University of New Hampshire

About: University of New Hampshire is a education organization based out in Durham, New Hampshire, United States. It is known for research contribution in the topics: Population & Solar wind. The organization has 9379 authors who have published 24025 publications receiving 1020112 citations. The organization is also known as: UNH.

Free‐Time Activities in Middle Childhood: Links with Adjustment in Early Adolescence

Abstract: This study assessed links between free-time activities in middle childhood (hobbies, sports, toys and games, outdoor play, reading, television viewing, and hanging out) and school grades, conduct, and depression symptoms both concurrently and 2 years later, in early adolescence. It also explored two mechanisms that might underlie activity-adjustment links: whether the social contexts of children's activities mediate these links, child effects explain these connections, or both. Participants were 198 children (M = 10.9 years, SD = .54 years) in Year 1, and their parents. In home interviews in Years 1 and 3 of the study, mothers rated children's conduct problems, children reported on their depression symptoms, and information was collected on school grades from report cards. In seven evening phone interviews, children reported on the time they spent in free-time activities during the day of the call and their companions in each activity. Links were found between the nature of children's free-time activities and their adjustment. The social contexts of free-time activities explained activity-adjustment links to a limited degree; with respect to child effects, evidence also suggested that better adjusted children became more involved in adaptive activities over time.

Why do models overestimate surface ozone in the Southeast United States

Abstract: . Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≡ NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25° × 0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

A sufficient condition for backtrack-bounded search

Abstract: Backtrack search is often used to solve constraint satisfaction problems. A relationship involving the structure of the constraints is described that provides a bound on the backtracking required to advance deeper into the backtrack tree. This analysis leads to upper bounds on the effort required for solution of a class of constraint satisfaction problems. The solutions involve a combination of relaxation preprocessing and backtrack search. The bounds are expressed in terms of the structure of the constraint connections. Specifically, the effort is shown to have a bound exponential in the size of the largest biconnected component of the constraint graph, as opposed to the size of the graph as a whole.