Abstract
This article introduces BetaMemo models, a set of advanced probabilistic forecasting models aimed at predicting the hourly power output of photovoltaic plants. By employing a semiparametric approach based on beta distributions and deterministic models, BetaMemo offers detailed forecasts, including point forecasts, variance, quantiles, uncertainty measures, and probabilities of power generation falling within specific intervals or exceeding predefined thresholds. BetaMemo models rely on input data derived from weather forecasts generated by a Numerical Weather Prediction model coupled with variables pertaining to solar positioning in the forthcoming hours. Eleven BetaMemo models were created, each using a unique combination of explanatory variables. These variables include data related to the location of the plant and spatiotemporal variables from weather forecasts across a broad area surrounding the plant. The models were validated using a real-life case study of a photovoltaic plant in Portugal, including comparisons of their performance with benchmark forecasting models. The results demonstrate the superior performance of the BetaMemo models, surpassing those of benchmark models in terms of forecasting accuracy. The BetaMemo model that integrates the most extensive set of spatiotemporal explanatory variables provides notably better forecasting results than simpler versions of the model that rely exclusively on the local plant information. This model improves the continuous ranked probability score by 13.89% and the reliability index by 45.66% compared to those obtained from a quantile random forest model using the same explanatory variables. The findings highlight the potential of BetaMemo models to enhance decision-making processes related to photovoltaic power bidding in electricity markets.

Abstract
Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of measurements retrieved from images. In this study, deep learning-based motion estimation architectures were used to determine the left ventricular longitudinal strain in echocardiography. Three motion estimation approaches, pretrained on popular optical flow datasets, were applied to a simulated echocardiographic dataset. Results show that PWC-Net, RAFT and FlowFormer achieved an average end point error of 0.20, 0.11 and 0.09 mm per frame, respectively. Additionally, global longitudinal strain was calculated from the FlowFormer outputs to assess strain correlation. Notably, there is variability in strain accuracy among different vendors. Thus, optical flow-based motion estimation has the potential to facilitate the use of strain imaging in clinical practice.

Abstract
In the context of the ongoing energy transition towards renewable sources and the decentralization of generation, multi-carrier energy systems emerge as a comprehensive solution that allows the synergic integration of different energy carriers, such as electricity, natural gas, heat, and storage, offering an effective response to the challenges posed by the variability of renewable generation and the fluctuation of energy demand. In addition, the inherent flexibility of these systems facilitates the management of the variability of renewable generation and adaptation to changes in energy demand, thus contributing to the stability and reliability of supply. In this context, the participation of prosumers who contribute their distributed generation and load flexibility through energy aggregators that effectively coordinate energy supply and demand in real-time ensures a constant balance in the energy system stands out. This paper explores the potential for various prosumer groups, facilitated by multi-carrier energy aggregators, to offer flexible services to electric distribution and natural gas grid utilities, given that natural gas is the prosumers' primary fuel for heating and cooking. The model is formulated as a two-level optimization problem. The upper level results in the emulation of the distribution system, while the lower level minimizes the flexible demand of prosumers. The interaction of the two levels is not through the price of electricity but through prosumer demand. The resulting optimization problem is a mixed-integer linear programming formulation. The results on the IEEE 33-bus distribution and 20-bus natural gas systems allow us to observe that the supply costs in the distribution and natural gas networks are efficiently reduced considering the coordination of prosumers' participation.

Abstract
This research's aim is to check if computer engineers have an appropriate set of rules on how to develop eXtended Reality (XR) applications for users with Autism Spectrum Disorder (ASD). In order to answer this question, a literature review has been performed on the main computer science association publishing avenues: the Digital Library of the Association for Computer Machinery (ACM DL), and the Institute of Electrical and Electronics Engineers (IEEE). The research findings are synthesized in 12 recommendations, but a gap is identified in established methodologies and consolidated knowledge. © 2025 Elsevier B.V., All rights reserved.

Abstract
The fourth industrial revolution is based on the production process’s digitisation to promote traceability, reduction of waste and decision support. This transition from the physical to the information domain requires, besides the process’s digital representation, sensors and transducers capable of capturing the system states. In the case of agricultural processes, due to the heterogeneity of production conditions, as well as the large area in which it takes place, physical characterisation often requires a large number of sensors spread over several hectares. This fact makes the economic costs associated with the digitisation of agriculture a very relevant aspect. In particular, if robust and precise sensors are to be deployed. Within this reference frame, this paper describes the approach taken to develop low-cost sensors capable of measuring soil moisture at three different depths. Robustness is achieved through the use of materials with high mechanical strength and corrosion resistance and both accuracy and repeatability are accomplished by using a signal conditioning strategy based on a programmable analogue front-end. Details about the methodology used are presented throughout the article. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Over the last decades, district heating has been under development, especially the technologies like heat pumps, solar thermal and cogeneration. However, there is still a long way to go regarding regulation, legislation and market liberalization, which varies across countries and regions. The objective of this work is to investigate the potential benefits of decentralized district heating systems in residential areas. By studying a case study of EnergyLab Nordhavn, a residential area in Copenhagen, Denmark, the paper compares the market outcomes of decentralized systems such as community markets to the centralized pool market currently in practice, under the EMB3Rs platform. The study focuses on key market outputs such as dispatched production, revenues, and daily consumption patterns. Additionally, the paper examines the impact of advanced features such as flexible heat consumption and network awareness in the market. The results of this research suggest that decentralized district heating systems have the potential to improve market outcomes and increase energy efficiency in residential areas.