Using siting algorithms in the design of marine reserve networks

Using benthic habitat data from the Florida Keys (USA), we demonstrate how siting algorithms can help identify potential networks of marine reserves that comprehensively represent target habitat types. We applied a flexible optimization tool-simulated annealing-to represent a fixed proportion of different marine habitat types within a geographic area. We investigated the relative influence of spatial information, planning-unit size, detail of habitat classification, and magnitude of the overall conservation goal on the resulting network scenarios. With this method, we were able to identify many adequate reserve systems that met the conservation goals, e.g., representing at least 20% of each conservation target (i.e., habitat type) while fulfilling the overall aim of minimizing the system area and perimeter. One of the most useful types of information provided by this siting algorithm comes from an irreplaceability analysis, which is a count of the number of, times unique planning units were included in reserve system scenarios. This analysis indicated that many different combinations of sites produced networks that met the conservation goals. While individual 1-km(2) areas were fairly interchangeable, the irreplaceability analysis highlighted larger areas within the planning region that were chosen consistently to meet the goals incorporated into the algorithm. Additionally, we found that reserve systems designed with a high degree of spatial clustering tended to have considerably less perimeter and larger overall areas in reserve-a configuration that may be preferable particularly for sociopolitical reasons. This exercise illustrates the value of using the simulated annealing algorithm to help site marine reserves: the approach makes efficient use of;available resources, can be used interactively by conservation decision makers, and offers biologically suitable alternative networks from which an effective system of marine reserves can be crafted.

Occupational therapy assessment of self-awareness following traumatic brain injury

Impaired self-awareness is a common problem following traumatic brain injury. Without adequate self-awareness, a person's motivation to participate in rehabilitation may be limited, which in turn can have an adverse effect on his or her functional outcome. For this reason, it is important that brain injury rehabilitation professionals, including occupational therapists, both understand this phenomenon and use assessment and treatment approaches aimed at improving clients' self-awareness. This article provides an overview of self-awareness, reviewing the distinction between intellectual and online awareness. The current role of occupational therapy in the assessment of self-awareness is highlighted and the guidelines for new assessments of self-awareness suitable for use in occupational therapy are explored.

Allowance for thermodynamic non-ideality in the characterization of protein self-association by frontal exclusion chromatography: hemoglobin revisited

This investigation re-examines theoretical aspects of the allowance for effects of thermodynamic non-ideality on the characterization of protein self-association by frontal exclusion chromatography, and thereby provides methods of analysis with greater thermodynamic rigor than those used previously. Their application is illustrated by reappraisal of published exclusion chromatography data for hemoglobin on the controlled-pore-glass matrix CPG-120. The equilibrium constant of 100/M that is obtained for dimerization of the (02 species by this means is also deduced from re-examination of published studies of concentrated hemoglobin solutions by osmotic pressure and sedimentation equilibrium methods. (C) 2003 Elsevier Science B.V. All rights reserved.

Kinetic parameters associated with self-heating of New Zealand coals under adiabatic conditions

Adiabatic self-heating tests were carried out on five New Zealand coal samples ranging in rank from lignite to high-volatile bituminous. Kinetic parameters of oxidation were obtained front the self-heating curves assuming Arrhenius behaviour. The activation energy E (kJ mol(-1)) and the pre-exponential factor A (s(-1)) were determined in the temperature range of 70-140 degreesC. The activation energy exhibited a definite rank relationship with a minimum E of 55 kJ mol(-1) occurring at a Suggate rank of similar to6.2 corresponding to subbituminous C. Either side of this rank there was a noticeable increase in the activation energy indicating lower reactivity of the coal. A similar rank trend was also observed in the R-70 self-heating rate index values that were taken from the initial portion of the self-heating curve front 40 to 70 degreesC. From these results it is clear that the adiabatic method is capable of providing reliable kinetic parameters of coal oxidation.

Perceived stress as a predictor of the self-reported new diagnosis of symptomatic CHD in older women

This article describes one aspect of a prospective cohort study of 10,432 women aged between 70 and 75 years. After a 3-year period, 503 women self-reported a new diagnosis by a doctor of angina or myocardial infarction (symptomatic coronary heart disease [CHD]). Time one psychosocial variables (Duke Social Support Index, time pressure, Perceived Stress Scale, Mental Health Index, having a partner, educational attainment, and location of residence) were analyzed using univariate binary logistic regression for their ability to predict subsequent symptomatic CHD. Of these variables, the Duke Social Support Index, Perceived Stress Scale and the Mental Health Index were found to be significant predictors of symptomatic CHID diagnosis. Only the Perceived Stress Scale, however, proved to be a significant independent predictor. After controlling for time one nonpsychosocial variables, as well as the frequency of family doctor visits, perceived stress remained a significant predictor of the new diagnosis of symptomatic CHD in this cohort of older women over a 3-year period.

Fast, scalable master equation solution algorithms. IV. Lanczos iteration with diffusion approximation preconditioned iterative inversion

In this paper we propose a second linearly scalable method for solving large master equations arising in the context of gas-phase reactive systems. The new method is based on the well-known shift-invert Lanczos iteration using the GMRES iteration preconditioned using the diffusion approximation to the master equation to provide the inverse of the master equation matrix. In this way we avoid the cubic scaling of traditional master equation solution methods while maintaining the speed of a partial spectral decomposition. The method is tested using a master equation modeling the formation of propargyl from the reaction of singlet methylene with acetylene, proceeding through long-lived isomerizing intermediates. (C) 2003 American Institute of Physics.

Fast, scalable master equation solution algorithms. III. Direct time propagation accelerated by a diffusion approximation preconditioned iterative solver

In this paper we propose a novel fast and linearly scalable method for solving master equations arising in the context of gas-phase reactive systems, based on an existent stiff ordinary differential equation integrator. The required solution of a linear system involving the Jacobian matrix is achieved using the GMRES iteration preconditioned using the diffusion approximation to the master equation. In this way we avoid the cubic scaling of traditional master equation solution methods and maintain the low temperature robustness of numerical integration. The method is tested using a master equation modelling the formation of propargyl from the reaction of singlet methylene with acetylene, proceeding through long lived isomerizing intermediates. (C) 2003 American Institute of Physics.

Knowledge-based system on optimum design of liquid retaining structures with genetic algorithms

This paper delineates the development of a prototype hybrid knowledge-based system for the optimum design of liquid retaining structures by coupling the blackboard architecture, an expert system shell VISUAL RULE STUDIO and genetic algorithm (GA). Through custom-built interactive graphical user interfaces under a user-friendly environment, the user is directed throughout the design process, which includes preliminary design, load specification, model generation, finite element analysis, code compliance checking, and member sizing optimization. For structural optimization, GA is applied to the minimum cost design of structural systems with discrete reinforced concrete sections. The design of a typical example of the liquid retaining structure is illustrated. The results demonstrate extraordinarily converging speed as near-optimal solutions are acquired after merely exploration of a small portion of the search space. This system can act as a consultant to assist novice designers in the design of liquid retaining structures.