Optimization, high availability and redundancy in information communications and technology using networks and systems virtualization architectures

Mestrado em Engenharia Informática

Exploring bioactive properties of marine cyanobacteria Isolated from the Portuguese coast: high potential as a source of anticancer compounds

The oceans remain a major source of natural compounds with potential in pharmacology. In particular, during the last few decades, marine cyanobacteria have been in focus as producers of interesting bioactive compounds, especially for the treatment of cancer. In this study, the anticancer potential of extracts from twenty eight marine cyanobacteria strains, belonging to the underexplored picoplanktonic genera, Cyanobium, Synechocystis and Synechococcus, and the filamentous genera, Nodosilinea, Leptolyngbya, Pseudanabaena and Romeria, were assessed in eight human tumor cell lines. First, a crude extract was obtained by dichloromethane:methanol extraction, and from it, three fractions were separated in a Si column chromatography. The crude extract and fractions were tested in eight human cancer cell lines for cell viability/toxicity, accessed with the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and lactic dehydrogenase release (LDH) assays. Eight point nine percent of the strains revealed strong cytotoxicity; 17.8% showed moderate cytotoxicity, and 14.3% assays showed low toxicity. The results obtained revealed that the studied genera of marine cyanobacteria are a promising source of novel compounds with potential anticancer activity and highlight the interest in also exploring the smaller filamentous and picoplanktonic genera of cyanobacteria.

Towards high-available and energy-efficient virtual computing environments in the cloud

Empowered by virtualisation technology, cloud infrastructures enable the construction of flexi- ble and elastic computing environments, providing an opportunity for energy and resource cost optimisation while enhancing system availability and achieving high performance. A crucial re- quirement for effective consolidation is the ability to efficiently utilise system resources for high- availability computing and energy-efficiency optimisation to reduce operational costs and carbon footprints in the environment. Additionally, failures in highly networked computing systems can negatively impact system performance substantially, prohibiting the system from achieving its initial objectives. In this paper, we propose algorithms to dynamically construct and readjust vir- tual clusters to enable the execution of users’ jobs. Allied with an energy optimising mechanism to detect and mitigate energy inefficiencies, our decision-making algorithms leverage virtuali- sation tools to provide proactive fault-tolerance and energy-efficiency to virtual clusters. We conducted simulations by injecting random synthetic jobs and jobs using the latest version of the Google cloud tracelogs. The results indicate that our strategy improves the work per Joule ratio by approximately 12.9% and the working efficiency by almost 15.9% compared with other state-of-the-art algorithms.

Effect of temperature on the shear strength of aluminium single lap bonded joints for high temperature applications

An experimental and numerical investigation into the shear strength behaviour of adhesive single lap joints (SLJs) was carried out in order to understand the effect of temperature on the joint strength. The adherend material used for the experimental tests was an aluminium alloy in the form of thin sheets, and the adhesive used was a high-strength high temperature epoxy. Tensile tests as a function of temperature were performed and numerical predictions based on the use of a bilinear cohesive damage model were obtained. It is shown that at temperatures below Tg, the lap shear strength of SLJs increased, while at temperatures above Tg, a drastic drop in the lap shear strength was observed. Comparison between the experimental and numerical maximum loads representing the strength of the joints shows a reasonably good agreement.

Nanoparticles based Permeable Reactive Barriers as an Eco-efficient Technology for nitrate remediation in soil and groundwater

The need to increase agricultural yield led, among others, to an increase in the consumption of nitrogen based fertilizers. As a consequence, there are excessive concentrations of nitrates, the most abundant of the reactive nitrogen (Nr) species, in several areas of the world. The demographic changes and projected population growth for the next decades, and the economic shifts which are already shaping the near future are powerful drivers for a further intensification in the use of fertilizers, with a predicted increase of the nitrogen loads in soils. Nitrate easily diffuses in the subsurface environments, portraying high mobility in soils. Moreover, the presence of high nitrate loads in water has the potential to cause an array of health dysfunctions, such as methemoglobinemia and several cancers. Permeable Reactive Barriers (PRB) placed strategically relatively to the nitrate source constitute an effective technology to tackle nitrate pollution. Ergo, PRB avoid various adverse impacts resulting from the displacement of reactive nitrogen downstream along water bodies. A four stages literature review was carried out in 34 databases. Initially, a set of pertinent key words were identified to perform the initial databases searches. Then, the synonyms of those initial key words were used to carry out a second set of databases searches. The third stage comprised the identification of other additional relevant terms from the research papers identified in the previous two stages. Again, databases searches were performed with this third set of key words. The final step consisted of the identification of relevant papers from the bibliography of the relevant papers identified in the previous three stages of the literature review process. The set of papers identified as relevant for in-depth analysis were assessed considering a set of relevant characterization variables.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.