Next-Generation Public Safety Systems Based on Autonomous Vehicles and Opportunistic Communications

An emergency scenario is characterized by the unpredictability of the environment conditions and by the scarcity of the available communication infrastructures. After a natural or human disaster, the main public and private infrastructures are partially damaged or totally destroyed. These infrastructures include roads, bridges, water supplies, electrical grids, telecommunications and so on. In these conditions, the first rescue operations executed by the public safety organizations can be very difficult, due to the unpredictability of the disaster area environment and the lack in the communications systems. The aim of this work is to introduce next-generation public safety systems where the main focus is the use of unmanned vehicles that are able to exploit the self-organizing characteristics of such autonomous systems. With the proposed public safety systems, a team of autonomous vehicles will be able to overcome the hazardous environments of a post disaster scenario by introducing a temporary dynamic network infrastructure which enables the first responders to cooperate and to communicate with the victims involved. Furthermore, given the pervasive penetration of smart end-user devices, the emergence of spontaneous networks could constitute promising solutions to implement emergency communication systems. With these systems the survivors will be able to self-organize in a communication network that allows them to send alerts and information messages towards the rescue teams, even in absence of communication infrastructures

Relativistic Digital Twin: Bringing the IoT to the Future

Complex IoT ecosystems often require the usage of Digital Twins (DTs) of their physical assets in order to perform predictive analytics and simulate what-if scenarios. DTs are able to replicate IoT devices and adapt over time to their behavioral changes. However, DTs in IoT are typically tailored to a specific use case, without the possibility to seamlessly adapt to different scenarios. Further, the fragmentation of IoT poses additional challenges on how to deploy DTs in heterogeneous scenarios characterized by the usage of multiple data formats and IoT network protocols. In this paper, we propose the Relativistic Digital Twin (RDT) framework, through which we automatically generate general-purpose DTs of IoT entities and tune their behavioral models over time by constantly observing their real counterparts. The framework relies on the object representation via the Web of Things (WoT), to offer a standardized interface to each of the IoT devices as well as to their DTs. To this purpose, we extended the W3C WoT standard in order to encompass the concept of behavioral model and define it in the Thing Description (TD) through a new vocabulary. Finally, we evaluated the RDT framework over two disjoint use cases to assess its correctness and learning performance, i.e., the DT of a simulated smart home scenario with the capability of forecasting the indoor temperature, and the DT of a real-world drone with the capability of forecasting its trajectory in an outdoor scenario.Comment: 17 pages, 10 figures, 4 tables, 6 listing

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

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Dual-mode wake-up nodes for IoT monitoring applications: Measurements and algorithms

Internet of Things (IoTs)-based monitoring applications usually involve large-scale deployments of battery-enabled sensor nodes providing measurements at regular intervals. In order to guarantee the service continuity over time, the energy-efficiency of the networked system should be maximized. In this paper, we address such issue via a combination of novel hardware/software solutions including new classes of Wake-up radio IoT Nodes (WuNs) and novel data- and hardware-driven network management algorithms. Three main contributions are provided. First, we present the design and prototype implementation of WuN nodes able to support two different energy-saving modes; such modes can be configured via software, and hence dynamically tuned. Second, we show by experimental measurements that the optimal policy strictly depends on the application requirements. Third, we move from the node design to the network design, and we devise proper orchestration algorithms which select both the optimal set of WuN to wake-up and the proper energy-saving mode for each WuN, so that the application lifetime is maximized, while the redundancy of correlated measurements is minimized. The proposed solutions are extensively evaluated via OMNeT++ simulations under different IoT scenarios and requirements of the monitoring applications

IPO-V2: A prospective, multicenter, randomized, comparative clinical investigation of the effects of sulodexide in preventing cardiovascular accidents in the first year after acute myocardial infarction

AbstractObjectives. This study was conducted to assess the efficacy of sulodexide, a glycosaminoglycan compound with antithrombotic properties, in preventing death and thromboembotic events after acute myocardial infarction.Background. Antithrombotic therapy has been found to play an important role in the prevention of cardiovascular events and death after acute myocardial infarction. Glycosaminoglycan-containing compounds, including sulodexide, show profibrinolytic and antithrombotic properties that render them suitable for use in patients after infarction.Methods. A total of 3,986 patients who had recovered from acute myocardial infarction were randomized to receive either the standard therapy routinely administered at each study center, excluding antiplatelet and anticoagulant drugs (control group, 1,970 patients), or the standard therapy plus sulodexide (treated group, 2,016 patients). Between 7 and 10 days after the episode of acute myocardial infarction, sulodexide was administered as a single daily 600-lipoprotein-lipase-releasing unit (LRU) intramuscular injection for the 1st month, followed by oral capsules of 500 LRU twice daily. Patients were evaluated for ≥12 months.Results. At the end of the study, 140 (7.1%) were recorded in the control group and 97 (4.8%) in the sulodexide group (32% risk reduction, p = 0.0022, chi-square test). A total of 90 patients (4.6%) in the control group had a further infarction, compared with 66 (33%) in the sulodexide group (28% risk reduction, p = 0.035). Furthermore, a reduction in left ventricular thrombus formation (evaluated by echocardiography) was observed in the sulodeside group (n = 12; 0.6%), compared with values in the control group (n = 25; 1.3%) (53% risk reduction, p = 0.027). Sulodexide was well tolerated and devoid of significant adverse events. All significant results were confirmed by “actual treatment” analyses.Conclusions. The study provides evidence that long-term therapy with sulodexide started early after an episode of acute myocardial infarction is associated with reductions in total mortality, rate of reinfarction and mural thrombus formation

Prolonged higher dose methylprednisolone vs. conventional dexamethasone in COVID-19 pneumonia: a randomised controlled trial (MEDEAS)

Dysregulated systemic inflammation is the primary driver of mortality in severe COVID-19 pneumonia. Current guidelines favor a 7-10-day course of any glucocorticoid equivalent to dexamethasone 6 mg·day-1. A comparative RCT with a higher dose and a longer duration of intervention was lacking

Prescription appropriateness of anti-diabetes drugs in elderly patients hospitalized in a clinical setting: evidence from the REPOSI Register

Diabetes is an increasing global health burden with the highest prevalence (24.0%) observed in elderly people. Older diabetic adults have a greater risk of hospitalization and several geriatric syndromes than older nondiabetic adults. For these conditions, special care is required in prescribing therapies including anti- diabetes drugs. Aim of this study was to evaluate the appropriateness and the adherence to safety recommendations in the prescriptions of glucose-lowering drugs in hospitalized elderly patients with diabetes. Data for this cross-sectional study were obtained from the REgistro POliterapie-Società Italiana Medicina Interna (REPOSI) that collected clinical information on patients aged ≥ 65 years acutely admitted to Italian internal medicine and geriatric non-intensive care units (ICU) from 2010 up to 2019. Prescription appropriateness was assessed according to the 2019 AGS Beers Criteria and anti-diabetes drug data sheets.Among 5349 patients, 1624 (30.3%) had diagnosis of type 2 diabetes. At admission, 37.7% of diabetic patients received treatment with metformin, 37.3% insulin therapy, 16.4% sulfonylureas, and 11.4% glinides. Surprisingly, only 3.1% of diabetic patients were treated with new classes of anti- diabetes drugs. According to prescription criteria, at admission 15.4% of patients treated with metformin and 2.6% with sulfonylureas received inappropriately these treatments. At discharge, the inappropriateness of metformin therapy decreased (10.2%, P < 0.0001). According to Beers criteria, the inappropriate prescriptions of sulfonylureas raised to 29% both at admission and at discharge. This study shows a poor adherence to current guidelines on diabetes management in hospitalized elderly people with a high prevalence of inappropriate use of sulfonylureas according to the Beers criteria