Analysis of feeding strategies for small-scale dairy systems in central Mexico using linear programming

A limitation of small-scale dairy systems in central Mexico is that traditional feeding strategies are less effective when nutrient availability varies through the year. In the present work, a linear programming (LP) model that maximizes income over feed cost was developed, and used to evaluate two strategies: the traditional one used by the small-scale dairy producers in Michoacan State, based on fresh lucerne, maize grain and maize straw; and an alternative strategy proposed by the LIP model, based on ryegrass hay, maize silage and maize grain. Biological and economic efficiency for both strategies were evaluated. Results obtained with the traditional strategy agree with previously published work. The alternative strategy did not improve upon the performance of the traditional strategy because of low metabolizable protein content of the maize silage considered by the model. However, the Study recommends improvement of forage quality to increase the efficiency of small-scale dairy systems, rather than looking for concentrate supplementation.

Forage production and nutrient availability in small-scale dairy systems in central Mexico using linear programming and partial budgeting

Small-scale dairy systems play an important role in the Mexican dairy sector and farm planning activities related to resource allocation have a significant impact on the profitability of such enterprises. Linear programming is a technique widely used for planning and ration formulation, and partial budgeting is a technique for assessing the impact of changes on the profitability of an enterprise. This study used both methods to optimise land use for forage production and nutrient availability, and to evaluate the economic impact of such changes in small-scale Mexican dairy systems. The model showed satisfactory performance when optimal solutions were compared with the traditional strategy. The strategy using fresh ryegrass, maize silage and oat hay, and the strategy using a combination of alfalfa hay, maize silage, fresh ryegrass and oat hay appeared attractive options for providing a better nutrient supply and maintaining a higher stocking rate throughout the year than the traditional strategy.

Developmental programming: Deficits in reproductive hormone dynamics and ovulatory outcomes in prenatal, testosterone-treated sheep

Prenatal testosterone excess leads to neuroendocrine, ovarian, and metabolic disruptions, culminating in reproductive phenotypes mimicking that of women with polycystic ovary syndrome (PCOS). The objective of this study was to determine the consequences of prenatal testosterone treatment on periovulatory hormonal dynamics and ovulatory outcomes. To generate prenatal testosterone-treated females, pregnant sheep were injected intramuscularly (days 30-90 of gestation, term = 147 days) with 100 mg of testosterone-propionate in cottonseed oil semi-weekly. Female offspring born to untreated control females and prenatal testosterone-treated females were then studied during their first two breeding seasons. Sheep were given two injections of prostaglandin F-2alpha 11 days apart, and blood samples were collected at 2-h intervals for 120 h, 10-min intervals for 8 h during the luteal phase (first breeding season only), and daily for an additional 15 days to characterize changes in reproductive hormonal dynamics. During the first breeding season, prenatal testosterone-treated females manifested disruptions in the timing and magnitude of primary gonadotropin surges, luteal defects, and reduced responsiveness to progesterone negative feedback. Disruptions in the periovulatory sequence of events during the second breeding season included: 1) delayed but increased preovulatory estradiol rise, 2) delayed and severely reduced primary gonadotropin surge in prenatal testosterone-treated females having an LH surge, 3) tendency for an amplified secondary FSH surge and a shift in the relative balance of FSH regulatory proteins, and 4) luteal responses that ranged from normal to anovulatory. These outcomes are likely to be of relevance to developmental origin of infertility disorders and suggest that differences in fetal exposure or fetal susceptibility to testosterone may account for the variability in reproductive phenotypes.

The parallel programming of voluntary and reflexive saccades

A novel two-step paradigm was used to investigate the parallel programming of consecutive, stimulus-elicited ('reflexive') and endogenous ('voluntary') saccades. The mean latency of voluntary saccades, made following the first reflexive saccades in two-step conditions, was significantly reduced compared to that of voluntary saccades made in the single-step control trials. The latency of the first reflexive saccades was modulated by the requirement to make a second saccade: first saccade latency increased when a second voluntary saccade was required in the opposite direction to the first saccade, and decreased when a second saccade was required in the same direction as the first reflexive saccade. A second experiment confirmed the basic effect and also showed that a second reflexive saccade may be programmed in parallel with a first voluntary saccade. The results support the view that voluntary and reflexive saccades can be programmed in parallel on a common motor map. (c) 2006 Elsevier Ltd. All rights reserved.

The remote distractor effect in saccade programming: channel interactions and lateral inhibition

We explored the dependency of the saccadic remote distractor effect (RDE) on the spatial frequency content of target and distractor Gabor patches. A robust RDE was obtained with low-medium spatial frequency distractors, regardless of the spatial frequency of the tat-get. High spatial frequency distractors interfered to a similar extent when the target was of the same spatial frequency. We developed a quantitative model based on lateral inhibition within an oculomotor decision unit. This lateral inhibition mechanism cannot account for the interaction observed between target and distractor spatial frequency, pointing to the existence of channel interactions at an earlier level. (C) 2004 Elsevier Ltd. All rights reserved.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

An exploratory study of the effect of aspect-oriented programming on maintainability

In this paper we describe an exploratory assessment of the effect of aspect-oriented programming on software maintainability. An experiment was conducted in which 11 software professionals were asked to carry out maintenance tasks on one of two programs. The first program was written in Java and the second in AspectJ. Both programs implement a shopping system according to the same set of requirements. A number of statistical hypotheses were tested. The results did seem to suggest a slight advantage for the subjects using the object-oriented system since in general it took the subjects less time to answer the questions on this system. Also, both systems appeared to be equally difficult to modify. However, the results did not show a statistically significant influence of aspect-oriented programming at the 5% level. We are aware that the results of this single small study cannot be generalized. We conclude that more empirical research is necessary in this area to identify the benefits of aspect-oriented programming and we hope that this paper will encourage such research.

An Efficient Parameterization of Dynamic Neural Networks for Nonlinear System Identification

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.