A simple optimal randomized algorithm for sorting on the PDM

The Parallel Disks Model (PDM) has been proposed to alleviate the I/O bottleneck that arises in the processing of massive data sets. Sorting has been extensively studied on the PDM model due to the fundamental nature of the problem. Several randomized algorithms are known for sorting. Most of the prior algorithms suffer from undue complications in memory layouts, implementation, or lack of tight analysis. In this paper we present a simple randomized algorithm that sorts in optimal time with high probablity and has all the desirable features for practical implementation

Computational Physics on Graphics Processing Units

The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various different algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics, and on quantum simulations for electronic structure calculations using the density functional theory, wave function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012, Helsinki, Finland, June 10-13, 201

Wholesale pricing in a small open economy

This paper addresses the empirical analysis of wholesale profit margins using data of the Dutch wholesale sector, 1986. At the heart of the analysis is the typical nature of wholesale production: wholesalers do not produce a tangible product, but offer a service capacity. This has an immediate impact on the identification, interprelation and measurement of determinants of profit variations. A model is set up to explain variations in wholesale profit margins, which is inspired by two widely applied approaches to industry pricing: the behavioural mark-up model and the marginalist price-cost model

Nanoparticle display of prefusion coronavirus spike elicits S1-focused cross-reactive antibody response against diverse coronavirus subgenera

Multivalent antigen display is a fast-growing area of interest toward broadly protective vaccines. Current nanoparticle-based vaccine candidates demonstrate the ability to confer antibody-mediated immunity against divergent strains of notably mutable viruses. In coronaviruses, this work is predominantly aimed at targeting conserved epitopes of the receptor binding domain. However, targeting conserved non-RBD epitopes could limit the potential for antigenic escape. To explore new potential targets, we engineered protein nanoparticles displaying coronavirus prefusion-stabilized spike (CoV_S-2P) trimers derived from MERS-CoV, SARS-CoV-1, SARS-CoV-2, hCoV-HKU1, and hCoV-OC43 and assessed their immunogenicity in female mice. Monotypic SARS-1 nanoparticles elicit cross-neutralizing antibodies against MERS-CoV and protect against MERS-CoV challenge. MERS and SARS nanoparticles elicit S1-focused antibodies, revealing a conserved site on the S N-terminal domain. Moreover, mosaic nanoparticles co-displaying distinct CoV_S-2P trimers elicit antibody responses to distant cross-group antigens and protect male and female mice against MERS-CoV challenge. Our findings will inform further efforts toward the development of pan-coronavirus vaccines

SARS-CoV-2 mRNA vaccine design enabled by prototype pathogen preparedness

A vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is needed to control the coronavirus disease 2019 (COVID-19) global pandemic. Structural studies have led to the development of mutations that stabilize Betacoronavirus spike proteins in the prefusion state, improving their expression and increasing immunogenicity1. This principle has been applied to design mRNA-1273, an mRNA vaccine that encodes a SARS-CoV-2 spike protein that is stabilized in the prefusion conformation. Here we show that mRNA-1273 induces potent neutralizing antibody responses to both wild-type (D614) and D614G mutant2 SARS-CoV-2 as well as CD8+ T cell responses, and protects against SARS-CoV-2 infection in the lungs and noses of mice without evidence of immunopathology. mRNA-1273 is currently in a phase III trial to evaluate its efficacy

Failure Analysis of Adhesively Bonded Structures: From Coupon Level Data to Structural Level Predictions and Verification

This paper presents a predictive methodology and verification through experiment for the analysis and failure of adhesively bonded, hat stiffened structures using coupon level input data. The hats were made of steel and carbon fiber reinforced polymer composite, respectively, and bonded to steel adherends. A critical strain energy release rate criterion was used to predict the failure loads of the structure. To account for significant geometrical changes observed in the structural level test, an adaptive virtual crack closure technique based on an updated local coordinate system at the crack tip was developed to calculate the strain energy release rates. Input data for critical strain energy release rates as a function of mode mixity was obtained by carrying out coupon level mixed mode fracture tests using the Fernlund–Spelt (FS) test fixture. The predicted loads at failure, along with strains at different locations, were compared with those measured from the structural level tests. The predictions were found to agree well with measurements for multiple replicates of adhesively bonded hat-stiffened structures made with steel hat/adhesive/steel and composite hat/adhesive/steel, thus validating the proposed methodology for failure prediction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42764/1/10704_2005_Article_0646.pd