From a Chinese kindergarten: A personal journey

How do Chinese kindergarten teachers understand their teaching practices? How are these influenced by cultural, political and economic forces? What do their classrooms look like from the perspective of a Chinese New Zealander

The Role of Dietary Safflower Oil in the Management of Glucose Levels in Obese Postmenopausal Women with Type 2 Diabetes Mellitus

With more than 8% of people in the United States diagnosed with type 2 diabetes, it is imperative to develop treatments that improve glycemic control (1). Previously, we reported that dietary safflower oil (8 g qd) significantly decreased trunk adipose mass, increased adiponectin levels, and decreased HbA1c levels in post-menopausal, obese women with type 2 diabetes mellitus (T2DM) (2). However, the specific components of safflower oil that contributed to altering adipose mass distribution and improving glycemic control are not known. Safflower oil (SAF) contains several compounds (e.g., tocopherols, tocotrienols, and linoleic acid) that may improve insulin sensitivity. The objectives of this study were to determine if supplementation with SAF (8 g qd for 16 weeks) increases alpha tocopherol or gamma tocopherol levels in post-menopausal women with T2DM and to measure the relationship of serum tocopherols or linoleic acid with the insulin sensitizing cytokine, adiponectin, HbA1c, and trunk adipose. Tocopherols were extracted from frozen serum samples via solid phase extraction and analyzed using high performance liquid chromatography (HPLC) for gamma and alpha tocopherol concentrations at the baseline and the endpoints of the (SAF) supplementation. While not all of the subjects exhibited increased tocopherol, the subjects who showed an increase in tocopherol concentration had a significant increase in plasma adiponectin and a significant decrease in trunk adipose mass and HbA1c. Overall, the data suggest that alpha and gamma tocopherol may contribute to the improvements in markers of glycemia in obese post-menopausal women with T2DM. Confounding factors that may have contributed to variability within our observations are related to the nature of this study being a secondary analysis derived from a primary study (2). The number of observations (n) was limited due to the primary objective (2). In addition, these considerations include possible exposure of samples to more than one freeze-thaw cycle, unregulated exposure to light, and duration of storage in -80C freezer. This study provides evidence that in addition to linoleic acid, tocopherols found in dietary SAF may contribute to improved glycemic control in women with (T2DM).The Ohio State University Arts and Sciences Undergraduate Research Scholarship, NIH R21 Grant, Carol S. Kennedy ProfessorshipNo embarg

A Comparative Study of Immigrant Children Starting Childcare

This comparative study investigated the experiences of starting childcare of three immigrant children in three different learning environments in New Zealand. The notion of learning environment was explored as a way of thinking about how different people, places, and approaches to learning have interacted to create a particular site for the children’s beginning experiences in early childhood settings. The study sheds light on multiple perceptions and experiences with regard to immigrant children and their learning. Child observations, child interviews, and teacher and parent interviews were conducted in each child’s setting. Findings suggest that early childhood environments played a critical role in supporting immigrant children’s transition from homes to early childhood centres if they were informed by the principles of familiarity, care, and collaboration. Immigrant children’s motivation to drive their own learning also provoked reflection on education both in New Zealand and other immigrant-receiving countries such as Canada, the United States, and Australia

Aperture efficiency of chemically etched horns at 93 GHz

The aperture efficiency of monolithic two-dimensional horn imaging arrays has been optimized at 93 GHz. The imaging arrays consist of several silicon wafers into which arrays of pyramidal horns are etched chemically. Dipole antennas and detectors are suspended on thin silicon oxynitride membranes on one of the central silicon wafers about halfway down the horns. The devices are 7×7 arrays with a 1 λ opening and a 71° flare angle. Antenna impedances have been measured on a low-frequency model. A variety of millimeter-wave dipole antennas and bolometers have been designed and tested. A large-area bismuth thin-film power meter is used to obtain accurate absolute power. The measured aperture efficiency improved from 44% to 72%. The highest system coupling efficiency with a lens was 36% including lens absorption and reflection losses

Single-shot determination of spin-polarization for ultrarelativistic electron beams via nonlinear Compton scattering

Impacts of spin-polarization of an ultrarelativistic electron beam head-on colliding with a strong laser pulse on emitted photon spectra and electron dynamics have been investigated in the quantum radiation regime. We simulate photon emissions quantum mechanically and electron dynamics semiclassically via taking spin-resolved radiation probabilities in the local constant field approximation. A small ellipticity of the laser field brings about an asymmetry in angle-resolved photon spectrum, which sensitively relies on the polarization of the electron beam. The asymmetry is particularly significant in high-energy photon spectra, and is employed for the polarization detection of a high-energy electron beam with extraordinary precision, e.g., better than 0.3\% for a few-GeV electron beam at a density of the scale of $10^{16}$ cm$^{-3}$ with currently available strong laser fields. This method demonstrates for the first time a way of single-shot determination of polarization for ultrarelativistic electron beams via nonlinear Compton scattering. A similar method based on the asymmetry in the electron momentum distribution after the interaction due to spin-dependent radiation reaction is proposed as well

MODELING DIFFERENTIATED TREATMENT EFFECTS FOR MULTIPLE OUTCOMES DATA

Multiple outcomes data are commonly used to characterize treatment effects in medical research, for instance, multiple symptoms to characterize potential remission of a psychiatric disorder. Often either a global, i.e. symptom-invariant, treatment effect is evaluated. Such a treatment effect may over generalize the effect across the outcomes. On the other hand individual treatment effects, varying across all outcomes, are complicated to interpret, and their estimation may lose precision relative to a global summary. An effective compromise to summarize the treatment effect may be through patterns of the treatment effects, i.e. differentiated effects. In this paper we propose a two-category model to differentiate treatment effects into two groups. A model fitting algorithm and simulation study are presented, and several methods are developed to analyze heterogeneity presenting in the treatment effects. The method is illustrated using an analysis of schizophrenia symptom data

Policies and Practices of Professional Development in China: What do Early Childhood Teachers Think?

This paper focuses on early childhood teachers’ professional development in China. It reports a study which aims to elicit twelve in-service early childhood teachers’ perspectives of the values and issues of professional development policies and the learning opportunities they experienced. Two themes arising from the study are addressed, namely the teachers’ positive responses to the government aspirations for enhancing teaching in early childhood education, and the complexities of the organizational and role structures of the early childhood community in ChangChun where the study took place. An important aspect of the teachers’ perspectives of their professional development, which connects up to the early childhood environment in ChangChun, is the view that professional development was oriented to their own employment continuity. Teachers’ learning was perceived as a useful means to offset the insecurity of their careers, but not closely related to children’s learning

Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method

In order to optimally design materials, it is crucial to understand the structure-property relations in the material by analyzing the effect of microstructure parameters on the macroscopic properties. In computational homogenization, the microstructure is thus explicitly modeled inside the macrostructure, leading to a coupled two-scale formulation. Unfortunately, the high computational costs of such multiscale simulations often render the solution of design, optimization, or inverse problems infeasible. To address this issue, we propose in this work a non-intrusive reduced basis method to construct inexpensive surrogates for parametrized microscale problems; the method is specifically well-suited for multiscale simulations since the coupled simulation is decoupled into two independent problems: (1) solving the microscopic problem for different (loading or material) parameters and learning a surrogate model from the data; and (2) solving the macroscopic problem with the learned material model. The proposed method has three key features. First, the microscopic stress field can be fully recovered. Second, the method is able to accurately predict the stress field for a wide range of material parameters; furthermore, the derivatives of the effective stress with respect to the material parameters are available and can be readily utilized in solving optimization problems. Finally, it is more data efficient, i.e. requiring less training data, as compared to directly performing a regression on the effective stress. For the microstructures in the two test problems considered, the mean approximation error of the effective stress is as low as 0.1% despite using a relatively small training dataset. Embedded into the macroscopic problem, the reduced order model leads to an online speed up of approximately three orders of magnitude while maintaining a high accuracy as compared to the FE$^2$ solver

A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In previous works, such models were constructed in a data-driven manner using methods such as Neural Networks (NN) or Gaussian Process Regression (GPR). However, these approaches currently suffer from issues, such as need for large amounts of training data, lack of physics, and considerable extrapolation errors. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior