Abstract
The relevance of the tourism industry to the overall sustainability of rural territories grows along with the demand for rural tourism destinations. Likewise, as the digital transition of rural tour operators takes place, their marketing initiatives also evolve towards a digital nature, which is why it is crucial to comprehend how the overall calibre of these activities might affect the perception of rural places, while also motivating tourists' travel intentions and, as a result, promoting the general sustainability of the destination. Thus, in this paper, we propose a novel conceptual model based on Delone and McLean's representative model of Information Systems Success Model, on Tan and Wu's arguments on tourism destinations' image relationship with tourists' visit intentions, and also on Verma's tourism destination brand equity concept. To validate the proposed model, an online focus group was developed involving several specialists whose opinions and perspectives corroborated the potential adequacy of the proposed artefact and, consequently, assumed its contribution and value. From this validation process, it was possible to highlight that digital marketing initiatives' overall quality influences both rural destinations' image and tourists' intention to visit these territories, that a positive image will trigger tourists' visit behaviour, and that these behaviours represent a valuable asset to rural destinations' global sustainability.

Abstract
This paper investigates the impact of different immersive Virtual Reality (iVR) technological approaches in teaching and learning English as a Foreign Language (EFL). Specifically, this paper explores the passive iVR and interactive iVR in a real authentic learning context as didactic possibilities compared to the conventional method of listening, consisting of audio-only listening exercises. The study was conducted using university students of B1 level EFL classes. The dependent variables considered in the study were Knowledge Retention, Presence, User Satisfaction, Cybersickness, and Preferred Technology. Results indicated that users showed significant satisfaction and preference for using this technology for learning, revealing enjoyment and motivation which are vital factors when learning a foreign language. However, no significant differences were found between learning via traditional listening exercises or the virtual system. Correlation tests were conducted between the questionnaire subscales to understand better which elements can influence learning. The study concludes that using iVR-based learning tools to learn a foreign language as an alternative to audio listening can only produce a broader positive impact and greater motivation. The results also suggest that iVR can be a valuable tool in the education field for knowledge transfer and motivation when learning foreign languages.

Abstract
The decarbonisation of the building sector is crucial for Portugal's goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse Gas (GHG) emissions reduction. Among the alternatives assessed, the proposed one (Alternative 4) presents the best returns on investment in terms of cost savings and emissions reduction. It allows for 52% investment cost savings in the residential sector and 13% in the service sector when compared to the current national roadmap to carbon neutrality (Alternative 2). The estimated emission reduction in 2050 for Alternative 4 is 0.64% for the residential sector and 3.2% for the service sector when compared to Alternative 2.

Abstract

Abstract

Abstract
PPG signal is a valuable resource for continuous heart rate monitoring; however, this signal suffers from artifact movements, which is particularly relevant during physical exercise and makes this biomedical signal difficult to use for heart rate detection during those activities. The purpose of this study was to develop learning models to determine heart rate using data from wearables (PPG and acceleration signals) and dealing with noise during physical exercise. Learning models based on CNNs and LSTMs were developed to predict the heart rate. The PPG signal was combined with data from accelerometers trying to overcome the noise movement on the PPG signal. Two datasets were used on this work: the 2015 IEEE Signal Processing Cup (SPC) dataset was used for training and testing, and another dataset was used for validation of the learning model (PPG-DaLiA dataset). The predictions obtained by the learning model represented a mean average error of 7.033±5.376 bpm for the SCP dataset, while a mean average error of 9.520±8.443 bpm for the validation set. The use of acceleration data increases the performance of the learning models on the prediction of the heart rate, showing the benefits of using this source of data to overcome the noise movement problem on the PPG signal. The combination of PPG signal with acceleration data could allow the learning models to use more information regarding the motion artifacts that affect the PPG and improve performance on the physiological event detections, which will largely spread the use of wearables on the healthcare applications for continuous monitor the physiological state allowing early and accurate detection of pathological events.