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.

eHealth e a adoção da inovação em assisted living technologies : o caso das demências

RESUMO - Assistimos hoje a um contexto marcado (i) pelo progressivo envelhecimento das sociedades ocidentais, (ii) pelo aumento da prevalência das doenças crónicas, de que as demências são um exemplo, (iii) pelo significativo aumento dos custos associados a estas patologias, (iv) por orçamentos públicos fortemente pressionadas pelo controlo da despesa, (v) por uma vida moderna que dificulta o apoio intergeracional, tornando o suporte proporcionado pelos filhos particularmente difícil, (vi) por fortes expectativas relativamente à prestação de cuidados de saúde com qualidade. Teremos assim de ser capazes de conseguir melhorar os serviços de saúde, ao mesmo tempo que recorremos a menos recursos financeiros e humanos, pelo que a inovação parece ser crítica para a sustentabilidade do sistema. Contudo a difusão das Assistive Living Technologies, apesar do seu potencial, tem sido bastante baixa, nomeadamente em Portugal. Porquê? Hamer, Plochg e Moreira (2012), no editorial do International Journal of Healthcare Management, enquadram a Inovação como “podendo ser imprevisível e mesmo dolorosa, pelo que talvez possamos não ficar surpreendidos se surgirem resistências e que, inovações bastante necessárias, capazes de melhorar os indicadores de saúde, tenham sido de adoção lenta ou que tenham mesmo sido insustentáveis”. Em Portugal não há bibliografia que procure caracterizar o modelo de difusão da inovação em eHealth ou das tecnologias de vivência assistida. A bibliografia internacional é igualmente escassa. O presente projeto de investigação, de natureza exploratória, tem como objetivo principal, identificar barreiras e oportunidades para a implementação de tecnologias eHealth, aplicadas ao campo das demências. Como objetivos secundários pretendemse identificar as oportunidades e limitações em Portugal: mapa de competências nacionais, e propor medidas que possa acelerar a inovação em ALT, no contexto nacional. O projeto seguirá o modelo de um estudo qualitativo. Para o efeito foram conduzidas entrevistas em profundidade junto de experts em ALT, procurando obter a visão daqueles que participam do lado da Oferta- a Indústria; do lado da Procura- doentes, cuidadores e profissionais de saúde; bem como dos Reguladores. O instrumento utilizado para a recolha da informação pretendida foi o questionário não estruturado. A análise e interpretação da informação recolhida foram feitas através da técnica de Análise de Conteúdo. Os resultados da Análise de Conteúdo efetuada permitiram expressar a dicotomia barreira/oportunidade, nas seguintes categorias aqui descritas como contextos (i) Contexto Tecnológico, nas subcategorias de Acesso às Infraestruturas; Custo da Tecnologia; Interoperabilidade, (ii) Contexto do Valor Percecionado, nas subcategorias de Utilidade; Eficiência; Divulgação, (iii) Contexto Político, compreendendo a Liderança; Organização; Regulação; Recursos, (iv) Contexto Sociocultural, incluindo nomeadamente Idade; Literacia; Capacidade Económica, (v) Contexto Individual, incluindo como subcategorias, Capacidade de Adaptação a Novas tecnologias; Motivação; Acesso a equipamentos (vi) Contexto Específico da Doença, nomeadamente o Impacto Cognitivo; Tipologia Heterogénea e a Importância do Cuidador. Foi proposto um modelo exploratório, designado de Modelo de Contextos e Forças, que estudos subsequentes poderão validar. Neste modelo o Contexto Tecnológico é um Força Básica ou Fundamental; o Contexto do Valor Percecionado, constitui-se numa Força Crítica para a adoção de inovação, que assenta na sua capacidade para oferecer valor aos diversos stakeholders da cadeia de cuidados. Temos também o Contexto Político, com capacidade de modelar a adoção da inovação e nomeadamente com capacidade para o acelerar, se dele emitir um sinal de urgência para a mudança. O Contexto Sociocultural e Individual expressam uma Força Intrínseca, dado que elas são características internas, próprias e imutáveis no curto-prazo, das sociedade e das pessoas. Por fim há que considerar o Contexto Específico da Doença, nesta caso o das demências. Das conclusões do estudo parece evidente que as condições tecnológicas estão medianamente satisfeitas em Portugal, com evidentes progressos nos últimos anos (exceção para a interoperabilidade aonde há necessidade de maiores progressos), não constituindo portanto barreira à introdução de ALT. Aonde há necessidade de investir é nas áreas do valor percebido. Da análise feita, esta é uma área que constitui uma barreira à introdução e adoção das ALT em Portugal. A falta de perceção do valor que estas tecnologias trazem, por parte dos profissionais de saúde, doentes, cuidadores e decisores políticos, parece ser o principal entrave à sua adoção. São recomendadas estratégias de modelos colaborativos de Investigação e Desenvolvimento e de abordagens de cocriação com a contribuição de todos os intervenientes na cadeia de cuidados. Há também um papel que cabe ao estado no âmbito das prioridades e da mobilização de recursos, sendo-lhe requerida a expressão do sentido de urgência para que esta mudança aconteça. Foram também identificadas oportunidades em diversas áreas, como na prevenção, no diagnóstico, na compliance medicamentosa, na terapêutica, na monitorização, no apoio à vida diária e na integração social. O que é necessário é que as soluções encontradas constituam respostas àquilo que são as verdadeiras necessidades dos intervenientes e não uma imposição tecnológica que só por si nada resolve. Do estudo resultou também a perceção de que há que (i) continuar a trabalhar no sentido de aproximar a comunidade científica, da clínica e do doente, (ii) fomentar a colaboração entre centros, com vista à criação de escala a nível global. Essa colaboração já parece acontecer a nível empresarial, tendo sido identificadas empresas Portuguesas com vocação global. A qualidade individual das instituições de ensino, dos centros de investigação, das empresas, permite criar as condições para que Portugal possa ser país um piloto e um case-study internacional em ALT, desde que para tal pudéssemos contar com um trabalho colaborativo entre instituições e com decisões políticas arrojadas.

Numerical prediction of diffusion and electric field-induced iron nanoparticle transport

Zero valent iron nanoparticles (nZVI) are considered very promising for the remediation of contaminated soils and groundwaters. However, an important issue related to their limited mobility remains unsolved. Direct current can be used to enhance the nanoparticles transport, based on the same principles of electrokinetic remediation. In this work, a generalized physicochemical model was developed and solved numerically to describe the nZVI transport through porous media under electric field, and with different electrolytes (with different ionic strengths). The model consists of the Nernst–Planck coupled system of equations, which accounts for the mass balance of ionic species in a fluid medium, when both the diffusion and electromigration of the ions are considered. The diffusion and electrophoretic transport of the negatively charged nZVI particles were also considered in the system. The contribution of electroosmotic flow to the overall mass transport was included in the model for all cases. The nZVI effective mobility values in the porous medium are very low (10−7–10−4 cm2 V−1 s−1), due to the counterbalance between the positive electroosmotic flow and the electrophoretic transport of the negatively charged nanoparticles. The higher the nZVI concentration is in the matrix, the higher the aggregation; therefore, low concentration of nZVI suspensions must be used for successful field application.

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

INTRODUCTION: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. METHODS: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. RESULTS: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases. CONCLUSIONS: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

Simplified wind turbine models for wind energy integration into power systems

This paper analyses the boundaries of simplified wind turbine models used to represent the behavior of wind turbines in order to conduct power system stability studies. Based on experimental measurements, the response of recent simplified (also known as generic) wind turbine models that are currently being developed by the International Standard IEC 61400-27 is compared to complex detailed models elaborated by wind turbine manufacturers. This International Standard, whose Technical Committee was convened in October 2009, is focused on defining generic simulation models for both wind turbines (Part 1) and wind farms (Part 2). The results of this work provide an improved understanding of the usability of generic models for conducting power system simulations.

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.

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

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.

Correlation between microdilution, Etest, and disk diffusion methods for antifungal susceptibility testing of fluconazole against Candida sp. blood isolates

INTRODUCTION: Antifungal susceptibility testing assists in finding the appropriate treatment for fungal infections, which are increasingly common. However, such testing is not very widespread. There are several existing methods, and the correlation between such methods was evaluated in this study. METHODS: The susceptibility to fluconazole of 35 strains of Candida sp. isolated from blood cultures was evaluated by the following methods: microdilution, Etest, and disk diffusion. RESULTS: The correlation between the methods was around 90%. CONCLUSIONS: The disk diffusion test exhibited a good correlation and can be used in laboratory routines to detect strains of Candida sp. that are resistant to fluconazole.

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

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

Contém resumo

Damage propagation in composite Materials meso-mechanical models

Composite materials have a complex behavior, which is difficult to predict under different types of loads. In the course of this dissertation a methodology was developed to predict failure and damage propagation of composite material specimens. This methodology uses finite element numerical models created with Ansys and Matlab softwares. The methodology is able to perform an incremental-iterative analysis, which increases, gradually, the load applied to the specimen. Several structural failure phenomena are considered, such as fiber and/or matrix failure, delamination or shear plasticity. Failure criteria based on element stresses were implemented and a procedure to reduce the stiffness of the failed elements was prepared. The material used in this dissertation consist of a spread tow carbon fabric with a 0°/90° arrangement and the main numerical model analyzed is a 26-plies specimen under compression loads. Numerical results were compared with the results of specimens tested experimentally, whose mechanical properties are unknown, knowing only the geometry of the specimen. The material properties of the numerical model were adjusted in the course of this dissertation, in order to find the lowest difference between the numerical and experimental results with an error lower than 5% (it was performed the numerical model identification based on the experimental results).

Understanding multi-asset factor models: Factor exposure interpretation

Field lab: Business project