Vernal Pool Conservation: Enhancing Existing Regulation Through the Creation of the Maine Vernal Pool Special Area Management Plan

Conservation of natural resources is challenging given the competing economic and ecological goals humans have for landscapes. Vernal pools in the northeastern US are seasonal, small wetlands that provide critical breeding habitat for amphibians and invertebrates adapted to temporary waters, and are exceptionally hard to conserve as their function is dependent on connections to other wetlands and upland forests. A team of researchers in Maine joined forces with a diverse array of governmental and private stakeholders to develop an alternative to existing top-down vernal pool regulation. Through creative adoption and revision of various resource management tools, they produced a vernal pool conservation mechanism, the Maine Vernal Pool Special Management Area Plan that meets the needs of diverse stakeholders from developers to ecologists. This voluntary mitigation tool uses fees from impacts to vernal pools in locally identified growth areas to fund conservation of “poolscapes” (pools plus appropriate adjacent habitat) in areas locally designated for rural use. In this case study, we identify six key features of this mechanism that illustrate the use of existing tools to balance growth and pool conservation. This case study will provide readers with key concepts that can be applied to any conservation problem: namely, how to work with diverse interests toward a common goal, how to evaluate and use existing policy tools in new ways, and how to approach solutions to sticky problems through a willingness to accept uncertainty and risk

Model for Dissipative Highly Nonlinear Waves in Dry Granular Systems

A model is presented for the characterization of dissipative effects on highly nonlinear waves in one-dimensional dry granular media. The model includes three terms: Hertzian, viscoelastic, and a term proportional to the square of the relative velocity of particles. The model outcomes are confronted with different experiments where the granular system is subject to several constraints for different materials. Excellent qualitative and quantitative agreement between theory and experiments is found.Comment: Link to the Journal: http://prl.aps.org/abstract/PRL/v104/i11/e11800

Condensation temperature of interacting Bose gases with and without disorder

The momentum-shell renormalization group (RG) is used to study the condensation of interacting Bose gases without and with disorder. First of all, for the homogeneous disorder-free Bose gas the interaction-induced shifts in the critical temperature and chemical potential are determined up to second order in the scattering length. The approach does not make use of dimensional reduction and is thus independent of previous derivations. Secondly, the RG is used together with the replica method to study the interacting Bose gas with delta-correlated disorder. The flow equations are derived and found to reduce, in the high-temperature limit, to the RG equations of the classical Landau-Ginzburg model with random-exchange defects. The random fixed point is used to calculate the condensation temperature under the combined influence of particle interactions and disorder.Comment: 7 pages, 2 figure

The Ising Model for Neural Data: Model Quality and Approximate Methods for Extracting Functional Connectivity

We study pairwise Ising models for describing the statistics of multi-neuron spike trains, using data from a simulated cortical network. We explore efficient ways of finding the optimal couplings in these models and examine their statistical properties. To do this, we extract the optimal couplings for subsets of size up to 200 neurons, essentially exactly, using Boltzmann learning. We then study the quality of several approximate methods for finding the couplings by comparing their results with those found from Boltzmann learning. Two of these methods- inversion of the TAP equations and an approximation proposed by Sessak and Monasson- are remarkably accurate. Using these approximations for larger subsets of neurons, we find that extracting couplings using data from a subset smaller than the full network tends systematically to overestimate their magnitude. This effect is described qualitatively by infinite-range spin glass theory for the normal phase. We also show that a globally-correlated input to the neurons in the network lead to a small increase in the average coupling. However, the pair-to-pair variation of the couplings is much larger than this and reflects intrinsic properties of the network. Finally, we study the quality of these models by comparing their entropies with that of the data. We find that they perform well for small subsets of the neurons in the network, but the fit quality starts to deteriorate as the subset size grows, signalling the need to include higher order correlations to describe the statistics of large networks.Comment: 12 pages, 10 figure

Competition between ferro-retrieval and anti-ferro orders in a Hopfield-like network model for plant intelligence

We introduce a simple cellular-network model to explain the capacity of the plants as memory devices. Following earlier observations (Bose \cite{Bose} and others), we regard the plant as a network in which each of the elements (plant cells) are connected via negative (inhibitory) interactions. To investigate the performance of the network, we construct a model following that of Hopfield, whose energy function possesses both Hebbian spin glass and anti-ferromagnetic terms. With the assistance of the replica method, we find that the memory state of the network decreases enormously due to the effect of the anti-ferromagnetic order induced by the inhibitory connections. We conclude that the ability of the plant as a memory device is rather weak.Comment: To be pulished in Physica A (Proc. STATPHYS-KOLKATA V), 9 pages, 6 eps fig

Synchronization from Disordered Driving Forces in Arrays of Coupled Oscillators

The effects of disorder in external forces on the dynamical behavior of coupled nonlinear oscillator networks are studied. When driven synchronously, i.e., all driving forces have the same phase, the networks display chaotic dynamics. We show that random phases in the driving forces result in regular, periodic network behavior. Intermediate phase disorder can produce network synchrony. Specifically, there is an optimal amount of phase disorder, which can induce the highest level of synchrony. These results demonstrate that the spatiotemporal structure of external influences can control chaos and lead to synchronization in nonlinear systems.Comment: 4 pages, 4 figure

Scaling of the magnetic entropy and magnetization in YbRh_2(Si_{0.95}Ge_{0.05})_2

The magnetic entropy of YbRh_2(Si_{0.95}Ge_{0.05})_2 is derived from low-temperature ($T\geq 18$ mK) specific heat measurements. Upon field-tuning the system to its antiferromagnetic quantum critical point unique temperature over magnetic field scaling is observed indicating the disintegration of heavy quasiparticles. The field dependence of the entropy equals the temperature dependence of the dc-magnetization as expected from the Maxwell relation. This proves that the quantum-critical fluctuations affect the thermal and magnetic properties in a consistent way.Comment: 6 pages, 2 figures, manuscript submitted to SCES2004 conferenc

Design of Copolymeric Materials

We devise a method for designing materials that will have some desired structural characteristics. We apply it to multiblock copolymers that have two different types of monomers, A and B. We show how to determine what sequence of A's and B's should be synthesised in order to give a particular structure and morphology. %For example in a melt of such %polymers, one may wish to engineer a body-centered %cubic structure. Using this method in conjunction with the theory of microphase separation developed by Leibler, we show it is possible to efficiently search for a desired morphology. The method is quite general and can be extended to design isolated heteropolymers, such as proteins, with desired structural characteristics. We show that by making certain approximations to the exact algorithm, a method recently proposed by Shakhnovich and Gutin is obtained. The problems with this method are discussed and we propose an improved approximate algorithm that is computationally efficient.Comment: 15 pages latex 2.09 and psfig, 1 postscript figure