Tbx1 and Bmp2 in the development of the ear, neural crest and pharyngeal system in mice

Dissertation presented to obtain the Ph.D degree in Biology

Uncertainty, generalization, and neural representation of relevant variables for decision making

Dissertation presented to obtain the Ph.D degree in Biology, Computational Biology.

Implementação de uma solução open source de Business Intelligence com metodologia Ágil

A importância dos sistemas de data warehousing e business intelligence é cada vez mais pronunciada, no sentido de dotar as organizações com a capacidade de guardar, explorar e produzir informação de valor acrescido para os seus processos de tomada de decisão. Esta realidade é claramente aplicável aos sectores da administração pública portuguesa e, muito em particular, aos organismos com responsabilidades centrais no Ministério da Saúde. No caso dos Serviços Partilhados do Ministério da Saúde (SPMS), que tem como missão prover o SNS de sistemas centrais de business intelligence, o apelo dos seus clientes, para que possam contar com capacidades analíticas nos seus sistemas centrais, tem sido sentido de forma muito acentuada. Todavia, é notório que, tanto os custos, como a complexidade, de grande parte destes projetos têm representado uma séria ameaça à sua adoção e sucesso. Por um lado, a administração pública tem recebido um forte encorajamento para integrar e adotar soluções de natureza open source (modelo de licenciamento gratuito), para os seus projetos de sistemas de informação. Por outro lado, temos vindo a assistir a uma vaga de aceitação generalizada de novas metodologias de desenvolvimento de projetos informáticos, nomeadamente no que diz respeito às metodologias Agéis, que se assumem como mais flexíveis, menos formais e com maior grau de sucesso. No sentido de averiguar da aplicabilidade do open source e das metodologias Ágeis aos sistemas de business intelligence, este trabalho documenta a implementação de um projeto organizacional para a SPMS, com recurso a ferramentas open source de licenciamento gratuito e através de uma metodologia de desenvolvimento de natureza Ágil.

A utilização de uma rede neural artificial para previsão da incidência da malária no município de Cantá, estado de Roraima

INTRODUÇÃO: A malária é uma doença endêmica na Amazônia Legal Brasileira, apresentando riscos diferentes para cada região. O Município de Cantá, no Estado de Roraima, apresentou para todo o período estudado, um dos maiores índices parasitários anuais do Brasil, com valor sempre maior que 50. O presente estudo visa à utilização de uma rede neural artificial para previsão da incidência da malária nesse município, a fim de auxiliar os coordenadores de saúde no planejamento e gestão dos recursos. MÉTODOS: Os dados foram coletados no site do Ministério da Saúde, SIVEP - Malária entre 2003 e 2009. Estruturou-se uma rede neural artificial com três neurônios na camada de entrada, duas camadas intermediárias e uma camada de saída com um neurônio. A função de ativação foi à sigmoide. No treinamento, utilizou-se o método backpropagation, com taxa de aprendizado de 0,05 e momentum 0,01. O critério de parada foi atingir 20.000 ciclos ou uma meta de 0,001. Os dados de 2003 a 2008 foram utilizados para treinamento e validação. Comparam-se os resultados com os de um modelo de regressão logística. RESULTADOS: Os resultados para todos os períodos previstos mostraram-se que as redes neurais artificiais obtiveram um menor erro quadrático médio e erro absoluto quando comparado com o modelo de regressão para o ano de 2009. CONCLUSÕES: A rede neural artificial se mostrou adequada para um sistema de previsão de malária no município estudado, determinando com pequenos erros absolutos os valores preditivos, quando comparados ao modelo de regressão logística e aos valores reais.

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

Nanostructuring silicon probes via electrodeposition: characterization of electrode coatings for acute in vivo neural recordings

Understanding how the brain works will require tools capable of measuring neuron elec-trical activity at a network scale. However, considerable progress is still necessary to reliably increase the number of neurons that are recorded and identified simultaneously with existing mi-croelectrode arrays. This project aims to evaluate how different materials can modify the effi-ciency of signal transfer from the neural tissue to the electrode. Therefore, various coating materials (gold, PEDOT, tungsten oxide and carbon nano-tubes) are characterized in terms of their underlying electrochemical processes and recording ef-ficacy. Iridium electrodes (177-706 μm2) are coated using galvanostatic deposition under different charge densities. By performing electrochemical impedance spectroscopy in phosphate buffered saline it is determined that the impedance modulus at 1 kHz depends on the coating material and decreased up to a maximum of two orders of magnitude for PEDOT (from 1 MΩ to 25 kΩ). The electrodes are furthermore characterized by cyclic voltammetry showing that charge storage capacity is im-proved by one order of magnitude reaching a maximum of 84.1 mC/cm2 for the PEDOT: gold nanoparticles composite (38 times the capacity of the pristine). Neural recording of spontaneous activity within the cortex was performed in anesthetized rodents to evaluate electrode coating performance.

Electromyography function, disability degree, and pain in leprosy patients undergoing neural mobilization treatment

INTRODUCTION: This study aimed to evaluate the effect of the neural mobilization technique on electromyography function, disability degree, and pain in patients with leprosy. METHODS: A sample of 56 individuals with leprosy was randomized into an experimental group, composed of 29 individuals undergoing treatment with neural mobilization, and a control group of 27 individuals who underwent conventional treatment. In both groups, the lesions in the lower limbs were treated. In the treatment with neural mobilization, the procedure used was mobilization of the lumbosacral roots and sciatic nerve biased to the peroneal nerve that innervates the anterior tibial muscle, which was evaluated in the electromyography. RESULTS: Analysis of the electromyography function showed a significant increase (p<0.05) in the experimental group in both the right (Δ%=22.1, p=0.013) and the left anterior tibial muscles (Δ%=27.7, p=0.009), compared with the control group pre- and post-test. Analysis of the strength both in the movement of horizontal extension (Δ%right=11.7, p=0.003/Δ%left=27.4, p=0.002) and in the movement of back flexion (Δ%right=31.1; p=0.000/Δ%left=34.7, p=0.000) showed a significant increase (p<0.05) in both the right and the left segments when comparing the experimental group pre- and post-test. The experimental group showed a significant reduction (p=0.000) in pain perception and disability degree when the pre- and post-test were compared and when compared with the control group in the post-test. CONCLUSIONS: Leprosy patients undergoing the technique of neural mobilization had an improvement in electromyography function and muscle strength, reducing disability degree and pain.

Factors associated with neural alterations and physical disabilities in patients with leprosy in São Luis, State of Maranhão, Brazil

Introduction Leprosy is a chronic infectious disease that is caused by Mycobacterium leprae. The objective of this study was to evaluate the risk factors that are associated with neural alterations and physical disabilities in leprosy patients at the time of diagnosis. Methods A prospective cross-sectional study was conducted on 155 leprosy patients who participated in a program that aimed to eliminate leprosy from São Luis, State of Maranhão. Results Patients who were 31-45 years of age, were older than 60 years of age or had a partner were more likely to have a disability. Patients with partners were 1.14 times more likely (p = 0.025) to have disabilities of the hands. The frequency of disabilities in the feet among the patients with different clinical forms of leprosy was statistically significant. Conclusions The identification of risk factors that are associated with neural alterations and physical disabilities in leprosy patients is important for diagnosing the disease because this approach enables physicians to plan and prioritize actions for the treatment and monitoring of patients.

Dimensions of source credibility in the case of user-generated advertisements

User-generated advertising changed the world of advertising and changed the strategies used by marketers. Many researchers explored the dimensions of source credibility in traditional media and online advertising. However, little previous research explored the dimensions of source credibility in the context of user-generated advertising. This exploratory study aims to investigate the different dimensions of source credibility in the case of user-generated advertising. More precisely, this study will explore the following factors: (1) objectivity, (2) trustworthiness, (3) expertise, (4) familiarity, (5) attractiveness and (6) frequency. The results suggest that some of the dimensions of source credibility (objectivity, trustworthiness, expertise, familiarity and attractiveness) remain the same in the case of user-generated advertising. Additionally, a new dimension is added to the factors that explain source credibility (reputation). Furthermore, the analysis suggests that the dimension “frequency” is not an explanatory factor of credibility in the case of user-generated advertisement. The study also suggests that companies using user-generated advertisement as part of their overall marketing strategy should focus on objectivity, trustworthiness, expertise, attractiveness and reputation when selecting users that will communicate sponsored user-generated advertisements.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

MANGROVE BARK: A RENEWABLE RESIN SOURCE FOR WOOD ADHESIVES

From four solutions tested to extract tannins from mangrove bark for wood adhesives, hot water is recommended. Hot water extracted 21.4% of formaldehyde-hydrochloric acid reactive polyphenols on oven-dry bark basis.

Comprehensive comparison of a current-source and a voltage-source converter for three-phase EV fast battery chargers

This paper presents a comprehensive comparison of a current-source converter and a voltage-source converter for three-phase electric vehicle (EV) fast battery chargers. Taking into account that the current-source converter (CSC) is a natural buck-type converter, the output voltage can assume a wide range of values, which varies between zero and the maximum instantaneous value of the power grid phase-to-phase voltage. On the other hand, taking into account that the voltage-source converter (VSC) is a natural boost-type converter, the output voltage is always greater than the maximum instantaneous value of the power grid phase-to-phase voltage, and consequently, it is necessary to use a dc-dc buck-type converter for applications as EV fast battery chargers. Along the paper is described in detail the principle of operation of both the CSC and the VSC for EV fast chargers, as well as the main equations of the power theory and current control strategies. The comparison between both converters is mainly established in terms of the total harmonic distortion of the grid current and the estimated efficiency for a range of operation between 10 kW and 50 kW.

Three-phase three-level current-source converter for EVs fast battery charging systems

This paper presents a three-phase three-level fast battery charger for electric vehicles (EVs) based in a current-source converter (CSC). Compared with the traditional voltage-source converters used for fast battery chargers, the CSC can be seen as a natural buck-type converter, i.e., the output voltage can assume a wide range of values, which varies between zero and the maximum instantaneous value of the power grid phase-to-phase voltage. Moreover, using the CSC it is not necessary to use a dc-dc back-end converter in the battery side, and it is also possible to control the grid current in order to obtain a sinusoidal waveform, and in phase with the power grid voltage (unitary power factor). Along the paper is described in detail the proposed CSC for EVs fast battery charging systems: the circuit topology, the power control theory, the current control strategy and the grid synchronization algorithm. Several simulation results of the EV fast battery charger operating with a maximum power of 50 kW are presented.

Predictive control of a current-source inverter for solar photovoltaic grid interface

Solar photovoltaic systems are an increasing option for electricity production, since they produce electrical energy from a clean renewable energy resource, and over the years, as a result of the research, their efficiency has been increasing. For the interface between the dc photovoltaic solar array and the ac electrical grid is necessary the use of an inverter (dc-ac converter), which should be optimized to extract the maximum power from the photovoltaic solar array. In this paper is presented a solution based on a current-source inverter (CSI) using continuous control set model predictive control (CCS-MPC). All the power circuits and respective control systems are described in detail along the paper and were tested and validated performing computer simulations. The paper shows the simulation results and are drawn several conclusions.