Abstract
The Data2MV dataset contains gaze fixation data obtained through experimental procedures from a total of 45 participants using an Intel RealSense F200 camera module and seven different video playlists. Each of the playlists had an approximate duration of 20 minutes and was viewed at least 17 times, with raw tracking data being recorded with a 0.05 second interval. The Data2MV dataset encompasses a total of 1.0 0 0.845 gaze fixations, gathered across a total of 128 experiments. It is also composed of 68.393 image frames, extracted from each of the 6 videos selected for these experiments, and an equal quantity of saliency maps, generated from aggregate fixation data. Software tools to obtain saliency maps and generate complementary plots are also provided as an open source software package. The Data2MV dataset was publicly released to the research community on Mendeley Data and constitutes an important contribution to reduce the current scarcity of such data, particularly in immersive, multi-view streaming scenarios. (c) 2023 Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Abstract
Answering a query through a peer-to-peer database presents one of the greatest challenges due to the high cost and time required to obtain a comprehensive response. Consequently, these systems were primarily designed to handle approximation queries. In our research, the primary objective was to develop an intelligent system capable of responding to approximate set-value inquiries. This paper explores the use of particle optimization to enhance the system's intelligence. In contrast to previous studies, our proposed method avoids the use of sampling. Despite the utilization of the best sampling methods, there remains a possibility of error, making it difficult to guarantee accuracy. Nonetheless, achieving a certain degree of accuracy is crucial in handling approximate queries. Various factors influence the accuracy of sampling procedures. The results of our studies indicate that the suggested method has demonstrated improvements in terms of the number of queries issued, the number of peers examined, and its execution time, which is significantly faster than the flood approach. Answering queries poses one of the most arduous challenges in peer-to-peer databases, as obtaining a complete answer is both costly and time-consuming. Consequently, approximation queries have been adopted as a solution in these systems. Our research evaluated several methods, including flood algorithms, parallel diffusion algorithms, and ISM algorithms. When it comes to query transmission, the proposed method exhibits superior cost-effectiveness and execution times.

Abstract
In the last 30 years, several academic and commercial projects have explored the context-awareness concept in multiple domains. Ubiquitous computing and ambient intelligence are features associated with the 4th generation industry empowering space to interact and respond appropriately according to context. In the scope of Industry 4.0, context-aware systems aim to improve productivity in smart factories and offer assistance to workers through services, applications, and devices, delivering functionalities and contextualised content. This article, through descriptive research, discusses the concepts related to context, presents and analyses projects related to ubiquitous computing and associated with Industry 4.0, and discusses the main challenges in systems and applications development to support intelligent environments for increased productivity, supporting informed decision-making in the factories of the future. The study results indicate that many research questions regarding the analysed projects remain the same, leading the research in the context-aware systems area to minimise issues related to context-aware features, improving the incorporation of Industry 4.0 paradigm concepts.

Abstract
The increasing integration of Distributed Energy Resources (DER) in the distribution network has brought more importance to Peer-to-Peer (P2P) markets. However, energy traded in P2P markets can lead to voltage and congestion constraints in distribution networks operated by Distribution System Operators (DSOs). At the same time, Transmission System Operators (TSOs) may need to solve system problems, requesting the participation of DERs in frequency regulation services. To ensure competitive participation in P2P markets, as well as to ensure a correct operation of distribution networks and to contribute to mitigate problems at the system level, coordination mechanisms between the P2P market and the System Operators (SOs) are required. This paper introduces a set of mathematical models considering P2P flexibility trading at the distribution system, while assisting the DSO and TSO in solving the congestion, voltage and frequency problems, respectively. The models are assessed on an IEEE 37bus distribution network with high DER penetration. The first and second models are based on product differentiation to avoid violating the lines' thermal limits and the nodes' voltage limits, respectively. The second model also considers reactive power control in order to impact voltage constraints. The third model uses a virtual load, connected to the TSO network (before the power transformer), to model frequency regulation services. The last model proposes the integration of all methods. Results showed that each model was effective in solving its constraint. However, they do not dismiss the use of the peers' flexibility assets to assure an overall feasible techno-economic solution. The use of the methodology proposed in the present paper can significantly facilitate the adoption of full P2P markets as well as the confidence of the system operators in the integration of these markets.& COPY; 2023 Elsevier Ltd. All rights reserved.

Abstract
The high costs associated with implementing and maintaining a training program based on immersive Virtual Reality (VR) technologies are a barrier to its adoption and widespread. This paper presents an Immersive VR training platform that intends to overcome such barriers. The Immersive VR platform was developed based on a real-usage case study conducted with an industry adopter. The case scenario focuses on training in the operation and maintenance of excavator machines. The industry partner has participated in the whole Immersive VR platform creation process, from conceptualization to its evaluation and validation. The Immersive VR training platform comprises two main modules: an authoring tool for an easy creation/update of training scenarios that supports industry-standard 3D models to ensure that they are continually updated when new products are released to the market and the training simulators that allow running training sessions regarding operation and maintenance of forestry machines. An exploratory usability evaluation of the training simulators created with the Authoring Tool revealed them as viable, validating the immersive VR platform. Limitations and future research directions are discussed to pave the way in this application field.

Abstract
Identifying the types of transportation modes that people use is a central problem in transportation research. Effective feature construction plays a crucial role in developing a successful machine learning model. In this study, we demonstrate an approach to identify commuters' transportation modes solely using raw GPS trajectory data. First, we transform the representation of location data points into a vector of motion features in the time domain. Next, we create fixed-length instances in the time domain. Subsequently, we transform the instances time-domain features into frequency-domain features using the fast Fourier transform. This results in a pool of features for the instances in both the time and frequency domains. We use the Sequential Forward Floating Selection technique to select the most informative features to train our models. We evaluate our approach using two distinct real-world GPS trajectory datasets. Our results show that the random forest classifier achieved an ROC-AUC scores of 79% and 89% on the respective datasets.