The Influence of in-medium NN cross-sections, symmetry potential and impact parameter on the isospin observables

We explore the influence of in-medium nucleon-nucleon cross section, symmetry potential and impact parameter on isospin sensitive observables in intermediate-energy heavy-ion collisions with the ImQMD05 code, a modified version of Quantum Molecular Dynamics model. At incident velocities above the Fermi velocity, we find that the density dependence of symmetry potential plays a more important role on the double neutron to proton ratio $DR(n/p)$ and the isospin transport ratio $R_i$ than the in-medium nucleon-nucleon cross sections, provided that the latter are constrained to a fixed total NN collision rate. We also explore both $DR(n/p)$ and $R_i$ as a function of the impact parameter. Since the copious production of intermediate mass fragments is a distinguishing feature of intermediate-energy heavy-ion collisions, we examine the isospin transport ratios constructed from different groups of fragments. We find that the values of the isospin transport ratios for projectile rapidity fragments with $Z\ge20$ are greater than those constructed from the entire projectile rapidity source. We believe experimental investigations of this phenomenon can be performed. These may provide significant tests of fragmentation time scales predicted by ImQMD calculations.Comment: 24 pages, 9 figures, to be published in Phys. Rev.

Are science students ready for university mathematics?

At UTS students in science courses often struggle with the first year first semester mathematics subject. This year we requested all commencing science students to take a Readiness Survey so that we could advise them of suitable pathways for the maths subjects in their degree. One such pathway includes taking a one-semester subject of introductory calculus before the regular mathematics subject. This paper reports on the practicalities of running such a test before semester starts, and the pathways taken up with varying levels of success by Science students. Insights from a parallel survey and pathway used for some years now with Engineering students in the same institution are offered

A Neural Network for Interpolating Light-Sources

This study combines two novel deterministic methods with a Convolutional Neural Network to develop a machine learning method that is aware of directionality of light in images. The first method detects shadows in terrestrial images by using a sliding-window algorithm that extracts specific hue and value features in an image. The second method interpolates light-sources by utilising a line-algorithm, which detects the direction of light sources in the image. Both of these methods are single-image solutions and employ deterministic methods to calculate the values from the image alone, without the need for illumination-models. They extract real-time geometry from the light source in an image, rather than mapping an illumination-model onto the image, which are the only models used today. Finally, those outputs are used to train a Convolutional Neural Network. This displays greater accuracy than previous methods for shadow detection and can predict light source-direction and thus orientation accurately, which is a considerable innovation for an unsupervised CNN. It is significantly faster than the deterministic methods. We also present a reference dataset for the problem of shadow and light direction detection. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

It is all in the looks: A rapid field-based visual assessment tool for evaluating the spawning likelihood of the Asian green mussel, Perna viridis (Linnaeus, 1758)

Numerous interceptions of Perna viridis on vessels entering Western Australian waters prompted the development of a rapid field-based assessment technique for determining reproductive status and hence spawning likelihood of P. viridis. The visual assessment tool and spawning likelihood matrix were developed using correlations between laboratory-based assessments of P. viridis size, colour and egg size in combination with field-based validations from mussels collected on vessels in Western Australian waters. The spawning likelihood matrix provides an immediate indicator of whether the mussel is low, medium or high likelihood of spawning. Mussels were recorded initiating gonad tissue development from approximately 6.5 mm in length, with the mean size of mature animals 59.6 mm. There was a positive correlation between mussel size and stage of reproductive development. Gonad colour, however, appeared to be a more accurate indicator of gonad maturity than mussel size. Female mussels showed a decrease in gonad colour intensity following spawning. Mussels that scored 1 for colour (potential score 1–3) generally had a low proportion of mature eggs (< 70 % mature eggs). Over 60% of the mussels with a colour score of 2 contained 70–100% mature eggs, indicating the capacity for further spawning. Mussels were assigned an overall spawning likelihood score (through the spawning likelihood matrix) based on the proportion of the visceral mass occupied by gonad tissues (% gonad cover, value from 1–3) and overall colour of gonads (value from 1–3). The spawning likelihood score was significantly related to the percentage of mature eggs present, and hence the spawning potential of the mussel. The matrix provides an immediate indicator of the risk of spawning posed by the sample. As such, it is expected that application of the matrix in situ would enable the potential likelihood posed by P. viridis translocated on vessels to be determined quickly and efficiently