Abstract
Broadcast is a communication primitive building block widely used in mobile ad-hoc networks (MANETs) for the exchange of control packets and resource location for upper level services such as routing and management protocols. Flooding is the most simple broadcast algorithm, but it wastes a lot of energy and bandwidth, as flooding leads to many redundant radio transmissions. An optimization to flooding is to contain it, once the resource has been found. In this paper, we compare the impact on the latency and power consumption of four competing approaches for flooding containment. The results show that stopping ongoing broadcasts can achieve promising performance increases over other flooding base techniques, when applied in large scale MANETs with scarce power resources. In addition, results show that both network topology and the number of copies of the resource influence differently the performance of each searching approach. © 2012 ACM.

Abstract
This paper is on offshore wind energy conversion systems with full-power converter and permanent magnet synchronous generator. The new drive train considered in this paper corresponds to a five-mass model and the resistant stiffness torque (structure and tower) in the deep water due to the moving surface elevation. Also, a multilevel converter and a fractional-order control strategy are considered. Simulation studies are carried out in order to adequately assess the harmonic content of the energy injected into the electric grid. Conclusions are duly drawn.

Abstract
This paper presents a new approach for considering all possible contingencies in short-term power system operation. Based on this new approach, both generator and transmission line outages would be modelled in network-based power system analysis. Multi generator and also parallel transmission lines is modelled in this methodology. We also investigate this claim that feasibility and applicability of this approach is much more than the previous analytical methodologies. Security Constrained Unit commitment (SCUC) program which is carried out by Independent System Operator (ISO), is one of the complex problems which would be handled by this approach. In this paper, a DC-Optimal Power Flow (DCOPF) methodology has been considered for hourly Locational Marginal Price (LMP) calculations. This approach can be applied in market simulation and planning owing to its robustness and speed. Unlike, previous admittance based matrix methodologies, which solidly depended on the network topology, independency of network in the presented approach; it would be an effective tool for considering possible contingencies in the grid. The simulation results show that the presented method is both satisfactory and consistent with expectation. © Indian Society for Education and Environment (iSee).

Abstract
We present a nonlinear mathematical model for a low-size and micro-power vibrational energy harvester targeted to power smart prostheses and in particular hip prostheses. Constraints such as available volume, allowable total weight and energy transducer mechanism difficult the development of an optimal generator system, especially considering the low frequencies involved in the human gait. Since non-linear behavior often conducts to better experimental results than those obtained using linear models, a nonlinear model is being pursued to reduce the volume of the generator and maximize performance. By including the non-linear effects of magnetic levitation used to suspend the inertial mass, influence of friction and effects of inductor non-idealities, preliminary results indicate better generator's performance prediction which allows a more effective customization of the energy harvester. (C) 2012 Elsevier Ltd....Selection and/or peer-review under responsibility of the Symposium Cracoviense Sp. z.o.o.

Abstract
Mining data streams poses many challenges to existing Machine Learning algorithms. Algorithms designed to learn in this scenario need to constantly update their decision models in accordance with current data behavior. Therefore, the ability to detect when the behavior of the stream is changing is an important feature of any learning technique approaching data streams. This work is concerned with unsupervised behavior change detection. It suggests the use of density-based clustering and an entropy measurement for change detection that is independent of the number and format of clusters. The proposed approach uses a modified version of the Den Stream algorithm that is designed to better cope with the entropy calculation. Experimental results using synthetic data provide insight on how clustering and novelty detection algorithms can be used for change detection in data streams. © 2012 IEEE.

Abstract
Studies on the impact of foreign direct investment (FDI) on the Chinese economy have essentially focused on the relationship between FDI, productivity and economic growth, revealing a tendency toward sectoral, regional and macroeconomic empirical studies. This work aims to complement these approaches and contribute to the rather limited literature on the relationship between FDI, Human Capital and Innovation at a corporate level. Based on a set of large and innovative firms (national and foreign capital) located in China, we have concluded that (1) the direct impact of foreign capital on the level of human capital in firms is negative, that is no evidence was found suggesting that FDI has a positive influence on their human capital and (ii) in indirect terms, by means of investment in R&D (research and development) activities, FDI has a positive impact on general human capital (i.e. formal education). These results suggest that for China to benefit from FDI, it is necessary to implement a selective policy to attract FDI, taking into account more technologically advanced, R&D-based projects.