Abstract
All over the world, there are several unoccupied spaces without adequate constant control mechanisms to reduce and prevent mold and provide good internal conditions and indoor air quality. A widespread way to reduce building humidity is through heating and dehumidification, which are costly to maintain and have high energy consumption. In addition, there are few studies on adjustable hygro ventilation systems, which do not consider the influence of temperature fluctuations. This work describes the operation of a prototype, which fills existing research gaps by considering not only the control of relative humidity (RH) but also the temperature peaks in indoor air conditions, allowing the maintenance of good air quality. The prototype Smart Hygrothermal Ventilation system uses two pairs of sensors related to RH and temperature, one pair placed inside an unoccupied compartment of the building and the other pair in the external environment, in order to activate a fan and the respective speed. The proposed prototype was applied in a compartment located on the ground floor in an unoccupied old rural building in a village near Porto during the winter period. The results show that the system performed adequately for different configurations of its functionalities. Therefore, the system offers an efficient alternative to minimize mold and the fluctuation of internal RH and temperature. Furthermore, it could be a vital mechanism for the conservation of historic buildings.

Abstract
The ongoing energy transition and the rapid electrification of transport increase the importance of integrating renewable energy sources into smart mobility systems. Among these, solar energy plays a central role, but the variability of solar radiation poses significant challenges for planning electric vehicle (EV) charging and ensuring the reliable operation of transport networks. This work addresses these challenges by combining Big Data approaches and High-Performance Computing (HPC) to improve solar radiation forecasting and assess its implications for sustainable transport as a novelty from previous works. A Long Short-Term Memory (LSTM) neural network was the focus, and it was trained to predict key meteorological variables - global solar radiation, temperature, and wind speed - using both the original dataset of 13 years and expanded datasets of up to 130 years, generated to simulate Big Data scenarios. Forecasting performance remained stable across datasets, with R2 values above 0.85 for all variables. The best predictive results were obtained for the original dataset, achieving R2 = 0.9884 for solar radiation, while the HPC reduced execution time compared to conventional desktop environments. These results demonstrate that larger datasets improve model scalability and robustness, but significantly increase computational demands. The Deucalion supercomputer achieved the best performance, processing the largest dataset (130 years) in 44.24 minutes, while the same task on a Ryzen 7 required 51.00 minutes. The proposed approach highlights the potential of integrating Big Data and HPC to support EV charging optimisation, smart grid operation, and sustainable mobility strategies, contributing to faster, more reliable, and data-driven decision-making in the energy-transport ecosystem. © 2025 IEEE.

Abstract
Industrial companies operate in a context of dynamic technological innovation, in which new technologies are adopted with a high impact internally and externally, leveraging their competitive advantages. Usually, managers decide to adopt technologies, often without realising the impacts on the company, but mainly supported by a strategic vision and the pursuit of differentiation. This study aims to describe the impacts of adopting Industry 4.0 technologies in industrial companies, focusing on sustainability's economic, social, and environmental dimensions and explaining which Industry 4.0 technologies contribute to each impact. This study used qualitative methodology, collecting data through interviews, internal documents, and observation. The results of this study identified new impacts in the three dimensions of sustainability, as well as the relationships between impacts and respective technologies. This study contributes to the literature by enriching and validating the impacts of adopting Industry 4.0 technologies on sustainability dimensions and linking these impacts with the technologies. In practice, it provides important insights to managers and decision-makers of manufacturing companies in making more informed decisions on adopting i4.0 technologies.

Abstract

Abstract
This study presents and applies time delay analysis of maximum cross-correlation between quadriceps and gastrocnemius sEMG neuromuscular control with lower limb joint angular coordination of the hip, the knee and the ankle joint angles, angular velocities and accelerations to assess long countermovement (CM) and stretch-shortening cycle (SSC) at countermovement jump (CMJ), short CM and SSC on drop jump (DJ), and no CM on squat jump (SJ), with different and shared features at each CM complementing functional anatomy analysis. © 2025 Elsevier B.V., All rights reserved.

Abstract
Portugal's electricity generation relies heavily on renewable sources, which accounted for over half of the country's production in recent years. The Portuguese government has set ambitious renewable energy targets for 2030. The R3EA project (https://r3ea.web.ua.pt/pt/projeto) evaluates the impact of new investments in solar and wind energy capacity in the Centro Region of Portugal, focusing on the costs and benefits of externalities. This study examines Portugal's electricity market outcomes in terms of prices, generation mix, and emissions for different wind and solar capacities, using the National Energy and Climate Plans (NECP) of Portugal and Spain as the reference scenario. The electricity markets of both countries are modelled together, reflecting the integrated Iberian market with significant interconnections. The NECP scenario results in lower market prices and emissions, but less significantly than scenarios with lower demand and higher renewable energy share. In all scenarios, increasing renewable energy sources drives market prices down from over 200/MWh in 2022 to under 100/MWh during peak hours in 2030. Demand is the main driver of emissions, as higher demand leads to more reliance on fossil fuel plants. Lower demand scenarios in 2030 show 20 % fewer CO2 emissions per TWh than higher demand ones.