H2∣2\mathbb{H}^{2|2}-model and Vertex-Reinforced Jump Process on Regular Trees: Infinite-Order Transition and an Intermediate Phase

We explore the supercritical phase of the vertex-reinforced jump process (VRJP) and the $\mathbb{H}^{2|2}$-model on rooted regular trees. The VRJP is a random walk, which is more likely to jump to vertices on which it has previously spent a lot of time. The $\mathbb{H}^{2|2}$-model is a supersymmetric lattice spin model, originally introduced as a toy model for the Anderson transition. On infinite rooted regular trees, the VRJP undergoes a recurrence/transience transition controlled by an inverse temperature parameter $\beta > 0$. Approaching the critical point from the transient regime, $\beta \searrow \beta_{\mathrm{c}}$, we show that the expected total time spent at the starting vertex diverges as $\sim \exp(c/\sqrt{\beta - \beta_{\mathrm{c}}})$. Moreover, on large finite trees we show that the VRJP exhibits an additional intermediate regime for parameter values $\beta_{\mathrm{c}} < \beta < \beta_{\mathrm{c}}^{\mathrm{erg}}$. In this regime, despite being transient in infinite volume, the VRJP on finite trees spends an unusually long time at the starting vertex with high probability. We comment on the absence of such an intermediate phase for wired boundary conditions, as a consequence of recent results by Rapenne. We translate these results to the $\mathbb{H}^{2|2}$-model. This provides rigorous evidence for predictions previously made in the physics literature on the Anderson transition. We also demonstrate an approach to study the $\mathbb{H}^{2|2}$-model on trees via a recursive integral equation in polar coordinates for $\mathbb{H}^{2|2}$. Our proofs rely on the application of branching random walk methods to a horospherical marginal of the $\mathbb{H}^{2|2}$-model

Mental Toughness and Coping Skills in Male Sprinters

The predictive quality of psychological skills in relation to sports and more specifically track and field athletes continues to be explored. Purpose: To profile psychological adaptations in Jamaican male sprinters and to assess the differences between elite and sub-elite athletes. Methods: A cross-sectional study of 30 male participants who were grouped based on previous athletic achievement into the elite group and sub-elite group. Following a simulated competitive run; the athletes completed the Athletic Coping Skills Inventory-28 and the Mental Toughness Questionnaire-48. Results: The elite athletes exhibited greater mental toughness than the sub-elite group and coping skills were a significant predictor of mental toughness. Conclusion: Assessment of psychological skills may distinguish elite from sub-elite athletes

Fit for fire: A 10-week low-cost hift experiential learning initiative between underrepresented kinesiology undergraduates and hypertensive deconditioned firefighters improves their health and fitness

The aims of this study were to investigate the feasibility of an experiential learning initiative led by minority exercise science undergraduates and to observe the adaptations after a 10-week high-intensity functional training (HIFT) program in 34 underrepresented, hypertensive, and overweight/obese professional firefighters (PFF; age: 36.8 ± 11.0 years, body weight: 97.3 ± 21.5 kg, height: 181.7 ± 6.6 cm; BMI: 29.2 ± 4.9 kg/m2). Data were analyzed for muscular strength and endurance, cardiorespiratory endurance, body composition, agility, flexibility, and readiness for change. The PFFs trained two to three times weekly during their work shifts at vigorous intensity for 40 min. Their resting diastolic blood pressure and resting heart rate significantly decreased. Improvements in agility, muscular strength, and readiness for change were observed. This HIFT experiential learning initiative was feasible and beneficial and improved the PFFs’ health and physical fitness with limited resources. Accredited programs in exercise science participating in low-cost initiatives may aid in mitigating public service workers’ compensation and injury rates to better respond to occupational demands

Estimating the size distribution of plastics ingested by animals

The ingestion of plastics appears to be widespread throughout the animal kingdom with risks to individuals, ecosystems and human health. Despite growing information on the location, abundance and size distribution of plastics in the environment, it cannot be assumed that any given animal will ingest all sizes of plastic encountered. Here, we use published data to develop an allometric relationship between plastic consumption and animal size to estimate the size distribution of plastics feasibly ingested by animals. Based on more than 2000 gut content analyses from animals ranging over three orders of magnitude in size (lengths 9 mm to 10 m), body length alone accounts for 42% of the variance in the length of plastic an animal may ingest and indicates a size ratio of roughly 20:1 between animal body length and the largest plastic the animal may ingest. We expect this work to improve global assessments of plastic pollution risk by introducing a quantifiable link between animals and the plastics they can ingest

MaWGAN: a generative adversarial network to create synthetic data from datasets with missing data

The creation of synthetic data are important for a range of applications, for example, to anonymise sensitive datasets or to increase the volume of data in a dataset. When the target dataset has missing data, then it is common to just discard incomplete observations, even though this necessarily means some loss of information. However, when the proportion of missing data are large, discarding incomplete observations may not leave enough data to accurately estimate their joint distribution. Thus, there is a need for data synthesis methods capable of using datasets with missing data, to improve accuracy and, in more extreme cases, to make data synthesis possible. To achieve this, we propose a novel generative adversarial network (GAN) called MaWGAN (for masked Wasserstein GAN), which creates synthetic data directly from datasets with missing values. As with existing GAN approaches, the MaWGAN synthetic data generator generates samples from the full joint distribution. We introduce a novel methodology for comparing the generator output with the original data that does not require us to discard incomplete observations, based on a modification of the Wasserstein distance and easily implemented using masks generated from the pattern of missing data in the original dataset. Numerical experiments are used to demonstrate the superior performance of MaWGAN compared to (a) discarding incomplete observations before using a GAN, and (b) imputing missing values (using the GAIN algorithm) before using a GA