A comparison of artificial neural network and time series models for forecasting GDP in Palestine

Time series of quarterly observations on Gross Domestic Product (GDP) is collected and used in this study. Forecasting results of ANNs are compared with those of the Autoregressive Integrated Moving Average (ARIMA) and regression as benchmark methods. Using Root Mean Square Error (RMSE), the empirical results show that ANN performs better than the traditional methods in forecasting GDP

Application of optimization algorithms in the design of a superconducting A.C. generator rotor

The superconducting a.c. generator is expected to be the optimum choice among a.c. generation systems in future because of its reduced size, high efficiency, high terminal voltage and its contribution to the stability of the power system. Such machines also exhibit unique design problems which remain unsolved. The optimal selection of the basic design parameters is a current problem of interest. This thesis is intended as a contribution in this direction, and a general design strategy has been developed for the superconducting a.c. generator. Elements of the design process include magnetic field analysis, losses, and mechanical performance all which of are discussed in the thesis. An analytical model has been developed to help determine the distribution of magnetic flux density inside the superconducting machine. This model takes into account the number, and the geometric structure, of the winding slots and allows the rotor of the superconducting machine to be designed with optimum magnetic field distribution. A general design strategy has been developed for the superconducting a.c. generator rotor for predicting the optimum design. The design optimization process incorporates "direct search" and random-shrinkage methods. Two direct search methods of minimization have been compared on mathematical functions and also on machine design problems. The best method is highlighted and discussed. A general computer program package is presented that will optimize and analyse machine design problems. The package is organised in such away that future addition or deletion of performance specifications, constraints, optimization methods and design process elements are readily implemented

Generalized Heteroskedasticity ACF for Moving Average Models in Explicit Forms

The autocorrelation function (ACF) measures the correlation between observations at different distances apart. We derive explicit equations for generalized heteroskedasticity ACF for moving average of order q, MA (q). We consider two cases: Firstly: when the disturbance term follow the general covariance matrix structure Cov (w i, w j)= S with s i, j ¹ 0" i¹j. Secondly: when the diagonal elements of S are not all identical but s ij= 0" i¹j, ie S= diag (s 11, s 22,…, s tt). The forms of the explicit equations depend essentially on the moving average coefficients and covariance structure of the disturbance terms

Male sexually coercive behaviour drives increased swimming efficiency in female guppies

Sexual coercion of females by males is widespread across sexually reproducing species. It stems from a conflict of interest over reproduction and exerts selective pressure on both sexes. For females, there is often a significant energetic cost of exposure to male sexually coercive behaviours. Our understanding of the efficiency of female resistance to male sexually coercive behaviour is key to understanding how sexual conflict contributes to population level dynamics and ultimately to the evolution of sexually antagonistic traits. Overlooked within this context are plastic physiological responses of traits within the lifetime of females that could moderate the energetic cost imposed by coercive males. Here, we examined whether conflict over the frequency and timing of mating between male and female guppies Poecilia reticulata can induce changes in swimming performance and aerobic capacity in females as they work to escape harassment by males. Females exposed to higher levels of harassment over a 5-month period used less oxygen to swim at a given speed, but displayed no difference in resting metabolic rate, maximal metabolic rate, maximal sustained swimming speed or aerobic scope compared to females receiving lower levels of harassment. The observed increase in swimming efficiency is at least partially related to differences in swimming mechanics, likely brought on by a training effect of increased activity, as highly harassed females spent less time performing pectoral fin-assisted swimming. Sexual conflict results in sexually antagonistic traits that impose a variety of costs, but our results show that females can reduce costs through phenotypic plasticity. It is also possible that phenotypic plasticity in swimming physiology or mechanics in response to sexual coercion can potentially give females more control over matings and affect which male traits are under selection

Spatial Distribution of Metal Emissions in SNR 3C 397 Viewed with Chandra and XMM

We present X-ray equivalent width imaging of the supernova remnant (SNR) 3C 397 for Mg He\alpha, Si He\alpha, S He\alpha, and Fe K\alpha complex lines with the Chandra and XMM-Newton observations. The images reveal that the heavier the element is, the smaller the extent of the element distribution is. The Mg emission is evidently enhanced in the southeastern blow-out region, well along the radio boundary there, and appears to partially envelope the eastern Fe knot. Two bilateral hat-like Si line-emitting structures are along the northern and southern borders, roughly symmetric with respect to the southeast-northwest elongation axis. An S line-emitting shell is located just inner to the northern radio and IR shell, indicating of a layer of reversely shocked sulphur in the ejecta. A few enhanced Fe features are basically aligned along the diagonal of the rectangular shape of the SNR, which implicates an early asymmetric SN explosion.Comment: 4 pages, 4 figures, appears in Science China Physics, Mechanics & Astronomy, 2010, 53 (Suppl.1), 267-27

Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine

Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine

Building Logistic Regression Model to Identify Key Determinants of Poverty in Palestine

This paper aims to identify the key determinants of poverty which affect the poverty status of a household in Palestine since the implementation of the economic reform program. A logistic regression model will be used to identify the level of poverty faced by a household. We find that the chance of a household tripping to poverty increased due to large number of children less than 18 years, unemployed adults, and large dependency ratio. Moreover, refugee status, residential region, non-skilled or semi-skilled employee, and inadequate housing and sanitation facilities also play a significant role for increasing the likelihood of being a poor

Mediation of spatial organization in the swift fox, Vulpes velox

The SGS-LTER research site was established in 1980 by researchers at Colorado State University as part of a network of long-term research sites within the US LTER Network, supported by the National Science Foundation. Scientists within the Natural Resource Ecology Lab, Department of Forest and Rangeland Stewardship, Department of Soil and Crop Sciences, and Biology Department at CSU, California State Fullerton, USDA Agricultural Research Service, University of Northern Colorado, and the University of Wyoming, among others, have contributed to our understanding of the structure and functions of the shortgrass steppe and other diverse ecosystems across the network while maintaining a common mission and sharing expertise, data and infrastructure.Animals communicate in a network of individuals. Acoustic, chemical, and visual signals can operate in this network to convey information about a signaller's identity, behaviour, physiological state and location. Poster presented at the 7th Shortgrass Steppe Symposium held in Fort Collins, Colorado on 1/14/05

Transport through quasi-ballistic quantum wires: the role of contacts

We model one-dimensional transport through each open channel of a quantum wire by a Luttinger liquid with three different interaction parameters for the leads, the contact regions and the wire, and with two barriers at the contacts. We show that this model explains several features of recent experiments, such as the flat conductance plateaux observed even at finite temperatures and for different lengths, and universal conductance corrections in different channels. We discuss the possibility of seeing resonance-like structures of a fully open channel at very low temperatures.Comment: revtex, 5 pages, 1 eps figure; clarifications added in light of new experiment