The main leachate emissions of uncontrolled landfills

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente, Perfil de Engenharia Sanitária

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Novel antibiotic-loaded orthopaedic bone cements: insights on drug release profiles and biocompatibility

Acrylic bone cement (BC) is widely used as an anchor of artificial joints. Bacterial infection due to biofilm formation and inflammation are common and difficult to treat problems associated with commercial available BC formulations. Research on novel BC compositions is urgently needed. The main objective of this thesis was to develop a new biocompatible antibiotic-loaded BC with improved release profile. To achieve that aim several additives were incorporated, as an antibiotic (levofloxacin) to combat bacterial growth, an anti-inflammatory drug (diclofenac) to decrease the inflammatory process and two well-known and broadly used biopolymers, alginate and chitosan in order to increase matrix porosity, and in this way to intensify the amount of released drug. Novel BC formulations were tested in order to find the most suitable one that had potential to proceed to clinical application. Numerous tests were conducted as: a) evaluation of drug release profiles in different biomimetic media, b) mechanical and surface studies, c) microbiological activity testing against Staphylococcus aureus and d) in vitro biocompatibility assays (fibroblasts and osteoblasts). In general, the addition of biopolymers increased drug release, didn’t compromised BC mechanical properties and increased BC hydrophilicity. Microbiological testing revealed that Lev[BC]Chi was the only matrix that reduced significantly biofilm formation. On the contrary, alginate and diclofenac loading into BC seemed to increase biofilm growth. Biocompatibility studies showed some decrease in cell viability, in particularly on osteoblasts, mainly due to the high amounts of released drugs. In conclusion, the present work has shown that the matrix with more potential to proceed in further investigations was Lev[BC]Chi. Other conditions (namely additives and drugs concentrations) should be evaluated with the other tested BC matrices before being discharged.

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.

How to increase brand value expanding the category and brand presence to the main meal occasion for Sumol- Compal

Having registered negative retail value growth of 4% in Portugal in 2014, the juice category is set to decline further by 5,5% until 2019. Manufacturers of juices and nectars are therefore increasingly looking for new categories in order to balance this negative forecast in their home territory. One apparent growth opportunity for Compal, the leading producer of juices and nectars, is to expand its commercial reach to new occasions of consumption. This report carefully analyzes the opportunities related to an expansion to the main meal occasion and introduces a complete marketing and communications plan for a possible new main meal juice, Compal à Mesa. The product concept represents a rather premium positioning for the main meal occasion, including new flavor mixes that are targeted at different occasions of meals. The justification of the introduced concept includes a discussion of the primary and secondary research that was performed

Return on assets in the Tobacco Industry analysis of the four main Tobacco Manufacturers

This paper studies operating profitability drivers in the Four Main Tobacco Manufacturers for the period 2004-2014. The operating profitability is analyzed as return on assets (ROA) based on the DuPont Extended Model breakdown in degree of operational risk, gross sales margin and assets turnover. The sources of ROA are market share and price strategies appraised through the drivers: firm-size, global value and strategic choices. Using consolidated data, results suggest that firm-size and global value holds a positive relationship with ROA. Also innovation through less harmful tobacco products can lead to better ROA despite no correlation between R&D and ROA.