Classification of users' transportation modalities from mobiles in real operating conditions.

The modern mobile phones and the complete digitalization of the public and private transport networks have allowed to access useful information to understand the user's mean of transportation. This enables a plethora of old and new applications in the fields of sustainable mobility, smart transportation, assistance, and e-health. The precise understanding of the travel means is at the basis of the development of a large range of applications. In this paper, a number of metrics has been identified to understand whether an individual on the move is stationary, walking, on a motorized private or public transport, with the aim of delivering to city users personalized assistance messages for: sustainable mobility, health, and/or for a better and enjoyable life, etc. Differently from the state-of-the-art solutions, the proposed approach has been designed to provide results, and thus collect metrics, in real operating conditions (imposed on the mobile phones as: a range of different mobile phone kinds, operating system constraints managing Applications, active battery consumption manager, etc.). The paper reports the whole experimentations and results. The solution has been developed in the context of Sii-Mobility Km4City Research Project infrastructure and tools, performed with the collaboration of public transport operators, and GDPR compliant. The same solution has been used in Snap4City mobile Apps with experiments performed in Antwerp and Helsinki.

A case of acute demyelinating polyradiculoneuropathy with bilateral facial palsy after ChAdOx1 nCoV-19 vaccine.

BACKGROUND: The coronavirus disease 2019 (COVID-19) global pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in late 2019. Researchers around the world are aggressively working to develop a vaccine. One of the vaccines approved against COVID-19 is Oxford-AstraZeneca chimpanzee adenovirus vectored vaccine ChAdOx1 nCoV-19. CASE REPORT: We described a patient who developed four limb distal paraesthesia, postural instability, and facial diplegia, ten days after vaccination with ChAdOx1 nCoV-19 (ABW1277). The electrophysiological findings were compatible with acute demyelinating motor polyneuropathy (Guillain-Barrè syndrome). DISCUSSION: We therefore want to describe a temporal correlation between administration of ChAdOx1 nCoV-19 (ABW1277) vaccine and GBS without evidence of other predisposing infectious or autoimmune factors. This paper aims to highlight the importance of pharmacovigilance and subsequent reports will be needed to evaluate the possible correlation between these two events.

An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities.

Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.

High Density Real-Time Air Quality Derived Services from IoT Networks.

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices' dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

Accuracy of Ultrasonographic Measurements of Inferior Vena Cava to Determine Fluid Responsiveness: A Systematic Review and Meta-Analysis.

OBJECTIVE: Fluid responsiveness is the ability to increase the cardiac output in response to a fluid challenge. Only about 50% of patients receiving fluid resuscitation for acute circulatory failure increase their stroke volume, but the other 50% may worsen their outcome. Therefore, predicting fluid responsiveness is needed. In this purpose, in recent years, the assessment of the inferior vena cava (IVC) through ultrasound (US) has become very popular. The aim of our work was to systematically review all the previously published studies assessing the accuracy of the diameter of IVC or its respiratory variations measured through US in predicting fluid responsiveness. DATA SOURCES: We searched in the MEDLINE (PubMed), Embase, Web of Science databases for all relevant articles from inception to September 2017. STUDY SELECTION: Included articles specifically addressed the accuracy of IVC diameter or its respiratory variations assessed by US in predicting the fluid responsiveness in critically ill ventilated or not, adult or pediatric patients. DATA EXTRACTION: We included 26 studies that investigated the role of the caval index (IVC collapsibility or distensibility) and 5 studies on IVC diameter. DATA SYNTHESIS: We conducted a meta-analysis for caval index with 20 studies: The pooled area under the curve, logarithmic diagnostic odds ratio, sensitivity, and specificity were 0.71 (95% confidence interval [CI]: 0.46-0.83), 2.02 (95% CI: 1.29-2.89), 0.71 (95% CI: 0.62-0.80), and 0.75 (95% CI: 0.64-0.85), respectively. CONCLUSION: An extreme heterogeneity of included studies was highlighted. Ultrasound evaluation of the diameter of the IVC and its respiratory variations does not seem to be a reliable method to predict fluid responsiveness.

Modulation of nociceptive threshold by combined hormonal contraceptives in women with oestrogen-withdrawal migraine attacks: a pilot study.

BACKGROUND: Menstrually-related headache and headaches associated with oestrogen withdrawal are common conditions, whose pathophysiology has not been completely elucidated. In this study we evaluated the influence of combined hormonal contraceptives (CHC) on pain threshold in women presenting migraine attacks during hormone-free interval. FINDINGS: Eleven women with migraine attacks recurring exclusively during the oestrogen-withdrawal period were studied with the nociceptive flexion reflex, a neurophysiological assessment of the pain control systems, during the third week of active treatment and during the hormone-free interval. During the hormone-free interval, nociceptive withdrawal reflex threshold was significantly lower (12.8 ± 8.0 mA) as compared to the third week of hormonal treatment (15.6 ± 6.6 mA) (p = 0.02). No change was observed in the pain perceived and in the temporal summation. CONCLUSIONS: Oestrogen withdrawal may mediate an increased sensitivity to somatosensory stimuli in women with migraine attacks recurring during the hormone-free interval.