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Reliability Analysis of Unmanned Aerial Vehicles Flight Control System Based on Reliability Analysis Technologies
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作者 CAI Shuang JIANG Guangjun 《Journal of Donghua University(English Edition)》 EI CAS 2018年第3期264-269,共6页
The unmanned aerial vehicles( UAV) has been becoming more and more important in the aviation industry.Despite the superior performance and advanced technology,major accident of UAV happens frequently due to the impact... The unmanned aerial vehicles( UAV) has been becoming more and more important in the aviation industry.Despite the superior performance and advanced technology,major accident of UAV happens frequently due to the impact of their systems,long distance of remote control and skill of manipulator technology.According to the application of engineering application,failure mode effects and criticality analysis( FMECA),failure reporting analysis and corrective action comprehensive analysis systems( FRACAS)and fault tree analysis( FTA)( 3 F) were combined.And also a set of user-friendly,more time,more efficient and accurate reliability analysis system were explored. 展开更多
关键词 failure mode effect and criticality analysis(FMECA) failure reporting analysis and corrective action comprehensive analysis systems(FRACAS) fault tree analysis(FTA) 3F integrated system analysis
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Spatial-temporal heterogeneity and determinants of HIV prevalence in the Mano River Union countries
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作者 Idrissa Laybohr Kamara Liang Wang +7 位作者 Yaxin Guo Shuting Huo Yuanyuan Guo Chengdong Xu Yilan Liao William J.Liu Wei Ma George F.Gao 《Infectious Diseases of Poverty》 SCIE 2022年第6期96-96,共1页
Background:Utilizing population-based survey data in epidemiological research with a spatial perspective can integrate valuable context into the dynamics of HIV prevalence in West Africa.However,the situation in the M... Background:Utilizing population-based survey data in epidemiological research with a spatial perspective can integrate valuable context into the dynamics of HIV prevalence in West Africa.However,the situation in the Mano River Union(MRU)countries is largely unknown.This research aims to perform an ecological study to determine the HIV prevalence patterns in MRU.Methods:We analyzed Demographic and Health Survey(DHS)and AIDS Indicator Survey(AIS)data on HIV prevalence in MRU from 2005 to 2020.We examined the country-specifc,regional-specifc and sex-specifc ratios of respondents to profle the spatial–temporal heterogeneity of HIV prevalence and determine HIV hot spots.We employed Geodetector to measure the spatial stratifed heterogeneity(SSH)of HIV prevalence for adult women and men.We assessed the comprehensive correct knowledge(CCK)about HIV/AIDS and HIV testing uptake by employing the Least Absolute Shrinkage and Selection Operator(LASSO)regression to predict which combinations of CCKs can scale up the ratio of HIV testing uptake with sex-specifc needs.Results:In our analysis,we leveraged data for 158,408 respondents from 11 surveys in the MRU.From 2005–2015,Cote d’Ivoire was the hot spot for HIV prevalence with a Gi_Bin score of 3,Z-Score 8.0–10.1 and P<0.001.From 2016 to 2020,Guinea and Sierra Leone were hot spots for HIV prevalence with a Gi_Bin score of 2,Z-Score of 3.17 and P<0.01.The SSH confrmed the signifcant diferences in HIV prevalence at the national level strata,with a higher level for Cote d’Ivoire compared to other countries in both sexes with q-values of 0.61 and 0.40,respectively.Our LASSO model predicted diferent combinations of CCKs with sex-specifc needs to improve HIV testing uptake.Conclusions:The spatial distribution of HIV prevalence in the MRU is skewed and the CCK about HIV/AIDS and HIV testing uptake are far below the threshold target set by UNAIDS for ending the epidemic in the sub-region.Geodetector detected statistically signifcant SSH within and between countries in the MRU.Our LASSO model predicted that diferent emphases should be implemented when popularizing the CCK about HIV/AIDS for adult women and men. 展开更多
关键词 Spatial distribution of HIV prevalence Geodetector Spatial stratifed heterogeneity Least Absolute Shrinkage and Selection Operator comprehensive correct knowledge Machine learning Africa
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