Abstract
Crowdsourcing is considered an emergent form of digital work, being able to delegate tasks simultaneously to many crowd workers. As a viable form of digital labor, crowdsourcing is used by a wide spectrum of people, including those who have physical or mental limitations. However, there are crowdsourcing platforms that lack accessibility, which can cause its users to never use it again. The main goal of this thesis is to empower each crowd worker, by matching the cognitive capabilities of each individual to the most suitable crowdsourcing tasks. The work developed in this thesis started with conducting a systematic literature review on the theme of cognitive personalization for microtasks platforms. The studies analyzed indicated that personalization should be performed in the task design and task assignment. Furthermore, in the systematic review, it were identified several cognitive features derived from psychology theories which could be then implemented for achieving personalization of microtasks. Most of the studies analyzed mentioned these theories, which were mainly grouped into cognitive abilities, cognitive bias, and cognitive styles. These cognitive features were explored in several studies conducted in the context of this thesis. To perform cognitive personalization in crowdsourcing, it was measured the cognitive features of the crowd workers using the administration of short online cognitive tests and applying task fingerprinting to identify crowd worker behaviors through the analysis of the interaction’s logs. The combination of both approaches has the potential to complement each part and enhance the performance of crowd workers and the work quality delivered, thus providing the means for achieving a significant cognitive personalization of microtasks. A study was conducted on microtask design with cognitive personalization based on the analysis of cognitive styles. A framework for a crowdsourcing system intended to support cognitive personalization in microtask design was elaborated. The framework includes an ontology built based on the concepts of microtasks, cognitive abilities, and types of adaptation to personalize the interface to the crowd worker. Subsequently, a study was conducted to research the complementarity of applying online cognitive tests and task fingerprinting techniques (i.e., identification of crowd workers' behavior traces) in a microtask scenario, focusing on the personalization of task design. A study was conducted with 134 crowd workers recruited from a crowdsourcing marketplace. The results ind icate that both techniques were combined successfully, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model that can predict the accuracy of the microtasks. A subsequent preliminary study was conducted to assess the feasibility of applying cognitive tests and task fingerprinting with neurodivergent people, which obtained positive results. Regarding cognitive bias, one study was conducted to research the feasibility of task fingerprinting alongside the stigmergic effect (i.e., coordination without direct communication) occurring in a crowdsourcing setting through a user event logger. The study was conducted using a real-world scenario of extreme weather phenomena represented on interactive maps. Each user could observe the traces of other crowd members while providing annotations and the results indicated that task fingerprinting can be used for tracking the stigmergic effect. The methods proposed in this work have been shown to be effective for performing cognitive personalization in different crowdsourcing scenarios. Thus, this work can be considered an important contribution towards the increase of accessibility in the crowdsourcing domain and support more people to enter this emerging digital work marketplace.;
O crowdsourcing é considerado uma emergente forma de trabalho digital, sendo capaz de delegar tarefas simultaneamente a vários crowd workers. Como uma viável forma de trabalho digital, o crowdsourcing é utilizado por um amplo espectro de pessoas, incluindo aquelas com limitações físicas ou mentais. No entanto, existem plataformas de crowdsourcing que carecem de acessibilidade, e que podem fazer com que os seus utilizadores nunca mais a utilizem. O principal objetivo desta tese é capacitar cada crowd worker, combinando as capacidades cognitivas de cada indivíduo com as tarefas de crowdsourcing mais adequadas. O trabalho nesta tese começou com a realização de uma revisão sistemática da literatura sobre o tema da personalização cognitiva para plataformas de microtarefas. Os estudos analisados indicaram que a personalização deve ser realizada no design e atribuição de tarefas. Além disso, foram identificadas várias características cognitivas derivadas de teorias da área da psicologia que poderiam ser implementadas para alcançar a personalização de microtarefas. A maioria dos estudos analisados mencionou essas teorias, que foram agrupadas principalmente em capacidades cognitivas, vieses cognitivos e estilos cognitivos. Estas características cognitivas foram exploradas em vários estudos realizados no contexto desta tese. Para realizar a personalização cognitiva no crowdsourcing, foram avaliadas as características cognitivas dos crowd workers utilizando a administração de pequenos testes cognitivos online e analisando as impressões digitais de tarefas para identificar os comportamentos dos crowd workers através da análise dos registos de interação. A combinação de ambas abordagens tem o potencial de complementar cada parte e melhorar o desempenho dos trabalhadores nas plataformas digitais, proporcionando assim os meios para alcançar uma personalização cognitiva significativa das microtarefas. Foi realizado um estudo sobre design de microtarefas com personalização cognitiva com base na análise de estilos cognitivos. Foi elaborada uma framework para um sistema de crowdsourcing destinado a apoiar a personalização cognitiva no design de microtarefas. A framework inclui uma ontologia com base nos conceitos de microtarefas, capacidades cognitivas e tipos de adaptação para personalizar a interface. Posteriormente, foi desenvolvido um estudo que pesquisou a complementaridade dos testes cognitivos online e técnicas de impressão digital de tarefas em um cenário de microtarefa, com foco na personalização do design. Um estudo foi realizado com 134 crowd workers recrutados. Os resultados indicam que ambas as técnicas foram combinadas com sucesso, validando o uso de testes cognitivos e da técnica de impressão digital de tarefas como mecanismos eficazes para personalização de microtarefas, incluindo o desenvolvimento de um modelo de deep learning que pode prever a eficácia das microtarefas. Foi também conduzido um estudo preliminar para avaliar a viabilidade da administração de testes cognitivos e da técnica de impressão digital de tarefas com pessoas neurodivergentes, sendo que os resultados obtidos foram positivos. Em relação ao viés cognitivo, foi realizado um estudo para pesquisar a viabilidade da impressão digital de tarefas juntamente com o efeito stigmergic (ou seja, coordenação sem comunicação direta) que ocorre em um ambiente de crowdsourcing por meio de um registrador de eventos do utilizador. O estudo foi conduzido utilizando um cenário real de fenómenos climáticos extremos representados em mapas interativos. Cada utilizador observou os rastros de outros utilizadores enquanto fornecia anotações e os resultados indicaram que a impressão digital da tarefa pode ser usada para rastrear o efeito stigmergic. Os métodos propostos neste trabalho demonstraram ser eficazes para realizar a personalização cognitiva em diferentes cenários de crowdsourcing. Assim, este trabalho pode ser considerado como um contributo importante para o aumento da acessibilidade no domínio do crowdsourcing e apoiar mais pessoas a entrar neste mercado de trabalho digital emergente.

Abstract
This work compares six AC/DC power conversion chain topologies commonly employed by industrial companies for implementing electrolyzers. The main purpose is to help identify the eventual advantages of joining the traditional high-power rectifiers to an additional stage based on DC/DC conversion. The comparison is based on the current ripple, power factor, total harmonic distortion, scalability, and solution complexity. A Simulink model corresponding to each topology was developed to determine comparison criteria. The procedure consists of performing a steady-state analysis of each topology through simulations to obtain the main waveforms and the values of the established criteria and then calculating the scores for each technical solution. The findings indicated that the 24-pulse diode bridge rectifier plus DC-DC without interphase reactor exhibited the best performance. © 2024 IEEE.

Abstract
Many natural signals exhibit quasi-periodic behaviors and are conveniently modeled as combinations of several harmonic sinusoids whose relative frequencies, magnitudes, and phases vary with time. The waveform shapes of those signals reflect important physical phenomena underlying their generation, requiring those parameters to be accurately estimated and modeled. In the literature, accurate phase estimation and modeling have received significantly less attention than frequency or magnitude estimation. This paper first addresses accurate DFT-based phase estimation of individual sinusoids across six scenarios involving two DFT-based filter banks and three different windows. It has been shown that bias in phase estimation is less than 0.001 radians when the SNR is equal to or larger than 2.5 dB. Using the Cram & eacute;r-Rao lower bound as a reference, it has been demonstrated that one particular window offers performance of practical interest by better approximating the CRLB under favorable signal conditions and minimizing performance deviation under adverse conditions. This paper describes the development of a shift-invariant phase-related feature that characterizes the harmonic phase structure. This feature motivates a new signal processing paradigm that greatly simplifies the parametric modeling, transformation, and synthesis of harmonic signals. It also aids in understanding and reverse engineering the phasegram. The theory and results are discussed from a reproducible perspective, with dedicated experiments supported by code, allowing for the replication of figures and results presented in this paper and facilitating further research.

Abstract
In this study we explore the impact of multisensory stimuli in virtual reality on users' emotional responses, addressing a knowledge gap in this rapidly evolving field. Utilizing a range of sensory inputs, including taste, haptics, and smell, in addition to audiovisual cues, this study aims to understand how different combinations of these stimuli affect the users' emotional experience. Two immersive virtual experiences have been developed for this purpose. One included a scenario to evoke positive emotions through selectively chosen pleasant multisensory stimuli, validated in a focus group. The other sought the contrary: to trigger negative emotions by integrating selected combinations of unpleasant multisensory stimuli, also validated in the same focus group. Through a comparative analysis, our findings revealed significant differences in emotional responses between the groups exposed to positive and negative stimuli combinations. Results indicated that combinations involving haptics and taste were particularly effective in eliciting intense emotions using positive stimuli, but their impact was less significant with negative stimuli. This investigation suggests that a fully multisensory virtual environment integrating positive stimuli might lead to cognitive overload, reducing overall emotional responses. In contrast, environments with negative stimuli could enhance emotional engagement and be more likely to avoid cognitive overload. These findings have important implications for designing emotionally resonant and compelling virtual reality experiences. This research enhances the understanding of sensory integration in virtual reality and its effects on emotional engagement, offering valuable insights for developing more impactful virtual experiences.

Abstract
Remaining current with emerging trends and technologies is crucial for businesses to stay at the forefront, satisfy consumer demands, and maintain competitiveness. As marketing strategies such as phygital and omnichannel tactics continue to evolve, technologies like augmented reality are becoming increasingly relevant and disruptive. Augmented reality is an innovative technology that is currently revolutionizing omnichannel marketing strategies. It offers numerous opportunities in both the metaverse and phygital marketing, greatly improving the overall customer experience, increasing sale success rate, and improving brand image. A systematic review using PRISMA methodology incorporating a total of six studies explores augmented reality (AR) technology’s influence on omnichannel marketing strategies in the retail industry. The findings analyze AR, omnichannel marketing, and the metaverse in-depth, their interplay, and how they influence the customer journey, experience, and behavior. This study explores how to effectively integrate AR into omnichannel marketing for retail, emphasizing on harnessing synergies between channels and devising targeted strategies. Research gaps in the literature are identified and future steps to seamlessly integrate channels through AR technology in retail. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The current surge in the deployment of web applications underscores the need to consider users' individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform's capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset.