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Surface wetting can be simulated using a phase field approach which describes the continuous liquid-gas transition with the help of an order parameter. In this publication, wetting of non-planar surfaces is investigated based on a phase field model by Diewald et al. [1, 2]. Different scenarios of droplets on rough surfaces are simulated. The static equilibrium for those scenarios is calculated using an Allen-Cahn evolution equation. The influence of the surface morphology on the resulting contact angle is investigated while the width of the phase transition from liquid to gas is varied as a model parameter.
The great flexibility of direct laser writing (DLW) arises from the possibility to fabricate precise three-dimensional structures on very small scales as well as the broad range of applicable materials. However, there is still a vast number of promising materials, which are currently inaccessible requiring the continuous development of novel photoresists. Herein, a new bio-sourced resist is reported that uses the monomeric unit of chitin, N-acetyl-D-glucosamine, paving the way from existing hydrogel resists based on animal carbohydrates to a new class of non-hydrogel ones. In addition, it is shown that the combined use of two photoinitiators is advantageous over the use of a single one. In this approach, the first photoinitiator is a good two-photon absorber at the applied wavelength, while the second photoinitiator exhibits poor two-photon absorbtion abilities, but is better suited for cross-linking of the monomer. The first photoinitiator absorbs the light acting as a sensitizer and transfers the energy to the second initiator, which subsequently forms a radical and initializes the polymerization. This sensitization effect enables a new route to utilize reactive photointiators with a small two-photon absorption cross section for DLW without changing their chemical structure.
Starting from [(η5-cyclopentadienyl)(η6-phenyl)iron(II)]imidazole, dicationic imidazolium salts were prepared by N-alkylation. Reaction of these compounds with basic metal precursors such as mesityl copper(I) or palladium(II) acetate led to mono respectively dicationic transition metal NHC complexes (NHC=N-heterocyclic carbene). Transmetalation using the copper(I) complexes opened up the access to NHC gold(I) compounds. PEPPSI-type NHC complexes of palladium(II) and platinum(II) were prepared by offering a neutral pyridine ligand to the transition metal center. A rhodium(I) NHC complex was accessible by deprotonation of the dicationic imidazolium precursor and subsequent treatment with [(COD)Rh(μ2-Cl)]2 (COD=1,5-cyclooctadiene). The new NHC complexes were investigated by means of NMR spectroscopy, mass spectrometry as well as single crystal X-ray structure analysis. Both, the palladium(II) containing PEPPSI-type and the gold(I) complex, were investigated for their catalytic properties in typical model reactions such as cyclization reactions, Suzuki coupling and cyanation. In addition, a selenium adduct was synthesized in order to study the electronic properties of the underlying ligand backbone. Based on the chemical shift in the 77Se NMR spectrum, it is evident that these NHC ligands possess rather poor π-acidity.
Janus materials are anisotropic nano- and microarchitectures with two different faces consisting of distinguishable or opposite physicochemical properties. In parallel with the discovery of new methods for the fabrication of these materials, decisive progress has been made in their application, for example, in biological science, catalysis, pharmaceuticals, and, more recently, in battery technology. This Minireview systematically covers recent and significant achievements in the application of task-specific Janus nanomaterials as heterogeneous catalysts in various types of chemical reactions, including reduction, oxidative desulfurization and dye degradation, asymmetric catalysis, biomass transformation, cascade reactions, oxidation, transition-metal-catalyzed cross-coupling reactions, electro- and photocatalytic reactions, as well as gas-phase reactions. Finally, an outlook on possible future applications is given.
The folding of newly synthesized polypeptides requires the coordinated action of molecular chaperones. Prokaryotic cells and the chloroplasts of plant cells possess the ribosome-associated chaperone trigger factor, which binds nascent polypeptides at their exit stage from the ribosomal tunnel. The structure of bacterial trigger factor has been well characterized and it has a dragon-shaped conformation, with flexible domains responsible for ribosome binding, peptidyl-prolyl cis–trans isomerization (PPIase) activity and substrate protein binding. Chloroplast trigger-factor sequences have diversified from those of their bacterial orthologs and their molecular mechanism in plant organelles has been little investigated to date. Here, the crystal structure of the plastidic trigger factor from the green alga Chlamydomonas reinhardtii is presented at 2.6 Å resolution. Due to the high intramolecular flexibility of the protein, diffraction to this resolution was only achieved using a protein that lacked the N-terminal ribosome-binding domain. The eukaryotic trigger factor from C. reinhardtii exhibits a comparable dragon-shaped conformation to its bacterial counterpart. However, the C-terminal chaperone domain displays distinct charge distributions, with altered positioning of the helical arms and a specifically altered charge distribution along the surface responsible for substrate binding. While the PPIase domain shows a highly conserved structure compared with other PPIases, its rather weak activity and an unusual orientation towards the C-terminal domain points to specific adaptations of eukaryotic trigger factor for function in chloroplasts.
An improved route for the highly stereoselective synthesis of (Z)-2-oxyenamides is reported. The desired products can be accessed in only three steps from aminoacetaldehyde dimethyl acetal as common, readily available building block in a highly modular fashion. The improved procedure has been applied to the synthesis of various acylated and sufonylated oxyenamides. Mechanistic and theoretical studies provide a conclusive rationale for the observed stereoselectivities.
Scaled boundary isogeometric analysis (SB-IGA) describes the computational domain by proper boundary NURBS together with a well-defined scaling center; see [5]. More precisely, we consider star convex domains whose domain boundaries correspond to a sequence of NURBS curves and the interior is determined by a scaling of the boundary segments with respect to a chosen scaling center. However, providing a decomposition into star shaped blocks one can utilize SB-IGA also for more general shapes. Even though several geometries can be described by a single patch, in applications frequently there appear multipatch structures. Whereas a C0 continuous patch coupling can be achieved relatively easily, the situation becomes more complicated if higher regularity is required. Consequently, a suitable coupling method is inevitably needed for analyses that require global C1 continuity.In this contribution we apply the concept of analysis-suitable G1 parametrizations [2] to the framework of SB-IGA for the C1 coupling of planar domains with a special consideration of the scaling center. We obtain globally C1 regular basis functions and this enables us to handle problems such as the Kirchhoff-Love plate and shell, where smooth coupling is an issue. Furthermore, the boundary representation within SB-IGA makes the method suitable for the concept of trimming. In particular, we see the possibility to extend the coupling procedure to study trimmed plates and shells.The approach was implemented using the GeoPDEs package [1] and its performance was tested on several numerical examples. Finally, we discuss the advantages and disadvantages of the proposed method and outline future perspectives.
Print path-dependent contact temperature dependency for 3D printing using fused filament fabrication
(2022)
This paper focuses on the effects of different time spans and thus different contact temperatures when a molten strand contacts an adjacent already solidified strand in a plane during 3D printing with fused filament fabrication. For this purpose, both the manufacturing parameters and the geometry of the component are systematically varied and the effect on morphology and mechanical properties is investigated. The results clearly show that even with identical printing parameters, the transitions between the individual layers are much more visible with long time spans until fusion and lead to low mechanical properties. In contrast, short spans lead to hardly visible welds and high mechanical properties. Transferring the findings to different component sizes ultimately verifies that the average temperature at the time of contact between the already solidified and the currently deposited strand is decisive for component quality. In order to generate high component qualities, this finding must therefore be taken into account in the future in the path generation strategy, i.e., in so-called slicing.
Methods for predicting Henry's law constants Hij are important as experimental data are scarce. We introduce a new machine learning approach for such predictions: matrix completion methods (MCMs) and demonstrate its applicability using a data base that contains experimental Hij values for 101 solutes i and 247 solvents j at 298 K. Data on Hij are only available for 2661 systems i + j. These Hij are stored in a 101 × 247 matrix; the task of the MCM is to predict the missing entries. First, an entirely data-driven MCM is presented. Its predictive performance, evaluated using leave-one-out analysis, is similar to that of the Predictive Soave-Redlich-Kwong equation-of-state (PSRK-EoS), which, however, cannot be applied to all studied systems. Furthermore, a hybrid of MCM and PSRK-EoS is developed in a Bayesian framework, which yields an unprecedented performance for the prediction of Hij of the studied data set.
Based on conservation of resources theory, this paper examines the mediating mechanisms in the relationship between digital affordances and employee corporate entrepreneurship participation likelihood. Findings from an experimental study with 207 employees show a statistically significant and positive indirect effect of digital affordances on employee corporate entrepreneurship participation likelihood through employee-perceived information technology support for innovation and a statistically significant and—contrary to our expectations—positive indirect effect through employee-perceived work overload. Results are corroborated by insights from in-depth interviews with senior managers. They provide support for digital affordances as action potentials that are associated with resource gains that in turn foster employee corporate entrepreneurship participation likelihood.
Overexpression of the vacuolar sugar transporter TST1 in Arabidopsis leads to higher seed lipid levels and higher total seed yield per plant. However, effects on fruit biomass have not been observed in crop plants like melon, strawberry, cotton, apple, or tomato with increased tonoplast sugar transporter (TST) activity. Thus, it was unclear whether overexpression of TST in selected crops might lead to increased fruit yield, as observed in Arabidopsis. Here, we report that constitutive overexpression of TST1 from sugar beet in the important crop species Camelina sativa (false flax) resembles the seed characteristics observed for Arabidopsis upon increased TST activity. These effects go along with a stimulation of sugar export from source leaves and not only provoke optimised seed properties like higher lipid levels and increased overall seed yield per plant, but also modify the root architecture of BvTST1 overexpressing Camelina lines. Such mutants grew longer primary roots and showed an increased number of lateral roots, especially when developed under conditions of limited water supply. These changes in root properties result in a stabilisation of total seed yield under drought conditions. In summary, we demonstrate that increased vacuolar TST activity may lead to optimised yield of an oil-seed crop species with high levels of healthy ω3 fatty acids in storage lipids. Moreover, since BvTST1 overexpressing Camelina mutants, in addition, exhibit optimised yield under limited water availability, we might devise a strategy to create crops with improved tolerance against drought, representing one of the most challenging environmental cues today and in future.
Living systems incessantly engage in the regulation of their cellular processes to fulfill their biological functions. Beyond development-related adjustments or cell cycle oscillations, environmental fluctuations compel the system to reorganize metabolic pathways, structural components, or molecular repair and reconstitution mechanisms. These responses manifest across diverse temporal scales, necessitating an intricate regulatory orchestration. Time series experiments have become increasingly popular for charting the chronological order and elucidating the underlying mechanisms. In the era of high-throughput technologies, the majority of cellular molecules can be analyzed in one fell swoop, generating a comprehensive snapshot of the status quo of most present molecules. Methodological advancements also permit the monitoring not only of molecular abundances but also the functional status of transcripts and proteins. However, due to the still high efforts associated with such experiments, the number of measured time points and the replication of measurements remains limited. Resulting datasets contain signals from thousands of molecules, yet they are sparse in temporal resolution and are often imprecise due to biological variability and technical measurement inaccuracies.
This thesis explores the complexities arising from the examination of short time series data and introduces pioneering tools that offer fresh insights into the realm of biological time series analysis. The broad spectrum of analytic possibilities ranges from a molecule-centric investigation of individual time courses to a holistic aggregation of the system’s response to its main characteristics. By creating a modeling framework that applies domain-specific constraints, time-course signals can be transformed from a series of discrete data points into a continuous curve. These curves align with current biological conjectures about molecule kinetics being smooth and devoid of superfluous oscillations. Noise present at individual time points is judiciously accounted for during curve fitting, mitigating the impact of time points with high variance on the curve. Subsequent classification is based on the features of these curves (extreme points and inflection points) and ensures a reduction in data amount and complexity. Succinct labels assigned to each molecule's kinetics encapsulate the signal's most notable features. Besides this modeling approach, an innovative enrichment strategy is introduced, that is independent of prior data partitioning and capable of segregating the temporal response into its thermodynamically relevant components. This approach allows for a continuous assessment of each molecule's contribution to these components, obviating the need for exclusive allocation. The application of various analytical approaches to heat acclimation experiments in Chlamydomonas highlights the relevance and potential of time series experiments and specifically tailored analysis techniques. The integration of different system levels has led to the identification of regulatory peculiarities, such as an increased correlation between transcripts and corresponding proteins during acclimation responses. These and other insights may herald new avenues of research that could ultimately enhance plant robustness in the face of increasing environmental perturbations.
The growing popularity of time series experiments necessitates dedicated analytical approaches that empower researchers and analysts to decipher patterns, discern trends, and unravel the underlying structures within the data, facilitating predictions and the derivation of meaningful conclusions that could potentially build bridges between the interweaved systems levels.
Cyanobacteria oxygenated Earth's atmosphere ~2.4 billion years ago, during the Great Oxygenation Event (GOE), through oxygenic photosynthesis. Their high iron requirement was presumably met by high levels of Fe(II) in the anoxic Archean environment. We found that many deeply branching Cyanobacteria, including two Gloeobacter and four Pseudanabaena spp., cannot synthesize the Fe(II) specific transporter, FeoB. Phylogenetic and relaxed molecular clock analyses find evidence that FeoB and the Fe(III) transporters, cFTR1 and FutB, were present in Proterozoic, but not earlier Archaean lineages of Cyanobacteria. Furthermore Pseudanabaena sp. PCC7367, an early diverging marine, benthic strain grown under simulated Archean conditions, constitutively expressed cftr1, even after the addition of Fe(II). Our genetic profiling suggests that, prior to the GOE, ancestral Cyanobacteria may have utilized alternative metal iron transporters such as ZIP, NRAMP, or FicI, and possibly also scavenged exogenous siderophore bound Fe(III), as they only acquired the necessary Fe(II) and Fe(III) transporters during the Proterozoic. Given that Cyanobacteria arose 3.3–3.6 billion years ago, it is possible that limitations in iron uptake may have contributed to the delay in their expansion during the Archean, and hence the oxygenation of the early Earth.
In selective laser melting (SLM), a powdered material is locally melted by a laser and, after cooling, forms a coherent solid structure that enables the production of complex geometries with various materials. The process involves extreme heating and cooling rates and, thus, large temperature gradients, which lead to anisotropic material properties on the macroscopic scale and, in the worst case, reduced mechanical properties. In order to reliably predict the final mechanical component properties, simulations can be performed at different time and length scales. Enormous computational resources are often required to perform such simulations. In order to transform these simulations into suitable surrogate models, the generated data must be compressed and evaluated in a suitable way. This paper shows first preliminary work and a possible new data description of such simulations.
Tracking waterborne microplastic (MP) in urban areas is a challenging task because of the various sources and transport pathways involved. Since MP occurs in low concentrations in most wastewater and stormwater streams, large sample volumes need to be captured, prepared, and carefully analyzed. The recent research in urban areas focused mainly on MP emissions at wastewater treatment plants (WWTPs), as obvious entry points into receiving waters. However, important transport pathways under wet-weather conditions are yet not been investigated thoroughly. In addition, the lack of comprehensive and comparable sampling strategies complicated the attempts for a deeper understanding of occurrence and sources. The goal of this paper is to (i) introduce and describe sampling strategies for MP at different locations in a municipal catchment area under dry and wet-weather conditions, (ii) quantify MP emissions from the entire catchment and two other smaller ones within the bigger catchment, and (iii) compare the emissions under dry and wet-weather conditions. WWTP has a high removal rate of MP (>96%), with an estimated emission rate of 189 kg/a or 0.94 g/[population equivalents (PEQ · a)], and polyethylene (PE) as the most abundant MP. The specific dry-weather emissions at a subcatchment were ≈30 g/(PEQ · a) higher than in the influent of WWTP with 23 g/(PEQ · a). Specific wet-weather emissions from large sub-catchment with higher traffic and population densities were 1952 g/(ha · a) higher than the emissions from smaller catchment (796 g/[ha · a]) with less population and traffic. The results suggest that wet-weather transport pathways are likely responsible for 2–4 times more MP emissions into receiving waters compared to dry-weather ones due to tire abrasion entered from streets through gullies. However, more investigations of wet-weather MP need to be carried out considering additional catchment attributes and storm event characteristics.
Model-based prediction is becoming increasingly important to meet the ever-increasing demands on manufacturing. In grinding, the prediction of the process forces and the generated surface by physical models are particularly important.Since cooling lubricants are almost always used on an industrial scale, the grinding model, developed at our institut, must be extended to include this component. Therefore, in order to implement cooling lubricants into the FEM-based model, it is first necessary to investigate the behaviors and effects of cooling lubricants in real experiments. Various influencing factors such as the scratching speed of individual abrasive grains in interaction with cooling lubricants need to be investigated. However, the existing physical grinding model is not limited exclusively to the prediction of the resulting forces. It is also supposed to be able to qualitatively predict the expected resulting surface of the workpiece. Hence, this paper will focus on the topographic characteristics that can occur in the scratch test due to different cooling lubricants and scratching speeds.Based on real experiments on a test rig for such scratch tests, it has been shown that different scratch speeds have a negligible influence on the topographical nature and expression of a scratch. In contrast, however, there is a direct influence of cooling lubricants on the topographic properties. This effect is additionally influenced by the viscosity of the cooling lubricant used.
In gravity separators, also known as settlers, two immiscible liquid phases separate due to differences in density. In extraction mixer-settler units, a dispersion needs to be separated within the separator unit. In order to overcome the hitherto purely experimental design, a knitted mesh adapted model as well as an automated test facility were developed in this work, which easily enable a scale-up to industrial units. An automation allows for a controlled investigation of knitted meshes as coalescing aids in settlers, and this was achieved via photo-optical probes with an optimized image analysis technique. It overcomes the limitations of neuronal network training based on manually annotating images using computer-generated image data. Therefore, the new methodology and setup are explained in detail, and the derivation and application of a new model to design separators with knitted meshes as coalescing aid is presented and compared to experimental results using meshes of different structures and materials. Finally, case studies and scale-up are discussed.
Within a biorefinery platform several conversion steps such as pretreatment, saccharification, fermentation and downstream processing are necessary to obtain the final bio-based product(s) from lignocellulosic biomass. The structural composition of the biomass, especially the lignin content, determines the necessary pretreatment steps. To obtain sugar monomers, the hydrolysis of lignocellulosic biomass is an essential step. This work examines the impact of different pretreatments on the sugar release during biocatalysis. Even without prior pretreatment the biocatalysis of low lignin biomass achieves glucose yields of up to 93 %, while the biocatalysis of high lignin biomass requires an upstream hydrothermal procedure to achieve a glucose yield of 74
Distributed message-passing systems have become ubiquitous and essential for our daily lives. Hence, designing and implementing them correctly is of utmost importance. This is, however, very challenging at the same time. In fact, it is well-known that verifying such systems is algorithmically undecidable in general due to the interplay of asynchronous communication (messages are buffered) and concurrency. When designing communication in a system, it is natural to start with a global protocol specification of the desired communication behaviour. In such a top-down approach, the implementability problem asks, given such a global protocol, if the specified behaviour can be implemented in a distributed setting without additional synchronisation. This problem has been studied from two perspectives in the literature. On the one hand, there are Multiparty Session Types (MSTs) from process algebra, with global types to specify protocols. Key to the MST approach is a so-called projection operator, which takes a global type and tries to project it onto every participant: if successful, the local specifications are safe to use. This approach is efficient but brittle. On the other hand, High-level Message Sequence Charts (HMSCs) study the implementability problem from an automata-theoretic perspective. They employ very few restrictions on protocol specifications, making the implementability problem for HMSCs undecidable in general. The work in this thesis is the first to formally build a bridge between the world of MSTs and HMSCs. To start, we present a generalised projection operator for sender-driven choice. This allows a sender to send to different receivers when branching, which is crucial to handle common communication patterns from distributed computing. Despite this first step, we also show that the classical MST projection approach is inherently incomplete. We present the first formal encoding from global types to HMSCs. With this, we prove decidability of the implementability problem for global types with sender-driven choice. Furthermore, we develop the first direct and complete projection operator for global types with sender-driven choice, using automata-theoretic techniques, and show its effectiveness with a prototype implementation. We are the first to provide an upper bound for the implementability problem for global types with sender-driven (or directed) choice and show it to be in PSPACE. We also provide a session type system that uses the results from our projection operator. Last, we introduce protocol state machines (PSMs) – an automata-based protocol specification formalism – that subsume both global types from MSTs and HMSCs with regard to expressivity. We use transformations on PSMs to show that many of the syntactic restrictions of global types are not restrictive in terms of protocol expressivity. We prove that the implementability problem for PSMs with mixed choice, which requires no dedicated sender for a branch but solely all labels to be distinct, is undecidable in general. With our results on expressivity, this answers an open question: the implementability problem for mixed-choice global types is undecidable in general.
Sound localization involves information analysis in the lateral superior olive (LSO), a conspicuous nucleus in the mammalian auditory brainstem. LSO neurons weigh interaural level differences (ILDs) through precise integration of glutamatergic excitation from the cochlear nucleus (CN) and glycinergic inhibition from the medial nucleus of the trapezoid body (MNTB). Sound sources can be localized even during sustained perception, an accomplishment that requires robust neurotransmission. Virtually nothing is known about the sustained performance and the temporal precision of MNTB–LSO inputs after postnatal day (P)12 (time of hearing onset) and whether acoustic experience guides development. Here we performed whole-cell patch-clamp recordings to investigate neurotransmission of single MNTB-LSO fibres upon sustained electrical stimulation (1–200 Hz/60 s) at P11 and P38 in wild-type (WT) and deaf otoferlin (Otof) knock-out (KO) mice. At P11, WT and KO inputs performed remarkably similarly. In WTs, the performance increased drastically between P11 and P38, e.g. manifested by an 8 to 11-fold higher replenishment rate (RR) of synaptic vesicles and action potential robustness. Together, these changes resulted in reliable and highly precise neurotransmission at frequencies ≤100 Hz. In contrast, KO inputs performed similarly at both ages, implying impaired synaptic maturation. Computational modelling confirmed the empirical observations and established a reduced RR per release site for P38 KOs. In conclusion, acoustic experience appears to contribute massively to the development of reliable neurotransmission, thereby forming the basis for effective ILD detection. Collectively, our results provide novel insights into experience-dependent maturation of inhibitory neurotransmission and auditory circuits at the synaptic level.
Development of a simple substitute model to describe the normal force of fluids in narrow gaps
(2023)
Fluids in narrow gaps are employed frequently in many applications. The motivation for their use is diverse and ranges from hydrodynamic lubrication in plain bearings to the transport of hard particles into the working gap for the purpose of machining workpiece surfaces in lapping processes. Depending on the focus of the analysis, it may be useful to investigate the entire pressure field or to calculate only individual quantities. For example, in sophisticated simulations it may be of interest to know the resulting force of a fluid as a function of the external system state in order to describe its damping characteristics. Especially for the simulation of flows in narrow gaps, the Reynolds equation is a convenient choice, which, in contrast to the more general Navier-Stokes equations, can lead to considerable savings in computational time because no three-dimensional discretization is required, but only a two-dimensional discretization. However, if not a highly detailed pressure field is of interest, but only simple relations such as the resulting force as a function of distance and velocity, and if this relation to be evaluated many times for different parameter combinations over a wide range of values, the use of a robust substitute model is a good choice. This article deals with the creation of such a substitute model based on the Reynolds equation taking cavitation into account.
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high-dimensional input space) is quickly exceeded. To explain a model's decisions, black-box methods have been proposed that provide either non-linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What-If'-reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high-dimensional hyperplanes—2D-slices of the high-dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real-world datasets and illustrate that it allows identification of instance-based decision boundary structures and can answer multi-dimensional ‘What-If'-questions, thereby identifying counterfactual scenarios visually.
In the last decades, the phase field method has drawn much attention for its application in fracture mechanics because it offers a simple unified framework for crack propagation. The core idea of phase field models for fracture is to introduce a continuous scalar field representing the discontinuous crack. Recently, a phase field model for fatigue has been proposed along this path. The fatigue failure differs from the other fracture scenarios since cracks only occur after a considerable number of load cycles. As fracturing happens, changes of the material microstructure are involved, which causes the evolution of the structural configuration. Thus, a new mathematical description not based on traditional spatial coordinates but the material manifold is desired, which will serve as an elegant analysis tool to understand the energetic forces for crack propagation. Configurational forces are a suitable choice for this purpose, as they describe the energetic driving forces associated with phenomena changing the material itself. In this work, we present a phase field model for fatigue. Furthermore, the phase field fatigue model is analyzed within the concept of configurational forces, which provides a straightforward way to understand the phase field simulations of fatigue fracture.
Citizen conceptions of democracy and support for artificial intelligence in government and politics
(2022)
How much do citizens support artificial intelligence (AI) in government and politics at different levels of decision-making authority and to what extent is this AI support associated with citizens’ conceptions of democracy? Using original survey data from Germany, the analysis shows that people are overall sceptical toward using AI in the political realm. The findings suggest that how much citizens endorse democracy as liberal democracy as opposed to several of its disfigurations matters for AI support, but only in high-level politics. While a stronger commitment to liberal democracy is linked to lower support for AI, the findings contradict the idea that a technocratic notion of democracy lies behind greater acceptance of political AI uses. Acceptance is higher only among those holding reductionist conceptions of democracy which embody the idea that whatever works to accommodate people's views and preferences is fine. Populists, in turn, appear to be against AI in political decision making.
CFD-DEM Simulation of Superquadric Cylindrical Particles in a Spouted Bed and a Rotor Granulator
(2023)
The fluidization behavior of cylindrical particles in a spouted bed was first investigated experimentally using a camera setup. The obtained average spouted bed height was used to evaluate the accuracy of different drag models in CFD-DEM simulations with the superquadric approach to model the particle shape. The drag model according to Sanjeevi et al. showed the best agreement. With this model, cylindrical particles were simulated in a rotor granulator and the particle dynamics were compared with the fluidization of volume equivalent spherical particles.
Drawing on theorising on digital technologies as external enablers of entrepreneurial activities and an interactionist perspective on corporate entrepreneurship, this article examines the relationship between digital technology support and employee intrapreneurial behaviour. We propose that management support for innovation as an organisational characteristic and intrapreneurial self-efficacy as an individual characteristic moderate this relationship. Findings from a metric conjoint experiment with 1360 decisions nested within 85 employees showed that support by social media, support by collaborative technologies, and support by intelligent decision support systems were significant predictors of employee intrapreneurial behaviour. However, the relative impact of support by these digital technologies varied with different levels of management support for innovation and intrapreneurial self-efficacy.
Edit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small-scale perturbations. For trees with 𝒪(n) nodes, we describe an 𝒪(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition-independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real-world examples to demonstrate its practicality and utility.
The direct regioselective C−H-functionalization of simple, unfunctionalized pyridines is considered a long-standing challenge in heterocyclic chemistry. Herein, we report a novel one-pot protocol for the C4-selective sulfonylation of pyridines via triflic anhydride (Tf2O) activation, base-mediated addition of a sulfinic acid salt, and subsequent elimination/re-aromatization. Contrary to previous approaches employing tailored blocking groups, positional selectivity can be controlled by using N-methylpiperidine as simple, readily available external base. This method offers a highly modular and streamlined access to C4-sulfonylated pyridines.
Algorithms increasingly govern people's lives, including through rapidly spreading applications in the public sector. This paper sheds light on acceptance of algorithms used by the public sector emphasizing that algorithms, as parts of socio-technical systems, are always embedded in a specific social context. We show that citizens' acceptance of an algorithm is strongly shaped by how they evaluate aspects of this context, namely the personal importance of the specific problems an algorithm is supposed to help address and their trust in the organizations deploying the algorithm. The objective performance of presented algorithms affects acceptance much less in comparison. These findings are based on an original dataset from a survey covering two real-world applications, predictive policing and skin cancer prediction, with a sample of 2661 respondents from a representative German online panel. The results have important implications for the conditions under which citizens will accept algorithms in the public sector.
The development of algorithmic differentiation (AD) tools focuses mostly on handling floating point types in the target language. Taping optimizations in these tools mostly focus on specific operations like matrix vector products. Aggregated types like std::complex are usually handled by specifying the AD type as a template argument. This approach provides exact results, but prevents the use of expression templates. If AD tools are extended and specialized such that aggregated types can be added to the expression framework, then this will result in reduced memory utilization and improve the timing for applications where aggregated types such as complex number or matrix vector operations are used. Such an integration requires a reformulation of the stored data per expression and a rework of the tape evaluation process. We will demonstrate the overheads on a synthetic benchmark and show the improvement when aggregated types are handled properly by the expression framework of the AD tool.
A stereoselective synthesis of isoindolo[2,1-a]quinolin-11(5H)-ones containing three contiguous stereogenic centers is described. This Lewis-acid mediated reaction of enamides with N-aryl-acylimines affords the desired fused heterocyclic isoindolinones in high yields and diastereoselectivities. Scope and limitations of this method are discussed. The stereochemical outcome of this transformation indicates a stepwise reaction pathway.
The measurement of self-diffusion coefficients using pulsed-field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well-established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high-field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model-based approach for the estimation of self-diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model-based approach, the self-diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self-diffusion coefficients estimated from high-field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self-diffusion coefficients, even in complex mixtures.
We compute three-dimensional displacement vector fields to estimate the deformation of microstructural data sets in mechanical tests. For this, we extend the well-known optical flow by Brox et al. to three dimensions, with special focus on the discretization of nonlinear terms. We evaluate our method first by synthetically deforming foams and comparing against this ground truth and second with data sets of samples that underwent real mechanical tests. Our results are compared to those from state-of-the-art algorithms in materials science and medical image registration. By a thorough evaluation, we show that our proposed method is able to resolve the displacement best among all chosen comparison methods.
Coastal port-industrial areas are becoming increasingly significant due to urban shrinkage, population
decline, and climate change. To address social and economic issues and enhance climate resilience, it
is crucial to anticipate urban shrinkage in both stable and growing coastal areas that are undergoing
economic transformation. Urban planning can better understand the dynamics of planning for urban
shrinkage and climate resilience, as port-industrial areas have a large economic impact on nearby
coastal communities.
This dissertation examines the long-term implications of urban shrinkage in coastal port-industrial
areas in the context of climate change and sea level rise in England. The research problem is that
current urban policy does not adequately address the challenges of urban shrinkage and climate
resilience in these areas. The research questions are: What are the population changes in local areas
in England? What effect does population decline have on changing urbanisation patterns in older
industrial areas? What type of adaptation efforts were made in North East Lincolnshire, England, and
Bremerhaven, Germany, in response to the 2013 tidal surge, and how did this affect urban
shrinkage?
The dissertation applies an integrated concept of Shrinkage-Resilience as a framework for analysis.
The methodology includes a review of existing models and frameworks, as well as case studies of
international and local contexts. The findings suggest that between 2013-2019, 68% of older
industrial areas (including coastal ports) in England are undergoing changing urbanisation patterns
relative to population, land use, and green belt areas, and are key areas for urban policy, such as the
Levelling Up agenda. One of the areas, North East Lincolnshire is discussed and compared to
Bremerhaven. These examples demonstrate the link between Shrinkage-Resilience approaches and
their practical implementation in coastal port-industrial areas affected by urban shrinkage.
This research advances the scientific practice of urban planning and policy-making for shrinking cities
by introducing the approach of Shrinkage-Resilience, which emphasises the importance of
considering long-term social, economic, and environmental impacts in urban shrinkage contexts. This
approach is crucial in the transition to a more sustainable and inclusive society, where the welfare of
present and future generations, the environment, and economic development are taken into
account. The dissertation provides recommendations for urban planning to incorporate policy
changes for shrinking cities and coastal port-industrial areas worldwide, to include disaster risk
reduction and climate change adaptation approaches.
To increase situational awareness of the crane operator, the aim of this thesis is to develop a vision-based deep learning object detection from crane load-view using an adaptive perception in the construction area. Conventional worker detection methods are based on simple shape or color features from the worker's appearances. Nonetheless, these methods can fail to recognize the workers who do not wear the protective gears. To find out an image representation of the object from the top view manually or handcrafted feature is crucial. We, therefore, employed deep learning methods to automatically learn those features.
To yield optimal results, deep learning methods require mass amount of data.
Due to the data deficit especially in the construction domain, we developed the photorealistic world to create the data in addition to our samples collected from the real construction area. The simulated platform does not benefit only from diverse data types, but also concurrent research development which speeds up the pipeline at a low cost.
Our research findings indicate that the combination of synthetic and real training samples improved the state-of-the-art detector. In line with previous studies to bridge the gap between synthetic and real data, the results of preprocessed synthetic images are substantially better than using the raw data by approximately 10%.
Finding the right deep learning model for load-view detection is challenging.
By investigating our training data, it becomes evident that the majority of bounding box sizes are very small with a complex background.
In addition, we gave the priority to speed over accuracy based on the construction safety criteria. Finally, RetinaNet is chosen out of the three primary object detection models.
Nevertheless, the data-driven detection algorithm can fail to handle scale invariance, especially for detectors whose input size is changed in an extremely wide range.
The adaptive zoom feature can enhance the quality of the worker detection.
To avoid further data gathering and extensive retraining, the proposed automatic zoom method of the load-view crane camera supports the deep learning algorithm, specifically in the high scale variant problem. The finite state machine is employed for control strategies to adapt the zoom level to cope not only with inconsistent detection but also abrupt camera movement during lifting operation. Consequently, the detector is able to detect a small size object by smooth continuous zoom control without additional training.
The adaptive zoom control not only enhances the performance of the top-view object detection but also reduces the interaction of the crane operator with camera system, reducing the risk of fatality during load lifting operation.
We study the sensor fault estimation and accommodation problems in a data-driven \(\mathcal{H}_\infty\) setting, leading to a data-driven sensor fault-tolerant control scheme. First, we formulate the fault estimation problem as a finite-horizon minimax \(\mathcal{H}_\infty\)-optimization problem in a data-driven setup, whose solution yields the fault estimate. The estimated fault is then used for output compensation. This compensated output and the experimental input are used to achieve certain control objectives in a data-driven \(\mathcal{H}_\infty\) setting. Next, the data-driven \(\mathcal{H}_\infty\) fault estimation and control problems are solved using a subspace predictor-based approach. Finally, the proposed algorithm is applied to the steering subsystem of the remotely operated underwater vehicle.
Opposition parties under minority governments find themselves in a fundamental dilemma. They are competing with other parties, including the government, for electoral support while also having a common responsibility to make stable government work. This dilemma is especially pronounced for opposition parties signing support agreements with the government. While not formally in a coalition, they nonetheless publicly commit to supporting a government. They may thus be concerned about losing distinctiveness and have an interest in strategically timing cooperation with the minority government. The present paper tests whether this is the case using data on opposition party voting on committee proposals from 23 years of Swedish minority governments between 1991 and 2018. The findings indicate that support parties are less likely to support the government towards the beginning and end of the election cycle, that is, when public attention is intense – a pattern that is not observable for other opposition parties.
With direct laser writing micro structures can be manufactured by solidifying a photo resist when the laser beam triggers a photochemical reaction in the focal voxel. We have used direct laser writing to fabricate a thermally actuated microgripper, which can move its two cantilever like arms to grip micro-objects. One cantilever consists thereby of two strips with different coefficients of thermal expansion such that both cantilevers bends towards each other for an increasing temperature like a welded bimetal.This work investigates the impact of each cantilever's geometry on the gripping performance of the micro gripper theoretically. The tip deflection of the gripper is calculated by the analytical model of Timoshenko's theory of elasticity. After fabricaiton of the microgripper, its gripping performance is observed under the microscope while heated by a heating element.
The quality of risk reports: Integrating requirement levels of standard setters into text analysis
(2021)
The intention of this paper is to shed light on the analysis of financial disclosure through the integration of requirement levels. This in return will lead to the development of a general applicable evaluation methodology based on Bloom's taxonomy system. Therefore, it will be possible to explicitly consider the relevance of the given information. To underline the appropriateness of our method, we combine the requirement levels with a qualitative content analysis. Based on the German accounting standard DRS 20, we clarify the respective application of the requirement levels in the context of the qualitative content analysis. Hence, we will discuss the limitations of our developed approach. In addition, we analyze further areas of application in the context of qualitative analysis of financial disclosure. All things considered, it is evident that our chosen approach, through the integration of a taxonomy system, contributes to the validity of established text analyzing methods.
Firn describes the interstage product between snow and ice in cold regions of the earth, where annual snow fall exceeds the amount of snow melting. The continuing accumulation of snow leads to its densificiation due to overburden stress until it becomes ice. In the field of glaciology various attempts on simulating firn densification have been made and new models are still developed, as the knowledge of the firn column's density structure allows important derivations.
The presented study reassesses a model description for low density firn based on the process of grain boundary sliding presented by Alley in 1987 [1] using an optimisation approach. By comparing simulation results to 159 measured firn density profiles from Greenland and Antarctica it finds a possible additional dependency of the constitutive relation on the mean surface mass balance. This result is interpreted as an insufficient description of the stress regime.
Disorder and photonics have long been seen as natural adversaries and designers of optical systems have often driven systems to perfection by minimizing deviations from the ideal design. Especially in the field of photonic crystals and metamaterials but also for optical circuits, disorder has been avoided as a nuisance for many years. However, starting from the very robust structural colors found in nature, scientists learn to analyze and tailor disorder to achieve functionalities beyond what is possible with perfectly ordered or ideal systems alone. This review article covers theoretical and materials aspects of tailored disorder as well as experimental results. Furthermore selected examples are highlighted in greater detail, for which the intentional use of disorder adds additional functionality or provides novel functionality impossible without disorder.
A novel method for the synthesis of nitro fatty acids (NFAs), an intriguing class of endogenously occurring lipid mediators, is reported. This one-pot procedure enables the controlled and stereoselective construction of nitro fatty acids from a simple set of common building blocks in a highly facile manner. Thereby, this methodology offers a streamlined, highly modular access to naturally occurring nitro fatty acids as well as non-natural NFA derivatives.
Sulfones play a pivotal role in modern organic chemistry. They are highly versatile building blocks and find various applications as drugs, agrochemicals, or functional materials. Therefore, sustainable access to this class of molecules is of great interest. Herein, the goal was to provide a summary on recent developments in the field of sustainable sulfone synthesis. Advances and existing limitations in traditional approaches towards sulfones were reviewed on selected examples. Furthermore, novel emerging technologies for a more sustainable sulfone synthesis and future directions were discussed.
A concept for the quantification of cooperative effects in transition-metal complexes is presented. It is demonstrated for a series of novel N,N- (mononuclear) and C,N-coordinated homo- and heterometallic binuclear complexes based on the (2-dimethylamino)-4-(2-pyrimidinyl)pyrimidine ligand, which are accessible by applying roll-over cyclometallation. These iridium-, platinum-, and palladium-containing compounds are investigated with respect to their absorption and fluorescence spectra. The cooperative effects in the electronic absorptions, i. e., the energetic shifts between mononuclear and dinuclear complexes, and free ligands are analyzed on the basis of the lowest energy π-π* transitions and compared to calculated data, obtained from TD-DFT calculations. Furthermore the corresponding fluorescence spectra are presented and analyzed with respect to the concept of cooperativity.
Aquatic habitats are closely linked to the adjacent riparian area. Fluxes of nutrients, energy and matter through emerging aquatic insects are a key component of the aquatic subsidy to terrestrial systems. In fact, adult insects serve as high-quality prey for riparian predators. Stressors impacting the aquatic subsidy can thus translate to consequences for the receiving terrestrial food web, while mechanistic knowledge is extremely limited. Against this background, this thesis aimed at (i) assessing the impact of a model stressor specifically targeting insect emergence, that is the mosquito control agent Bacillus thuringiensis var. israelensis, on quantity, temporal dynamics and (ii) quality of emerging aquatic insects. For this purpose, outdoor floodplain pond mesocosms (n = 6) were employed. Since emergence is, in most cases, no point event but occurs over a longer period emergence was monitored over 3.5 months. The model stressor, i.e., Bti applied three times during spring at 2.88 × 10^9 ITU/ha, shifted the emergence time of aquatic insects, especially of non-biting midges (Diptera: Chironomidae), by ten days with a 26% reduced peak, while the nutrient content was not altered. On this basis, (ii) the propagation of the effects in aquatic subsidy emergence to riparian predators was investigated. Stable isotope analyses were used to assess the diet of a model predator, that is the web-building riparian spider Tetragnatha extensa. Results suggested changes in the composition of the spider’s diet to replace missing Chironomidae by other aquatic and terrestrial prey organisms pointing to further negative consequences. Finally, the thesis aimed at (iii) the understanding of processes underlying an altered emergence of aquatic subsidy mainly consisting of chironomids. Using a laboratory-based test design, populations of Chironomus riparius (n = 6) were assessed for their sensitivity towards Bti under different food qualities (high and low nutritious) before and after a long-term (six months) Bti exposure. Signs of phenotypic adaptation were observed in emergence time and nutrient content over multiple generations, resulting in changes in chironomids’ quantity and quality as food source. Overall, it can be concluded that direct and indirect effects of an aquatic stressor, as well as the adaptive response to it, can alter ecosystems at different levels, including individual, population and community level. Furthermore, this thesis highlights the importance of a temporal perspective when investigating the impact of aquatic stressors beyond ecosystem boundaries. It illustrates potential bottom-up effects on riparian predators through altered emergence of aquatic insects, feeding our understanding of meta-ecosystems and how stressors and their effects are transferred across systems. These insights will support efforts to protect and conserve natural ecosystems.
In nanobiotechnology, viral nanoparticles have come into focus as interesting nano building blocks. In this context, the formation of 2D and 3D structures is of particular interest. Herein, the creation of defined 2D patterns of an icosahedral plant virus, the tomato bushy stunt virus (TBSV), by means of different techniques is reported on: the top-down lithography ebeam and focused ion beam (FIB) as well as the bottom-up fluidic force microscope (FluidFM) approach. The obtained layer structures are imaged by scanning force and scanning electron microscopy. The data show that a defined 2D structure can successfully be created either top down by FIB or bottom up by FluidFM. Electron beam lithography is not able to remove viruses from the substrate under the chosen conditions. FIB has an advantage if larger areas covered with viruses combined with smaller areas without being desired. FluidFM is advantageous if only small areas with viruses are required. A further benefit is that the uncovered areas are not affected. The pattern formation in FluidFM is influenced not only by the spotting parameters, but in particular by the drying process. Deegan and Marangoni effects are shown to play a role if the spotted droplets are not very small.
A highly diastereoselective one-pot synthesis of the 1,3-diamino-2-alcohol unit bearing three continuous stereocenters is described. This method utilizes 2-oxyenamides as a novel type of building block for the rapid assembly of the 1,3-diamine scaffold containing an additional stereogenic oxygen functionality at the C2 position. A stereoselective preparation of the required (Z)-oxyenamides is reported as well.
In the present work, microfibrillar composites (MFCs) consisting of polypropylene (PP) and poly(ethylene terephthalate) (PET) were successfully produced by melt extrusion and cold stretching. The resulting filaments were then printed using fused filament fabrication. The morphological results demonstrate that the highly oriented PET fibrils after stretching are still well preserved in the printed components. Since the printing process defines the alignment of the fibrils in the final component the fibers can be perfectly adapted to the load paths. Comparative analyses of the mechanical properties reveal that the PET fibrils act as an effective reinforcement in the 3D printed components, resulting in the superior mechanical performance of the PP/PET MFCs compared to a PP/PET blend and a neat PP. Due to the combination of material and innovative processing, the study opens up a new way of using the morphology-based enormous potential of polymer fibers for lightweight, cost-effective and recyclable full polymer solutions in compact components.
Turbulence models, which are a means to fix the closure problem arising from Reynolds averaging of Navier-Stokes equations, are economical stop-gaps but suffer from accuracy issues. Modifying turbulence models by incorporating corrections in their functional form is one approach to improve their accuracy. We estimate correction functionals for the Spalart - Allmaras turbulence model, based on an inverse problem with PDE constraints emphasizing the issue of regularization.