An exploration of women elementary school teachers' perceptions of how their decision to return to teaching part-time from a maternity leave influences their professional and personal lives /

The purpose of this research was to explore women elementary teachers' perceptions of how their decision to return to teaching part-time from a maternity leave influences their professional and personal lives. The investigation focused on the decisions surrounding a mother's choice to reenter the teaching profession parttime in a field where each mother had previously been employed full-time. A collective case study was undertaken based on an in-depth interview with five mothers who had made the choice to return to the classroom part-time. The data collected in this study were analyzed and interpreted using qualitative methods. The following four major themes emerged from the interviews: decisionmaking process, challenges faced by mothers who teach part-time, the importance of support, and the enhancement of instructional practice from parenthood. Using these four themes, an analysis was conducted to examine the similarities and differences among the experiences of the participants. The mothers' reflections, my analysis, and the related literature were used at the conclusion of this report to compile implications for teaching practice, theory, and further research.

Permit to Leave Canada, Simon E. Hansler - September 14, 1918

A permit to leave Canada requested by Simon E. Hansler September 14, 1918. The request states that he is permitted to leave Canada on or before December 1, 1918. The document includes his date of birth, eye colour, hair colour, height, occupation and a photograph. Also included on the form, four names to refer for identification. The document states that he is planning to visit relatives in the United States for "a few days".

Mapping quantitative trait loci for flag leave senescence as a yield determinant in winter wheat under optimal and drought stressed environments

The timing of flag leaf senescence (FLS) is an important determinant of yield under stress and optimal environments. A doubled haploid population derived from crossing the photo period-sensitive variety Beaver,with the photo period-insensitive variety Soissons, varied significantly for this trait, measured as the percent green flag leaf area remaining at 14 days and 35 days after anthesis. This trait also showed a significantly positive correlation with yield under variable environmental regimes. QTL analysis based on a genetic map derived from 48 doubled haploid lines using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers, revealed the genetic control of this trait. The coincidence of QTL for senescence on chromosomes 2B and 2D under drought-stressed and optimal environments, respectively, indicate a complex genetic mechanism of this trait involving the re-mobilisation of resources from the source to the sink during senescence.

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.