High temperature series tests for helical order.

Thesis. 1977. Ph.D.--Massachusetts Institute of Technology. Dept. of Physics.M̲i̲c̲ṟo̲f̲i̲c̲ẖe̲ c̲o̲p̲y̲ a̲v̲a̲i̲ḻa̲ḇḻe̲ i̲ṉ A̲ṟc̲ẖi̲v̲e̲s̲ a̲ṉḏ S̲c̲i̲e̲ṉc̲e̲.Vita.Bibliography : leaf 134.Ph.D

How many species have mass M?

Within large taxonomic assemblages, the number of species with adult body mass M is characterized by a broad but asymmetric distribution, with the largest mass being orders of magnitude larger than the typical mass. This canonical shape can be explained by cladogenetic diffusion that is bounded below by a hard limit on viable species mass and above by extinction risks that increase weakly with mass. Here we introduce and analytically solve a simplified cladogenetic diffusion model. When appropriately parameterized, the diffusion-reaction equation predicts mass distributions that are in good agreement with data on 4002 terrestrial mammal from the late Quaternary and 8617 extant bird species. Under this model, we show that a specific tradeoff between the strength of within-lineage drift toward larger masses (Cope's rule) and the increased risk of extinction from increased mass is necessary to produce realistic mass distributions for both taxa. We then make several predictions about the evolution of avian species masses.Comment: 7 pages, 3 figure

Citation Statistics from 110 Years of Physical Review

Publicly available data reveal long-term systematic features about citation statistics and how papers are referenced. The data also tell fascinating citation histories of individual articles.Comment: This is esssentially identical to the article that appeared in the June 2005 issue of Physics Toda

Edge fires drive the shape and stability of tropical forests

In tropical regions, fires propagate readily in grasslands but typically consume only edges of forest patches. Thus forest patches grow due to tree propagation and shrink by fires in surrounding grasslands. The interplay between these competing edge effects is unknown, but critical in determining the shape and stability of individual forest patches, as well the landscape-level spatial distribution and stability of forests. We analyze high-resolution remote-sensing data from protected areas of the Brazilian Cerrado and find that forest shapes obey a robust perimeter-area scaling relation across climatic zones. We explain this scaling by introducing a heterogeneous fire propagation model of tropical forest-grassland ecotones. Deviations from this perimeter-area relation determine the stability of individual forest patches. At a larger scale, our model predicts that the relative rates of tree growth due to propagative expansion and long-distance seed dispersal determine whether collapse of regional-scale tree cover is continuous or discontinuous as fire frequency changes.Comment: 21 pages, 4 figure

Introduction: Third Annual Gallery of Nonlinear Images (Baltimore, Maryland, 2006)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87884/2/041101_1.pd

Size limiting in Tsallis statistics

Power law scaling is observed in many physical, biological and socio-economical complex systems and is now considered as an important property of these systems. In general, power law exists in the central part of the distribution. It has deviations from power law for very small and very large step sizes. Tsallis, through non-extensive thermodynamics, explained power law distribution in many cases including deviation from the power law, both for small and very large steps. In case of very large steps, they used heuristic crossover approach. In real systems, the size is limited and thus, the size limiting factor is important. In the present work, we present an alternative model in which we consider that the entropy factor q decreases with step size due to the softening of long range interactions or memory. This explains the deviation of power law for very large step sizes. Finally, we apply this model for distribution of citation index of scientists and examination scores and are able to explain the entire distribution including deviations from power law.Comment: 22 pages, 8 figure

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

Fate of the Kinetic Ising Model

Presented on February 25, 2013 from 3:00 pm to 4:00 pm in Room 1116 of the Marcus Nanotechnology building.Runtime: 62:32 minutesWhat could possibly be new in the Ising model, arguably the most-studied model of statistical physics? Plenty! Consider the Ising model initially at infinite temperature that is suddenly cooled to zero temperature and evolves by single spin-flip dynamics. What happens? In one dimension, the ground state is always reached and the evolution can be solved exactly. In two dimensions, the ground state is reached only about 2/3 of the time, and the long-time evolution is characterized by two distinct time scales, the longer of which arises from topological defects. In three dimensions, the ground state is never reached and the evolution is quite rich: (i) domains are topologically complex, with average genus growing algebraically with system size; (ii) the long-time state always contains "blinker" spins that can flip ad infinitum with no energy cost; (iii) the relaxation time grows exponentially with system size