Environmental life cycle assessment of concrete made with fine recycled concrete aggregates

The majority of worldwide structures use concrete as its main material. This happens because concrete is economically feasible, due to its undemanding production technology and case Of use. However, it is widely recognized that concrete production has a strong environmental impact in the planet. Natural aggregates use is one of the most important problems of concrete production nowadays, since they are obtained from limited, and in some countries scarce, resources. In Portugal, although there are enough stone quarries to cover coarse aggregates needs for several more years, Supplies of fine aggregates are becoming scarcer, especially in the northern part of the country. On the other hand, as concrete structures' life cycle comes to an end, an urgent need emerges to establish technically and economically viable solutions for demolition debris, other than for use as road base and quarry fill. This paper presents a partial life cycle assessment (LCA) of concrete made with fine recycled concrete aggregates performed with EcoConcrete tool. EcoConcrete is a tailor-made, interactive, learning and communications tool promoted by the Joint Project Group (JPG) on the LCA of concrete, to qualify and quantify the overall environment impact of concrete products. It consists of an interactive Excel-spreadsheet in which several environmental inputs (material quantities, distances from origin to production Site, production processes) and outputs (material, energy, emissions to air, water, soil or waste) are collected in a life cycle inventory, and are then processed to determine the environmental impact (assessment) of the analysed concrete, in terms of ozone layer depletion, smog or "greenhouse" effect.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

An artificial neural network approach for short-term wind power forecasting in Portugal

This paper presents an artificial neural network approach for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. The accuracy of the wind power forecasting attained with the proposed approach is evaluated against persistence and ARIMA approaches, reporting the numerical results from a real-world case study.

Nonlinear optimization method for short-term hydro scheduling considering head-dependency

This paper is on the problem of short-term hydro scheduling, particularly concerning head-dependent reservoirs under competitive environment. We propose a new nonlinear optimization method to consider hydroelectric power generation as a function of water discharge and also of the head. Head-dependency is considered on short-term hydro scheduling in order to obtain more realistic and feasible results. The proposed method has been applied successfully to solve a case study based on one of the main Portuguese cascaded hydro systems, providing a higher profit at a negligible additional computation time in comparison with a linear optimization method that ignores head-dependency.

New Steel Alloys for the Design of Heat Recovery Steam Generator Components of Combined Cycle Gas Plants

Demand for power is growing every day, mainly due to emerging economies in countries such as China, Russia, India, and Brazil. During the last 50 years steam pressure and temperature in power plants have been continuously raised to improve thermal efficiency. Recent efforts to improve efficiency leads to the development of a new generation of heat recovery steam generator, where the Benson once-through technology is applied to improve the thermal efficiency. The main purpose of this paper is to analyze the mechanical behavior of a high pressure superheater manifold by applying finite element modeling and a finite element analysis with the objective of analyzing stress propagation, leading to the study of damage mechanism, e.g., uniaxial fatigue, uniaxial creep for life prediction. The objective of this paper is also to analyze the mechanical properties of the new high temperature resistant materials in the market such as 2Cr Bainitic steels (T/P23 and T/P24) and also the 9-12Cr Martensitic steels (T/P91, T/P92, E911, and P/T122). For this study the design rules for construction of power boilers to define the geometry of the HPSH manifold were applied.

Enhanced short wavelength response in laser-scanned-photodiode image sensor using an a-SiC : H/a-Si : H tandem structure

We report in this paper the recent advances we obtained in optimizing a color image sensor based on the laser-scanned-photodiode (LSP) technique. A novel device structure based on a a-SiC:H/a-Si:H pin/pin tandem structure has been tested for a proper color separation process that takes advantage on the different filtering properties due to the different light penetration depth at different wavelengths a-SM and a-SiC:H. While the green and the red images give, in comparison with previous tested structures, a weak response, this structure shows a very good recognition of blue color under reverse bias, leaving a good margin for future device optimization in order to achieve a complete and satisfactory RGB image mapping. Experimental results about the spectral collection efficiency are presented and discussed from the point of view of the color sensor applications. The physics behind the device functioning is explained by recurring to a numerical simulation of the internal electrical configuration of the device.

Short-term electricity prices forecasting in a competitive market by a hybrid PSO-ANFIS approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn. (C) 2012 Elsevier Ltd. All rights reserved.

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

Microstrip antenna array for multiband dedicated short range communications systems

Dedicated Short Range Communications (DSRC) is the key enabling technology for the present and future vehicular communication for various applications, such as safety improvement and traffic jam mitigation. This paper describes the development of a microstrip antenna array for the roadside equipment of a DSRC system, whose characteristics are according with the vehicular communications standards. The proposed antenna, with circular polarization, has a wide bandwidth, enough to cover the current European DSRC 5.8 GHz band and the future 5.9 GHz band for next generation DSRC communications. (C) 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53: 2794-2796, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26394

Semiconductor Device as Optical Demultiplexer for Short Range Optical Communications

In this paper we present results on the use of a multilayered a-SiC:H heterostructure as a wavelength-division demultiplexing device for the visible light spectrum. The proposed device is composed of two stacked p-i-n photodiodes with intrinsic absorber regions adjusted to short and long wavelength absorption and carrier collection. An optoelectronic characterisation of the device was performed in the visible spectrum. Demonstration of the device functionality for WDM applications was done with three different input channels covering the long, the medium and the short wavelengths in the visible range. The recovery of the input channels is explained using the photocurrent spectral dependence on the applied voltage. An electrical model of the WDM device is proposed and supported by the solution of the respective circuit equations. Short range optical communications constitute the major application field, however other applications are also foreseen.

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.

Short-term wind power forecasting in Portugal by neural networks and wavelet transform

This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.

Tubulin cofactor A gene silencing in mammalian cells induces changes in microtubule cytoskeleton, cell cycle arrest and cell death

Microtubules are polymers of alpha/beta-tubulin participating in essential cell functions. A multistep process involving distinct molecular chaperones and cofactors produces new tubulin heterodimers competent to polymerise. In vitro cofactor A (TBCA) interacts with beta-tubulin in a quasi-native state behaving as a molecular chaperone. We have used siRNA to silence TBCA expression in HeLa and MCF-7 mammalian cell lines. TBCA is essential for cell viability and its knockdown produces a decrease in the amount of soluble tubulin, modifications in microtubules and G1 cell cycle arrest. In MCF-7 cells, cell death was preceded by a change in cell shape resembling differentiation.