Reinforced thermal-shock resistant ceramics

Composite material, made by dispersing short tungsten-rhenium fibers randomly throughout zirconium oxide, is highly resistant to oxidizing environments at temperatures above 2000 degrees F. This reinforced ceramic is also thermal stress resistant

Improved throat inserts for ablative thrust chambers

Composite material development and structural design of improved throat inserts for ablative thrust chamber

Binscatter Regressions

We introduce the \texttt{Stata} (and \texttt{R}) package \textsf{Binsreg}, which implements the binscatter methods developed in \citet*{Cattaneo-Crump-Farrell-Feng_2019_Binscatter}. The package includes the commands \texttt{binsreg}, \texttt{binsregtest}, and \texttt{binsregselect}. The first command (\texttt{binsreg}) implements binscatter for the regression function and its derivatives, offering several point estimation, confidence intervals and confidence bands procedures, with particular focus on constructing binned scatter plots. The second command (\texttt{binsregtest}) implements hypothesis testing procedures for parametric specification and for nonparametric shape restrictions of the unknown regression function. Finally, the third command (\texttt{binsregselect}) implements data-driven number of bins selectors for binscatter implementation using either quantile-spaced or evenly-spaced binning/partitioning. All the commands allow for covariate adjustment, smoothness restrictions, weighting and clustering, among other features. A companion \texttt{R} package with the same capabilities is also available

On Binscatter

Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity or monotonicity of the regression function. This paper presents a foundational, thorough analysis of binscatter: we give an array of theoretical and practical results that aid both in understanding current practices (i.e., their validity or lack thereof) and in offering theory-based guidance for future applications. Our main results include principled number of bins selection, confidence intervals and bands, hypothesis tests for parametric and shape restrictions of the regression function, and several other new methods, applicable to canonical binscatter as well as higher-order polynomial, covariate-adjusted and smoothness-restricted extensions thereof. In particular, we highlight important methodological problems related to covariate adjustment methods used in current practice. We also discuss extensions to clustered data. Our results are illustrated with simulated and real data throughout. Companion general-purpose software packages for \texttt{Stata} and \texttt{R} are provided. Finally, from a technical perspective, new theoretical results for partitioning-based series estimation are obtained that may be of independent interest

Why Medical Students Choose Rural Clinical Campuses For Training: A Report From Two Campuses At Opposite Ends Of The Commonwealth

Introduction Although US medical schools have increased their enrollment by about 30%, most of the increase has occurred in urban areas. The affinity model proposes that rural training of a rural student will more likely result in a rural physician, but the exact role of these rural campuses is unclear. Do they solidify and reinforce a pre-existing career plan, do they create social and marital ties that make the transition to rural medicine easier, or could they be replaced with a briefer and more efficient rural rotation? We administered a questionnaire to students attending two different two year rural clinical campuses in the same state in order to explore their opinions regarding the advantages of a rural campus. Methods Two different rural M3-M4 year clinical campuses, affiliated with different medical schools in the same state, administered surveys to 70 medical students across all four years of medical school. Both schools selectively recruit rural students to the rural campuses, and require a campus decision at admission. Both schools require students to attend their first two years at an urban campus, and transfer to the rural campus for clinical education. Questions addressed student opinions on rural campus location, recommendations from others regarding attendance, campus atmosphere and social life, teaching methods and involvement in patient care. Comparisons were analyzed using the non-parametric Mann Whitney U test. Results The top five reasons students chose a rural campus included three aspects of rural training and two features of being rural. There were small differences between the two campuses regarding the importance of more procedures during training and more outdoor activities, the opportunity to study with friends, and strength of local leadership, reflecting differences in the practice setting and the environment of the two campuses. Differences were also noted between upper-level and lower-level students regarding the importance of studying with friends, and the chances of meeting a future spouse. Finally, very rural students (30 miles from urban area) were less concerned with availability of scholarships, and lack of fine dining, but viewed the opportunity to study with friends more favorably. Conclusions This study adds to the published literature by surveying students at multiple rural campuses by year of training. There were many more similarities than differences, but there were differences between the two campuses, and there were also differences as the students progressed in their training, and differences between very rural students and other students attending the campus. Rural campuses provide both clinical and social support for students contemplating rural practice. Results of the survey indicate both are of importance to the students as well, with quality of training the most important factor

Links between biodiversity and human infectious and non-communicable diseases: a review

INTRODUCTION: Biodiversity has intrinsic value and a fundamental role in human health. The relationship between them is complex, and the specific sustaining processes are still not well understood. In view of the rapidly evolving landscape, this literature review investigated scientific evidence for specific links between biodiversity and human infectious and non-communicable diseases to characterise identifiable relationships. METHODS: A search of the PubMed and Web of Science databases using keyword algorithms identified relevant manuscripts published between 1 January 2000 and 18 April 2019. Qualitative data were extracted from 155 studies investigating links between or mechanisms linking biodiversity and infectious disease, non-communicable disease, allergic/inflammatory disease and microbiomes. RESULTS: None of the reviewed studies documented causal evidence for a mechanism linking biodiversity and human health. The main mechanisms proposed to link biodiversity and transmission of infectious disease were dilution and amplification. The dilution hypothesis argues that an increase in species diversity leads to a decrease in pathogen prevalence. The amplification effect is the converse, that there is a positive correlation between species diversity and disease risk/infection prevalence. Several driving factors are postulated, including encounter reduction, interspecies competition and predation. In addition, it appears that scale, both spatial and temporal, highly impacts diversity-disease relationships. There is strong evidence that the early environment of a child, including maternally transferred prenatal signals, affects immune maturation, modifying later disease risk. Bi-directional axes communicate between the gut microbiome and the brain, as well as between the skin microbiome and the lung, leading to direct and indirect immune, humoral and neural mechanisms. The main challenges in assessing links between biodiversity and human health are the wide variation in definitions of health and biodiversity, and the heterogeneity in types of studies encountered, as well as the complexity of interactions in dynamic systems. CONCLUSIONS: Contextually adapted integrative approaches, which maintain dialogue across disciplines and amongst all stakeholders, are most likely to generate robust evidence. Because of the relevance of local scale, research engagement must occur across levels to generate legitimate practices and translate into sustainable, equitable policies. Recommendations for future action include: improve the knowledge base on contribution of biodiversity to health, increase awareness of health effects of natural and near-natural environments and biodiversity, and promote synergies by increasing policy coherence