Twist and Measure: Characterizing the Effective Radius of Strings and Bundles under Twisting Contraction

We test the standard model for the length contraction of a bundle of strings under twist, and find deviation that is significantly greater than typically appreciated and that has a different nature at medium and large twist angles. By including volume conservation, we achieve better fits to data for single-, double-, and triple-stranded bundles of Nylon monofilament as an ideal test case. This gives a well-defined procedure for extracting an effective twist radius that characterizes contraction behavior. While our approach accounts for the observed faster-than-expected contraction up to medium twist angles, we also find that the contraction is nevertheless slower than expected at large twist angles for both Nylon monofilament bundles and several other string types. The size of this effect varies with the individual-string braid structure and with the number of strings in the bundle. We speculate that it may be related to elastic deformation within the material. However, our first modeling attempt does not fully capture the observed behavior.Comment: 8 pages, 8 figure

Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation

The conversion of raw images into quantifiable data can be a major hurdle in experimental research, and typically involves identifying region(s) of interest, a process known as segmentation. Machine learning tools for image segmentation are often specific to a set of tasks, such as tracking cells, or require substantial compute or coding knowledge to train and use. Here we introduce an easy-to-use (no coding required), image segmentation method, using a 15-layer convolutional neural network that can be trained on a laptop: Bellybutton. The algorithm trains on user-provided segmentation of example images, but, as we show, just one or even a portion of one training image can be sufficient in some cases. We detail the machine learning method and give three use cases where Bellybutton correctly segments images despite substantial lighting, shape, size, focus, and/or structure variation across the regions(s) of interest. Instructions for easy download and use, with further details and the datasets used in this paper are available at pypi.org/project/Bellybuttonseg.Comment: 6 Pages 3 Figure

Aqueous foams in microgravity, measuring bubble sizes

The paper describes a study of wet foams in microgravity whose bubble size distribution evolves due to diffusive gas exchange. We focus on the comparison between the size of bubbles determined from images of the foam surface and the size of bubbles in the bulk foam, determined from Diffuse Transmission Spectroscopy (DTS). Extracting the bubble size distribution from images of a foam surface is difficult so we have used three different procedures : manual analysis, automatic analysis with a customized Python script and machine learning analysis. Once various pitfalls were identified and taken into account, all the three procedures yield identical results within error bars. DTS only allows the determination of an average bubble radius which is proportional to the photon transport mean free path $\ell^*$. The relation between the measured diffuse transmitted light intensity and {$\ell^*$} previously derived for slab-shaped samples of infinite lateral extent does not apply to the cuboid geometry of the cells used in the microgravity experiment. A new more general expression of the diffuse intensity transmitted with specific optical boundary conditions has been derived and applied to determine the average bubble radius. The temporal evolution of the average bubble radii deduced from DTS and of the same average radii of the bubbles measured at the sample surface are in very good agreement throughout the coarsening. Finally, ground experiments were performed to compare bubble size distributions in a bulk wet foam and at its surface at times so short that diffusive gas exchange is insignificant. They were found to be similar, confirming that bubbles seen at the surface are representative of the bulk foam bubbles