The Impact of Instructional Coaching on the Self-Efficacy of Beginning Teachers in the Facilitation of Student Learning

This dissertation was designed to examine the impact of instructional coaching on the self-efficacy of beginning teachers in the facilitation of student learning. Beginning teachers are still learning their craft when they enter the teaching profession. Research indicates that schools are losing beginning teachers before they have adequately developed their abilities. Many systems are providing support to beginning teachers through mentoring and instructional coaching. It is essential that schools support beginning teachers as they hone their abilities to facilitate learning in their classrooms. To best meet the needs of beginning teachers, an understanding of best practices for coaching needs to be developed. This case study utilized the mixed-methods approach to collect and analyze data to advance an understanding of how instructional coaching impacts the facilitation of learning for beginning teachers. In order to gather data to determine the impact of instructional coaching, the following data collection tools were utilized: teacher surveys, focus groups, and individual teacher interviews. Quantitative data gathered from survey responses were analyzed using descriptive statistics, primarily frequency distributions, mean scores, and percent positive by item, element, and construct; the researcher also chose to cross tabulate specific items to better understand correlations. Qualitative data were gathered from individual interviews and compared to find areas of convergence. The data suggest that beginning teachers perceive that working with instructional coach mentors has a positive impact on their own self-efficacy. Additionally, beginning teacher responses indicated that more time with their coaches would be helpful, but more focused time with their coaches would also be of benefit. The findings can be used by districts to assess and inform their own mentoring and induction programs

An Ensemble Model of QSAR Tools for Regulatory Risk Assessment

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study

Health Priorities, Current Lifestyle Behaviors, and Barriers to a Healthy Lifestyle Among Emergency Department Nurses

Background: Emergency nurses are tasked with managing the hectic, unpredictable, and constantly changing environment of an ED. In addition, emergency nurses have been shown to have high levels of stress, irregular meal schedules, rotating shift work, long hours, and a lack of physical activity. Furthermore, research has suggested that nurses are at an increased risk for non-communicable diseases, such as diabetes, hypertension, and coronary heart disease (Phiri, et al., 2014), in addition to a high prevalence of obesity (Kyle, et al., 2016). Methodology: In this study, 23 emergency nurses completed a 43-item survey regarding current behaviors and constructs of the Theory of Planned Behavior (TPB) model (attitudes, subjective norms, and perceived control) as it related to 8 identified health behaviors, and their intention to change at least one behavior in the following 30 days. Results: Analysis revealed a non-significant relationship between components of the TPB, however findings indicated strong correlations between multiple health behaviors (e.g. physical activity and sleep, overall wellness and workplace stress, and, co-worker support and healthy eating). Discussion: Although a small sample was obtained, the trends identified in the data are discussed along with potential interventions for ED nurses. Additionally, the implications of the current healthcare climate on the stress and wellbeing of ED nurses and the need for further research are considered

CanMEDs and the discourse of nature: on petals, beauty and the symmetry of flowers

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P.R.I.D.E.: Positive Racial Identity Development in Early Education

Racism negatively affects children of color in the United States, particularly Black children. Theirs is a history of marginalization since the slavery era, and the impacts are cognitive, social, and psychological. Additionally, Black children face unique challenges upon entering formal education, resulting in disturbing academic outcomes. Yet, adults can facilitate Black children’s development of positive racial identity to help them handle the negative implications of experiencing racism across their lifespan. A description of the research related to positive racial identity is provided along with presentation of the P.R.I.D.E. program, a Pittsburgh-based effort that is designed to help adults build the knowledge and skills needed to support Black children with this aspect of healthy development, thereby interrupting the cycle of racial oppression. A developmental framework for exploring race and child development is introduced. Strategies for teacher growth and classroom application are described along with suggestions for future direction

Intensifying pastoralism may not reduce greenhouse gas emissions : wildlife-dominated landscape scenarios as a baseline in life cycle analysis

The general public is increasingly critical of extensive, ruminant-dominated systems for their attributed high greenhouse gas emissions. However, advocates of low input, grass-fed systems present them as paradigmatic sustainable production systems because of their biodiversity, land use, rural development and animal welfare benefits. We reconcile both analyses by proposing to assess baseline emissions in grazed ecosystems. We show that policies aiming at transitioning grass-fed systems towards fodder-based (concentrate- or grain-based) systems can be ineffective at reducing emissions because wild ruminants or termites fill livestock's ecological niche. Climate change policies targeting livestock should carefully evaluate derived emissions scenarios.Peer reviewe

Considering Natural Baselines When Calculating Livestock Impacts Point to a Negligible Role of Grass-Fed Livestock Systems in Climate Change

ISBN: 978-9966-30-094-2The use of baselines is common in a variety of academic disciplines, including environmental science, but they are subjected to relativity depending on the geographical or historical reference considered. Such considerations are illustrated by how invasive species are evaluated or what reference baselines are considered in biodiversity assessments. The measurement of livestock effects on climate change has, however, disregarded the use of baselines. Current methodology is based exclusively on greenhouse gas emissions by individual animals, without putting them in their ecological context. As a consequence, current analyses of livestock impacts put grass-fed ruminant systems in the spotlight, because of their high methane emissions. Conversion into intensive, grain-fed chicken and pork systems is recommended to cope with increased meat demand, an approach that is being echoed by media. In this study we reviewed existing literature on baseline greenhouse gas emissions by wild ruminants, with models available for North America and northern Russia. We also considered the potential of termites in filling herbivore niches in an ungulate-free scenario and reviewed the literature for possible consequences of ensuing wildfires. We found consistent evidence for natural baseline scenarios to be of the same order of magnitude as current livestock scenarios. This implies that the current policy recommendations for tackling climate change through the livestock sector are likely to be much less effective than currently thought. Other studies on livestock environmental impacts, such as for water or biodiversity, have also not taken into account natural baseline levels from wild herbivores, hence depicting an exaggerated negative image on grass-fed livestock. Policy recommendations should take baseline levels into account, concentrate on reducing intensive use of fossil fuel and focus on double-win strategies for methane emission reduction, such as the use of manure-fed biogas cooking stoves. This paper uses concepts originally developed at Manzano & White (2019).Peer reviewe

Aerogel-Based Multilayer Insulation with Micrometeoroid Protection

Ultra-low-density, highly hydrophobic, fiber-reinforced aerogel material integrated with MLI (aluminized Mylar reflectors and B4A Dacron separators) offers a highly effective insulation package by providing unsurpassed thermal performance and significant robustness, delivering substantial MMOD protection via the addition of a novel, durable, external aerogel layer. The hydrophobic nature of the aerogel is an important property for maintaining thermal performance if the material is exposed to the environment (i.e. rain, snow, etc.) during ground installations. The hybrid aerogel/MLI/MMOD solution affords an attractive alternative because it will perform thermally in the same range as MLI at all vacuum levels (including high vacuum), and offers significant protection from micrometeoroid damage. During this effort, the required low-density and resilient aerogel materials have been developed that are needed to optimize the thermal performance for space (high vacuum) cryotank applications. The proposed insulation/MMOD package is composed of two sections: a stack of interleaved aerogel layers and MLI intended for cryotank thermal insulation, and a 1.5- to 1-in. (.2.5- to 3.8- cm) thick aerogel layer (on top of the insulation portion) for MMOD protection. Learning that low-density aerogel cannot withstand the hypervelocity impact test conditions, the innovators decided during the course of the program to fabricate a high-density and strong material based on a cross-linked aerogel (X-aerogel; developed elsewhere by the innovators) for MMOD protection. This system has shown a very high compressive strength that is capable of withstanding high-impact tests if a proper configuration of the MMOD aerogel layer is used. It was learned that by stacking two X-aerogel layers [1.5-in. (.3.8-cm) thick] separated by an air gap, the system would be able to hold the threat at a speed of 5 km/s and gpass h the test. The first aerogel panel stopped the projectile from damaging the second aerogel panel. The impacted X-aerogel (the back specimen from the successful test) was further tested in comparison to another similar sample (not impacted) at Kennedy Space Center for thermal conductivity evaluation at cryogenic conditions. The specimens were tested under high vacuum and cryogenic temperatures, using Cryostat 500. The results show that the specimen did not lose a significant amount of thermal performance due to the impact test, especially at high vacuum

Quantum data compression, quantum information generation, and the density-matrix renormalization group method

We have studied quantum data compression for finite quantum systems where the site density matrices are not independent, i.e., the density matrix cannot be given as direct product of site density matrices and the von Neumann entropy is not equal to the sum of site entropies. Using the density-matrix renormalization group (DMRG) method for the 1-d Hubbard model, we have shown that a simple relationship exists between the entropy of the left or right block and dimension of the Hilbert space of that block as well as of the superblock for any fixed accuracy. The information loss during the RG procedure has been investigated and a more rigorous control of the relative error has been proposed based on Kholevo's theory. Our results are also supported by the quantum chemistry version of DMRG applied to various molecules with system lengths up to 60 lattice sites. A sum rule which relates site entropies and the total information generated by the renormalization procedure has also been given which serves as an alternative test of convergence of the DMRG method.Comment: 8 pages, 7 figure

Bears Remain Top Summer Predators

In the ten years since wolves (Canis lupus) were restored to Yellowstone National Park (YNP), elk (Cervus elaphus) numbers have substantially decreased. The northern range elk herd is the largest elk herd in Yellowstone, and constitutes the majority of the park’s elk population. During 1994–2005, early winter counts of northern Yellowstone elk decreased from 19,045 to 9,545. Also, during winters 2000–2004, calf:cow ratios declined from 29:100 to 12:100, and were among the lowest recorded during the past several decades. Though many factors (e.g., predation, hunting, and drought) likely contributed to this decreasing abundance and low recruitment, several state and federal legislators continue to speculate that wolves are the primary reason for the recent decrease in elk recruitment rates, and have called for the immediate delisting and liberal control of the abundance and distribution of wolves. Because both wolves and elk are culturally, economically, and ecologically important in the Yellowstone area, it is vital to determine the basis for the decline in the elk population. To help this effort, we initiated a three-year study of northern Yellowstone elk calf mortality in May 2003. Our study was designed to follow up on Dr. Francis Singer et al.’s baseline pre–wolf restoration elk calf mortality study (1987–1990)