Influence of foliar diseases and their control by fungicides on grain yield and quality in wheat

Twenty-eight field experiments on sandy-loam soils in the UK (1982-2003) are reviewed by relating the extension of the green area duration of the flag leaf (GLADF) by fungicides to effects on yield and quality of winter wheat. Over all experiments mean grain yield = 8.85t ha(-1) at 85% DM. With regards quality, mean values were: thousand grain weight (TGW) = 44.5 g; specific weight (SWT) = 76.9 kg hl(-1); crude protein concentration (CP (N x 5.7)) = 12.5 % DM; Hagberg falling number (HFN) = 285 s; and sodium dodecyl sulphate (SDS)-sedimentation volume = 69ml. For each day (d) that fungicides increased GLADF there were associated average increases in yield (0.144 1 ha(-1) d(-1), se 0.0049, df = 333), TGW (0.56 gd(-1), se = 0.017) and SWT (0.22 kg hl(-1) d(-1), se 0.011). Some curvature was evident in all these relationships. When GLADF was delayed beyond 700 degrees Cd after anthesis, as was possible in cool wet seasons, responses were curtailed, or less reliable. Despite this apparent terminal sink limitation, fungicide effects on sink size, eg endosperm cell numbers or maximum water mass per grain, were not prerequisites for large effects on grain yield, TGW or SWT. Fungicide effects on CP were variable. Although the average response of CP was negative (-0.029%DM/d; se = 0.00338), this depended on cultivar and disease controlled. Controlling biotrophs such as rusts, (Puccinia spp.) tended to increase CP, whereas controlling a more necrotrophic pathogen (Septoria tritici) usually reducedCP. Irrespective of pathogen controlled, delaying senescence of the flag leaf was associated with increased nitrogen yields in the grain (averaging 2.24 kg N ha-1 d(-1), se = 0.0848) due to both increased N uptake into the above ground crop, and also more efficient remobilisation of N from leaf laminas. When sulphur availability appeared to be adequate, fungicide x cultivar interactions were similar on S as for CP, although N:S ratios tended to decline (i.e. improve for bread making) when S. tritici was controlled. On average, SDS-sedimentation volume declined (-0. 18 ml/d, se = 0.027) with increased GLADF, broadly commensurate with the average effect on CP. Hagberg falling number decreased as fungicide increased GLADF (-2.73 s/d, se = 0.178), indicating an increase in alpha-amylase activity.

Probability density function estimation using orthogonal forward regression

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

Fully complex-valued radial basis function networks for orthogonal least squares regression

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

Sparse kernel density estimator using orthogonal regression based on D-optimality experimental design

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

An autonomic approach in swarm-array computing

Can autonomic computing concepts be applied to traditional multi-core systems found in high performance computing environments? In this paper, we propose a novel synergy between parallel computing and swarm robotics to offer a new computing paradigm, `Swarm-Array Computing' that can harness and apply autonomic computing for parallel computing systems. One approach among three proposed approaches in swarm-array computing based on landscapes of intelligent cores, in which the cores of a parallel computing system are abstracted to swarm agents, is investigated. A task gets executed and transferred seamlessly between cores in the proposed approach thereby achieving self-ware properties that characterize autonomic computing. FPGAs are considered as an experimental platform taking into account its application in space robotics. The feasibility of the proposed approach is validated on the SeSAm multi-agent simulator.