Abstract
This article reports a pedagogical experience developed within the scope of a Ph.D. program in Electrical and Computer Engineering with application to Education. Starting with a contextualization on the evolution of computers and Computational Thinking, the article describes the platform used in this study - code.org -, highlighting the strengths that captivate the students. In the Case Study topic, we describe the study carried out, starting with a description of the students involved, followed by a description of the process and the analysis of the results, ending with the evaluation process performed by the students. The article ends concluding that code.org is a valid option to develop computational thinking at early-ages. Copyright

Abstract
The optimisation of vineyards management requires efficient and automated methods able to identify individual plants. In the last few years, Unmanned Aerial Vehicles (UAVs) have become one of the main sources of remote sensing information for Precision Viticulture (PV) applications. In fact, high resolution UAV-based imagery offers a unique capability for modelling plant's structure making possible the recognition of significant geometrical features in photogrammetric point clouds. Despite the proliferation of innovative technologies in viticulture, the identification of individual grapevines relies on image-based segmentation techniques. In that way, grapevine and non-grapevine features are separated and individual plants are estimated usually considering a fixed distance between them. In this study, an automatic method for grapevine trunk detection, using 3D point cloud data, is presented. The proposed method focuses on the recognition of key geometrical parameters to ensure the existence of every plant in the 3D model. The method was tested in different commercial vineyards and to push it to its limit a vineyard characterised by several missing plants along the vine rows, irregular distances between plants and occluded trunks by dense vegetation in some areas, was also used. The proposed method represents a disruption in relation to the state of the art, and is able to identify individual trunks, posts and missing plants based on the interpretation and analysis of a 3D point cloud. Moreover, a validation process was carried out allowing concluding that the method has a high performance, especially when it is applied to 3D point clouds generated in phases in which the leaves are not yet very dense (January to May). However, if correct flight parametrizations are set, the method remains effective throughout the entire vegetative cycle.

Abstract
The current literature focuses on the cocreation of brands in dynamic contexts, but the impact of the relationships among brands on branding is poorly documented. To address this gap a concept is proposed concerning the relationships between brands and a model is developed, showing the influence of the latter on the identity and reputation of brands. Therefore, the goal of this study is to develop a brand relationships concept and to build a framework relating it with corporate brand identity and reputation, in a higher consumer involvement context like higher education. Structural equation modelling (SEM) was used for this purpose. In line with this, interviews, cooperatively developed by higher education lecturers and brand managers, were carried out with focus groups of higher education students, and questionnaires conducted, with 216 complete surveys obtained. Data are analyzed using confirmatory factor analysis and structural equation modelling. Results demonstrate that the concept of brand relationships comprises three dimensions: trust, commitment, and motivation. The structural model reveals robustness regarding the selected fit indicators, demonstrating that the relationships between brands influence brand identity and reputation. This suggests that managers must choose and promote brand relationships that gel with the identity and reputation of the primary brand they manage, to develop an integrated balanced product range.

Abstract
Background: Over the past few years, various new types of technologies have been introduced, which have been tailored to meet the specific needs of older adults by incorporating gerontological design principles (i.e., "gerontechnologies"). However, it has been difficult to motivate older adults to adopt and use these new technologies. Therefore, it is crucial to better understand not only the role of personal drivers but also the family influences on older adults. Objective: This research goes beyond traditional technology acceptance theories by investigating the role of personal (e.g., inherent novelty seeking) and interpersonal drivers (e.g., influence of family) in stimulating older adults to use gerontechnologies. Nine hypotheses, building on traditional and new technology acceptance theories, were developed and tested. Methods: This research applies a cross-sectional study design. Therefore, a face-to-face survey instrument was developed building on a qualitative pilot study and validated scales. Three hundred and four older adults (minimum age = 70 years) were willing to participate as well as one of their family members. Structural equation modeling was applied to analyze the hypothesized conceptual model. Results: Our results extend the seminal technology acceptance theories by adding personal (i.e., inherent novelty seeking p = 0.017) and interpersonal drivers. More specifically, it was found that the attitude toward gerontechnologies was influenced by family tech savviness (i.e., people who often use technology), as this relationship is fully mediated through the social norms of older adults (p = 0.014). The same was found for older adults' trust in the family member's technology knowledge (p <= 0.001). Here, the relationship with older adults' attitude toward gerontechnologies was partially mediated by the older adults' trust in technology. Conclusion: This study identified important personal and interpersonal drivers that influence attitudes toward and intentions to use gerontechnologies. To foster technology acceptance among older adults, it was found that it is important to strengthen the trust in and the attitude toward gerontechnologies. Furthermore, family members' knowledge and beliefs in technology were the keys to promoting the actual use of gerontechnologies among older adults. Furthermore, the families' trust in gerontechnologies and the provision of access to technology can improve their attitudes toward technology and usage intentions for the older relative.

Abstract
The predawn leaf water potential (psi(pd)) is an eco-physiological indicator widely used for assessing vines water status and thus supporting irrigation management in several wine regions worldwide. However, the.pd is measured in a short time period before sunrise and the collection of a large sample of points is necessary to adequately represent a vineyard, which constitute operational constraints. In the present study, an alternative method based on hyperspectral data derived from a handheld spectroradiometer and machine learning algorithms was tested and validated for assessing grapevine water status. Two test sites in Douro wine region, integrating three grapevine cultivars, were studied for the years of 2014, 2015, and 2017. Four machine learning regression algorithms were tested for predicting the psi(pd) as a continuous variable, namely Random Forest (RF), Bagging Trees (BT), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VH-GPR). Three predicting variables, including two vegetation indices (NRI554,561 and WI900,970) and a time-dynamic variable based on the psi(pd) (psi(pd_0)), were applied for modelling the response variable (psi(pd)). Additionally, the predicted values of psi(pd) were aggregated into three classes representing different levels of water deficit (low, moderate, and high) and compared with the corresponding classes of.pd observed values. A root mean square error (RMSE) and a mean absolute error (MAE) lower or equal than 0.15 MPa and 0.12 MPa, respectively, were obtained with an external validation data set (n= 71 observations) for the various algorithms. When the modelling results were assessed through classes of values, a high overall accuracy was obtained for all the algorithms (82-83%), with prediction accuracy by class ranging between 79% and 100%. These results show a good performance of the predictive models, which considered a large variability of climatic, environmental, and agronomic conditions, and included various grape cultivars. By predicting both continuous values of.pd and classes of psi(pd), the approach presented in this study allowed obtaining 2-levels of accurate information about vines water status, which can be used to feed management decisions of different types of stakeholders.

Abstract
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under: Technologies > Machine Learning Technologies > Classification Application Areas > Health Care