Abstract
Literature on analysis of inter-organizational networks mentions the benefits that collaboration networks can provide to firms, in terms of managerial decision-making, although rarely analysed in terms of their overall performance. This paper aims to identify the existence of common factors of evolutionary patterns in the networks that determine its performance and evolution through a Multiple Factor Analysis (MFA). Subsequently, a hierarchical clustering procedure was performed on the factors that determine these networks, trying to find similarities in the evolutionary behavior. Data were collected on twelve real collaboration networks, characterized by four variables: Operational Result, Stock of Knowledge, Operational Costs and Technological Distance. The hierarchical clustering allowed the identification and distinction of the networks with the worst and best performances, as well as the variables that characterize them, allowing to recognize poorly defined strategies in the constitution of some networks. © Springer Nature Switzerland AG 2020.

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[No abstract available]

Abstract
The COVID-19 outbreak has proven to be a challenge for most communities, requiring them to adapt to a newfound reality. Cities need now to accommodate the circulation of their populations in a safe manner, dealing with economic repercussions, and avoiding to oversaturate the countries' healthcare facilities. So far, the latter has happened with dramatic consequences in terms of loss of human lives. In this context, we propose a social simulation meta-model suitable to represent the complex socio-technical system of a campaign hospital, created to support existing healthcare facilities as a response to the demands created by the coronavirus pandemic. With this model we intend to support the analysis of social coordination policies towards the improvement of a given set of characteristics of the system. By considering both technical and social dimensions, we expect to gain insights into how certain aspects such as the collaborativeness of patients or the nature of staff might affect the healing speed of patients and, similarly, the efficiency of the campaign hospital. Ultimately, all emergent behaviour should provide useful insights allowing for the identification of key social practices influencing its performance. © 2020 IEEE.

Abstract
Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality. © 2020 ACM.

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Abstract
Background As of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detection to accelerate its diagnosis and help its prevention. Among several approaches, one is to automatically classify tumor samples through their gene expression analysis. Methods In this work, we aim to distinguish five different types of cancer through RNA-Seq datasets: thyroid, skin, stomach, breast, and lung. To do so, we have adopted a previously described methodology, with which we compare the performance of 3 different autoencoders (AEs) used as a deep neural network weight initialization technique. Our experiments consist in assessing two different approaches when training the classification model - fixing the weights after pre-training the AEs, or allowing fine-tuning of the entire network - and two different strategies for embedding the AEs into the classification network, namely by only importing the encoding layers, or by inserting the complete AE. We then study how varying the number of layers in the first strategy, the AEs latent vector dimension, and the imputation technique in the data preprocessing step impacts the network's overall classification performance. Finally, with the goal of assessing how well does this pipeline generalize, we apply the same methodology to two additional datasets that include features extracted from images of malaria thin blood smears, and breast masses cell nuclei. We also discard the possibility of overfitting by using held-out test sets in the images datasets. Results The methodology attained good overall results for both RNA-Seq and image extracted data. We outperformed the established baseline for all the considered datasets, achieving an average F(1)score of 99.03, 89.95, and 98.84 and an MCC of 0.99, 0.84, and 0.98, for the RNA-Seq (when detecting thyroid cancer), the Malaria, and the Wisconsin Breast Cancer data, respectively. Conclusions We observed that the approach of fine-tuning the weights of the top layers imported from the AE reached higher results, for all the presented experiences, and all the considered datasets. We outperformed all the previous reported results when comparing to the established baselines.