Automated deployment and scaling of automotive safety services in 5G-Transformer

There is a growing interest of verticals (in this case, the automotive industry) to reap the benefits of 5G networks. At the same time, there is a clear trend of the telco industry to under-stand their needs. These are also some of the main goals of the EU 5G-TRANSFORMER (5GT) project. This demo focuses on the need of verticals to dynamically deploy services at the edge and to adapt the vertical service to network operational conditions. In particular, it is presented the extended virtual sensing (EVS) service, which deployed on demand at the distributed computing infrastructure (i.e. in the network), complements sensing and processing functions running in the car to detect the risk of collisions and take appropriate action, even if there is no direct communication between cars. The stringent latency constraints imposed by the EVS network service leave a limited processing budget at the vertical service level. Since such processing time is correlated with the CPU consumption of a virtual machine running a VNF of the EVS network service, in this demo we also show how the vertical service exploits the automated scaling capabilities offered by the 5GT service orchestrator to deploy a new instance of the EVS VNF upon reception of a CPU consumption alert generated by the available 5GT monitoring platform.Grant numbers : grant TEC2017-88373-R (5G-REFINE) and Generalitat de Catalunya grant 2017 SGR 1195.© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%.JRC.C.5-Air and Climat

Holomorphic spheres and four-dimensional symplectic pairs

We classify four-dimensional manifolds endowed with symplectic pairs admitting embedded symplectic spheres with nonnegative self-intersection, following the strategy of McDuff’s classification of rational and ruled symplectic four-manifolds

Evaluation of receptor and chemical transport models for PM10 source apportionment

In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models.JRC.C.5-Air and Climat

Development, validation, and prognostic evaluation of a risk score for long-term liver-related outcomes in the general population: a multicohort study

Liver cirrhosis is a major cause of death worldwide. Cirrhosis develops after a long asymptomatic period of fibrosis progression, with the diagnosis frequently occurring late, when major complications or cancer develop. Few reliable tools exist for timely identification of individuals at risk of cirrhosis to allow for early intervention. We aimed to develop a novel score to identify individuals at risk for future liver-related outcomes. We derived the LiverRisk score from an international prospective cohort of individuals from six countries without known liver disease from the general population, who underwent liver fibrosis assessment by transient elastography. The score included age, sex, and six standard laboratory variables. We created four groups: minimal risk, low risk, medium risk, and high risk according to selected cutoff values of the LiverRisk score (6, 10, and 15). The model's discriminatory accuracy and calibration were externally validated in two prospective cohorts from the general population. Moreover, we ascertained the prognostic value of the score in the prediction of liver-related outcomes in participants without known liver disease with median follow-up of 12 years (UK Biobank cohort). We included 14 726 participants: 6357 (43·2%) in the derivation cohort, 4370 (29·7%) in the first external validation cohort, and 3999 (27·2%) in the second external validation cohort. The score accurately predicted liver stiffness in the development and external validation cohorts, and was superior to conventional serum biomarkers of fibrosis, as measured by area under the receiver-operating characteristics curve (AUC; 0·83 [95% CI [0·78-0·89]) versus the fibrosis-4 index (FIB-4; 0·68 [0·61-0·75] at 10 kPa). The score was effective in identifying individuals at risk of liver-related mortality, liver-related hospitalisation, and liver cancer, thereby allowing stratification to different risk groups for liver-related outcomes. The hazard ratio for liver-related mortality in the high-risk group was 471 (95% CI 347-641) compared with the minimal risk group, and the overall AUC of the score in predicting 10-year liver-related mortality was 0·90 (0·88-0·91) versus 0.84 (0·82-0·86) for FIB-4. The LiverRisk score, based on simple parameters, predicted liver fibrosis and future development of liver-related outcomes in the general population. The score might allow for stratification of individuals according to liver risk and thus guide preventive care. None. [Abstract copyright: Copyright © 2023 Elsevier Ltd. All rights reserved.