Recent publications
Waterlogging is a significant stressor for crops, particularly in lowland regions where soil conditions exacerbate the problem. Waterlogged roots experience hypoxia, disrupting oxidative phosphorylation and triggering metabolic reorganization to sustain energy production. Here, we investigated the metabolic aspects that differentiate two soybean sister lines contrasting for waterlogging tolerance. After 11 days of waterlogging, roots of the tolerant line (PELBR15-7015C) modulated their fermentative metabolism by exporting key metabolites (lactate, malate, and succinate) to the shoot. These metabolites were metabolized in the leaves, supporting photosynthesis and facilitating sugar export to the roots, sustaining a root-shoot-root cycling process. In contrast, the sensitive line (PELBR15-7060) entered a quiescent state, depleting its carbon stock and accumulating protective metabolites. Our study reveals that long-term waterlogging tolerance is primarily achieved through lactate detoxification in the leaves, along with malate and succinate metabolism, enabling root metabolism to withstand hypoxia. This mechanism offers new insights into crop resilience under waterlogged conditions, with implications for modern agriculture as climate change intensifies the frequency and duration of such stress events.
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Wake vortex studies using Light Detection and Ranging (lidar) measurements constitute a decisive element for determining appropriate and efficient aircraft separations. Algorithms for characterizing the position and strength of wake vortices within lidar scans are available, for example the Radial Velocity (RV) method. However, due to the lack of a ground-truth from field measurements, no reliable accuracy bound for these algorithms could be given so far. Thus, we perform virtual lidar measurements via Large-Eddy-Simulation (LES) Lidar Simulators (LLS’s) employing high-fidelity LES’s of landing aircraft, where the position and strength of vortices is fully known. Lidar measurements are simulated to realistic detail by including spatial averaging along a Line of Sight and flow field distortions caused by the measurement geometry. Previous studies either focused on the wake vortex simulation or the lidar simulation, but never both aspects in full detail. Through simulations under various atmospheric conditions, the accuracy of the RV method can be defined as a 4% strength overestimation and 6% dislocation for vortices within an altitude range of below 2.5 initial vortex separations. Within a highly turbulent atmosphere the RV method performs worse. The main driver of RV method inaccuracy is revealed as the lack of modeling mirror vortices i.e. imaginary vortices caused by walls. This work enables rating the accuracy of studies employing the RV method realistically. Furthermore, the LLS’s allow generating a labeled dataset for evaluating further algorithms and developing new ones which may increase the data accuracy and thus reduce the effort of costly field measurements.
Objective. In preclinical research, in vivo imaging of mice and rats is more common than any other animal species, since their physiopathology is very well-known and many genetically altered disease models exist. Animal studies based on small rodents are usually performed using dedicated preclinical imaging systems with high spatial resolution. For studies that require animal models such as mini-pigs or New-Zealand White (NZW) rabbits, imaging systems with larger bore sizes are required. In case of hybrid imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI), clinical systems have to be used, as these animal models do not typically fit in preclinical simultaneous PET-MRI scanners. Approach. In this paper, we present initial imaging results obtained with the Hyperion IID PET insert which can accommodate NZW rabbits when combined with a large volume MRI RF coil. First, we developed a rabbit-sized image quality phantom of comparable size to a NZW rabbit in order to evaluate the PET imaging performance of the insert under high count rates. For this phantom, radioactive spheres with inner diameters between 3.95 and 7.86mm were visible in a warm background with a tracer activity ratio of 4.1 to 1 and with a total ¹⁸F activity in the phantom of 58MBq at measurement start. Second, we performed simultaneous PET-MR imaging of atherosclerotic plaques in a rabbit in vivo using a single injection containing ¹⁸F-FDG for detection of inflammatory activity, and Gd-ESMA for visualization of the aortic vessel wall and plaques with MRI. Main results. The fused PET-MR images reveal ¹⁸F-FDG uptake within an active plaques with plaque thicknesses in the sub-millimeter range. Histology showed colocalization of ¹⁸F-FDG uptake with macrophages in the aortic vessel wall lesions. Significance. Our initial results demonstrate that this PET insert is a promising system for simultaneous high-resolution PET-MR atherosclerotic plaque imaging studies in NZW rabbits.
Purpose
Single motherhood is associated with increased psychosocial risks, affecting both mothers and their minor children. However, little is known about the specific psychosocial impact of maternal cancer in single mothers (SMs). This study compared psychological burden, quality of life, specific problems, and parental concerns between SMs and partnered mothers (PMs) affected by cancer and caring for minor children.
Methods
Cross-sectional analysis of baseline data from a multicenter, non-randomized, controlled trial in Germany (Family-SCOUT). SMs and PMs affected by cancer were assessed for psychological burden (anxiety, depression, distress), quality of life, practical, family, emotional, spiritual/religious, and physical problems and parental concerns.
Results
A total of 54 SMs and 245 PMs were included. SMs reported more practical problems (p = 0.008, d = 0.44) and parental concerns than PMs (p = 0.011, d = 0.40). After controlling for demographic and clinical group differences, practical problems (p = 0.009, OR = 1.53) and parental concerns (p = 0.015, OR = 1.73) remained significantly associated with single motherhood. SMs and PMs did not differ in anxiety, depression, distress or quality of life. Overall, a large proportion of mothers reported clinically relevant elevated levels of anxiety (71.9%), depression (46.8%) and heightened distress (82.3%).
Conclusion
In this sample, the psychological burden of mothers with cancer who care for minor children did not differ based on whether they were parenting alone or together with a co-parent. However, SMs reported more practical problems and parental concerns than PMs, emphasizing the need for targeted support in practical problem-solving and child care for SMs.
Trial Registration Number: NCT04186923, 5. December 2019.
In many applications of cylindrical gearboxes, the reduction of noise from the gear mesh is an important criterion. In addition to electrified vehicle drives, gear excitations also play an important role in stationary applications such as wind turbines. When designing gearboxes, elastic surrounding structures or material combinations are increasingly being used in order to reduce mass and manufacturing costs. This has a decisive effect on the misalignment behavior of the cylindrical gears and the need for micro geometric corrections. At the same time, it must be taken into account that deviations can occur during the production of the micro geometries, which must be tolerated and have different effects on the NVH behavior.
The aim is to develop a method for the design of profile corrections for elastic environmental conditions and materials. It takes into account that the nominal design is subject to different manufacturing deviations, which have to be tolerated. This is done by means of a tolerance design mechanism that determines the best compromise between tolerance definition and manufacturing costs depending on assumptions for the relationship between manufacturing accuracy and resulting costs.
In the first step of the nominal design, the load-dependent misalignments are determined by FE-simulation. This is followed by an optimization of the micro geometric gear profile using a penetration calculation in order to optimize the excitations and improve the NVH behavior. An AI-supported substitute model of the excitation behavior of the entire gearbox is then determined. This is used to determine a suitable compromise between tolerance requirements and expected production costs using a metaheuristic optimization process. This is demonstrated using a cylindrical gearbox for electric cars. The calculation method presented enables the design of profile modifications and production tolerances using modern tools and shows further potential for noise reduction.
Wind power constitutes a significant share in the German energy mix, and future increase is forecast. About 20% of the levelized cost of electricity for wind power stem from maintenance and repair efforts. Wind turbine main bearings are a component especially prone to failure with a failure probability of up to 30% over the wind turbine design lifetime. The current commercially available generation of wind turbines exclusively use rolling element bearings as main bearings. Their exchange or repair is very elaborate as they require a dismantling of the rotor.
At present there are efforts within industry and science to explore plain bearings as a possible alternative to rolling element bearings as wind turbine main bearings. Segmented plain bearings promise reduced repair costs as their segments can be exchanged individually in case of damage or failure without dismantling of the rotor. Three such plain bearing concepts (HydroLa, FlexPad and Z‑Pad) were developed. FlexPad features stationary sliding segments with a flexible support structure. The FlexPad however, demonstrated challenges for upscaling towards multi-megawatt turbines regarding e.g. its maintenance. To address the maintenance challenge, the Z‑Pad was developed. Z‑Pad features its sliding segments on the rotating shaft. Thus, allowing for easier access of the sliding segments. As with the FlexPad bearing no standardised design process exists. However, a process can be developed analogues to the FlexPad design approach. The proposed design process follows the following steps parameter space definition, sampling, elasto-hydrodynamic simulations, surrogate model creation and lastly mathematical optimisation. This study aims to evaluate the underlying parameter set for the future design process. Results from a systematic sensitivity analysis are explored, highlighting the design influence of individual parameters. The study shows, that during design the global design parameters can no longer be considered concurrently with the parameters governing the bearings flexibility. Moreover, unnecessary design features are identified and removed.
As gearbox induced excitations can lead to acoustic abnormalities in wind turbines, one main target of the development process of gearboxes for wind turbines are low-excitation levels. To achieve this excitation-relevant characteristics such as stiffness variation and transmission error of the gears are optimized over large parameter search spaces. In this paper it is proven that Machine Learning (ML) methods can predict these features depending on the geometry and flank modifications of a gear stage’s gears. Regarding the need of data for application of ML methods, Datasets based on gear calculations are used for training purposes within this work. The underlying data structure enables the usage of efficient gradient-boosting methods to predict the gear meshing characteristics. Here, the high prediction performance is used to evaluate large parameter spaces. Outside the range of the geometric quantities covered by the data set, the validity of the developed models must be evaluated carefully.
Applying the developed methods to the optimization of the flank modifications of an existing gear stage shows the effectiveness of these techniques regarding the prediction of geometry influence on gear-meshing.
Subsequently, the process is validated by a test run of the gearbox whereby an optimized excitation-behavior of the gearbox can be expected, and results are presented here.
There is a great need for high-quality and comprehensive data in the energy sector. This data is collected and preprocessed at considerable expense and is not only required for research, but also by planning offices and other industries in connection with planning activities, such as the creation of municipal heat planning. The NEED ecosystem will accelerate these processes establishing an efficient, robust, and scalable energy data ecosystem. Heterogeneous energy-related data sources will be brought together and automatically linked consistently across different sectors as well as temporal and spatial levels. In this context, existing data sources will not be replaced but rather integrated into the NEED ecosystem as dedicated sources including a semantic description on how to utilize them. In addition to conventional data sources from the various planning levels, we envision a quality assessment scheme based on the FAIR criteria. In reality, we are often faced with missing data, too. To close this gap we explore data-driven, model-driven, AI-based, and tool-driven generation of synthetic data. These heterogeneous data sources will be interlinked using ontology modules which will be represented in a knowledge graph. Via a semantic API, queries will be generated to identify the required data sources, which will be orchestrated to provide the data needed. This will enable researchers, planners, and others including their tools to interact with the NEED ecosystem, while a tool proxy will be able to translate the resulting data into proprietary formats, required by some tools to operate. The NEED ecosystem is planned to be a robust, easy-to-maintain, and flexible infrastructure to enhance planning energy measures at different spatial levels and with different time horizons. We envision to evaluate our NEED approach for the transparent provision of data by integrating relevant data sources as microservices, definition and analysis of application scenarios in the planning domain, as well as the integration of various tools for different planning purposes. With these elements, we will be able to quantify the efficiency of data procurement and demonstrate the functionality of the approach using practical use cases.
It's now over two years since Julian F. Miller sadly passed away in February 2022 and lots of development has taken place within the graph-based genetic programming (GGP) community since then.
Continuous access to electronic health records will fuel the digital transformation of medicine. For data-sharing initiatives, the challenge lies in ensuring data access aligns with the interests of data holders. Federated data access authorization, where data remains controlled locally, may offer a solution to balance these interests. This paper reports on a digital health implementation of the federated data access authorization system used in the German National Emergency Department Data Registry. Using data from 2017 to 2024, we analyzed the system’s effectiveness in managing data access in a nationwide research network of 58 emergency departments. Facilitating access to more than 7.9 million records, 75% of data access queries were authorized within 15 days. The system also supports periodic queries, enabling recurring real-time access. Query volumes grew from 15 to over 23,000 by 2024, with completion rates of 86%. The system may thus serve as a blueprint for data-sharing initiatives worldwide.
The production of electrical components for vehicles, battery systems, and power electronics components using ultrasonic metal welding is now well established. The industrial applications take advantage of the large joining surface and the associated excellent electrical and thermal conduction properties. However, this also prevents the weld quality from being monitored based on resistance measurements. For this reason, mechanical properties are used as a substituting quality criterion for process optimization and control. The type of mechanical testing depends on the component geometry, resulting in different quality features not necessarily comparable to each other. The ultrasonic metal welding process takes place in successive phases, which are characterized by the varying relative movements of the parts to be joined and the welding tools, prone to changes of material hardness, surface cleanliness, and geometric deviations. In this study, we investigate the influence of different mechanical quality features as target parameters for quality prediction on prediction accuracy and robustness. For this purpose, we consider the joint formation and the mechanical strength over the welding time for different quality parameters, fracture behaviors, and joint geometries. Supervised machine-learning quality prediction models are developed for the respective processes. The predictions are based on external and internal welding machine sensors. In addition to the sensor configuration, the importance of the individual process phases as a data source for the prediction accuracy is also investigated. We show that the overall process predictability strongly depends on the quality of the quality feature itself. Combining different sensor signals improves prediction quality. Furthermore, an analysis of the first process phases is sufficient for a robust quality prediction.
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Aachen, Germany
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Dr. rer. nat. Dr. h. c. mult., Universitätsprofessor Ulrich Rüdiger
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