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In the study of relativistic jets one of the key open questions is their interaction with the environment on the microscopic level. Here, we study the initial evolution of both electron-proton (e(-)-p(+)) and electron-positron (e(+/-)) relativistic jets containing helical magnetic fields, focusing on their interaction with an ambient plasma. We have performed simulations of "global" jets containing helical magnetic fields in order to examine how helical magnetic fields affect kinetic instabilities such as the Weibel instability, the kinetic Kelvin-Helmholtz instability (kKHI) and the Mushroom instability (MI). In our initial simulation study these kinetic instabilities are suppressed and new types of instabilities can grow. In the e(-)-p(+) jet simulation a recollimation-like instability occurs and jet electrons are strongly perturbed. In the e(+/-) jet simulation a recollimation-like instability occurs at early times followed by a kinetic instability and the general structure is similar to a simulation without helical magnetic field. Simulations using much larger systems are required in order to thoroughly follow the evolution of global jets containing helical magnetic fields.
The relationship between climate and forest productivity is an intensively studied subject in forest science. This Thesis is embedded within the general framework of future forest growth under climate change and its implications for the ongoing forest conversion. My objective is to investigate the future forest productivity at different spatial scales (from a single specific forest stand to aggregated information across Germany) with focus on oak-pine forests in the federal state of Brandenburg. The overarching question is: how are the oak-pine forests affected by climate change described by a variety of climate scenarios. I answer this question by using a model based analysis of tree growth processes and responses to different climate scenarios with emphasis on drought events. In addition, a method is developed which considers climate change uncertainty of forest management planning.
As a first 'screening' of climate change impacts on forest productivity, I calculated the change in net primary production on the base of a large set of climate scenarios for different tree species and the total area of Germany. Temperature increases up to 3 K lead to positive effects on the net primary production of all selected tree species. But, in water-limited regions this positive net primary production trend is dependent on the length of drought periods which results in a larger uncertainty regarding future forest productivity. One of the regions with the highest uncertainty of net primary production development is the federal state of Brandenburg.
To enhance the understanding and ability of model based analysis of tree growth sensitivity to drought stress two water uptake approaches in pure pine and mixed oak-pine stands are contrasted. The first water uptake approach consists of an empirical function for root water uptake. The second approach is more mechanistic and calculates the differences of soil water potential along a soil-plant-atmosphere continuum. I assumed the total root resistance to vary at low, medium and high total root resistance levels. For validation purposes three data sets on different tree growth relevant time scales are used. Results show that, except the mechanistic water uptake approach with high total root resistance, all transpiration outputs exceeded observed values. On the other hand high transpiration led to a better match of observed soil water content. The strongest correlation between simulated and observed annual tree ring width occurred with the mechanistic water uptake approach and high total root resistance. The findings highlight the importance of severe drought as a main reason for small diameter increment, best supported by the mechanistic water uptake approach with high root resistance. However, if all aspects of the data sets are considered no approach can be judged superior to the other. I conclude that the uncertainty of future productivity of water-limited forest ecosystems under changing environmental conditions is linked to simulated root water uptake.
Finally my study aimed at the impacts of climate change combined with management scenarios on an oak-pine forest to evaluate growth, biomass and the amount of harvested timber. The pine and the oak trees are 104 and 9 years old respectively. Three different management scenarios with different thinning intensities and different climate scenarios are used to simulate the performance of management strategies which explicitly account for the risks associated with achieving three predefined objectives (maximum carbon storage, maximum harvested timber, intermediate). I found out that in most cases there is no general management strategy which fits best to different objectives. The analysis of variance in the growth related model outputs showed an increase of climate uncertainty with increasing climate warming. Interestingly, the increase of climate-induced uncertainty is much higher from 2 to 3 K than from 0 to 2 K.
Recently, due to an increasing demand on functionality and flexibility, beforehand isolated systems have become interconnected to gain powerful adaptive Systems of Systems (SoS) solutions with an overall robust, flexible and emergent behavior. The adaptive SoS comprises a variety of different system types ranging from small embedded to adaptive cyber-physical systems. On the one hand, each system is independent, follows a local strategy and optimizes its behavior to reach its goals. On the other hand, systems must cooperate with each other to enrich the overall functionality to jointly perform on the SoS level reaching global goals, which cannot be satisfied by one system alone. Due to difficulties of local and global behavior optimizations conflicts may arise between systems that have to be solved by the adaptive SoS.
This thesis proposes a modeling language that facilitates the description of an adaptive SoS by considering the adaptation capabilities in form of feedback loops as first class entities. Moreover, this thesis adopts the Models@runtime approach to integrate the available knowledge in the systems as runtime models into the modeled adaptation logic. Furthermore, the modeling language focuses on the description of system interactions within the adaptive SoS to reason about individual system functionality and how it emerges via collaborations to an overall joint SoS behavior. Therefore, the modeling language approach enables the specification of local adaptive system behavior, the integration of knowledge in form of runtime models and the joint interactions via collaboration to place the available adaptive behavior in an overall layered, adaptive SoS architecture.
Beside the modeling language, this thesis proposes analysis rules to investigate the modeled adaptive SoS, which enables the detection of architectural patterns as well as design flaws and pinpoints to possible system threats. Moreover, a simulation framework is presented, which allows the direct execution of the modeled SoS architecture. Therefore, the analysis rules and the simulation framework can be used to verify the interplay between systems as well as the modeled adaptation effects within the SoS. This thesis realizes the proposed concepts of the modeling language by mapping them to a state of the art standard from the automotive domain and thus, showing their applicability to actual systems. Finally, the modeling language approach is evaluated by remodeling up to date research scenarios from different domains, which demonstrates that the modeling language concepts are powerful enough to cope with a broad range of existing research problems.
Decreasing groundwater levels in many parts of Germany and decreasing low flows in Central Europe have created a need for adaptation measures to stabilize the water balance and to increase low flows. The objective of our study was to estimate the impact of ditch water level management on stream-aquifer interactions in small lowland catchments of the mid-latitudes. The water balance of a ditch-irrigated area and fluxes between the subsurface and the adjacent stream were modeled for three runoff recession periods using the Hydrus-2D software package. The results showed that the subsurface flow to the stream was closely related to the difference between the water level in the ditch system and the stream. Evapotranspiration during the growing season additionally reduced base flow. It was crucial to stop irrigation during a recession period to decrease water withdrawal from the stream and enhance the base flow by draining the irrigated area. Mean fluxes to the stream were between 0.04 and 0.64 ls(-1) for the first 20 days of the low-flow periods. This only slightly increased the flow in the stream, whose mean was 57 ls(-1) during the period with the lowest flows. Larger areas would be necessary to effectively increase flows in mesoscale catchments.
In many statistical applications, the aim is to model the relationship between covariates and some outcomes. A choice of the appropriate model depends on the outcome and the research objectives, such as linear models for continuous outcomes, logistic models for binary outcomes and the Cox model for time-to-event data. In epidemiological, medical, biological, societal and economic studies, the logistic regression is widely used to describe the relationship between a response variable as binary outcome and explanatory variables as a set of covariates. However, epidemiologic cohort studies are quite expensive regarding data management since following up a large number of individuals takes long time. Therefore, the case-cohort design is applied to reduce cost and time for data collection. The case-cohort sampling collects a small random sample from the entire cohort, which is called subcohort. The advantage of this design is that the covariate and follow-up data are recorded only on the subcohort and all cases (all members of the cohort who develop the event of interest during the follow-up process).
In this thesis, we investigate the estimation in the logistic model for case-cohort design. First, a model with a binary response and a binary covariate is considered. The maximum likelihood estimator (MLE) is described and its asymptotic properties are established. An estimator for the asymptotic variance of the estimator based on the maximum likelihood approach is proposed; this estimator differs slightly from the estimator introduced by Prentice (1986). Simulation results for several proportions of the subcohort show that the proposed estimator gives lower empirical bias and empirical variance than Prentice's estimator.
Then the MLE in the logistic regression with discrete covariate under case-cohort design is studied. Here the approach of the binary covariate model is extended. Proving asymptotic normality of estimators, standard errors for the estimators can be derived. The simulation study demonstrates the estimation procedure of the logistic regression model with a one-dimensional discrete covariate. Simulation results for several proportions of the subcohort and different choices of the underlying parameters indicate that the estimator developed here performs reasonably well. Moreover, the comparison between theoretical values and simulation results of the asymptotic variance of estimator is presented.
Clearly, the logistic regression is sufficient for the binary outcome refers to be available for all subjects and for a fixed time interval. Nevertheless, in practice, the observations in clinical trials are frequently collected for different time periods and subjects may drop out or relapse from other causes during follow-up. Hence, the logistic regression is not appropriate for incomplete follow-up data; for example, an individual drops out of the study before the end of data collection or an individual has not occurred the event of interest for the duration of the study. These observations are called censored observations. The survival analysis is necessary to solve these problems. Moreover, the time to the occurence of the event of interest is taken into account. The Cox model has been widely used in survival analysis, which can effectively handle the censored data. Cox (1972) proposed the model which is focused on the hazard function. The Cox model is assumed to be
λ(t|x) = λ0(t) exp(β^Tx)
where λ0(t) is an unspecified baseline hazard at time t and X is the vector of covariates, β is a p-dimensional vector of coefficient.
In this thesis, the Cox model is considered under the view point of experimental design. The estimability of the parameter β0 in the Cox model, where β0 denotes the true value of β, and the choice of optimal covariates are investigated. We give new representations of the observed information matrix In(β) and extend results for the Cox model of Andersen and Gill (1982). In this way conditions for the estimability of β0 are formulated. Under some regularity conditions, ∑ is the inverse of the asymptotic variance matrix of the MPLE of β0 in the Cox model and then some properties of the asymptotic variance matrix of the MPLE are highlighted. Based on the results of asymptotic estimability, the calculation of local optimal covariates is considered and shown in examples. In a sensitivity analysis, the efficiency of given covariates is calculated. For neighborhoods of the exponential models, the efficiencies have then been found. It is appeared that for fixed parameters β0, the efficiencies do not change very much for different baseline hazard functions. Some proposals for applicable optimal covariates and a calculation procedure for finding optimal covariates are discussed.
Furthermore, the extension of the Cox model where time-dependent coefficient are allowed, is investigated. In this situation, the maximum local partial likelihood estimator for estimating the coefficient function β(·) is described. Based on this estimator, we formulate a new test procedure for testing, whether a one-dimensional coefficient function β(·) has a prespecified parametric form, say β(·; ϑ). The score function derived from the local constant partial likelihood function at d distinct grid points is considered. It is shown that the distribution of the properly standardized quadratic form of this d-dimensional vector under the null hypothesis tends to a Chi-squared distribution. Moreover, the limit statement remains true when replacing the unknown ϑ0 by the MPLE in the hypothetical model and an asymptotic α-test is given by the quantiles or p-values of the limiting Chi-squared distribution. Finally, we propose a bootstrap version of this test. The bootstrap test is only defined for the special case of testing whether the coefficient function is constant. A simulation study illustrates the behavior of the bootstrap test under the null hypothesis and a special alternative. It gives quite good results for the chosen underlying model.
References
P. K. Andersen and R. D. Gill. Cox's regression model for counting processes: a large samplestudy. Ann. Statist., 10(4):1100{1120, 1982.
D. R. Cox. Regression models and life-tables. J. Roy. Statist. Soc. Ser. B, 34:187{220, 1972.
R. L. Prentice. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika, 73(1):1{11, 1986.
The LEA (late embryogenesis abundant) proteins COR15A and COR15B from Arabidopsis thaliana are intrinsically disordered under fully hydrated conditions, but obtain α-helical structure during dehydration, which is reversible upon rehydration. To understand this unusual structural transition, both proteins were investigated by circular dichroism (CD) and molecular dynamics (MD) approaches. MD simulations showed unfolding of the proteins in water, in agreement with CD data obtained with both HIS-tagged and untagged recombinant proteins. Mainly intramolecular hydrogen bonds (H-bonds) formed by the protein backbone were replaced by H-bonds with water molecules. As COR15 proteins function in vivo as protectants in leaves partially dehydrated by freezing, unfolding was further assessed under crowded conditions. Glycerol reduced (40%) or prevented (100%) unfolding during MD simulations, in agreement with CD spectroscopy results. H-bonding analysis indicated that preferential exclusion of glycerol from the protein backbone increased stability of the folded state.
Foam fractionation of surfactant and protein solutions is a process dedicated to separate surface active molecules from each other due to their differences in surface activities. The process is based on forming bubbles in a certain mixed solution followed by detachment and rising of bubbles through a certain volume of this solution, and consequently on the formation of a foam layer on top of the solution column. Therefore, systematic analysis of this whole process comprises of at first investigations dedicated to the formation and growth of single bubbles in solutions, which is equivalent to the main principles of the well-known bubble pressure tensiometry. The second stage of the fractionation process includes the detachment of a single bubble from a pore or capillary tip and its rising in a respective aqueous solution. The third and final stage of the process is the formation and stabilization of the foam created by these bubbles, which contains the adsorption layers formed at the growing bubble surface, carried up and gets modified during the bubble rising and finally ends up as part of the foam layer.
Bubble pressure tensiometry and bubble profile analysis tensiometry experiments were performed with protein solutions at different bulk concentrations, solution pH and ionic strength in order to describe the process of accumulation of protein and surfactant molecules at the bubble surface. The results obtained from the two complementary methods allow understanding the mechanism of adsorption, which is mainly governed by the diffusional transport of the adsorbing protein molecules to the bubble surface. This mechanism is the same as generally discussed for surfactant molecules. However, interesting peculiarities have been observed for protein adsorption kinetics at sufficiently short adsorption times. First of all, at short adsorption times the surface tension remains constant for a while before it decreases as expected due to the adsorption of proteins at the surface. This time interval is called induction time and it becomes shorter with increasing protein bulk concentration. Moreover, under special conditions, the surface tension does not stay constant but even increases over a certain period of time. This so-called negative surface pressure was observed for BCS and BLG and discussed for the first time in terms of changes in the surface conformation of the adsorbing protein molecules. Usually, a negative surface pressure would correspond to a negative adsorption, which is of course impossible for the studied protein solutions. The phenomenon, which amounts to some mN/m, was rather explained by simultaneous changes in the molar area required by the adsorbed proteins and the non-ideality of entropy of the interfacial layer. It is a transient phenomenon and exists only under dynamic conditions.
The experiments dedicated to the local velocity of rising air bubbles in solutions were performed in a broad range of BLG concentration, pH and ionic strength. Additionally, rising bubble experiments were done for surfactant solutions in order to validate the functionality of the instrument. It turns out that the velocity of a rising bubble is much more sensitive to adsorbing molecules than classical dynamic surface tension measurements. At very low BLG or surfactant concentrations, for example, the measured local velocity profile of an air bubble is changing dramatically in time scales of seconds while dynamic surface tensions still do not show any measurable changes at this time scale. The solution’s pH and ionic strength are important parameters that govern the measured rising velocity for protein solutions. A general theoretical description of rising bubbles in surfactant and protein solutions is not available at present due to the complex situation of the adsorption process at a bubble surface in a liquid flow field with simultaneous Marangoni effects. However, instead of modelling the complete velocity profile, new theoretical work has been started to evaluate the maximum values in the profile as characteristic parameter for dynamic adsorption layers at the bubble surface more quantitatively.
The studies with protein-surfactant mixtures demonstrate in an impressive way that the complexes formed by the two compounds change the surface activity as compared to the original native protein molecules and therefore lead to a completely different retardation behavior of rising bubbles. Changes in the velocity profile can be interpreted qualitatively in terms of increased or decreased surface activity of the formed protein-surfactant complexes. It was also observed that the pH and ionic strength of a protein solution have strong effects on the surface activity of the protein molecules, which however, could be different on the rising bubble velocity and the equilibrium adsorption isotherms. These differences are not fully understood yet but give rise to discussions about the structure of protein adsorption layer under dynamic conditions or in the equilibrium state.
The third main stage of the discussed process of fractionation is the formation and characterization of protein foams from BLG solutions at different pH and ionic strength. Of course a minimum BLG concentration is required to form foams. This minimum protein concentration is a function again of solution pH and ionic strength, i.e. of the surface activity of the protein molecules. Although at the isoelectric point, at about pH 5 for BLG, the hydrophobicity and hence the surface activity should be the highest, the concentration and ionic strength effects on the rising velocity profile as well as on the foamability and foam stability do not show a maximum. This is another remarkable argument for the fact that the interfacial structure and behavior of BLG layers under dynamic conditions and at equilibrium are rather different. These differences are probably caused by the time required for BLG molecules to adapt respective conformations once they are adsorbed at the surface.
All bubble studies described in this work refer to stages of the foam fractionation process. Experiments with different systems, mainly surfactant and protein solutions, were performed in order to form foams and finally recover a solution representing the foamed material. As foam consists to a large extent of foam lamella – two adsorption layers with a liquid core – the concentration in a foamate taken from foaming experiments should be enriched in the stabilizing molecules. For determining the concentration of the foamate, again the very sensitive bubble rising velocity profile method was applied, which works for any type of surface active materials. This also includes technical surfactants or protein isolates for which an accurate composition is unknown.
Molecular paleoclimate reconstructions over the last 9 ka from a peat sequence in South China
(2016)
To achieve a better understanding of Holocene climate change in the monsoon regions of China, we investigated the molecular distributions and carbon and hydrogen isotope compositions delta C-13 and delta D values) of long-chain n-alkanes in a peat core from the Shiwangutian SWGT) peatland, south China over the last 9 ka. By comparisons with other climate records, we found that the delta C-13 values of the long-chain n-alkanes can be a proxy for humidity, while the dD values of the long-chain n-alkanes primarily recorded the moisture source dD signal during 9-1.8 ka BP and responded to the dry climate during 1.8-0.3 ka BP. Together with the average chain length ACL) and the carbon preference index CPI) data, the climate evolution over last 9 ka in the SWGT peatland can be divided into three stages. During the first stage 9-5 ka BP), the delta C-13 values were depleted and CPI and Paq values were low, while ACL values were high. They reveal a period of warm and wet climate, which is regarded as the Holocene optimum. The second stage 5-1.8 ka BP) witnessed a shift to relatively cool and dry climate, as indicated by the more positive delta C-13 values and lower ACL values. During the third stage 1.8-0.3 ka BP), the delta C-13, delta D, CPI and Paq values showed marked increase and ACL values varied greatly, implying an abrupt change to cold and dry conditions. This climate pattern corresponds to the broad decline in Asian monsoon intensity through the latter part of the Holocene. Our results do not support a later Holocene optimum in south China as suggested by previous studies.
The speciation of 2-Mercaptopyridine in aqueous solution has been investigated with nitrogen 1s Near Edge X-ray Absorption Fine Structure spectroscopy and time dependent Density Functional Theory. The prevalence of distinct species as a function of the solvent basicity is established. No indications of dimerization towards high concentrations are found. The determination of different molecular structures of 2-Mercaptopyridine in aqueous solution is put into the context of proton-transfer in keto-enol and thione-thiol tautomerisms. (C) 2016 The Authors. Published by Elsevier B.V.
More effort — more results
(2016)
The development of 'omics' technologies has progressed to address complex biological questions that underlie various plant functions thereby producing copious amounts of data. The need to assimilate large amounts of data into biologically meaningful interpretations has necessitated the development of statistical methods to integrate multidimensional information. Throughout this review, we provide examples of recent outcomes of 'omics' data integration together with an overview of available statistical methods and tools.