Model Comparison in the Introductory Physics Laboratory

Model comparison is at the heart of all scientific methodologies. Progress is made in science by constructing many models (possibly of different complexities), testing them against measurements, and determining which of them explain the data the best. It is my observation, however, that in many introductory physics labs we provide students with the materials and methods to verify the “correct” model of the experiment they are performing, e.g. measuring “g” or verifying the period of a pendulum. In this way, we do our students a disservice and don’t allow them to experience the richness and creativity that constitutes the scientific enterprise. Limiting the lab to the “correct” model can have its uses—for example, getting the students to practice the proper methods to measure lengths and times or to support the specific theory covered in the lecture portion of the class. However, when students perform these labs, they come to view these activities as repetitive and mechanical, reinforcing the notion that science concerns not the true exploration of nature but simply the verification of what we already know. By verifying what we already know, the laboratory experience does not improve overall understanding and can mislead students about the methods of science overall

A STUDY OF THE DEFENSIVE BEHAVIORS OF FREE-RANGING DEKAY’S BROWNSNAKES, STORERIA DEKAYI (HOLBROOK, 1836)

The defensive behaviors of free-ranging Dekay’s Brownsnakes, Storeria dekayi, were studied at a site in Erie County, Pennsylvania, USA. Twenty-nine unique sequences of defensive behavior were documented. A total of 50 individual snakes (26 males and 24 females) provided 88 observations during the initial phase, of which 78% (n = 69) were of snakes that remained in place. Snakes were tapped with the investigator’s hand to elicit defensive behaviors during the contact phase. Snakes were more than twice as likely to attempt to flee during the contact phase (46%) than during the initial phase (22%). During the contact phase, mean surface body temperatures were significantly higher in snakes attempting to flee (22.3 ± 1.3 °C) than those that remained in place (16.1 ± 2.2 °C). The most frequently observed response during the contact phase was dorso-ventral flattening of the head and body (n = 42). During capture, most snakes (94%) smeared their cloacal contents on themselves and the investigator’s hand

NATURAL HISTORY OF DEKAY’S BROWNSNAKE, STORERIA DEKAYI (HOLBROOK, 1836), AT A SITE IN NORTHWESTERN PENNSYLVANIA

A population of Dekay’s Brownsnake, Storeria dekayi was studied using mark-recapture techniques in Erie County, Pennsylvania, USA during the spring, summer and autumn of 2012. Morphometric data were similar to that reported for the species, with adult females averaging larger and more massive than adult males. However, sexual dimorphism in snout-vent length (SVL) and total length (TL) was not significantly different (P>0.05) in juveniles, although relative tail length (tl/TL) was dimorphic. Relative tail length in both juveniles and adults was greater in males (tl/TL = 0.22-0.27) than females (tl/TL = 0.18-0.23). Storeria dekayi were active from 21 March through 22 October, and displayed a bimodal activity pattern, with peaks in April and August. Using the Schnabel and the Schumacher-Eschmeyer methods, population size was estimated to be 122 ± 19 and 130 ± 35 individuals, respectively. Density was estimated to be 244 and 260 snakes/ha, and biomass 1.60 and 1.71 kg/ha. Additional data regarding population structure, mortality, diet, reproduction, body temperature, movements and site fidelity are also presented

Results from the OLYMPUS Experiment on the Contribution of Hard Two-Photon Exchange to Elastic Electron-Proton Scattering

Measurements of the ratio of the elastic form factors of the proton ($\mu_pG_E/G_M$) exhibit a strong discrepancy. Experiments using unpolarized beams and Rosenbluth separation to determine the form factors have found values of the ratio approximately consistent with unity over a wide range of $Q^2$, while polarization transfer experiments suggest that the ratio decreases as a function of $Q^2$. The most widely-accepted hypothesis to explain this discrepancy is that hard two-photon exchange (TPE) significantly contributes to the elastic $ep$ cross section. Hard TPE has been neglected in previous analyses of electron-proton scattering scattering experiments, in part due to the fact that there exists no model independent way to calculate the contribution. The effect of hard TPE may be measured experimentally, however, via precise determination of the ratio of the electron-proton and positron-proton elastic cross sections. The OLYMPUS experiment collected more than 3 fb$^{-1}$ of exclusive $e^- p$ and $e^+ p$ elastic scattering data at DESY in 2012, and has determined the elastic $\sigma_{e^+p}/\sigma_{e^-p}$ ratio to unprecedented precision up to $Q^2\approx2.2$ (GeV/$c$)$^2$, $\epsilon\approx0.4$. This presentation will discuss the OLYMPUS experiment and analysis, and present the recently published results from OLYMPUS in the context of the results from the other two TPE experiments.Comment: 8 pages, 5 figures, contribution to the proceedings of the XVII International Conference on Hadron Spectroscopy and Structure (2017

Using Python to Program LEGO MINDSTORMS Robots: The PyNXC Project

LEGO MINDSTORMS® NXT (Lego Group, 2006) is a perfect platform for introducing programming concepts, and is generally targeted toward children from age 8-14. The language which ships with the MINDSTORMS®, called NXTg, is a graphical language based on LabVIEW (Jeff Kodosky, 2010). Although there is much value in graphical languages, such as LabVIEW, a text-based alternative can be targeted at an older audiences and serve as part of a more general introduction to modern computing. Other languages, such as NXC (Not Exactly C) (Hansen, 2010) and PbLua (Hempel, 2010), fit this description. Here we introduce PyNXC, a subset of the Python language which can be used to program the NXT MINDSTORMS®. We present results using PyNXC, comparisons with other languages, and some challenges and future possible extensions