Effects of reduced height (Rht) and photoperiod insensitivity (Ppd) alleles on yield of wheat in contrasting production systems

Near isogenic lines (NILs) varying for reduced height (Rht) and photoperiod insensitivity (Ppd-D1) alleles in a cv. Mercia background (rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht8c+Ppd-D1a, Rht-D1c, Rht12) were compared for interception of photosynthetically active radiation (PAR), radiation use efficiency (RUE), above-ground biomass (AGB), harvest index (HI), height, weed prevalence, lodging and grain yield, at one field site but within contrasting (‘organic’ v ‘conventional’) rotational and agronomic contexts, in each of three years. In the final year, further NILs (rht (tall), Rht-B1b, Rht-D1b, Rht-B1c, Rht-B1b+Rht-D1b, Rht-D1b+Rht-B1c) in Maris Huntsman and Maris Widgeon backgrounds were added together with 64 lines of a doubled haploid (DH) population [Savannah (Rht-D1b) × Renesansa (Rht-8c+Ppd-D1a)]. There were highly significant genotype × system interactions for grain yield, mostly because differences were greater in the conventional system than in the organic system. Quadratic fits of NIL grain yield against height were appropriate for both systems when all NILs and years were included. Extreme dwarfing was associated with reduced PAR, RUE, AGB, HI, and increased weed prevalence. Intermediate dwarfing was often associated with improved HI in the conventional system, but not in the organic system. Heights in excess of the optimum for yield were associated particularly with reduced HI and, in the conventional system, lodging. There was no statistical evidence that optimum height for grain yield varied with system although fits peaked at 85cm and 96cm in the conventional and organic systems, respectively. Amongst the DH lines, the marker for Ppd-D1a was associated with earlier flowering, and just in the conventional system also with reduced PAR, AGB and grain yield. The marker for Rht-D1b was associated with reduced height, and again just in the conventional system, with increased HI and grain yield. The marker for Rht8c reduced height, and in the conventional system only, increased HI. When using the System × DH line means as observations grain yield was associated with height and early vegetative growth in the organic system, but not in the conventional system. In the conventional system, PAR interception after anthesis correlated with yield. Savannah was the highest yielding line in the conventional system, producing significantly more grain than several lines that out yielded it in the organic system.

Sparse kernel density estimation technique based on zero-norm constraint

A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathemtical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the -optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Regression based D-optimality experimental design for sparse kernel density estimation

This paper derives an efficient algorithm for constructing sparse kernel density (SKD) estimates. The algorithm first selects a very small subset of significant kernels using an orthogonal forward regression (OFR) procedure based on the D-optimality experimental design criterion. The weights of the resulting sparse kernel model are then calculated using a modified multiplicative nonnegative quadratic programming algorithm. Unlike most of the SKD estimators, the proposed D-optimality regression approach is an unsupervised construction algorithm and it does not require an empirical desired response for the kernel selection task. The strength of the D-optimality OFR is owing to the fact that the algorithm automatically selects a small subset of the most significant kernels related to the largest eigenvalues of the kernel design matrix, which counts for the most energy of the kernel training data, and this also guarantees the most accurate kernel weight estimate. The proposed method is also computationally attractive, in comparison with many existing SKD construction algorithms. Extensive numerical investigation demonstrates the ability of this regression-based approach to efficiently construct a very sparse kernel density estimate with excellent test accuracy, and our results show that the proposed method compares favourably with other existing sparse methods, in terms of test accuracy, model sparsity and complexity, for constructing kernel density estimates.

Effect of abomasal glucose infusion on plasma concentrations of gut peptides in periparturient dairy cows

Six Holstein cows fitted with ruminal cannulas and permanent indwelling catheters in the portal vein, hepatic vein, mesenteric vein, and an artery were used to study the effects of abomasal glucose infusion on splanchnic plasma concentrations of gut peptides. The experimental design was a randomized block design with repeated measurements. Cows were assigned to one of 2 treatments: control or infusion of 1,500 g of glucose/d into the abomasum from the day of parturition to 29 d in milk. Cows were sampled 12 ± 6 d prepartum and at 4, 15, and 29 d in milk. Concentrations of glucose-dependent insulinotropic polypeptide, glucagon-like peptide 1(7–36) amide, and oxyntomodulin were measured in pooled samples within cow and sampling day, whereas active ghrelin was measured in samples obtained 30 min before and after feeding at 0800 h. Postpartum, dry matter intake increased at a lower rate with infusion compared with the control. Arterial, portal venous, and hepatic venous plasma concentrations of the measured gut peptides were unaffected by abomasal glucose infusion. The arterial, portal venous, and hepatic venous plasma concentrations of glucose-dependent insulinotropic polypeptide and glucagon-like peptide 1(7–36) amide increased linearly from 12 d prepartum to 29 d postpartum. Plasma concentrations of oxyntomodulin were unaffected by day relative to parturition. Arterial and portal venous plasma concentrations of ghrelin were lower postfeeding compared with prefeeding concentrations. Arterial plasma concentrations of ghrelin were greatest prepartum and lowest at 4 d postpartum, giving a quadratic pattern of change over the transition period. Positive portal venous-arterial and hepatic venous–arterial concentration differences were observed for glucagon-like peptide 1(7–36) amide. A negative portal venous–arterial concentration difference was observed for ghrelin pre-feeding. The remaining portal venous–arterial and hepatic venous–arterial concentration differences of gut peptides did not differ from zero. In conclusion, increased postruminal glucose supply to postpartum transition dairy cows reduced feed intake relative to control cows, but did not affect arterial, portal venous, or hepatic venous plasma concentrations of gut peptide hormones. Instead, gut peptide plasma concentrations increased as lactation progressed. Thus, the lower feed intake of postpartum dairy cows receiving abomasal glucose infusion was not attributable to changes in gut peptide concentrations.

Effect of feeding fat or intrajugular infusion of glucagon-like peptide-1 and cholecystokinin on dry matter intake, digestibility, and digesta rate of passage in growing wethers

A cause and effect relationship between glucagon-like peptide 1 (7, 36) amide (GLP-1) and cholecystokinin (CCK) and DMI regulation has not been established in ruminants. Three randomized complete block experiments were conducted to determine the effect of feeding fat or infusing GLP-1 or CCK intravenously on DMI, nutrient digestibility, and Cr rate of passage (using Cr(2)O(3) as a marker) in wethers. A total of 18 Targhee × Hampshire wethers (36.5 ± 2.5 kg of BW) were used, and each experiment consisted of four 21-d periods (14 d for adaptation and 7 d for infusion and sampling). Wethers allotted to the control treatments served as the controls for all 3 experiments; experiments were performed simultaneously. The basal diet was 60% concentrate and 40% forage. In Exp. 1, treatments were the control (0% added fat) and addition of 4 or 6% Ca salts of palm oil fatty acids (DM basis). Treatments in Exp. 2 and 3 were the control and 3 jugular vein infusion dosages of GLP-1 (0.052, 0.103, or 0.155 µg•kg of BW(-1)•d(-1)) or CCK (0.069, 0.138, or 0.207 µg•kg of BW(-1)•d(-1)), respectively. Increases in plasma GLP-1 and CCK concentrations during hormone infusions were comparable with increases observed when increasing amounts of fat were fed. Feeding fat and infusion of GLP-1 tended (linear, P = 0.12; quadratic, P = 0.13) to decrease DMI. Infusion of CCK did not affect (P > 0.21) DMI. Retention time of Cr in the total gastrointestinal tract decreased (linear, P < 0.01) when fat was fed, but was not affected by GLP-1 or CCK infusion. In conclusion, jugular vein infusion produced similar plasma CCK and GLP-1 concentrations as observed when fat was fed. The effects of feeding fat on DMI may be partially regulated by plasma concentration of GLP-1, but are not likely due solely to changes in a single hormone concentration.

Relationship between åström control and the kalman linear regulator — Caines revisited

The relationship between minimum variance and minimum expected quadratic loss feedback controllers for linear univariate discrete-time stochastic systems is reviewed by taking the approach used by Caines. It is shown how the two methods can be regarded as providing identical control actions as long as a noise-free measurement state-space model is employed.

Modelling the likely impact of healthy eating guidelines on agricultural production and land use in England and Wales

Quadratic programming techniques were applied to household food consumption data in England and Wales to estimate likely changes in diet under healthy eating guidelines, and the consequences this would have on agriculture and land use in England and Wales. The first step entailed imposing nutrient restrictions on food consumption following dietary recommendations suggested by the UK Department of Health. The resulting diet was used, in a second step as a proxy for demand in agricultural commodities, to test the impact of such a scenario on food production and land use in England and Wales and the impacts of this on agricultural landscapes. Results of the diet optimisation indicated a large drop in consumption of foods rich in saturated fats and sugar, essentially cheese and sugar-based products, along with lesser cuts of fat and meat products. Conversely, consumption of fruit and vegetables, cereals, and flour would increase to meet dietary fibre recommendations. Such a shift in demand would dramatically affect production patterns: the financial net margin of England and Wales agriculture would rise, due to increased production of high market value and high economic margin crops. Some regions would, however, be negatively affected, mostly those dependent on beef cattle and sheep production that could not benefit from an increased demand for cereals and horticultural crops. The effects of these changes would also be felt in upstream industries, such as animal feed suppliers. While arable dominated landscapes would be little affected, pastoral landscapes would suffer through loss of grazing management and, possibly, land abandonment, especially in upland areas.

On-line handwritten segmentation in linear drawings

This paper addresses the problem of tracking line segments corresponding to on-line handwritten obtained through a digitizer tablet. The approach is based on Kalman filtering to model linear portions of on-line handwritten, particularly, handwritten numerals, and to detect abrupt changes in handwritten direction underlying a model change. This approach uses a Kalman filter framework constrained by a normalized line equation, where quadratic terms are linearized through a first-order Taylor expansion. The modeling is then carried out under the assumption that the state is deterministic and time-invariant, while the detection relies on double thresholding mechanism which tests for a violation of this assumption. The first threshold is based on an approach of layout kinetics. The second one takes into account the jump in angle between the past observed direction of layout and its current direction. The method proposed enables real-time processing. To illustrate the methodology proposed, some results obtained from handwritten numerals are presented.

Optimal control of a class of discrete–continuous non-linear systems — decomposition and hierarchical structure

A technique is derived for solving a non-linear optimal control problem by iterating on a sequence of simplified problems in linear quadratic form. The technique is designed to achieve the correct solution of the original non-linear optimal control problem in spite of these simplifications. A mixed approach with a discrete performance index and continuous state variable system description is used as the basis of the design, and it is shown how the global problem can be decomposed into local sub-system problems and a co-ordinator within a hierarchical framework. An analysis of the optimality and convergence properties of the algorithm is presented and the effectiveness of the technique is demonstrated using a simulation example with a non-separable performance index.

Novel developments in process optimisation using predictive control

In industrial practice, constrained steady state optimisation and predictive control are separate, albeit closely related functions within the control hierarchy. This paper presents a method which integrates predictive control with on-line optimisation with economic objectives. A receding horizon optimal control problem is formulated using linear state space models. This optimal control problem is very similar to the one presented in many predictive control formulations, but the main difference is that it includes in its formulation a general steady state objective depending on the magnitudes of manipulated and measured output variables. This steady state objective may include the standard quadratic regulatory objective, together with economic objectives which are often linear. Assuming that the system settles to a steady state operating point under receding horizon control, conditions are given for the satisfaction of the necessary optimality conditions of the steady-state optimisation problem. The method is based on adaptive linear state space models, which are obtained by using on-line identification techniques. The use of model adaptation is justified from a theoretical standpoint and its beneficial effects are shown in simulations. The method is tested with simulations of an industrial distillation column and a system of chemical reactors.

Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems

An algorithm for solving nonlinear discrete time optimal control problems with model-reality differences is presented. The technique uses Dynamic Integrated System Optimization and Parameter Estimation (DISOPE), which achieves the correct optimal solution in spite of deficiencies in the mathematical model employed in the optimization procedure. A version of the algorithm with a linear-quadratic model-based problem, implemented in the C+ + programming language, is developed and applied to illustrative simulation examples. An analysis of the optimality and convergence properties of the algorithm is also presented.

A new approach for the solution of the optimal set-point tracking problem for nonlinear systems

In this paper, a discrete time dynamic integrated system optimisation and parameter estimation algorithm is applied to the solution of the nonlinear tracking optimal control problem. A version of the algorithm with a linear-quadratic model-based problem is developed and implemented in software. The algorithm implemented is tested with simulation examples.

Hierarchical control and optimisation of the Tennessee Eastman process

Based on integrated system optimisation and parameter estimation a method is described for on-line steady state optimisation which compensates for model-plant mismatch and solves a non-linear optimisation problem by iterating on a linear - quadratic representation. The method requires real process derivatives which are estimated using a dynamic identification technique. The utility of the method is demonstrated using a simulation of the Tennessee Eastman benchmark chemical process.

Optimal control of nonlinear differential algebraic equation systems

A novel iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified linear quadratic model based problem with parameter updating in such a manner that the correct solution of the original non-linear problem is achieved. The resulting algorithm has a particular advantage in that the solution is achieved without the need to solve the differential algebraic equations . Convergence aspects are discussed and a simulation example is described which illustrates the performance of the technique. 1. Introduction When modelling industrial processes often the resulting equations consist of coupled differential and algebraic equations (DAEs). In many situations these equations are nonlinear and cannot readily be directly reduced to ordinary differential equations.