Factors Influencing the Purchase of Live Seafood in the North Central Region of the United States

This study assesses the preferences of shoppers of live seafood products in the North Central Region of the US accounting for heterogeneity in their preferences. The results suggest that quality assurance considerations and high incomes are factors that would increase the probability of higher expenditures on live fish/shellfish. The purchase of saltwater fish and shellfish also increased the probability of higher expenditures. The North Central Region produces freshwater seafood, and maintaining fish quality through the production process is important to this niche market. Shoppers also purchased live seafood frequently, signifying the importance of availability.Live fish, preferences, random parameters ordered probit, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Institutional and Behavioral Economics, Public Economics, Q11, Q21, Q22,

Crossed Andreev effects in two-dimensional quantum Hall systems

We study the crossed Andreev effects in two-dimensional conductor/superconductor hybrid systems under a perpendicular magnetic field. Both a graphene/superconductor hybrid system and an electron gas/superconductor one are considered. It is shown that an exclusive crossed Andreev reflection, with other Andreev reflections being completely suppressed, is obtained in a high magnetic field because of the chiral edge states in the quantum Hall regime. Importantly, the exclusive crossed Andreev reflection not only holds for a wide range of system parameters, e.g., the size of system, the width of central superconductor, and the quality of coupling between the graphene and the superconductor, but also is very robust against disorder. When the applied bias is within the superconductor gap, a robust Cooper-pair splitting process with high-efficiency can be realized in this system.Comment: 10 pages, 10 figure

A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance

An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO) algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO) problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results