The influence of ultraviolet radiation on growth, photosynthesis and phenolic levels of green and red lettuce: potential for exploiting effects of ultraviolet radiation in a production system

Studies have shown that natural ultraviolet (UV) radiation increases secondary products such as phenolics but can significantly inhibit biomass accumulation in lettuce plants. In the work presented here, the effect of UV radiation on phenolic concentration and biomass accumulation was assessed in relation to photosynthetic performance in red and green lettuce types. Lettuce plants in polythene clad tunnels were exposed to either ambient (UV transparent film) or UV-free conditions (UV blocking film). The study tested whether growth reduction in lettuce plants exposed to natural UV radiation is because of inhibition of photosynthesis by direct damage to the photosynthetic apparatus or by internal shading by anthocyanins. Ambient levels of UV radiation did not limit the efficiency of photosynthesis suggesting that phenolic compounds may effectively protect the photosynthetic apparatus. Growth inhibition does, however, occur in red lettuce and could be explained by the high metabolic cost of phenolic compounds for UV protection. From a commercial perspective, UV transparent and UV blocking films offer opportunities because, in combination, they could increase plant quality as well as productivity. Growing plants continuously under a UV blocking film, and then 6 days before the final harvest transferring them to a UV transparent film, showed that high yields and high phytochemical content can be achieved complementarily.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Preparation of (–)-epigallocatechin gallate from commercial green tea by caffeine precipitation and solvent partition

A method has been developed which enables the easy and inexpensive preparation of gram quantities of (–)-epigallocatechin gallate from green tea (Camellia sinensis). A decaffeinated aqueous brew of commercial green tea is treated with caffeine (30 m ). The precipitate is redissolved after decaffeination with chloroform and further purified by solvent partition with ethyl hexanoate and propyl acetate. Commercial leaf (25 g) yields 400 mg (–)-epigallocatechin gallate at better than 80% purity, as judged by reversed phase HPLC.

New evidence on the green building rent and price premium

This paper investigates the effect of voluntary eco-certification on the rental and sale prices of US commercial office properties. Hedonic and logistic regressions are used to test whether there are rental and sale price premiums for LEED and Energy Star certified buildings. The results of the hedonic analysis suggest that there is a rental premium of approximately 6% for LEED and Energy Star certification. A sale price premium of approximately 35% was found for 127 price observations involving LEED rated buildings and 31% for 662 buildings involving Energy Star rated buildings. When compared to samples of similar buildings identified by a binomial logistic regression for LEED-certified buildings, the existence of a rent and sales price premium is confirmed albeit with differences regarding the magnitude of the premium. Overall, the results of this study confirm that LEED and Energy Star buildings exhibit higher rental rates and sales prices per square foot controlling for a large number of location- and property-specific factors.