The Universe and the Quantum Computer

It is first pointed out that there is a common mathematical model for the universe and the quantum computer. The former is called the histories approach to quantum mechanics and the latter is called measurement-based quantum computation. Although a rigorous concrete model for the universe has not been completed, a quantum measure and integration theory has been developed which may be useful for future progress. In this work we show that the quantum integral is the unique functional satisfying certain basic physical and mathematical principles. Since the set of paths (or trajectories) for a quantum computer is finite, this theory is easier to treat and more developed. We observe that the sum of the quantum measures of the paths is unity and the total interference vanishes. Thus, constructive interference is always balanced by an equal amount of destructive interference. As an example we consider a simplified two-slit experiment.     

Optimal crop planting schedules and financial hedging strategies under ENSO-based climate forecasts

This paper investigates the impact of ENSO-based climate forecasts on optimal planting schedules and financial yield-hedging strategies in a framework focused on downside risk. In our context, insurance and futures contracts are available to hedge against yield and price risks, respectively. Furthermore, we adopt the Conditional-Value-at-Risk (CVaR) measure to assess downside risk, and Gaussian copula to simulate scenarios of correlated non-normal random yields and prices. The resulting optimization problem is a mixed 0?C1 integer programming formulation that is solved efficiently through a two-step procedure, first through an equivalent linear form by disjunctive constraints, followed by decomposition into sub-problems identified by hedging strategies. With data for a representative cotton producer in the Southeastern United States, we conduct a study that considers a wide variety of optimal planting schedules and hedging strategies under alternative risk profiles for each of the three ENSO phases (Niña, Niño, and Neutral.) We find that the Neutral phase generates the highest expected profit with the lowest downside risk. In contrast, the Niña phase is associated with the lowest expected profit and the highest downside risk. Additionally, yield-hedging insurance strategies are found to vary significantly, depending critically on the ENSO phase and on the price bias of futures contracts.     

Stability of symmetric vortices for two-component Ginzburg–Landau systems

We study Ginzburg–Landau equations for a complex vector order parameter Ψ=(_ψ+,_ψ−)∈C²$\Psi =({\psi }_{+},{\psi }_{-})\in {\mathbb{C}}^2$. We consider the Dirichlet problem in the disk in R²${\mathbb{R}}^2$ with a symmetric, degree-one boundary condition, and study its stability, in the sense of the spectrum of the second variation of the energy. We find that the stability of the degree-one equivariant solution depends on the Ginzburg–Landau parameter as well as the sign of the interaction term in the energy.     

Efficient Syntheses of β-Sesquiphellandrene and Zingiberenol Employing a Tandem Arylation-Multistep Reduction-Hydrolysis Sequence1

The title sesquiterpenes are synthesized in one step from the common intermediate 4-(6-methyl-5-hepten-2-yl)-2-cyclohexen-1-one, which is prepared using a one-pot tandem arylation-multistep reduction-hydrolysis sequence.     

Optimal design of a 2 DOF parallel robot

This paper is aimed at presenting a study on the optimization of the Biglide mini parallel robot, which comprises two-degree-of-freedom (DOF) mini parallel robots with constant struts. The robot workspace is characterized and the inverse kinematics equation is obtained. In the paper, design optimization is implemented with Genetic Algorithms (GA) for optimization considering transmission quality index and workspace. Here, intended to show the advantages of using the GA, we applied it to a multicriteria optimization problem of 2 DOF mini parallel robot. Genetic algorithms (GA) are so far generally the best and most robust kind of evolutionary algorithms. A GA has a number of advantages. It can quickly scan a vast solution set. Bad proposals do not affect the end solution negatively as they are simply discarded. The obtained results have shown that the use of GA in such kind of optimization problem enhances the quality of the optimization outcome, providing a better and more realistic support for the decision maker. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)