Far-red light filtering by plastic film, greenhouse-cladding materials: effects on growth and flowering in Petunia and Impatiens

Photoselective plastic films with low transmission to far-red (FR) light (700-800 nm) are now available so that plants grown in greenhouses clad with such plastics exhibit reduced stem extension and, consequently, plant height. Here we compare the action of three FR-absorbing polythene films on extension growth of Petunia (Petunia X hybrida) cv. 'Express Blue' and Impatiens walleriana cv. 'Accent Deep Pink' with plants grown under a control polythene film (standard UVI/EVA film). Half of the plants under the control film were treated with a chemical plant growth regulator (PGR; diaminozide, B-Nine) and half were sprayed with water alone. Possible negative effects of such film plastics on flowering, and on fresh and dry weight accumulation, were also quantified. Plants were harvested destructively when all plants in each treatment had reached the first open flower stage. In Petunia, plant height was reduced by all three FR-filtering films and by PGR-treatment. The FR-filtering films giving the highest R:FR ratios also reduced plant height in Impatiens. Leaf number, leaf area and total dry Weight in both species. were greatest in the controls and smallest under films with the lowest PAR transmission. The film giving the highest R:FR ratio and PAR transmission also produced the most compact Petunia plants;, while the film. with. the lowest PAR transmission produced the least compact plants in both species. There was no significant effect of treatments on time to first flower in Impatiens. However, Petunia plants under low PAR transmission films took longer to flower. Plastic-films which filter out FR light to increase the R:FR ratio, combined With high PAR transmission, can therefore be used as an alternative to conventional PGRs.

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.

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.

Integrated optics for nulling interferometry in the thermal infrared

Modal filtering is based on the capability of single-mode waveguides to transmit only one complex amplitude function to eliminate virtually any perturbation of the interfering wavefronts, thus making very high rejection ratios possible in a nulling interferometer. In the present paper we focus on the progress of Integrated Optics in the thermal infrared [6-20 mu m] range, one of the two candidate technologies for the fabrication of Modal Filters, together with fiber optics. In conclusion of the European Space Agency's (ESA) "Integrated Optics for Darwin" activity, etched layers of clialcogenide material deposited on chalcogenide glass substrates was selected among four candidates as the technology with the best potential to simultaneously meet the filtering efficiency, absolute and spectral transmission, and beam coupling requirements. ESA's new "Integrated Optics" activity started at mid-2007 with the purpose of improving the technology until compliant prototypes can be manufactured and validated, expectedly by the end of 2009. The present paper aims at introducing the project and the components requirements and functions. The selected materials and preliminary designs, as well as the experimental validation logic and test benches are presented. More details are provided on the progress of the main technology: vacuum deposition in the co-evaporation mode and subsequent etching of chalcogenide layers. In addition., preliminary investigations of an alternative technology based on burying a chalcogenide optical fiber core into a chalcogenide substrate are presented. Specific developments of anti-reflective solutions designed for the mitigation of Fresnel losses at the input and output surface of the components are also introduced.

EMG signal filtering based on Empirical Mode Decomposition

This paper introduces a procedure for filtering electromyographic (EMG) signals. Its key element is the Empirical Mode Decomposition, a novel digital signal processing technique that can decompose my time-series into a set of functions designated as intrinsic mode functions. The procedure for EMG signal filtering is compared to a related approach based on the wavelet transform. Results obtained from the analysis of synthetic and experimental EMG signals show that Our method can be Successfully and easily applied in practice to attenuation of background activity in EMG signals. (c) 2006 Elsevier Ltd. All rights reserved.

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.

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.

Wavelet filtered modelling applied to measurements of a waveguide's THz time domain response

A quasi-optical de-embedding technique for characterizing waveguides is demonstrated using wideband time-resolved terahertz spectroscopy. A transfer function representation is adopted for the description of the signal in the input and output port of the waveguides. The time domain responses were discretised and the waveguide transfer function was obtained through a parametric approach in the z-domain after describing the system with an ARX as well as with a state space model. Prior to the identification procedure, filtering was performed in the wavelet domain to minimize signal distortion and the noise propagating in the ARX and subspace models. The model identification procedure requires isolation of the phase delay in the structure and therefore the time-domain signatures must be firstly aligned with respect to each other before they are compared. An initial estimate of the number of propagating modes was provided by comparing the measured phase delay in the structure with theoretical calculations that take into account the physical dimensions of the waveguide. Models derived from measurements of THz transients in a precision WR-8 waveguide adjustable short will be presented.