Investigation of biodegradable nonwoven composites based on cotton, bagasse and other annual plants

In this study a new method of preparing biodegradable all-cellulosic composite nonwoven materials composed of cotton and kenaf or cotton and bagasse has been developed. Alkaline extracted kenaf or bagasse fibers were used as the main component of composite nonwovens. Recyclable or low value cotton fibers were used to entangle coarser kenaf or bagasse fibers in a web on which the nonwoven architecture was based. The novel adhesive system developed in this work for the web bonding was cellulose from a solution, in N-methyl morpholine N-oxide monohydrate. The completely biodegradable composite nonwovens were obtained by sandwiching and hot-pressing the cellulosic webs and the adhesive into a bonded sheet. It was shown that synthetic polymers can be substituted for the stabilization of nonwovens by a solution of cellulose prepared from recyclable cotton textiles. Some relevant properties of final nonwoven products, such as strength, viscoelastic characteristics and thermal properties were determined and compared among several compositions. The physical characteristics of all-cellulosic composite nonwovens were comparable to that of biodegradable composite nonwovens prepared earlier at LSU from natural fibers and a biodegradable synthetic polyester. Practical application of all-cellulosic composite nonwovens will be determined by the economics of delignification of composing fibers

Leisure Time Budget, Time Price and Consumption of Traditional News Media and New News Media [Slides]

Slides presented at Media Management and Economics Division, Association for Education in Journalism and Mass Communication Association Annual Convention, August 10-13, 2011, St. Louis, Missouri by Louisa Ha and Xiaoqun Zhang

A Hybrid Segmentation and D-bar Method for Electrical Impedance Tomography

The Regularized D-bar method for Electrical Impedance Tomography provides a rigorous mathematical approach for solving the full nonlinear inverse problem directly, i.e. without iterations. It is based on a low-pass filtering in the (nonlinear) frequency domain. However, the resulting D-bar reconstructions are inherently smoothed leading to a loss of edge distinction. In this paper, a novel approach that combines the rigor of the D-bar approach with the edge-preserving nature of Total Variation regularization is presented. The method also includes a data-driven contrast adjustment technique guided by the key functions (CGO solutions) of the D-bar method. The new TV-Enhanced D-bar Method produces reconstructions with sharper edges and improved contrast while still solving the full nonlinear problem. This is achieved by using the TV-induced edges to increase the truncation radius of the scattering data in the nonlinear frequency domain thereby increasing the radius of the low pass filter. The algorithm is tested on numerically simulated noisy EIT data and demonstrates significant improvements in edge preservation and contrast which can be highly valuable for absolute EIT imaging