Location-based systems currently represent a suitable solution to enhance cultural experiences inside museums, as they can satisfy visitors’ needs through the provision of contextualized contents and services. In this framework, a promising approach to captivate the attention of teenagers—a hard to please target audience—is represented by mobile serious games, such as playful activities aiming to primarily fulfil educational purposes. The use of a mobile digital tool during the visit definitely discloses new opportunities for contextual learning scenarios; however, so far, only a few studies have analysed the impact of different communication approaches on visitors’ degree of exploration and acquisition of knowledge. This work aims to enrich this field of research, presenting the conceptual framework; the design principles; and the evaluation results of “Gossip at palace,” a location-based mobile game integrating a storytelling approach. The game was developed for an Italian historical residence to communicate its 18th-century history to teenagers, capitalizing on narrative and game mechanics to foster young visitors’ motivations to explore the museum and facilitate their meaning-making process. Following a mixed-methods perspective, the article firstly describes to what extent the components of the application were appreciated by teenagers as well as by other visitor segments. Secondly, it provides an insight on the effectiveness of the game in facilitating the acquisition of historical knowledge by participants, enriched by considerations on the methods to be adopted when evaluating mobile learning in informal educational settings. Thirdly, players’ degree of use of the digital game throughout the visit is compared to analogous patterns registered for people using a multimedia mobile guide in the same venue. On the one hand, the study pointed out that the game facilitated a wider exploration of the museum; on the other, it highlighted that players mainly gained a superficial knowledge of the proposed contents.
The overwhelming increase of parcel transports has prompted the need for effective and scalable intelligent logistics systems. In parallel, with the advent of Industry 4.0, a tight integration of Internet of Things technologies and Big Data analytics solution has become necessary to effectively manage industrial processes and to early predict product faults or service disruptions. In the context of good transports, the development of smart monitoring tools is particularly useful for couriers to ensure effective and efficient parcel deliveries. However, the existing predictive maintenance frameworks are not tailored to parcel delivery services. We present REDTag Service, an integrated framework to track and monitor the shipped packages. It relies on a network of IoT-enabled devices, called REDTags, allowing courier employees to easily collect the status of the package at each delivery step. The framework provides back-end functionalities for smart data transmission, management, storage, and analytics. A machine-learning process is included to promptly analyze the features describing event-related data to predict potential breaks of the goods in the packages. The framework provides also a dynamic view on the integrated data tailored to the different stakeholders, as well as on the prediction outcomes, enabling immediate feedback and model improvements. We analyze a realworld dataset including event-related data about parcel transports. To validate the hypothesis that the acquired data contains information relevant to predict the package status (i.e., broken or safe), we empirically analyze the performance of different, scalable classifiers. The experimental results confirm, in good approximation, the predictive power of the models extracted from the event-related features. To the best of the authors' knowledge, this work is the first attempt to address predictive maintenance in smart good transport logistics to predict package breaks from real-world data. INDEX TERMS Big data analytics, Industry 4.0, intelligent transports and logistics, Internet of Things, machine learning, predictive maintenance.
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