Abstract
Conventional power distribution networks (PDNs), in which individual voltage regulators power the entire integrated circuit (IC), are ineffective for high-power, large-area ICs. In highperformance systems-on-chip (SoCs) and microprocessors (in particular those designed for AI applications), shrinking technology nodes are leading to higher current densities, which impose thermal constraints and limit the portion of the chip that can be simultaneously powered (“dark silicon”). PDNs with point-of-load regulation offer a promising alternative. The distributed nature of their design inherently relaxes thermal constraints while minimizing high-current routing overhead (IR drops), thereby improving the PDN efficiency. In this work, the concept of on-chip distributed voltage regulation is introduced. Previously reported distributed voltage regulator designs are reviewed, emphasizing their major achievements and limitations. Then, the challenges that hinder a more ubiquitous adoption of such designs, namely stability (analysis) and unbalanced load sharing, are discussed. Existing solutions addressing these challenges are also presented. Finally, a comparative analysis of the performance of these regulators is presented, and insights into the future direction of distributed voltage regulation are offered. © (2025), (Universidade do Porto - Faculdade de Engenharia). All rights reserved.

Abstract
Robotics education plays a crucial role in developing STEM skills. However, university-level courses often emphasize theoretical learning, which can lead to decreased student engagement and motivation. In this paper, we tackle the challenge of providing hands-on robotics experience in higher education by adapting a mobile robot originally designed for competitions to be used in laboratory classes. Our approach integrates real-world robot operation into coursework, bridging the gap between simulation and physical implementation while maintaining accessibility. The robot's software is developed using ROS, and its effectiveness is assessed through student surveys. The results indicate that the platform increases student engagement and interest in robotics topics. Furthermore, feedback from teachers is also collected and confirmed that the platform boosts students' confidence and understanding of robotics.

Abstract
The food industry faces growing challenges due to evolving consumer demands, requiring digital technologies to enhance sensory analysis. Innovations such as eye tracking, FaceReader, virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) are transforming consumer behavior research by providing deeper insights into sensory experiences. For instance, FaceReader captures emotional responses to food by analyzing facial expressions, offering valuable data on consumer preferences for taste, texture, and aroma. Together, these technologies provide a comprehensive understanding of the sensory experience, aiding product development and branding. Electronic nose, tongue, and eye technologies also replicate human sensory capabilities, enabling objective and efficient assessment of aroma, taste, and color. The electronic nose (E-nose) detects volatile compounds for aroma evaluation, while the electronic tongue (E-tongue) evaluates taste through electrochemical sensors, ensuring accuracy and consistency in sensory analysis. The electronic eye (E-eye) analyzes food color, supporting quality control processes. These advancements offer rapid, non-invasive, reproducible assessments, benefiting research and industrial applications. By improving the precision and efficiency of sensory analysis, digital tools help enhance product quality and consumer satisfaction in the competitive food industry. This review explores the latest digital methods shaping food sensory research and innovation.

Abstract
This study addresses the variability of gamma radiation measurements over the Atlantic Ocean. The analysis of back trajectories shows that the path of the air masses is the main factor determining gamma radiation levels over the ocean, rather than the distance to the coast. Different gamma values were recorded at different times in the same location as a result of the distinct origin of the corresponding air masses. Higher counts observed in the northeast Atlantic in winter compared with the spring values result from air masses coming from Europe and the African continent. In general, gamma radiation values over the ocean increase with increasing continental influence on the air mass above. A predictive classifica-tion model is developed showing that marine gamma observations can be used to classify marine boundary layer air masses according to the degree of continental influence.

Abstract
The paradigm of Device as a Service (DaaS) is one where devices are used as part of a service, with the user having no ownership over them. A centralised, web-based approach can be envisioned to support such a business model, but such lacks transparency, availability, and global scalability. A blockchain-based solution is proposed to support such a business model. The concept of a blockchain-assisted DaaS is novel and, by using smart contracts to support key interactions between relevant entities, marks a shift in device ownership, management, and revenue generation.

Abstract
The retention of public transport users is widely acknowledged as a paramount challenge in the path towards the establishment of more sustainable cities and societies. In this setting, in which no contractual relationship with customers exists, an early and accurate prediction of whether a customer will remain with the company or leave, assumes great significance for businesses to develop effective retention strategies. This work focuses on this topic by identifying potential churners based on their past travel behavior. To achieve this, we developed a set of classification models using various machine learning techniques. These models were then employed as base learners within a stacking ensemble. All classifiers were developed with a profit-driven approach, optimizing for expected maximum profit. Finally, we calculated Shapley Additive Explanation values to enhance the interpretability of the proposed classifiers. The performance of the predictive models was evaluated using the data of taxi services recorded in a Portuguese city for 52 months. A broad range of predictors is proposed, including recency and frequency measures of taxi usage as well as others related to customers' satisfaction level. The predictive power of the models was also assessed for specific proportions of higher risk customers. All models have shown the capability to identify churners accurately. This study innovates in evaluating the one-to-one service provider company-customer relationship in the context of taxi industry. Retention actions to promote customers loyalty and enhance retention are also suggested.