Effect of cellulose-derivatives and emulsifiers on creep-recovery and crumb properties of gluten-free bread prepared from sorghum and gelatinised cassava starch

The effect of cellulose-derivatives and emulsifiers on the creep-recovery behaviour of gluten-free dough prepared from gelatinised cassava starch and sorghum was studied. Cellulose treated doughs, except the treatment with 2.4% w/w fwb CMC, had lower resistances to deformation (range 10–33%) than emulsifier-treated doughs (range 3–13%). The higher elastic recovery of emulsifier-treated doughs corresponded to lower compliances and higher zero shear viscosities than for doughs treated with cellulose-derivatives. Addition of egg white powder (6.7% w/w fwb) eliminated several textural defects associated with gluten-free bread. Cellulose-derivatives did not decrease crumb firmness or staling rate when compared to the control. Though increasing emulsifier concentration (from 0.4% to 2.4% w/w fwb) decreased crumb firmness, crumbs treated with 2.4% w/w fwb emulsifiers, except diacetyl tartaric acid esters of mono- and diglycerides, were weak and difficult to handle after slicing. Nevertheless, all gluten-free breads treated with 2.4% w/w fwb emulsifiers staled at a slower rate than the control.     

The effect of resin finish on the dimensional stability of cotton knitted fabric

In this article, the effect of resin finish on the degree of set is discussed. Furthermore, the relationship of the degree of set and dimensional stability is analyzed. After relaxation treatments, the yarns in a fabric would be set at a certain level. As the fabric is subjected to prolonged relaxation treatments, the yarn will become more set until the shape of the unraveled yarn is similar to the shape of the loop in the fabric. The yarn that is removed from a knitted fabric has a curvature value close to zero, and the curvature is exactly as the corresponding position at the knitted loop. From previous research, it is known that the degree of set affects the dimensional properties in a plain knitted fabric. In this article, the degree of set increases with resin level. It explains why the resin finish can stabilize the dimensional properties indirectly.     

Strategies for handling treatment failure successfully.

This article addresses the topic of treatment failure in psychotherapy. The authors argue that treatment failure is common, that it is rarely discussed, and that clinicians do not manage it effectively. They elaborate on a well-established hypothesis-testing model of clinical work to provide an algorithm that includes strategies clinicians can use to prevent, identify, overcome, and accept treatment failure. They illustrate the use of the algorithm to guide the initially failing treatment of a hypochondriacal attorney. They conclude with a discussion of obstacles to using the algorithm. The algorithm is largely orientation neutral, but cognitive-behavior therapists are likely to find it most compatible with their theoretical model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The brain/mind and document design

Four very different books from four very different fields, (neuroscience, linguistics, cognitive psychology and philosophy) are studied. The books all accept the model of the brain as modular and share a common view of the general organization of the human brain. Although only one of the books deals specifically with usable design, the author believes that all of them have potential interest for document and page designers     

Investigation of air gaps entrapped in protective clothing systems

Air gaps entrapped in protective clothing are known as one of the major factors affecting heat transfer through multiple layers of flexible clothing fabrics. The identification and quantification of the air gaps are two aspects of a multidisciplinary research effort directed toward improving the flame/thermal protective performance of the clothing. Today's three‐dimensional (3‐D) whole body digitizers, which provide accurate representations of the surface of the human body, can be a novel means for visualizing and quantifying the air gaps between the wearer and his clothing. In this paper we discuss how images from a 3‐D whole body digitizer are used to determine local and global distributions of air gaps and the quantification of air gap sizes in single and multilayer clothing systems dressed on a thermal manikin. Examples are given that show concordance between air gap distributions and burn patterns obtained from full‐scale manikin fire tests. We finish with a discussion of the application of air gap information to bench‐scale testing to improve the protective performance of current flame/thermal protective clothing. Copyright © 2002 John Wiley & Sons, Ltd.     

Sensitivity to time change: The role of context and skill.

Three experiments used a 2-alterative, forced-choice procedure to examine the performance of skilled (musician) and unskilled (nonmusician) participants in detecting small time changes embedded within the test cycles of rhythmical patterns. Relational versus statistical properties of rhythms as well as rate and magnitude of to-be-detected time changes were varied in Experiments 1 and 2. Experiment 3 continued to vary structural properties of sequences (relational vs. statistical) and of experimental sessions. In general, listeners were better with relationally simple than with complex patterns in spite of the fact that the former were, on average, more variable; in addition, musicians were superior to nonmusicians only with simple sequences. In Experiment 3, more marked effects of relational structure within both pattens and sessions were evident. Results were interpreted in terms of an oscillator model of dynamic attending. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction dataNeural network for modelling spatial interaction data

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.