Short report: Molecular genetic characterization of an unusually severe case of hydatid disease in Alaska caused by the cervid strain of Echinococcus granulosusi

Distinct Echinococcus granulosus life cycle patterns have been described in North America: domestic and sylvatic. Gene sequences of the sylvatic E. granulosus indicate that it represents a separate variant. Case-based data have suggested that the course of sylvatic disease is less severe than that of domestic disease. which led to the recommendation to treat cystic echinococcosis patients in the Arctic by careful medical management rather than by aggressive surgery. We recently reported the first two documented E. granalosus human cases in Alaska with accompanying severe sequelae. Here we describe the results of molecular genetic analysis of the cyst material of one of the subjects that supported identification of the parasite as the sylvatic (cervid) strain and not the domestic (common sheep strain), which was initially thought to be implicated in these unusually severe Alaskan cases.

Short-term autocorrelation in Australian equities

This paper examines the statistical and economic significance of short-term autocorrelation in Australian equities. We document large negative first-order autocorrelation in individual stock returns. Preliminary results suggest this autocorrelation is economically significant, as two simple trading strategies based on the autocorrelation structure appear to yield large risk-adjusted returns. Further analysis, however, shows that these results are driven by the inclusion of small-capitalisation and low-priced stocks which are vulnerable to a number of market-microstructure-related problems. After revising the dataset to mitigate these problems, little evidence of economic significance remains.

On the robustness of short-term interest rate models

This paper investigates the robustness of a range of short–term interest rate models. We examine the robustness of these models over different data sets, time periods, sampling frequencies, and estimation techniques. We examine a range of popular one–factor models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate. We find that parameter estimates are highly sensitive to all of these factors in the eight countries that we examine. Since parameter estimates are not robust, these models should be used with caution in practice.

Simultaneous observations of ionospheric quasiperiodic scintillations from short and long meridional baselines using VHF transmissions from transit satellites

For the first time it was possible to observe regular quasiperiodic scintillations (QPS) in VHF radio-satellite transmissions from orbiting satellites simultaneously at short (2.1 km) and long (121 km) meridional baselines in the vicinity of a typical mid-latitude station (Brisbane; 27.5degreesS and 152.9degreesE geog. and 35.6degrees invar.lat.), using three sites (St. Lucia-S, Taringa-T in Brisbane and Boreen Pt.-B, north of Brisbane). A few pronounced quasiperiodic (QP) events were recorded showing unambiguous regular structures at the sites which made it possible to deduce a time displacement of the regular fading minimum at S, T and B. The QP structure is highly dependent on the geometry of the ray-path from a satellite to the observer which is manifested as a change of a QP event from symmetrical to non-symmetrical for stations separated by 2.1 km, and to a radical change in the structure of the event over a distance of 121 km. It is suggested the short-duration intense QP events are due to a Fresnel diffraction (or a reflection mechanism) of radio-satellite signals by a single ionospheric irregularity in a form of an ellipsoid with a large ionization gradient along the major axis. The structure of a QP event depends on the angle of viewing of the irregular blob from a radio-satellite. In view of this it is suggested that the reported variety of the ionization formation, responsible for different types of QPS, is only apparent but not real. (C) 2003 Elsevier Science Ltd. All rights reserved.

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.

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.

Short-term wind forecasting for energy resources scheduling

This paper proposes a wind power forecasting methodology based on two methods: direct wind power forecasting and wind speed forecasting in the first phase followed by wind power forecasting using turbines characteristics and the aforementioned wind speed forecast. The proposed forecasting methodology aims to support the operation in the scope of the intraday resources scheduling model, namely with a time horizon of 5 minutes. This intraday model supports distribution network operators in the short-term scheduling problem, in the smart grid context. A case study using a real database of 12 months recorded from a Portuguese wind power farm was used. The results show that the straightforward methodology can be applied in the intraday model with high wind speed and wind power accuracy. The wind power forecast direct method shows better performance than wind power forecast using turbine characteristics and wind speed forecast obtained in first phase.