Enterprise reference lexicon building from business models

Nowadays, a significant increase on the demand for interoperable systems for exchanging data in business collaborative environments has been noticed. Consequently, cooperation agreements between each of the involved enterprises have been brought to light. However, due to the fact that even in a same community or domain, there is a big variety of knowledge representation not semantically coincident, which embodies the existence of interoperability problems in the enterprises information systems that need to be addressed. Moreover, in relation to this, most organizations face other problems about their information systems, as: 1) domain knowledge not being easily accessible by all the stakeholders (even intra-enterprise); 2) domain knowledge not being represented in a standard format; 3) and even if it is available in a standard format, it is not supported by semantic annotations or described using a common and understandable lexicon. This dissertation proposes an approach for the establishment of an enterprise reference lexicon from business models. It addresses the automation in the information models mapping for the reference lexicon construction. It aggregates a formal and conceptual representation of the business domain, with a clear definition of the used lexicon to facilitate an overall understanding by all the involved stakeholders, including non-IT personnel.

Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

Real-time collaborative editing of OutSystems DSL models

Real-time collaborative editing systems are common nowadays, and their advantages are widely recognized. Examples of such systems include Google Docs, ShareLaTeX, among others. This thesis aims to adopt this paradigm in a software development environment. The OutSystems visual language lends itself very appropriate to this kind of collaboration, since the visual code enables a natural flow of knowledge between developers regarding the developed code. Furthermore, communication and coordination are simplified. This proposal explores the field of collaboration on a very structured and rigid model, where collaboration is made through the copy-modify-merge paradigm, in which a developer gets its own private copy from the shared repository, modifies it in isolation and later uploads his changes to be merged with modifications concurrently produced by other developers. To this end, we designed and implemented an extension to the OutSystems Platform, in order to enable real-time collaborative editing. The solution guarantees consistency among the artefacts distributed across several developers working on the same project. We believe that it is possible to achieve a much more intense collaboration over the same models with a low negative impact on the individual productivity of each developer.

Synthesis of Polymeric Nanoparticles for biomedical delivery applications

Polymeric nanoparticles (PNPs) have attracted considerable interest over the last few years due to their unique properties and behaviors provided by their small size. Such materials could be used in a wide range of applications such as diagnostics and drug delivery. Advantages of PNPs include controlled release, protection of drug molecules and its specific targeting, with concomitant increasing of the therapeutic index. In this work, novel sucrose and cholic acid based PNPs were prepared from different polymers, namely polyethylene glycol (PEG), poly(D,L-lactic-co-glycolic acid) (PLGA) and PLGA-co-PEG copolymer. In these PNP carriers, cholic acid will act as a drug incorporation site and the carbohydrate as targeting moiety. The uptake of nanoparticles into cells usually involves endocytotic processes, which depend primarily on their size and surface characteristics. These properties can be tuned by the nanoparticle preparation method. Therefore, the nanoprecipitation and the emulsion-solvent evaporation method were applied to prepare the PNPs. The influence of various parameters, such as concentration of the starting solution, evaporation method and solvent properties on the nanoparticle size, size distribution and morphology were studied. The PNPs were characterized by using atomic force microscopy (AFM), scanning electron microscopy (SEM) and dynamic light scattering (DLS) to assess their size distribution and morphology. The PNPs obtained by nanoprecipitation ranged in size between 90 nm and 130 nm with a very low polydispersity index (PDI < 0.3). On the other hand, the PNPs produced by the emulsion-solvent evaporation method revealed particle sizes around 300 nm with a high PDI value. More detailed information was found in AFM and SEM images, which demonstrated that all these PNPs were regularly spherical. ζ-potential measurements were satisfactory and evidenced the importance of sucrose moiety on the polymeric system, which was responsible for the obtained negative surface charge, providing colloidal stability. The results of this study show that sucrose and cholic acid based polymeric conjugates can be successfully used to prepare PNPs with tunable physicochemical characteristics. In addition, it provides novel information about the materials used and the methods applied. It is hoped that this work will be useful for the development of novel carbohydrate based nanoparticles for biomedical applications, specifically for targeted drug delivery.

Development of 3D in vitro models for prediction of hepatic metabolism and toxicity

The development of human cell models that recapitulate hepatic functionality allows the study of metabolic pathways involved in toxicity and disease. The increased biological relevance, cost-effectiveness and high-throughput of cell models can contribute to increase the efficiency of drug development in the pharmaceutical industry. Recapitulation of liver functionality in vitro requires the development of advanced culture strategies to mimic in vivo complexity, such as 3D culture, co-cultures or biomaterials. However, complex 3D models are typically associated with poor robustness, limited scalability and compatibility with screening methods. In this work, several strategies were used to develop highly functional and reproducible spheroid-based in vitro models of human hepatocytes and HepaRG cells using stirred culture systems. In chapter 2, the isolation of human hepatocytes from resected liver tissue was implemented and a liver tissue perfusion method was optimized towards the improvement of hepatocyte isolation and aggregation efficiency, resulting in an isolation protocol compatible with 3D culture. In chapter 3, human hepatocytes were co-cultivated with mesenchymal stem cells (MSC) and the phenotype of both cell types was characterized, showing that MSC acquire a supportive stromal function and hepatocytes retain differentiated hepatic functions, stability of drug metabolism enzymes and higher viability in co-cultures. In chapter 4, a 3D alginate microencapsulation strategy for the differentiation of HepaRG cells was evaluated and compared with the standard 2D DMSO-dependent differentiation, yielding higher differentiation efficiency, comparable levels of drug metabolism activity and significantly improved biosynthetic activity. The work developed in this thesis provides novel strategies for 3D culture of human hepatic cell models, which are reproducible, scalable and compatible with screening platforms. The phenotypic and functional characterization of the in vitro systems performed contributes to the state of the art of human hepatic cell models and can be applied to the improvement of pre-clinical drug development efficiency of the process, model disease and ultimately, development of cell-based therapeutic strategies for liver failure.

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.

Flood resilient housing recovery models: a theoretical case study in Maldives

Natural disasters are events that cause general and widespread destruction of the built environment and are becoming increasingly recurrent. They are a product of vulnerability and community exposure to natural hazards, generating a multitude of social, economic and cultural issues of which the loss of housing and the subsequent need for shelter is one of its major consequences. Nowadays, numerous factors contribute to increased vulnerability and exposure to natural disasters such as climate change with its impacts felt across the globe and which is currently seen as a worldwide threat to the built environment. The abandonment of disaster-affected areas can also push populations to regions where natural hazards are felt more severely. Although several actors in the post-disaster scenario provide for shelter needs and recovery programs, housing is often inadequate and unable to resist the effects of future natural hazards. Resilient housing is commonly not addressed due to the urgency in sheltering affected populations. However, by neglecting risks of exposure in construction, houses become vulnerable and are likely to be damaged or destroyed in future natural hazard events. That being said it becomes fundamental to include resilience criteria, when it comes to housing, which in turn will allow new houses to better withstand the passage of time and natural disasters, in the safest way possible. This master thesis is intended to provide guiding principles to take towards housing recovery after natural disasters, particularly in the form of flood resilient construction, considering floods are responsible for the largest number of natural disasters. To this purpose, the main structures that house affected populations were identified and analyzed in depth. After assessing the risks and damages that flood events can cause in housing, a methodology was proposed for flood resilient housing models, in which there were identified key criteria that housing should meet. The same methodology is based in the US Federal Emergency Management Agency requirements and recommendations in accordance to specific flood zones. Finally, a case study in Maldives – one of the most vulnerable countries to sea level rise resulting from climate change – has been analyzed in light of housing recovery in a post-disaster induced scenario. This analysis was carried out by using the proposed methodology with the intent of assessing the resilience of the newly built housing to floods in the aftermath of the 2004 Indian Ocean Tsunami.

The future of airline business models:

This research is titled “The Future of Airline Business Models: Which Will Win?” and it is part of the requirements for the award of a Masters in Management from NOVA BSE and another from Luiss Guido Carlo University. The purpose is to elaborate a complete market analysis of the European Air Transportation Industry in order to predict which Airlines, strategies and business models may be successful in the next years. First, an extensive literature review of the business model concept has been done. Then, a detailed overview of the main European Airlines and the strategies that they have been implementing so far has been developed. Finally, the research is illustrated with three case studies

Technical optimization of the synthesis of flavours

The goal of this thesis is the investigation and optimization of the synthesis of potential fragrances. This work is projected as collaboration between the University of Applied Sciences in Merseburg and the company Miltitz Aromatics GmbH in Bitterfeld‐Wolfen (Germany). Flavoured compounds can be synthesized in different ways and by various methods. In this work, methods like the phase transfer catalysis and the Cope‐rearrangement were investigated and applied, for getting a high yield and quantity of the desired substances and without any by‐products or side reactions. This involved the study of syntheses with different process parameters such as temperature, solvent, pressure and reaction time. The main focus was on Cope‐rearrangement, which is a common method in the synthesis of new potential fragrance compounds. The substances synthesized in this work have a hepta‐1,5‐diene‐structure and that is why they can easily undergo this [3,3]‐sigma tropic rearrangement. The lead compound of all research was 2,5‐dimethyl‐2‐vinyl‐4‐hexenenitrile (Neronil). Neronil is synthesized by an alkylation of 2‐methyl‐3‐butenenitrile with prenylchloride under basic conditions in a phase‐transfer system. In this work the yield of isolated Neronil is improved from about 35% to 46% by according to the execution conditions of the reaction. Additionally the amount of side product was decreased. This synthesized hexenenitrile involved not only the aforementioned 1,5‐diene‐structure, but also a cyano group, that makes this structure a suitable base for the synthesis of new potential fragrance compounds. It was observed that Neronil can be transferred into 2,5‐dimethyl‐2‐vinyl‐4‐hexenoic acid by a hydrolysis under basic conditions. After five hours the acid can be obtained with a yield of 96%. The following esterification is realized with isobutanol to produce 2,5‐dimethyl‐2‐vinyl‐4‐hexenoic acid isobutyl ester with quantitative conversion. It was observed that the Neronil and the corresponding ester can be converted into the corresponding Cope‐product, with a conversion of 30 % and 80%. Implementing the Cope‐rearrangement, the acid was heated and an unexpected decarboxylated product is formed. To achieve the best verification of reaction development and structure, scrupulous analyses were done using GC‐MS, 1H‐NMR and 13C‐ NMR.

Synthesis of precursors of the rare 3-O-methylmannose polysaccharides present in Nontuberculous Mycobacteria

3-O-methylmannose polysaccharides (MMPs) are cytoplasmic carbohydrates synthesized by mycobacteria, which play important intracellular roles, such as for example in metabolism regulation. An important way to confirm if the inhibition of the synthesis of these polysaccharides will critically affect the survival of mycobacteria is the study of the biosynthetic pathways from these molecules on these microorganisms. The purpose of this work is the efficient synthesis of three saccharides, which are rare cellular precursors from the biosynthesis of the mycobacterial polysaccharides, allowing its study. In order to obtain these molecules, a chemical strategy to connect two precursors was used. This process is called chemical glycosylation and its importance will be highlighted as an important alternative to enzymatic glycosylation. The first objective was the synthesis of the disaccharides Methyl (3-O-methyl-α-D-mannopyranosyl)-(1→4)-3-O-methyl-α-D-mannopyranoside and (3-O-Methyl-α-D-mannopyra- nosyl)-(1→4)-3-O-methyl-(α/β)-D-mannopyranose. The mannose precursors were prepared before the glycosylation reaction. The same mannosyl donor was used in the preparation of both molecules and its efficient synthesis was achieved using a 8 step synthetic route from D-mannose. A different mannosyl acceptor was used in the synthesis of each disaccharide and their syntheses were also efficient, the first one a 4 step synthetic route from α-methyl-D-mannose and the second one as an intermediate from the synthesis of the mannosyl donor. The stereoselective preparation of these disaccharides was performed successfully. The second and last objective of the proposed work was the synthesis of the tetrasaccharide methyl (3-O-methyl-α-D-mannopyranosyl-(1→4)-3-O-methyl-α-D-mannopyra- nosyl-(1→4)-3-O-methyl-α-D-mannopyranosyl-(1→4)-3-O-methyl-α-D-mannopyranoside. The disaccharide acceptor and donor to be linked through a stereoselective glycosidic reaction had to be first synthesized. Several synthetic strategies were studied. Neither the precursors nor the tetrasaccharide were synthesized, but a final promising synthetic route for its preparation has been proposed.

Study of the role of wall teichoic acids in the localization Staphylococcus aureus cell wall synthesis protein PBP4

The cell wall of Staphylococcus aureus is a highly complex network mainly composed of highly cross-linked peptidoglycan (PG) and teichoic acids (TAs), both important for the maintenance of the integrity and viability of bacteria. The penicillin binding proteins (PBPs), which catalyse the final stage of PG biosynthesis, are targets of β-lactam antibiotics and have been a key focus of antibacterial research. S. aureus has four native PBPs, PBP1-4 carried by both methicillin-sensitive (MSSA) and –resistant (MRSA) strains. PBP4 is required for the synthesis of the highly cross-linked PG and, as shown in recent studies, is essential for the expression of β-lactam resistance in community-acquired strains (CA-MRSA). This protein has a septal localization that seems to be spatially and temporally regulated by an unknown intermediate of the wall teichoic acids (WTA) biosynthesis pathway. Therefore, if WTA synthesis is compromised, PBP4 becomes dispersed throughout the entire cell membrane. The aim of this project was to identify the WTA precursor responsible for the septal recruitment of PBP4. In order to do so, inducible mutants of tarB and tarL genes in the background of NCTCPBP4-YFP were constructed allowing for the study of PBP4 localization in the presence and absence of these specific tar genes.With this work we were able to show that the absence of TarB or TarL leads to the delocalization of PBP4, indicating that TarL or a protein/WTA precursor whose localization/synthesis is dependent on TarL is responsible for the recruitment of PBP4.

Building 3D City Models: Testing and Comparing Laser Scanning and Low-Cost UAV Data Using FOSS Technologies

Contém resumo

Al-doped ZnO ceramic sputtering targets based on nanocrystalline powders produced by emulsion detonation synthesis – deposition and application as a transparent conductive oxide material

Transparent conducting oxides (TCOs) have been largely used in the optoelectronic industry due to their singular combination of low electrical resistivity and high optical transmittance. They are usually deposited by magnetron sputtering systems being applied in several devices, specifically thin film solar cells (TFSCs). Sputtering targets are crucial components of the sputtering process, with many of the sputtered films properties dependent on the targets characteristics. The present thesis focuses on the development of high quality conductive Al-doped ZnO (AZO) ceramic sputtering targets based on nanostructured powders produced by emulsion detonation synthesis method (EDSM), and their application as a TCO. In this sense, the influence of several processing parameters was investigated from the targets raw-materials synthesis to the application of sputtered films in optoelectronic devices. The optimized manufactured AZO targets present a final density above 99 % with controlled grain size, an homogeneous microstructure with a well dispersed ZnAl2O4 spinel phase, and electrical resistivities of ~4 × 10-4 Ωcm independently on the Al-doping level among 0.5 and 2.0 wt. % Al2O3. Sintering conditions proved to have a great influence on the properties of the targets and their performance as a sputtering target. It was demonstrated that both deposition process and final properties of the films are related with the targets characteristics, which in turn depends on the initial powder properties. In parallel, the influence of several deposition parameters in the film´s properties sputtered from these targets was investigated. The sputtered AZO TCOs showed electrical properties at room temperature that are superior to simple oxides and comparable to a reference TCO – indium tin oxide (ITO), namely low electrical resistivity of 5.45 × 10-4 Ωcm, high carrier mobility (29.4 cm2V-1s-1), and high charge carrier concentration (3.97 × 1020 cm-3), and also average transmittance in the visible region > 80 %. These superior properties allowed their successful application in different optoelectronic devices.

VO2 thin films and nanoparticles for energy-saving applications in architectural elements

Vanadium dioxide (VO2) is a promising material with large interest in construction industry and architecture, due to its thermochromic properties. This material may be used to create "smart" coatings that result in improvements in the buildings energy efficiency, by reducing heat exchanges and, consequently, the need for acclimatization. In this work, VO2 thin films and coatings were produced and tested in laboratory, to apply in architectural elements, such as glass, rooftop tiles and exterior paints. Thin films were produced by RF magnetron sputtering and VO2 nanoparticles were obtained through hydrothermal synthesis, aiming to create "smart" windows and tiles, respectively. These coatings have demonstrated the capability to modulate the transmittance of infrared radiation by around 20%. The VO2 nanoparticle coatings were successfully applied on ceramic tiles. The critical temperature was reduced to around 40ºC by tungsten doping. Ultimately, two identical house models were built, in order to test the VO2 coatings, in real atmospheric conditions during one of the hottest months of the year, in Portugal – August.