Gestão de três equipas por parte do(a) treinador(a) : relatório de estágio em futebol realizado na Sociedade União 1º Dezembro, Campeonato Distrital de Juniores C da 2ª Divisão, série 7

O Futebol é a forma de desporto mais popular no Mundo, sendo praticado em todas as Nações (Reilly, 2003). Em Portugal, nenhum outro desporto atrai tantos adeptos como o Futebol. É este o desporto que movimenta mais paixões e mais dinheiro. O seu poder e influência são cada vez maiores, sendo já considerado uma importante referência do setor económico, social e cultural (Santos, 2011). E uma verdade é inegável: o Futebol joga-se praticamente em todo o lado (Ramos, 2002). O estágio profissionalizante propõe um trabalho de planeamento, intervenção e reflexão da prática profissional diária de um(a) Treinador(a) de Futebol, que vive confrontado com a necessidade constante de encontrar soluções para os mais variados problemas, com que se depara regularmente no exercício da sua profissão. O estágio foi realizado na Sociedade União 1ºDezembro no escalão de Iniciados B, arcando a signatária, pela primeira vez na carreira, a função de Treinadora Principal. Esta Equipa B competiu no Campeonato Distrital de Juniores “C” da 2ª Divisão na série 7 e no Torneio Extraordinário. O documento segue a exposição de toda a atividade desenvolvida ao longo do estágio.

Two roads to planet detection

One of the most exciting discoveries in astrophysics of the last last decade is of the sheer diversity of planetary systems. These include "hot Jupiters", giant planets so close to their host stars that they orbit once every few days; "Super-Earths", planets with sizes intermediate to those of Earth and Neptune, of which no analogs exist in our own solar system; multi-planet systems with planets smaller than Mars to larger than Jupiter; planets orbiting binary stars; free-floating planets flying through the emptiness of space without any star; even planets orbiting pulsars. Despite these remarkable discoveries, the field is still young, and there are many areas about which precious little is known. In particular, we don't know the planets orbiting Sun-like stars nearest to our own solar system, and we know very little about the compositions of extrasolar planets. This thesis provides developments in those directions, through two instrumentation projects.

The first chapter of this thesis concerns detecting planets in the Solar neighborhood using precision stellar radial velocities, also known as the Doppler technique. We present an analysis determining the most efficient way to detect planets considering factors such as spectral type, wavelengths of observation, spectrograph resolution, observing time, and instrumental sensitivity. We show that G and K dwarfs observed at 400-600 nm are the best targets for surveys complete down to a given planet mass and out to a specified orbital period. Overall we find that M dwarfs observed at 700-800 nm are the best targets for habitable-zone planets, particularly when including the effects of systematic noise floors caused by instrumental imperfections. Somewhat surprisingly, we demonstrate that a modestly sized observatory, with a dedicated observing program, is up to the task of discovering such planets.

We present just such an observatory in the second chapter, called the "MINiature Exoplanet Radial Velocity Array," or MINERVA. We describe the design, which uses a novel multi-aperture approach to increase stability and performance through lower system etendue, as well as keeping costs and time to deployment down. We present calculations of the expected planet yield, and data showing the system performance from our testing and development of the system at Caltech's campus. We also present the motivation, design, and performance of a fiber coupling system for the array, critical for efficiently and reliably bringing light from the telescopes to the spectrograph. We finish by presenting the current status of MINERVA, operational at Mt. Hopkins observatory in Arizona.

The second part of this thesis concerns a very different method of planet detection, direct imaging, which involves discovery and characterization of planets by collecting and analyzing their light. Directly analyzing planetary light is the most promising way to study their atmospheres, formation histories, and compositions. Direct imaging is extremely challenging, as it requires a high performance adaptive optics system to unblur the point-spread function of the parent star through the atmosphere, a coronagraph to suppress stellar diffraction, and image post-processing to remove non-common path "speckle" aberrations that can overwhelm any planetary companions.

To this end, we present the "Stellar Double Coronagraph," or SDC, a flexible coronagraphic platform for use with the 200" Hale telescope. It has two focal and pupil planes, allowing for a number of different observing modes, including multiple vortex phase masks in series for improved contrast and inner working angle behind the obscured aperture of the telescope. We present the motivation, design, performance, and data reduction pipeline of the instrument. In the following chapter, we present some early science results, including the first image of a companion to the star delta Andromeda, which had been previously hypothesized but never seen.

A further chapter presents a wavefront control code developed for the instrument, using the technique of "speckle nulling," which can remove optical aberrations from the system using the deformable mirror of the adaptive optics system. This code allows for improved contrast and inner working angles, and was written in a modular style so as to be portable to other high contrast imaging platforms. We present its performance on optical, near-infrared, and thermal infrared instruments on the Palomar and Keck telescopes, showing how it can improve contrasts by a factor of a few in less than ten iterations.

One of the large challenges in direct imaging is sensing and correcting the electric field in the focal plane to remove scattered light that can be much brighter than any planets. In the last chapter, we present a new method of focal-plane wavefront sensing, combining a coronagraph with a simple phase-shifting interferometer. We present its design and implementation on the Stellar Double Coronagraph, demonstrating its ability to create regions of high contrast by measuring and correcting for optical aberrations in the focal plane. Finally, we derive how it is possible to use the same hardware to distinguish companions from speckle errors using the principles of optical coherence. We present results observing the brown dwarf HD 49197b, demonstrating the ability to detect it despite it being buried in the speckle noise floor. We believe this is the first detection of a substellar companion using the coherence properties of light.

MODELING OF TRANSFER PATH FOR DETERMINATION OF COMBUSTION AND NOISE METRICS ON DIESEL ENGINES

Determination of combustion metrics for a diesel engine has the potential of providing feedback for closed-loop combustion phasing control to meet current and upcoming emission and fuel consumption regulations. This thesis focused on the estimation of combustion metrics including start of combustion (SOC), crank angle location of 50% cumulative heat release (CA50), peak pressure crank angle location (PPCL), and peak pressure amplitude (PPA), peak apparent heat release rate crank angle location (PACL), mean absolute pressure error (MAPE), and peak apparent heat release rate amplitude (PAA). In-cylinder pressure has been used in the laboratory as the primary mechanism for characterization of combustion rates and more recently in-cylinder pressure has been used in series production vehicles for feedback control. However, the intrusive measurement with the in-cylinder pressure sensor is expensive and requires special mounting process and engine structure modification. As an alternative method, this work investigated block mounted accelerometers to estimate combustion metrics in a 9L I6 diesel engine. So the transfer path between the accelerometer signal and the in-cylinder pressure signal needs to be modeled. Depending on the transfer path, the in-cylinder pressure signal and the combustion metrics can be accurately estimated - recovered from accelerometer signals. The method and applicability for determining the transfer path is critical in utilizing an accelerometer(s) for feedback. Single-input single-output (SISO) frequency response function (FRF) is the most common transfer path model; however, it is shown here to have low robustness for varying engine operating conditions. This thesis examines mechanisms to improve the robustness of FRF for combustion metrics estimation. First, an adaptation process based on the particle swarm optimization algorithm was developed and added to the single-input single-output model. Second, a multiple-input single-output (MISO) FRF model coupled with principal component analysis and an offset compensation process was investigated and applied. Improvement of the FRF robustness was achieved based on these two approaches. Furthermore a neural network as a nonlinear model of the transfer path between the accelerometer signal and the apparent heat release rate was also investigated. Transfer path between the acoustical emissions and the in-cylinder pressure signal was also investigated in this dissertation on a high pressure common rail (HPCR) 1.9L TDI diesel engine. The acoustical emissions are an important factor in the powertrain development process. In this part of the research a transfer path was developed between the two and then used to predict the engine noise level with the measured in-cylinder pressure as the input. Three methods for transfer path modeling were applied and the method based on the cepstral smoothing technique led to the most accurate results with averaged estimation errors of 2 dBA and a root mean square error of 1.5dBA. Finally, a linear model for engine noise level estimation was proposed with the in-cylinder pressure signal and the engine speed as components.

Nitrogen removal and sustainability of vertical flow constructed wetlands for small scale wastewater treatment

The challenge for wastewater professionals is to design and operate treatment processes that support human well being and are environmentally sensitive throughout the life-cycle. This research focuses on one technology for small-scale wastewater treatment: the vertical flow constructed wetland (VFCW), which is herein investigated for the capacity to remove ammonium and nitrate nitrogen from wastewater. Hydraulic regime and presence/absence of vegetation are the basis for a three-phase bench scale experiment to determine oxygen transfer and nitrogen fate in VFCWs. Results show that 90% NH4+-N removal is achieved in aerobic downflow columns, 60% NO3--N removal occurs in anaerobic upflow columns, and 60% removal of total nitrogen can be achieved in downflow-upflow in-series. The experimental results are studied further using a variably saturated flow and reactive transport model, which allows a mechanistic explanation of the fate and transport of oxygen and nitrogen. The model clarifies the mechanisms of oxygen transport and nitrogen consumption, and clarifies the need for readily biodegradable COD for denitrification. A VFCW is then compared to a horizontal flow constructed wetland (HFCW) for life cycle environmental impacts. High areal emissions of greenhouse gases from VFCWs compared to HFCWs are the driver for the study. The assessment shows that because a VFCW is only 25% of the volume of an HFCW designed for the same treatment quality, the VFCW has only 25-30% of HFCW impacts over 12 impact categories and 3 damage categories. Results show that impacts could be reduced by design improvements. Design recommendations are downflow wetlands for nitrification, upflow wetlands for denitrification, series wetlands for total nitrogen removal, hydraulic load of 142 L/m2d, 30 cm downflow wetland depth, 1.0 m upflow wetland depth, recycle, vegetation and medium-grained sand. These improvements will optimize nitrogen removal, minimize gaseous emissions, and reduce wetland material requirements, thus reducing environmental impact without sacrificing wastewater treatment quality.

Matlab simulink modeling of photovoltaic cells for understanding shadow effect

Irradiation is the main component for producing the electricity from solar energy. When obstacles come in between the sun and the PV cell then it doesn’t get sufficient irradiance to produce enough electricity. Shadowing has a great impact on photovoltaic cell. The main fuel of PV cell is solar radiation. Using solar radiation, a photovoltaic cell produces electricity. The shadow on a PV cell decreases the output of the photovoltaic cell. It has been already shown in different papers that shadow effect decreases the output of the PV cell. There are different kinds of shadow effects which are observed, some minimize the PV cell output and some reduce the output to zero. There are different types of shadow based on their effects on the photovoltaic cell. The shadow has also effects depending on whether the PV cells are connected in series connection or in parallel connection. In series when one cell is out of order then the whole series of the PV cells will not work but in parallel connection if one cell is damaged, the others will work because they work independently. According to the output requirement the arrangement of the PV cells are made in series or parallel. Simulink modeling is made for series and parallel connection between two PV cells and the shadow effect is analyzed on one of the PV cells. Using SIMULINK, the shadowing is simulated on the two PV cells, where in one system they are in series and in another system they are in parallel. Slowly the irradiance is decreased to simulate the shadow effect. Simulation of the shadow effect gives an idea about the output of the PV cell system when system has shadow on the PV cells. Here the shadow effect on the two PV cells using series and parallel combinations are simulated and analyzed for understanding the effects on output.

Sticking and erosion at carbon-containing plasma-facing materials in fusion reactors

Controlled nuclear fusion is one of the most promising sources of energy for the future. Before this goal can be achieved, one must be able to control the enormous energy densities which are present in the core plasma in a fusion reactor. In order to be able to predict the evolution and thereby the lifetime of different plasma facing materials under reactor-relevant conditions, the interaction of atoms and molecules with plasma first wall surfaces have to be studied in detail. In this thesis, the fundamental sticking and erosion processes of carbon-based materials, the nature of hydrocarbon species released from plasma-facing surfaces, and the evolution of the components under cumulative bombardment by atoms and molecules have been investigated by means of molecular dynamics simulations using both analytic potentials and a semi-empirical tight-binding method. The sticking cross-section of CH3 radicals at unsaturated carbon sites at diamond (111) surfaces is observed to decrease with increasing angle of incidence, a dependence which can be described by a simple geometrical model. The simulations furthermore show the sticking cross-section of CH3 radicals to be strongly dependent on the local neighborhood of the unsaturated carbon site. The erosion of amorphous hydrogenated carbon surfaces by helium, neon, and argon ions in combination with hydrogen at energies ranging from 2 to 10 eV is studied using both non-cumulative and cumulative bombardment simulations. The results show no significant differences between sputtering yields obtained from bombardment simulations with different noble gas ions. The final simulation cells from the 5 and 10 eV ion bombardment simulations, however, show marked differences in surface morphology. In further simulations the behavior of amorphous hydrogenated carbon surfaces under bombardment with D^+, D^+2, and D^+3 ions in the energy range from 2 to 30 eV has been investigated. The total chemical sputtering yields indicate that molecular projectiles lead to larger sputtering yields than atomic projectiles. Finally, the effect of hydrogen ion bombardment of both crystalline and amorphous tungsten carbide surfaces is studied. Prolonged bombardment is found to lead to the formation of an amorphous tungsten carbide layer, regardless of the initial structure of the sample. In agreement with experiment, preferential sputtering of carbon is observed in both the cumulative and non-cumulative simulations

Dengue fever and el nino-southern oscillation in Queensland, Australia : a time series predictive model

Background It remains unclear over whether it is possible to develop an epidemic forecasting model for transmission of dengue fever in Queensland, Australia. Objectives To examine the potential impact of El Niño/Southern Oscillation on the transmission of dengue fever in Queensland, Australia and explore the possibility of developing a forecast model of dengue fever. Methods Data on the Southern Oscillation Index (SOI), an indicator of El Niño/Southern Oscillation activity, were obtained from the Australian Bureau of Meteorology. Numbers of dengue fever cases notified and the numbers of postcode areas with dengue fever cases between January 1993 and December 2005 were obtained from the Queensland Health and relevant population data were obtained from the Australia Bureau of Statistics. A multivariate Seasonal Auto-regressive Integrated Moving Average model was developed and validated by dividing the data file into two datasets: the data from January 1993 to December 2003 were used to construct a model and those from January 2004 to December 2005 were used to validate it. Results A decrease in the average SOI (ie, warmer conditions) during the preceding 3–12 months was significantly associated with an increase in the monthly numbers of postcode areas with dengue fever cases (β=−0.038; p = 0.019). Predicted values from the Seasonal Auto-regressive Integrated Moving Average model were consistent with the observed values in the validation dataset (root-mean-square percentage error: 1.93%). Conclusions Climate variability is directly and/or indirectly associated with dengue transmission and the development of an SOI-based epidemic forecasting system is possible for dengue fever in Queensland, Australia.

Understanding the Aquifier : 3D Visualisation and Groundwater Time-Series Animation in Lockyer Valley

The Lockyer Valley, southeast Queensland, hosts intensive irrigated agriculture using groundwater from over 5000 alluvial bores. A current project is considering introduction of PRW (purified recycled water) to augment groundwater supplies. To assess this, a valley-wide MODFLOW simulation model is being developed plus a new unsaturated zone flow model. To underpin these models and provide a realistic understanding of the aquifer framework a 3D visualisation model has been developed using Groundwater Visualisation System (GVS) software produced at QUT.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

The short-term effect of air pollution on cardiovascular mortality in Tianjin, China : comparison of time series and case–crossover analyses

Background: Many studies have illustrated that ambient air pollution negatively impacts on health. However, little evidence is available for the effects of air pollution on cardiovascular mortality (CVM) in Tianjin, China. Also, no study has examined which strata length for the time-stratified case–crossover analysis gives estimates that most closely match the estimates from time series analysis. Objectives: The purpose of this study was to estimate the effects of air pollutants on CVM in Tianjin, China, and compare time-stratified case–crossover and time series analyses. Method: A time-stratified case–crossover and generalized additive model (time series) were applied to examine the impact of air pollution on CVM from 2005 to 2007. Four time-stratified case–crossover analyses were used by varying the stratum length (Calendar month, 28, 21 or 14 days). Jackknifing was used to compare the methods. Residual analysis was used to check whether the models fitted well. Results: Both case–crossover and time series analyses show that air pollutants (PM10, SO2 and NO2) were positively associated with CVM. The estimates from the time-stratified case–crossover varied greatly with changing strata length. The estimates from the time series analyses varied slightly with changing degrees of freedom per year for time. The residuals from the time series analyses had less autocorrelation than those from the case–crossover analyses indicating a better fit. Conclusion: Air pollution was associated with an increased risk of CVM in Tianjin, China. Time series analyses performed better than the time-stratified case–crossover analyses in terms of residual checking.