Uranium and other natural radionuclides in the sediments of a Mediterranean fjord-like embayment, Amvrakikos Gulf (Ionian Sea), Greece.

The distribution of the natural radionuclides ((238)U, (232)Th, (226)Ra, (40)K) and the artificial (137)Cs was studied in sediment cores collected from Amvrakikos Gulf, a seasonal anoxic marine basin, using γ-ray spectrometry. The activity of radionuclides, along with the concentrations of Fe and Mn, were also studied in relation to the total organic carbon and the granulometric fractions of the sediments. The results obtained revealed higher (238)U activity concentrations in all the examined sediment samples compared to the world and Greek average values for soil. The high activity values of (238)U are attributed, besides the lattice-held fraction, to phosphate fertilizer inputs in the Gulf via major rivers and/or to alteration processes of phosphate ores located mainly in the drainage basin of the river Louros. The elevated activity values of (40)K could be attributed to the mineralogical composition of the sediments and to phosphate fertilizers containing potassium. Organic matter seems to be a more efficient sorbent for U than clay minerals and amorphous Fe and Mn-oxyhydroxides. Scanning electron microscopy, together with qualitative analysis of some smectites, reveals the occurrence of U, suggesting a limited absorption of U onto clay minerals. The applied BCR sequential extraction procedure revealed that U was found mainly in the refractory phase or associated with organic matter and to a lesser extent as surface-coating oxides, with the exception of one sediment core which is characterized by high content of fresh marine organic matter and presents high percentage of U in the exchangeable fraction.

Application of a novel modeling method to the nonstationary properties of potentiation in the rabbit hippocampus.

This paper presents the first application of a novel methodology for nonstationary nonlinear modeling to neurobiological data consisting of extracellular population field potentials recorded from the dendritic layer of the dentate gyrus of the rabbit hippocampus under conditions of stimulus-induced potentiation. The experimental stimulus was a Poisson random sequence with a mean rate of 5 impulses/s applied to the perforant path, which was sufficient to induce a progressive potentiation of perforant path-evoked granule cell response. The modeling method utilizes a novel artificial neural network architecture, which is based on the general time-varying Volterra model. The artificial neural network is composed of parallel subnets of three-layer perceptrons with polynomial activation functions, with the output of each subnet modulated by an appropriate time function that models the system nonstationarities and gives the summative output its time-varying characteristics. For the specific application presented herein these time functions are sigmoidal functions with trainable slopes and inflection points. A possible mapping between the nonstationary components of the model and the mechanisms underlying potentiation changes in the hippocampus is discussed.

Modeling of nonlinear nonstationary dynamic systems with a novel class of artificial neural networks.

This paper introduces a novel neural-network architecture that can be used to model time-varying Volterra systems from input-output data. The Volterra systems constitute a very broad class of stable nonlinear dynamic systems that can be extended to cover nonstationary (time-varying) cases. This novel architecture is composed of parallel subnets of three-layer perceptrons with polynomial activation functions, with the output of each subnet modulated by an appropriate time function that gives the summative output its time-varying characteristics. The paper shows the equivalence between this network architecture and the class of time-varying Volterra systems, and demonstrates the range of applicability of this approach with computer-simulated examples and real data. Although certain types of nonstationarities may not be amenable to this approach, it is hoped that this methodology will provide the practical tools for modeling some broad classes of nonlinear, nonstationary systems from input-output data, thus advancing the state of the art in a problem area that is widely viewed as a daunting challenge.