Abstract
To identify the most developed terms in the field of Tourism and virtual reality, a quantitative analysis was developed in February 2020. This analysis was focused on seven hundred and eighty-four (784) publications from the Scopus database, published between 1995 and February 2022 and limited to the following areas: Business Management and Accounting, Computer Science, Social Sciences and Engineering. A bibliometric analysis was performed using the VOSviewer software and a technique of matching terms and co-authoring by authors and countries. Were found 5 clusters for the co-occurrence of terms and 7 clusters for the co-authorship of authors and countries. © 2022 IEEE Computer Society. All rights reserved.

Abstract
The urge for robust and reliable localization systems for autonomous mobile robots (AMR) is increasing since the demand for these automated systems is rising in service, industry, and other areas of the economy. The localization of AMRs is one of the crucial challenges, and several approaches exist to solve this. The most well-known localization systems are based on LiDAR data due to their reliability, accuracy, and robustness. One standard method is to match the reference map information with the actual readings from LiDAR or camera sensors, allowing localization to be performed. However, this approach has difficulties handling anything that does not belong to the original map since it affects the matching algorithm's performance. Therefore, they should be considered outliers. In this paper, a deep learning-based object detection algorithm is not only used for detection but also to classify them as outliers from the localization's perspective. This is an innovative approach to improve the localization results in a realmobile platform. Results are encouraging, and the proposed methodology is being tested in a real robot.

Abstract
The relevance of international research collaboration (IRC) in bolstering intellectual capital, in-creasing embeddedness in networks, and promoting innovation has been acknowledged by sci-entists and policymakers. However, large-scale studies involving different scientific domains and periods aimed at exploring the factors that influence IRC are missing, which could deepen our understanding of the factors affecting IRC. Based on a novel dataset of 193 countries over three periods, 1990-1999, 2000-2009 and 2010-2018, we have examined the impact of geographical, socioeconomic, political, cultural, intellectual, and excellence distances on the propensity to engage in IRC at the global level, by scientific domain and over time. In general, all the distances considered obstruct IRC, with geographical and cultural distance emerging as the barriers with the highest impact. Two exceptions are worthwhile noting: excel-lence distance fosters IRC in the Medical & Health Sciences (MHS) and intellectual distance fosters IRC in the Agricultural Sciences (AS). At the global level, the negative impact of socioeconomic, political, and intellectual distances on IRC has increased over time, whereas the negative impact of geographical and cultural dis-tances has decreased.

Abstract
The paper presents the Scratch4All project, co-financed by the Portuguese Social Innovation Mission Structure, namely the presentation of the methodology applied in the impact assessment of the first year of the project. A project that uses Scratch software to teach programming languages to fourth grade students (elementary school). Based on the defined indicators, on the characteristics of the beneficiaries and on the activities implemented, adequate measurement instruments were developed to verify the impacts on the target audience. Using secondary and primary sources, indicators were developed to measure the level of change that occurred in each project beneficiary, using quantitative tools. Regarding the software used, Scratch, it was possible to perceive that it enables greater group work skills, working on current social issues and/or technological profiles, fosters greater responsibility in the use of technologies, within the scope of digital citizenship, generates an increase in creativity and innovation, greater willingness to investigate and research different topics, greater communication and collaborative work. © 2022 IEEE Computer Society. All rights reserved.

Abstract

Abstract
This paper proposes an optimal allocation of a Wind-Storage Unit (WSU). Since transmission lines congestion varies according to the size, the location, and the operation of a generation unit in power systems, we assess the optimal location of a unit as a function of its variable operating condition. An independently operated wind storage unit is assumed as a price-maker that seeks to maximize its market payoff without any prior information on optimally locating the wind and storage units. The main problem is provided as a tri-level optimization problem in which the first level is the WSU profit maximization, the second level is the power system operation cost minimization from the perspective of the independent system operator (ISO), and the third level is the maximization of the robustness of the system by using an appropriate transmission switching interval robust based chance constrained (TSIRC) method in order to minimize the operation cost of the system and transmission lines congestion problem. The tri-level model is converted to a bi-level optimization model by using KarushKuhn-Tucker (KKT) conditions provided as a Mathematical Programming with Equilibrium Constraint (MPEC). An effective binary particle swarm optimization algorithm (BPSO) is used in order to find the optimal location of the wind and storage units. Unscented Transform (UT) as a key element is suggested to model the uncertainties associated with the output power of the wind turbines. The proposed method is tested on an IEEE 24-bus test system and the results reveal the validity of this work.