Abstract
High-performance computing relies on performance-oriented infrastructures with access to powerful computing resources to complete tasks that contribute to solve complex problems in society. The intensive use of resources and the increase in service demand due to emerging fields of science, combined with the exascale paradigm, climate change concerns, and rising energy costs, ultimately means that the decarbonization of these centers is key to improve their environmental and financial performance. Therefore, a review on the main opportunities and challenges for the decarbonization of high-performance computing centers is essential to help decision-makers, operators and users contribute to a more sustainable computing ecosystem. It was found that state-of-the-art supercomputers are growing in computing power, but are combining different measures to meet sustainability concerns, namely going beyond energy efficiency measures and evolving simultaneously in terms of energy and information technology infrastructure. It was also shown that policy and multiple entities are now targeting specifically HPC, and that identifying synergies with the energy sector can reveal new revenue streams, but also enable a smoother integration of these centers in energy systems. Computing-intensive users can continue to pursue their scientific research, but participating more actively in the decarbonization process, in cooperation with computing service providers. Overall, many opportunities, but also challenges, were identified, to decrease carbon emissions in a sector mostly concerned with improving hardware performance.

Abstract
This paper presents the development of a polishing prototype with a rotating sponge to be applied in the automation of a finishing process for the ceramic industry, focusing on increasing mechanical robustness. The prototype includes an AC motor, encoder, microcontroller, motor drive, and a collaborative robot to assist in the tests. Validation experiments related to the speed and force control were performed followed by the trajectory control tests using pieces printed using 3D printing technology to simulate the ceramic pieces. The results were satisfactory and showed a good performance of the polishing prototype, being this a good teaching aid tool to assist in the teaching and practical classes of mechatronics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
This article delves into the pivotal role of expert systems in bolstering information security, with a specific emphasis on their effectiveness in awareness and training programs aimed at thwarting social engineering attacks. Employing a snowball methodology, the research expands upon seminal works, highlighting the intersection between expert systems and cybersecurity. The study identifies a gap in current understanding and aims to contribute valuable insights to the field. By analyzing five key articles as seeds, the research explores the landscape of expert systems in information security, emphasizing their potential impact on cultivating robust defenses against evolving cyber threats.

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.