Cognitive deficits in docosahexaenoic acid-deficient rats.

This study investigated the influence of brain docosahexaenoic acid (DHA) deficiency on simple and complex olfactory-based learning and memory in 2nd generation (F2) adult male rats. Rats raised and maintained on either an n-3-adequate or an n-3-deficient diet were tested for acquisition of an olfactory learning set and an olfactory memory task, and for motivation to obtain a water reward. Despite a 76% decrease in brain DHA, n-3-deficient rats were able to acquire most simple 2-odor discrimination tasks but were deficient in the acquisition of a 20-problem olfactory learning set. This deficit could not be attributed to changes in sensory capacity but, instead, appeared to represent a deficit in higher order learning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Clarification of suspensions by colloidal gas aphrons

Colloidal gas aphrons (CGAs), generated from dilute solutions of four surfactants, were used to clarify palm oil mill effluent (POME), suspensions of microalgae and suspensions of three inorganic minerals. In POME and the algal suspensions, each CGA was most effective at a pH value close to the pK value of the surfactant concerned. This effect was not tested in the inorganic suspensions. The efficiency of air utilization was directly related to the concentration of solids in the suspensions, the size, density and nature of the solids having secondary effects. Comparison with data in the literature led to a general correlation embracing a variety of suspensions and flotation systems. Shedding of collected material from the foam layer was also a direct function of solids concentration. CGAs offer advantages over other systems of air-assisted flotation in relation to the requirements for equipment and to the management of process operations.     

Early stimulated emission amplification by an inverted nuclear spin population and the H-maser

Two different topics are discussed: (1) early (1950) stimulated emission amplification observed with a pure LiF crystal having an inverted population of the nuclear spin system and (2) the atomic H-maser.     

The Routineness of Routines: Measuring Rhythms of Media Interaction

The routines of information work are commonplace yet difficult to characterize. Although cognitive models have successfully characterized routine tasks within which there is little variation, a large body of ethnomethodological research has identified the inherent nonroutineness of routines in information work. We argue that work does not fall into discrete classes of routine versus nonroutine; rather, task performance lies on a continuum of routineness, and routineness metrics are important to the understanding of workplace multitasking.

In a study of 10 information workers shadowed for 3 whole working days each, we utilize the construct of working sphere to model projects/tasks as a network of humans and artifacts. Employing a statistical technique called T-pattern analysis, we derive measures of routineness from these real-world data. In terms of routineness, we show that information workers experience archetypes of working spheres. The results indicate that T-patterns of interactions with information and computational media are important indicators of facets of routineness and that these measures are correlated with workers' affective states. Our results are some of the first to demonstrate how regular temporal patterns of media interaction in tasks are related to stress. These results suggest that designs of systems to facilitate so-called routine work should consider the degrees to which a person's working spheres fall along varying facets of routineness.     

A Genetic‐Based Iterative Quantile Regression Algorithm for Analyzing Fatigue Curves

Accurate prediction of fatigue failure times of materials such as fracture and plastic deformation at various stress ranges has a strong bearing on practical fatigue design of materials. In this study, we propose a novel genetic‐based iterative quantile regression (GA‐IQR) algorithm for analyzing fatigue curves that represent a nonlinear relationship between a given stress amplitude and fatigue life. We reduce the problem to a linear framework and develop the iterative algorithm for determining the model coefficients including unknown fatigue limits. The procedure keeps updating the estimates in a direction to reduce its resulting error. Also, our approach benefits from the population‐based stochastic search of the genetic algorithms so that the algorithm becomes less sensitive to its initialization. Compared with conventional approaches, the proposed GA‐IQR requires fewer assumptions to develop fatigue model, capable of exploring the data structure in a relatively flexible manner. All procedures and calculations are quite straightforward, such that the proposed quantile regression model has a high potential value in a wide range of applications for exploring nonlinear relationships with lifetime data. Computational results for real data sets found in the literature present good evidences to support the argument. Copyright © 2012 John Wiley & Sons, Ltd.