A Secure and Fair Double Auction Framework for Cloud Virtual Machines

Double auction is one of the most promising solutions to allocate virtual machine (VM) resources in two-sided cloud markets, which can increase the utilization rate of VM resources. However, most cloud auction mechanisms simply assume that the auctioneer is fully trusted while ignoring bid-privacy preservation and trade fairness in the process of auction. Previous studies have indicated that some cryptographic tools can be used to resolve the above issues, but the poor performance makes those techniques difficult to practice. In this paper, we propose a Secure and Fair Double AuCtion framework (named SF-DAC) for cloud virtual machines, which performs cloud auction efficiently while guaranteeing both bid privacy and trade fairness. We design secure 3-party computation protocols that support secure comparison and secure sorting, which enable us to construct a secure double auction scheme that outperforms all prior comparable solutions. Furthermore, we propose a fair trading mechanism based on smart contracts to prevent the bidders from halting the auction without financial penalties. The extensive experiments demonstrate that SF-DAC achieves an order of magnitude reduction in computation and communication costs than prior arts

Heterogeneity of work alienation and its relationship with job embeddedness among Chinese nurses: a cross-sectional study using latent profile analysis

Abstract Objective To identify the distinct profiles of work alienation among Chinese nurses, examine the demographic factors associated with profile memberships, and then explore the relationship between latent categories of work alienation and job embeddedness. Methods A cross-sectional survey of 523 nurses was conducted from July to August 2023. Latent profile analysis (LPA) was performed to identify distinct profiles of nurses based on three aspects: powerlessness, helplessness, and meaningfulness. A multinomial logistic regression analysis was conducted to explore the predictors of profile membership. Hierarchical regression analysis was carried out to examine the association between profile memberships and job embeddedness. Results Three subgroups of work alienation of nurses were identified: 23.1%, 57.8%, and 19.1% in the low work alienation group (profile 1), the moderate work alienation group (profile 3), and the high work alienation group (profile 2), respectively. Nurses with college degrees were more likely to be grouped into moderate work alienation. Nurses who did not work night shifts were more likely to have low or moderate levels of work alienation. Nurses earning 2,000–3,000 and 3,001–5,000 yuan per month were likely to be in the low work alienation group. The different categories of work alienation significantly predicted job embeddedness among nurses (ΔR 2 = 0.103, p < 0.001). Conclusions Work alienation has an important impact on clinical nurses’ job embeddedness. Nursing managers should pay attention to the differences in individual work alienation status and adopt reasonable management strategies to improve the level of job embeddedness, ensure the quality of care, and reduce nursing turnover

The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis

Abstract Background The purpose of this paper was to systematically evaluate the application value of artificial intelligence in predicting mortality among COVID-19 patients. Methods The PubMed, Embase, Web of Science, CNKI, Wanfang, China Biomedical Literature, and VIP databases were systematically searched from inception to October 2022 to identify studies that evaluated the predictive effects of artificial intelligence on mortality among COVID-19 patients. The retrieved literature was screened according to the inclusion and exclusion criteria. The quality of the included studies was assessed using the QUADAS-2 tools. Statistical analysis of the included studies was performed using Review Manager 5.3, Stata 16.0, and Meta-DiSc 1.4 statistical software. This meta-analysis was registered in PROSPERO (CRD42022315158). Findings Of 2193 studies, 23 studies involving a total of 25 AI models met the inclusion criteria. Among them, 18 studies explicitly mentioned training and test sets, and 5 studies did not explicitly mention grouping. In the training set, the pooled sensitivity was 0.93 [0.87, 0.96], the pooled specificity was 0.94 [0.87, 0.97], and the area under the ROC curve was 0.98 [0.96, 0.99]. In the validation set, the pooled sensitivity was 0.84 [0.78, 0.88], the pooled specificity was 0.89 [0.85, 0.92], and the area under the ROC curve was 0.93 [1.00, 0.00]. In the subgroup analysis, the areas under the summary receiver operating characteristic (SROC) curves of the artificial intelligence models KNN, SVM, ANN, RF and XGBoost were 0.98, 0.98, 0.94, 0.92, and 0.91, respectively. The Deeks funnel plot indicated that there was no significant publication bias in this study (P > 0.05). Interpretation Artificial intelligence models have high accuracy in predicting mortality among COVID-19 patients and have high prognostic value. Among them, the KNN, SVM, ANN, RF, XGBoost, and other models have the highest levels of accuracy

Additional file 1 of Heterogeneity of work alienation and its relationship with job embeddedness among Chinese nurses: a cross-sectional study using latent profile analysis

Additional file 1. STROBE Statement—Checklist of items that should be included in reports of cross-sectional studies