Long-term Morphodynamic Modelling of Coastal Evolution of the German Bight

The morphodynamics of coastal landscapes, such as embayments, estuaries, tidal channels and sandbars, have been systematically studied for several decades by field studies, empirical formulations and, increasingly, numerical models. Process-based morphodynamic models have been recognized as useful comprehensive tools for studying coastal evolution. However, in practice, one of the difficulties of studying coastal morphodynamics arises from the broad range of temporal and spatial scales involved. Over decadal temporal scales, morphodynamic models often show limitations in reproducing natural complex morphological evolution patterns, such as the movement of bars and tidal channels. Due to the known shortcomings of the numerical modelling system, data assimilation techniques, whereby model output is combined with measurements, have sprung up recently for application in coastal morphodynamic modelling. The main objective of this research is to investigate the coastal meso-scale morphological features of tidal channel migration and channel-shoal patterns by means of process-based morphodynamic modelling (using the Delft3D modelling suite) and data assimilation (DA) techniques. The study area is in the Jade and Elbe Estuary in the German Bight, North Sea. Decadal channel migration patterns are observed in the Jade Channel and the Medem Channel of the Elbe Estuary. The predictive ability of morphodynamic modelling of tidal channel migration is firstly examined. The improvement of current model predictions by DA techniques is subsequently evaluated. For the implementation of DA methods, assumptions and approximations have to be made (often based on experience) in order to define the observation and background error covariance metrics. A systematic analysis of the user defined correlation length scale for the definition of the background error covariance matrix has been conducted. The research starts with a simplified configuration of DA by neglecting the correlation length scale (set to 0). The DA scheme is then reduced to an optimization scheme which improved the model perditions, although to a limited extent. Furthermore, the correlation length scale is extended spatially and defined in both a homogeneous and heterogeneous way. This method is referred to as a nudging method, with which the model-predicted bathymetry is nudged towards predefined true states. This study has highlighted the definition of the correlation length scale with regard to morphological features, and an optimal value of the correlation length scale is suggested with respect to the grid cell size. In order to understand and interpret the tidal channel migration and associated channel-shoal patterns in estuaries, a schematized morphodynamic model has been applied to investigate the oscillation frequency of flood/ebb dominance which controls the net sediment transport and long-term estuarine morphologies. The oscillation frequency of flood/ebb dominance is formulated and decomposed under three time scales. The long-term annual river discharge is found to be of importance for the oscillation of flood/ebb dominance. Sea level rise increases the depth and enhance the flood dominance and increase estuarine infill

To Harvest and Jam: A Paradigm of Self-Sustaining Friendly Jammers for Secure AF Relaying

This paper studies the use of multi-antenna harvest-and-jam (HJ) helpers in a multi-antenna amplify-and-forward (AF) relay wiretap channel assuming that the direct link between the source and destination is broken. Our objective is to maximize the secrecy rate at the destination subject to the transmit power constraints of the AF relay and the HJ helpers. In the case of perfect channel state information (CSI), the joint optimization of the artificial noise (AN) covariance matrix for cooperative jamming and the AF beamforming matrix is studied using semi-definite relaxation (SDR) which is tight, while suboptimal solutions are also devised with lower complexity. For the imperfect CSI case, we provide the equivalent reformulation of the worst-case robust optimization to maximize the minimum achievable secrecy rate. Inspired by the optimal solution to the case of perfect CSI, a suboptimal robust scheme is proposed striking a good tradeoff between complexity and performance. Finally, numerical results for various settings are provided to evaluate the proposed schemes.Comment: 16 pages (double column), 8 figures, submitted for possible journal publicatio

Limits to growth of forest biomass carbon sink under climate change.

Widely recognized as a significant carbon sink, North American forests have experienced a history of recovery and are facing an uncertain future. This growing carbon sink is dictated by recovery from land-use change, with growth trajectory modified by environmental change. To address both processes, we compiled a forest inventory dataset from North America to quantify aboveground biomass growth with stand age across forest types and climate gradients. Here we show, the biomass grows from 90 Mg ha-1 (2000-2016) to 105 Mg ha-1 (2020 s), 128 Mg ha-1 (2050 s), and 146 Mg ha-1 (2080 s) under climate change scenarios with no further disturbances. Climate change modifies the forest recovery trajectory to some extent, but the overall growth is limited, showing signs of biomass saturation. The future (2080s) biomass will only sequester at most 22% more carbon than the current level. Given such a strong sink has limited growth potential, our ground-based analysis suggests policy changes to sustain the carbon sink

Interatomic Fe-H potential for irradiation and embrittlement simulations

The behavior of hydrogen in iron and iron alloys is of interest in many fields of physics and materials science. To enable large-scale molecular dynamics simulations of systems with Fe-H interactions, we develop, based on density-functional theory calculations, an interatomic Fe-H potential in the Tersoff-Brenner formalism. The obtained analytical potential is suitable for simulations of H in bulk Fe as well as for modeling small FeH molecules, and it can be directly combined with our previously constructed potential for the stainless steel Fe-Cr-C system. This will allow simulations of, e.g., hydrocarbon molecule chemistry on steel surfaces. In the current work, we apply the potential to simulating hydrogen-induced embrittlement in monocrystalline bulk Fe and in an Fe bicrystal with a grain boundary. In both cases, hydrogen is found to soften the material.Comment: 23 pages, 4 color figures; identical in content to the published articl

Pharmacokinetics and Disposition of Green Tea Catechins

Green tea reportedly possesses many health beneficial effects as a beverage. Its usage has even been elevated to therapeutic level for treatment of diseases, including cancer, after increasing the catechin constituents in green tea extract or through purified catechins compounds. However, the therapeutic effectiveness of green tea extract or catechin formulae on different diseases is still questionable and inconsistent in reported studies. One reason is the low and variable bioavailability of catechins or unknown constituents in green tea extract. The plasma levels of total catechins are usually at submicromolar level which is well below the effective dose in many in vitro studies. Besides their variable chemical structures that cause heterogeneity of absorption, green tea catechins are subject to extensive metabolism by phase II process and catabolism by colonic microbes that result in complicated pharmacokinetics. It is essential to understand the factors affecting the pharmacokinetics and metabolic profiles in plasma and tissues based on animal and human studies before green tea catechins can be applied for therapeutic use

Introducing the VIMM toolbox for the (re)calibration of the SCALDIS model

Hydrodynamic

Visual Weather Temperature Prediction

In this paper, we attempt to employ convolutional recurrent neural networks for weather temperature estimation using only image data. We study ambient temperature estimation based on deep neural networks in two scenarios a) estimating temperature of a single outdoor image, and b) predicting temperature of the last image in an image sequence. In the first scenario, visual features are extracted by a convolutional neural network trained on a large-scale image dataset. We demonstrate that promising performance can be obtained, and analyze how volume of training data influences performance. In the second scenario, we consider the temporal evolution of visual appearance, and construct a recurrent neural network to predict the temperature of the last image in a given image sequence. We obtain better prediction accuracy compared to the state-of-the-art models. Further, we investigate how performance varies when information is extracted from different scene regions, and when images are captured in different daytime hours. Our approach further reinforces the idea of using only visual information for cost efficient weather prediction in the future.Comment: 8 pages, accepted to WACV 201