Evaluating relative impact of virtual reality system variables on architectural design comprehension and presence - A variable-centered approach using fractional factorial experiment

The relative contributions of five variables (Stereoscopy, screen size, field of view, level of realism and level of detail) of virtual reality systems on spatial comprehension and presence are evaluated here. Using a variable-centered approach instead of an object-centric view as its theoretical basis, the contributions of these five variables and their two-way interactions are estimated through a 25-1 fractional factorial experiment (screening design) of resolution V with 84 subjects. The experiment design, procedure, measures used, creation of scales and indices, results of statistical analysis, their meaning and agenda for future research are elaborated.

A benchmark-driven modelling approach for evaluating deployment choices on a multicore architecture

The complexity of current and emerging high performance architectures provides users with options about how best to use the available resources, but makes predicting performance challenging. In this work a benchmark-driven performance modelling approach is outlined that is appro- priate for modern multicore architectures. The approach is demonstrated by constructing a model of a simple shallow water code on a Cray XE6 system, from application-specific benchmarks that illustrate precisely how architectural char- acteristics impact performance. The model is found to recre- ate observed scaling behaviour up to 16K cores, and used to predict optimal rank-core affinity strategies, exemplifying the type of problem such a model can be used for.

Anticipatory objects and uncertain imminence: cattle grids, landscape and the presencing of climate change on the Lizard Peninsula, UK

In this paper we show how a seemingly unremarkable object – a cattle grid – has come to presence climate change in partial and contingent ways on the Lizard Peninsula, Cornwall, UK. We identify the cattle grid as an ‘anticipatory object’ through which conservation organisations seek to manage the future and adapt to climate change, but which at the same time presences that unthought-of future for others in the landscape. We explore the ways in which the cattle grid acts to presence something that is not only absent – climate change – but has uncertain imminence. We investigate the ways in which the cattle grid make climate relevant as an embodied and experiential process, a physical and intellectual artefact, and the means to imagine climate and the ways it might change. Drawing upon interpretative approaches informed by theorisations of materiality, presence and absence to understand climate change as a social phenomenon, we go beyond a consideration of this ordinary object defined by its function to consider how the object is experienced, the processes and practices through which people relate to it, and the ways in which social meaning accumulates around it. The empirical basis for this argument is provided by in-depth interviews with local representatives of Natural England, residents and farmers.

An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams

This research has responded to the need for diagnostic reference tools explicitly linking the influence of environmental uncertainty and performance within the supply chain. Uncertainty is a key factor influencing performance and an important measure of the operating environment. We develop and demonstrate a novel reference methodology based on data envelopment analysis (DEA) for examining the performance of value streams within the supply chain with specific reference to the level of environmental uncertainty they face. In this paper, using real industrial data, 20 product supply value streams within the European automotive industry sector are evaluated. Two are found to be efficient. The peer reference groups for the underperforming value streams are identified and numerical improvement targets are derived. The paper demonstrates how DEA can be used to guide supply chain improvement efforts through role-model identification and target setting, in a way that recognises the multiple dimensions/outcomes of the supply chain process and the influence of its environmental conditions. We have facilitated the contextualisation of environmental uncertainty and its incorporation into a specific diagnostic reference tool.

Evaluating forecasts from SETAR models of exchange rates

We consider the forecasting performance of two SETAR exchange rate models proposed by Kräger and Kugler [J. Int. Money Fin. 12 (1993) 195]. Assuming that the models are good approximations to the data generating process, we show that whether the non-linearities inherent in the data can be exploited to forecast better than a random walk depends on both how forecast accuracy is assessed and on the ‘state of nature’. Evaluation based on traditional measures, such as (root) mean squared forecast errors, may mask the superiority of the non-linear models. Generalized impulse response functions are also calculated as a means of portraying the asymmetric response to shocks implied by such models.

Evaluating multivariate forecast densities: a comparison of two approaches

We consider methods of evaluating multivariate density forecasts. A recently proposed method is found to lack power when the correlation structure is mis-specified. Tests that have good power to detect mis-specifications of this sort are described. We also consider the properties of the tests in the presence of more general mis-specifications.

Evaluating interval forecasts of high-frequency financial data

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated.

Evaluating the Bank of England density forecasts of inflation

We consider evaluating the UK Monetary Policy Committee's inflation density forecasts using probability integral transform goodness-of-fit tests. These tests evaluate the whole forecast density. We also consider whether the probabilities assigned to inflation being in certain ranges are well calibrated, where the ranges are chosen to be those of particular relevance to the MPC, given its remit of maintaining inflation rates in a band around per annum. Finally, we discuss the decision-based approach to forecast evaluation in relation to the MPC forecasts

Evaluating the Survey of Professional Forecasters probability distributions of expected inflation based on derived event probability forecasts

Techniques are proposed for evaluating forecast probabilities of events. The tools are especially useful when, as in the case of the Survey of Professional Forecasters (SPF) expected probability distributions of inflation, recourse cannot be made to the method of construction in the evaluation of the forecasts. The tests of efficiency and conditional efficiency are applied to the forecast probabilities of events of interest derived from the SPF distributions, and supplement a whole-density evaluation of the SPF distributions based on the probability integral transform approach.

A comprehensive benchmarking system for evaluating global vegetation models

We present a benchmark system for global vegetation models. This system provides a quantitative evaluation of multiple simulated vegetation properties, including primary production; seasonal net ecosystem production; vegetation cover; composition and height; fire regime; and runoff. The benchmarks are derived from remotely sensed gridded datasets and site-based observations. The datasets allow comparisons of annual average conditions and seasonal and inter-annual variability, and they allow the impact of spatial and temporal biases in means and variability to be assessed separately. Specifically designed metrics quantify model performance for each process, and are compared to scores based on the temporal or spatial mean value of the observations and a "random" model produced by bootstrap resampling of the observations. The benchmark system is applied to three models: a simple light-use efficiency and water-balance model (the Simple Diagnostic Biosphere Model: SDBM), the Lund-Potsdam-Jena (LPJ) and Land Processes and eXchanges (LPX) dynamic global vegetation models (DGVMs). In general, the SDBM performs better than either of the DGVMs. It reproduces independent measurements of net primary production (NPP) but underestimates the amplitude of the observed CO2 seasonal cycle. The two DGVMs show little difference for most benchmarks (including the inter-annual variability in the growth rate and seasonal cycle of atmospheric CO2), but LPX represents burnt fraction demonstrably more accurately. Benchmarking also identified several weaknesses common to both DGVMs. The benchmarking system provides a quantitative approach for evaluating how adequately processes are represented in a model, identifying errors and biases, tracking improvements in performance through model development, and discriminating among models. Adoption of such a system would do much to improve confidence in terrestrial model predictions of climate change impacts and feedbacks.

Effect of cattle urine addition on the surface emissions and subsurface concentrations of greenhouse gases in a UK peat grassland

Grazing systems represent a substantial percentage of the global anthropogenic flux of nitrous oxide (N2O) as a result of nitrogen addition to the soil. The pool of available carbon that is added to the soil from livestock excreta also provides substrate for the production of carbon dioxide (CO2) and methane (CH4) by soil microorganisms. A study into the production and emission of CO2, CH4 and N2O from cattle urine amended pasture was carried out on the Somerset Levels and Moors, UK over a three-month period. Urine-amended plots (50 g N m−2) were compared to control plots to which only water (12 mg N m−2) was applied. CO2 emission peaked at 5200 mg CO2 m−2 d−1 directly after application. CH4 flux decreased to −2000 μg CH4 m−2 d−1 two days after application; however, net CH4 flux was positive from urine treated plots and negative from control plots. N2O emission peaked at 88 mg N2O m−2 d−1 12 days after application. Subsurface CH4 and N2O concentrations were higher in the urine treated plots than the controls. There was no effect of treatment on subsurface CO2 concentrations. Subsurface N2O peaked at 500 ppm 12 days after and 1200 ppm 56 days after application. Subsurface NO3− concentration peaked at approximately 300 mg N kg dry soil−1 12 days after application. Results indicate that denitrification is the key driver for N2O release in peatlands and that this production is strongly related to rainfall events and water-table movement. N2O production at depth continued long after emissions were detected at the surface. Further understanding of the interaction between subsurface gas concentrations, surface emissions and soil hydrological conditions is required to successfully predict greenhouse gas production and emission.