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Jamal Asfahani, Rashad Al-Hent and Mosa Aissa

Abstract

A scored lithological map including nine litho-factor units is established through applying the statistical factor analysis technique (SFAT) to aerial spectrometric data of Area-2 (Al-Rassafeh Area), which includes T.C, eU, eTh, K, eU/eTh, eU/K, and eTh/K. A model of four rotated factors F1, F2, F3, and F4 is adapted for representing 61712 data measured points in Area-2, where 90.3% of total data variance is interpreted. The isolated lithological units related to F1, F2, and F3 are characterized by an eU average of 2.15, 0.99, and 1.57 ppm respectively. Two geological scored pseudo-sections derived from the lithological scored map are established and analyzed in order to show the mutual environmental geological relationships between different lithological isolated units. This scored map will be the base for further geological investigations in Area-2. SFAT has proven its efficacy in the research study Area-2, and allowed the different isolated sectors to be characterized and interpreted geologically and radioactively.

Open access

Miloš Revallo, Fridrich Valach, Pavel Hejda and Josef Bochníček

Abstract

A model of geomagnetic storms based on the method of artificial neural networks (ANN) combined with an analytical approach is presented in the paper. Two classes of geomagnetic storms, caused by coronal mass ejections (CMEs) and those caused by corotating interaction regions (CIRs), of medium and week intensity are subject to study. As the model input, the hourly solar wind parameters measured by the ACE satellite at the libration point L1 are used. The time series of the Dst index is obtained as the model output. The simulated Dst index series is compared with the corresponding observatory data. The model reliabilty is assessed using the skill scores, namely the correlation coefficient CC and the prediction efficiency PE. The results show that the model performance is better for the CME driven storms than for the CIR driven storms. At the same time, it appears that in the case of medium and weak storms the model performance is worse than in the case of intense storms

Open access

Grazia Caradonna, Antonio Novelli, Eufemia Tarantino, Raffaela Cefalo and Umberto Fratino

on multivariate statistical modeling using remote sensing data. Environmental Modeling & Assessment, 18(5), 547-558. Coscarelli, R., Caloiero, T., Minervino, I., & Sorriso-Valvo, M. (2015). Sensitivity to desertification of a high productivity area in Southern Italy. Journal of Maps, 1-9. Figorito, B., Mancini, F., Novelli, A., & Tarantino, E. (2014). Monitoring land cover changes at watershed scale using LANDSAT imagery. Score@Poliba. Han, L., Zhang, Z., Zhang, Q., & Wan, X. (2015). Desertification assessments in the

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Volodymyr Hlotov, Alla Hunina, Mariana Yurkiv and Zbigniew Siejka

of freedom, F-value – assessment of the relationship between independent factors and the dependent variable, Significance level F– lower level shows better connection. So, according to Tab. 2 , the multiple correlation coefficient R = 0.378, which indicates a moderate relationship between the score ĸ and factors α andω, and also the presence of additional unaccounted factors of influence. The equation of dependence (regression model), in accordance with Table 3 , has the form ĸ = 4.482 – 1.233α – 0.248!. As we can see, roll angle ω and pitch α have an

Open access

Rafał Myszka and Kinga Niedziółka-Rybak

described in two contexts, firstly as democratic (including) space, secondly, as totalitarian (egalitarian) space. The score suggests whether each particular space is of a democratic or totalitarian character ( Nawratek 2005 : 46) ( Tab. 1 ). Table 1 Defining terms Source: ( Nawratek 2005 : 46), modified by the authors Totalitarian space Democratic space Size of structure Monumental Exceeding the in the scale traditional of its context meaning Respecting the scale of its context Form of structure Monumental Harking back to

Open access

Jarosław Kazimierczak and Piotr Kosmowski

each block and horizontal intensity of development (I H ) in each block. In field studies we used a 4-degree evaluation scale for buildings and their elevations. Each positive change in the years 2013–2016 scored +1 point while each negative change scored -1. The demolition of buildings which degraded the urban landscape scored +4 points, and a new building +5 points. Much less visible improvements than in block G took place in blocks F (I T =15.62), E (I T =13.21), C (I T =9.17), B (I T =6.83), and A (I T =2.34). The technical condition of buildings deteriorated in

Open access

Simon Huhndorf and Jarosław Działek

libraries such a strict number does not exist. Therefore, the E2SFCA is applied in this paper. Even though the results (E2SFCA-values) were finally calculated with a free ArcGIS add-in Download link: https://www.researchgate.net/publica-tion/261398233_USWFCA_An_ArcGIS_101102_Add-In_tool_to_compute_Enhanced_Two-Step_Floating_Catchment_Area_accessibility_scores , the underlying background process of this calculation will be summarised For further and more detailed explanations of the method see Luo and Qi (2009) . in the following outline. As the name of the method

Open access

Nick Bailey, Joanna L. Stewart and Jon Minton

in 2004 and in 2015/2016, and hence the change in share. Using this score, LSOAs are grouped into neighbourhood poverty change quintiles within each city, from those with the largest decrease in their poverty share (1) through those where poverty was little changed (3) to those with the largest increases in poverty share (5). It is worth noting that this approach compares the distribution of poverty within the city at one point in time with its distribution at another point in time. It is measuring the relative distribution rather than absolute changes. It is not

Open access

Adam Radzimski

( van Gent et al. 2009 ). Neither is there some common understanding on the criteria to be applied in delineating such areas. According to W. van Gent et al. (2009 : 55), deprived areas are places where place-based liveability issues (like vandalism, anti-social behaviour, crime etc.) are coupled with, and are assumed to be a source of, sustained economic deprivation. Multiple socio-economic variables and composite scores are used more frequently and are believed to be more appropriate measures of spatial disadvantage than simple income indicators ( Pawson & Herath