Mission Analysis Program for Solar Electric Propulsion (MAPSEP). Volume 1: Analytical manual

The mission analysis program for solar electric propulsion (MAPSEP) is comprised of the basic modes: TOPSEP (trajectory generation), GODSEP (linear error analysis), and SIMSEP (simulation). The program is designed to analyze any low thrust mission with respect to trajectory performance, guidance and navigation, and to provide system related requirements for the purpose of vehicle design. The MAPSEP organization is described along with all models and algorithms. Topics discussed include: trajectory and error covariance propagation methods, orbit determination processes, thrust modeling, and trajectory correction (guidance) schemes

Low thrust orbit determination program

Logical flow and guidelines are provided for the construction of a low thrust orbit determination computer program. The program, tentatively called FRACAS (filter response analysis for continuously accelerating spacecraft), is capable of generating a reference low thrust trajectory, performing a linear covariance analysis of guidance and navigation processes, and analyzing trajectory nonlinearities in Monte Carlo fashion. The choice of trajectory, guidance and navigation models has been made after extensive literature surveys and investigation of previous software. A key part of program design relied upon experience gained in developing and using Martin Marietta Aerospace programs: TOPSEP (Targeting/Optimization for Solar Electric Propulsion), GODSEP (Guidance and Orbit Determination for SEP) and SIMSEP (Simulation of SEP)

Bataillean Ecology: An Introduction to the Theory of Sustainable Excess

A new ecology and a new environmentalism have arisen from the study of the thought of Georges Bataille. This paper both introduces this scholarship and advocates for environmentalist action based on it. Through both intellectual biography and theoretical inquiry, this paper distils a principle for guiding environmentalism from small-scale communities to the limits of the biosphere, a principle of sustainable excess, a kind of perpetual decadence

Mission Analysis Program for Solar Electric Propulsion (MAPSEP). Volume 3: Program manual

The internal structure of MAPSEP is described. Topics discussed include: macrologic, variable definition, subroutines, and logical flow. Information is given to facilitate modifications to the models and algorithms of MAPSEP

Mission Analysis Program for Solar Electric Propulsion (MAPSEP). Volume 2: User's manual

A user's manual which describes input/output routines and recommended operating procedures relating to MAPSEP is presented. Samples runs are included

The In Vivo Wear Resistance of 12 Composite Resins

: The in vivo wear resistance of 12 composite resins were compared with an amalgam control using the Latin Square experimental design. Sixteen edentulous patients wearing specially designed complete dentures formed the experimental population. Materials and Methods : The Michigan Computer Graphics Measurement System was used to digitize the surface of the control and composite resin samples before and after 3-month test periods to obtain wear data. The 12 composite resins selected for this investigation based on their published composite classification types were seven fine particle composites, three blends, and two microfilled composite resins. The Latin Square experimental design was found to be valid with the factor of material being statistically different at the 5% level of significance. Wear was computed as volume loss (mm 3 /mm 2 ), and all of the composites studied had more wear than the amalgam control ( P = .001). Results : After 3 months, the mean (error) of wear of the amalgam was 0.028 (0.006). Means (error) of wear for the 12 composites were ranked from most to least wear by mean wear volume loss. Conclusions : The absence of any relationship between mean wear volume loss and the volume percentage filler was confirmed by the correlation coefficient r = -0.158.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72960/1/j.1532-849X.1992.tb00419.x.pd

The Periotest Method: Implant-Supported Framework Precision of Fit Evaluation

: In this study, the Periotest instrument was used to measure the precision of fit between cast high noble-metal frameworks and the supporting implants in a patient-simulation model. Three framework conditions and three implant-location variables were used to evaluate the rigidity of the assembly as measured by the Periotest method. The framework variables were (1) one-piece castings (OPC); (2) sectioned-soldered inaccurate castings (SSIC); and (3) sectioned-soldered accurate castings (SSAC). The implant-location variables were right anterior (RA), center (C), and left anterior (LA). Materials and Methods : The patient simulation model used consisted of three self-tapping BrÅnemark implants in a reasonable arch curvature in bovine bone. Three working casts were fabricated from the patient-simulation model using polyvinyl siloxane and tapered impression copings. From the working casts, three sets of three frameworks were fabricated as OPCs, SSICs, and SSACs using type 3 high noble alloy. The SSICs were fabricated with a quantitative misfit of 101.6 Μm at the facial surface, between the abutment-to-gold cylinder interface at the C implant location. Periotest value (PTV) measurements were made at the midfacial surface of the frameworks directly above each abutment-to-gold cylinder interface. Three measurements were made for each test condition. The data were analyzed to compare framework condition(s) and implant location(s) using ANOVA and Fisher's Protected Least Significant Difference Comparison Test. Results : The ANOVA showed that significant differences exist between the mean PTV data for framework condition and for implant location (p < .01). Significant differences were shown between the mean PTV data for the SSAC assemblies and the OPC and SSIC assemblies. The SSICs displayed a more positive (+) mean PTV than the OPCs. The OPC assemblies had a more positive mean PTV than the SSAC assemblies. The mean PTV data for the SSAC assemblies had a significantly different PTV (p < .01) than the other two framework condition assemblies. The OPC and the SSIC assemblies had PTVs that were not significantly different. The C implant location was significantly different from the RA and the LA implant locations (p < .01). The RA and the LA implant locations were not significantly different from each other. The C implant location always demonstrated the most positive mean PTV regardless of the framework condition being tested. Conclusions : The Periotest instrument quantified differences in the precision of fit between three framework conditions. The SSAC assemblies were significantly more rigid than the OPC and SSIC assemblies. The OPC and SSIC assemblies' mean PTVs were not significantly different. The mean PTVs for the C implant location and the RA and LA implant locations were significantly different (p < .01). The mean PTVs of the RA and LA implant locations were not significantly different. The implant-location PTVs followed the same rank order for all three framework conditions. The procedures used to fabricate a more precise fit between the framework and the supporting implants is influenced by the skill of the clinician and technician.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75096/1/j.1532-849X.1996.tb00298.x.pd

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients’ predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types