Sensitivity of nucleon-nucleus scattering to the off-shell behavior of on-shell equivalent NN potentials

The sensitivity of nucleon-nucleus elastic scattering to the off-shell behavior of realistic nucleon-nucleon interactions is investigated when on-shell equivalent nucleon-nucleon potentials are used. The study is based on applications of the full-folding optical model potential for an explicit treatment of the off-shell behavior of the nucleon-nucleon effective interaction. Applications were made at beam energies between 40 and 500 MeV for proton scattering from 40Ca and 208Pb. We use the momentum-dependent Paris potential and its local on-shell equivalent as obtained with the Gelfand-Levitan and Marchenko inversion formalism for the two nucleon Schroedinger equation. Full-folding calculations for nucleon-nucleus scattering show small fluctuations in the corresponding observables. This implies that off-shell features of the NN interaction cannot be unambiguously identified with these processes. Inversion potentials were also constructed directly from NN phase-shift data (SM94) in the 0-1.3 GeV energy range. Their use in proton-nucleus scattering above 200 MeV provide a superior description of the observables relative to those obtained from current realistic NN potentials. Limitations and scope of our findings are presented and discussed.Comment: 17 pages tightened REVTeX, 8 .ps figures, submitted to Phys. Rev.

Nucleon-Nucleon Optical Model for Energies to 3 GeV

Several nucleon-nucleon potentials, Paris, Nijmegen, Argonne, and those derived by quantum inversion, which describe the NN interaction for T-lab below 300$ MeV are extended in their range of application as NN optical models. Extensions are made in r-space using complex separable potentials definable with a wide range of form factor options including those of boundary condition models. We use the latest phase shift analyses SP00 (FA00, WI00) of Arndt et al. from 300 MeV to 3 GeV to determine these extensions. The imaginary parts of the optical model interactions account for loss of flux into direct or resonant production processes. The optical potential approach is of particular value as it permits one to visualize fusion, and subsequent fission, of nucleons when T-lab above 2 GeV. We do so by calculating the scattering wave functions to specify the energy and radial dependences of flux losses and of probability distributions. Furthermore, half-off the energy shell t-matrices are presented as they are readily deduced with this approach. Such t-matrices are required for studies of few- and many-body nuclear reactions.Comment: Latex, 40 postscript pages including 17 figure

Predicting green: really radical (plant) predictive processing

In this article we account for the way plants respond to salient features of their environment under the free-energy principle for biological systems. Biological self-organization amounts to the minimization of surprise over time. We posit that any self-organizing system must embody a generative model whose predictions ensure that (expected) free energy is minimized through action. Plants respond in a fast, and yet coordinated manner, to environmental contingencies. They pro-actively sample their local environment to elicit information with an adaptive value. Our main thesis is that plant behaviour takes place by way of a process (active inference) that predicts the environmental sources of sensory stimulation. This principle, we argue, endows plants with a form of perception that underwrites purposeful, anticipatory behaviour. The aim of the article is to assess the prospects of a radical predictive processing story that would follow naturally from the free-energy principle for biological systems; an approach that may ultimately bear upon our understanding of life and cognition more broadly

Genetical genomics: spotlight on QTL hotspots

No abstract available

Transferring research from a university to the United Kingdom National Health Service : The implications for impact

This is an Open Access article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.The aim of this article is to inform readers of the author's reflections on the experience of transferring universitybased research into the commercial sector, and of the processes and strategies employed when preparing for impact in so doing. Concepts for the transfer are illustrated by the author's reflection on aspects that arose during the birthing and subsequent start-up of a university spin-off, Pathways2Wellbeing, a form of reflection-on-action. This is the vehicle for the adaption required to transfer research into the delivery of a specialised clinic in the United Kingdom National Health Service for people with medically unexplained, persistent, bodily symptoms such as fibromyalgia, chronic fatigue and chronic pain. It is hoped that the article will provide readers with an insight into how knowledge transfer can take place through engagement with stakeholders to create an exchange of knowledges to result in impact on health service policy for service users, despite the challenges, and the enablers that facilitated this process. The reflections on the process of knowledge transfer and the implications for impact are underpinned by relevant theory.Peer reviewedFinal Published versio

A view of Neural Networks as dynamical systems

We consider neural networks from the point of view of dynamical systems theory. In this spirit we review recent results dealing with the following questions, adressed in the context of specific models. 1. Characterizing the collective dynamics; 2. Statistical analysis of spikes trains; 3. Interplay between dynamics and network structure; 4. Effects of synaptic plasticity.Comment: Review paper, 51 pages, 10 figures. submitte