The Deformation Analysis of the 3D Alignment Control Network Based on the Multiple Congruence Models

In the construction and maintenance of particle accelerators,all the accelerator elements should be installed in the same coordinate system,only in this way could the devices in the actual world be consistent with the design drawings.However,with the occurrence of the movements of the reinforced concrete cover plates at short notice or building deformations in the long term,the control points upon the engineering structure will be displaced,and the fitness between the subnetwork and the global control network may be irresponsible.Therefore,it is necessary to evaluate the deformations of the 3D alignment control network.Different from the extant investigations,in this paper,to characterize the deformations of the control network,all of the congruent models between the points measured in different epochs have been identified,and the congruence model with the most control points is considered as the primary or fundamental model,the remaining models are recognized as the additional ones.Furthermore,the discrepancies between the primary S-transformation parameters and the additional S-transformation parameters can reflect the relative movements of the additional congruence models.Both the iterative GCT method and the iterative combinatorial theory are proposed to detect multiple congruence models in the control network.Considering the actual work of the alignment,it is essential to identify the competitive models in the monitoring network,which can provide us a hint that,even the fitness between the subnetwork and the global control network is good,there are still deformations which may be ignored.The numerical experiments show that the suggested approaches can describe the deformation of the 3D alignment control network roundly.

Normal-Weight Abdominal Obesity: A Risk Factor for Hypertension and Cardiometabolic Dysregulation

Objective:This study aimed to examine the associations of waist circumference with hypertension and cardiometabolic dysregulation among normal-weight adults.Methods:This cross-sectional study included 8795 normal-weight participants aged 20 to 79 years from the 2009–2018 US National Health and Nutrition Examination Survey.The demographic characteristics and cardiometabolic risk factors across waist circumference quartiles were summarized.We used adjusted multivariate logistic regression models,subgroup analysis,and restricted cubic spline to analyze the association between waist circumference and the prevalence of hypertension.Thereafter,we used the random forest supervised machine learning method,together with least absolute shrinkage and selection operator regression,to select hypertension-related features and created a predictive model based on regression analysis to identify hypertension in normal-weight individuals.Results:Waist circumference was positively correlated with hypertension in the non-adjusted,minimally adjusted,and fully adjusted models,with odds ratios(95%confidence interval)of 2.28(2.14–2.44),1.27(1.12–1.44),and 1.27(1.12–1.44),respectively.In the fully adjusted model,participants in the highest waist circumference quartile had a higher risk of hypertension relative to those in the lowest quartile,with an odds ratio(95%confidence interval)of 3.87(1.59–10.34).Sensitivity analysis demonstrated the robustness of the association.In the testing set,the predictive model exhibited good performance,with an area under the curve of 0.803,sensitivity of 0.72,specificity of 0.76,and negative predictive value of 0.84.Conclusions:Measuring waist circumference may improve the evaluation of the risk of hypertension and help to manage cardiometabolic risk in normal-weight individuals.