What does community resilience look like in practice? How institutions see the role of communities in responding to heatwaves in the UK

The concept of resilience is well-established in policy, as well as popular and professional discourse. The notion of community resilience, though, is relatively new, and has only recently been taken up in policy (Cabinet Office 2011b; Defra 2012; 2013). Twigger-Ross et al (2011) define community resilience as an ongoing process of communities working with local resources – alongside local expertise – to help themselves and others to prepare for, respond to, and recover from emergencies. However, when regional and national policy documents mention community and voluntary groups – and local residents – the roles of these actors in developing and implementing resilience are not clearly explained. The documents tend to focus on infrastructure development and institutional emergency responses (Greater London Authority 2011; Defra 2013; UK Government 2013; Public Health England 2014b). In this context, community resilience seems to be something that is bestowed on passive communities by active local institutions; all of the local agency of Twigger-Ross’s definition is lost or missing. The challenge that policymakers face in trying to define the roles of communities in resilience raises various problems. Research and practice in a range of domains (and over a long period) highlights the limits of institutional responses, and emphasises that community-led action and other forms of public participation and engagement can effectively complement institutional responses (Arnstein 1969; INVOLVE 2005; Twigger Ross et al 2011; Cinderby et al 2014; DECC 2014). An active community with local agency could play a key role in preparing for, responding to and recovering from emergencies

On the Use of Group Theoretical and Graphical Techniques toward the Solution of the General N-body Problem

Group theoretic and graphical techniques are used to derive the N-body wave function for a system of identical bosons with general interactions through first-order in a perturbation approach. This method is based on the maximal symmetry present at lowest order in a perturbation series in inverse spatial dimensions. The symmetric structure at lowest order has a point group isomorphic with the S_N group, the symmetric group of N particles, and the resulting perturbation expansion of the Hamiltonian is order-by-order invariant under the permutations of the S_N group. This invariance under S_N imposes severe symmetry requirements on the tensor blocks needed at each order in the perturbation series. We show here that these blocks can be decomposed into a basis of binary tensors invariant under S_N. This basis is small (25 terms at first order in the wave function), independent of N, and is derived using graphical techniques. This checks the N^6 scaling of these terms at first order by effectively separating the N scaling problem away from the rest of the physics. The transformation of each binary tensor to the final normal coordinate basis requires the derivation of Clebsch-Gordon coefficients of S_N for arbitrary N. This has been accomplished using the group theory of the symmetric group. This achievement results in an analytic solution for the wave function, exact through first order, that scales as N^0, effectively circumventing intensive numerical work. This solution can be systematically improved with further analytic work by going to yet higher orders in the perturbation series.Comment: This paper was submitted to the Journal of Mathematical physics, and is under revie

Denoising Autoencoders for fast Combinatorial Black Box Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.Comment: corrected typos and small inconsistencie

A Decision-Support Model for Selecting Additive Manufacturing Versus Subtractive Manufacturing Based on Energy Consumption

This paper presents a simple computational model for determining whether additive manufacturing or subtractive manufacturing is more energy efficient for production of a given metallic part. The key discriminating variable is the fraction of the bounding envelope that contains material i.e. the volume fraction of solid material. For both the additive process and the subtractive process, the total energy associated with the production of a part is defined in terms of the volume fraction of that part. The critical volume fraction is that for which the energy consumed by subtractive manufacturing equals the energy consumed by additive manufacturing. For volume fractions less than the critical value, additive manufacturing is more energy efficient. For volume fractions greater than the critical value, subtractive manufacturing is more efficient. The model considers the entire manufacturing lifecycle from production and transport of feedstock material through processing to return of post-production scrap for recycling. Energy consumed by processing equipment while idle is also accounted for in the model. Although the individual energy components in the model are identified and accounted for in the expressions for additive and subtractive manufacturing, values for many of these components may not be currently available. Energy values for some materials production and subtractive and additive manufacturing processes can be found in the literature. However, since many of these data are reported for a very specific application, it may be difficult, if not impossible, to reliably apply these data to new process-material manufacturing scenarios since, very often, insufficient information is provided to enable extrapolation to broader use. Consequently, this paper also highlights the need to develop improved knowledge of the energy embodied in each phase of the manufacturing process. To be most valuable, users of the model should determine the energy consumed by their manufacturing process equipment on the basis of energy-per-unit-volume of production for each material of interest considering both alloy composition and form. Energy consumed during machine idle per unit time should also be determined by the user then scaled to specific processing scenarios. Energy required to generate feedstock material (billet, plate, bar, wire, powder) must be obtained from suppliers

Analytic, Group-Theoretic Density Profiles for Confined, Correlated N-Body Systems

Confined quantum systems involving $N$ identical interacting particles are to be found in many areas of physics, including condensed matter, atomic and chemical physics. A beyond-mean-field perturbation method that is applicable, in principle, to weakly, intermediate, and strongly-interacting systems has been set forth by the authors in a previous series of papers. Dimensional perturbation theory was used, and in conjunction with group theory, an analytic beyond-mean-field correlated wave function at lowest order for a system under spherical confinement with a general two-body interaction was derived. In the present paper, we use this analytic wave function to derive the corresponding lowest-order, analytic density profile and apply it to the example of a Bose-Einstein condensate.Comment: 15 pages, 2 figures, accepted by Physics Review A. This document was submitted after responding to a reviewer's comment

Subduction Duration and Slab Dip

The dip angles of slabs are among the clearest characteristics of subduction zones, but the factors that control them remain obscure. Here, slab dip angles and subduction parameters, including subduction duration, the nature of the overriding plate, slab age, and convergence rate, are determined for 153 transects along subduction zones for the present day. We present a comprehensive tabulation of subduction duration based on isotopic ages of arc initiation and stratigraphic, structural, plate tectonic and seismic indicators of subduction initiation. We present two ages for subduction zones, a long‐term age and a reinitiation age. Using cross correlation and multivariate regression, we find that (1) subduction duration is the primary parameter controlling slab dips with slabs tending to have shallower dips at subduction zones that have been in existence longer; (2) the long‐term age of subduction duration better explains variation of shallow dip than reinitiation age; (3) overriding plate nature could influence shallow dip angle, where slabs below continents tend to have shallower dips; (4) slab age contributes to slab dip, with younger slabs having steeper shallow dips; and (5) the relations between slab dip and subduction parameters are depth dependent, where the ability of subduction duration and overriding plate nature to explain observed variation decreases with depth. The analysis emphasizes the importance of subduction history and the long‐term regional state of a subduction zone in determining slab dip and is consistent with mechanical models of subduction

Near-Infrared, Adaptive Optics Observations of the T Tauri Multiple-Star System

With high-angular-resolution, near-infrared observations of the young stellar object T Tauri at the end of 2002, we show that, contrary to previous reports, none of the three infrared components of T Tau coincide with the compact radio source that has apparently been ejected recently from the system (Loinard, Rodriguez, and Rodriguez 2003). The compact radio source and one of the three infrared objects, T Tau Sb, have distinct paths that depart from orbital or uniform motion between 1997 and 2000, perhaps indicating that their interaction led to the ejection of the radio source. The path that T Tau Sb took between 1997 and 2003 may indicate that this star is still bound to the presumably more massive southern component, T Tau Sa. The radio source is absent from our near-infrared images and must therefore be fainter than K = 10.2 (if located within 100 mas of T Tau Sb, as the radio data would imply), still consistent with an identity as a low-mass star or substellar object.Comment: 11 pages, 3 figures, submitted to ApJ