14,388 research outputs found

    Economia colaborativa

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    A importância de se proceder à análise dos principais desafios jurídicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutível, correspondendo à necessidade de se fomentar a segurança jurídica destas práticas, potenciadoras de crescimento económico e bem-estar social. O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domínio, com o objetivo de identificar os problemas jurídicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação. O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202

    A direct-laser-written heart-on-a-chip platform for generation and stimulation of engineered heart tissues

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    In this dissertation, we first develop a versatile microfluidic heart-on-a-chip model to generate 3D-engineered human cardiac microtissues in highly-controlled microenvironments. The platform, which is enabled by direct laser writing (DLW), has tailor-made attachment sites for cardiac microtissues and comes with integrated strain actuators and force sensors. Application of external pressure waves to the platform results in controllable time-dependent forces on the microtissues. Conversely, oscillatory forces generated by the microtissues are transduced into measurable electrical outputs. After characterization of the responsivity of the transducers, we demonstrate the capabilities of this platform by studying the response of cardiac microtissues to prescribed mechanical loading and pacing. Next, we tune the geometry and mechanical properties of the platform to enable parametric studies on engineered heart tissues. We explore two geometries: a rectangular seeding well with two attachment sites, and a stadium-like seeding well with six attachment sites. The attachment sites are placed symmetrically in the longitudinal direction. The former geometry promotes uniaxial contraction of the tissues; the latter additionally induces diagonal fiber alignment. We systematically increase the length for both configurations and observe a positive correlation between fiber alignment at the center of the microtissues and tissue length. However, progressive thinning and “necking” is also observed, leading to the failure of longer tissues over time. We use the DLW technique to improve the platform, softening the mechanical environment and optimizing the attachment sites for generation of stable microtissues at each length and geometry. Furthermore, electrical pacing is incorporated into the platform to evaluate the functional dynamics of stable microtissues over the entire range of physiological heart rates. Here, we typically observe a decrease in active force and contraction duration as a function of frequency. Lastly, we use a more traditional ?TUG platform to demonstrate the effects of subthreshold electrical pacing on the rhythm of the spontaneously contracting cardiac microtissues. Here, we observe periodic M:N patterns, in which there are ? cycles of stimulation for every ? tissue contractions. Using electric field amplitude, pacing frequency, and homeostatic beating frequencies of the tissues, we provide an empirical map for predicting the emergence of these rhythms

    Evaluation of image quality and reconstruction parameters in recent PET-CT and PET-MR systems

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    In this PhD dissertation, we propose to evaluate the impact of using different PET isotopes for the National Electrical Manufacturers Association (NEMA) tests performance evaluation of the GE Signa integrated PET/MR. The methods were divided into three closely related categories: NEMA performance measurements, system modelling and evaluation of the image quality of the state-of-the-art of clinical PET scanners. NEMA performance measurements for characterizing spatial resolution, sensitivity, image quality, the accuracy of attenuation and scatter corrections, and noise equivalent count rate (NECR) were performed using clinically relevant and commercially available radioisotopes. Then we modelled the GE Signa integrated PET/MR system using a realistic GATE Monte Carlo simulation and validated it with the result of the NEMA measurements (sensitivity and NECR). Next, the effect of the 3T MR field on the positron range was evaluated for F-18, C-11, O-15, N-13, Ga-68 and Rb-82. Finally, to evaluate the image quality of the state-of-the-art clinical PET scanners, a noise reduction study was performed using a Bayesian Penalized-Likelihood reconstruction algorithm on a time-of-flight PET/CT scanner to investigate whether and to what extent noise can be reduced. The outcome of this thesis will allow clinicians to reduce the PET dose which is especially relevant for young patients. Besides, the Monte Carlo simulation platform for PET/MR developed for this thesis will allow physicists and engineers to better understand and design integrated PET/MR systems

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri

    The potential of shade trees to improve microclimate in coffee production systems and contribute to the protection of coffee yield and quality in a changing climate

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    Climate change is a major challenge to which global coffee production must adapt. With Coffea arabica being especially sensitive to rising temperatures, shade trees present a promising adaptation strategy, as there is some evidence that they can modify microclimate. Employing an interdisciplinary approach, combining biophysical and sociological research, this study investigated the effect of shade on coffee production on the southern slope of Mt. Kilimanjaro with the aim of finding suitable strategies to optimise coffee production systems and ensure optimal yield and quality, thus assuring farmers’ livelihoods into the future, in the face of climate change. Precipitation records from coffee plantations were analysed for changes in weather patterns in the last two decades. The influence of shade on microclimate, leaf temperature, coffee yield and physical quality aspects was assessed in coffee plantations and smallholder systems. Additionally, focus group discussions and interviews with small-scale farmers were conducted to explore farmers’ knowledge on the impacts of weather extremes on coffee production and the ecosystem services different tree species provide. This research shows that climate change at Mt. Kilimanjaro manifests as droughts and shorter wet seasons with less frequent but heavier rainfall events, challenges to which coffee farmers will have to adapt. Shade trees show potential in adaptation of coffee production systems to climate change, as they reduce maximum air temperatures and can reduce leaf temperature extremes during hot periods, without having negative effects on nocturnal temperatures, which are beneficial for coffee production. In coffee plantations, no effect of shade on yields was observed while a slight reduction was observed for smallholder systems. Coffee quality benefits from shade, as different shade components are associated with an increase in bean size and weight. Farmers identified Albizia schimperiana as an important tree species, providing regulatory ecosystem services to improve coffee production. Recommendations need to take farmers’ priorities into account, including their willingness to trade some reduction in coffee production for other services, such as food, fodder or firewood, which were identified as the most important ecosystem services for farmers at Mt. Kilimanjaro

    Generación de textos en ruso mediante técnicas de Aprendizaje Automático para la industria del lenguaje

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    [ES] Hoy en día los avances en el área del Procesamiento del Lenguaje Natural y el Aprendizaje Automático permiten el análisis, la comprensión y la generación de texto automáticamente cada vez más precisa y fluida. El objetivo de este trabajo final de grado es la creación automática de ejemplos de texto en ruso, a partir de datos de texto ya existentes mediante técnicas de aprendizaje automático. Se han empleado redes neuronales y recursos lingüísticos para la generación automática de texto en ruso. Para el desarrollo del trabajo se han utilizado datos de dominio público. El sistema genera nuevos textos utilizando información de embeddings entrenadas con una ingente cantidad de datos en modelos de lenguaje neuronales. La generación de dichos textos incrementa el corpus utilizado para el entrenamiento de modelos para tareas del Procesamiento del Lenguaje Natural como la traducción automática. También podría aplicarse a otras tareas como la generación de resúmenes automáticos o parafraseadores de textos. Por último, se ha realizado un análisis de los resultados obtenidos evaluando la calidad de los textos generados y se han añadido al entrenamiento de modelos de traducción automática neuronal. Estos modelos se han comparado realizando un análisis cuantitativo, comparando los distintos métodos mediante varias métricas automáticas típicas utilizadas en traducción automática y se han medido los tiempos empleados y la cantidad de texto generado para un buen uso en la industria del lenguaje, y un análisis cualitativo, donde se han expuesto ejemplos de traducción generados por los modelos de traducción entrenados y se han comparado entre sí.[EN] Current progress in the areas of Natural Language Processing and Machine Learning allows for the analysis, understanding and automatic generation of increasingly accurate and fluid text. The objective of this final degree project is automatically creating text examples in Russian from existing text data using machine learning techniques. Neural networks and linguistic resources have been used for the automatic generation of text in Russian. To develop this project, data from the public domain have been used. The system generates new texts using information from embeddings trained with a huge amount of data in neural language models. The generation of these texts increases the corpus used to train models for several Natural Language Processing tasks, for instance, machine translation. It could also be applied to other tasks such as generating automatic summaries or to text paraphrasers. Finally, an analysis of the results obtained evaluating the quality of generated texts has been carried out and those texts have been added to the training process of neural machine translation models. On the one hand, these models have been compared by performing a quantitative analysis, comparing the different methods by means of several typical automatic metrics used in machine translation and measuring the times spent and the amount of text generated for good use in the language industry. On the other hand, they have been compared through a qualitative analysis, where examples of translation generated by the trained translation models have been exposed and compared with each other.[CA] Hui dia, els avanços en l’àrea del Processament del Llenguatge Natural i l’Aprenentatge Automàtic permeten l’anàlisi, la comprensió i la generació automàtica de text cada vegada més precís i fluid. L’objectiu d’aquest treball final de grau és la creació automàtica d’exemples de text en rus a partir de dades de text ja existents mitjançant tècniques d’aprenentatge automàtic. S’han emprat xarxes neuronals i recursos lingüístics per a la generació automàtica de text en rus. Per al desenvolupament del treball s’han utilitzat dades de domini públic. El sistema genera nous textos utilitzant informació d’embeddings entrenades amb una ingent quantitat de dades en models de llenguatge neuronals. La generació d’aquests textos incrementa el corpus utilitzat a l’entrenament de models per a tasques de Processament del Llenguatge Natural com ara la traducció automàtica. També podria aplicar-se a d’altres tasques com, per exemple, la generació de resums automàtics o als parafrasejadors de textos. Finalment, s’ha realitzat una anàlisi dels resultats obtinguts mitjançant l’avaluació de la qualitat dels textos generats, els quals s’han afegit a l’entrenament de models de traducció automàtica neuronal. Aquests models s’han comparat realitzant, d’una banda, una anàlisi quantitativa amb la comparació dels diferents mètodes mitjançant diverses mètriques automàtiques típiques utilitzades en traducció automàtica, així com el mesurament dels temps emprats i la quantitat de text generat per un bon ús en la indústria del llenguatge i, d’altra banda, una anàlisi qualitativa, on s’han exposat exemples de traducció generats pels models de traducció entrenats i s’han comparat entre ells.Gregoryev, M. (2022). Generación de textos en ruso mediante técnicas de Aprendizaje Automático para la industria del lenguaje. Universitat Politècnica de València. http://hdl.handle.net/10251/182213TFG

    A preliminary research to identify the biomimetic entities for generating novel wave energy converters

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    Biomimetics and creatures could contribute to novel design inspirations for wave energy converter as to other engineering branches since we have seen numerous examples in engineering applications. But how to obtain valuable biological entities or bionic design cases that could produce inspirations for novel designs may be challenging for the designers of wave energy converters (WECs). This research work carries out a preliminary research on acquiring the biological entities for designers, so to obtain the innovative bio-inspired ideas for designing novel WECs. In the proposed method, the first step is to draw out the engineering terminologies based on the functions, structures and energy extraction principles of existing WECs. Then by applying ‘WordNet’, the candidate biological terminologies can be obtained. Next, using ‘AskNature’ and through manual selection and filtering, the biological terminologies can be acquired. Lastly, to use the biological terminologies to establish the reference biological entities and the information and knowledge so for designing an innovative WEC. Using the proposed methodology, a novel WEC was conceived and verified

    From wallet to mobile: exploring how mobile payments create customer value in the service experience

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    This study explores how mobile proximity payments (MPP) (e.g., Apple Pay) create customer value in the service experience compared to traditional payment methods (e.g. cash and card). The main objectives were firstly to understand how customer value manifests as an outcome in the MPP service experience, and secondly to understand how the customer activities in the process of using MPP create customer value. To achieve these objectives a conceptual framework is built upon the Grönroos-Voima Value Model (Grönroos and Voima, 2013), and uses the Theory of Consumption Value (Sheth et al., 1991) to determine the customer value constructs for MPP, which is complimented with Script theory (Abelson, 1981) to determine the value creating activities the consumer does in the process of paying with MPP. The study uses a sequential exploratory mixed methods design, wherein the first qualitative stage uses two methods, self-observations (n=200) and semi-structured interviews (n=18). The subsequent second quantitative stage uses an online survey (n=441) and Structural Equation Modelling analysis to further examine the relationships and effect between the value creating activities and customer value constructs identified in stage one. The academic contributions include the development of a model of mobile payment services value creation in the service experience, introducing the concept of in-use barriers which occur after adoption and constrains the consumers existing use of MPP, and revealing the importance of the mobile in-hand momentary condition as an antecedent state. Additionally, the customer value perspective of this thesis demonstrates an alternative to the dominant Information Technology approaches to researching mobile payments and broadens the view of technology from purely an object a user interacts with to an object that is immersed in consumers’ daily life

    Digital asset management via distributed ledgers

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    Distributed ledgers rose to prominence with the advent of Bitcoin, the first provably secure protocol to solve consensus in an open-participation setting. Following, active research and engineering efforts have proposed a multitude of applications and alternative designs, the most prominent being Proof-of-Stake (PoS). This thesis expands the scope of secure and efficient asset management over a distributed ledger around three axes: i) cryptography; ii) distributed systems; iii) game theory and economics. First, we analyze the security of various wallets. We start with a formal model of hardware wallets, followed by an analytical framework of PoS wallets, each outlining the unique properties of Proof-of-Work (PoW) and PoS respectively. The latter also provides a rigorous design to form collaborative participating entities, called stake pools. We then propose Conclave, a stake pool design which enables a group of parties to participate in a PoS system in a collaborative manner, without a central operator. Second, we focus on efficiency. Decentralized systems are aimed at thousands of users across the globe, so a rigorous design for minimizing memory and storage consumption is a prerequisite for scalability. To that end, we frame ledger maintenance as an optimization problem and design a multi-tier framework for designing wallets which ensure that updates increase the ledger’s global state only to a minimal extent, while preserving the security guarantees outlined in the security analysis. Third, we explore incentive-compatibility and analyze blockchain systems from a micro and a macroeconomic perspective. We enrich our cryptographic and systems' results by analyzing the incentives of collective pools and designing a state efficient Bitcoin fee function. We then analyze the Nash dynamics of distributed ledgers, introducing a formal model that evaluates whether rational, utility-maximizing participants are disincentivized from exhibiting undesirable infractions, and highlighting the differences between PoW and PoS-based ledgers, both in a standalone setting and under external parameters, like market price fluctuations. We conclude by introducing a macroeconomic principle, cryptocurrency egalitarianism, and then describing two mechanisms for enabling taxation in blockchain-based currency systems
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