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Racah Institute of Physics
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Department of Psychology
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    ABSTRACT: Spatial Decision Support Systems (SDSS) combine Decision Support Systems (DSS) and Geographical Information Systems (GIS) to support decision makers in managing complex spatial problems. Such problems are commonly evaluated based on multiple criteria, consist of a large number of decision alternatives, involve uncertainty in their decisions and have therefore been adopted to manage various complex environmental processes such as natural hazards and water resources. An SDSS must be able, in addition to providing requirements common to any DSS, to support the input and storage of spatial data and the application of spatial analysis methods and to build prediction models that can simulate dynamic spatial processes and evaluate the effect of different future scenarios for decision-making. To support farmers in efficient precision management of tree crops, a GIS-based SDSS is under construction within the framework of the '3D-Mosaic' project which aims at optimizing water consumption in tree plantations by employing precision agriculture methods. Tree plantation test sites in Turkey and in Germany are currently monitored for plant and fruit growth under different environmental conditions and irrigation regimes using an autonomous mobile sensing and image analysis platform. Data is collected using various sensors, on a variety of plant-related and site-related variables. The constructed 3D GIS database is currently analyzed for variability using spatial statistic methods to assess the spatiotemporal patterns of a-biotic and plant related data. Recognized patterns will be used to develop algorithms for deriving: (a) tree adapted management zones and (b) site-specific irrigation maps. These maps will serve as the basis for developing SDSS interface to support farmers in orchard management. 1 INTRODUCTION The challenge to ensure food security within a global climate change together with increased concern to reduce the agricultural footprint on the environment while maintaining its economic viability pursued the evolvement of precision agriculture. Research and practice in precision agriculture aim at optimizing the management of sustainable agricultural production by addressing the spatial variability in plant properties and in their environmental conditions, such as in the soil. Remote sensing, information technology (IT) and geospatial methods are being harnessed to quantify the spatial variability in the agricultural plots (Corwin and Plant, 2005). This variability is the basis for developing management zones that optimize inputs, such as irrigation and fertilization, while improving the quantity and quality of yields and improve sustainability of agricultural practices. Spatial analysis is considered as the core of Geographical Information Systems (GIS) and consists of the methods and techniques for analyzing data in its spatial context (Longley, 2010). Spatial statistical methods can be used to recognize and quantify patterns and relations which vary in space, i.e. quantify the statistical significance of a recognized pattern (Mitchell, 2005). These features make spatial statistics suitable for analyzing and quantifying the spatial variability of various parameters related to crops and to their environmental conditions.
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    ABSTRACT: We studied the temperature dependence of the photocurrent spectra of a Ge-SiO2 composite thin film. We found that the spectral position of the photocurrent peak is determined by the competition between absorption and non-radiative recombination and that its temperature dependence is associated with the population variation of the energetically deep levels in the system under "thermal quenching" conditions. Combining these results with our previous deep-level transient spectroscopy data enables the association of these levels with the quantum confinement effect. We thus identify here a non-radiative recombination process associated with deep-level sensitization that stems from quantum confinement.
    Thin Solid Films 01/2015; 574:184-188. DOI:10.1016/j.tsf.2014.12.004
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    ABSTRACT: A method for supporting palladium nanoparticles on magnetically separable organosilica nanoparticles functionalized with ionic liquid groups is described. The system was prepared by sol–gel condensation of two silica precursors: tetraethyl orthosilicate (TEOS) and bis-silylated ionic liquid monomer, on hydrophobic magnetic nanoparticles modified with oleate groups. The support of palladium nanoparticles on the magnetic organo-silica hybrid nanoparticles was achieved by adsorbing palladium salts (Na2PdCl4) on their surface via ion exchange with the ionic liquid groups, followed by reduction with sodium borohydride. The resulted system was applied in three different catalytic transformations: carbonylation of iodoarenes and Heck and Suzuki coupling reactions. The catalyst demonstrated high catalytic activity and was easily separated from the reaction mixture by applying an external magnetic field. The catalyst was recycled over five times without showing a significant loss in its activity.
    The Journal of Physical Chemistry C 12/2014; 118(51):30045-30056. DOI:10.1021/jp510472t


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    Amir Steinman
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Nature Cell Biology 01/2007; 8(12):1327-36. DOI:10.1038/ncb1500
Nature Communications 01/2015; 6:7414. DOI:10.1038/ncomms8414

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