The photochemical reflectance index (PRI), based on reflectance signatures at 531 and 570 nm, and associated with xanthophyll pigment inter-conversion and related thylakoid energisation, was evaluated as an indicator of photosynthetic function in a Mediterranean holm oak (Quercus ilex L.) coppice. The chlorophyll fluorescence pulse-amplitude-modulation and the eddy correlation techniques were used to estimate the photosystem 2 photochemical efficiency of leaves and the CO2 flux over the canopy, respectively. The reflectance and fluorescence techniques yielded identical estimates of the photosynthetic activity in leaves exposed to dark-light-dark cycles or to a variable irradiance in laboratory. However, there was no such correlation between photosynthetic performance and PRI when applied to a sun-exposed canopy in field conditions. Fluorescence profiles inside the canopy and especially a helpful use of multispectral reflectance imaging highlight the limitations of such method.
A measuring system evaluating a Point Spread Function generated in an ultrasonographic image by scanning a spherical target was developed. The target is moved in measuring bath filled by water over scanned volume via 3D computer controlled positioning system. A video signal obtained is converted to digital form and analyzed by original software to derive various objective parameters of the imager as follows: Focal areas in both the azimuth and the elevation directions, Ultrasound scanning lines visualisation, Manufacturer preloaded TGC, Width of the scanning plane, Side lobe levels and Amplification uniformity in the azimuth direction. The method was verified by testing 18 different equipments in 282 measurements. Samples of particular measurement results in form of graphical outputs are included. Medical and physiological impacts of this approach are discussed., L. Doležal, J. Mazura, J. Tesařík, H. Kolářová, D. Korpas, S. Binder, J. Hálek., and Obsahuje bibliografii
We show that the learning of (uncertain) conditional and/or causal information may be modelled by (Jeffrey) imaging on Stalnaker conditionals. We adapt the method of learning uncertain conditional information proposed in Günther (2017) to a method of learning uncertain causal information. The idea behind the adaptation parallels Lewis (1973c)’s analysis of causal dependence. The combination of the methods provides a unified account of learning conditional and causal information that manages to clearly distinguish between conditional, causal and conjunctive information. Moreover, our framework seems to be the first general solution that generates the correct predictions for Douven (2012)’s benchmark examples and the Judy Benjamin Problem., Ukazujeme, že učení (neurčitých) podmíněných a / nebo kauzálních informací může být modelováno zobrazením (Jeffrey) na Stalnakerových podmínkách. Metodu učení nejistých podmíněných informací navrhovaných v Güntheru (2017) přizpůsobujeme metodě učení nejistých kauzálních informací. Myšlenka adaptačních paralel Lewisova (1973c) analýza kauzální závislosti. Kombinace metod poskytuje jednotný popis učení podmíněných a příčinných informací, které dokáží jasně rozlišit mezi podmíněnými, příčinnými a spojovacími informacemi. Náš rámec se navíc jeví jako první obecné řešení, které vytváří správné předpovědi pro příklady benchmarku Douven (2012) a problém Judy Benjaminové., and Mario Günther