A CP-safe solution of the μ/Bμ problem of gauge mediation

AbstractWe construct a model that naturally generates μ and B of the same order without producing large CP violating phases. This is easily accomplished once one permits these mass scales to be determined independently of the ordinary gauge-mediated soft masses. The alignment of phases is shown to emerge dynamically upon coupling to supergravity and is not unique to the model presented here

Pulling the Group Together: The Role of the Social Identity Approach

First paragraph: How do coaches successfully pull a group together? This chapter focuses on the role and importance of creating and maintaining social identities for group functioning and performance. Research documenting the role and importance of social identities has increased considerably over recent years, with over 200 research articles published across a variety of psychological domains in 2012 alone (Haslam, 2014). Given the wealth of empirical studies available, we have chosen to focus on key research articles within our review of social identity literature to highlight the role and importance of social identities in coaching contexts. Ultimately, social identity researchers recognise that groups are dynamic and have the capacity to change individuals which means that groups and organisations are much more than an aggregation of their individual parts (Haslam, 2004). Therefore, the key to successfully pulling a group together from a social identity perspective lies in the understanding and promotion of a shared sense of social identity among group members. For a coach to understand their role in optimising group functioning and performance, the social identity approach to leadership (Haslam, Reicher, & Platow, 2011) contains four principles that can be implemented within coaching practice. This chapter will also explore each principle of social identity leadership for a coaching audience

Higgs Messengers

We explore the consequences of the Higgs fields acting as messengers of supersymmetry breaking. The hidden-sector paradigm in the gauge mediation framework is relaxed by allowing two types of gauge-invariant, renormalizable operators that are typically discarded: direct coupling between the Higgses and supersymmetry breaking singlets, and Higgs-messenger mixing terms. The most important phenomenological consequence is a flavor-dependent shift in sfermion masses. This is from a one-loop contribution, which we compute for a general set of weak doublet messengers. We also study a couple of explicit models in detail, finding that precision electroweak constraints can be satisfied with a spectrum significantly different from that of gauge mediation.Comment: 20 pages, 5 figure

Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet

As the number of novel data-driven approaches to material science continues to grow, it is crucial to perform consistent quality, reliability and applicability assessments of model performance. In this paper, we benchmark the Materials Optimal Descriptor Network (MODNet) method and architecture against the recently released MatBench v0.1, a curated test suite of materials datasets. MODNet is shown to outperform current leaders on 6 of the 13 tasks, whilst closely matching the current leaders on a further 2 tasks; MODNet performs particularly well when the number of samples is below 10,000. Attention is paid to two topics of concern when benchmarking models. First, we encourage the reporting of a more diverse set of metrics as it leads to a more comprehensive and holistic comparison of model performance. Second, an equally important task is the uncertainty assessment of a model towards a target domain. Significant variations in validation errors can be observed, depending on the imbalance and bias in the training set (i.e., similarity between training and application space). By using an ensemble MODNet model, confidence intervals can be built and the uncertainty on individual predictions can be quantified. Imbalance and bias issues are often overlooked, and yet are important for successful real-world applications of machine learning in materials science and condensed matter

Reaction Bonded Silicon Carbide: SFF, Process Refinement and Applications

Reaction bonded silicon carbide (RBSiC) has a wide variety of industrial applications and a manufacturing process based on Selective Laser Sintering (SLS) has been demonstrated in previous research at the University of Texas. That study was directed toward semiconductor manufacturing applications and was based on prior indirect SLS methods. Several key research questions were addressed for three main manufacturing phases: preform SLS, binder burnout and reactive infiltration. The current research is focused on development of material systems and manufacturing capability and is directed toward a broader set of potential applications. Preform formation utilizes SiC powder of an appropriate average particle size mixed with a multicomponent binder. The preform or green part is then placed in a vacuum furnace to carbonize the binder. The details of the binder chemistry must support accurate SFF shapes and acceptable surface roughness, a strong green part and maintenance of the part shape during the first furnace operation. Finally, the physics and chemistry of the infiltration process, based on the microstructure of the initial green preform, determine the viability of the manufacturing process and the characteristics of the final composite material. The functionality of metal, polymer and ceramic matrix composites can support the growing SFF industry desire to move beyond functional prototyping and into manufacturing arenas. This project is being explored for more general application to matrix composite materials, especially highly functional systems tailored specifically for SLS. The goal is to establish the governing principles of binder function, carbonization and infiltration as well as to understand the interdependence of these phases in terms of manufacturing application. With this understanding new applications and special SLS composites can support the development of new products and a greater SFF manufacturing presence. This paper provides an introduction to the material, a look at basic rapid manufacturing trends, an overview of the previous work, a review of relevant RBSiC material science issues, and an outline of the current study.Mechanical Engineerin

PASCal Python: A Principal Axis Strain Calculator

The response of crystalline materials to external stimuli: whether temperature, pressure or electrochemical potential, is critical for both our understanding of materials and their use. This information can be readily obtained through in-situ diffraction experiments, however if the intrinsic anisotropy of crystals is not taken into account, the true behaviour of crystals can be overlooked. This is particularly true for anomalous mechanical properties of great topical interest, such as negative linear or area compressibility (Cairns & Goodwin, 2015; Hodgson et al., 2014), negative thermal expansion (Chen et al., 2015) or strongly anisotropic electrochemical strain (Kondrakov et al., 2017). We have developed PASCal, Principal Axis Strain Calculator, a widely used web tool that implements the rapid calculation of principal strains and fitting to many common models for equations of state. It provides a simple web form user interface designed to be able to be used by all levels of experience. This new version of PASCal is written in Python using the standard scientific Python stack (Harris et al., 2020; Virtanen et al., 2020), is released open source under the MIT license, and significantly extends the feature set of the original closed-source Fortran, Perl and Gnuplot webtool (Cliffe & Goodwin, 2012). Significant additional attention has been paid to testing, documentation, modularisation and reproducibility, enabling the main app functionality to now also be accessed directly through a Python API. The web app is deployed online at https://www.pascalapp.co.uk with the associated source code and documentation available on GitHub at MJCliffe/PASCal

Long Days Enhance Recognition Memory and Increase Insulin-like Growth Factor 2 in the Hippocampus

Light improves cognitive function in humans; however, the neurobiological mechanisms underlying positive effects of light remain unclear. One obstacle is that most rodent models have employed lighting conditions that cause cognitive deficits rather than improvements. Here we have developed a mouse model where light improves cognitive function, which provides insight into mechanisms underlying positive effects of light. To increase light exposure without eliminating daily rhythms, we exposed mice to either a standard photoperiod or a long day photoperiod. Long days enhanced long-term recognition memory, and this effect was abolished by loss of the photopigment melanopsin. Further, long days markedly altered hippocampal clock function and elevated transcription of Insulin-like Growth Factor2 (Igf2). Up-regulation of Igf2 occurred in tandem with suppression of its transcriptional repressor Wilm’s tumor1. Consistent with molecular de-repression of Igf2, IGF2 expression was increased in the hippocampus before and after memory training. Lastly, long days occluded IGF2-induced improvements in recognition memory. Collectively, these results suggest that light changes hippocampal clock function to alter memory, highlighting novel mechanisms that may contribute to the positive effects of light. Furthermore, this study provides insight into how the circadian clock can regulate hippocampus-dependent learning by controlling molecular processes required for memory consolidation

Constraining the structure of the non-spherical preprotostellar core L1544

A series of self-consistent, three-dimensional continuum radiative transfer models are constructed of the pre-protostellar core L1544, with the results compared with existing SCUBA and ISO data. The source is well-fit by a prolate spheroid, having an ellipsoidal power-law density distribution of index m ~ 2 (1.75 < m < 2.25) in to at least r ~ 1600AU. For r<1600 AU, the data are consistent with either an extension of the power law to smaller radii, or a flattened (Bonner-Ebert like) density distribtion. We can further constrain the optical depth along the short axis at 1300um to be ~ 5e-3, the central luminosity to be L < 1e-3 solar luminosities, the long axis diameter D ~ 0.1 pc, the axis ratio to be q ~ 2, and the external ISRF to be similar to that defined by Mathis, Mezger, & Panagia (1983) to within 50 per cent. The outer diameter and axis ratio may each be somewhat larger due to potential on-source chopping in the observations, and the projection of the long axis onto the plane of the sky. While these results are similar to those inferred directly from observations or spherical modeling due to the source transparency at submillimeter wavelengths, we infer a smaller size, lower mass, and higher optical depth / column density, exposed to a stronger external radiation field than previously assumed. Finally, we find that both the spectral energy distribution (SED) and surface brightness distribution are necessary to constrain the source properties in this way.Comment: 9 pages; 8 figures; accepted for publication in MNRA

The King–Devick test for sideline concussion screening in collegiate football

AbstractPurposeSports-related concussion has received increasing attention as a result of neurologic sequelae seen among athletes, highlighting the need for a validated, rapid screening tool. The King–Devick (K–D) test requires vision, eye movements, language function and attention in order to perform and has been proposed as a promising tool for assessment of concussion. We investigated the K–D test as a sideline screening tool in a collegiate cohort to determine the effect of concussion.MethodsAthletes (n=127, mean age 19.6±1.2 years) from the Wheaton College football and men's and women's basketball teams underwent baseline K–D testing at pre-season physicals for the 2012–2013 season. K–D testing was administered immediately on the sidelines for football players with suspected head injury during regular games and changes compared to baseline were determined. Post-season testing was also performed to compare non-concussed athletes’ test performance.ResultsConcussed athletes (n=11) displayed sideline K–D scores that were significantly higher (worse) than baseline (36.5±5.6s vs. 31.3±4.5s, p<0.005, Wilcoxon signed-rank test). Post-season testing demonstrated improvement of scores and was consistent with known learning effects (35.1±5.2s vs. 34.4±5.0s, p<0.05, Wilcoxon signed-rank test). Test-retest reliability was analyzed between baseline and post-season administrations of the K–D test resulting in high levels of test-retest reliability (intraclass correlation coefficient (ICC)=0.95 [95% Confidence Interval 0.85–1.05]).ConclusionsThe data show worsening of K–D test scores following concussion further supporting utility of the K–D test as an objective, reliable and effective sideline visual screening tool to help identify athletes with concussion