Development of a superconductor magnetic suspension and balance prototype facility for studying the feasibility of applying this technique to large scale aerodynamic testing

The basic research and development work towards proving the feasibility of operating an all-superconductor magnetic suspension and balance device for aerodynamic testing is presented. The feasibility of applying a quasi-six-degree-of freedom free support technique to dynamic stability research was studied along with the design concepts and parameters for applying magnetic suspension techniques to large-scale aerodynamic facilities. A prototype aerodynamic test facility was implemented. Relevant aspects of the development of the prototype facility are described in three sections: (1) design characteristics; (2) operational characteristics; and (3) scaling to larger facilities

On Quantum Algorithms

Quantum computers use the quantum interference of different computational paths to enhance correct outcomes and suppress erroneous outcomes of computations. In effect, they follow the same logical paradigm as (multi-particle) interferometers. We show how most known quantum algorithms, including quantum algorithms for factorising and counting, may be cast in this manner. Quantum searching is described as inducing a desired relative phase between two eigenvectors to yield constructive interference on the sought elements and destructive interference on the remaining terms.Comment: 15 pages, 8 figure

Cargo/Logistics Airlift System Study (CLASS), Volume 2

Air containerization is discussed in terms of lower freight rates, size and pallet limitations, refrigeration, backhaul of empties, and ownership. It is concluded that there is a need for an advance air cargo system as indicated by the industry/transportation case studies, and a stimulation of the air cargo would result in freight rate reductions

Cargo/Logistics Airlift System Study (CLASS), Volume 1

Current and advanced air cargo systems are evaluated using industrial and consumer statistics. Market and commodity characteristics that influence the use of the air mode are discussed along with a comparison of air and surface mode on typical routes. Results of on-site surveys of cargo processing facilities at airports are presented, and institutional controls and influences on air cargo operations are considered

Cargo/Logistics Airlift System Study (CLASS), Executive Summary

The current air cargo system is analyzed along with advanced air cargo systems studies. A forecast of advanced air cargo system demand is presented with cost estimates. It is concluded that there is a need for a dedicated advance air cargo system, and with application of advanced technology, reductions of 45% in air freight rates may be achieved

A general low frequency acoustic radiation capability for NASTRAN

A new capability called NASHUA is described for calculating the radiated acoustic sound pressure field exterior to a harmonically-excited arbitrary submerged 3-D elastic structure. The surface fluid pressures and velocities are first calculated by coupling a NASTRAN finite element model of the structure with a discretized form of the Helmholtz surface integral equation for the exterior fluid. After the fluid impedance is calculated, most of the required matrix operations are performed using the general matrix manipulation package (DMAP) available in NASTRAN. Far field radiated pressures are then calculated from the surface solution using the Helmholtz exterior integral equation. Other output quantities include the maximum sound pressure levels in each of the three coordinate planes, the rms and average surface pressures and normal velocities, the total radiated power and the radiation efficiency. The overall approach is illustrated and validated using known analytic solutions for submerged spherical shells subjected to both uniform and nonuniform applied loads

Struggling to a monumental triumph : Re-assessing the final stages of the smallpox eradication program in India, 1960-1980

The global smallpox program is generally presented as the brainchild of a handful of actors from the WHO headquarters in Geneva and at the agency's regional offices. This article attempts to present a more complex description of the drive to eradicate smallpox. Based on the example of India, a major focus of the campaign, it is argued that historians and public health officials should recognize the varying roles played by a much wider range of participants. Highlighting the significance of both Indian and international field officials, the author shows how bureaucrats and politicians at different levels of administration and society managed to strengthen—yet sometimes weaken—important program components. Centrally dictated strategies developed at WHO offices in Geneva and New Delhi, often in association with Indian federal authorities, were reinterpreted by many actors and sometimes changed beyond recognition

Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP

[EN] Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.FEDER; INRA; Universidad Nacional de Lomas de Zamora; European Unions' Horizon 2020 Research & Innovation Programme, Grant/Award Number: No772787Aguilar, I.; Fernandez, EN.; Blasco Mateu, A.; Ravagnolo, O.; Legarra, A. (2020). Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP. Journal of Animal Breeding and Genetics. 137(4):356-364. https://doi.org/10.1111/jbg.12470S3563641374Bijma, P. (2012). Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics, 129(5), 345-358. doi:10.1111/j.1439-0388.2012.00991.xChristensen, O. F., Madsen, P., Nielsen, B., Ostersen, T., & Su, G. (2012). Single-step methods for genomic evaluation in pigs. Animal, 6(10), 1565-1571. doi:10.1017/s1751731112000742Colleau, J.-J., Palhière, I., Rodríguez-Ramilo, S. T., & Legarra, A. (2017). A fast indirect method to compute functions of genomic relationships concerning genotyped and ungenotyped individuals, for diversity management. Genetics Selection Evolution, 49(1). doi:10.1186/s12711-017-0363-9Edel, C., Pimentel, E. C. G., Erbe, M., Emmerling, R., & Götz, K.-U. (2019). Short communication: Calculating analytical reliabilities for single-step predictions. Journal of Dairy Science, 102(4), 3259-3265. doi:10.3168/jds.2018-15707Fernández, E. N., Sánchez, J. P., Martínez, R., Legarra, A., & Baselga, M. (2017). Role of inbreeding depression, non-inbred dominance deviations and random year-season effect in genetic trends for prolificacy in closed rabbit lines. Journal of Animal Breeding and Genetics, 134(6), 441-452. doi:10.1111/jbg.12284Golden, B. L., Brinks, J. S., & Bourdon, R. M. (1991). A performance programmed method for computing inbreeding coefficients from large data sets for use in mixed-model analyses. Journal of Animal Science, 69(9), 3564-3573. doi:10.2527/1991.6993564xGroeneveld E. Kovac M. &Wang T.(1990).PEST a general purpose BLUP package for multivariate prediction and estimation. Proceedings of the 4th World Congress on Genetics Applied to Livestock Production Edinburgh 13 488–491.Henderson, C. R. (1975). Best Linear Unbiased Estimation and Prediction under a Selection Model. Biometrics, 31(2), 423. doi:10.2307/2529430Henderson, C. R. (1976). A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding Values. Biometrics, 32(1), 69. doi:10.2307/2529339Legarra, A., Aguilar, I., & Colleau, J. J. (2020). Short communication: Methods to compute genomic inbreeding for ungenotyped individuals. Journal of Dairy Science, 103(4), 3363-3367. doi:10.3168/jds.2019-17750Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92(9), 4656-4663. doi:10.3168/jds.2009-2061Legarra A. Lourenco D. A. L. &Vitezica Z. G.(2018).Bases for genomic prediction. Retrieved fromhttp://genoweb.toulouse.inra.fr/~alegarra/Masuda, Y., Aguilar, I., Tsuruta, S., & Misztal, I. (2015). Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements1,2. Journal of Animal Science, 93(10), 4670-4674. doi:10.2527/jas.2015-9395Matilainen, K., Strandén, I., Aamand, G. P., & Mäntysaari, E. A. (2018). Single step genomic evaluation for female fertility in Nordic Red dairy cattle. Journal of Animal Breeding and Genetics, 135(5), 337-348. doi:10.1111/jbg.12353Mehrabani-Yeganeh, H., Gibson, J. P., & Schaeffer, L. R. (2000). Including coefficients of inbreeding in BLUP evaluation and its effect on response to selection. Journal of Animal Breeding and Genetics, 117(3), 145-151. doi:10.1046/j.1439-0388.2000.00241.xMeyer, K. (2007). WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University SCIENCE B, 8(11), 815-821. doi:10.1631/jzus.2007.b0815Misztal, I., & Wiggans, G. R. (1988). Approximation of Prediction Error Variance in Large-Scale Animal Models. Journal of Dairy Science, 71, 27-32. doi:10.1016/s0022-0302(88)79976-2Mrode, R. A., & Thompson, R. (Eds.). (2005). Linear models for the prediction of animal breeding values. doi:10.1079/9780851990002.0000Pryce, J. E., Gonzalez-Recio, O., Nieuwhof, G., Wales, W. J., Coffey, M. P., Hayes, B. J., & Goddard, M. E. (2015). Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows. Journal of Dairy Science, 98(10), 7340-7350. doi:10.3168/jds.2015-9621Sargolzaei, M., Chesnais, J. P., & Schenkel, F. S. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15(1), 478. doi:10.1186/1471-2164-15-478Strandén, I., Matilainen, K., Aamand, G. P., & Mäntysaari, E. A. (2017). Solving efficiently large single-step genomic best linear unbiased prediction models. Journal of Animal Breeding and Genetics, 134(3), 264-274. doi:10.1111/jbg.12257Ten Napel J. Vandenplas J. Lidauer M. Stranden I. Taskinen M. Mäntysaari E. Veerkamp R. F.(2017).MiXBLUP user‐friendly software for large genetic evaluation systems–Manual V2. Retrived from:https://www.mixblup.eu/documents/Manual%20MiXBLUP%202.1_June%202017_V2.pdfTier B. Schneeberger M. Hammond K. &Fuchs W. C.(1991).Determining the accuracy of estimated breeding values in multiple trait animal models. Proceedings of the 9th AAABG Conference 239–242Van Vleck, L. D. (1993). Variance of prediction error with mixed model equations when relationships are ignored. Theoretical and Applied Genetics, 85(5), 545-549. doi:10.1007/bf00220912VanRaden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11), 4414-4423. doi:10.3168/jds.2007-0980Xiang, T., Christensen, O. F., & Legarra, A. (2017). Technical note: Genomic evaluation for crossbred performance in a single-step approach with metafounders1. Journal of Animal Science, 95(4), 1472-1480. doi:10.2527/jas.2016.115