Influence of disaccharide structure on prebiotic selectivity in vitro

To obtain structure-function information of a range of carbohydrates, which are available only in very small quantities, an in vitro fermentation method using 7 mg of carbohydrate, 0.7 mL of basal medium, and 1% (w/v) of fecal bacteria was validated against a pH-controlled batch culture with 150 mL of basal medium and 1.5g of test carbohydrate. This method was used to determine the influence of different glycosidic linkages and monosaccharide compositions of disaccharides on the selectivity of microbial fermentation. A prebiotic index (PI) was calculated for each disaccharide. Generally, disaccharides with linkages of 1-2, 1-4, and 1-6 generated a high PI score, with kojibiose and sophorose showing the greatest values (21.62 and 18.63, respectively). Apart from 6 alpha-mannobiose, mannose-containing disaccharicles gave a low PI due to low numbers of bifidobacteria and lactobacilli and an increase in bacteroides. The structure-function information obtained in this study may lead to a predictive understanding of how specific structures are fermented by the human gut microflora.

Application of constrained predictive control on a 3D crane system

This paper describes the SIMULINK implementation of a constrained predictive control algorithm based on quadratic programming and linear state space models, and its application to a laboratory-scale 3D crane system. The algorithm is compatible with Real Time. Windows Target and, in the case of the crane system, it can be executed with a sampling period of 0.01 s and a prediction horizon of up to 300 samples, using a linear state space model with 3 inputs, 5 outputs and 13 states.

Predictive control using feedback linearization based on dynamic neural models

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.

Green noise or green value? Measuring the effects of environmental certification on office values

This study investigates the price effects of environmental certification on commercial real estate assets. It is argued that there are likely to be three main drivers of price differences between certified and noncertified buildings. These are additional occupier benefits, lower holding costs for investors and a lower risk premium. Drawing upon the CoStar database of U.S. commercial real estate assets, hedonic regression analysis is used to measure the effect of certification on both rent and price. The results suggest that, compared to buildings in the same submarkets, eco-certified buildings have both a rental and sale price premium.

Religious schools, social values, and economic attitudes: evidence from Bangladesh

This paper uses new data on female graduates of registered secondary secular schools and madrasas from rural Bangladesh and tests whether there exist attitudinal gaps by school type and what teacher-specific factors explain these gaps. Even after controlling for a rich set of individual, family and school traits, we find that madrasa graduates differ on attitudes associated with issues such as working mothers, desired fertility, and higher education for girls, when compared to their secular schooled peers. On the other hand, madrasa education is associated with attitudes that are still conducive to democracy. We also find that exposure to female and younger teacher is associated with more favorable attitudes among graduates.

A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Weighted input predictive controller

A discrete-time algorithm is presented which is based on a predictive control scheme in the form of dynamic matrix control. A set of control inputs are calculated and made available at each time instant, the actual input applied being a weighted summation of the inputs within the set. The algorithm is directly applicable in a self-tuning format and is therefore suitable for slowly time-varying systems in a noisy environment.

Predictive algorithm for the roll control autopilot of a jet fighter aircraft

Predictive controllers are often only applicable for open-loop stable systems. In this paper two such controllers are designed to operate on open-loop critically stable systems, each of which is used to find the control inputs for the roll control autopilot of a jet fighter aircraft. It is shown how it is quite possible for good predictive control to be achieved on open-loop critically stable systems.