Wie wirksam sind neue Therapien für Hepatitis C? : Mathematische Modellierung charakterisiert den individuellen Behandlungserfolg

Die derzeitige Therapie der chronischen Hepatitis C ist komplex und mit zahlreichen Nebenwirkungen assoziiert. Um den Therapieerfolg frühzeitig vorhersagen zu können und die Behandlung individuell zu optimieren, greifen Forscher des Frankfurter Leberzentrums auf mathematische Modellierung zurück. ..

Rheological Model for Wood

Wood as the most important natural and renewable building material plays an important role in the construction sector. Nevertheless, its hygroscopic character basically affects all related mechanical properties leading to degradation of material stiffness and strength over the service life. Accordingly, to attain reliable design of the timber structures, the influence of moisture evolution and the role of time- and moisture-dependent behaviors have to be taken into account. For this purpose, in the current study a 3D orthotropic elasto-plastic, visco-elastic, mechano-sorptive constitutive model for wood, with all material constants being defined as a function of moisture content, is presented. The corresponding numerical integration approach, with additive decomposition of the total strain is developed and implemented within the framework of the finite element method (FEM). Moreover to preserve a quadratic rate of asymptotic convergence the consistent tangent operator for the whole model is derived. Functionality and capability of the presented material model are evaluated by performing several numerical verification simulations of wood components under different combinations of mechanical loading and moisture variation. Additionally, the flexibility and universality of the introduced model to predict the mechanical behavior of different species are demonstrated by the analysis of a hybrid wood element. Furthermore, the proposed numerical approach is validated by comparisons of computational evaluations with experimental results.Comment: 37 pages, 13 figures, 10 table

Directionality of THz emission from photoinduced gas plasmas

Forward and backward THz emission by ionizing two-color laser pulses in gas is investigated by means of a simple semi-analytical model based on Jefimenko's equation and rigorous Maxwell simulations in one and two dimensions. We find the emission in backward direction having a much smaller spectral bandwidth than in forward direction and explain this by interference effects. Forward THz radiation is generated predominantly at the ionization front and thus almost not affected by the opacity of the plasma, in excellent agreement with results obtained from a unidirectional pulse propagation model

On the power and size properties of cointegration tests in the light of high-frequency stylized facts

This paper first establishes a selection of stylized facts for high-frequency cointegration processes in the European equity market. Empirical evidence is given by one minute-binned transaction data of all DAX 30 constituents as traded on Deutsche Börse's Xetra market in 2014. A methodology is introduced to simulate cointegrated stock pairs, following none, some or all of the discussed stylized facts. In particular, AR(1), AR(1)-GARCH(1,1) and multiple regime STAR(1)-GARCH(1,1) processes are used to model the cointegration relationship. Furthermore, this cointegration relationship is contaminated with jumps. Based on these processes, the power and size properties of ten contemporary cointegration tests are assessed. We provide an economic interpretation of our approach by relating cointegration to relative-value arbitrage strategies in near-efficient markets. Quintessentially, we find that in a high-frequency setting typical for stock price data, selected cointegration tests still exhibit high power. Especially the Phillips-Perron and the Pantula, Gonzalez-Farias and Fuller tests perform best at very limited size distortions

Components of intraday volatility and their prediction at different sampling frequencies with application to DAX and BUND futures

The adjusted measure of realized volatility suggested in [20] is applied to high- frequency orderbook and transaction data of DAX and BUND futures from EU- REX in order to identify the drivers of intraday volatility. Four components are identified to have predictive power: an auto-regressive pattern, a seasonal pattern, long-term memory and scheduled data releases. These components are analyzed in detail. Some evidence for two additional components, market microstrucuture events and unscheduled news, is given. Depending on the sampling frequency we estimate that between one and two thirds of the variation in realized volatility can be predicted by a simple linear model based on the components identified. It is shown how the predictive power of the different components depends on sampling frequencies