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Theoretical calculations to identify and design transition metal-based additives for hydrogen storage materials
No full textThis study demonstrates the successful design of transition metal boride-based additives to enhance the hydrogen absorption and desorption kinetics of hydrogen storage materials. Density functional theory (DFT) was used to predict a range of boride compounds, with (Ta:Ti)B2 and (Nb:Ti)B2 identified as promising candidates. In particular, the Nb1/2Ti1/2B2 and Ta1/2Ti1/2B2 compositions significantly improve the kinetic properties of the 2LiH-MgB2 (LiMgB) system. When a small amount of these additives is incorporated into LiMgB, its kinetics are
improved twice in comparison to the undoped material while maintaining stable reversibility. This substantial improvement is attributed to the presence of Nb1/2Ti1/2B2 and Ta1/2Ti1/2B2 nanoparticles, which act as heterogeneous nucleation sites for MgB2. The study highlights how computational methods can accelerate the design and discovery of optimal additive compositions for hydrogen storage, minimizing the need for extensive experimental testing
Charakterisierung und Bewertung von freischwebenden Magnetantrieben für gerührte Bioreaktoren
No full textA portable compiler-runtime approach for scalability prediction
No full textHighly scalable parallel applications can efficiently solve expensive computational problems when run on a large number of compute nodes. However, selecting the optimal number of nodes for a compute job of a given size is non-trivial, and allocating too few or too many nodes may not yield the expected performance. Knowing the scaling behavior of an application in advance enables us, for example, to make optimal use of the available hardware resources. We introduce a novel, portable approach to predict the scalability of parallel applications written in modern high-level programming models. We propose a predictive compiler-runtime framework based on Celerity, a task-based distributed runtime system that enables executing SYCL codes on clusters. The framework targets a broad range of computing systems, from CPU to GPU clusters, and proposes a model that combines machine learning, communication modeling and DAG heuristics. Experimental results on two large-scale clusters, JUWELS and Marconi-100, show accurate scalability prediction of unseen single and multi-task applications
Impact of identifier normalization on vulnerability detection techniques
No full textThis study examines the impact of identifier normalization on software vulnerability detection using three approaches: static application security testing (SAST), specialized machine learning (ML) models, and Large Language Models (LLM). Using the BigVul dataset of vulnerabilities in C/C++ projects, the research evaluates the performance of these methods under normalized (generalized variables / functions names) and their original conditions. SAST tools such as Flawfinder and CppCheck exhibit limited effectiveness (F1 ∼ scores 0.1) and are unaffected by normalization. Specialized ML models, such as LineVul, achieve high F1 scores on nonnormalized data (F1 ∼ 0.9) but suffer significant performance drops when tested on normalized inputs, highlighting their lack of generalizability. In contrast, LLMs such as Llama3, although underperforming in their pre-trained state, show substantial improvement after fine-tuning, achieving robust and consistent results across both normalized and non-normalized datasets. The findings suggest that while SAST tools are less effective, fine-tuned LLMs hold strong potential for scalable and generalized vulnerability detection. The study recommends further exploration of hybrid approaches that combine ML models, LLMs, and traditional tools to enhance accuracy and adaptability in diverse scenarios
Development of an additively manufactured head and neck phantom for computed tomography studies
No full textAdditive manufacturing (AM) offers significant potential for the design of medical phantoms used in quality assurance for medical imaging and treatment planning. This study presents the design and fabrication of a head and neck phantom for computed tomography (CT) quality assurance. Appropriate infill densities of Polylactic acid (PLA) were selected to achieve tissue-equivalent CT values, enabling the integration of many anatomical structures in the head and neck region. A bone surrogate material was incorporated post-processing to achieve high density values that are unachievable with common 3D printing materials. CT validation confirmed the phantom’s ability to replicate appropriate Hounsfield unit (HU), demonstrating its suitability for imaging-based assessments. This phantom provides a reproducible and customizable solution for treatment verification in head and neck cancer therapies
Establishing a single source of truth for avionics platform verification through UCoF
No full textThe increasing complexity of avionics development requires new approaches to manage workload and improve efficiency. Early and continuous validation has emerged as a solution to reduce unnecessary iterations by using limited system information for simulations from the earliest stages. However, simultaneous hardware and software development means validation must occur without the final hardware or software. While additional validation steps initially increase workload, they enhance system confidence earlier in the process. A streamlined workflow is essential to minimize this burden. The Universal Configuration Format (UCoF) addresses these challenges by providing a single-source-of-truth database for all development stages. Instead of merging common data (i.e., targetindependent data like signal data types) and target-specific data (e.g., hardware pins), UCoF separates them, linking specific information through extensions. This enables efficient configuration of various test environments - virtual, hybrid, and hardware without being restricted by proprietary tools. UCoF supports varying levels of information granularity, accommodating different software and hardware maturity levels with minimal additional workload. It facilitates functional integration (basic simulations), hybrid integration (hardware-in-the-loop testing), and emulation integration (full platform partitioning). By ensuring traceability, UCoF streamlines change management and revision control, crucial during for the development process. Additionally, UCoF's open format allows seamless automation and integration into existing toolchains. Bidirectional data transformation ensures platform updates are reflected across environments, enhancing transparency. This paper demonstrates the advantages of UCoF over proprietary formats in a virtual environment, showcasing how it reduces configuration workload and improves avionics development efficiency
Prediction of on-board consumption: generating synthetic data for machine learning to optimize catering weight
No full textThere is a great potential for reducing CO2 emissions resulting from passenger aircraft cabin. In addition to further lowering the power consumption and reducing the mass of large monuments and seats, it is above all the amount of catering that can improve the CO2 balance. The environmental impact of overloading, as well as the disposal and incineration of catering waste makes it necessary to organize the catering process more effectively. In our today’s digital world, there is still no information about the quantities consumed and the subsequent disposal of catering on board airplanes. Real world catering on the consumption are practically non-existent and represent a major challenge for the development of machine learning (ML) -based predictive models for the actual amount of meals and beverages required. This research paper focuses on overcoming the challenges posed by unavailability of in-flight catering data and enabling on-board consumption prediction. To this end, synthetic datasets were generated based on information such as statistics on consumer age, food choices, nationality, flight details, and other catering-relevant parameters. To generate the datasets closer to realism, two regions have been chosen to differentiate passenger meals and beverage preferences: A flight from Europe to east (India) and a flight to the west (USA). Utilizing the synthetic datasets, ML models were developed to predict in-flight consumption per flight, facilitating optimized catering orders and minimizing aircraft catering overload and waste, thereby reducing CO2 emissions per flight and contributing positively to environmental sustainability. The models are trained and validated using historical synthetic data from various flights journeys to ensure reliability and robustness. Based on the generated synthetic datasets, this study shows that a good prediction of catering demand that avoids overstocking of meals and beverages can be achieved. It is expected, that future access to authentic catering data on a larger scale can be used for improving and validating the synthetic data generation concept and will enhance the authenticity of synthetic datasets, resulting in precise predictions
How born-sustainable and sustainability-driven companies contribute to the SDGS
No full textThe principal values and drivers of born-sustainable companies are built into the core of their business models and are crucial to achieve some of the critical SDGs. In this chapter, we analyze how born-sustainable and sustainability-driven companies present themselves, including how they have built their businesses, by focusing on their core sustainability values. These components offer guidance for companies that need to develop their business models to become more sustainable in alignment with the SDGs. We apply a case study methodology to assess companies from the clothes and apparel, cosmetics, and food industries and use secondary data. Based on our findings, we present a conceptual model of how these case companies build their business models on a sustainable foundation that promotes key SDGs. This helps build more sustainable business and practices, which will help ensure overall well-being in the society and have a positive impact on the natural environment
An optical study of nanofluidics in mesoporous silicon
No full textThis thesis presents a novel method for studying capillary imbibition and vapor sorption in mesoporous silicon (PSi) with white light spectroscopy. By Fast-Fourier-Transformation of thin film interference and parallel analysis of an optical microcavity within the porous layer, it achieves high precision in monitoring fluid dynamics. The research includes a constriction model for capillary imbibition, the resolution of liquid menisci and a percolation mechanism for vapor sorption. Moreover, a new rapid and non-destructive technique is introduced for assessing pore pathways of PSi, by calulating the pore geometry from liquid dynamics and capillary pressure.Diese Arbeit stellt eine neuartige Methode zur Untersuchung der kapillaren Imbibition und Dampfsorption in mesoporösem Silizium (PSi) mit Weißlichtspektroskopie vor. Durch die Fast-Fourier-Transformation von Dünnschichtinterferenz und parallele Analyse einer optischen Mikrokavität wird eine hohe Präzision bei der Messung der Fluiddynamik erreicht. Die Forschung umfasst ein Flaschenhalsmodell für die kapillare Imbibition, die Auflösung von Flüssigkeitsmenisken und einen Perkolationsmechanismus für die Dampfsorption. Darüber hinaus wird eine schnelle und zerstörungsfreie Technik zur Auflösung des Porenradiusverlaufs in PSi eingeführt, der aus Flüssigkeitsdynamiken und dem Kapillardruck errechnet wird.Deutsche Forschungsgemeinschaft (DFG)Landesforschungsförderung HamburgTechnische Universität Hambur
Full-body vs. head-only modeling: Full wave computational SAR and adaptation of corresponding ANN models
No full textElectromagnetic compatibility (EMC) analysis is often computationally expensive, with partial modeling and domain-specific approximations commonly employed to improve efficiency, although these simplifications can introduce accuracy trade-offs. To address these challenges, this work focuses on bioelectromagnetic compatibility (Bio-EMC) problems, particularly the Specific Absorption Rate (SAR) calculations, by evaluating SAR in human head tissues using Full-Body and Head-Only models with finite element method (FEM) solvers under plane wave (PW) and near field (NF) exposures at 13.56 MHz. More than 2,000 full wave simulations are conducted, incorporating uncertainties in material properties and exposure angles, with machine learning techniques applied for enhanced analysis. Results show that while model truncation can impact SAR, certain scenarios allow Head-Only data to effectively replace Full-Body data. In these cases, parameter prioritization in artificial neural networks (ANNs) achieves over 90% accuracy while reducing input parameters by up to 70%. For cases where truncation effects are more significant, the ANN trained on Head-Only data is refined using Full-Body data, improving predictive accuracy up to 85% while maintaining computational efficiency. The proposed ANN-based approach enhances both computational efficiency and prediction reliability in Bio-EMC analysis, making it applicable to other emission susceptibility scenarios by reducing system complexity and improving the physical interpretation of results