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

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

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

Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

Kernel density estimation for multivariate data is an important technique that has a wide range of applications in econometrics and finance. However, it has received significantly less attention than its univariate counterpart. The lower level of interest in multivariate kernel density estimation is mainly due to the increased difficulty in deriving an optimal data-driven bandwidth as the dimension of data increases. We provide Markov chain Monte Carlo (MCMC) algorithms for estimating optimal bandwidth matrices for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters whose posterior density can be obtained through the likelihood cross-validation criterion. Numerical studies for bivariate data show that the MCMC algorithm generally performs better than the plug-in algorithm under the Kullback-Leibler information criterion, and is as good as the plug-in algorithm under the mean integrated squared errors (MISE) criterion. Numerical studies for 5 dimensional data show that our algorithm is superior to the normal reference rule. Our MCMC algorithm is the first data-driven bandwidth selector for kernel density estimation with more than two variables, and the sampling algorithm involves no increased difficulty as the dimension of data increaseBandwidth matrices; Cross-validation; Kullback-Leibler information; mean integrated squared errors; Sampling algorithms.

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

Gamma-ray spectrometry in the field: Radioactive heat production in the Central Slovakian Volcanic Zone

We report 62 sets of measurements from central-southern Slovakia, obtained using a modern portable gamma-ray spectrometer, which reveal the radioactive heat production in intrusive and extrusive igneous rocks of the Late Cenozoic Central Slovakian Volcanic Zone. Sites in granodiorite of the Štiavnica pluton are thus shown to have heat production in the range ~ 2.2–4.9 μW m− 3, this variability being primarily a reflection of variations in content of the trace element uranium. Sites in dioritic parts of this pluton have a lower, but overlapping, range of values, ~ 2.1–4.4 μW m− 3. Sites that have been interpreted in adjoining minor dioritic intrusions of similar age have heat production in the range ~ 1.4–3.3 μW m− 3. The main Štiavnica pluton has zoned composition, with potassium and uranium content and radioactive heat production typically increasing inward from its margins, reflecting variations observed in other granodioritic plutons elsewhere. It is indeed possible that the adjoining dioritic rocks, hitherto assigned to other minor intrusions of similar age, located around the periphery of the Štiavnica pluton, in reality provide further evidence for zonation of the same pluton. The vicinity of this pluton is associated with surface heat flow ~ 40 mW m− 2 above the regional background. On the basis of our heat production measurements, we thus infer that the pluton has a substantial vertical extent, our preferred estimate for the scale depth for its downward decrease in radioactive heat production being ~ 8 km. Nonetheless, this pluton lacks any significant negative Bouguer gravity anomaly. We attribute this to the effect of the surrounding volcanic caldera, filled with relatively low-density lavas, ‘masking’ the pluton's own gravity anomaly. We envisage that emplacement occurred when the pluton was much hotter, and thus of lower density, than at present, its continued uplift, evident from the local geomorphology, being the isostatic consequence of localized erosion. The heat production in this intrusion evidently plays a significant role, hitherto unrecognized, in the regional geothermics

Optimisation in ‘Self-modelling’ Complex Adaptive Systems

When a dynamical system with multiple point attractors is released from an arbitrary initial condition it will relax into a configuration that locally resolves the constraints or opposing forces between interdependent state variables. However, when there are many conflicting interdependencies between variables, finding a configuration that globally optimises these constraints by this method is unlikely, or may take many attempts. Here we show that a simple distributed mechanism can incrementally alter a dynamical system such that it finds lower energy configurations, more reliably and more quickly. Specifically, when Hebbian learning is applied to the connections of a simple dynamical system undergoing repeated relaxation, the system will develop an associative memory that amplifies a subset of its own attractor states. This modifies the dynamics of the system such that its ability to find configurations that minimise total system energy, and globally resolve conflicts between interdependent variables, is enhanced. Moreover, we show that the system is not merely ‘recalling’ low energy states that have been previously visited but ‘predicting’ their location by generalising over local attractor states that have already been visited. This ‘self-modelling’ framework, i.e. a system that augments its behaviour with an associative memory of its own attractors, helps us better-understand the conditions under which a simple locally-mediated mechanism of self-organisation can promote significantly enhanced global resolution of conflicts between the components of a complex adaptive system. We illustrate this process in random and modular network constraint problems equivalent to graph colouring and distributed task allocation problems

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