Metabolic flux analysis of neural cell metabolism in primary cultures

Dissertation presented to obtain the Ph.D degree in Biochemistry, Neuroscience

A Visual Programming Framework for Wireless Sensor Networks in Smart Home Applications

International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2015). 7 to 9, Apr, 2015. Singapure, Singapore.

Bringing Context Awareness to IoT-Based Wireless Sensor Networks

IEEE International Conference on Pervasive Computing and Communications (PerCom). 23 to 26, Mar, 2015, PhD Forum. Saint Louis, U.S.A..

On the Scalability of Constructive Interference in Low-Power Wireless Networks

12th European Conference on Wireless Sensor Networks (EWSN 2015). 9 to 11, Feb, 2015. Porto, Portugal

AUV Control and Communication using Underwater Acoustic Networks

Underwater acoustic networks can be quite effective to establish communication links between autonomous underwater vehicles (AUVs) and other vehicles or control units, enabling complex vehicle applications and control scenarios. A communications and control framework to support the use of underwater acoustic networks and sample application scenarios are described for single and multi-AUV operation.

Poster/Demo Session Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN'15) C

Proceedings of the 12th European Conference on Wireless Sensor Networks (EWSN'15), 9-11 Feb 2015, Porto-Portugal.

A Modular Programming Approach for IoTBased Wireless Sensor Networks

Poster presented in 12th European Conference on Wireless Sensor Network (EWSN 2015). 9 to 11, Feb, 2015. Porto, Portugal.

Models for pheromone evaluation in Ant Systems for Mobile Ad-hoc networks

On a mobile ad-hoc network environment, where the resources are scarce, the knowledge about the network's link state is essential to optimize the routing procedures. This paper presents a study about different pheromone evaluation models and how they react to possible changes in traffic rate. Observing how the pheromone value on a link changes, it could be possible to identify certain patterns which can indicate the path status. For this study, the behavior of the Ant System evaluation model was compared with a Temporal Active Pheromone model (a biological approach) and a Progressive Pheromone Reduction model with and without a maximum pheromone limit.

Sensor Integration for Smart Cities Using Multi-Hop Networks

Smart Cities are designed to be living systems and turn urban dwellers life more comfortable and interactive by keeping them aware of what surrounds them, while leaving a greener footprint. The Future Cities Project [1] aims to create infrastructures for research in smart cities including a vehicular network, the BusNet, and an environmental sensor platform, the Urban Sense. Vehicles within the BusNet are equipped with On Board Units (OBUs) that offer free Wi-Fi to passengers and devices near the street. The Urban Sense platform is composed by a set of Data Collection Units (DCUs) that include a set of sensors measuring environmental parameters such as air pollution, meteorology and noise. The Urban Sense platform is expanding and receptive to add new sensors to the platform. The parnership with companies like TNL were made and the need to monitor garbage street containers emerged as air pollution prevention. If refuse collection companies know prior to the refuse collection which route is the best to collect the maximum amount of garbage with the shortest path, they can reduce costs and pollution levels are lower, leaving behind a greener footprint. This dissertation work arises in the need to monitor the garbage street containers and integrate these sensors into an Urban Sense DCU. Due to the remote locations of the garbage street containers, a network extension to the vehicular network had to be created. This dissertation work also focus on the Multi-hop network designed to extend the vehicular network coverage area to the remote garbage street containers. In locations where garbage street containers have access to the vehicular network, Roadside Units (RSUs) or Access Points (APs), the Multi-hop network serves has a redundant path to send the data collected from DCUs to the Urban Sense cloud database. To plan this highly dynamic network, the Wi-Fi Planner Tool was developed. This tool allowed taking measurements on the field that led to an optimized location of the Multi-hop network nodes with the use of radio propagation models. This tool also allowed rendering a temperature-map style overlay for Google Earth [2] application. For the DCU for garbage street containers the parner company provided the access to a HUB (device that communicates with the sensor inside the garbage containers). The Future Cities use the Raspberry pi as a platform for the DCUs. To collect the data from the HUB a RS485 to RS232 converter was used at the physical level and the Modbus protocol at the application level. To determine the location and status of the vehicles whinin the vehicular network a TCP Server was developed. This application was developed for the OBUs providing the vehicle Global Positioning System (GPS) location as well as information of when the vehicle is stopped, moving, on idle or even its slope. To implement the Multi-hop network on the field some scripts were developed such as pingLED and “shark”. These scripts helped upon node deployment on the field as well as to perform all the tests on the network. Two setups were implemented on the field, an urban setup was implemented for a Multi-hop network coverage survey and a sub-urban setup was implemented to test the Multi-hop network routing protocols, Optimized Link State Routing Protocol (OLSR) and Babel.

Cost allocation model for distribution networks considering high penetration of distributed energy resources

The high penetration of distributed energy resources (DER) in distribution networks and the competitive environment of electricity markets impose the use of new approaches in several domains. The network cost allocation, traditionally used in transmission networks, should be adapted and used in the distribution networks considering the specifications of the connected resources. The main goal is to develop a fairer methodology trying to distribute the distribution network use costs to all players which are using the network in each period. In this paper, a model considering different type of costs (fixed, losses, and congestion costs) is proposed comprising the use of a large set of DER, namely distributed generation (DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehicles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). The proposed model includes three distinct phases of operation. The first phase of the model consists in an economic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen's and Bialek's tracing algorithms are used and compared to evaluate the impact of each resource in the network. Finally, the MW-mile method is used in the third phase of the proposed model. A distribution network of 33 buses with large penetration of DER is used to illustrate the application of the proposed model.

Multi-criteria optimisation approach to increase the delivered power in radial distribution networks

This study proposes a new methodology to increase the power delivered to any load point in a radial distribution network, through the identification of new investments in order to improve the repair time. This research work is innovative and consists in proposing a full optimisation model based on mixed-integer non-linear programming considering the Pareto front technique. The goal is to achieve a reduction in repair times of the distribution networks components, while minimising the costs of that reduction as well as non-supplied energy costs. The optimisation model considers the distribution network technical constraints, the substation transformer taps, and it is able to choose the capacitor banks size. A case study based on a 33-bus distribution network is presented in order to illustrate in detail the application of the proposed methodology.

Impact of sensor nodes scaling and velocity on handover mechanisms for healthcare wireless sensor networks with mobility support

Health promotion in hospital environments can be improved using the most recent information and communication technologies. The Internet connectivity to small sensor nodes carried by patients allows remote access to their bio-signals. To promote these features the healthcare wireless sensor networks (HWSN) are used. In these networks mobility support is a key issue in order to keep patients under realtime monitoring even when they move around. To keep sensors connected to the network, they should change their access points of attachment when patients move to a new coverage area along an infirmary. This process, called handover, is responsible for continuous network connectivity to the sensors. This paper presents a detailed performance evaluation study considering three handover mechanisms for healthcare scenarios (Hand4MAC, RSSI-based, and Backbone-based). The study was performed by simulation using several scenarios with different number of sensors and different moving velocities of sensor nodes. The results show that Hand4MAC is the best solution to guarantee almost continuous connectivity to sensor nodes with less energy consumption.

Does Metabolic Syndrome Predict Significant Angiographic Coronary Artery Disease?

INTRODUCTION: Metabolic syndrome (MS) is an independent predictor of acute cardiovascular events. However, few studies have addressed the relationship between MS and stable angiographic coronary artery disease (CAD), which has a different pathophysiological mechanism. We aimed to study the independent predictors for significant CAD, and to analyze the impact of MS (by the AHA/NHLBI definition) on CAD. METHODS: We prospectively included 300 patients, mean age 64±9 years, 59% male, admitted for elective coronary angiography (suspected ischemic heart disease), excluding patients with known cardiac disease. All patients underwent assessment of demographic, anthropometric, and laboratory data and risk factors, and subsequently underwent coronary angiography. RESULTS: In the study population, 23.0% were diabetic, 40.5% had MS (and no diabetes) and 36.7% had neither diagnosis. Significant CAD was present in 51.3% of patients. CAD patients were older and more frequently male and diabetic, with increased triglycerides and glucose and lower HDL cholesterol. Abdominal obesity was also less prevalent. MS was not associated with the presence of CAD (OR 0.94, 95% CI 0.59-1.48, p=0.778). Of the MS components, the most important predictors of CAD were increased glucose and triglycerides. Abdominal obesity was associated with a lower risk of CAD. In a multivariate logistic regression model for CAD, independent predictors of CAD were age, male gender, glucose and triglycerides. Body mass index had a protective effect. CONCLUSIONS: Although MS is associated with cardiovascular events, the same was not found for stable angiographically proven CAD. Age, gender, diabetes and triglycerides are the most influential factors for CAD, with abdominal obesity as a protective factor.

Prevalence of Metabolic Syndrome and Diabetes on a High-Risk Population with Suspected Coronary Artery Disease

Objectivos: A prevalência de Sindroma Metabólica (SM) e diabetes é variável consoante a definição utilizada, assim como com a região geográfica e o grupo étnico estudado. Não existem estudos em indivíduos portugueses com suspeita de doença arterial coronária. Analisámos a prevalência de SM e diabetes nesta população específica de doentes, comparando também definições. Métodos: Incluíram-se no estudo 300 indivíduos, com uma idade media de 64 ± 9 anos, 59% do género masculino, admitidos para angiografia coronária electiva, tendo sido excluídos os doentes com doença cardíaca previamente conhecida. Avaliou-se a prevalência de SM e de diabetes. Resultados: A prevalência ajustada de SM foi de 39,3% (critério NCEP-ATP III), 53,8% (critério IDF) e 48,4% (critério AHA/NHLBI). A prevalência ajustada de diabetes foi de 14,8% pela definição prévia da ADA e de 36.4% com a definição mais recente. A concordância global entre as definições de SM foi de 45,3%, sendo mais elevada entre as definições da AHA/NHLBI e da NCEP-ATP III (Kappa 0,821). A prevalência de SM está altamente dependente da idade em ambos os géneros, sendo mais prevalente no género feminino. A prevalência de diabetes é também dependente da idade, sendo semelhante em ambos os géneros. O componente de SM mais frequente é a hipertensão arterial, seguido pela obesidade abdominal, elevação da glicose, colesterol-HDL baixo e finalmente elevação dos triglicéridos. É também importante referir que 60% dos doentes estavam sob terapêutica hipolipemiante (56,6% com estatinas, 1,7% com fibratos e 1,7% com ambos). A diferença mais significativa entre géneros no que diz respeito aos componentes de SM é a elevada prevalência de obesidade abdominal no género feminino. Conclusões: Nesta população de alto risco, a prevalência de SM é elevada, sendo contudo a prevalência de diabetes semelhante à registada em estudos epidemiológicos na população geral.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.