The use of quantum dots can turn the old concept of a luminescent solar collector into a practical concentrator. The quantum efficiency, tunability of absorption threshold, and size of the redshift make quantum dots an ideal replacement for the organic dyes whose performance limited this inexpensive technology. Progress in photovoltaic cells, in particular, the ability of quantum-well cells to tune the band gap, also suggests high efficiency is possible in solar and thermophotovoltaic applications. A thermodynamic model is used to show quantitatively how the separation of absorption and luminescent peaks under global illumination is related to the spread of quantum-dot sizes. Hence, the redshift can be determined during the growth process. The model can be used to optimize concentrator performance and to study the effect of reabsorption, which is important for high concentration even if the quantum efficiency is unity. This model provides a quantitative explanation for the contribution of the spread of sizes to the redshift, which should help in the extraction of the much smaller, single-dot effects.

Measurements of the e + e~ cross section above BB threshold are reported. Structures are observed which could be the Y(5S) and Y(6S) resonances. The masses and widths are given and compared with various potential-model predictions. Average charged multiplicities and inclusive lepton yields are also presented.

Abstract:In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a twolayer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.

In this paper, we present a distributed infrastructure based on wireless sensors network and Grid computing technology for air pollution monitoring and mining, which aims to develop low-cost and ubiquitous sensor networks to collect real-time, large scale and comprehensive environmental data from road traffic emissions for air pollution monitoring in urban environment. The main informatics challenges in respect to constructing the high-throughput sensor Grid are discussed in this paper. We present a two-layer network framework, a P2P e-Science Grid architecture, and the distributed data mining algorithm as the solutions to address the challenges. We simulated the system in TinyOS to examine the operation of each sensor as well as the networking performance. We also present the distributed data mining result to examine the effectiveness of the algorithm.

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