Elementary Teachers’ Implementation of Inquiry-Based Instruction

A mid-Atlantic state has recently adopted the Next Generation Science Standards (NGSS) that require teachers to integrate inquiry-based instruction into the classroom. The problem at the local level is a new inquiry-based curriculum, based on the NGSS, is being mandated without identifying the instructional strategies teachers are using to implement the new standards. The purpose of this project study was to explore fourth- and fifth-grade science teachers’ inquiry-based instructional strategies, why the strategies were chosen, and teachers’ concerns about the implementation of the strategies. In this case study, the concern-based adoption model and self-efficacy were used as a conceptual framework to capture the experiences and perceptions of the participants’ implementation of inquiry-based instruction. Data were collected, in the form of interviews, lesson plans, and classroom observations, from nine fourth- and fifth-grade elementary teachers in a rural, mid-Atlantic school system setting. The participants were interviewed about their implementation of inquiry-based instruction and classroom observations and documents were gathered to provide corroborating evidence. Open-coding strategies were used to analyze the data. The findings from this study supported the need for increased professional development for elementary teachers to implement inquiry-based lessons. Consequently, a professional development plan was developed to help address teachers’ concerns by providing information on the implementation of a new inquiry-based curriculum, based on NGSS, and give voice to elementary science teachers. The results influence positive social change by supporting teachers’ implementation of practices that support students’ learning in science

A New Technique for Multidimensional Signal Compression

The problem of efficiently compressing a large number, L, of N dimensional signal vectors is considered. The approach suggested here achieves efficiencies over current pre-processing and Karhunen-Loeve techniques when both L and N are large

Further evidence for the planet around 51 Pegasi

The discovery of the planet around the solar-type star 51 Pegasi marked a watershed in the search for extrasolar planets. Since then seven other solar-type stars have been discovered, of which several have surprisingly short orbital periods, like the planet around 51 Peg. These planets were detected using the indirect technique of measuring variations in the Doppler shifts of lines in the spectra of the primary stars. But it is possible that oscillations of the stars themselves (or other effects) could mimic the signature of the planets, particularly around the short-period planets. The apparent lack of spectral and brightness variations, however, led to widespread acceptance that there is a planet around 51 Peg. This conclusion was challenged by the observation of systematic variations in the line profile shapes of 51 Peg, which suggested stellar oscillations. If these observations are correct, then there is no need to invoke a planet around 51 Peg to explain the data. Here we report observations of 51 Peg at a much higher spectral resolution than those in ref.9, in which we find no evidence for systematic changes in the line shapes. The data are most consistent with a planetary companion to 51 Peg.Comment: LaTeX, 6 pages, 2 figures. To appear in 8 January 1998 issue of Natur

Impact experiments into multiple-mesh targets: Concept development of a lightweight collisional bumper

The utility of multiple-mesh targets as potential lightweight shields to protect spacecraft in low-Earth orbit against collisional damage is explored. Earlier studies revealed that single meshes comminute hypervelocity impactors with efficiencies comparable to contiguous targets. Multiple interaction of projectile fragments with any number of meshes should lead to increased comminution, deceleration, and dispersion of the projectile, such that all debris exiting the mesh stack possesses low specific energies (ergs/sq cm) that would readily be tolerated by many flight systems. The study is conceptually exploring the sensitivity of major variables such as impact velocity, the specific areal mass (g/sq cm) of the total mesh stack (SM), and the separation distance (S) between individual meshes. Most experiments employed five or ten meshes with total SM typically less than 0.5 the specific mass of the impactor, and silicate glass impactors rather than metal projectiles. While projectile comminution increases with increasing impact velocity due to progressively higher shock stresses, encounters with multiple-meshes at low velocity (1-2 km/s) already lead to significant disruption of the glass impactors, with the resulting fragments being additionally decelerated and dispersed by subsequent meshes, and, unlike most contiguous single-plate bumpers, leading to respectable performance at low velocity. Total specific bumper mass must be the subject of careful trade-off studies; relatively massive bumpers will generate too much debris being dislodged from the bumper itself, while exceptionally lightweight designs will not cause sufficient comminution, deceleration, or dispersion of the impactor. Separation distance was found to be a crucial design parameter, as it controls the dispersion of the fragment cloud. Substantial mass savings could result if maximum separation distances were employed. The total mass of debris dislodged by multiple-mesh stacks is modestly smaller than that of single, contiguous-membrane shields. The cumulative surface area of all penetration holes in multiple mesh stacks is an order of magnitude smaller than that in analog multiple-foil shields, suggesting good long-term performance of the mesh designs. Due to different experimental conditions, direct and quantitative comparison with other lightweight shields is not possible at present

Improving the Predictions of Computational Models of Convection-Enhanced Drug Delivery by Accounting for Diffusion Non-gaussianity

Convection-enhanced delivery (CED) is an innovative method of drug delivery to the human brain, that bypasses the blood-brain barrier by injecting the drug directly into the brain. CED aims to target pathological tissue for central nervous system conditions such as Parkinson's and Huntington's disease, epilepsy, brain tumors, and ischemic stroke. Computational fluid dynamics models have been constructed to predict the drug distribution in CED, allowing clinicians advance planning of the procedure. These models require patient-specific information about the microstructure of the brain tissue, which can be collected non-invasively using magnetic resonance imaging (MRI) pre-infusion. Existing models employ the diffusion tensor, which represents Gaussian diffusion in brain tissue, to provide predictions for the drug concentration. However, those predictions are not always in agreement with experimental observations. In this work we present a novel computational fluid dynamics model for CED that does not use the diffusion tensor, but rather the diffusion probability that is experimentally measured through diffusion MRI, at an individual-participant level. Our model takes into account effects of the brain microstructure on the motion of drug molecules not taken into account in previous approaches, namely the restriction and hindrance that those molecules experience when moving in the brain tissue, and can improve the drug concentration predictions. The duration of the associated MRI protocol is 19 min, and therefore feasible for clinical populations. We first prove theoretically that the two models predict different drug distributions. Then, using in vivo high-resolution diffusion MRI data from a healthy participant, we derive and compare predictions using both models, in order to identify the impact of including the effects of restriction and hindrance. Including those effects results in different drug distributions, and the observed differences exhibit statistically significant correlations with measures of diffusion non-Gaussianity in brain tissue. The differences are more pronounced for infusion in white-matter areas of the brain. Using experimental results from the literature along with our simulation results, we show that the inclusion of the effects of diffusion non-Gaussianity in models of CED is necessary, if reliable predictions that can be used in the clinic are to be generated by CED models

Improving the predictions of computational models of convection-enhanced drug delivery by accounting for diffusion non-gaussianity

Convection-enhanced delivery (CED) is an innovative method of drug delivery to the human brain, that bypasses the blood-brain barrier by injecting the drug directly into the brain. CED aims to target pathological tissue for central nervous system conditions such as Parkinson's and Huntington's disease, epilepsy, brain tumors, and ischemic stroke. Computational fluid dynamics models have been constructed to predict the drug distribution in CED, allowing clinicians advance planning of the procedure. These models require patient-specific information about the microstructure of the brain tissue, which can be collected non-invasively using magnetic resonance imaging (MRI) pre-infusion. Existing models employ the diffusion tensor, which represents Gaussian diffusion in brain tissue, to provide predictions for the drug concentration. However, those predictions are not always in agreement with experimental observations. In this work we present a novel computational fluid dynamics model for CED that does not use the diffusion tensor, but rather the diffusion probability that is experimentally measured through diffusion MRI, at an individual-participant level. Our model takes into account effects of the brain microstructure on the motion of drug molecules not taken into account in previous approaches, namely the restriction and hindrance that those molecules experience when moving in the brain tissue, and can improve the drug concentration predictions. The duration of the associated MRI protocol is 19 min, and therefore feasible for clinical populations. We first prove theoretically that the two models predict different drug distributions. Then, using in vivo high-resolution diffusion MRI data from a healthy participant, we derive and compare predictions using both models, in order to identify the impact of including the effects of restriction and hindrance. Including those effects results in different drug distributions, and the observed differences exhibit statistically significant correlations with measures of diffusion non-Gaussianity in brain tissue. The differences are more pronounced for infusion in white-matter areas of the brain. Using experimental results from the literature along with our simulation results, we show that the inclusion of the effects of diffusion non-Gaussianity in models of CED is necessary, if reliable predictions that can be used in the clinic are to be generated by CED models