Abstract
Phobias are characterized as the excessive or irrational fear of an object or situation, and specific phobias affect about 10% of the world population. Blood-injection-injury phobia is a specific phobia that has a unique physical response to phobic stimuli, that is, a vasovagal syncope that causes the person to faint. Phobos is a serious game intended for blood phobia treatment that was created to be played in virtual reality with an HTC Vive that has photorealistic graphics to provide a greater immersion. We also developed a console application in C# for electrocardiography sensor connectivity and data acquisition, which gathers a 1 min baseline reading and then has continuous data acquisition during gameplay. Usability tests were conducted with self-reported questionnaires and with a case study population of 10 testers, which gave insight into the previous game experience of the tester for both digital games and virtual reality games, evaluating the discomfort for hardware on both the sensor and the virtual reality headset, as well as the game regarding usability, user experience, level of immersion, and the existence of motion sickness and its source. The results corroborate that the immersion of the game is good, which suggests that it will help with triggering the phobia.

Abstract
AbstractWith the rapid growth of Deep Learning models and neural networks, the medical data available for training – which is already significantly less than other types of data – is becoming scarce. For that purpose, Generative Adversarial Networks (GANs) have received increased attention due to their ability to synthesize new realistic images. Our preliminary work shows promising results for brain MRI images; however, there is a need to distribute the workload, which can be supported by High-Performance Computing (HPC) environments. In this paper, we generate 256×256 MRI images of the brain in a distributed setting. We obtained an FIDRadImageNetof 10.67 for the DCGAN and 23.54 for the WGAN-GP, which are consistent with results reported in several works published in this scope. This allows us to conclude that distributing the GAN generation process is a viable option to overcome the computational constraints imposed by these models and, therefore, facilitate the generation of new data for training purposes.

Abstract
Intervention studies have been explored to identify actions to effectively remediate indoor environmental quality (IEQ) problems and to improve people's health, well-being, comfort, and productivity. This study assessed a comprehensive set of IEQ indicators related to ventilation, air pollution, thermal comfort, illuminance, and noise for the first time in Portuguese office buildings. The purpose was to derive evidence-based corrective measures for a further environmental intervention program. The study monitored and surveyed 15 open-space offices from six modern office buildings in Porto (Portugal) during a workday between September and December 2022. Illuminance was of most concern among the assessed IEQ indicators since the measured levels were below the minimum limit required in 27% of the evaluated workplaces. For CO2, although mean concentrations were below 1000 ppm, absolute values exceeding that level were consistently registered in 20% of the offices during the afternoon period. Mean levels of PM2.5, PM10, and ultrafine particles exceeding the WHO guidelines were found in 13%, 7%, and 7% of the offices, respectively. The assessed thermal comfort levels were typically neutral, corresponding to an estimated mean of 6% of dissatisfied people. Based on the findings, an intervention plan was designed to be implemented in the further stages of this work. The priority interventions to test include relocation of printers (PM source removal), optimisation of ventilation rates (using real-time data from CO2 sensors), adjustment of desk positions to improve illuminance, and introduction of indoor plants.

Abstract
The research objective of this work is to develop and evaluate an enterprise architecture for rural accommodation in the Iberian Peninsula that responds to the demand of the remote labor market. Through an extensive literature review and the application of ArchiMate modeling, this study focuses on providing an enterprise architecture that promotes business resilience and environmental sustainability and boosts the local economy. The proposed enterprise architecture is remotely evaluated by experts, highlighting potential benefits, challenges, and areas for improvement. The results show that the proposed enterprise architecture has the potential to improve the long-term success of rural lodging businesses, enhance the customer experience, promote sustainability, and contribute to economic growth in rural areas through value exchange among stakeholders. The ArchiMate model provides a holistic perspective on stakeholder interactions and interoperability across all functional business areas: Customer Service, Product Management, Omnichannel Commerce, Human Resources, Business Strategy, Marketing, and Sustainability Management. The idea is to empower rural lodging businesses to create a better customer experience, achieve energy and environmental efficiency, contribute to local development, respond quickly to regulatory changes and compliance, and develop new revenue streams. The main goal is to improve offers, mitigate seasonal effects, and reverse the continuous cycle of decline in areas with low population density. Therefore, this ArchiMate modeling can be the initial basis for the digitization or expansion of the rural lodging industry in other geographies.

Abstract
The adoption of open innovation poses significant challenges that are important to explore. Studies in this field have mainly focused on exploring the causes of the failure of open innovation among large companies. This study addresses this research gap by employing a sample of 297 Portuguese small- and medium-sized enterprises (SMEs) to explore, through a quantitative study, whether the dimensions and causes of failure differ between large organizations and SMEs. A total of seven dimensions of causes of failure are considered, including strategy-related, organizational structure, organizational culture, knowledge and intellectual property management, management skill and action, resources, and interfirm collaboration. The findings reveal significant differences in four of these seven dimensions: the main causes of failure are related to the resources and management processes of open innovation in SMEs, while large companies face more challenges in the organizational structure and culture components. This study offers theoretical insights into the gaps in the literature to better understand the challenges facing open innovation. Furthermore, this study offers practical guidelines for SMEs to identify and mitigate these main obstacles, promoting better innovation management practices.

Abstract
Given the high prevalence of lung cancer, an accurate diagnosis is crucial. In the diagnosis process, radiologists play an important role by examining numerous radiology exams to identify different types of nodules. To aid the clinicians' analytical efforts, computer-aided diagnosis can streamline the process of identifying pulmonary nodules. For this purpose, medical reports can serve as valuable sources for automatically retrieving image annotations. Our study focused on converting medical reports into nodule annotations, matching textual information with manually annotated data from the Lung Nodule Database (LNDb)-a comprehensive repository of lung scans and nodule annotations. As a result of this study, we have released a tabular data file containing information from 292 medical reports in the LNDb, along with files detailing nodule characteristics and corresponding matches to the manually annotated data. The objective is to enable further research studies in lung cancer by bridging the gap between existing reports and additional manual annotations that may be collected, thereby fostering discussions about the advantages and disadvantages between these two data types.