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The large literature that aims to find evidence of climate migration delivers mixed findings. This meta-regression analysis i) summarizes direct links between adverse climatic events and migration, ii) maps patterns of climate migration, and iii) explains the variation in outcomes. Using a set of limited dependent variable models, we meta-analyze thus-far the most comprehensive sample of 3,625 estimates from 116 original studies and produce novel insights on climate migration. We find that extremely high temperatures and drying conditions increase migration. We do not find a significant effect of sudden-onset events. Climate migration is most likely to emerge due to contemporaneous events, to originate in rural areas and to take place in middle-income countries, internally, to cities. The likelihood to become trapped in affected areas is higher for women and in low-income countries, particularly in Africa. We uniquely quantify how pitfalls typical for the broader empirical climate impact literature affect climate migration findings. We also find evidence of different publication biases.
The increasing development of antibiotic resistance in bacteria has been a major problem for years, both in human and veterinary medicine. Prophylactic measures, such as the use of vaccines, are of great importance in reducing the use of antibiotics in livestock. These vaccines are mainly produced based on formaldehyde inactivation. However, the latter damages the recognition elements of the bacterial proteins and thus could reduce the immune response in the animal. An alternative inactivation method developed in this work is based on gentle photodynamic inactivation using carbon nanodots (CNDs) at excitation wavelengths λex > 290 nm. The photodynamic inactivation was characterized on the nonvirulent laboratory strain Escherichia coli K12 using synthesized CNDs. For a gentle inactivation, the CNDs must be absorbed into the cytoplasm of the E. coli cell. Thus, the inactivation through photoinduced formation of reactive oxygen species only takes place inside the bacterium, which means that the outer membrane is neither damaged nor altered. The loading of the CNDs into E. coli was examined using fluorescence microscopy. Complete loading of the bacterial cells could be achieved in less than 10 min. These studies revealed a reversible uptake process allowing the recovery and reuse of the CNDs after irradiation and before the administration of the vaccine. The success of photodynamic inactivation was verified by viability assays on agar. In a homemade flow photoreactor, the fastest successful irradiation of the bacteria could be carried out in 34 s. Therefore, the photodynamic inactivation based on CNDs is very effective. The membrane integrity of the bacteria after irradiation was verified by slide agglutination and atomic force microscopy. The method developed for the laboratory strain E. coli K12 could then be successfully applied to the important avian pathogens Bordetella avium and Ornithobacterium rhinotracheale to aid the development of novel vaccines.
Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.
Portal Wissen = Departure
(2021)
On October 20, 1911, the Norwegian Roald Amundsen left the safe base camp “Framheim” at the Bay of Whales together with four other explorers and 52 sledge dogs to be the first person to reach the South Pole. Ahead of them lay the perpetual ice at temperatures of 20 to 30 degrees Celsius below zero and a distance of 1,400 kilometers. After eight weeks, the group reached its destination on December 13. The men planted the Norwegian flag in the lonely snow and shortly afterwards set off to make their way back – celebrated, honored as conquerors of the South Pole and laden with information and knowledge from the world of Antarctica. The voyage of Amundsen and his companions is undoubtedly so extraordinary because the five proved that it was possible and were the first to succeed. It is, however, also a symbol of what enables humans to push the boundaries of their world: the urge to set out into the unknown, to discover what has not yet been found, explored, and described.
What distinguishes science - even before each discovery and new knowledge – is the element of departure. Questioning apparent certainties, taking a critical look at outdated knowledge, and breaking down encrusted thought patterns is the starting point of exploratory curiosity. And to set out from there for new knowledge is the essence of scientific activities – neither protected nor supported by the reliable and known. Probing, trying, courageously questioning, and sensing that the solid ground, which still lies hidden, can only be reached again in this way. “Research is always a departure for new shoreless waters,” said chemist Prof. Dr. Hans-Jürgen Quadbeck-Seeger. Leaving behind the safe harbor, trusting that new shores are waiting and can be reached is the impetus that makes science so important and valuable.
For the current issue of the University of Potsdam’s research magazine, we looked over the shoulders of some researchers as they set out on new research journeys – whether in the lab, in the library, in space, or in the mind. Astrophysicist Lidia Oskinova, for example, uses the Hubble telescope to search for particularly massive stars, while hydrologist Thorsten Wagener is trying to better understand the paths of water on Earth. Economists and social scientists such as Elmar Kriegler and Maik Heinemann are researching in different projects what politics can do to achieve a turnaround in climate policy and stop climate change.
Time and again, however, such departures are themselves the focus of research: And a group of biologists and environmental scientists is investigating how nature revives forest fire areas and how the newly emerging forests can become more resilient to future fires.
Since – as has already been said – a departure is inherent in every research question, this time the entire issue of “Portal Wissen” is actually devoted to the cover topic. And so we invite you to set out with Romance linguist Annette Gerstenberg to research language in old age, with immunologist Katja Hanack to develop a quick and safe SARS-CoV-2 test, and with the team of the Potsdam Center for Industry 4.0 to the virtual factory of tomorrow. And we will show you how evidence- based economic research can inform and advise politicians, and how a warning system is intended to prevent future accidents involving cyclists.
So, what are you waiting for?!
Noise is ubiquitous in nature and usually results in rich dynamics in stochastic systems such as oscillatory systems, which exist in such various fields as physics, biology and complex networks. The correlation and synchronization of two or many oscillators are widely studied topics in recent years.
In this thesis, we mainly investigate two problems, i.e., the stochastic bursting phenomenon in noisy excitable systems and synchronization in a three-dimensional Kuramoto model with noise. Stochastic bursting here refers to a sequence of coherent spike train, where each spike has random number of followers due to the combined effects of both time delay and noise. Synchronization, as a universal phenomenon in nonlinear dynamical systems, is well illustrated in the Kuramoto model, a prominent model in the description of collective motion.
In the first part of this thesis, an idealized point process, valid if the characteristic timescales in the problem are well separated, is used to describe statistical properties such as the power spectral density and the interspike interval distribution. We show how the main parameters of the point process, the spontaneous excitation rate, and the probability to induce a spike during the delay action can be calculated from the solutions of a stationary and a forced Fokker-Planck equation. We extend it to the delay-coupled case and derive analytically the statistics of the spikes in each neuron, the pairwise correlations between any two neurons, and the spectrum of the total output from the network.
In the second part, we investigate the three-dimensional noisy Kuramoto model, which can be used to describe the synchronization in a swarming model with helical trajectory. In the case without natural frequency, the Kuramoto model can be connected with the Vicsek model, which is widely studied in collective motion and swarming of active matter. We analyze the linear stability of the incoherent state and derive the critical coupling strength above which the incoherent state loses stability. In the limit of no natural frequency, an exact self-consistent equation of the mean field is derived and extended straightforward to any high-dimensional case.
Reciprocal space slicing
(2021)
An experimental technique that allows faster assessment of out-of-plane strain dynamics of thin film heterostructures via x-ray diffraction is presented. In contrast to conventional high-speed reciprocal space-mapping setups, our approach reduces the measurement time drastically due to a fixed measurement geometry with a position-sensitive detector. This means that neither the incident (ω) nor the exit (2θ) diffraction angle is scanned during the strain assessment via x-ray diffraction. Shifts of diffraction peaks on the fixed x-ray area detector originate from an out-of-plane strain within the sample. Quantitative strain assessment requires the determination of a factor relating the observed shift to the change in the reciprocal lattice vector. The factor depends only on the widths of the peak along certain directions in reciprocal space, the diffraction angle of the studied reflection, and the resolution of the instrumental setup. We provide a full theoretical explanation and exemplify the concept with picosecond strain dynamics of a thin layer of NbO2.
This thesis focuses on the study of marked Gibbs point processes, in particular presenting some results on their existence and uniqueness, with ideas and techniques drawn from different areas of statistical mechanics: the entropy method from large deviations theory, cluster expansion and the Kirkwood--Salsburg equations, the Dobrushin contraction principle and disagreement percolation.
We first present an existence result for infinite-volume marked Gibbs point processes. More precisely, we use the so-called entropy method (and large-deviation tools) to construct marked Gibbs point processes in R^d under quite general assumptions. In particular, the random marks belong to a general normed space S and are not bounded. Moreover, we allow for interaction functionals that may be unbounded and whose range is finite but random. The entropy method relies on showing that a family of finite-volume Gibbs point processes belongs to sequentially compact entropy level sets, and is therefore tight.
We then present infinite-dimensional Langevin diffusions, that we put in interaction via a Gibbsian description. In this setting, we are able to adapt the general result above to show the existence of the associated infinite-volume measure. We also study its correlation functions via cluster expansion techniques, and obtain the uniqueness of the Gibbs process for all inverse temperatures β and activities z below a certain threshold. This method relies in first showing that the correlation functions of the process satisfy a so-called Ruelle bound, and then using it to solve a fixed point problem in an appropriate Banach space. The uniqueness domain we obtain consists then of the model parameters z and β for which such a problem has exactly one solution.
Finally, we explore further the question of uniqueness of infinite-volume Gibbs point processes on R^d, in the unmarked setting. We present, in the context of repulsive interactions with a hard-core component, a novel approach to uniqueness by applying the discrete Dobrushin criterion to the continuum framework. We first fix a discretisation parameter a>0 and then study the behaviour of the uniqueness domain as a goes to 0. With this technique we are able to obtain explicit thresholds for the parameters z and β, which we then compare to existing results coming from the different methods of cluster expansion and disagreement percolation.
Throughout this thesis, we illustrate our theoretical results with various examples both from classical statistical mechanics and stochastic geometry.
When it comes to teacher attitudes towards teaching and learning, research relies heavily on explicit measures (e.g., questionnaires). These attitudes are generally conceptualized as constructivist and transmissive views on teaching and learning with constructivism often considered to be more desirable. In explicit measures, this can have drawbacks like socially desirable responding. It is for this reason that, in this study, we investigated implicit attitudes as well as explicit attitudes towards constructivism and transmission. N = 100 preservice teachers worked on a questionnaire and two Single-Target Implicit Association Tests (ST-IAT constructivism and ST-IAT transmission) before (T1) and after (T2) a single master’s semester. One group (n = 50) did student teaching while a second group (n = 50) took master’s courses. We evaluated preservice teachers’ views on teaching at the end of their masters’ studies. Participants agreed with transmission and constructivism (T1) on both an explicit and implicit level. Implicit measures seem to exceed explicit measures in differentially assessing constructivist and transmissive views on teaching and learning. After student teaching (T2), there was no overall effect of attitude development but changes in rank indicate that participants’ implicit attitudes towards constructivism and transmission developed differently for each individual.
BACKGROUND: The orbitofrontal cortex (OFC) is implicated in depression. The hypothesis investigated was whether the OFC sensitivity to reward and nonreward is related to the severity of depressive symptoms.
METHODS: Activations in the monetary incentive delay task were measured in the IMAGEN cohort at ages 14 years (n = 1877) and 19 years (n = 1140) with a longitudinal design. Clinically relevant subgroups were compared at ages 19 (high-severity group: n = 116; low-severity group: n = 206) and 14.
RESULTS: The medial OFC exhibited graded activation increases to reward, and the lateral OFC had graded activation increases to nonreward. In this general population, the medial and lateral OFC activations were associated with concurrent depressive symptoms at both ages 14 and 19 years. In a stratified high-severity depressive symptom group versus control group comparison, the lateral OFC showed greater sensitivity for the magnitudes of activations related to nonreward in the high-severity group at age 19 (p = .027), and the medial OFC showed decreased sensitivity to the reward magnitudes in the high-severity group at both ages 14 (p = .002) and 19 (p = .002). In a longitudinal design, there was greater sensitivity to nonreward of the lateral OFC at age 14 for those who exhibited high depressive symptom severity later at age 19 (p = .003).
CONCLUSIONS: Activations in the lateral OFC relate to sensitivity to not winning, were associated with high depressive symptom scores, and at age 14 predicted the depressive symptoms at ages 16 and 19. Activations in the medial OFC were related to sensitivity to winning, and reduced reward sensitivity was associated with concurrent high depressive symptom scores.
With ongoing anthropogenic global warming, some of the most vulnerable components of the Earth system might become unstable and undergo a critical transition. These subsystems are the so-called tipping elements. They are believed to exhibit threshold behaviour and would, if triggered, result in severe consequences for the biosphere and human societies. Furthermore, it has been shown that climate tipping elements are not isolated entities, but interact across the entire Earth system. Therefore, this thesis aims at mapping out the potential for tipping events and feedbacks in the Earth system mainly by the use of complex dynamical systems and network science approaches, but partially also by more detailed process-based models of the Earth system.
In the first part of this thesis, the theoretical foundations are laid by the investigation of networks of interacting tipping elements. For this purpose, the conditions for the emergence of global cascades are analysed against the structure of paradigmatic network types such as Erdös-Rényi, Barabási-Albert, Watts-Strogatz and explicitly spatially embedded networks. Furthermore, micro-scale structures are detected that are decisive for the transition of local to global cascades. These so-called motifs link the micro- to the macro-scale in the network of tipping elements. Alongside a model description paper, all these results are entered into the Python software package PyCascades, which is publicly available on github.
In the second part of this dissertation, the tipping element framework is first applied to components of the Earth system such as the cryosphere and to parts of the biosphere. Afterwards it is applied to a set of interacting climate tipping elements on a global scale. Using the Earth system Model of Intermediate Complexity (EMIC) CLIMBER-2, the temperature feedbacks are quantified, which would arise if some of the large cryosphere elements disintegrate over a long span of time. The cryosphere components that are investigated are the Arctic summer sea ice, the mountain glaciers, the Greenland and the West Antarctic Ice Sheets. The committed temperature increase, in case the ice masses disintegrate, is on the order of an additional half a degree on a global average (0.39-0.46 °C), while local to regional additional temperature increases can exceed 5 °C. This means that, once tipping has begun, additional reinforcing feedbacks are able to increase global warming and with that the risk of further tipping events.
This is also the case in the Amazon rainforest, whose parts are dependent on each other via the so-called moisture-recycling feedback. In this thesis, the importance of drought-induced tipping events in the Amazon rainforest is investigated in detail. Despite the Amazon rainforest is assumed to be adapted to past environmental conditions, it is found that tipping events sharply increase if the drought conditions become too intense in a too short amount of time, outpacing the adaptive capacity of the Amazon rainforest. In these cases, the frequency of tipping cascades also increases to 50% (or above) of all tipping events. In the model that was developed in this study, the southeastern region of the Amazon basin is hit hardest by the simulated drought patterns. This is also the region that already nowadays suffers a lot from extensive human-induced changes due to large-scale deforestation, cattle ranching or infrastructure projects.
Moreover, on the larger Earth system wide scale, a network of conceptualised climate tipping elements is constructed in this dissertation making use of a large literature review, expert knowledge and topological properties of the tipping elements. In global warming scenarios, tipping cascades are detected even under modest scenarios of climate change, limiting global warming to 2 °C above pre-industrial levels. In addition, the structural roles of the climate tipping elements in the network are revealed. While the large ice sheets on Greenland and Antarctica are the initiators of tipping cascades, the Atlantic Meridional Overturning Circulation (AMOC) acts as the transmitter of cascades. Furthermore, in our conceptual climate tipping element model, it is found that the ice sheets are of particular importance for the stability of the entire system of investigated climate tipping elements.
In the last part of this thesis, the results from the temperature feedback study with the EMIC CLIMBER-2 are combined with the conceptual model of climate tipping elements. There, it is observed that the likelihood of further tipping events slightly increases due to the temperature feedbacks even if no further CO$_2$ would be added to the atmosphere.
Although the developed network model is of conceptual nature, it is possible with this work for the first time to quantify the risk of tipping events between interacting components of the Earth system under global warming scenarios, by allowing for dynamic temperature feedbacks at the same time.
As competition over peer status becomes intense during adolescence, some adolescents develop insecure feelings regarding their social standing among their peers (i.e., social status insecurity). These adolescents sometimes use aggression to defend or promote their status. The aim of this study was to examine the relationships among social status insecurity, callous-unemotional (CU) traits, and popularity-motivated aggression and prosocial behaviors among adolescents, while controlling for gender. Another purpose was to examine the potential moderating role of CU traits in these relationships. Participants were 1,047 (49.2% girls; Mage = 12.44 years; age range from 11 to 14 years) in the 7th or 8th grades from a large Midwestern city. They completed questionnaires on social status insecurity, CU traits, and popularity-motivated relational aggression, physical aggression, cyberaggression, and prosocial behaviors. A structural regression model was conducted, with gender as a covariate. The model had adequate fit. Social status insecurity was associated positively with callousness, unemotional, and popularity-motivated aggression and related negatively to popularity-motivated prosocial behaviors. High social status insecurity was related to greater popularity-motivated aggression when adolescents had high callousness traits. The findings have implications for understanding the individual characteristics associated with social status insecurity.
Exendin-4 is a pharmaceutical peptide used in the control of insulin secretion. Structural information on exendin-4 and related peptides especially on the level of quaternary structure is scarce. We present the first published association equilibria of exendin-4 directly measured by static and dynamic light scattering. We show that exendin-4 oligomerization is pH dependent and that these oligomers are of low compactness. We relate our experimental results to a structural hypothesis to describe molecular details of exendin-4 oligomers. Discussion of the validity of this hypothesis is based on NMR, circular dichroism and fluorescence spectroscopy, and light scattering data on exendin-4 and a set of exendin-4 derived peptides. The essential forces driving oligomerization of exendin-4 are helix–helix interactions and interactions of a conserved hydrophobic moiety. Our structural hypothesis suggests that key interactions of exendin-4 monomers in the experimentally supported trimer take place between a defined helical segment and a hydrophobic triangle constituted by the Phe22 residues of the three monomeric subunits. Our data rationalize that Val19 might function as an anchor in the N-terminus of the interacting helix-region and that Trp25 is partially shielded in the oligomer by C-terminal amino acids of the same monomer. Our structural hypothesis suggests that the Trp25 residues do not interact with each other, but with C-terminal Pro residues of their own monomers.
3D point clouds are a universal and discrete digital representation of three-dimensional objects and environments. For geospatial applications, 3D point clouds have become a fundamental type of raw data acquired and generated using various methods and techniques. In particular, 3D point clouds serve as raw data for creating digital twins of the built environment.
This thesis concentrates on the research and development of concepts, methods, and techniques for preprocessing, semantically enriching, analyzing, and visualizing 3D point clouds for applications around transport infrastructure. It introduces a collection of preprocessing techniques that aim to harmonize raw 3D point cloud data, such as point density reduction and scan profile detection. Metrics such as, e.g., local density, verticality, and planarity are calculated for later use. One of the key contributions tackles the problem of analyzing and deriving semantic information in 3D point clouds. Three different approaches are investigated: a geometric analysis, a machine learning approach operating on synthetically generated 2D images, and a machine learning approach operating on 3D point clouds without intermediate representation.
In the first application case, 2D image classification is applied and evaluated for mobile mapping data focusing on road networks to derive road marking vector data. The second application case investigates how 3D point clouds can be merged with ground-penetrating radar data for a combined visualization and to automatically identify atypical areas in the data. For example, the approach detects pavement regions with developing potholes. The third application case explores the combination of a 3D environment based on 3D point clouds with panoramic imagery to improve visual representation and the detection of 3D objects such as traffic signs.
The presented methods were implemented and tested based on software frameworks for 3D point clouds and 3D visualization. In particular, modules for metric computation, classification procedures, and visualization techniques were integrated into a modular pipeline-based C++ research framework for geospatial data processing, extended by Python machine learning scripts. All visualization and analysis techniques scale to large real-world datasets such as road networks of entire cities or railroad networks.
The thesis shows that some use cases allow taking advantage of established image vision methods to analyze images rendered from mobile mapping data efficiently. The two presented semantic classification methods working directly on 3D point clouds are use case independent and show similar overall accuracy when compared to each other. While the geometry-based method requires less computation time, the machine learning-based method supports arbitrary semantic classes but requires training the network with ground truth data. Both methods can be used in combination to gradually build this ground truth with manual corrections via a respective annotation tool.
This thesis contributes results for IT system engineering of applications, systems, and services that require spatial digital twins of transport infrastructure such as road networks and railroad networks based on 3D point clouds as raw data. It demonstrates the feasibility of fully automated data flows that map captured 3D point clouds to semantically classified models. This provides a key component for seamlessly integrated spatial digital twins in IT solutions that require up-to-date, object-based, and semantically enriched information about the built environment.
Biodiversity decline causes a loss of functional diversity, which threatens ecosystems through a dangerous feedback loop: This loss may hamper ecosystems’ ability to buffer environmental changes, leading to further biodiversity losses. In this context, the increasing frequency of human-induced excessive loading of nutrients causes major problems in aquatic systems. Previous studies investigating how functional diversity influences the response of food webs to disturbances have mainly considered systems with at most two functionally diverse trophic levels. We investigated the effects of functional diversity on the robustness, that is, resistance, resilience, and elasticity, using a tritrophic—and thus more realistic—plankton food web model. We compared a non-adaptive food chain with no diversity within the individual trophic levels to a more diverse food web with three adaptive trophic levels. The species fitness differences were balanced through trade-offs between defense/growth rate for prey and selectivity/half-saturation constant for predators. We showed that the resistance, resilience, and elasticity of tritrophic food webs decreased with larger perturbation sizes and depended on the state of the system when the perturbation occurred. Importantly, we found that a more diverse food web was generally more resistant and resilient but its elasticity was context-dependent. Particularly, functional diversity reduced the probability of a regime shift toward a non-desirable alternative state. The basal-intermediate interaction consistently determined the robustness against a nutrient pulse despite the complex influence of the shape and type of the dynamical attractors. This relationship was strongly influenced by the diversity present and the third trophic level. Overall, using a food web model of realistic complexity, this study confirms the destructive potential of the positive feedback loop between biodiversity loss and robustness, by uncovering mechanisms leading to a decrease in resistance, resilience, and potentially elasticity as functional diversity declines.
The effects of exercise interventions on unspecific chronic low back pain (CLBP) have been investigated in many studies, but the results are inconclusive regarding exercise types, efficiency, and sustainability. This may be because the influence of psychosocial factors on exercise induced adaptation regarding CLBP is neglected. Therefore, this study assessed psychosocial characteristics, which moderate and mediate the effects of sensorimotor exercise on LBP. A single-blind 3-arm multicenter randomized controlled trial was conducted for 12-weeks. Three exercise groups, sensorimotor exercise (SMT), sensorimotor and behavioral training (SMT-BT), and regular routines (CG) were randomly assigned to 662 volunteers. Primary outcomes (pain intensity and disability) and psychosocial characteristics were assessed at baseline (M1) and follow-up (3/6/12/24 weeks, M2-M5). Multiple regression models were used to analyze whether psychosocial characteristics are moderators of the relationship between exercise and pain, meaning that psychosocial factors and exercise interact. Causal mediation analysis were conducted to analyze, whether psychosocial characteristics mediate the exercise effect on pain. A total of 453 participants with intermittent pain (mean age = 39.5 ± 12.2 years, f = 62%) completed the training. It was shown, that depressive symptomatology (at M4, M5), vital exhaustion (at M4), and perceived social support (at M5) are significant moderators of the relationship between exercise and the reduction of pain intensity. Further depressive mood (at M4), social-satisfaction (at M4), and anxiety (at M5 SMT) significantly moderate the exercise effect on pain disability. The amount of moderation was of clinical relevance. In contrast, there were no psychosocial variables which mediated exercise effects on pain. In conclusion it was shown, that psychosocial variables can be moderators in the relationship between sensorimotor exercise induced adaptation on CLBP which may explain conflicting results in the past regarding the merit of exercise interventions in CLBP. Results suggest further an early identification of psychosocial risk factors by diagnostic tools, which may essential support the planning of personalized exercise therapy.
Level of Evidence: Level I.
Clinical Trial Registration: DRKS00004977, LOE: I, MiSpEx: grant-number: 080102A/11-14. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00004977.
Supernova remnants (SNRs) are discussed as the most promising sources of galactic cosmic rays (CR). The diffusive shock acceleration (DSA) theory predicts particle spectra in a rough agreement with observations. Upon closer inspection, however, the photon spectra of observed SNRs indicate that the particle spectra produced at SNRs shocks deviate from the standard expectation. This work suggests a viable explanation for a softening of the particle spectra in SNRs. The basic idea is the re-acceleration of particles in the turbulent region immediately downstream of the shock. This thesis shows that at the re-acceleration of particles by the fast-mode waves in the downstream region can be efficient enough to impact particle spectra over several decades in energy. To demonstrate this, a generic SNR model is presented, where the evolution of particles is described by the reduced transport equation for CR. It is shown that the resulting particle and the corresponding synchrotron spectra are significantly softer compared to the standard case. Next, this work outlines RATPaC, a code developed to model particle acceleration and corresponding photon emissions in SNRs. RATPaC solves the particle transport equation in test-particle mode using hydrodynamic simulations of the SNR plasma flow. The background magnetic field can be either computed from the induction equation or follows analytic profiles. This work presents an extended version of RATPaC that accounts for stochastic re-acceleration by fast-mode waves that provide diffusion of particles in momentum space. This version is then applied to model the young historical SNR Tycho. According to radio observations, Tycho’s SNR features the radio spectral index of approximately −0.65. In previous modeling approaches, this fact has been attributed to the strongly distinctive Alfvénic drift, which is assumed to operate in the shock vicinity. In this work, the problems and inconsistencies of this scenario are discussed. Instead, stochastic re-acceleration of electrons in the immediate downstream region of Tycho’s SNR is suggested as a cause for the soft radio spectrum. Furthermore, this work investigates two different scenarios for magnetic-field distributions inside Tycho’s SNR. It is concluded that magnetic-field damping is needed to account for the observed filaments in the radio range. Two models are presented for Tycho’s SNR, both of them feature strong hadronic contribution. Thus, a purely leptonic model is considered as very unlikely. Additionally, to the detailed modeling of Tycho’s SNR, this dissertation presents a relatively simple one-zone model for the young SNR Cassiopeia A and an interpretation for the recently analyzed VERITAS and Fermi-LAT data. It shows that the γ-ray emission of Cassiopeia A cannot be explained without a hadronic contribution and that the remnant accelerates protons up to TeV energies. Thus, Cassiopeia A is found to be unlikely a PeVatron.
Geochemical processes such as mineral dissolution and precipitation alter the microstructure of rocks, and thereby affect their hydraulic and mechanical behaviour. Quantifying these property changes and considering them in reservoir simulations is essential for a sustainable utilisation of the geological subsurface. Due to the lack of alternatives, analytical methods and empirical relations are currently applied to estimate evolving hydraulic and mechanical rock properties associated with chemical reactions. However, the predictive capabilities of analytical approaches remain limited, since they assume idealised microstructures, and thus are not able to reflect property evolution for dynamic processes. Hence, aim of the present thesis is to improve the prediction of permeability and stiffness changes resulting from pore space alterations of reservoir sandstones.
A detailed representation of rock microstructure, including the morphology and connectivity of pores, is essential to accurately determine physical rock properties. For that purpose, three-dimensional pore-scale models of typical reservoir sandstones, obtained from highly resolved micro-computed tomography (micro-CT), are used to numerically calculate permeability and stiffness. In order to adequately depict characteristic distributions of secondary minerals, the virtual samples are systematically altered and resulting trends among the geometric, hydraulic, and mechanical rock properties are quantified. It is demonstrated that the geochemical reaction regime controls the location of mineral precipitation within the pore space, and thereby crucially affects the permeability evolution. This emphasises the requirement of determining distinctive porosity-permeability relationships
by means of digital pore-scale models. By contrast, a substantial impact of spatial alterations patterns on the stiffness evolution of reservoir sandstones are only observed in case of certain microstructures, such as highly porous granular rocks or sandstones comprising framework-supporting cementations. In order to construct synthetic granular samples a process-based approach is proposed including grain deposition and diagenetic cementation. It is demonstrated that the generated samples reliably represent the microstructural complexity of natural sandstones. Thereby, general limitations of imaging techniques can be overcome and various realisations of granular rocks can be flexibly produced. These can be further altered by virtual experiments, offering a fast and cost-effective way to examine the impact of precipitation, dissolution or fracturing on various petrophysical correlations.
The presented research work provides methodological principles to quantify trends in permeability and stiffness resulting from geochemical processes. The calculated physical property relations are directly linked to pore-scale alterations, and thus have a higher accuracy than commonly applied analytical approaches. This will considerably improve the predictive capabilities of reservoir models, and is further relevant to assess and reduce potential risks, such as productivity or injectivity losses as well as reservoir compaction or fault reactivation. Hence, the proposed method is of paramount importance for a wide range of natural and engineered subsurface applications, including geothermal energy systems, hydrocarbon reservoirs, CO2 and energy storage as well as hydrothermal deposit exploration.
The Big Five personality traits play a major role in student achievement. As such, there is consistent evidence that students that are more conscientious receive better teacher-assigned grades in secondary school. However, research often does not support the claim that students that are more conscientious similarly achieve higher scores in domain-specific standardized achievement tests. Based on the Invest-and-Accrue Model, we argue that conscientiousness explains to some extent why certain students receive better grades despite similar academic accomplishments (i.e., achieving similar scores in domain-specific standardized achievement tests). Therefore, the present study examines to what extent the relationship between student personality and teacher-assigned grades consists of direct as opposed to indirect associations (via subject-specific standardized test scores). We used a representative sample of 14,710 ninth-grade students to estimate these direct and indirect pathways in mathematics and German. Structural equation models showed that test scores explained between 8 and 11% of the variance in teacher-assigned grades in mathematics and German. The Big Five personality traits in students additionally explained between 8 and 10% of the variance in grades. Finally, the personality-grade relationship consisted of direct (0.02 | β| ≤ 0.27) and indirect associations via test scores (0.01 | β| ≤ 0.07). Conscientiousness explained discrepancies between teacher-assigned grades and students’ scores in domain-specific standardized tests to a greater extent than any of the other Big Five personality traits. Our findings suggest that students that are more conscientious may invest more effort to accomplish classroom goals, but fall short of mastery.
Background
Building on the Realistic Accuracy Model, this paper explores whether it is easier for teachers to assess the achievement of some students than others. Accordingly, we suggest that certain individual characteristics of students, such as extraversion, academic self-efficacy, and conscientiousness, may guide teachers' evaluations of student achievement, resulting in more appropriate judgements and a stronger alignment of assigned grades with students' actual achievement level (as measured using standardized tests).
Aims
We examine whether extraversion, academic self-efficacy, and conscientiousness moderate the relations between teacher-assigned grades and students' standardized test scores in mathematics.
Sample
This study uses a representative sample of N = 5,919 seventh-grade students in Germany (48.8% girls; mean age: M = 12.5, SD = 0.62) who participated in a national, large-scale assessment focusing on students' academic development.
Methods
We specified structural equation models to examine the inter-relations of teacher-assigned grades with students' standardized test scores in mathematics, Big Five personality traits, and academic self-efficacy, while controlling for students' socioeconomic status, gender, and age.
Results
The correlation between teacher-assigned grades and standardized test scores in mathematics was r = .40. Teacher-assigned grades more closely related to standardized test scores when students reported higher levels of conscientiousness (beta = .05, p = .002). Students' extraversion and academic self-efficacy did not moderate the relationship between teacher-assigned grades and standardized test scores.
Conclusions
Our findings indicate that students' conscientiousness is a personality trait that seems to be important when it comes to how closely mathematics teachers align their grades to standardized test scores.