Expert judgement in the processes of commercial property market forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

Necessary and sufficient conditions for realizability of point processes

We give necessary and sufficient conditions for a pair of (generali- zed) functions 1(r1) and 2(r1, r2), ri 2X, to be the density and pair correlations of some point process in a topological space X, for ex- ample, Rd, Zd or a subset of these. This is an infinite-dimensional version of the classical “truncated moment” problem. Standard tech- niques apply in the case in which there can be only a bounded num- ber of points in any compact subset of X. Without this restriction we obtain, for compact X, strengthened conditions which are necessary and sufficient for the existence of a process satisfying a further re- quirement—the existence of a finite third order moment. We general- ize the latter conditions in two distinct ways when X is not compact.

The potential of electrospinning in rapid manufacturing processes

We review the process of electrospinning and how this new technique for generating a rich morphology of nano and micro scale fibres sits alongside established procedures for rapid manufacturing. We introduce the key elements of electrospinning and how these influence the nature and distribution of the fibres produced. We describe the range of polymers available for electrospinning and the limitations to the use of these materials. Using this base we review the potential approaches to using electrospinning as part of a broader rapid manufacturing system and the possible applications for such a hybrid system.

Discrete time representation of continuous time ARMA processes

This paper derives exact discrete time representations for data generated by a continuous time autoregressive moving average (ARMA) system with mixed stock and flow data. The representations for systems comprised entirely of stocks or of flows are also given. In each case the discrete time representations are shown to be of ARMA form, the orders depending on those of the continuous time system. Three examples and applications are also provided, two of which concern the stationary ARMA(2, 1) model with stock variables (with applications to sunspot data and a short-term interest rate) and one concerning the nonstationary ARMA(2, 1) model with a flow variable (with an application to U.S. nondurable consumers’ expenditure). In all three examples the presence of an MA(1) component in the continuous time system has a dramatic impact on eradicating unaccounted-for serial correlation that is present in the discrete time version of the ARMA(2, 0) specification, even though the form of the discrete time model is ARMA(2, 1) for both models.

Processes controlling DOC in pore water during simulated drought cycles in six different UK peats

The effect of episodic drought on dissolved organic carbon (DOC) dynamics in peatlands has been the subject of considerable debate, as decomposition and DOC production is thought to increase under aerobic conditions, yet decreased DOC concentrations have been observed during drought periods. Decreased DOC solubility due to drought-induced acidification driven by sulphur (S) redox reactions has been proposed as a causal mechanism; however evidence is based on a limited number of studies carried out at a few sites. To test this hypothesis on a range of different peats, we carried out controlled drought simulation experiments on peat cores collected from six sites across Great Britain. Our data show a concurrent increase in sulphate (SO4) and a decrease in DOC across all sites during simulated water table draw-down, although the magnitude of the relationship between SO4 and DOC differed between sites. Instead, we found a consistent relationship across all sites between DOC decrease and acidification measured by the pore water acid neutralising capacity (ANC). ANC provided a more consistent measure of drought-induced acidification than SO4 alone because it accounts for differences in base cation and acid anions concentrations between sites. Rewetting resulted in rapid DOC increases without a concurrent increase in soil respiration, suggesting DOC changes were primarily controlled by soil acidity not soil biota. These results highlight the need for an integrated analysis of hydrologically driven chemical and biological processes in peatlands to improve our understanding and ability to predict the interaction between atmospheric pollution and changing climatic conditions from plot to regional and global scales.

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli.