Abstract
Wind power is expected to deliver a significant part of power generation in future smart grid. However, many economic challenges have arisen from the intermittent nature of wind power. In this paper, a multi-stage stochastic model is proposed for self-scheduling problem of Wind Power Producers (WPPs) in competitive electricity markets. The proposed model includes three trading levels namely; forward, day-ahead, and balancing sessions. The problem uncertainties, such as wind power, market prices and quantity of activated reserve by ISO are considered by the Monte Carlo method. Moreover, Conditional Value-at-Risk (CVaR) is employed in the model as an appropriate risk measuring technique. The proposed model yields the optimal behavior of WPPs to participate in day-ahead energy and ancillary services markets (i.e. spinning reserve and regulation). Simulation results indicate that simultaneous participation of the WPPs in the mentioned markets not only augments their profit but also can significantly decrease the associated risks.

Abstract
We show a one-to-one correspondence between circle diffeomorphism sequences that are C1+ n-periodic points of renormalization and smooth Markov sequences. © Springer Science+Business Media New York 2014.

Abstract
This paper presents a study based on the application of cascade ANN prediction models to an earthwork construction database. Results show not only a good adjustment to the data, but also the influence of each earthwork construction process on the work rate of the global production line. Furthermore, the obtained results emphasize the importance of optimal resource allocation and management throughout earthwork construction phases. Following this framework, the study concludes with a demonstration of how the developed models can be integrated into a more complex system in order to pursue that purpose.

Abstract
This work reports a new type of optical fiber tweezers based on polymeric micro-lenses. The lenses are achieved by means of an economical and fast fabrication process, using an in-fiber photo-polymerization technique. The polymerization radiation is guided towards the fiber tip creating a polymeric waveguide. The method allows tailoring the geometry of the tip by adjusting the fabrication parameters. Furthermore, more complex shapes can be fabricated by exploring modal effects at the polymerization/trapping wavelengths, which can be used for different applications such as trapping, beam shaping and patterned illumination.

Abstract
A model based on a set of bibliometric indicators is proposed for the prediction of the ranking of applicants to an academic position as produced by a committee of peers. The results show that a very small number of indicators may lead to a robust prediction of about 75% of the cases. We start with 12 indicators to build a few composite indicators by factor analysis. Following a discrete choice model, we arrive at 3 comparatively good predicative models. We conclude that these models have a surprisingly good predictive power and may help peers in their selection process.

Abstract
An effective analytical model combining geometrical optics with the transfer-matrix theory for stratified optical media is applied to investigate the sensing properties of tapered optical fiber surface plasmon resonance (SPR) sensors incorporating Ag-Au bimetallic layers, particularly in the context of phase interrogation. The performance of the sensing structures is studied as a function of the tapering parameters and thickness of the metallic layers. It is shown that the Ag-Au bimetallic combination is capable of improving the resolution and tuning working region of SPR fiber-optic sensors and that by tapering the sensing structures enhanced sensitivity can be achieved when phase interrogation is considered.