Abstract
There is an increasing number of academic publications on studying the impact of the gig economy and digital platforms. Some of them involve entrepreneurship and business models. However, there is a lack of a global picture depicting the scientific structure of knowledge regarding the gig economy and entrepreneurship. This paper presents a conceptual, intellectual, and social bibliometric overview, using Bibliometrix and Biblioshiny (R-packages). To this end, total of 345 published articles were analyzed, covering 245 sources, 44 countries and 751 authors. There are several important findings: five main clusters emerged from the study (Self-employment and social economy; Sharing economy and sustainable development; Entrepreneurship and innovation; Gig economy and platform economy; and Digitalization); the main themes that emerge deal with sharing, gig, and platform economy, digitalization, teleworking, career participation and platforms; finally, gig workers are key for developing strategies, policies, and actions to achieve a social welfare through entrepreneurship in the platform ecosystem. It is also important to highlight the role of communities and social capital in the development of sustainable collaborative initiatives through digital entrepreneurship.

Abstract
With the evolution of technology and all the associated paradigms, the business reality had the need to adapt and incorporate all this evolution. Gamification strategies in social networks are used to optimize the use and involvement of users. Thus, in a business context, these strategies are used to increase user engagement in the company/brand's social networks with the aim of increasing and strengthening trust. This chapter's main objectives are to understand if internet users and social media users know the term gamification and if the associated strategies are perceptible in the social media context. Therefore, to better analyze the users' behavior regarding these strategies and their knowledge about gamification, an online survey was developed and applied. After the survey, it was possible to conclude that 16.3% of the respondents already knew the term gamification. Through the survey it was also possible to conclude that most respondents had already been in contact with gamification strategies, even if 90.6% did not adhere to them frequently.

Abstract
Deep Learning (DL) based classification algorithms have been shown to achieve top results in clinical diagnosis, namely with lung cancer datasets. However, the complexity and opaqueness of the models together with the still scant training datasets call for the development of explainable modeling methods enabling the interpretation of the results. To this end, in this paper we propose a novel interpretability approach and demonstrate how it can be used on a malignancy lung cancer DL classifier to assess its stability and congruence even when fed a low amount of image samples. Additionally, by disclosing the regions of the medical images most relevant to the resulting classification the approach provides important insights to the correspondent clinical meaning apprehended by the algorithm. Explanations of the results provided by ten different models against the same test sample are compared. These attest the stability of the approach and the algorithm focus on the same image regions.

Abstract
The objective of this article is to analyze and empirically validate the differential effects in the daily schedules of the induced electricity prices by selling bids for three different technologies, namely hydraulic, thermal and renewable energy sources (RES), in hourly values, by daily observations for the year 2018. To achieve this objective, we employ an autoregressive distributed lag (ARDL) model-bound testing approach The results of the ADRL-ECM method, which also reports the long-run analysis, show that (a) the renewable and thermal technologies positively and significantly affect the electricity price for Endesa and Hidroeletrica del Cantabrico generators and (b) the hydraulic technology impacts negatively the electricity price, both at a 1% level of significance. In addition, following a long-term perspective it must be highlighted that RES negatively impact the price of electricity with a 1% level of significance for the Iberdrola, E.ON Energy, Union Fenosa and EDP Energy of Portugal generators. However based on a short-term perspective, the results report a positive effect between the quantities traded by hydraulic and thermal technologies on the electricity price for Endesa, Iberdrola, Hidroeletrica del Cantabrico and EDP Energy of Portugal, at a 1% level of significance. (C) 2022 The Author(s). Published by Elsevier Ltd.

Abstract
The grape variety plays an important role in the wine production chain, thus identifying it is crucial for production control. Ampelographers, professionals who identify grape varieties through plant visual analysis, are scarce, and molecular markers are expansive to identify grape varieties on a large scale. In this context, Deep Learning models become an effective way to handle ampelographers scarcity. In this work, we explore the benefit of using deep learning vision transformers architecture relative to conventional CNN to identify 12 grapevine varieties using leaf-centred RGB images acquired in the field. We train an Xception model as a baseline and four different configurations of the ViT_B model. The best model achieved 0.96 of F1-score, outperforming the state-of-the-art convolutional-based model in the used dataset.

Abstract
The use of deep learning methods in medical imaging has been able to deliver promising results; however, the success of such models highly relies on large, properly annotated datasets. The annotation of medical images is a laborious, expensive, and time-consuming process. This difficulty is increased for the mutations status label since these require additional exams (usually biopsies) to be obtained. On the other hand, raw images, without annotations, are extensively collected as part of the clinical routine. This work investigated methods that could mitigate the labelled data scarcity problem by using both labelled and unlabelled data to improve the efficiency of predictive models. A semi-supervised learning (SSL) approach was developed to predict epidermal growth factor receptor (EGFR) mutation status in lung cancer in a less invasive manner using 3D CT scans.The proposed approach consists of combining a variational autoencoder (VAE) and exploiting the power of adversarial training, intending that the features extracted from unlabelled data to discriminate images can help in the classification task. To incorporate labelled and unlabelled images, adversarial training was used, extending a traditional variational autoencoder. With the developed method, a mean AUC of 0.701 was achieved with the best-performing model, with only 14% of the training data being labelled. This SSL approach improved the discrimination ability by nearly 7 percentage points over a fully supervised model developed with the same amount of labelled data, confirming the advantage of using such methods when few annotated examples are available.