The recent 3GPP NR-V2X standard (Rel. 16) has largely built upon its precursor Cellular-V2X (Rels. 14 & 15) but has introduced new approaches for dealing with application traffic exhibiting aperiodic arrival rates in the sidelink. This is vital as safety services based on ETSI Cooperative Awareness Messages (CAMs) and Decentralised Environmental Notification Messages (DENM) exhibit such characteristics. It is further envisaged that future vehicular services will also exhibit high aperiodicity to support increased autonomy. In this paper we quantitatively evaluate the reasons why the Sensing based Semi-Peristent Scheduling (SB-SPS) mechanism performs poorly when scheduling aperiodic traffic. We then provide the first in-depth evaluation of the NR-V2X Dynamic Grant mechanism in contrast to schemes that parameterise the existing C-V2X SPS algorithm and evaluate the performance of alternative dedicated scheduling mechanisms specifically designed for aperiodicity. This paper highlights th at the level of aperiodicity exhibited by the application model greatly impacts scheduling performance, both for the default SB-SPS and dedicated approaches. As such we conclude that a novel aperiodic scheduling mechanism must be devised, or more promisingly, an approach to enable application traffic to mimic periodic characteristics allowing it to co-exist with the existing scheduling approach.

This paper presents a detailed quantitative evaluation of standardised ETSI & 3GPP Decentralised Congestion Control (DCC) and packet dropping mechanisms for Cellular V2X (C-V2X) and New Radio (NR) V2X. Based on the identified shortcomings, an Access layer scheme, RRI DCC, is then proposed. RRI DCC explicitly accommodates the sidelink scheduling mechanism Sensing Based Semi-Persistent Scheduling (SB-SPS), eliminating incompatibilities between current standards and the scheduling mechanism, to avoid unnecessary and recurring collisions. Three variants are proposed; one is an evolution of the ETSI Reactive DCC mechanism, the second is based on the ETSI Adaptive DCC mechanism and the final aligns with the 3GPP approach based on channel occupancy ratio (CR). All approaches are compared with current ETSI and 3GPP standards and exhibit improved performance. An evaluation of the proposed RRI DCC mechanisms and existing DCC standards, to meet the Quality of Service (QoS) requirements of vehicular cooperative awareness applications is also conducted.

Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our state- of-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics - empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.

Adverse drug reactions (ADRs) caused by drug-drug interactions (DDI) are a major cause of morbidity and mortality worldwide. There is a growing need for computing-supported methods that facilitate the automated signaling of DDI related ADRs (DIARs) that otherwise would remain undiscovered in millions of ADR reports. In this demonstration, we showcase our MeDIAR technology - an end-to-end DIAR signal generation, exploration and validation solution for pharmaceutical regulatory agencies to detect true DIAR signals from a drug surveillance database. MeDIAR's innovations include an efficient rule-driven learning algorithm for deriving DIAR signals from ADR reports, an innovative scoring methodology based on the proposed contextual association cluster model to rank the generated signals by their importance. Further, these ranked signals are augmented with meta information such as their significance level and their severity, along with links to their supporting ADR reports. Lastly, MeDIAR features an interactive visual analytics interface to support drug safety evaluators in reviewing and discovering unknown severe DIARs.