Our results demonstrate that in highly interconnected ecosystems, noise can act as an ordering force, dynamically generating ecological patterns even in environments lacking explicit niches.”
“Removal of dyes from the industrial discharge water is an important issue for safety of the environment. In this study, magnetic (magnetite, Fe3O4) nanoparticles were coated with chitosan (CS) and the efficiency of these chitosan coated

magnetic nanoparticles (Fe3O4-CS) for the adsorption of a reactive textile dye (Reactive Yellow 145, RY145) was examined first time in literature. TEM, XRD, and EPR results revealed that the thickness of PFTα research buy the coat was about 25 nm, no phase change in the spinel structure of magnetic particles existed after coating, and particles had paramagnetic property, respectively. Adsorption of RY145 on Fe3O4-CS nanoparticles occurs according to Langmuir model in the temperature range 25 degrees C-45 degrees

C with a maximum adsorption capacity of 47.62 mg g(-1) at 25 degrees C, in aqueous media. Thermodynamic parameters demonstrated that the adsorption process was endothermic and spontaneous, and the maximum desorption of the dye was 80% over a single adsorption/desorption cycle. In this study, the high efficiency of the CS coated magnetic nanoparticles in the adsorption and find more removal of reactive dyes from water was shown on model RY145. This type of nanoparticles can be good candidates in industrial applications for the decolorization of waste waters. (C) 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012″
“We show that the optical infrared magneto-refractive effect can be used to probe magneto-resistance effects in absolute terms. A spin valve sample with synthetic anti-ferromagnet has been studied using non-contact infrared reflection spectroscopy measurements. Direct experimental comparison shows excellent agreement between electrical and optical measurements in both shape of the magneto-resistance curves and absolute values. This opens the possibility CP-868596 order of developing fast and efficient tests of magneto-resistive samples and sensors using an in situ, non-contact,

non-destructive optical approach, without the need to determine a calibration factor between the electrical and optical measurements. [doi:10.1063/1.3631778]“
“The multidimensional computations performed by many biological systems are often characterized with limited information about the correlations between inputs and outputs. Given this limitation, our approach is to construct the maximum noise entropy response function of the system, leading to a closed-form and minimally biased model consistent with a given set of constraints on the input/output moments; the result is equivalent to conditional random field models from machine learning. For systems with binary outputs, such as neurons encoding sensory stimuli, the maximum noise entropy models are logistic functions whose arguments depend on the constraints.