Extensions and applications of a second-order landsurface parameterization

Extensions and applications of a second order land surface parameterization, proposed by Andreou and Eagleson are developed. Procedures for evaluating the near surface storage depth used in one cell land surface parameterizations are suggested and tested by using the model. Sensitivity analysis to the key soil parameters is performed. A case study involving comparison with an "exact" numerical model and another simplified parameterization, under very dry climatic conditions and for two different soil types, is also incorporated

A Neural Network Measurement of Relative Military Security: The Case of Greece and Cyprus

This paper aims at introducing a relative security measure, applicable to evaluating the impact of arms races on the military security of allies. This measure is based on demographic criteria, which play a dominant role in a number of arms races involving military alliances. The case of Greece and Cyprus, on one hand, and Turkey on the other, is the one to which our relative security measure is applied and tested. Artificial neural networks were trained to forecast the future behaviour of relative security. The high forecasting performance permitted the application of alternative scenarios for predicting the impact of the Greek - Turkish arms race on the relative security of the Greek - Cypriot alliance.Arms Race, Neural Networks, Relative Military Security

Computational Intelligence in Exchange-Rate Forecasting

This paper applies computational intelligence methods to exchange rate forecasting. In particular, it employs neural network methodology in order to predict developments of the Euro exchange rate versus the U.S. Dollar and the Japanese Yen. Following a study of our series using traditional as well as specialized, non-parametric methods together with Monte Carlo simulations we employ selected Neural Networks (NNs) trained to forecast rate fluctuations. Despite the fact that the data series have been shown by the Rescaled Range Statistic (R/S) analysis to exhibit random behaviour, their internal dynamics have been successfully captured by certain NN topologies, thus yielding accurate predictions of the two exchange-rate series.Exchange - rate forecasting, Neural networks

A second-order Budkyo-type parameterization of landsurface hydrology

A simple, second order parameterization of the water fluxes at a land surface for use as the appropriate boundary condition in general circulation models of the global atmosphere was developed. The derived parameterization incorporates the high nonlinearities in the relationship between the near surface soil moisture and the evaporation, runoff and percolation fluxes. Based on the one dimensional statistical dynamic derivation of the annual water balance, it makes the transition to short term prediction of the moisture fluxes, through a Taylor expansion around the average annual soil moisture. A comparison of the suggested parameterization is made with other existing techniques and available measurements. A thermodynamic coupling is applied in order to obtain estimations of the surface ground temperature

Financial Versus Human Resources in the Greek-Turkish Arms Race: A Forecasting Investigation Using Artificial Neural Networks

This paper aims at forecasting the burden on the Greek economy resulting from the arms race against Turkey and at concentrating on the leading determinants of this burden. The military debt and the defence share of GDP are employed alternatively in order to approximate the measurement of the arms race pressure on Greece, and the method used is that of artificial neural networks. The use of a wide variety of explanatory variables in combination with the promising results derived, suggest that the impact on the Greek economy resulting from this arms race is determined, to a large extent, by demographic factors which strongly favour the Turkish side. Prediction on both miltary debt and defence expenditure exhibited highly satisfactory accuracy, while the estimation of input significance, indicates that variables describing the Turkish side are often dominant over the corresponding Greek ones.Greek Military Debt, Defence Expenditure, Neural Networks

Involvement of kainate glutamate receptors in the modulation of neuronal transmission in brain areas involved in migraine pathophysiology

Migraine pathophysiology is thought to involve activation of the trigeminal fibres which innervate dural structures. The nociceptive inflow from the meninges is relayed to the trigeminocervical complex (TCC), before ascending to higher brain areas, including the thalamus. Glutamate is implicated in the transmission of the nociceptive information and thus an increased understanding of the nature and effects of glutamate receptors activation has major implications in migraine pathophysiology and treatment. Here the role of kainate receptors, a member of the ionotropic glutamate receptors subfamily, was investigated in relaying sensory information upon activation of the trigeminovascular system. In order to study the role of kainate receptors on the periphery, we used the neurogenic dural vasodilation (NDV) model, in which electrical stimulation of the dura mater causes reproducible vasodilation, due to calcitonic gene-related peptide (CGRP) release. In this set of experiments kainate receptor activation but not blockade was effective in inhibiting NDV. Vasodilation induced by systemic administration of CGRP was not changed by administration of a kainate receptor agonist. In the TCC, local application by microiontophoresis of a selective kainate receptor antagonist on second order neurons which were excited by meningeal electrical stimulation, caused dual effects; 50% of the neurons tested were inhibited, whereas in a second subpopulation, activation in response to meningeal stimulation was facilitated. However, in all neurons tested, post-synaptic activation in response to kainate receptor agonists application was selectively inhibited. Microiontophoretic ejection of a kainate receptor antagonist in the ventroposteromedial thalamus (VPM) was able to inhibit cell firing in response to dural stimulation, as well as post-synaptic firing in response to kainate receptor activation. Both effects were reversed when the kainate receptor antagonist was co-ejected with a 5-HT1B receptor antagonist. We also carried electrophysiology studies in both the TCC and the VPM nucleus in order to compare the effects of the clinically active kainate receptor antagonist LY466195. Systemic and local application of LY466195 was able to inhibit cell firing in response to dura mater stimulation in both the TCC and VPM nucleus. Moreover, further to the kainate binding, a significant action of the compound on N-methyl-Daspartate receptors was observed

The Greek Current Account Deficit:Is it Sustainable after all?

The large Greek current account deficit figures reported during the past few years have become the source of increasing concern regarding its sustainability. Bearing in mind the variety of techniques employed and the views expressed as regards the analysis and the assessment of the size of the current account deficit, this paper resorts to using neural network architectures to demonstrate that, despite its size, the current account deficit of Greece can be considered sustainable. This conclusion, however, is not meant to neglect the structural weaknesses that lead to such a deficit. In fact, even in the absence of any financing requirements these high deficit figures point to serious competitiveness losses with everything that these may entail for the future performance of the Greek economy.Neural Networks; Current Account Deficit Sustainability

Impact Ionization and Hot-Electron Injection Derived Consistently from Boltzmann Transport

We develop a quantitative model of the impact-ionizationand hot-electron–injection processes in MOS devices from first principles. We begin by modeling hot-electron transport in the drain-to-channel depletion region using the spatially varying Boltzmann transport equation, and we analytically find a self consistent distribution function in a two step process. From the electron distribution function, we calculate the probabilities of impact ionization and hot-electron injection as functions of channel current, drain voltage, and floating-gate voltage. We compare our analytical model results to measurements in long-channel devices. The model simultaneously fits both the hot-electron- injection and impact-ionization data. These analytical results yield an energydependent impact-ionization collision rate that is consistent with numerically calculated collision rates reported in the literature

Of Masters and Servants: Hybrid Power in Theodore Laskaris’ 'Response to Mouzalon' and in the 'Tale of Livistros and Rodamne'

The present paper examines two Byzantine texts from the middle of the thirteenth century, ostensibly unrelated to each other: a political essay written by a young emperor and an anonymous love romance. The analysis is conducted through the concept of hybrid power, a notion initially developed by postcolonial criticism. It is shown that in the two texts authority (that of the Byzantine emperor and that of Eros as emperor) is constructed as hybrid and thus as an impossibility, though in the case of the political essay this impossibility remains unresolved, while in the romance it is actually resolved. The pronounced similarities between the two texts on the level of political ideology (e.g. the notion of friendship between master and servant, the performance of power relations, shared key concepts) informing the hybrid form of authority and its relation to its servants is a clear indication that they belong to the same socio-cultural and intellectual environment, namely the Laskarid imperial court in Nicaea around 1250

Forecasting Exchange-Rates via Local Approximation Methods and Neural Networks

There has been an increased number of papers in the literature in recent years, applying several methods and techniques for exchange - rate prediction. This paper focuses on the Greek drachma using daily observations of the drachma rates against four major currencies, namely the U.S. Dollar (USD), the Deutsche Mark (DM), the French Franc (FF) and the British Pound (GBP) for a period of 11 years, aiming at forecasting their short-term course by applying local approximation methods based on both chaotic analysis and neural networks.Key Words: Exchange Rates, Forecasting, Neural Networks