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

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 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

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

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 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

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

RAS mutation status predicts survival and patterns of recurrence in patients undergoing hepatectomy for colorectal liver metastases.

ObjectiveTo determine the impact of RAS mutation status on survival and patterns of recurrence in patients undergoing curative resection of colorectal liver metastases (CLM) after preoperative modern chemotherapy.BackgroundRAS mutation has been reported to be associated with aggressive tumor biology. However, the effect of RAS mutation on survival and patterns of recurrence after resection of CLM remains unclear.MethodsSomatic mutations were analyzed using mass spectroscopy in 193 patients who underwent single-regimen modern chemotherapy before resection of CLM. The relationship between RAS mutation status and survival outcomes was investigated.ResultsDetected somatic mutations included RAS (KRAS/NRAS) in 34 (18%), PIK3CA in 13 (7%), and BRAF in 2 (1%) patients. At a median follow-up of 33 months, 3-year overall survival (OS) rates were 81% in patients with wild-type versus 52.2% in patients with mutant RAS (P = 0.002); 3-year recurrence-free survival (RFS) rates were 33.5% with wild-type versus 13.5% with mutant RAS (P = 0.001). Liver and lung recurrences were observed in 89 and 83 patients, respectively. Patients with RAS mutation had a lower 3-year lung RFS rate (34.6% vs 59.3%, P < 0.001) but not a lower 3-year liver RFS rate (43.8% vs 50.2%, P = 0.181). In multivariate analyses, RAS mutation predicted worse OS [hazard ratio (HR) = 2.3, P = 0.002), overall RFS (HR = 1.9, P = 0.005), and lung RFS (HR = 2.0, P = 0.01), but not liver RFS (P = 0.181).ConclusionsRAS mutation predicts early lung recurrence and worse survival after curative resection of CLM. This information may be used to individualize systemic and local tumor-directed therapies and follow-up strategies

Structural Change in (Economic) Time Series

Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. Several recent methods for dating structural changes are compared using a time series of oil prices spanning more than 60 years. The methods broadly agree for the first part of the series up to the mid-1980s, for which changes are associated with major historical events, but provide somewhat different solutions thereafter, reflecting a gradual increase in oil prices that is not well described by a step function. As a further illustration, 1990s data on the volatility of the Hang Seng stock market index are reanalyzed.Comment: 12 pages, 6 figure