Drought Severity Assessment Based on Bivariate Probability Analysis

Conventionally drought severity is assessed based on drought indices. Recently the Reconnaissance Drought Index (RDI) was proposed to assess drought severity based on the precipitation to potential evapotranspiration ratio (P/PET). In this paper RDI is studied as a bivariate index under a set of assumptions and simplifications. The paper presents a simple computational procedure for estimating the P/PET ratio for selected reference periods varying from 3 to 12 months, for any return period of drought. Alternatively, based on this procedure, the severity of any drought episode is rationally assessed. A bivariate probability analysis is employed based on the assumption that P and PET values are normally distributed and often negatively correlated. Examples for the application of the proposed procedure are presented using data from several meteorological stations in Greece. It is shown that the assumption of normality of both P and PET holds for long periods at all examined stations.     

On the discovery of subsumption relations for the alignment of ontologies

For the effective alignment of ontologies, the subsumption mappings between the elements of the source and target ontologies play a crucial role, as much as equivalence mappings do. This paper presents the “Classification-Based Learning of Subsumption Relations” (CSR) method for the alignment of ontologies. Given a pair of two ontologies, the objective of CSR is to learn patterns of features that provide evidence for the subsumption relation among concepts, and thus, decide whether a pair of concepts from these ontologies is related via a subsumption relation. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series of both artificially created and real world cases, and discusses the potential of the method.     

Intelligent Network Provisioning for Dynamically Downloadable Applications in Beyond 3G Mobile Networks

Mobile communications beyond 3G will integrate different (but complementary) access technologies into a common platform to deliver value-added services and multimedia content in an optimum way. However, the numerous possible configurations of mobile networks complicated the dynamic deployment of mobile applications. Therefore, research is intensely seeking a service provisioning framework that is technology-independent, supports multiple wireless network technologies, and can interwork high-level service management tasks to network management operations. This paper presents an open value chain paradigm, a model for downloadable applications and a mediating platform for service provisioning in beyond 3G mobile settings. Furthermore, we introduce mechanisms that support a coupled interaction between service deployment and network configuration operations, focusing on the dynamic provisioning of QoS state to data path devices according to the requirements of dynamically downloadable mobile value-added services (VAS).

Named-Entity Recognition from Greek and English Texts

Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.     

Chemical investigation and antimicrobial properties of mastic water and its major constituents

Mastic water is a commercial flavouring obtained during the steam distillation of mastic resin (the resin of Pistacia lentiscus var. chia) for the production of mastic oil. The mastic water extracts were analysed by GC−MS. The major compounds identified were verbenone, α-terpineol, linalool and trans-pinocarveol. Overall the composition was found to be very different from that of mastic oil. Additional GC−MS revealed the enantiomeric ratio of the chiral constituents of mastic water. The antimicrobial activity of mastic water extract, as well as that of its major constituents, was examined against Escherichia coli, Staphylococcus aureus and Candida spp. including ATCC wild clinical and food-borne strains. Linalool and α-terpineol were found to be the most potent antimicrobial constituents. Finally the stability of mastic water at different temperatures was studied, showing no change in the GC−MS profile of the organic extract for a period of 4 months at storage temperatures up to 4 °C.     

A survey on the structure of approximation classes

The structure of approximability classes by the introduction of approximation preserving reductions has been one of the main research programmes in theoretical computer science during the last thirty years. This paper surveys the main results achieved in this domain.     

Automated Remote Sensing with Near Infrared Reflectance Spectra: Carbonate Recognition

Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the reliability of several other automated methods adapted to the same purpose. Evidence is given that the procedure can be applied to some other mineral classes as well. Since the procedure is fast with low memory requirements, it is suitable for on-board scientific analysis by orbiters or surface rovers.