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OBJECTIVES Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

Lung ultrasound (LUS) is currently utilized worldwide to assess COVID-19 patients. However, imaging protocols are often defined arbitrarily, and studies on post-COVID-19 are lacking. In this work, we report on the capabilities of standardized LUS to monitor and stratify COVID-19 and post-COVID-19 patients. A validated and standardized imaging protocol based on 14 scanning-areas and a 4-level scoring system were utilized to collect and analyze data from 220 patients, 100 COVID-19 positive, and 120 post-COVID-19. Next, the capability of five imaging protocols (based on 4, 8, 10, 12, and 14 scanning-areas) to intercept the most significant LUS findings was compared. Moreover, a longitudinal-study was conducted aiming at investigating the possibility to simplify the protocol during follow-up. Results on the agreement between AI-models and LUS experts with respect to LUS data evaluation are also reported. In conclusion, a 12-areas protocol emerges as the optimal trade-off between a time-efficient and an accurate LUS examination. However, it appears not to be possible to reduce further the number of scanning-areas during follow-up. Finally, COVID-19 and post-COVID-19 data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19.

Personal data provides important business value, for example, in the personalization of services. In addition, companies are moving toward new business models, in which products and services are offered without charge to users, but in exchange for targeted advertising revenue. New privacy regulations require organizations to explicitly state their data practices in privacy policies, including which data types will be collected. By consenting to data collections described in a policy, the user acknowledges that he or she is granting the company the authorizations needed to access their data. When data practices change, a new version of the policy is released. This release can occur a few times a year, when requirements are rapidly changing for the collection and processing of personal data. Furthermore, the user may change his or her privacy consent by opting in or out of the policy. We propose a formal framework to support companies and users in their understanding of policies evolution under consent regime that supports both retroactive and non-retroactive consent and consent revocation. Preliminary results include an ontology for policy evolution, expressed in Description Logic, that can be used to formalize consent and data collection logs and then query for which data types can be legally accessed.

Part 1: Regular Papers; International audience; Privacy is a key aspect for the European Union (EU), where it is regulated by a specific law, the General Data Protection Regulation (GDPR). Compliance to the GDPR is a problem for organizations, it imposes strict constraints whenever they deal with personal data and, in case of infringement, it specifies severe consequences such as legal and monetary penalties. Such organizations frequently are complex systems, where personal data is processed by humans and technical services. Therefore, it becomes fundamental to consider privacy from the social perspective when designing such system, i.e., when relations between different components are specified. This is, indeed, also specified in the GDPR, which encourages to apply privacy-by-design principles. This paper proposes a method to support the design of GDPR compliant systems, based on a socio-technical approach composed of a modeling language and a reasoning framework.

Smart cities are founded on complex interactions among architectural and urban designs, sensors, actuators and crowds of people with their devices. In this context, simulation becomes essential to study the effects of the technology and to understand how to improve its effectiveness on the social environment. The majority of the current pedestrian and traffic simulations adopt a bird-eye view and are driven by statistical models. While this is enough in many cases, e.g. to study traffic flow under common conditions assuming average cases, it is not appropriate when a higher level of fidelity is required. Simulated people need to show both a plausible behavior and mechanisms to coordinate with human participants in a natural way. Much of this coordination happens silently and is driven by social norms, that may vary according to culture and context. In this paper, we propose an approach to represent social norms in multi-agent systems that enables implicit coordination driven by observations of others' behaviors. This is applied specifically to the case of pedestrian movement. In order to allow for a more effective participation of humans in the simulation, our approach does not use central coordinators or coordination protocol, but rather each agent takes its own decision so to make more realistic interactions. A software architecture and initial experimental results are presented and discussed.