Plasmonic Light Trapping in an Ultrathin Photovoltaic Layer with Film-Coupled Metamaterial Structures

A film-coupled metamaterial structure is numerically investigated for enhancing the light absorption in an ultrathin photovoltaic layer of crystalline gallium arsenide (GaAs). The top subwavelength concave grating and the bottom metallic film could not only effectively trap light with the help of wave interference and magnetic resonance effects excited above the bandgap, but also practically serve as electrical contacts for photon-generated charge collection. The energy absorbed by the active layer is greatly enhanced in the film-coupled metamaterial structure, resulting in significant enhancement on the short-circuit current density by three times over a free-standing GaAs layer at the same thickness. The results would facilitate the development of next-generation ultrathin solar cells with lower cost and higher efficiency

Extenics-based Study on Evaluation of Urban Community Home-care Service for the Elderly

AbstractThis paper tries to introduce extenics theory into the evaluation of the urban community home-care service for the elderly. The paper analyzes the feasibility of using extenics to evaluate the service, uses analytic hierarchy process to decide the weight of index and constructs a comprehensive evaluation model for the service on the basis of extenics. Based on the case study of communities in Ningbo, the paper has completed the evaluation of home-care service in operation and put forward countermeasures to the existing problems

A Decentralized Primal Dual Algorithm with Quasi-Newton Tracking

This paper considers the decentralized optimization problem of minimizing a finite sum of strongly convex and twice continuously differentiable functions over a fixed connected undirected network. A fully decentralized primal dual method (DPDM) is proposed. In DPDM, both primal and dual updates use second order information while Hessian approximations are constructed by simple algebraic calculations and at most matrix-vector products. Additionally, on each node in the network the local direction asymptotically approaches the centralized quasi-Newton direction. Under some mild assumptions, the proposed algorithm is showed to have global linear convergence rate for solving strongly convex decentralized optimization problems. Numerical results are also provided for demonstrating the effectiveness of this new algorithm.Comment: 24 pages, 18 figure