11,252 research outputs found
The structure of spider's web fast escaping sets
Building on recent work by Rippon and Stallard, we explore the intricate
structure of the spider's web fast escaping sets associated with certain
transcendental entire functions. Our results are expressed in terms of the
components of the complement of the set (the 'holes' in the web). We describe
the topology of such components and give a characterisation of their possible
orbits under iteration. We show that there are uncountably many components
having each of a number of orbit types, and we prove that components with
bounded orbits are quasiconformally homeomorphic to components of the filled
Julia set of a polynomial. We also show that there are singleton periodic
components and that these are dense in the Julia set.Comment: 18 page
Advanced study of video signal processing in low signal to noise environments Quarterly progress report, Oct. 1969 - Jan. 1970
Video signal processing in low signal to noise environment
Threshhold analysis of phase locked loops
Computer technique for predicting threshold in phased locked loops with and without frequency modulatio
Advanced study of video signal processing in low signal to noise environments Semiannual progress report, 1967-1968
Mathematical model for Apollo video signal processing in low signal to noise ratio environment
Quantitative estimates of fish abundance from boat electrofishing
Multiple removals by boat electro-fishing were used to estimate fish populations in non-wadeable habitats in New Zealand lakes and rivers. Mean capture probability was 0.47±h0.10 (± 95% CI) from 35 population estimates made with 2-7 successive removals. The relationship between the population estimate from the Zippin method (Y)and the number of fish caught in the first removal (X) was significant (adjusted r2=0.84, P<0.001; Figure 2). The least-squares regression was Y = 1.55X 1.23. Mean density ± 95% confidence interval for 13 fishing occasions was 30±27 fish 100 m-
2. Mean biomass of fish for sites was 78±39 g m-2 (range 29 to 245 g m-2). Koi carp comprised the largest proportion of the fish biomass wherever they were present. The high biomasses of koi carp estimated in these results (mean 56±33 g m-2) suggest that they can reach problematic abundances in New Zealand. Bioniass of spawning koi carp can exceed 400 g m-2
Pure xenon hexafluoride prepared for thermal properties studies
Preparation of a xenon hexafluoride and sodium fluoride salt yields a sample of the highest possible purity for use in thermal measurements. The desired hexafluoride can easily be freed from the common contaminants, xenon tetra-fluoride, xenon difluoride, and xenon oxide tetrafluoride, because none of these compounds reacts with sodium fluoride
Fostering Students\u27 Identification with Mathematics and Science
Book Summary: Interest in Mathematics and Science Learning is the first volume to assemble findings on the role of interest in mathematics and science learning. As the contributors illuminate across the volume’s 22 chapters, interest provides a critical bridge between cognition and affect in learning and development. This volume will be useful to educators, researchers, and policy makers, especially those whose focus is mathematics, science, and technology education.
Chapter Summary: The primary purpose of this chapter is to explore the process whereby students transition from a short-term, situational interest in mathematics or science to a more enduring individual interest in which they incorporate performance in mathematics or science into their self-definitions (e.g. I am a scientist ). We do so by examining the research related to domain identification, which is the extent to which students define themselves through a role or performance in a domain, such as mathematics or science. Understanding the process of domain identification is important because it can contribute to an understanding of how individual interest develops over time. The means through which students become highly domain identified involves many factors that are internal (e.g. goals and beliefs) and external (e.g. family environment and educational experiences) to them. Students who are more identified with an academic domain tend to demonstrate increased motivation, effort, perseverance (when faced with failure), and achievement. Importantly, students with lower domain identification tend to demonstrate less motivation, lower effort, and fewer desirable outcomes. Student outcomes in a domain can reciprocally influence domain identification by reinforcing or altering it. This feedback loop can help explain incremental changes in motivation, self-concept, individual interest, and, ultimately, important outcomes such as achievement, choice of college major, and career path. This dynamic model presents possible mechanisms for influencing student outcomes. Furthermore, assessing students\u27 domain identification can allow practitioners to intervene to prevent undesirable outcomes. Finally, we present research on how mathematics and science instructors could use the principles of the MUSIC Model of Academic Motivation to enhance students\u27 domain identification, by (a) empowering students, (b) demonstrating the usefulness of the domain, (c) supporting students\u27 success, (d) triggering students\u27 interests, and (e) fostering a sense of caring and belonging. We conclude that by using the MUSIC model, instructors can intentionally design educational experiences to help students progress from a situational interest to one that is more enduring and integrated into their identities
Recommended from our members
Logits and tigers and bears, oh my! A brief look at the simple math of logistic regression and how it can improve dissemination of results
Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher’s toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These outcomes represent important social science lines of research: retention in, or dropout from school, using illicit drugs, underage alcohol consumption, antisocial behavior, purchasing decisions, voting patterns, risky behavior, and so on. The goal of this paper is to briefly lead the reader through the surprisingly simple mathematics that underpins logistic regression: probabilities, odds, odds ratios, and logits. Anyone with spreadsheet software or a scientific calculator can follow along, and in turn, this knowledge can be used to make much more interesting, clear, and accurate presentations of results (especially to non-technical audiences). In particular, I will share an example of an interaction in logistic regression, how it was originally graphed, and how the graph was made substantially more user-friendly by converting the original metric (logits) to a more readily interpretable metric (probability) through three simple steps. Accessed 7,862 times on https://pareonline.net from June 06, 2012 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right
Recommended from our members
What is Rotating in Exploratory Factor Analysis?
Exploratory factor analysis (EFA) is one of the most commonly-reported quantitative methodology in the social sciences, yet much of the detail regarding what happens during an EFA remains unclear. The goal of this brief technical note is to explore what rotation is, what exactly is rotating, and why we use rotation when performing EFAs. Some commentary about the relative utility and desirability of different rotation methods concludes the narrative. Accessed 43,804 times on https://pareonline.net from January 07, 2015 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right
- …