Minimum aberration construction results for nonregular two-level fractional factorial designs

Nonregular two-level fractional factorial designs are designs which cannot be specified in terms of a set of defining contrasts. The aliasing properties of nonregular designs can be compared by using a generalisation of the minimum aberration criterion called minimum G2-aberration.Until now, the only nontrivial designs that are known to have minimum G2-aberration are designs for n runs and m n–5 factors. In this paper, a number of construction results are presented which allow minimum G2-aberration designs to be found for many of the cases with n = 16, 24, 32, 48, 64 and 96 runs and m n/2–2 factors.

Modeling data with structural and temporal correlation using lower level and higher level multilevel models

Novel imaging techniques are playing an increasingly important role in drug development, providing insight into the mechanism of action of new chemical entities. The data sets obtained by these methods can be large with complex inter-relationships, but the most appropriate statistical analysis for handling this data is often uncertain - precisely because of the exploratory nature of the way the data are collected. We present an example from a clinical trial using magnetic resonance imaging to assess changes in atherosclerotic plaques following treatment with a tool compound with established clinical benefit. We compared two specific approaches to handle the correlations due to physical location and repeated measurements: two-level and four-level multilevel models. The two methods identified similar structural variables, but higher level multilevel models had the advantage of explaining a greater proportion of variation, and the modeling assumptions appeared to be better satisfied.

Elastic-net prefiltering for two-class classification

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

Output envelope adjustment for an environmental fuzzy logic controller

A two-level fuzzy logic controller for use in air-conditioning systems is outlined in this paper. At the first level a simplified controller is produced from expert knowledge and envelope adjustment is introduced, while the second level provides a means for adapting this controller to different working spaces. The mechanism for adaption is easily implemented and can be used in real time. A series of simulations is presented to illustrate the proposed schema.

An elastic net orthogonal forward regression algorithm

In this paper we propose an efficient two-level model identification method for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularization parameters in the elastic net are optimized using a particle swarm optimization (PSO) algorithm at the upper level by minimizing the leave one out (LOO) mean square error (LOOMSE). Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.

Elastic net orthogonal forward regression

An efficient two-level model identification method aiming at maximising a model׳s generalisation capability is proposed for a large class of linear-in-the-parameters models from the observational data. A new elastic net orthogonal forward regression (ENOFR) algorithm is employed at the lower level to carry out simultaneous model selection and elastic net parameter estimation. The two regularisation parameters in the elastic net are optimised using a particle swarm optimisation (PSO) algorithm at the upper level by minimising the leave one out (LOO) mean square error (LOOMSE). There are two elements of original contributions. Firstly an elastic net cost function is defined and applied based on orthogonal decomposition, which facilitates the automatic model structure selection process with no need of using a predetermined error tolerance to terminate the forward selection process. Secondly it is shown that the LOOMSE based on the resultant ENOFR models can be analytically computed without actually splitting the data set, and the associate computation cost is small due to the ENOFR procedure. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.

Investigation of air quality, comfort parameters and effectiveness for two floor-level air supply systems in classrooms

The method of distributing the outdoor air in classrooms has a major impact on indoor air quality and thermal comfort of pupils. In a previous study, ([11] Karimipanah T, Sandberg M, Awbi HB. A comparative study of different air distribution systems in a classroom. In: Proceedings of Roomvent 2000, vol. II, Reading, UK, 2000. p. 1013-18; [13] Karimipanah T, Sandberg M, Awbi HB, Blomqvist C. Effectiveness of confluent jets ventilation system for classrooms. In: Idoor Air 2005, Beijing, China, 2005 (to be presented).) presented results for four and two types of air distribution systems tested in a purpose built classroom with simulated occupancy as well as computational fluid dynamics (CFD) modelling. In this paper, the same experimental setup has been used to investigate the indoor environment in the classroom using confluent jet ventilation, see also ([12]Cho YJ, Awbi HB, Karimipanah T. The characteristics of wall confluent jets for ventilated enclosures. In: Proceedings of Roomvent 2004, Coimbra, Portugal, 2004.) Measurements of air speed, air temperature and tracer gas concentrations have been carried out for different thermal conditions. In addition, 56 cases of CFD simulations have been carried to provide additional information on the indoor air quality and comfort conditions throughout the classroom, such as ventilation effectiveness, air exchange effectiveness, effect of flow rate, effect of radiation, effect of supply temperature, etc., and these are compared with measured data.

Mountain waves in two-layer sheared flows: Critical-level effects, wave reflection, and drag enhancement

Internal gravity waves generated in two-layer stratified shear flows over mountains are investigated here using linear theory and numerical simulations. The impact on the gravity wave drag of wind profiles with constant unidirectional or directional shear up to a certain height and zero shear above, with and without critical levels, is evaluated. This kind of wind profile, which is more realistic than the constant shear extending indefinitely assumed in many analytical studies, leads to important modifications in the drag behavior due to wave reflection at the shear discontinuity and wave filtering by critical levels. In inviscid, nonrotating, and hydrostatic conditions, linear theory predicts that the drag behaves asymmetrically for backward and forward shear flows. These differences primarily depend on the fraction of wavenumbers that pass through their critical level before they are reflected by the shear discontinuity. If this fraction is large, the drag variation is not too different from that predicted for an unbounded shear layer, while if it is small the differences are marked, with the drag being enhanced by a considerable factor at low Richardson numbers (Ri). The drag may be further enhanced by nonlinear processes, but its qualitative variation for relatively low Ri is essentially unchanged. However, nonlinear processes seem to interact constructively with shear, so that the drag for a noninfinite but relatively high Ri is considerably larger than the drag without any shear at all.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

An eclectic morphostratigraphic model for the sedimentary response to Holocene sea-level rise in northwest Europe

The improved empirical understanding of silt facies in Holocene coastal sequences provided by such as diatom, foraminifera, ostracode and testate amoebae analysis, combined with insights from quantitative stratigraphic and hydraulic simulations, has led to an inclusive, integrated model for the palaeogeomorphology, stratigraphy, lithofacies and biofacies of northwest European Holocene coastal lowlands in relation to sea-level behaviour. The model covers two general circumstances and is empirically supported by a range of field studies in the Holocene deposits of a number of British estuaries, particularly, the Severn. Where deposition was continuous over periods of centuries to millennia, and sea level fluctuated about a rising trend, the succession consists of repeated cycles of silt and peat lithofacies and biofacies in which series of transgressive overlaps (submergence sequences) alternate with series of regressive overlaps (emergence sequences) in association with the waxing and waning of tidal creek networks. Environmental and sea-level change are closely coupled, and equilibrium and secular pattern is of the kind represented ideally by a closed limit cycle. In the second circumstance, characteristic of unstable wetland shores and generally affecting smaller areas, coastal erosion ensures that episodes of deposition in the high intertidal zone last no more than a few centuries. The typical response is a series of regressive overlaps (emergence sequence) in erosively based high mudflat and salt-marsh silts that record, commonly as annual banding, exceptionally high deposition rates and a state of strong disequilibrium. Environmental change, including creek development, and sea-level movement are uncoupled. Only if deposition proceeds for a sufficiently long period, so that marshes mature, are equilibrium and close coupling regained. (C) 2002 Elsevier Science B.V. All rights reserved.

Granulometric characterization and evaluation of annually banded mid-Holocene estuarine silts, Welsh Severn Estuary (UK): coastal change, sea level and climate

Holocene silts (salt marshes) and highest intertidal-supratidal peats are superbly exposed on a 15 kin coastal transect which reveals two laterally extensive units of annually banded silts (Beds 3, 7) associated with three transgressive-regressive silt-peat cycles (early sixth-early fourth millennium BC). Bed 3 in places is concordantly and gradationally related to peats above and below, but in others transgresses older strata. Bed 7 also grades up into peat, but everywhere overlies a discordance. The banding in Bed 3 at three main and two minor sites was resolved and characterized texturally at high-resolution (2.5/5 mm contiguous slices) using laser granulometry (LS230 with PIDS) and a comprehensive scheme of data-assessment. Most of Bed 3 formed very rapidly, at peak values of several tens of millimetres annually, in accordance with modelled effects of sea-level fluctuations on mature marshes (bed concordant and gradational) and on marshes growing up after coastal erosion and retreat (bed with discordant base). Using data from the modern Severn Estuary, the textural contrast within bands, and its variation between bands, points to a variable but overall milder mid-Holocene climate than today. The inter-annual variability affected marsh dynamics, as shown by the behaviour of the finely divided plant tissues present. Given local calibration, the methodology is applicable to other tidal systems with banded silts in Britain and mainland northwest Europe. (c) 2006 Elsevier Ltd. All rights reserved.

Genetically modified insect resistance in cotton: some farm level economic impacts in India

The paper explores the impact of insect-resistant Bacillus thuringiensis (Bt) cotton on costs and returns over the first two seasons of its commercial release in three sub-regions of Maharashtra State, India. It is the first such research conducted in India based on farmers' own practices rather than trial plots. Data were collected for a total of 7793 cotton plots in 2002 and 1577 plots in 2003. Results suggest that while the cost of cotton seed was much higher for farmers growing Bt cotton relative to those growing non-Bt cotton, the costs of bollworm spray were much lower. While Bt plots had greater costs (seed plus insecticide) than non-Bt plots, the yields and revenue from Bt plots were much higher than those of non-Bt plots (some 39% and 63% higher in 2002 and 2003, respectively). Overall, the gross margins of Bt plots were some 43% (2002) and 73% (2003) higher than those of non-Bt plots, although there was some variation between the three sub-regions of the state. The results suggest that Bt cotton has provided substantial benefits for farmers in India over the 2 years, but there are questions as to whether these benefits are sustainable. (c) 2004 Elsevier Ltd. All rights reserved.

Social interactions and resource ownership in two private protected areas of Paraguay

This paper describes the results of field research to dissect how social interactions differ between two reserves in Paraguay having very different styles of governance. The two reserves were Mbaracayu Natural Forest Reserve (Reserva Natural del Bosque de Mbaracayti, RNBM) and San Rafael Managed Resource Reserve (Reserva de Recursos Manejados San Rafael, RRMSR). RNBM is a private reserve owned by a non-governmental organisation. while RRNISR is a publicly-managed reserve, albeit with a substantial degree of private land ownership. Both reserves are intended to protect Atlantic Forest, one of the five world biodiversity 'hotspots', and also one of the most highly threatened. Each reserve and its buffer zone comprises a set of stakeholders, including indigenous communities and farmers, and the paper explores the interactions between these and the management regime. Indeed, while the management regimes of the two reserves are different, one being highly top-down (RNBM) and the other more socially inclusive (RRMSR), the issues that they have to deal with are much the same. However, while both management regimes will readily acknowledge the need to address poverty, inequality appears to be a far more sensitive issue. Whereas this may be expected for the privately-owned RNBM it is perhaps more surprising in RRNISR even when allowing for the fact that much of the land in the latter is in private hands. It is argued that the origins of this sensitivity rest within the broader features of Paraguayan society, and the prevalence of private land ownership. Yet ironically, it is the inequality in land ownership that is perhaps the most significant threat to conservation in both reserves. Therefore, while reserve-level analyses can provide some insight into the driving forces at play in the interaction between conservation and sustainable management, larger scales may be necessary to gain a fuller appreciation of the dynamics operating at site level. Even in a society with a history of centralised control these dynamics may be surprising. (c) 2005 Elsevier Ltd. All rights reserved.

Theory of enhanced magnetic anisotropy induced by Pt adatoms on two-dimensional Co clusters supported on a Pt(111) substrate

Calculations are reported of the magnetic anisotropy energy of two-dimensional (2D) Co nanostructures on a Pt(111) substrate. The perpendicular magnetic anisotropy (PMA) of the 2D Co clusters strongly depends on their size and shape, and rapidly decreases with increasing cluster size. The PMA calculated is in reasonable agreement with experimental results. The sensitivity of the results to the Co-Pt spacing at the interface is also investigated and, in particular, for a complete Co monolayer we note that the value of the spacing at the interface determines whether PMA or in-plane anisotropy occurs. We find that the PMA can be greatly enhanced by the addition of Pt adatoms to the top surface of the 2D Co clusters. A single Pt atom can induce in excess of 5 meV to the anisotropy energy of a cluster. In the absence of the Pt adatoms the PMA of the Co clusters falls below 1 meV/Co atom for clusters of about 10 atoms whereas, with Pt atoms added to the surface of the clusters, a PMA of 1 meV/Co atom can be maintained for clusters as large as about 40 atoms. The effect of placing Os atoms on the top of the Co clusters is also considered. The addition of 5d atoms and clusters on the top of ferromagnetic nanoparticles may provide an approach to tune the magnetic anisotropy and moment separately.