Abstract
The use of Virtual Reality (VR) technology to train professionals has increased over the years due to its advantages over traditional training. This paper presents a study comparing the effectiveness of a Virtual Environment (VE) and a Real Environment (RE) designed to train firefighters. To measure the effectiveness of the environments, a new method based on participants' Heart Rate Variability (HRV) was used. This method was complemented with self-reports, in the form of questionnaires, of fatigue, stress, sense of presence, and cybersickness. An additional questionnaire was used to measure and compare knowledge transfer enabled by the environments. The results from HRV analysis indicated that participants were under physiological stress in both environments, albeit with less intensity on the VE. Regarding reported fatigue and stress, the results showed that none of the environments increased such variables. The results of knowledge transfer showed that the VE obtained a significant increase while the RE obtained a positive but non-significant increase (median values, VE: before - 4 after - 7, p = .003; RE: before - 4 after - 5, p = .375). Lastly, the results of presence and cybersickness suggested that participants experienced high overall presence and no cybersickness. Considering all results, the authors conclude that the VE provided effective training but that its effectiveness was lower than that of the RE.

Abstract
Leadership styles and human capital are important drivers of innovation processes. The way the leader interacts with the organization members can pre-empt or leverage innovation processes as leaders influence, empower and motivate other individuals in the achievement of their goals. Human capital is an important driver of innovation and competitiveness, as it will shape the uniqueness of the company as well as the process to obtain skills, capabilities, knowledge and expertise. As such, the main objectives of the paper are to analyze the impact of leadership styles on the innovation process and also to address the moderation effect of the human capital on the previous relation. Four leadership styles-autocratic, transactional, democratic, and transformational-were considered to measure their impacts on the innovation process, considering the alternative types of innovations. The 2018 Community Innovation Survey (CIS) database was used, encompassing Portuguese data, covering the 2016-2018 period, with a sample of 13702 firms. In regard to the empirical part, first, an exploratory analysis was run to better understand the connection between the leadership styles and the innovative strategies followed by an econometric estimation encompassing 28 logit models to disentangle the specific impacts of each leader on each innovation type. Evidence proves that autocratic and transactional leadership styles have a negative impact on innovation and transformational and democratic leadership impact innovation positively. Furthermore, human capital was found to moderate the relationship between leadership styles and the innovation process; i.e., under the same leadership style, the presence of additional skills leverages innovative propensity. The paper brings relevant insights for both managers and policymakers, highlighting that innovation will be accelerated if firms implement more participatory (democratic and transformational) leadership styles and also if they invest in competences to promote knowledge internalization and share. All in all, participatory leadership combined with the internal skills is proved to be an efficient combination for innovation to take place; as such, policy instruments must promote the coexistence of these two factors.

Abstract
The application of machine learning algorithms to predict the mechanism of action (MoA) of drugs can be highly valuable and enable the discovery of new uses for known molecules. The developed methods are usually evaluated with small subsets of MoAs with large support, leading to deceptively good generalization. However, these datasets may not accurately represent a practical use, due to the limited number of target MoAs. Accurate predictions for these rare drugs are important for drug discovery and should be a point of focus. In this work, we explore different training strategies to improve the performance of a well established deep learning model for rare drug MoA prediction. We explored transfer learning by first learning a model for common MoAs, and then using it to initialize the learning of another model for rarer MoAs. We also investigated the use of a cascaded methodology, in which results from an initial model are used as additional inputs to the model for rare MoAs. Finally, we proposed and tested an extension of Mixup data augmentation for multilabel classification. The baseline model showed an AUC of 73.2% for common MoAs and 62.4% for rarer classes. From the investigated methods, Mixup alone failed to improve the performance of a baseline classifier. Nonetheless, the other proposed methods outperformed the baseline for rare classes. Transfer Learning was preferred in predicting classes with less than 10 training samples, while the cascaded classifiers (with Mixup) showed better predictions for MoAs with more than 10 samples. However, the performance for rarer MoAs still lags behind the performance for frequent MoAs and is not sufficient for the reliable prediction of rare MoAs.

Abstract
To ensure the safety of passengers concerning virus propagation, such as COVID-19, and keep the turnaround time at low levels, airlines should seek efficient aircraft boarding strategies in terms of both physical distancing and boarding times. This study seeks to analyze the impact of different boarding strategies in the context of the International Air Transport Association's recommendations during the pandemic to reduce interference and physical contact between passengers in airplanes. Boarding strategies such as back-to-front, outside-in, reverse pyramid, blocks, Steffen, and modified optimal have been tested in this context. This study extends the previous literature using discrete event simulation to evaluate the impact of the occupation of the middle seat by family members only. This study also analyses the impact of having passengers carrying hand luggage and priority passengers on the performance of these strategies concerning boarding times. In general, the simulation results revealed a 15% improvement in boarding times when the reverse pyramid strategy is used compared to a random strategy, which essentially results from a reduction in the boarding interferences between passengers. The results also show that Steffen's strategy is the best performing, while the blocks strategy results in the worst performance. This study has practical implications for airline companies concerning both operation efficiency and passenger safety.

Abstract
This article addresses the 3-D localization of a stand-alone acoustic beacon based on the Principle of Synthetic Baseline using a single receiver on board a surface vehicle. The process only uses the passive reception of an acoustic signal with no explicit synchronization, interaction, or communication with the acoustic beacon. The localization process exploits the transmission of periodic signals without synchronization to a known time reference to estimate the time-of-arrival (ToA) with respect to an absolute time basis provided by the global navigation satellite system (GNSS). We present the development of the acoustic signal acquisition system, the signal processing algorithms, the data processing of times-of-arrival, and an estimator that uses times-of-arrival and the coordinates where they have been collected to obtain the 3-D position of the acoustic beacon. The proposed approach was validated in a real field application on a search for an underwater glider lost in September 2021 near the Portuguese coast.

Abstract
Immersive video applications impose unpractical bandwidth requirements for best-effort networks. With Multi-View(MV) streaming, these can be minimized by resorting to view prediction techniques. SmoothMV is a multi-view system that uses a non-intrusive head tracking mechanism to detect the viewer's interest and select appropriate views. By coupling Neural Networks (NNs) to anticipate the viewer's interest, a reduction of view-switching latency is likely to be obtained. The objective of this paper is twofold: 1) Present a solution for acquisition of gaze data from users when viewing MV content; 2) Describe a dataset, collected with a large-scale testbed, capable of being used to train NNs to predict the user's viewing interest. Tracking data from head movements was obtained from 45 participants using an Intel Realsense F200 camera, with 7 video playlists, each being viewed a minimum of 17 times. This dataset is publicly available to the research community and constitutes an important contribution to reducing the current scarcity of such data. Tools to obtain saliency/heat maps and generate complementary plots are also provided as an open-source software package.