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Historical maps are popular reference tools for historical, archaeological and temporal analysis, and there has recently been an increase in their use. However, for various reasons, the planimetric accuracy of maps produced before the nineteenth century is usually considered to be lower than today. In this paper, a new method for assessing maps is proposed, using a series of processes, such as radial-based function artificial neural network, magnetic declination, and also MapAnalyst software. The map used in the current study (of Europe and the Mediterranean Sea) is a small-scale map; therefore, control points were produced by taking reference from large-scale maps drawn by the same cartographer, Piri Reis (c.1465–1553). While developing this method, affine transformation (six parameters) was compared in terms of planimetric accuracy. The results indicate that Piri Reis's Mediterranean map offers us unique information in many areas. [ABSTRACT FROM AUTHOR]

Central and East European (CEE) countries are in an economic transition process that involves convergence of their economic performance with the European Union. One of the principal engines for the necessary transformation toward EU average economic performance is inward foreign direct investment (FDI). Quantitatively examining the causes of FDI in the CEE region is thus an important research area. Traditional linear regression approaches have had difficulty achieving conceptually and statistically reliable results. In this paper, we offer a novel approach to examining FDI in the CEE region. The key tasks addressed in this research are a neural network (NN)—based FDI forecasting model and a nonlinear evaluation of the determinants of FDI. The methodology is nontraditional for this kind of research (compared with multiple linear regression estimates) and is applied primarily for the FDI dynamics in the CEE region, with some worldwide comparisons. In terms of mean square error (MSE) and R2 criteria, we find that NN approaches better explain FDI determinants' weights than do traditional regression methodologies. Our findings are preliminary, but offer important and novel implications for future research in this area, including more detailed comparisons across sectors, as well as countries over time. [ABSTRACT FROM AUTHOR]

ABSTRACT The paper focuses on the Transalpine Freight Transport systems in the light of the future integration of single national transport systems into the European transport network. The environmental, social and institutional peculiarities of this 'region' have favoured--in the past--the development of strong nationally-oriented policies, which are largely in contrast with the goals promoted by the European Union. The present analysis aims to highlight opportunities and limits inherent in the implementation of various new network projects, with a particular view on the planned changes of the Alpine transport system. In this framework, a concise description of the existing and 'planned' situation will be offered. In addition, some new forecasting analyses for road transport will be provided on the basis of environmentally-based transport scenarios. In particular, given the large size of our database on European transport flows, two different approaches will be compared, viz. the logit model and the neural network model. Logit models are well-known in the literature; however, applications of logit analysis to large samples are more rare. Neural networks are nowadays receiving a considerable attention as a new approach that is able to capture major patterns of spatial flows, on the basis of fuzzy and incomplete information. The tentative results of both approaches in this context may then be used as a benchmark for judging the results of other transport flow models and offer also a more 'flexible' range of results to policy actors. Furthermore, our study will present the assessment of trans-European freight flows based on interesting future scenarios related to further congestion and the introduction of eco-taxes on transport in Europe. [ABSTRACT FROM AUTHOR]

Continuous monitoring was carried out on the Ile Vierge lighthouse, the tallest stone lighthouse in Europe (82 m), built in 1902, where the upper part of the tower presents a crack pattern. The monitoring was implemented over 2.5 years to characterize the actions of temperature and wind and to determine their links with crack opening. The variation of each factor measured was analysed and possible correlations between the variables were sought. In addition, the temperatures measured on site were compared with the actions required by Eurocode 1. The quantity of data extracted made it necessary to develop a data analysis method combining Principal Component Analysis, Multiple Regression Analysis, and Artificial Neural Network Analysis. Thus, the long- and short-term behaviour of the instrumented cracks could be identified. The relative influences of the wind and the temperature on crack opening were given by a percentage assessment. [ABSTRACT FROM AUTHOR]

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested. [ABSTRACT FROM AUTHOR]

This AMPHORA study's aim was to investigate selected factors potentially affecting changes in consumption of alcoholic beverages in 12 European countries during the 1960s-2008 (an average increase in beer, decreases in wine and spirits, total alcohol drinking decrease). Both time series and artificial neural networks-based analyses were used. Results indicated that selected socio-demographic and economic factors showed an overall major impact on consumption changes; particularly urbanization, increased income, and older mothers' age at their childbirths were significantly associated with consumption increase or decrease, depending on the country. Alcoholic beverage control policies showed an overall minor impact on consumption changes: among them, permissive availability measures were significantly associated with consumption increases, while drinking and driving limits and availability restrictions were correlated with consumption decreases, and alcohol taxation and prices of the alcoholic beverages were not significantly correlated with consumption. Population ageing, older mother's age at childbirths, increased income and increases in female employment, as well as drink driving limitations were associated with the decrease of transport mortality. Study's limitations are noted. [ABSTRACT FROM AUTHOR]

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R 2) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R 2 ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location. [ABSTRACT FROM AUTHOR]

A freely available data processor for the B asic E RS & ENVISAT ( A )ATSR and M ERIS Toolbox (BEAM) was developed to retrieve atmospheric and oceanic properties above and of Case-2 waters from Medium Resolution Imaging Spectrometer (MERIS) Level1b data. The processor was especially designed for European coastal waters and uses MERIS Level1b Top-Of-Atmosphere (TOA) radiances to retrieve atmospherically corrected remote sensing reflectances at the Bottom-Of-Atmosphere (BOA), spectral aerosol optical thicknesses (AOT) and the concentration of three water constituents, namely chlorophyll-a (CHL), total suspended matter (TSM) and the absorption of yellow substance at 443 nm (YEL). The retrieval is based on four separate artificial neural networks which were trained on the basis of the results of extensive radiative transfer (RT) simulations by taking various atmospheric and oceanic conditions into account. The accuracy of the retrievals was acquired by comparisons with concurrent in situ ground measurements and was published in full detail elsewhere. For the remote sensing reflectance product a mean absolute percentage error (MAPE) of 18% was derived within the spectral range 412.5-708.75 nm while the accuracy of the AOT at 550 nm in terms of MAPE was calculated to be 40%. The accuracies for CHL, TSM and YEL were derived from match-up analysis with MAPEs of 50%, 60% and 71%, respectively. [ABSTRACT FROM AUTHOR]