Application of chemometric methods for assessment and modelling of microbiological quality data concerning coastal bathing water in Greece

Background. Worldwide, the aim of managing water is to safeguard human health whilst maintaining sustainable aquatic and associated terrestrial, ecosystems. Because human enteric viruses are the most likely pathogens responsible for waterborne diseases from recreational water use, but detection methods are complex and costly for routine monitoring, it is of great interest to determine the quality of coastal bathing water with a minimum cost and maximum safety. Design and methods. This study handles the assessment and modelling of the microbiological quality data of 2149 seawater bathing areas in Greece over 10-year period (1997-2006) by chemometric methods. Results. Cluster analysis results indicated that the studied bathing beaches are classified in accordance with the seasonality in three groups. Factor analysis was applied to investigate possible determining factors in the groups resulted from the cluster analysis, and also two new parameters were created in each group; VF1 includes E. coli, faecal coliforms and total coliforms and VF2 includes faecal streptococci/enterococci. By applying the cluster analysis in each seasonal group, three new groups of coasts were generated, group A (ultraclean), group B (clean) and group C (contaminated). Conclusions. The above analysis is confirmed by the application of discriminant analysis, and proves that chemometric methods are useful tools for assessment and modeling microbiological quality data of coastal bathing water on a large scale, and thus could attribute to effective and economical monitoring of the quality of coastal bathing water in a country with a big number of bathing coasts, like Greece

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

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

Co-Movement Analysis of Italian and Greek Electricity Market Wholesale Prices by Using a Wavelet Approach

We study the co-evolution of the dynamics or co-movement of two electricity markets, the Italian and Greek, by studying the dynamics of their wholesale day-ahead prices, simultaneously in the time-frequency domain. Co-movement is alternatively referred as market integration in financial economics and markets are internationally integrated if the reward for risk is identical regardless the market one trades in. The innovation of this work is the application of wavelet analysis and more specifically the wavelet coherence to estimate the dynamic interaction between these two prices. Our method is compared to other generic econometric tools used in Economics and Finance namely the dynamic correlation and coherence analysis, to study the co-movement of variables of the type related to these two fields. Our study reveals valuable information that we believe will be extremely useful to the authorities as well as other agents participating in these markets to better prepare the national markets towards the European target model, a framework in which the two markets will be coupled

Flexibility study of the Greek power system using a stochastic programming approach for estimating reserve requirements

International audienc

Ethyl (E)-(3-(4-((4-bromobenzyl)oxy)phenyl)acryloyl)glycinate

In an attempt to develop new potent anti-inflammatory agents, a cinnamic -amino acid hybrid molecule was synthesized and in silico drug-likeness, in vitro COX-2 inhibition, and pharmacokinetic properties were studied. The results showed high cyclooxygenase inhibitory activity (IC50 = 6 µM) and favorable pharmacokinetic properties, being orally bioavailable according to Lipinski’s rule of five, making this compound a possible lead to design and develop potent COX inhibitors. The new compound, in comparison with its cinnamic acid precursor (E)-(3-(4-((4-bromobenzyl)oxy)phenyl)acrylic acid, showed improved biological activities. Compound ethyl (E)-(3-(4-((4-bromobenzyl)oxy)phenyl)acryloyl)glycinate can be used as a lead for the synthesis of more effective hybrids

Dance North performance of Women's War Too, choreographed by Cheryl Stock, performed by Bradford Leeon and Bernadette Walong, 1992 [picture] /

Part of the collection: Papers of Cheryl Stock.; "Dance North 1992. Women's war too. Bradford Leeon & Bernadette Walong. Choreography: Cheryl Stock. Music: 40's medley. Design: Michael Pearce"--Verso.; Title devised by cataloguer.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.ms-ms8354-0-4