Unravelling the relative contributions of climate change and ground disturbance to subsurface temperature perturbations: Case studies from Tyneside, UK

When assessing subsurface urban heat islands (UHIs) it is important to distinguish between localized effects of land-use change and the impacts of global climate change. However, few investigations have successfully unraveled the two influences. We have investigated borehole temperature records from the urban centres of Gateshead and Newcastle upon Tyne in northeast England, to ascertain the effects on subsurface temperatures of climate change and changes in ground conditions due to historic coal mining and more recent urban development. The latter effects are shown to be substantial, albeit with significant variations on a very local scale. Significant subsurface UHIs are indeed evident in both urban centres, estimated as 2.0 °C in Newcastle and 4.5 °C in Gateshead, the former value being comparable to the 1.9 °C atmospheric UHI previously measured for the Tyneside conurbation as a whole. We interpret these substantial subsurface UHIs as a consequence of the region’s long history of urban and industrial development and associated surface energy use, possibly supplemented in Gateshead by the thermal effect of trains braking in an adjacent shallow railway tunnel. We also show that a large proportion of the expected conductive heat flux from the Earth’s interior beneath both Gateshead and Newcastle becomes entrained by groundwater flow and transported elsewhere, through former mineworkings in which the rocks have become ‘permeabilised’ during the region’s long history of coal mining. Discharge of groundwater at a nearby minewater pumping station, Kibblesworth, has a heat flux that we estimate as ∼7.5 MW; it thus ‘captures’ the equivalent of roughly two thirds of the geothermal heat flux through a >100 km2 surrounding region. Modelling of the associated groundwater flow regime provides first-order estimates of the hydraulic transport properties of ‘permeabilised’ Carboniferous Coal Measures rocks, comprising permeability ∼3 × 10−11 m2 or ∼30 darcies, hydraulic conductivity ∼2 × 10−4 m s−1, and transmissivity ∼2 × 10−3 m2 s−1 or ∼200 m2 day−1; these are very high values, comparable to what one might expect for karstified Carboniferous limestone. Furthermore, the large-magnitude subsurface UHIs create significant downward components of conductive heat flow in the shallow subsurface, which are supplemented by downward heat transport by groundwater movement towards the flow network through the former mineworkings. The warm water in these workings has thus been heated, in part, by heat drawn from the shallow subsurface, as well as by heat flowing from the Earth’s interior. Similar conductive heat flow and groundwater flow responses are expected in other urban former coalfield regions of Britain; knowledge of the processes involved may facilitate their use as heat stores and may also contribute to UHI mitigation

Modelling diffusion in crystals under high internal stress gradients

Diffusion of vacancies and impurities in metals is important in many processes occurring in structural materials. This diffusion often takes place in the presence of spatially rapidly varying stresses. Diffusion under stress is frequently modelled by local approximations to the vacancy formation and diffusion activation enthalpies which are linear in the stress, in order to account for its dependence on the local stress state and its gradient. Here, more accurate local approximations to the vacancy formation and diffusion activation enthalpies, and the simulation methods needed to implement them, are introduced. The accuracy of both these approximations and the linear approximations are assessed via comparison to full atomistic studies for the problem of vacancies around a Lomer dislocation in Aluminium. Results show that the local and linear approximations for the vacancy formation enthalpy and diffusion activation enthalpy are accurate to within 0.05 eV outside a radius of about 13 Å (local) and 17 Å (linear) from the centre of the dislocation core or, more generally, for a strain gradient of roughly up to 6 × 10^6 m^-1 and 3 × 10^6 m^-1, respectively. These results provide a basis for the development of multiscale models of diffusion under highly non-uniform stress

Review of the Inputs of Professor David Smythe in Relation to Planning Applications for Shale Gas Development in Lancashire (Planning Applications LCC/2014/0096 /0097 /0101 and /0102) and Associated Recommendations

No abstract available

Associative memory in gene regulation networks

The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organism’s gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily ‘Hebbian’ – ‘genes that fire together wire together’ – i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ‘recall’ capabilities normally reserved for cognitive systems

Transformations in the Scale of Behaviour and the Global Optimisation of Constraints in Adaptive Networks

The natural energy minimisation behaviour of a dynamical system can be interpreted as a simple optimisation process, finding a locally optimal resolution of problem constraints. In human problem solving, high-dimensional problems are often made much easier by inferring a low-dimensional model of the system in which search is more effective. But this is an approach that seems to require top-down domain knowledge; not one amenable to the spontaneous energy minimisation behaviour of a natural dynamical system. However, in this paper we investigate the ability of distributed dynamical systems to improve their constraint resolution ability over time by self-organisation. We use a ‘self-modelling’ Hopfield network with a novel type of associative connection to illustrate how slowly changing relationships between system components can result in a transformation into a new system which is a low-dimensional caricature of the original system. The energy minimisation behaviour of this new system is significantly more effective at globally resolving the original system constraints. This model uses only very simple, and fully-distributed positive feedback mechanisms that are relevant to other ‘active linking’ and adaptive networks. We discuss how this neural network model helps us to understand transformations and emergent collective behaviour in various non-neural adaptive networks such as social, genetic and ecological networks

Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

We provide Markov chain Monte Carlo (MCMC) algorithms for computing the bandwidth matrix for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters to be estimated, which we do by optimizing the likelihood cross-validation criterion. Numerical results show that the resulting bandwidths are superior to all existing methods; for dimensions greater than two, our algorithm is the first practical method for estimating the optimal bandwidth matrix. Moreover, the MCMC algorithm for bandwidth selection for multivariate data has no increased difficulty as the dimension of data increases.Bandwidth selection, cross-validation, multivariate kernel density estimation, sampling algorithms.

The Impact of the Restaurant Critic

Restaurant critiques have an effect on the sales volume of restaurants following the publication of the critique in the target markets’ media. The author discusses data from restaurant operations in the greater Cleveland, Ohio, metropolitan area which have had their operations publicly critiqued, and also addresses the credibility of critics

Incidence and post-pollination mechanisms of nonrandom mating in Arabidopsis thaliana

Compatible pollinations from many differenttaxa display nonrandom mating. Here we describe a systemfor examining questions of nonrandom mating in Arabidopsisthaliana. Using this system, we demonstrate thatArabidopsis thaliana displays nonrandom mating betweendistinct accessions. Statistical analysis of these data demonstratesaspects of both pollen competition and male–female complementarity in these matings. Cytologicalexperiments implicate pollen germination and pollen tubegrowth rates as possible causal factors in these nonrandommating efficiencies