Technische Universität Kaiserslautern
  • Kaiserslautern, Rheinland-Pfalz, Germany
Recent publications
We describe algorithms to compute splitting fields and towers of radical extensions in which a polynomial splits, without using polynomial factorisation in towers or constructing any field properly containing the constructed field, instead extending Galois group computations for this task. We also describe the computation of geometric Galois groups (monodromy groups) and their use in computing absolute factorizations.
The non-destructive determination of internal forces in bars for existing building structures is a forward-looking challenge for the construction industry. The application of validated techniques would not only allow the detection of overloads or structural changes according to the load-bearing behavior, it would also allow the verification of the original structural analysis. Based on an experimental modal analysis, tried and tested methods to determine the tensile forces of cables in the operating condition have existed for many years. Due to the slenderness of the cables, even normal forces have a large influence on their natural frequencies: The higher the tensile forces, the higher the geometrical system stiffness, which increases natural frequencies. An equivalent effect occurs when considering load-bearing elements subjected to compression, whereby larger compressive forces lead to a reduction in the geometrical system stiffness and thereby to a reduction in natural frequencies. While various methods have been experimentally investigated for tension cables and tension rods, only a few elaborations are known with respect to structural compression rods. This research presents a system identification method based on the experimental determination of the modal parameters of a compression steel bar. Using an iterative optimization procedure, the design parameters of a basic model including the compressive force are iteratively adjusted until the best possible agreement between the experimental measurements and a theoretical analysis is found. For this purpose, a deviation function is formulated and an evolutional algorithm — in this case facilitating a Particle Swarm Optimization — is used to solve the optimization problem. The Particle Swarm Optimization is an innovative metaheuristic algorithm that allows searching for the global minimum even for complicated nonlinear functions. The method is experimentally validated on slender steel beams with varying compressive forces. Laboratory results from three different steel profiles combined with two different bearing conditions each are presented. Therefore, six specimens were constructed and tested with four different force levels each. The average deviation over all 24 tests between the optimized force and the actual force directly measured by a load cell was determined as 7.4%.
Images and objects depend on the perception and the point of view. The focus of our research had been on exploring these relationships with experiments in various projects with a small group of students. Theory on perception and gestalt psychology as well as publications on anamorphoses formed the background of this research and determined the creations. The theme included also reflections on the extent to which perception can be included in a design or the designed space or object is transformed by the point of view. Possibilities arise in the expansion of space through images or the creation of objects that enable different perceptions depending on the point of view. The experiments studied effective spatial representations, merging of image and real object as well as the creation of images through elements in space. The anamorphoses are not just artistic gimmicks, but they are created by optical-geometric rules and thus provide thought-provoking impulses for reflection on perception and design.
How can we bring more flexibility into sitting? This was the initial question of the seminar “PerForm” of the department Descriptive Geometry and Perspective, Faculty of Architecture of the TU Kaiserslautern (TUK) in 2021. The course started with a workshop providing the students with crucial competencies in digital design, presentation and fabrication tools. After analyzing related studies and projects, the students then developed their own conceptual drafts based on their acquired knowledge and skills. As a central part of the seminar, the research involved designing kinetic and flexible furniture that encourages motion and agility using parametric design tools. In addition, the use of robotic fabrication techniques building 1:1 wooden prototypes was a part of the complex task. Finally, an exhibition was held on campus including the presentation of the final furniture, mockups, documentations and posters. This approach combining topics such as geometry of movement, construction techniques and graphic presentations shows possibilities for a future-proof teaching concept.
The visual appearance of an object is a function of stimulus properties as well as perceptual biases imposed by the observer. The context-specific trade-off between both can be measured accurately in a perceptual judgment task, involving grouping by proximity in ambiguous dot lattices. Such grouping depends lawfully on a stimulus parameter of the dot lattices known as their aspect ratio (AR), whose effect is modulated by a perceptual bias representing the preference for a cardinal orientation. In two experiments, we investigated how preceding context can lead to bias modulation, either in a top-down fashion via visual working memory (VWM) or bottom-up via sensory priming. In Experiment 1, we embedded the perceptual judgment task in a change detection paradigm and studied how the factors of VWM load (complexity of the memory array) and content (congruency in orientation to the ensuing dot lattice) affect the prominence of perceptual bias. A robust vertical orientation bias was observed, which was increased by VWM load and modulated by congruent VWM content. In Experiment 2, dot lattices were preceded by oriented primes. Here, primes regardless of orientation elicited a vertical orientation bias in dot lattices compared to a neutral baseline. Taken together, the two experiments demonstrate that top-down context (VWM load and content) effectively controls orientation bias modulation, while bottom-up context (i.e., priming) merely acts as an undifferentiated trigger to perceptual bias. These findings characterize the temporal context sensitivity of Gestalt perception, shed light on the processes responsible for different perceptual outcomes of ambiguous stimuli, and identify some of the mechanisms controlling perceptual bias.
The addition of flavorings to food and beverages provides practically unlimited opportunities for innovation, for maintaining and enhancing palatability, and is one essential element of a stable supply of nutritious consumer products. A safety evaluation by the Flavor and Extract Manufacturers Association (FEMA) Expert Panel provides a pathway for flavor producers and users to achieve regulatory authority to use for substances under the conditions of intended use as a flavoring. This chapter describes the factors that contribute to the safety assessment process that is conducted by the Expert Panel, and provides examples of specific flavorings and types of flavorings that are considered. The chapter also describes future issues and opportunities likely to be encountered within the context of the FEMA generally recognized as safe assessment of flavorings.
Our modern food chain may contain natural toxins from plants, either due to their natural presence in the plant-derived food or in the raw material used for food production or because these constituents are added during the regular production process, or present as contaminants. The present chapter presents an overview of situations where natural toxins from plants may raise concern, illustrated by examples of relevant structural features, toxic modes of action, their safety or risk assessment, and potential regulations related to their occurrence in the modern food chain. It is concluded that the safety concerns in part arise from the fact that botanicals and botanical preparations are considered food and thus a priori not subject to premarket evaluation and/or quality control for their safety in use. The chapter ends with a discussion presenting existing data gaps and research directions.
Large-scale energy storage is viewed as a key complementary technology in a power system fed by a large share of intermittent renewable energies (RE). However, subsidies for RE – a well-intended market intervention – may distort price signals, thereby adversely undermining the profitability of energy storages, and thus, adequate investment incentives. This study provides novel causal estimates supporting this notion, using an econometric instrumental-variables framework and data on Austrian pumped storages, operating in the German–Austrian electricity market, characterized by a large share of generously subsidized RE. The findings show that RE significantly depress storage profitability and that further deployment of RE will intensify this effect. This may pose an obstacle against adequate investment in bulk energy storage capacity. Moreover, the results indicate that intensifying carbon pricing would significantly counteract the problem via a market-based price signal. This study contributes to the general debate on the design and effects of environmental regulation and particularly shows that a non-market-based policy for a green technology may adversely affect complementary technologies.
Seniority-zero geminal wavefunctions are known to capture bond-breaking correlation. Among this class of wavefunctions, Richardson–Gaudin states stand out as they are eigenvectors of a model Hamiltonian. This provides a clear physical picture, clean expressions for reduced density matrix (RDM) elements, and systematic improvement (with a complete set of eigenvectors). Known expressions for the RDM elements require the computation of rapidities, which are obtained by first solving for the so-called eigenvalue based variables (EBV) and then root-finding a Lagrange interpolation polynomial. In this paper, we obtain expressions for the RDM elements directly in terms of the EBV. The final expressions can be computed at the same cost as the rapidity expressions. Therefore, except, in particular, circumstances, it is entirely unnecessary to compute rapidities at all. The RDM elements require numerically inverting a matrix, and while this is usually undesirable, we demonstrate that it is stable, except when there is degeneracy in the single-particle energies. In such cases, a different construction would be required.
Within this work, we report the results of nuclear inelastic scattering experiments of the low-spin phase of the iron(II) mononuclear SCO complex Fe[HBpz 3 ] 2 and density functional theory based calculations performed on a model molecule of the complex. We show that the calculated partial density of vibrational states based on the structure of a single iron(II) center which is linked by three pyrazole rings to borat is in good accordance with the experimentally obtained ⁵⁷ Fe-pDOS and assign the molecular vibrations to the prominent optical phonons.
Edges are image locations where the gray value intensity changes suddenly. They are among the most important features to understand and segment an image. Edge detection is a standard task in digital image processing, solved, for example, using filtering techniques. However, the amount of data to be processed grows rapidly and pushes even supercomputers to their limits. Quantum computing promises exponentially lower memory usage in terms of the number of qubits compared to the number of classical bits. In this paper, we propose a hybrid method for quantum edge detection based on the idea of a quantum artificial neuron. Our method can be practically implemented on quantum computers, especially on those of the current noisy intermediate-scale quantum era. We compare six variants of the method to reduce the number of circuits and thus the time required for the quantum edge detection. Taking advantage of the scalability of our method, we can practically detect edges in images considerably larger than reached before.
Background Neurotypical individuals categorize items even during ultra-rapid presentations (20 ms; see Thorpe et al. Nature 381: 520, 1996). In cognitively able autistic adults, these semantic categorization processes may be impaired and/or may require additional time, specifically for the categorization of atypical compared to typical items. Here, we investigated how typicality structures influence ultra-rapid categorization in cognitively able autistic and neurotypical male adults. Methods Images representing typical or atypical exemplars of two different categories (food/animals) were presented for 23.5 vs. 82.3 ms (short/long). We analyzed detection rates, reaction times, and the event-related potential components dN150, N1, P2, N2, and P3 for each group. Results Behavioral results suggest slower and less correct responses to atypical compared to typical images. This typicality effect was larger for the category with less distinct boundaries (food) and observed in both groups. However, electrophysiological data indicate a different time course of typicality effects, suggesting that neurotypical adults categorize atypical images based on simple features (P2), whereas cognitively able autistic adults categorize later, based on arbitrary features of atypical images (P3). Conclusions We found evidence that all three factors under investigation — category, typicality, and presentation time — modulated specific aspects of semantic categorization. Additionally, we observed a qualitatively different pattern in the autistic adults, which suggests that they relied on different cognitive processes to complete the task.
Traditionally, control strategies are applied to automate switchable electrochromic glazing systems (EC) to save energy and provide comfort for occupants indoors. In addition, the plants’ minimum requirements and the consequences of active shading on the supplemental artificial lighting for plants should be considered when designers want to embrace Biophilic design. This paper introduces a simulation workflow to evaluate the impact of shading activation on both human and plant requirements year-round using combined climate-based daylight (Radiance) and building energy simulation tool (TRNSYS). Finally, the simulated total electricity demand for supplemental lighting for plants in a prototypical office room in temperate climate condition are presented and discussed under different control strategies.
To achieve national and international climate goals, the greenhouse gas emissions from the building sector must be reduced. One option is the use of renewable energies, which must be stored. Building parts with thermal activation have the potential to store heat. As those building parts feature new active energetic functionalities – active heat storage and heating – they are called multifunctional building parts (MBP). To investigate the potential of MBP, a thermal model is deployed and verified. The model is used to examine the influence of the distance between thermal activation and the MBP’s inside.
The precise regulation of synaptic connectivity and function is essential to maintain neuronal circuits. Here, we show that the Drosophila pseudo-kinase Madm/NRBP1 (Mlf-1-adapter-molecule/nuclear-receptor-binding protein 1) is required presynaptically to maintain synaptic stability and to coordinate synaptic growth and function. Presynaptic Madm mediates these functions by controlling cap-dependent translation via the target of rapamycin (TOR) effector 4E-BP/Thor (eukaryotic initiation factor 4E binding protein/Thor). Strikingly, at degenerating neuromuscular synapses, postsynaptic Madm induces a compensatory, transsynaptic signal that utilizes the presynaptic homeostatic potentiation (PHP) machinery to offset synaptic release deficits and to delay synaptic degeneration. Madm is not required for canonical PHP but induces a neurodegeneration-specific form of PHP and acts via the regulation of the cap-dependent translation regulators 4E-BP/Thor and S6-kinase. Consistently, postsynaptic induction of canonical PHP or TOR activation can compensate for postsynaptic Madm to alleviate functional and structural synaptic defects. Our results provide insights into the molecular mechanisms underlying neurodegeneration-induced PHP with potential neurotherapeutic applications.
The bulk viscosity, a transport coefficient in the Navier-Stokes equation, is often neglected in the continuum mechanics of Newtonian fluids. Recently, however, the role of the bulk viscosity is highlighted in the area of surface and interface-related phenomena, in systematic model up-scaling and as an important quantity for the interpretation of acoustic sensor data. The prediction of the bulk viscosity usually employs molecular dynamics and the Green-Kubo linear response theory, which is used to sample transport properties in general from molecular trajectories. Since simulations are usually carried out at specified state points in concert with the evaluation of other thermodynamic properties, the role of thermostats in molecular dynamics needs to be explored systematically. In this work, we carefully investigate the role of thermostatting schemes and numerical implementations of the Green-Kubo formalism, in particular in the canonical ensemble, using two popular water force field models. It turns out that the sampling of the bulk and shear viscosities is a delicate challenge since details of thermostatting and numerical subtleties may have an influence on the results beyond statistical uncertainties. Based on the present findings, we conclude with hints on how to construct robust sampling in the canonical ensemble for the bulk viscosity.
Lichens and mosses play important functional roles in all terrestrial ecosystems, particularly in tundra and drylands. As with all taxa, to maintain their current niche in a changing climate, lichens and mosses will have to migrate. However, there are no published estimates of future habitat suitability or necessary rates of migration for members of these groups at the global scale. Lichens and mosses. Global. Using global occurrence data, we conducted ensemble distribution models in the ‘biomod2’ R package, parameterised with a range of climatic, land use and soil variables, to estimate current and future (2100) habitat suitability in 16 abundant species of lichen and moss. Without considering dispersal limitation, suitable area was forecast to expand for eight species and decline for four species. For species with predominantly boreo‐arctic distributions, suitable area typically declined at the temperate range edge and expanded across the High Arctic. Future suitable area available to dryland‐adapted species generally declined overall, likely relating to the desiccation‐tolerant physiology of lichens and mosses. The average migration rates required for species to disperse into new suitable habitat ranged from 1.7 (Placidium squamulosum) to 9.0 km year−1 (Syntrichia ruralis), although most species will need to migrate >16 km year−1 to completely fill their potential future suitable habitat. For mosses and lichens, as with all species, migration will be an important part of the adjustment to a warmer climate, but realisation of these potential migrations will require both rare dispersal events and habitat that is suitable in non‐climatic dimensions. Current evidence on dispersal in these groups suggests that these geographical shifts may be unlikely to be realised without intervention, especially in landscapes that are highly modified by humans.
Liver cancer is one of the most frequent tumor entities worldwide, which is causally linked to viral infection, fatty liver disease, life-style factors and food-borne carcinogens, particularly aflatoxins. Moreover, genotoxic plant toxins including phenylpropenes are suspected human liver carcinogens. The phenylpropene methyleugenol (ME) is a constituent of essential oils in many plants and occurs in herbal medicines, food, and cosmetics. Following its uptake, ME undergoes Cytochrome P450 (CYP) and sulfotransferase 1A1 (SULT1A1)-dependent metabolic activation, giving rise to DNA damage. However, little is known about the cellular response to the induced DNA adducts. Here, we made use of different SULT1A1-proficient cell models including primary hepatocytes that were treated with 1′-hydroxymethyleugenol (OH-ME) as main phase I metabolite. Firstly, mass spectrometry showed a concentration-dependent formation of N ² -MIE-dG as major DNA adduct, strongly correlating with SULT1A1 expression as attested in cells with and without human SULT1A1. ME-derived DNA damage activated mainly the ATR-mediated DNA damage response as shown by phosphorylation of CHK1 and histone 2AX, followed by p53 accumulation and CHK2 phosphorylation. Consistent with these findings, the DNA adducts decreased replication speed and caused replication fork stalling. OH-ME treatment reduced viability particularly in cell lines with wild-type p53 and triggered apoptotic cell death, which was rescued by pan-caspase-inhibition. Further experiments demonstrated mitochondrial apoptosis as major cell death pathway. ME-derived DNA damage caused upregulation of the p53-responsive genes NOXA and PUMA , Bax activation, and cytochrome c release followed by caspase-9 and caspase-3 cleavage. We finally demonstrated the crucial role of p53 for OH-ME triggered cell death as evidenced by reduced pro-apoptotic gene expression, strongly attenuated Bax activation and cell death inhibition upon genetic knockdown or pharmacological inhibition of p53. Taken together, our study demonstrates for the first time that ME-derived DNA damage causes replication stress and triggers mitochondrial apoptosis via the p53-Bax pathway.
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input–output pairs to train a neural network (NN) on it. We are able to learn the variables’ tendencies achieving an average $ {R}^2 $ score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model.
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5,591 members
Reiner Hartenstein
  • Department of Computer Science
Michael Fröhlich
  • Department of Science of Sport
Sven Panis
  • Center for Cognitive Science: Experimental Psychology Unit
Gottlieb-Daimler-Str., 67653, Kaiserslautern, Rheinland-Pfalz, Germany
Head of institution
Prof. Dr. Arnd Poetzsch-Heffter