N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

Climate change 2014 - Synthesis Report

Experimental Neurology By Prof Paul Glees Pp xii + 532 (Oxford: Clarendon Press; London: Oxford University Press, 1961) 75s net

The Nervous System

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

/pdf/nonlinear-time-series-analysis-1j3a35n4y3.pdf

Nonlinear Time Series Analysis.

Aims Carotid–femoral pulse wave velocity (PWV), a direct measure of aortic stiffness, has become increasingly important for total cardiovascular (CV) risk estimation. Its application as a routine tool for clinical patient evaluation has been hampered by the absence of reference values. The aim of the present study is to establish reference and normal values for PWV based on a large European population. Methods and results We gathered data from 16 867 subjects and patients from 13 different centres across eight European countries, in which PWV and basic clinical parameters were measured. Of these, 11 092 individuals were free from overt CV disease, non-diabetic and untreated by either anti-hypertensive or lipid-lowering drugs and constituted the reference value population, of which the subset with optimal/normal blood pressures (BPs) (n = 1455) is the normal value population. Prior to data pooling, PWV values were converted to a common standard using established conversion formulae. Subjects were categorized by age decade and further subdivided according to BP categories. Pulse wave velocity increased with age and BP category; the increase with age being more pronounced for higher BP categories and the increase with BP being more important for older subjects. The distribution of PWV with age and BP category is described and reference values for PWV are established. Normal values are proposed based on the PWV values observed in the non-hypertensive subpopulation who had no additional CV risk factors. Conclusion The present study is the first to establish reference and normal values for PWV, combining a sizeable European population after standardizing results for different methods of PWV measurement.

Determinants of pulse wave velocity in healthy people and in the presence of cardiovascular risk factors

The study of river-riparian vegetation interactions is an important and intriguing research field in geophysics. Vegetation is an active element of the ecological dynamics of a floodplain which interacts with the fluvial processes and affects the flow field, sediment transport, and the morphology of the river. In turn, the river provides water, sediments, nutrients, and seeds to the nearby riparian vegetation, depending on the hydrological, hydraulic, and geomorphological characteristic of the stream. In the past, the study of this complex theme was approached in two different ways. On the one hand, the subject was faced from a mainly qualitative point of view by ecologists and biogeographers. Riparian vegetation dynamics and its spatial patterns have been described and demonstrated in detail, and the key role of several fluvial processes has been shown, but no mathematical models have been proposed. On the other hand, the quantitative approach to fluvial processes, which is typical of engineers, has led to the development of several morphodynamic models. However, the biological aspect has usually been neglected, and vegetation has only been considered as a static element. In recent years, different scientific communities (ranging from ecologists to biogeographers and from geomorphologists to hydrologists and fluvial engineers) have begun to collaborate and have proposed both semiquantitative and quantitative models of river-vegetation interconnections. These models demonstrate the importance of linking fluvial morphodynamics and riparian vegetation dynamics to understand the key processes that regulate a riparian environment in order to foresee the impact of anthropogenic actions and to carefully manage and rehabilitate riparian areas. In the first part of this work, we review the main interactions between rivers and riparian vegetation, and their possible modeling. In the second part, we discuss the semiquantitative and quantitative models which have been proposed to date, considering both multi- and single-thread rivers.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/rog.20014

Modeling the interactions between river morphodynamics and riparian vegetation

[1] A model for the evaluation of the intra-meander hyporheic exchange fluxes is presented. The method relies on a physically-based morphodynamic model to predict the characteristics of the flow field in a meandering river and the temporal evolution of its planimetry. The hyporheic fluxes induced at the meander scale by the river sinuosity can therefore be computed. The application of the model to a simulated case has shown the fundamental role of the river planimetry on the hyporheic exchange pattern at the meander scale, and its influence on the long-term evolution of the hyporheic exchange. Citation: Boano, F., C. Camporeale, R. Revelli, and L. Ridolfi (2006), Sinuositydriven hyporheic exchange in meandering rivers, Geophys. Res. Lett., 33, L18406, doi:10.1029/2006GL027630.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006GL027630

Sinuosity-driven hyporheic exchange in meandering rivers

We review the importance of the physical mechanisms involved in river meandering by comparing some existing linear models and extensions thereof. Such models are hierarchically derived from a common and general mathematical framework and then analyzed with a detailed discussion of the physical processes and relevant hypotheses that are involved. Experiments and field data are also used to discuss the related morphodynamic processes. The analysis of the models shows the importance of the closure of secondary currents especially in the modeling of eddy viscosity. This aspect confirms the usefulness of using simplified models for some practical applications, provided the secondary currents are modeled in detail. On the other hand, the free response of the sediments, the phase lag of secondary currents, and the momentum redistribution due to the coupling between the main and the transverse flow are shown to be less relevant. Hence the second-order models, which neglect the effect of superelevation induced by the topography-driven lateral flow on the longitudinal flow, can reasonably be considered a good approximation for both predictive analysis and the computation of the resonant conditions. Finally, the analysis of higher harmonics suggests that the multilobed pattern can intrinsically be present in both second- and fourth-order models.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2005RG000185

Hierarchy of models for meandering rivers and related morphodynamic processes

[1] A river and its surrounding riparian vegetation are two dynamical systems that interact through several hydrological, geomorphological, and ecological processes. This work focuses on the role played by vegetation on meandering river morphodynamics: River planform evolution forces the riparian vegetation dynamics, which, in turn, affect the mechanical characteristics of the river banks and influence the meandering dynamics of the river itself. It follows that despite the fact that a traditional engineering approach considers vegetation as a static element the study of river morphodynamics should be coupled with the riparian vegetation evolution. To this end, a fluid dynamic model of meandering rivers is here coupled with a process-based model for the riparian biomass dynamics. The feedback of vegetation on river morphology is provided by a relation that links the biomass density to the bank erodibility. The numerical results highlight (1) the remarkable effects of the vegetation dynamics on meander evolution and (2) the role of the temporal scales of vegetation growth and decay in relation to typical morphodynamic scales. In particular, the differences with respect to the constant erodibility case can be of the order of tens or hundreds of meters (10–20% of the meander wavelength), and peculiar meander shapes that do not show the usual marked upstream skewness emerge.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006WR005234

Significance of the riparian vegetation dynamics on meandering river morphodynamics

[1] Riparian vegetation is part of one of the most diverse and fragile ecotones The key role played by river discharge on the dynamics of riparian vegetation has been widely studied and documented However, although randomness is a fundamental characteristic of river hydrology, very few quantitative vegetation studies take into account the random nature of river discharge Here we propose a stochastic model of riparian vegetation ecosystem dynamics forced by random variations in river discharge The model is solved, and the analytical expressions of the probability density function of the overall vegetation biomass and its first moments are obtained These theoretical results are used to investigate the effect of river hydrology on the distribution of vegetation along the riparian transect transverse to the river In particular, the influence of the type of riparian species and the statistical characteristics of discharge time series are discussed and compared with field observations

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2006WR004933

Carlo Vincenzo Camporeale

Papers

Modeling the interactions between river morphodynamics and riparian vegetation

Sinuosity-driven hyporheic exchange in meandering rivers

Hierarchy of models for meandering rivers and related morphodynamic processes

Significance of the riparian vegetation dynamics on meandering river morphodynamics

Riparian vegetation distribution induced by river flow variability: A stochastic approach