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
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.

Abstract
With the continued growth of wind power penetration into conventional power grid systems, wind power forecasting plays an increasingly competitive role in organizing and deploying electrical and energy systems. The wind power time series, though, often present non-linear and non-stationary characteristics, allowing them quite challenging to estimate precisely. The aim of this paper is in proposing a novel hybrid model named EvolCNN in order to predict the short-term wind power at 10-min interval up to 3-hr based on deep convolutional neural network (CNN) and evolutionary search optimizer. Specifically, we develop an improved version of Grey Wolf Optimization (GWO) algorithm by incorporating two effective modifications in its original structure. The proposed GWO algorithm is more effective than the original version due to performing in a faster way and the ability to escape from local optima. The proposed GWO algorithm is utilized to find the optimal values of hyperparameters for deep CNN model. Moreover, the optimal CNN model is employed to predict wind power time series. The main advantage of the proposed Evol-CNN model is to enhance the capability of time series forecasting models in obtaining more accurate predictions. Several forecasting benchmarks are compared with the Evol-CNN model to address its effectiveness. The simulation results indicate that the Evol-CNN has a significant advantage over the competitive benchmarks and also, has the minimum error regarding of 10-min, 1-hr and 3-hr ahead forecasting.

Abstract
Immersive technologies, such as virtual reality, could be effective marketing tools for destination marketing, namely in creating place attachment prior experience the destination. Place attachment plays a significant role in behavioural intentions to visit and to recommend a destination. However, place attachment research is relatively new in the tourism context. This study seeks to empirically examine the effectiveness of Virtual Reality in creating place attachment to destinations exploring the changes in the place attachment after two moments. First, after watching a video and after having an experience in the Virtual Reality environment. Second, after the experience in the Virtual Reality environment and after the 'real' visit to a representative viewpoint in Douro region. Students belonging to Gen Z were sampled. Findings reflect that Virtual Reality has potentialities for marketing destinations.

Abstract
Digital presence, especially using websites, is currently one of the pillars for effective communication in the field of marketing. However, it appears that many organizations, despite making high investments in development, even if they present a visually attractive website, face difficulties in attracting visitors. In the case of the tourism sector, in which competition takes place on a global level, it is even more pressing for technologies to reach potential stakeholders, first attracting visitors, then seducing them and converting them into customers. In this context, digital marketing techniques, and more specifically, website optimization techniques to improve their visibility by search engines (Search Engine Optimization) are the key to leveraging the potential for visits to any website. In this article, digital marketing concepts and the technological aspects that affect the indexing made by search engines will be explored. The case study presented intends to demonstrate, in a simplified way, the importance and applicability of these concepts and techniques, proposing a set of improvements that should be implemented to improve the positioning of the Termas de Chaves web site in the search engine page results.