Using a Rolling Vector Error Correction Model to Model Static and Dynamic Causal Relations between Electricity Spot Price and Related Fundamental Factors: The Case of Greek Electricity Market

The purpose of this study is to investigate short and long run relationships between electricity spot prices in Greece, Brent oil, natural gas, lignite fuel cost and carbon allowances using daily data from 2007 to 2014. Static and dynamic Johansen test are applied in order to identify long run relations and also to assess the evolution over time in the level of cointegration. Additionally we test for Granger Causality in a Vector error correction model and embrace impulse response and variance decomposition techniques to model the dynamic response of electricity prices in excitation of another variable. Overall our results suggest an important long run relation between spot electricity prices in Greece, natural gas price and carbon allowances, while in the short run electricity prices are not affected by any of the other variables, results that are of practical importance for the market regulator as well as the wholesale market participants. Keywords:  Vector Error Correction, Electricity Markets, Fuel Markets JEL Classifications: C4, C5 & C

Evaluation of genetic variants in miRNAs in patients with colorectal cancer

BACKGROUND: Aberrant expression and structural alteration of miRNAs are considered to participate in cancer development. It has been suggested that common single-nucleotide polymorphisms (SNPs) in miRNAs are associated with susceptibility to several human diseases including colorectal cancer (CRC). METHODS: A case-control study at 157 CRC patients and 299 healthy controls of Greek origin was undertaken in order to investigate the association between the genotype and allelic frequencies of three common SNPs (rs2910164, rs11614913 and rs3746444) in pre-miRNAs, miR-146a, miR-196a2 and miR-499. RESULTS: The risk for CRC was significantly higher at the carriers of miR-146a rs2910164 CC genotype and C allele (p=0.02 and p< 0.001, respectively). None of the other performed analysis showed any statistically significant results. CONCLUSIONS: Our findings suggest that the rs2910164 polymorphism in pre-miRNA, miR-146a may be associated with the risk of CRC. © 2015 - IOS Press and the authors. All rights reserved

Rainwater composition in Athens, Greece

Wet precipitation-only samplers were used to collect wet deposition at two sites in the Athens basin, Greece for the period March 1986-February 1987. Concentrations of major cations (H+, NH+4, Na+, K+, Ca2+ and Mg2+) and major anions (Cl-, NO-3 and SO2-4) were determined for the first time in rainwater samples in Greece. Bicarbonate concentrations were calculated. The relative importance of natural and anthropogenic sources were estimated by a chemical balance. The majority of rain collected has a neutral or alkaline character. Acidity was due to the presence of H2SO4 and HNO3. The statistical analysis of the correlation between the concentration of chemical species confirm the influence of natural and anthropogenic sources. In all samples, SO2-4 concentrations exceed NO-3 concentrations despite the dominance of low S oil burning in the region. The wet flux of S was calculatd to be 0.34 gm-2a-1. © 1989

FAILURE MANAGEMENT IN GRIDS: THE CASE OF THE EGEE INFRASTRUCTURE

The emergence of Grid infrastructures like EGEE has enabled the deployment of large-scale computational experiments that address challenging scientific problems in various fields. However, to realize their full potential, Grid infrastructures need to achieve a higher degree of dependability, i.e., they need to improve the ratio of Grid-job requests that complete successfully in the presence of Grid-component failures. To achieve this, however, we need to determine, analyze and classify the causes of job failures on Grids. In this paper we study the reasons behind Grid job failures in the context of EGEE, the largest Grid infrastructure currently in operation. We present points of failure in a Grid that affect the execution of jobs, and describe error types and contributing factors. We discuss various information sources that provide users and administrators with indications about failures, and assess their usefulness based on error information accuracy and completeness. We describe two real-life case studies, describing failures that occurred on a production site of EGEE and the troubleshooting process for each case. Finally, we propose the architecture for a system that could provide failure management support to administrators and end-users of large-scale Grid infrastructures like EGEE

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems

Cloud computing: Distributed internet computing for IT and scientific research

Cloud computing is location agnostic and provides dynamically scalable and virtualized resources as services over the Internet. Here, the authors provide broad introductory definitions to cloud computing concepts. Articles in this special issue investigate some of the most fundamental issues concerning cloud services' development and deployment. copy; 2009 IEEE