Abstract
Collaborative networks (CNs) of organizations are nowadays complex and intertwined compositions of technological, cognitive and social artifacts. The design of such compositions should be addressed as a socio-technical endeavor as a way to maximize the success probability. In despite of intensive research in this community, much has to be explored to achieve sound contributions to a design theory of CNs. In this paper, we make use of the context intervention -mechanism-outcome logic (CIMO-logic) as a way to improve the design propositions component of a CN design theory. Variations of the concept of "mechanism" are explored with the goal of making clearer the socio-technical perspective in the design propositions. This theoretical exploration is illustrated with a case of transforming an industrial business association (IBA) in a digital collaborative network.

Abstract
This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

Abstract
The Tele-Media-Art project aims to promote the improvement of the online distance learning and artistic teaching process applied in the teaching of two test scenarios, doctorate in digital art-media and the lifelong learning course 'the experience of diversity' by exploiting multimodal telepresence facilities encompassing the diversified visual, auditory and sensory channels, as well as rich forms of gestural/body interaction. To this end, a telepresence system was developed to be installed at Palácio Ceia, in Lisbon, Portugal, headquarters of the Portuguese Open University, from which methodologies of artistic teaching in mixed regime-face-to-face and online distance-that are inclusive to blind and partially sighted students. This system has already been tested against a group of subjects, including blind people. Although positive results were achieved, more development and further tests will be carried in the future.

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Abstract
Food trucks are a widely popular fast food restaurant alternative, whose differentiating factor is their proximity to customers. Their popularity has stimulated the expansion of available options, which now includes several different types of cuisines, consequently making the choice by customers a challenging issue. From data obtained via a market research, in which hundreds of participants provided their food truck preferences, this paper focuses on the problem of food truck recommendation using a multilabel approach. In particular, it investigates how to improve the recommendation task regarding a previous work, where some labels have never been predicted. In order to address this problem, different alternatives were investigated. One of these alternatives, the Ensemble of Single Label, proposed in this paper, was able to reduce it. Despite its simplicity, good predictive results were obtained when they were used in the investigated task. Among other benefits, all labels were correctly predicted at least for few instances.