UK construction orders and output: A comparison of government and commercially available data 1995-2001

Because of the importance and potential usefulness of construction market statistics to firms and government, consistency between different sources of data is examined with a view to building a predictive model of construction output using construction data alone. However, a comparison of Department of Trade and Industry (DTI) and Office for National Statistics (ONS) series shows that the correlation coefcient (used as a measure of consistency) of the DTI output and DTI orders data and the correlation coefficient of the DTI output and ONS output data are low. It is not possible to derive a predictive model of DTI output based on DTI orders data alone. The question arises whether or not an alternative independent source of data may be used to predict DTI output data. Independent data produced by Emap Glenigan (EG), based on planning applications, potentially offers such a source of information. The EG data records the value of planning applications and their planned start and finish dates. However, as this data is ex ante and is not correlated with DTI output it is not possible to use this data to describe the volume of actual construction output. Nor is it possible to use the EG planning data to predict DTI construc-tion orders data. Further consideration of the issues raised reveal that it is not practically possible to develop a consistent predictive model of construction output using construction statistics gathered at different stages in the development process.

Qualitative modelling of sustainable energy scenarios: An application and extension of the Bon Qualitative Input-Output model

Climate change is one of the major challenges facing economic systems at the start of the 21st century. Reducing greenhouse gas emissions will require both restructuring the energy supply system (production) and addressing the efficiency and sufficiency of the social uses of energy (consumption). The energy production system is a complicated supply network of interlinked sectors with 'knock-on' effects throughout the economy. End use energy consumption is governed by complex sets of interdependent cultural, social, psychological and economic variables driven by shifts in consumer preference and technological development trajectories. To date, few models have been developed for exploring alternative joint energy production-consumption systems. The aim of this work is to propose one such model. This is achieved in a methodologically coherent manner through integration of qualitative input-output models of production, with Bayesian belief network models of consumption, at point of final demand. The resulting integrated framework can be applied either (relatively) quickly and qualitatively to explore alternative energy scenarios, or as a fully developed quantitative model to derive or assess specific energy policy options. The qualitative applications are explored here.

Mind the gap: improving the dissemination of CBT

Empirically supported psychological treatments have been developed for a range of psychiatric disorders but there is evidence that patients are not receiving them in routine clinical care. Furthermore, even when patients do receive these treatments there is evidence that they are often not well delivered. The aim of this paper is to identify the barriers to the dissemination of evidence-based psychological treatments and then propose ways of overcoming them, hence potentially bridging the gap between research findings and clinical practice. (C) 2009 Elsevier Ltd. All rights reserved.

Input and output modality effects in immediate serial recall

In immediate recall tasks, visual recency is substantially enhanced when output interference is low (Cowan, Saults, Elliott, & Moreno, 2002; Craik, 1969) whereas auditory recency remains high even under conditions of high output interference. Ibis auditory advantage has been interpreted in terms of auditory resistance to output interference (e.g., Neath & Surprenant, 2003). In this study the auditory-visual difference at low output interference re-emerged when ceiling effects were accounted for, but only with spoken output. With written responding the auditory advantage remained significantly larger with high than with low output interference. These new data suggest that both superior auditory encoding and modality-specific output interference contribute to the classic auditory-visual modality effect.

Disruption of sitting balance after stroke: influence of spoken output

Objectives: To identify the extent of dual task interference between cognitive and motor tasks, (cognitive motor interference (CMI)) in sitting balance during recovery from stroke; to compare CMI in sitting balance between stroke and non-stroke groups; and to record any changes to CMI during sitting that correlate with functional recovery. Method: 36 patients from stroke rehabilitation settings in three NHS trusts. Healthy control group: 21 older volunteers. Measures of seated postural sway were taken in unsupported sitting positions, alone, or concurrently with either a repetitive utterance task or an oral word category generation task. Outcome measures were variability of sway area, path length of sway, and the number of valid words generated. Results: Stroke patients were generally less stable than controls during unsupported sitting tasks. They showed greater sway during repetitive speech compared with quiet sitting, but did not show increased instability to posture between repetitive speech and word category generation. When compared with controls, stroke patients experienced greater dual task interferences during repetitive utterance but not during word generation. Sway during repetitive speech was negatively correlated with concurrent function on the Barthel ADL index. Conclusions: The stroke patients showed postural instability and poor word generation skills. The results of this study show that the effort of verbal utterances alone was sufficient to disturb postural control early after stroke, and the extent of this instability correlated with concomitant Barthel ADL function.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

The British ‘failure’ that never was? The Anglo-American ‘productivity gap’ in large-scale interwar retailing—evidence from the department store sector

Interwar British retailing has been characterized as having lower productivity, less developed managerial hierarchies and methods, and weaker scale economies than its US counterpart. This article examines comparative productivity for one major segment of large-scale retailing in both countries—the department store sector. Drawing on exceptionally detailed contemporary survey data, we show that British department stores in fact achieved superior performance in terms of operating costs, margins, profits, and stock-turn. While smaller British stores had lower labour productivity than US stores of equivalent size, TFP was generally higher for British stores, which also enjoyed stronger scale economies. We also examine the reasons behind Britain's surprisingly strong relative performance, using surviving original returns from the British surveys. Contrary to arguments that British retailers faced major barriers to the development of large-scale enterprises, that could reap economies of scale and scope and invest in machinery and marketing to support the growth of their primary sales functions, we find that British department stores enthusiastically embraced the retail ‘managerial revolution’—and reaped substantial benefits from this investment.

Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

Performance analysis of the LMS blind minimum-output-energy CDMA detector

Adaptive filters used in code division multiple access (CDMA) receivers to counter interference have been formulated both with and without the assumption of training symbols being transmitted. They are known as training-based and blind detectors respectively. We show that the convergence behaviour of the blind minimum-output-energy (MOE) detector can be quite easily derived, unlike what was implied by the procedure outlined in a previous paper. The simplification results from the observation that the correlation matrix determining convergence performance can be made symmetric, after which many standard results from the literature on least mean square (LMS) filters apply immediately.