Abstract

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.