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

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

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

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

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

Long-term dependence in exchange rates

The extent to which exchange rates of four major currencies against the Greek Drachma exhibit long-term dependence is investigated using a R/S analysis testing framework. We show that both classic R/S analysis and the modified R/S statistic if enhanced by bootstrapping techniques can be proven very reliable tools to this end. Our findings support persistence and long-term dependence with non-periodic cycles for the Deutsche Mark and the French Franc series. In addition a noisy chaos explanation is favored over fractional Brownian motion. On the contrary, the US Dollar and British Pound were found to exhibit a much more random behavior and lack of any long-term structure

The changing landscape of OSH regulation in the UK

This report presents key findings of a research project on the changing landscape of OSH regulation in the UK. Understanding the changing landscape of OSH regulation and standards and its implications are of central importance for ensuring that OSH outcomes are not compromised and the needs of different types of organisations are met. This project examined the landscape of OSH in the UK since its origin and in particular looked at how regulation of OSH has evolved over time. It considered both policy and practice issues as well as the role of key stakeholders in this process with a view to identify how the right balance can be achieved to promote health and safety going forward. To achieve its aims, a qualitative methodology was implemented in five stages: a. a literature and policy review, including a comparative analysis with other countries; b. case study analysis; c. stakeholder interviews, d. stakeholder workshops including focus groups; and e. research output synthesis

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