Vehicle-to-everything (V2X) communication and services have been garnering significant interest from different stakeholders as part of future intelligent transportation systems (ITSs). This is due to the many benefits they offer. However, many of these services have stringent performance requirements, particularly in terms of the delay/latency. Multi-access/mobile edge computing (MEC) has been proposed as a potential solution for such services by bringing them closer to vehicles. Yet, this introduces a new set of challenges such as where to place these V2X services, especially given the limit computation resources available at edge nodes. To that end, this work formulates the problem of optimal V2X service placement (OVSP) in a hybrid core/edge environment as a binary integer linear programming problem. To the best of our knowledge, no previous work considered the V2X service placement problem while taking into consideration the computational resource availability at the nodes. Moreover, a low-complexity greedy-based heuristic algorithm named "Greedy V2X Service Placement Algorithm" (G-VSPA) was developed to solve this problem. Simulation results show that the OVSP model successfully guarantees and maintains the QoS requirements of all the different V2X services. Additionally, it is observed that the proposed G-VSPA algorithm achieves close to optimal performance while having lower complexity. 1) Communication Modes: To cover all possible on-road interactions, the 3GPP project proposed four different communication modes. This includes vehicle-to-network (V2N), vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and vehicle-to-pedestrian (V2P) communication [5] as shown in Fig. 2. Depending on the service or application, a communication mode can be chosen. A brief overview of each of these communication modes is provided below: a-V2N Communication: V2N communication refers to the communication between a vehicle and a V2X application server. This is typically done using a cellular network such as an LTE network [9], [10]. Through this connection, different services such as infotainment, traffic optimization, navigation, and safety can be offered [11], [12].

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With the growing demand for data connectivity, network service providers are faced with the task of reducing their capital and operational expenses while simultaneously improving network performance and addressing the increased connectivity demand. Although Network Function Virtualization (NFV) has been identified as a solution, several challenges must be addressed to ensure its feasibility. In this paper, we address the Virtual Network Function (VNF) placement problem by developing a machine learning decision tree model that learns from the effective placement of the various VNF instances forming a Service Function Chain (SFC). The model takes several performance-related features from the network as an input and selects the placement of the various VNF instances on network servers with the objective of minimizing the delay between dependent VNF instances. The benefits of using machine learning are realized by moving away from a complex mathematical modelling of the system and towards a data-based understanding of the system. Using the Evolved Packet Core (EPC) as a use case, we evaluate our model on different data center networks and compare it to the BACON algorithm in terms of the delay between interconnected components and the total delay across the SFC. Furthermore, a time complexity analysis is performed to show the effectiveness of the model in NFV applications.

BackgroundWe aimed to compare the clinical judgments of a reference panel of emergency medicine academic physicians against evidence-based likelihood ratios (LRs) regarding the diagnostic value of selected clinical and paraclinical findings in the context of a script concordance test (SCT).FindingsA SCT with six scenarios and five questions per scenario was developed. Subsequently, 15 emergency medicine attending physicians (reference panel) took the test and their judgments regarding the diagnostic value of those findings for given diseases were recorded. The LRs of the same findings for the same diseases were extracted from a series of published systematic reviews. Then, the reference panel judgments were compared to evidence-based LRs. To investigate the test-retest reliability, five participants took the test one month later, and the correlation of their first and second judgments were quantified using Spearman rank-order coefficient.In 22 out of 30 (73.3%) findings, the expert judgments were significantly different from the LRs. The differences included overestimation (30%), underestimation (30%), and judging the diagnostic value in an opposite direction (13.3%). Moreover, the score of a hypothetical test-taker was calculated to be 21.73 out of 30 if his/her answers were based on evidence-based LRs.The test showed an acceptable test-retest reliability coefficient (Spearman coefficient: 0.83).ConclusionsAlthough SCT is an interesting test to evaluate clinical decision-making in emergency medicine, our results raise concerns regarding whether the judgments of an expert panel are sufficiently valid as the reference standard for this test.