An automated classification system for urban areas matching the 'city country fingers' pattern: the cases of Kamakura (Japan) and Acireale (Italy) cities

AbstractThe uncontrolled expansion of urban areas is one of the main factors that reduce the liveability of cities. In recent years, to contrast urban sprawl, several nations have promoted policies aimed at developing urban green spaces. The importance of green oases within cities had already been highlighted, in 1977, by the architect Christopher Alexander who had developed a series of patterns including 'City Country Fingers' claiming that city development should consider the prolongation of country land in to the urban area.In several cities, especially in Japan, it is possible to recognize the imprint of urban development based on country fingers. This term refers to extensive urban intersections of agricultural land or wooded hills which, from the peripheral areas, penetrate the city. Inside them, there are urban windows, called city fingers, whose development direction is opposite to those of the country fingers. To recognize and analyze, in an automated way, these particular structures, a Python-based application was created. Starting from the original high-resolution image of Google Earth, a complete analysis was performed, labeling and delimiting urban and vegetational areas and extrapolating the main geometric parameters of the country and city fingers. The finalization of the results obtained was carried out through a classification model whose criteria were based on Alexander's pattern.Thanks to this classification scheme, the distinction between Active Green Areas (country fingers) and Passive Green Areas (gardens and public parks) have been revealed for the analyzed cities. The tests performed showed almost ideal conditions for the city of Kamakura and a limited match for the urban area of Acireale. The proposed method is suitable for fields of application that require a qualitative and quantitative determination of the vegetation cover present within the city, an essential condition for correct territorial planning

Laser Welding of Aluminium-Steel Clad Materials for Naval Applications

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FAMA: An automatic code for stellar parameter and abundance determination

The large amount of spectra obtained during the epoch of extensive spectroscopic surveys of Galactic stars needs the development of automatic procedures to derive their atmospheric parameters and individual element abundances. Starting from the widely-used code MOOG by C. Sneden, we have developed a new procedure to determine atmospheric parameters and abundances in a fully automatic way. The code FAMA (Fast Automatic MOOG Analysis) is presented describing its approach to derive atmospheric stellar parameters and element abundances. The code, freely distributed, is written in Perl and can be used on different platforms. The aim of FAMA is to render the computation of the atmospheric parameters and abundances of a large number of stars using measurements of equivalent widths as automatic and as independent of any subjective approach as possible. It is based on the simultaneous search for three equilibria: excitation equilibrium, ionization balance, and the relationship between \fei\ and the reduced equivalent widths. FAMA also evaluates the statistical errors on individual element abundances and errors due to the uncertainties in the stellar parameters. The convergence criteria are not fixed 'a priori' but are based on the quality of the spectra. In this paper we present tests performed on the Solar spectrum EWs which tests the dependency on the initial parameters, and the analysis of a sample of stars observed in Galactic open and globular clusters.Comment: A&A accepted, 12 pages, 6 figures, 3 table

Strain Diversity of Pseudomonas fluorescens Group with Potential Blue Pigment Phenotype Isolated from Dairy Products

The blue discoloration in Mozzarella cheese comes from bacterial spoilage due to contamination with Pseudomonas. Fourteen Pseudomonas fluorescens strains from international collections and 55 new isolates of dominant bacterial populations from spoiled fresh cheese samples were examined to assess genotypic and phenotypic strain diversity. Isolates were identified by 16S rRNA gene sequencing and tested for the production of the blue pigment at various temperatures on Mascarpone agar and in Mozzarella preserving fluid (the salty water in which the cheese is conserved, which becomes enriched by cheese minerals and peptides during storage). Pulsed-field gel electrophoresis analysis after treatment with the endonuclease SpeI separated the isolates into 42 genotypes at a similarity level of 80%. Based on the pulsotype clustering, 12 representative strains producing the blue discoloration were chosen for the multilocus sequence typing targeting the gyrB, glnS, ileS, nuoD, recA, rpoB, and rpoD genes. Four new sequence typing profiles were discovered, and the concatenated sequences of the investigated loci grouped the tested strains into the so-called ''blue branch'' of the P. fluorescens phylogenetic tree, confirming the linkage between pigment production and a specific genomic cluster. Growth temperature affected pigment production; the blue discoloration appeared at 4 and 14°C but not at 30°C. Similarly, the carbon source influenced the phenomenon; the blue phenotype was generated in the presence of glucose but not in the presence of galactose, sodium succinate, sodium citrate, or sodium lactate

An insight on the polyphase thermal history of the Internal Rif (Northern Morocco) through Raman micro-spectroscopy investigation

We are greatly indebted with M.N. Zaghloul for fruitful discussions and for introduce us to Rif geology. We kindly acknowledge the Editor in chief Federico Rossetti, the associated editor Giulio Viola and two anonymous reviewers for their constructive criticisms that improved the original version of this paper.Peer reviewedPostprin