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With an increasing number of cloud service providers (CSPs), research works on multi-cloud environments to provide solutions to avoid vendor lock-in and deal with the single-point failure problem have expanded considerably. However, a few schemes focus on the conditional privacy protection authentication of vehicular networks under a multi-cloud environment. In this regard, we propose a robust and extensible authentication scheme for vehicular networks to fulfil the evergrowing diversified service demands from users. According to our solution, the vehicles need to register with the trusted authority (TA) only once to achieve a fast and efficient authentication with CSPs. Additionally, as long as the new CSP is successfully registered in TA, it can participate in vehicular service. A cloud broker, which is managed by the TA, is responsible for connecting all the cloud services; consequently, the complexity involved in the selection of CSPs is hidden from the users' view. A detailed security analysis establishes that our scheme can fulfil conditional privacy protection and achieve the security objectives of vehicular networks. Our scheme is based on elliptic curve cryptography and does not employ the complex bilinear pairing operation. An evaluation of performance of the proposed scheme indicates that it is suitable for applications involving vehicular networks.

Network meta-analysis expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, synthesizing both direct and indirect information and thus strengthening inference. Since most of trials only compare two treatments, a typical data set in a network meta-analysis managed as a trial-by-treatment matrix is extremely sparse, like an incomplete block structure with significant missing data. Zhang et al. proposed an arm-based method accounting for correlations among different treatments within the same trial and assuming that absent arms are missing at random. However, in randomized controlled trials, nonignorable missingness or missingness not at random may occur due to deliberate choices of treatments at the design stage. In addition, those undertaking a network metaanalysis may selectively choose treatments to include in the analysis, which may also lead to missingness not at random. In this paper, we extend our previous work to incorporate missingness not at random using selection models. The proposed method is then applied to two network meta-analyses and evaluated through extensive simulation studies. We also provide comprehensive comparisons of a commonly used contrast-based method and the arm-based method via simulations in a technical appendix under missing completely at random and missing at random.

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Background Fever of unknown origin (FUO) is a group of diseases with heterogeneous complex causes that are misdiagnosed or have delayed diagnoses. Previous studies have focused mainly on the statistical analysis and research of the cases. The treatments are very different for the different categories of FUO. Therefore, how to intelligently diagnose FUO into one category is worth studying. Objective We aimed to fuse all of the medical data together to automatically predict the categories of the causes of FUO among patients using a machine learning method, which could help doctors diagnose FUO more accurately. Methods In this paper, we innovatively and manually built the FUO intelligent diagnosis (FID) model to help clinicians predict the category of the cause and improve the manual diagnostic precision. First, we classified FUO cases into four categories (infections, immune diseases, tumors, and others) according to the large numbers of different causes and treatment methods. Then, we cleaned the basic information data and clinical laboratory results and structured the electronic medical record (EMR) data using the bidirectional encoder representations from transformers (BERT) model. Next, we extracted the features based on the structured sample data and trained the FID model using LightGBM. Results Experiments were based on data from 2299 desensitized cases from Peking Union Medical College Hospital. From the extensive experiments, the precision of the FID model was 81.68% for top 1 classification diagnosis and 96.17% for top 2 classification diagnosis, which were superior to the precision of the comparative method. Conclusions The FID model showed excellent performance in FUO diagnosis and thus would be a potentially useful tool for clinicians to enhance the precision of FUO diagnosis and reduce the rate of misdiagnosis.