Broken Ergodicity in classically chaotic spin systems

A one dimensional classically chaotic spin chain with asymmetric coupling and two different inter-spin interactions, nearest neighbors and all-to-all, has been considered. Depending on the interaction range, dynamical properties, as ergodicity and chaoticity are strongly different. Indeed, even in presence of chaoticity, the model displays a lack of ergodicity only in presence of all to all interaction and below an energy threshold, that persists in the thermodynamical limit. Energy threshold can be found analytically and results can be generalized for a generic XY model with asymmetric coupling.Comment: 6 pages, 3 figure

Application of FT-IR spectroscopy to analyze casein in cow milk

AbstractInfrared spectrometry is, at present, the most frequently applied methodology for the composition analysis of milk and dairy products. The determination of fat, protein and lactose has been described during recent decades. The introduction of Fourier Transform Infra-red (FT-IR) technology in combination with the application of multi-dimensional procedures (i.e. principal component regression, partial least squares) has improved this methodology, opening new perspectives for the simultaneous and routinely determination of many new parameters such as casein, urea, specific sugars, etc.. The aim of our study was to develop on MilkoScanTM FT 120 (Foss Electric, Hillerod, Denmark) a calibration curve for the analysis of casein in cow milk and to execute a preliminary validation. The calibration curve was developed on 89 individual milk samples collected from 4 dairy herds in the Grana-Padano cheese district. In order to obtain a higher variability of milk protein content and composition, in each herd m..

Mapping Asbestos-Cement Roofing with Hyperspectral Remote Sensing over a Large Mountain Region of the Italian Western Alps

The World Health Organization estimates that 100 thousand people in the world die every year from asbestos-related cancers and more than 300 thousand European citizens are expected to die from asbestos-related mesothelioma by 2030. Both the European and the Italian legislations have banned the manufacture, importation, processing and distribution in commerce of asbestos-containing products and have recommended action plans for the safe removal of asbestos from public and private buildings. This paper describes the quantitative mapping of asbestos-cement covers over a large mountainous region of Italian Western Alps using the Multispectral Infrared and Visible Imaging Spectrometer sensor. A very large data set made up of 61 airborne transect strips covering 3263 km2 were processed to support the identification of buildings with asbestos-cement roofing, promoted by the Valle d’Aosta Autonomous Region with the support of the Regional Environmental Protection Agency. Results showed an overall mapping accuracy of 80%, in terms of asbestos-cement surface detected. The influence of topography on the classification’s accuracy suggested that even in high relief landscapes, the spatial resolution of data is the major source of errors and the smaller asbestos-cement covers were not detected or misclassified

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen’s kappa coefficient of 0.84 and an overall accuracy of 85%

Effect of space availability at feed bunk and rest area on metabolic conditions and productive responses in dairy cows

It has been estimated that dairy cows spend 3 to 5 h/d eating, consuming 9 to 14 meals per day. In addition, they ruminate 7 to 10 h/d, spend approximately 30 min/d drinking, 2 to 3 h/d being milked, and require approximately 10 h/d for lying time (Grant and Albright, 2000). Management decisions on dairy must not interfere with the cow’s ability to perform these activities which comprises her daily routines. It is well known that feed bunk space modifies eating behaviour, while the amount and characteristics of rest area modify resting activity of the cows. The traditional recommendation of 0.6 linear meter of bunk space per cow is the minimal amount of space needed for all cows to eat at one time (Grant and Albright, 2001)......

Multispectral technology for mining exploration in arid lands: a short review (tecnologia multispettrale per l’esplorazione d’idrocarburi in ambienti aridi: una breve review)

A oggi non esistono stime attendibili sull’effettiva consistenza delle risorse d’idrocarburi e solamente poco più di un quarto dei bacini potenzialmente interessanti ai fini dell’esplorazione sono stati adeguatamente studiati. Anche le tecniche d’indagine più sofisticate spesso si rivelano inadeguate o contraddittorie e per questo motivo numerose compagnie petrolifere stanno valutando le potenzialità offerte da altre nuove tecnologie, come ad esempio il Telerilevamento multispettrale. Questa breve review descrive lo stato dell’arte nella ricerca di microseep d’idrocarburi in ambienti aridi mediante l’utilizzo di tecniche di Telerilevamento ottico multispettrale

Remote Sensing Urban Analysis

Satellite remote sensing is the process of collecting information about the Earth’s surface from the space through the measure of electromagnetic radiation. Nowadays, remote sensing is a mature technology used to extract, analyze and detect changes of geographic and thematic information over large areas, inacces-sible sites or where only limited knowledge is available. In this chapter we describe how satellite’s data collected over Multan (Pakistan) have been used for mapping and monitoring the dynamics of the urban area. A multi-scale approach allowed to evaluate the urban growth of the Municipality area occurred in the last 2 dec-ades with medium-resolution Landsat-5/TM time series. Urban green plots and in-frastructures (buildings and roads) have been mapped at the local scale of the his-toric Walled City with the state of the art GeoEye-1 and WorldView-2 very high-resolution multispectral imagery

Minimum noise fraction transform for improving the classification of airborne hyperspectral data: two case studies

This paper investigates the use of Minimum Noise Fraction (MNF) components to improve the spectral separability of two specific thematic classes in airborne hyperspectral imagery using Spectral Angle Mapper (SAM). Particularly, we compared trends on data distribution before and after MNF transform. Two different data sets recorded with the Multispectral Infrared Visible Imaging Spectrometer (MIVIS) were analyzed. In the first case study, the classification of MNF-transformed data led to an overall enhancement in mapping asbestos roofs. In the second case study, the classification of MNF-transformed data succeeded to distinguish between two different artificial lakes, whereas classification of original hyperspectral data failed. Overall, this study showed how the use of MNF as pre-processing could improve the capability to extract information from two different airborne hyperspectral data sets

High-resolution SAR and high-resolution optical data integration for sub-urban land-cover classification

This study shows a comparison between pixel-based and object-based approaches in data fusion of high-resolution multispectral GeoEye-1 imagery and high-resolution COSMO-SkyMed SAR data for land-cover/land-use classification. The per-pixel method consisted of a maximum likelihood classification of fused data based on discrete wavelet transform and a classification from optical images alone. Optical and SAR data were then integrated into an object-oriented environment with the addition of texture measurements from SAR and classified with a nearest neighbor approach. Results were compared with the classification of the GeoEye-1 data alone and the outcomes pointed out that per-pixel data fusion did not improve the classification accuracy, while the object-based data integration increased the overall accuracy from 73% to 89%. According to results, an object-based approach with the introduction of adjunctive information layers proved to be more performing in land-cover/land-use classification than standard pixel-based methods