Given the ever-diversifying arena of multimedia instruction and the ability of students to be fully conversant with technology, this project demonstrates that students are ideal participants and creators of multimedia resources. It is hoped that such an approach will help to further develop the skill base of students, but will also provide an avenue of developing packages that are student user friendly, and that are focused towards particular curricula requirements.
BACKGROUNDWe consider the problem of quantifying the human lifespan using a statistical approach that probabilistically forecasts the maximum reported age at death (MRAD) through 2100. OBJECTIVEWe seek to quantify the probability that any person attains various extreme ages, such as those above 120, by the year 2100. METHODSWe use the exponential survival model for supercentenarians (people over age 110) of Rootzén and Zholud (2017) but extend the forecasting window, quantify population uncertainty using Bayesian population projections, and incorporate the most recent data from the International Database on Longevity (IDL) to obtain unconditional estimates of the distribution of MRAD this century in a fully Bayesian analysis. RESULTSWe find that the exponential survival model for supercentenarians is consistent with the most recent IDL data and that projections of the population aged 110-114 through 2080 are sensible. We integrate over the posterior distributions of the exponential model parameter and uncertainty in the supercentenarian population projections to estimate an unconditional distribution of MRAD by 2100. CONCLUSIONSBased on the Bayesian analysis, there is a greater than 99% probability that the current MRAD of 122 will be broken by 2100. We estimate the probabilities that a person lives to at least age 126, 128, or 130 this century, as 89%, 44%, and 13%, respectively.
Background: In many peer review settings, proposals are selected for funding onthe basis of some summary statistics – such as the mean, median, or percentile –of review scores. There are numerous challenges to working with scores. Theseinclude low inter-rater reliability, epistemological differences, susceptibility tovarying levels of leniency or harshness of reviewers, and the presence of ties. Adifferent approach that is able to mitigate some of these issues would be toadditionally collect rankings such as top-k preferences or paired comparisons andincorporate them in the analysis of review scores. Rankings and pairedcomparisons are scale-free and can enforce demarcation between proposals bydesign. However, analyzing scores and rankings simultaneously has not been doneuntil recently due to the lack of tools for principled modeling. Methods: We first introduce an innovative protocol for collecting rankingsamong top quality proposals. This rankings collection is done as an add-on to thetypical peer review procedures focused on scores and does not require reviewersto rank all proposals. We then present statistical methodology for obtaining anintegrated score for each proposal, and from the integrated scores an inducedpreference ordering, that captures both types of peer review inputs: scores andrankings. Our statistical methodology allows for the collected rankings to differfrom the score-implied rankings; this feature is essential when the two qualityassessments disagree which, as we find empirically, often happens in peer review.We illustrate how our method quantifies the uncertainty in order to betterunderstand reviewer preferences among similarly scored proposals. Results: Using artificial “toy” examples and real peer review data, wedemonstrate that incorporating top-k rankings into scores allows us to betterlearn when reviewers can distinguish between proposals. We also examine therobustness of this system to partial rankings, inconsistencies between ratings andrankings, and outliers. Finally, we discuss how, using panel data, this method canprovide information about funding priority that provides a level of accuracy in aformat that is well suited for the types of decisions research funders make. Conclusions: The gathering of both rating and ranking data and the use ofintegrated scores and its induced preference ordering can have many advantagesover methods relying on ratings alone, leveraging more information to mostaccurately distill reviewer opinion into a useful output to make the most informedfunding decision.
A new methodology to analyze two-component molecular tagging velocimetry image pairs is presented. Velocity measurements with high spatial resolution are achieved by determining grid displacements at the intersections as well as along the grid lines using a multivariate adaptive regression splines parameterization along the segments connecting adjacent grid intersections. The methodology can detect the orientation of the grid, contains redundant steps for increased reliability, and handles cases where parts of the grid are missing, indicating potential for automation. Initial demonstration of the algorithm’s performance was illustrated using synthetic data sets derived from Computational Fluid Dynamics simulations and compared to Hough-transform and cross-correlation methodologies. Besides providing comparable results in terms of precision and accuracy to previously reported methodologies, the analysis of images by the proposed methodology results in significantly increased spatial resolution of the flow displacement determinations along the grid lines with comparable precision and accuracy. This methodology’s ability to handle different grid orientations without modifications was assessed using synthetic datasets with grids formed by sets of parallel lines at 90, 45, and 30 degrees from the vertical axis. Comparable results in terms of precision and accuracy were obtained across grid orientations, with all uncertainties below 0.1 pixel for images with signal-to-noise levels exceeding 5, and within 0.5 pixel for the noisiest image sets.
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