Abstract
Renewable energy integration is gradually increasing in several European countries, such as Portugal and Spain, aiming to reduce emissions in power system operations. Particularly, significant investments in hydro energy systems are envisaged in Portugal, representing a secure and reliable energy source towards sustainable development. Additionally, a pool market structure implies that producers should aim at maximizing profits on a day-ahead basis. Usually, hydro power producers have been considered as price-takers, accepting the price signal as input data. Instead, a MILP approach is provided in this paper to determine the operations planning of a hydro producer acting as a price-maker in a day-ahead electricity market. A realistic case study is presented, based on a hydro energy system in cascaded configuration characterized by spatial-temporal interdependency between reservoirs, in order to thoroughly evaluate the expertise of the proposed approach.

Abstract
An incidence matrix approach for mitigating congestion in transmission network is presented in this paper. Based on this methodology, all congested power transmission lines is identified and main economical signals for investment planning is introduced. In this method, we can determine strong and weak transmission corridors in the network. The Flexible AC Transmission System (FACTS) device has been applied to enhance the controllability of power systems. New generations of FACTS device called Distributed FACTS such as distributed series impedance or distributed static series compensator have recently received increasing interests for power system control and are expected to be broadly deployed. This paper presents a detailed formulation and algorithm to find the best location and size of DFACTS to achieve the optimal utilization of transmission capacity to mitigate congestion. 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 long-term expansion planning criteria or implementing D-FACTS devices in modern power systems. The simulation results show that the presented method is both satisfactory and consistent with expectation. Simulation results are presented with the PJM 5-bus system to illustrate the capabilities of presented approach in compression with previous works. © Indian Society for Education and Environment (iSee).

Abstract
This paper describes a multi-purpose energy management system that can harvest energy from a multitude of power sources. In order to suffice power needs of a smart hip prosthesis, a prototype was built considering the use of two types of power sources: energy harvesting micro-power generators for intermittent implant electronics powering; wireless energy, by means of an activation system, responsible for, when needed, continuously powering the implant electronics and also configuring its mode of operation. Intermittently or continuously, it is now possible to energize more power demanding systems as the ones that uses a RF transceiver. The smart hip prosthesis can now become a wireless body sensor network node using LR-WPAN protocols such as Bluetooth low energy as is intended in this work. Preliminary results proved that is possible to energize a Bluetooth low energy module, for over 100 s, solely using the stored energy produced by one of the micro-power generators. (C) 2012 Elsevier Ltd....Selection and/or peer-review under responsibility of the Symposium Cracoviense Sp. z.o.o.

Abstract
Long-term travel time prediction (TTP) can be an important planning tool for both freight transport and public transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression (PPR), Support Vector Machine (SVM) and Random Forests (RF). For each one of these methods we study the best combination of input parameters. We also study the impact of different methods for the pre-processing tasks (feature selection, example selection and domain values definition) in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.

Abstract
The impact of human capital on foreign direct investment has been assessed in an essentially descriptive manner. Most quantitative studies focus on the macroeconomic level. Microeconomic studies are scarce internationally and even more so in the case of China. Based on a survey of innovative firms located in China, this study assesses the importance of human capital in attracting foreign direct investment (FDI) to China. Using a sample of 77 innovative firms, and logistic estimation techniques, we concluded that even though human capital does not constitute a direct factor in attracting FDI to China, it is a positive indirect factor through firms' R&D efforts. Moreover, we found that connections with universities have a positive impact on the attraction of FDI, although the impact of human capital on the attraction of FDI is not sustained on the basis of additional contacts with universities. Evidence gathered suggests that it is important that public authorities recognize the interconnections between education and innovation policies and the implementation of FDI policies - human capital is only capable of attracting innovative foreign capital when associated with high levels of R&D.

Abstract
A high-birefringent (Hi-Bi) Sagnac loop interferometer for torsion measurement is demonstrated. The sensing head is formed by a section of standard single mode fiber spliced between the output ports of a Hi-Bi coupler at 3 dB. The sensing configuration is characterized in torsion, temperature and strain. The results obtained indicate the viability of a torsion sensor independent of the temperature and strain cross-sensitivity effects. Additionally, in the proposed configuration all measurements are performed without the need of a polarization controller, a device most often required in standard Sagnac loops applied for sensing.