Automation of cranes can have a direct impact on the productivity of construction projects. In this paper, we focus on the control of one of the most used cranes, the boom crane. Tower cranes and overhead cranes have been widely studied in the literature, whereas the control of boom cranes has been investigated only by a few works. Typically, these works make use of simple models making use of a large number of simplifying assumptions (e.g. fixed length cable, assuming certain dynamics are uncoupled, etc.) A first result of this paper is to present a fairly complete nonlinear dynamic model of a boom crane taking into account all coupling dynamics and where the only simplifying assumption is that the cable is considered as rigid. The boom crane involves pitching and rotational movements, which generate complicated centrifugal forces, and consequently, equations of motion highly nonlinear. On the basis of this model, a control law has been developed able to perform position control of the crane while actively damping the oscillations of the load. The effectiveness of the approach has been tested in simulation with realistic physical parameters and tested in the presence of wind disturbances.

Fake news is a serious problem, which has received considerable attention from both industry and academic communities. Over the past years, many fake news detection approaches have been introduced, and most of the existing methods rely on either news content or the social context of the news dissemination process on social media platforms. In this work, we propose a generic model that is able to take into account both the news content and the social context for the identification of fake news. Specifically, we explore different aspects of the news content by using both shallow and deep representations. The shallow representations are produced with word2vec and doc2vec models while the deep representations are generated via transformer-based models. These representations are able to jointly or separately address four individual tasks, namely bias detection, clickbait detection, sentiment analysis, and toxicity detection. In addition, we make use of graph convolutional neural networks and mean-field layers in order to exploit the underlying structural information of the news articles. That way, we are able to take into account the inherent correlation between the articles by leveraging their social context information. Experiments on widely-used benchmark datasets indicate the effectiveness of the proposed method.

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