Patterns of size inequality in crowded plant populations are often taken to be indicative of the degree of size asymmetry of competition, but recent research suggests that some of the patterns attributed to size-asymmetric competition could be due to spatial structure. To investigate the theoretical relationships between plant density, spatial pattern, and competitive size asymmetry in determining size variation in crowded plant populations, we developed a spatially explicit, individual-based plant competition model based on overlapping zones of influence. The zone of influence of each plant is modeled as a circle, growing in two dimensions, and is allometrically related to plant biomass. The area of the circle represents resources potentially available to the plant, and plants compete for resources in areas in which they overlap. The size asymmetry of competition is reflected in the rules for dividing up the overlapping areas. Theoretical plant populations were grown in random and in perfectly uniform spatial patterns at four densities under size-asymmetric and size-symmetric competition. Both spatial pattern and size asymmetry contributed to size variation, but their relative importance varied greatly over density and over time. Early in stand development, spatial pattern was more important than the symmetry of competition in determining the degree of size variation within the population, but after plants grew and competition intensified, the size asymmetry of competition became a much more important * E-mail: jw@kvl.dk. † E-mail: stoll@sgi.unibe.ch. ‡ E-mail: helene@eno.princeton.edu. § E-mail: ajasen@concentric.net. source of size variation. Size variability was slightly higher at higher densities when competition was symmetric and plants were distributed nonuniformly in space. In a uniform spatial pattern, size variation increased with density only when competition was size asymmetric. Our results suggest that when competition is size asymmetric and intense, it will be more important in generating size variation than is local variation in density. Our results and the available data are consistent with the hypothesis that high levels of size inequality commonly observed within crowded plant populations are largely due to size-asymmetric competition, not to variation in local density.Keywords: asymmetric competition, individual-based models, population structure, size inequality, spatial effects, zone of influence.Competition among individuals usually increases size variation within plant populations, but there is controversy over the mechanisms through which this occurs. This controversy reflects a fundamental disagreement about the nature of competition among individual plants. Some studies have concluded that a major factor generating size variation in crowded plant populations is the "size asymmetry" of competition: larger plants have a disproportionate advantage (for their relative size) in competition with smaller plants, suppressing their growth (Begon 1984;Weiner 1990;Schwinning and Weiner 1...

We describe the architecture and functioning of an all-optical, continuous-time recurrent neural network. The network is a ring resonator which contains a saturable, two-beam amplifier; two volume holograms; and a linear, two-beam amplifier. The saturable amplifier permits, through the use of a spatially patterned signal beam, the realization of an optical neuron array; the two volume holograms provide global network interconnectivity; and the linear amplifier supplies sufficient cavity gain to permit resonant, convergent operation of the network. Numerical solutions of the network equations of motion indicate that, for real-valued neural state vectors, the network functions in much the same way as either Hopfield's continuous-time model or a continuous-time version of Anderson's BSB model. For complex-valued neural state vectors, the network always converges to the dominant network attractor, thereby suggesting a paradigm for solving optimization problems in which entrapment by local minima is avoided.

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The objective of this research was to identify current and future approaches to the design of highly automated systems for life science processes involving humans in control loops in applications such as high-throughput compound screening and high-performance analytical chemistry. (In some advanced applications, screening of biochemical reactions and analytics are performed together.) The identified approaches were classified according to existing theories of human-centered automation, which provided a basis for projecting human performance implications, including error recovery capability. We provide background on the life sciences domain and established theories of types and levels of automation in complex human-machine systems. We describe specific forms of robotic and automated technologies used in life science applications and the general design of high-throughput screening and analytical systems to accommodate particular process configurations. Some example classifications of life science automation (LSA) schemes are presented by referring to a taxonomy of levels of automation from the literature. Finally, we identify the need for future empirical research on human performance consequences of LSA and remedial measures, including enhanced supervisory control interface design.