Non-extensive resonant reaction rates in astrophysical plasmas

We study two different physical scenarios of thermonuclear reactions in stellar plasmas proceeding through a narrow resonance at low energy or through the low energy wing of a wide resonance at high energy. Correspondingly, we derive two approximate analytical formulae in order to calculate thermonuclear resonant reaction rates inside very coupled and non ideal astrophysical plasmas in which non-extensive effects are likely to arise. Our results are presented as simple first order corrective factors that generalize the well known classical rates obtained in the framework of Maxwell-Boltzmann statistical mechanics. As a possible application of our results, we calculate the dependence of the total corrective factor with respect to the energy at which the resonance is located, in an extremely dense and non ideal carbon plasma.Comment: 5 pages, 1 figur

Single molecule protein stabilisation translates to macromolecular mechanics of a protein network

Folded globular proteins are attractive building blocks for biopolymer-based materials, as their mechanically resistant structures carry out diverse biological functionality. While much is now understood about the mechanical response of single folded proteins, a major challenge is to understand and predictably control how single protein mechanics translates to the collective response of a network of connected folded proteins. Here, by utilising the binding of maltose to hydrogels constructed from photo-chemically crosslinked maltose binding protein (MBP), we investigate the effects of protein stabilisation at the molecular level on the macroscopic mechanical and structural properties of a protein-based hydrogel. Rheological measurements show an enhancement in the mechanical strength and energy dissipation of MBP hydrogels in the presence of maltose. Circular dichroism spectroscopy and differential scanning calorimetry measurements show that MBP remains both folded and functional in situ. By coupling these mechanical measurements with mesoscopic structural information obtained by small angle scattering, we propose an occupation model in which higher proportions of stabilised, ligand occupied, protein building blocks translate their increased stability to the macroscopic properties of the hydrogel network. This provides powerful opportunities to exploit environmentally responsive folded protein-based biomaterials for many broad applications

Old Blandford Church, Petersburg, Virginia

This postcard features a restored Blandford Church in Petersburg, Virginia.https://scholarsjunction.msstate.edu/fvw-artifacts/5947/thumbnail.jp

“The Dictator,” Petersburg, Virginia

This postcard features Fort Stedman in Petersburg, Virginia.https://scholarsjunction.msstate.edu/fvw-artifacts/5946/thumbnail.jp

Alpha decay rate enhancement in metals: An unlikely scenario

It has been recently suggested that one might drastically shorten the alpha lifetime of nuclear waste products, if these are embedded in metals at low temperatures. Using quantum mechanical tunneling arguments, we show that such an effect is likely to be very small, if present at all.Comment: RevTeX4. 5 pages, 1 figure. Accepted by Nucl. Phys.

Enhancement of Resonant Thermonuclear Reaction Rates in Extremely Dense Stellar Plasmas

The enhancement factor of the resonant thermonuclear reaction rates is calculated for the extremely dense stellar plasmas in the liquid phase. In order to calculate the enhancement factor we use the screening potential which is deduced from the numerical experiment of the classical one-component plasma. It is found that the enhancement is tremendous for white dwarf densities if the ^{12}C + ^{12}C fusion cross sections show resonant behavior in the astrophysical energy range. We summarize our numerical results by accurate analytic fitting formulae.Comment: 13 pages, 3 figures, accepted for publication in ApJ, replaced with revised versio

Comparative measurements of the energy consumption of 51 electric, hybrid and internal combustion engine vehicles

Accepted versio

Diversity of viscoelastic properties of an engineered muscle-inspired protein hydrogel

Folded protein hydrogels are prime candidates as tuneable biomaterials but it is unclear to what extent their mechanical properties have mesoscopic, as opposed to molecular origins. To address this, we probe hydrogels inspired by the muscle protein titin and engineered to the polyprotein I275, using a multimodal rheology approach. Across multiple protocols, the hydrogels consistently exhibit power-law viscoelasticity in the linear viscoelastic regime with an exponent β = 0.03, suggesting a dense fractal meso-structure, with predicted fractal dimension df = 2.48. In the nonlinear viscoelastic regime, the hydrogel undergoes stiffening and energy dissipation, indicating simultaneous alignment and unfolding of the folded proteins on the nanoscale. Remarkably, this behaviour is highly reversible, as the value of β, df and the viscoelastic moduli return to their equilibrium value, even after multiple cycles of deformation. This highlights a previously unrevealed diversity of viscoelastic properties that originate on both at the nanoscale and the mesoscopic scale, providing powerful opportunities for engineering novel biomaterials

Artificial intelligence in fracture detection: a systematic review and meta-analysis

Background: Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose: To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods: A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results: Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion: Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice