Intervehicle communication (IVC) is emerging in research prominence for the interest that it is generating in all major car manufacturers and for the benefits that its inception will produce. The specific features of IVC will allow the deployment of a wide set of possible applications, which span from road safety to entertainment. Even if, on the one hand, these applications share the common need for fast multihop message propagation, on the other hand, they possess distinct characteristics in terms of generated network traffic. The state of the art of current research only proposes solutions specifically designed for a single application (or class) that is not directly extendable to a general IVC context. Instead, we claim that a privileged architecture exists, which is able to support the whole spectrum of application classes. To this aim, we propose a novel IVC architecture that adapts its functionalities to efficiently serve applications by quickly propagating their messages over a vehicular network. We conducted an extensive set of experiments that demonstrate the efficacy of our approach. As representative case studies, we considered two application classes that, for their network traffic characteristics, are at the opposite boundaries of the application spectrum: safety and entertainment.

In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public–private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

Cerebral blood flow tests have increasingly been advocated for the confirmation of brain death (BD). Four-vessel angiography has been considered the most reliable investigation in the diagnosis of BD for >30 yrs, but it is invasive. 99mTc-HMPAO SPECT provides noninvasive, multiplanar imaging of brain tissue perfusion. The aim of this study was to check the reliability of SPECT compared with contrast angiography. Prospective, blind study. Neurointensive care unit of a university hospital. Consecutive clinically brain dead patients with flat electroencephalogram. BD was diagnosed according to Italian law. 99mTc-HMPAO SPECT and four-vessel angiography were performed in the same session; the rater of each investigation ignored the results of the other. Blood pressure, Spo2, and Peco2 were monitored throughout the study: any episode of hypoxia or hypotension caused exclusion of the patient from the study. Twenty brain dead patients were enrolled. The cause of BD was head injury in seven cases (35%), subarachnoid hemorrhage in seven (30%), spontaneous hemorrhage in one (10%), brain tumors in two (10%), stroke in two (10%), and thrombosis of the sagittal sinus in one (5%). Both angiography and SPECT confirmed BD in 19 of 20 patients: angiography showed the absence of filling of intracranial arteries, while SPECT showed a picture of “empty skull.” For the remaining patient, angiography showed slight and late filling of left vertebral, basilar, and posterior cerebral arteries, while SPECT showed faint traces of uptake in the posterior fossa on the right side and on the midline. For this patient, the tests were repeated 48 hrs later, and both showed the arrest of intracranial circulation, thus confirming BD. Our results confirm the reliability of SPECT in the diagnosis of BD; because SPECT is noninvasive, it is a good candidate for the “gold standard” of diagnosis.

In this paper we discuss the design and experimental evaluation of a middleware architecture that enables Service Level Agreement (SLA)-driven dynamic configuration, management and optimization of cloud resources and services. This architecture has been designed in order to respond effectively to the Quality of Service (QoS) requirements of the cloud customer applications. Typically, an application can be hosted in an execution platform constructed out of (real and virtual) cloud resources. In this context, the application QoS requirements can be specified in a SLA that binds the application to its hosting platform. Our architecture incorporates a load balancer that distributes the computational load across the platform resources, and monitors the QoS the platform delivers. If this deviates from that specified in the SLA, so as to violate it, the platform is reconfigured dynamically in order to incorporate additional resources from the cloud. In contrast, if the SLA is honored and platform resources result unused, platform reconfiguration occurs to release those unused resources.