markets is available. These data, concerning innumerous regards of electricity markets operation, is accessible free of charge, and it is essential for understanding and suitably modelling electricity markets. This paper proposes a tool which is able to handle, store and dynamically update data. The development of the proposed tool is expected to be of great importance to improve the comprehension of electricity markets and the interactions among the involved entities.

Modelling real solar cell using PSCAD/MATLAB

This paper presents the development of a solar photovoltaic (PV) model based on PSCAD/EMTDC - Power System Computer Aided Design – including a mathematical model study. An additional algorithm has been implemented in MATLAB software in order to calculate several parameters required by the PSCAD developed model. All the simulation study has been performed in PSCAD/MATLAB software simulation tool. A real data base concerning irradiance, cell temperature and PV power generation was used in order to support the evaluation of the implemented PV model.