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This exploratory study determined which set of student characteristics and disability-related services explained graduation success among college students with disabilities. The archived records of 1,289 unidentified students with disabilities in three public universities were examined ex-post-facto to collect demographic data on the students, the disability-related services they qualified for while enrolled in the institution, and student graduation status. A hierarchical logistic regression framework was used to compare models predicting graduation among students with disabilities in college. A model selection procedure was then used to construct a parsimonious model of the data. The final model constructed included predictors related to gender, age, disability type, and several disability-related services. Given the limitations of this study, more research is needed to validate this model using a similar sample of students with disabilities in 2-year and 4-year institutions. (Contains 4 tables.)

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component‐based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component‐based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly‐shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems. [ABSTRACT FROM AUTHOR]

The Arctic is warming two to three times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), have been significantly underestimated by more than 50%, with the smallest biases calculated for the nudged atmosphere-only simulations. CMIP6 simulations performed slightly better in simulating both surface concentrations of aerosols and climate parameters, compared to the Eclipse simulations. In addition, fully-coupled simulations had slightly smaller biases in aerosol levels compared to atmosphere only simulations without nudging. Arctic BC, organic carbon (OC) and SO42- burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m-2, of which - 0.24±0.01 W m-2 were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m-2 for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m-2), of which the anthropogenic aerosols accounted for -0.26 W m-2. Finally, all simulations showed an 46 increase in the Arctic surface air temperatures both throughout the simulation period. In 2050, surface air temperatures are projected to increase by 2.4 °C to 2.6 °C in the Eclipse ensemble and 1.9 °C to 2.6 °C in the CMIP6 ensemble, compared to the 1990-2010 mean. Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. [ABSTRACT FROM AUTHOR]

Identifying regions having large or unusual response in a spatial domain is frequently of interest in environmental and public health contexts. These regions having a large response are frequently called exceedance sets, exceedance regions, or excursion sets. We propose Bayesian methodology for constructing credible regions that either contain the exceedance sets or are contained within the exceedance sets with a specified level probability. The algorithms are simple to implement and use samples from the joint posterior predictive distribution of the process of interest. The R package ExceedanceTools implements the proposed methodology. We study the coverage properties of the methodology and also show that it has good coverage properties in practice. Simulation experiments demonstrate that the proposed method has good performance with respect to the empirical coverage and power. We apply this methodology to identify regions of high zinc concentration using soil samples collected in southwestern Wyoming. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This exploratory study determined which set of student characteristics and disability-related services explained graduation success among college students with disabilities. The archived records of 1,289 unidentified students with disabilities in three public universities were examined ex-post-facto to collect demographic data on the students, the disability-related services they qualified for while enrolled in the institution, and student graduation status. A hierarchical logistic regression framework was used to compare models predicting graduation among students with disabilities in college. A model selection procedure was then used to construct a parsimonious model of the data. The final model constructed included predictors related to gender, age, disability type, and several disability-related services. Given the limitations of this study, more research is needed to validate this model using a similar sample of students with disabilities in 2-year and 4-year institutions. [ABSTRACT FROM AUTHOR]