Síntesis de series temporales de centelleo troposférico

Los sistemas de telecomunicación que trabajan en frecuencias milimétricas pueden verse severamente afectados por varios fenómenos atmosféricos, tales como la atenuación por gases, nubes y el centelleo troposférico. Una adecuada caracterización es imprescindible en el diseño e implementación de estos sistemas. El presente Proyecto Fin de Grado tiene como objetivo el estudio estadístico a largo plazo de series temporales de centelleo troposférico en enlaces de comunicaciones en trayecto inclinado sobre la banda Ka a 19,7 GHz. Para la realización de este estudio, se dispone como punto de partida de datos experimentales procedentes de la baliza en banda Ka a 19,7 GHz del satélite Eutelsat Hot Bird 13A que han sido recopilados durante siete años entre julio de 2006 y junio de 2013. Además, se cuenta como referencia teórica con la aplicación práctica del método UIT-R P.618-10 para el modelado del centelleo troposférico en sistemas de telecomunicación Tierra-espacio. Esta información permite examinar la validez de la aplicación práctica del método UIT-R P.1853-1 para la síntesis de series temporales de centelleo troposférico. Sobre este sintetizador se variará la serie temporal de contenido integrado de vapor de agua en una columna vertical por datos reales obtenidos de bases de datos meteorológicas ERA-Interim y GNSS con el objetivo de comprobar el impacto de este cambio. La primera parte del Proyecto comienza con la exposición de los fundamentos teóricos de los distintos fenómenos que afectan a la propagación en un enlace por satélite, incluyendo los modelos de predicción más importantes. Posteriormente, se presentan los fundamentos teóricos que describen las series temporales, así como su aplicación al modelado de enlaces de comunicaciones. Por último, se describen los recursos específicos empleados en la realización del experimento. La segunda parte del Proyecto se inicia con la muestra del proceso de análisis de los datos disponibles que, más tarde, permiten obtener resultados que caracterizan el centelleo troposférico en ausencia de precipitación, o centelleo seco, para los tres casos de estudio sobre los datos experimentales, sobre el modelo P.618-10 y sobre el sintetizador P.1853-1 con sus modificaciones. Al haber mantenido en todo momento las mismas condiciones de frecuencia, localización, clima y periodo de análisis, el estudio comparativo de los resultados obtenidos permite extraer las conclusiones oportunas y proponer líneas futuras de investigación. ABSTRACT. Telecommunication systems working in the millimetre band are severely affected by various atmospheric impairments, such as attenuation due to clouds, gases and tropospheric scintillation. An adequate characterization is essential in the design and implementation of these systems. This Final Degree Project aims to a long-term statistical study of time series of tropospheric scintillation on slant path communications links in Ka band at 19.7 GHz. To carry out this study, experimental data from the beacon in Ka band at 19.7 GHz for the Eutelsat Hot Bird 13A satellite are available as a starting point. These data have been collected during seven years between July 2006 and June 2013. In addition, the practical application of the ITU-R P.618-10 method for tropospheric scintillation modeling of Earth-space telecommunication systems has been the theoretical reference used. This information allows us to examine the validity of the practical application of the ITU-R P.1853-1 method for tropospheric scintillation time series synthesis. In this synthesizer, the time series of water vapor content in a vertical column will be substituted by actual data from meteorological databases ERA-Interim and GNSS in order to test the impact of this change. The first part of the Project begins with the exposition of the theoretical foundations of the various impairments that affect propagation in a satellite link, including the most important prediction models. Subsequently, it presents the theoretical foundations that describe the time series, and its application to communication links modeling. Finally, the specific resources used in the experiment are described. The second part of the Project starts with the exhibition of the data analysis process to obtain results that characterize the tropospheric scintillation in the absence of precipitation, or dry scintillation, for the three study cases on the experimental data, on the P.618-10 model and on the P.1853-1 synthesizer with its modifications. The fact that the same conditions of frequency, location, climate and period of analysis are always maintained, allows us to draw conclusions and propose future research lines by comparing the results.

Influencia de la altura de lluvia en la atenuación por precipitación en comunicaciones por satélite

El objetivo principal de este Proyecto Fin de Grado es el estudio experimental de la atenuación, producida por las precipitaciones, en los enlaces Tierra – satélite en banda Ka. En particular, se ha realizado el estudio para una frecuencia de 19,701 GHz, que corresponde con la frecuencia de una de las balizas del satélite Hot Bird 13 de Eutelsat. Para la realización del estudio se dispone de datos experimentales recogidos por sondeos realizados en la estación meteorológica de Madrid - Barajas y de datos sinópticos. La primera parte del proyecto comienza con una descripción de los fundamentos teóricos de los distintos fenómenos que afectan a la propagación en un enlace por satélite, y se enumeran los distintos modelos de predicción más importantes. Posteriormente se describen en más detalle algunos modelos de predicción propuestos por la Unión Internacional de Telecomunicaciones (UIT). En la segunda parte del mismo se explica en detalle el proceso necesario para el procesado de los datos experimentales, con el fin de poder manejarlos de forma más sencilla a la hora de presentar los resultados pertinentes. En una tercera parte se recogen los resultados experimentales obtenidos para el caso de la altura de la isoterma a 0˚C y de la atenuación por lluvia. El capítulo dedicado al estudio de la altura de la isoterma a 0˚C se centra en obtener dicho dato a partir de los datos experimentales de los sondeos, que fueron procesados con anterioridad. Así mismo, se realizará una comparativa de estos datos experimentales con los proporcionados por la UIT en la Recomendación 839. En el capítulo dedicado al estudio de la atenuación producida por la lluvia se compararán los resultados obtenidos de forma experimental con los datos proporcionados por la UIT en la Recomendación 618. Por último se recoge en un capítulo las conclusiones obtenidas con la realización de este PFG y las líneas futuras de investigación. ABSTRACT. The main aim of this Final Degree Project is the experimental study of attenuation by precipitation, in links Earth – Ka band satellite. In particular, the study was performed at a frequency of 19.701 GHz, which corresponds to the frequency of one of the beacons of the satellite Hot Bird 13 (Eutelsat). There is experimental data and synoptic data provided, which was collected from surveys in the Madrid- Barajas weather station. The first part of the project outlines some theoretical foundations regarding different phenomena which affect propagation in a satellite link and includes the most important prediction models. Additionally, some prediction models proposed by the International Telecommunication Union (ITU) are outlined in detail. In the second part, the process for the processing of the experimental data is explained in detail. This process is necessary in order to be able to utilize the data more easily when presenting the results of this project. In the third part, the experimental results obtained in the study are presented for both cases: isotherm height at 0°C and rain attenuation. The chapter dedicated to the study of the isotherm height at 0°C focuses on obtaining the real isotherm height at 0°C from the experimental data processed previously. Furthermore, a comparison will be made between the experimental results and data proposed by ITU in Recommendation 839. The chapter dedicated to the study of rain attenuation focuses on making a comparison between the results from the experimental data and data proposed by ITU in Recommendation 618. Finally, there is a chapter which revises all the conclusions obtained throughout this project and provides recommendations for future research.

MULTIPRED: A computational system for prediction of promiscuous HLA binding peptides

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules ( proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability ( area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets termed T-cell epitope hotspots. MULTIPRED is available at http:// antigen.i2r.a-star.edu.sg/ multipred/.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

MHC class II binding prediction by molecular docking

Proteins of the Major Histocompatibility Complex (MHC) bind self and nonself peptide antigens or epitopes within the cell and present them at the cell surface for recognition by T cells. All T-cell epitopes are MHC binders but not all MCH binders are T-cell epitopes. The MHC class II proteins are extremely polymorphic. Polymorphic residues cluster in the peptide-binding region and largely determine the MHC's peptide selectivity. The peptide binding site on MHC class II proteins consist of five binding pockets. Using molecular docking, we have modelled the interactions between peptide and MHC class II proteins from locus DRB1. A combinatorial peptide library was generated by mutation of residues at peptide positions which correspond to binding pockets (so called anchor positions). The binding affinities were assessed using different scoring functions. The normalized scoring functions for each amino acid at each anchor position were used to construct quantitative matrices (QM) for MHC class II binding prediction. Models were validated by external test sets comprising 4540 known binders. Eighty percent of the known binders are identified in the best predicted 15% of all overlapping peptides, originating from one protein. © 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Distribution of Diatoms and Development of Diatom-Based Models for Inferring Salinity and Nutrient Concentrations in Florida Bay and Adjacent Coastal Wetlands of South Florida (USA)

The composition and distribution of diatom algae inhabiting estuaries and coasts of the subtropical Americas are poorly documented, especially relative to the central role diatoms play in coastal food webs and to their potential utility as sentinels of environmental change in these threatened ecosystems. Here, we document the distribution of diatoms among the diverse habitat types and long environmental gradients represented by the shallow topographic relief of the South Florida, USA, coastline. A total of 592 species were encountered from 38 freshwater, mangrove, and marine locations in the Everglades wetland and Florida Bay during two seasonal collections, with the highest diversity occurring at sites of high salinity and low water column organic carbon concentration (WTOC). Freshwater, mangrove, and estuarine assemblages were compositionally distinct, but seasonal differences were only detected in mangrove and estuarine sites where solute concentration differed greatly between wet and dry seasons. Epiphytic, planktonic, and sediment assemblages were compositionally similar, implying a high degree of mixing along the shallow, tidal, and storm-prone coast. The relationships between diatom taxa and salinity, water total phosphorus (WTP), water total nitrogen (WTN), and WTOC concentrations were determined and incorporated into weighted averaging partial least squares regression models. Salinity was the most influential variable, resulting in a highly predictive model (r apparent 2  = 0.97, r jackknife 2  = 0.95) that can be used in the future to infer changes in coastal freshwater delivery or sea-level rise in South Florida and compositionally similar environments. Models predicting WTN (r apparent 2  = 0.75, r jackknife 2  = 0.46), WTP (r apparent 2  = 0.75, r jackknife 2  = 0.49), and WTOC (r apparent 2  = 0.79, r jackknife 2  = 0.57) were also strong, suggesting that diatoms can provide reliable inferences of changes in solute delivery to the coastal ecosystem.

Adaptive software fault prediction approach using object-oriented metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

Diatom-based Models for Inferring Hydrology and Periphyton Abundance in a Subtropical Karstic Wetland: Implications for Ecosystem-Scale Bioassessment

We developed diatom-based prediction models of hydrology and periphyton abundance to inform assessment tools for a hydrologically managed wetland. Because hydrology is an important driver of ecosystem change, hydrologic alterations by restoration efforts could modify biological responses, such as periphyton characteristics. In karstic wetlands, diatoms are particularly important components of mat-forming calcareous periphyton assemblages that both respond and contribute to the structural organization and function of the periphyton matrix. We examined the distribution of diatoms across the Florida Everglades landscape and found hydroperiod and periphyton biovolume were strongly correlated with assemblage composition. We present species optima and tolerances for hydroperiod and periphyton biovolume, for use in interpreting the directionality of change in these important variables. Predictions of these variables were mapped to visualize landscape-scale spatial patterns in a dominant driver of change in this ecosystem (hydroperiod) and an ecosystem-level response metric of hydrologic change (periphyton biovolume). Specific diatom assemblages inhabiting periphyton mats of differing abundance can be used to infer past conditions and inform management decisions based on how assemblages are changing. This study captures diatom responses to wide gradients of hydrology and periphyton characteristics to inform ecosystem-scale bioassessment efforts in a large wetland.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

Adaptive Software Fault Prediction Approach Using Object-Oriented Metrics

As users continually request additional functionality, software systems will continue to grow in their complexity, as well as in their susceptibility to failures. Particularly for sensitive systems requiring higher levels of reliability, faulty system modules may increase development and maintenance cost. Hence, identifying them early would support the development of reliable systems through improved scheduling and quality control. Research effort to predict software modules likely to contain faults, as a consequence, has been substantial. Although a wide range of fault prediction models have been proposed, we remain far from having reliable tools that can be widely applied to real industrial systems. For projects with known fault histories, numerous research studies show that statistical models can provide reasonable estimates at predicting faulty modules using software metrics. However, as context-specific metrics differ from project to project, the task of predicting across projects is difficult to achieve. Prediction models obtained from one project experience are ineffective in their ability to predict fault-prone modules when applied to other projects. Hence, taking full benefit of the existing work in software development community has been substantially limited. As a step towards solving this problem, in this dissertation we propose a fault prediction approach that exploits existing prediction models, adapting them to improve their ability to predict faulty system modules across different software projects.

Investment decision framework for infrastructure asset management: a probability-based approach

Australia’s civil infrastructure assets of roads, bridges, railways, buildings and other structures are worth billions of dollars. Road assets alone are valued at around A$ 140 billion. As the condition of assets deteriorate over time, close to A$10 billion is spent annually in asset maintenance on Australia's roads, or the equivalent of A$27 million per day. To effectively manage road infrastructures, firstly, road agencies need to optimise the expenditure for asset data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. A procedure for assessing investment decision for road asset management has been developed. The procedure includes: • A methodology for optimising asset data collection; • A methodology for calibrating deterioration prediction models; • A methodology for assessing risk-adjusted estimates for life-cycle cost estimates. • A decision framework in the form of risk map

National practices in investment decision making in road sector

This document provides the findings of a national review of investment decision-making practices in road asset management. Efforts were concentrated on identifying the strategic objectives of agencies in road asset management, establishing and understanding criteria different organisations adopted and ascertaining the exact methodologies used by different sate road authorities. The investment objectives of Australian road authorities are based on triple-bottom line considerations (social, environmental, economic and political). In some cases, comparing with some social considerations, such as regional economic development, equity, and access to pubic service etc., Benefit-Cost Ratio has limited influence on the decision-making. Australian road authorities have developed various decision support tools. Although Multi-Criteria Analysis has been preliminarily used in case by case study, pavement management systems, which are primarily based on Benefit Cost Analysis, are still the main decision support tool. This situation is not compatible with the triple-bottom line objectives. There is need to fill the gap between decision support tools and decision-making itself. Different decision criteria should be adopted based on the contents of the work. Additional decision criteria, which are able to address social, environmental and political impacts, are needed to develop or identify. Environmental issue plays a more and more important role in decision-making. However, the criteria and respective weights in decision-making process are yet to be clearly identified. Social and political impacts resulted from road infrastructure investment can be identified through Community Perceptions Survey. With accumulative data, prediction models, which are similar as pavement performance models, can be established. Using these models, the decision-makers are able to foresee the social and political consequences of investment alternatives.

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion annually for road infrastructure asset management. To effectively manage road infrastructure, firstly road agencies not only need to optimise the expenditure for data collection, but at the same time, not jeopardise the reliability in using the optimised data to predict maintenance and rehabilitation costs. Secondly, road agencies need to accurately predict the deterioration rates of infrastructures to reflect local conditions so that the budget estimates could be accurately estimated. And finally, the prediction of budgets for maintenance and rehabilitation must provide a certain degree of reliability. This paper presents the results of case studies in using the probability-based method for an integrated approach (i.e. assessing optimal costs of pavement strength data collection; calibrating deterioration prediction models that suit local condition and assessing risk-adjusted budget estimates for road maintenance and rehabilitation for assessing life-cycle budget estimates). The probability concept is opening the path to having the means to predict life-cycle maintenance and rehabilitation budget estimates that have a known probability of success (e.g. produce budget estimates for a project life-cycle cost with 5% probability of exceeding). The paper also presents a conceptual decision-making framework in the form of risk mapping in which the life-cycle budget/cost investment could be considered in conjunction with social, environmental and political issues.

Social Participation of Community Dwelling Stroke Survivors [Published Abstract: From the Quality of Care and Outcomes Research in Cardiovascular Disease and Stroke Conference 2009]

Disability following a stroke can impose various restrictions on patients’ attempts at participating in life roles. The measurement of social participation, for instance, is important in estimating recovery and assessing quality of care at the community level. Thus, the identification of factors influencing social participation is essential in developing effective measures for promoting the reintegration of stroke survivors into the community. Data were collected from 188 stroke survivors (mean age 71.7 years) 12 months after discharge from a stroke rehabilitation hospital. Of these survivors, 128 (61 %) had suffered a first ever stroke, and 81 (43 %) had a right hemisphere lesion. Most (n = 156, 83 %) were living in their own home, though 32 (17 %) were living in residential care facilities. Path analysis was used to test a hypothesized model of participation restriction which included the direct and indirect effects between social, psychological and physical outcomes and demographic variables. Participation restriction was the dependent variable. Exogenous independent variables were age, functional ability, living arrangement and gender. Endogenous independent variables were depressive symptoms, state self-esteem and social support satisfaction. The path coefficients showed functional ability having the largest direct effect on participation restriction. The results also showed that more depressive symptoms, low state self-esteem, female gender, older age and living in a residential care facility had a direct effect on participation restriction. The explanatory variables accounted for 71% of the variance in explaining participation restriction. Prediction models have empirical and practical applications such as suggesting important factors to be considered in promoting stroke recovery. The findings suggest that interventions offered over the course of rehabilitation should be aimed at improving functional ability and promoting psychological aspects of recovery. These are likely to enhance stroke survivors resume or maximize their social participation so that they may fulfill productive and positive life roles.