Abstract
Purpose - This paper aims to generate knowledge of the impact of different virtual reality (VR) set-ups in tourism promotion regarding destination image, place attachment and behavioural intention.Design/methodology/approach - The paper presents a comparative study of the impact of different visualisation technologies (video, immersive VR and multisensory immersive VR) to promote tourism destinations. The study's dependent variables are destination image, place attachment and behaviour intention.Findings - Results show that VR content impacts these variables. Multisensory immersive VR is the preferred content type for destination promotion. It is also evidenced that female participants scored each variable higher than male participants. Males reported higher scores on the video set-up for destination image and place attachment. Behavioural intention reported higher values in the video when compared to immersive VR in both sexes.Practical implications - This paper concludes that there is a preference towards multisensory set-ups, which suggests that incorporating audiovisual and sensory elements can significantly enhance the effectiveness of VR experiences in attracting and engaging potential tourists.Originality/value - The paper contributes to the scarce body of knowledge regarding the impact of different VR factors on tourism promotion, including the multisensory VR component.

Abstract
This paper presents the results of field trials performed with the EVA autonomous underwater vehicle in the protection of critical infrastructures. The trials were conducted in the context of the REPMUS23 naval exercise organized by the Portuguese Navy. EVA was successfully deployed in a mission of detailed inspection of a submarine cable and in the detection and localization of a possible hostile attack with explosive charges. Multibeam sonar and structured laser light systems were also used to locate and obtain a detailed model of the TURTLE robotic lander deployed on the sea bottom.

Abstract
Over the years, sensitive data has been growing in software systems. To comply with ethical and legal requirements, the General Data Protection Regulation (GDPR) recommends using pseudonymization and anonymization techniques to ensure appropriate protection and privacy of personal data. Many anonymization techniques have been described in the literature, such as generalization or suppression, but deciding which methods to use in different contexts is not a straightforward task. Furthermore, anonymization poses two major challenges: choosing adequate techniques for a given context and achieving an optimal level of privacy while maintaining the utility of the data for the context within which it is meant to be used. To address these challenges, this paper describes four new design patterns: Generalization, Hierarchical Generalization, Suppress Outliers, and Relocate Outliers, building on existing literature to offer solutions for common anonymization challenges, including avoiding linkage attacks and managing the privacy-utility trade-off.

Abstract
Hospitalization has been identified as stress-inducing event that potentially contributes to depression and anxiety among children, particularly when the duration of hospital stay is prolonged. This scoping review seeks to identify the role of videogames and other interactive technology in reducing stress and promoting well-being, exploring the specific considerations for developing videogames for in- patient children and focusing on understanding various outcomes with different types of interactive technologies. The databases used in this research were ACM, PubMed, Wiley Library, yielding a total of 90 articles. Following the application of exclusion criteria 7 articles were selected for analysis. It is noteworthy that many of the included articles exhibit limitations, such as restricted study durations and a small number of participants. Addressing these limitations is crucial for establishing the long-term efficacy of interactive technology and videogames in promoting the well-being of in-patient children.

Abstract
Software is developed using programming languages whose choice is made based on a wide range of criteria, but it should be noted that the programming language selected can affect the quality of the software product. In this paper, we focus on analysing the differences in energy consumption when running certain algorithms that have been developed using different programming languages. Therefore, we focus on the software quality from the perspective of greenability, in concrete in the aspects related to energy efficiency. For this purpose, this study has conducted an empirical investigation about the most suitable programming languages from an energy efficiency perspective using a hardware-based consumption measurement instrument that obtains real data about energy consumption. The study builds upon a previous study in which energy efficiency of PL were ranked using a software-based approach where the energy consumption is an estimation. As a result, no significant differences are obtained between two approaches, in terms of ranking the PL. However, if it is required to have a more realistic knowledge of consumption, it is necessary to use hardware approaches. Furthermore, the hardware approach provides information about the energy consumption of specific DUT hardware components, such as, HDD, graphics card, and processor, and a ranking for each of component is elaborated. This can provide useful information to make a more informed decision on the choice of a PL, depending on several factors, such as the type of algorithms to be implemented, or the effects on power consumption not only in overall, but also depending on specific DUT hardware components.

Abstract
Almonds are becoming a central element in the gastronomic and food industry worldwide. Over the last few years, almond production has increased globally. Portugal has become the third most important producer in Europe, where this increasing trend is particularly evident. However, the susceptibility of almond trees to changing climatic conditions presents substantial risks, encompassing yield reduction and quality deterioration. Hence, yield forecasts become crucial for mitigating potential losses and aiding decisionmakers within the agri-food sector. Recent technological advancements and new data analysis techniques have led to the development of more suitable methods to model crop yields. Herein, an innovative approach to predict almond yields in the Tras-os-Montes region of Portugal was developed, by using machine learning regression models (i.e., the random forest regressor, XGBRegressor, gradient boosting regressor, bagging regressor, and AdaBoost regressor), coupled with remote sensing data obtained from different satellite platforms. Satellite data from both proprietary and free platforms at different spatial resolutions were used as features in the study (i.e., the GSMP: 11.13 km, Terra: 1 km, Landsat 8: 30 m, Sentinel-2: 10 m, and PlanetScope: 3 m). The best possible combination of features was analyzed and hyperparameter tuning was applied to enhance the prediction accuracy. Our results suggest that high-resolution data (PlanetScope) combined with irrigation information, vegetation indices, and climate data significantly improves almond yield prediction. The XGBRegressor model performed best when using PlanetScope data, reaching a coefficient of determination (R2) of 0.80. However, alternative options using freely available data with lower spatial resolution, such as GSMaP and Terra MODIS LST, also showed satisfactory performance (R2 = 0.68). This study highlights the potential of integrating machine learning models and remote sensing data for accurate crop yield prediction, providing valuable insights for informed decision support in the almond sector, contributing to the resilience and sustainability of this crop in the face of evolving climate dynamics.