86,307 research outputs found
Efficient minimization of multipole electrostatic potentials in torsion space
The development of models of macromolecular electrostatics capable of delivering improved fidelity to quantum mechanical calculations is an active field of research in computational chemistry. Most molecular force field development takes place in the context of models with full Cartesian coordinate degrees of freedom. Nevertheless, a number of macromolecular modeling programs use a reduced set of conformational variables limited to rotatable bonds. Efficient algorithms for minimizing the energies of macromolecular systems with torsional degrees of freedom have been developed with the assumption that all atom-atom interaction potentials are isotropic. We describe novel modifications to address the anisotropy of higher order multipole terms while retaining the efficiency of these approaches. In addition, we present a treatment for obtaining derivatives of atom-centered tensors with respect to torsional degrees of freedom. We apply these results to enable minimization of the Amoeba multipole electrostatics potential in a system with torsional degrees of freedom, and validate the correctness of the gradients by comparison to finite difference approximations. In the interest of enabling a complete model of electrostatics with implicit treatment of solvent-mediated effects, we also derive expressions for the derivative of solvent accessible surface area with respect to torsional degrees of freedom
Parameter Sensitivity Analysis of Social Spider Algorithm
Social Spider Algorithm (SSA) is a recently proposed general-purpose
real-parameter metaheuristic designed to solve global numerical optimization
problems. This work systematically benchmarks SSA on a suite of 11 functions
with different control parameters. We conduct parameter sensitivity analysis of
SSA using advanced non-parametric statistical tests to generate statistically
significant conclusion on the best performing parameter settings. The
conclusion can be adopted in future work to reduce the effort in parameter
tuning. In addition, we perform a success rate test to reveal the impact of the
control parameters on the convergence speed of the algorithm
A Laboratory Investigation of Compliance Behavior under Tradable Emissions Rights: Implications for Targeted Enforcement
This paper uses laboratory experiments to test the theoretical observations that both the violations of competitive risk-neutral firms and the marginal effectiveness of increased enforcement across firms are independent of differences in their abatement costs and their initial allocations of permits. This conclusion has important implications for enforcing emissions trading programs because it suggests that regulators have no justification for targeting their enforcement effort based on firm-level characteristics. Consistent with the theory, we find that subjects’ violations were independent of parametric differences in their abatement costs. However, those subjects that were predicted to buy permits tended to have higher violation levels than those who were predicted to sell permits. Despite this, we find no statistically significant evidence that the marginal effectiveness of enforcement depends on any firmspecific characteristic. We also examine the determinants of compliance behavior under fixed emissions standards. As expected, we find significant differences between compliance behavior under fixed standards and emissions trading programs.enforcement, compliance, emissions trading, permit markets, standards, commandand- control
Base Station Switching Problem for Green Cellular Networks with Social Spider Algorithm
With the recent explosion in mobile data, the energy consumption and carbon
footprint of the mobile communications industry is rapidly increasing. It is
critical to develop more energy-efficient systems in order to reduce the
potential harmful effects to the environment. One potential strategy is to
switch off some of the under-utilized base stations during off-peak hours. In
this paper, we propose a binary Social Spider Algorithm to give guidelines for
selecting base stations to switch off. In our implementation, we use a penalty
function to formulate the problem and manage to bypass the large number of
constraints in the original optimization problem. We adopt several randomly
generated cellular networks for simulation and the results indicate that our
algorithm can generate superior performance
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