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Toronto, Canada

York University is a public research university in Toronto, Ontario, Canada. It is Ontario's second-largest graduate school and Canada's third-largest university. York has a student population of approximately 51,000, 7,000 staff, and 250,000 alumni worldwide. Wikipedia.


Sadorsky P.,York University
Energy Economics | Year: 2013

Against a backdrop of concerns about climate change, peak oil, and energy security issues, reducing energy intensity is often advocated as a way to at least partially mitigate these impacts. This study uses recently developed heterogeneous panel regression techniques like mean group estimators and common correlated effects estimators to model the impact that income, urbanization and industrialization has on energy intensity for a panel of 76 developing countries. In the long-run, a 1% increase in income reduces energy intensity by -0.45% to -0.35%. Long-run industrialization elasticities are in the range 0.07 to 0.12. The impact of urbanization on energy intensity is mixed. In specifications where the estimated coefficient on urbanization is statistically significant, it is slightly larger than unity. The implications of these results for energy policy are discussed. © 2013 Elsevier B.V. Source


Density functional theory (DFT) calculations are carried out on an extensive series of ruthenium complexes with the non-innocent (redox active) o-benzoquinonediimine (bqdi) ligand, namely [Ru(WXYZ)(bqdi)]n+ where WXYZ are a range of spectator ligands including ammonia, phosphines, 2,2′-bipyridine, 2,2′,2″-terpyridine, carbon monoxide, water, halide, acetonitrile, triazacyclononane, nitrosyl, cyclam, etc. In addition, a smaller series, Ru(acac)2(R-bqdi) is explored, where acac = 2,4-pentanedionate, and R = H, Cl, Me, NO2 and N-SO2Me. A range of properties including Mulliken and Natural population analysis (NPA) charges, Mayer bond orders (Ru-N, C{double bond, long}N, C{double bond, long}C, etc.), net σ-donation and net π-back donation, and percentage Ru 4dπ in the LUMO, are derived and correlated with experimental properties including oxidation and reduction potentials and ligand electrochemical parameters, EL(L). The various properties are understood in terms of the primary involvement of π-back donation to the π*-LUMO of bqdi. Net π-back donation is derived from the contribution of the π*-LUMO (and higher virtual orbitals) of bqdi, to filled molecular orbitals of the complex. The question of whether these species should be considered exclusively as being represented as [RuIIL4(bqdi)] or [RuIIIL4(sqdi)] (sqdi = o-benzosemiquinonediimine) is briefly considered and evidence presented for the former electronic structure. This is written as a pedagogical treatise rather than a detailed research discussion of the electronic properties of these molecules. © 2010 Elsevier B.V. All rights reserved. Source


Zayed A.,York University | Robinson G.E.,University of Illinois at Urbana - Champaign
Annual Review of Genetics | Year: 2012

Behavior is a complex phenotype that is plastic and evolutionarily labile. The advent of genomics has revolutionized the field of behavioral genetics by providing tools to quantify the dynamic nature of brain gene expression in relation to behavioral output. The honey bee Apis mellifera provides an excellent platform for investigating the relationship between brain gene expression and behavior given both the remarkable behavioral repertoire expressed by members of its intricate society and the degree to which behavior is influenced by heredity and the social environment. Here, we review a linked series of studies that assayed changes in honey bee brain transcriptomes associated with natural and experimentally induced changes in behavioral state. These experiments demonstrate that brain gene expression is closely linked with behavior, that changes in brain gene expression mediate changes in behavior, and that the association between specific genes and behavior exists over multiple timescales, from physiological to evolutionary. © 2012 by Annual Reviews. Source


Meisner B.A.,York University
Journals of Gerontology - Series B Psychological Sciences and Social Sciences | Year: 2012

Objective.Evidence has shown that age stereotypes influence several behavioral outcomes in later life via stereotype valence-outcome assimilation; however, a direct comparison of positive versus negative age stereotyping effects has not yet been made.Methods.PsycINFO and Pubmed were used to generate a list of articles (n = 137), of which seven were applicable. From these articles, means, standard errors (SEs), and other relevant data were extracted for 52 dependent measures: 27 involved negative age primes and 25 involved positive age primes. Independent samples analysis of variance tests were used to explore the influence of prime valence and awareness on behavior compared with a neutral referent.Results.A significant main effect for prime valence was found such that negative age priming elicited a greater effect on behavior than did positive age priming (F(1,48) = 4.32, p =. 04). In fact, the effects from negative age priming were almost three times larger than those of positive priming when compared with a neutral baseline. This effect was not influenced by prime awareness, discipline of study, study design, or research group.Discussion.Findings show that negative age stereotyping has a much stronger influence on important behavioral outcomes among older adults than does positive age stereotyping. © The Author 2011. Source


Murray R.F.,York University
Journal of Vision | Year: 2011

Classification images have recently become a widely used tool in visual psychophysics. Here, I review the development of classification image methods over the past fifteen years. I provide some historical background, describing how classification images and related methods grew out of established statistical and mathematical frameworks and became common tools for studying biological systems. I describe key developments in classification image methods: use of optimal weighted sums based on the linear observer model, formulation of classification images in terms of the generalized linear model, development of statistical tests, use of priors to reduce dimensionality, methods for experiments with more than two response alternatives, a variant using multiplicative noise, and related methods for examining nonlinearities in visual processing, including secondorder Volterra kernels and principal component analysis. I conclude with a selective review of how classification image methods have led to substantive findings in three representative areas of vision research, namely, spatial vision, perceptual organization, and visual search. © ARVO. Source

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