Abstract
The integration of electric vehicles is paramount to the electrification of the transport sector, supporting the energy transition. The charging process of electric vehicles can be perceived as a controllable load and targeted with price or incentive-based programs. Demand-side management can optimize charging station performance and integrate renewable energy generation through electrical energy storage. Data flowing through charging stations can be used in computational approaches to solve open challenges and create new services, such as a dynamic pricing strategy, where the charging tariff depends on operating conditions. This work presents a data-driven service that optimizes day-ahead charging tariffs with a bilevel optimization problem and develops a case study around a large-scale pilot. The impact of photovoltaics and battery storage on the dynamic pricing scheme was assessed. A dynamic pricing strategy was found to benefit self-consumption and self-sufficiency of the charging station, increasing over 4 percentage points in some cases.

Abstract
Ego -motion estimation plays a critical role in autonomous driving systems by providing accurate and timely information about the vehicle's position and orientation. To achieve high levels of accuracy and robustness, it is essential to leverage a range of sensor modalities to account for highly dynamic and diverse scenes, and consequent sensor limitations. In this work, we introduce TEFu-Net, a Deep -Learning -based late fusion architecture that combines multiple ego -motion estimates from diverse data modalities, including stereo RGB, LiDAR point clouds and GNSS/IMU measurements. Our approach is non -parametric and scalable, making it adaptable to different sensor set configurations. By leveraging a Long Short -Term Memory (LSTM), TEFu-Net produces reliable and robust spatiotemporal ego -motion estimates. This capability allows it to filter out erroneous input measurements, ensuring the accuracy of the car's motion calculations over time. Extensive experiments show an average accuracy increase of 63% over TEFu-Net's input estimators and on par results with the state-of-the-art in real -world driving scenarios. We also demonstrate that our solution can achieve accurate estimates under sensor or input failure. Therefore, TEFu-Net enhances the accuracy and robustness of ego -motion estimation in real -world driving scenarios, particularly in challenging conditions such as cluttered environments, tunnels, dense vegetation, and unstructured scenes. As a result of these enhancements, it bolsters the reliability of autonomous driving functions.

Abstract
This paper presents a comprehensive study on the application of machine learning techniques in the prediction of respiratory rate via time-series-based statistical and machine learning methods using several physiological signals. Two different models, ARIMA and LSTM, were developed. The LSTM model showed a stronger capacity for learning and capturing complicated patterns in the data compared to the ARIMA model. The findings imply that LSTM models, by incorporating many variables, have the ability to provide predictions that are more accurate, particularly in situations where respiratory rate values vary significantly. © 2024 The Authors. Published by ELSEVIER B.V.

Abstract
Research have been driven by the increased demand for delivery and pick-up services to develop new formulations and algorithms for solving Vehicle Routing Problems (VRP). The main objective is to create algorithms that can identify paths considering execution time in real-world scenarios. This study focused on using the Guided Local Search (GLS) metaheuristic available in OR-Tools to solve the Capacitated Vehicle Routing Problem with Time Windows using the Solomons instances. The execution time was used as a stop criterion, with short runs ranging from 1 to 10 s and a long run of 360 s for comparison. The results showed that the GLS metaheuristic from OR-Tools is applicable for achieving high performance in finding the shortest path and optimizing routes within constrained execution times. It outperformed the best-known solutions from the literature in longer execution times and even provided a close-to-optimal solution within 10 s. These findings suggest the potential application of this tool for dynamic VRP scenarios that require faster algorithms.

Abstract
Renewable energy communities are increasingly becoming a field of great interest. This is mainly due to the advancement of technology but also the global concern to reduce carbon emissions and also create economic and social benefits. Business models play a crucial role in these communities, as a well-structured business model can facilitate the integration of innovative technologies, optimize the use of renewable energy sources, and promote economic and environmental sustainability. Therefore, it is a topic whose research is of great importance. This article presents an investigation and discussion on different aspects relating to renewable energy communities with special attention to Europe, concentrating in certain parts the focus on Portugal. This study was carried out with the aim of understanding which business models already exist and later understanding whether they can be improved or even considering the creation of new models. © 2024 IEEE.

Abstract
The online channel, particularly in the food retail area, has been evolving positively and exponentially in the world, including Portugal. Currently, this type of purchase is increasingly part of people's daily lives, even more so with the emergence of the Covid-19 pandemic. Consequently, in Portugal, most companies adopt a multichannel strategy, where the physical store and the online store operate independently from each other. However, it is necessary to rethink this channel integration model, which may go through an omnichannel strategy, where the physical store and the online store operate as a single store, and where several advantages are already recognized in terms of the consumer's shopping experience. The main objective of this study is to understand the strategy implemented by the company studied, Pingo Doce, through an analysis and description of its channels. To better understand the strategy of the company under study, a semi-structured exploratory interview was carried out with one of the people in charge of Pingo Doce's digital channels, to understand the strategy used by the company and thus complement the data obtained through direct observation and bibliographic research. At the end of the work developed it was possible to understand the positioning of Pingo Doce in the online food retail area and their online and offline distribution strategies.