Book Review - What it Takes to Thrive: Techniques for Severe Trauma and Stress Recovery

Steve McCarthy-Grunwald, University of Cumbria, reviews the book 'What it takes to thrive: techniques for severe trauma and stress recovery' by John Henden (World Scientific Publishing Co Ltd, 2017, ISBN 9789813230217). Within the eye of a global pandemic it could be said that there is no better time to consider ways of reducing stress, anxiety, and experiences of trauma (Hagger et al., 2020). Although this book was initially published in 2017, its value today seems even more prudent for many individuals across the globe. The opening passages clearly identify how the book has been developed from a person centric position, offering advice and self-help tips and techniques for the reader to contemplate trying. This genre of book in particular faces much criticism as to how effective they really are for readers seeking advice and guidance at a time of personal conflict and have been suggested to giving a potential false sense of an alternative to seeking professional help Bergsma (2008). Interestingly, Bergsma’s study which analysed 57 best-selling psychology selfhelp books in the Netherlands identified that the primary aim of them all was not to alleviate the symptoms of psychological disorders, but to enhance personal strengths and functioning. Considering Henden book, he clearly notes that the design was specifically for ‘survivors and practitioners alike’ (Henden, 2017), and offers a jargon-free and solution-focused guide with the hope of inspiring change

Lidar waveform based analysis of depth images constructed using sparse single-photon data

This paper presents a new Bayesian model and algorithm used for depth and intensity profiling using full waveforms from the time-correlated single photon counting (TCSPC) measurement in the limit of very low photon counts. The model proposed represents each Lidar waveform as a combination of a known impulse response, weighted by the target intensity, and an unknown constant background, corrupted by Poisson noise. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters and their constraints. In particular, a gamma Markov random field (MRF) is used to model the joint distribution of the target intensity, and a second MRF is used to model the distribution of the target depth, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm is then proposed to compute the Bayesian estimates of interest and perform Bayesian inference. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a serie of experiments using real data

Robust Bayesian target detection algorithm for depth imaging from sparse single-photon data

This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting (TCSPC) measurements in the limit of very low photon counts (i.e., typically less than 20 photons per pixel). The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixel-wise model selection and estimation problem which is solved using Bayesian inference. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters while accounting for their constraints. In particular, Markov random fields (MRFs) are used to model the joint distribution of the background levels and of the target presence labels, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm including reversible-jump updates is then proposed to compute the Bayesian estimates of interest. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.Comment: arXiv admin note: text overlap with arXiv:1507.0251

The endocrine pancreas during exercise in people with and without type 1 diabetes: Beyond the beta-cell

Although important for digestion and metabolism in repose, the healthy endocrine pancreas also plays a key role in facilitating energy transduction around physical exercise. During exercise, decrements in pancreatic β-cell mediated insulin release opposed by increments in α-cell glucagon secretion stand chief among the hierarchy of glucose-counterregulatory responses to decreasing plasma glucose levels. As a control hub for several major glucose regulatory hormones, the endogenous pancreas is therefore essential in ensuring glucose homeostasis. Type 1 diabetes (T1D) is pathophysiological condition characterised by a destruction of pancreatic β-cells resulting in pronounced aberrations in glucose control. Yet beyond the beta-cell perhaps less considered is the impact of T1D on all other pancreatic endocrine cell responses during exercise and whether they differ to those observed in healthy man. For physicians, understanding how the endocrine pancreas responds to exercise in people with and without T1D may serve as a useful model from which to identify whether there are clinically relevant adaptations that need consideration for glycaemic management. From a physiological perspective, delineating differences or indeed similarities in such responses may help inform appropriate exercise test interpretation and subsequent program prescription. With more complex advances in automated insulin delivery (AID) systems and emerging data on exercise algorithms, a timely update is warranted in our understanding of the endogenous endocrine pancreatic responses to physical exercise in people with and without T1D. By placing our focus here, we may be able to offer a nexus of better understanding between the clinical and engineering importance of AIDs requirements during physical exercise

RASEM Squared: Assisting Students in their Transition to the STEM Workforce

RASEM Squared works to increase the number of people with disabilities pursuing careers in science, technology, engineering, and mathematics (STEM) fields. To accomplish this goal, it provides funding directly to students to support their college education and to educators who accommodate students with disabilities in their STEM curricula. This paper describes several projects that illustrate RASEM Squared’s activities, and identifies six major issues that have arisen in its work. These issues involve student self-disclosure, transition from supports under the Individuals with Disabilities Education Act (IDEA) to those under the American with Disabilities Act (ADA), training of special education and science education teachers, use of assistive technology (AT) devices, compatibility of software and hardware, and links between services

The Rotational Spectrum and Potential Energy Surface of the Ar-SiO Complex

The rotational spectra of five isotopic species of the Ar-SiO complex have been observed at high-spectral resolution between 8 and 18 GHz using chirped Fourier transform microwave spectroscopy and a discharge nozzle source; follow-up cavity measurements have extended these measurements to as high as 35 GHz. The spectrum of the normal species is dominated by an intense progression of a-type rotational transitions arising from increasing quanta in the Si-O stretch, in which lines up to v = 12 (~14 500 cm-1) were identified. A structural determination by isotopic substitution and a hyperfine analysis of the Ar-Si17O spectrum both suggest that the complex is a highly fluxional prolate symmetric rotor with a vibrationally averaged structure between T-shaped and collinear in which the oxygen atom lies closer to argon than the silicon atom, much like Ar-CO. To complement the experimental studies, a full dimensional potential and a series of effective vibrationally averaged, two-dimensional potential energy surfaces of Ar + SiO have been computed at the CCSD(T)-F12b/CBS level of theory. The equilibrium structure of Ar-SiO is predicted to be T-shaped with a well depth of 152 cm-1, but the linear geometry is also a minimum, and the potential energy surface has a long, flat channel between 140 and 180°. Because the barrier between the two wells is calculated to be small (of order 5 cm-1) and well below the zero-point energy, the vibrationally averaged wavefunction is delocalized over nearly 100° of angular freedom. For this reason, Ar-SiO should exhibit large amplitude zero-point motion, in which the vibrationally excited states can be viewed as resonances with long lifetimes. Calculations of the rovibrational level pattern agree to within 2% with the transition frequencies of normal and isotopic ground state Ar-SiO, and the putative Ka = ±1 levels for Ar-28SiO, suggesting that the present theoretical treatment well reproduces the salient properties of the intramolecular potential