Refine
Year of publication
- 2024 (75) (remove)
Institute
- Fachbereich Medizintechnik und Technomathematik (27)
- Fachbereich Elektrotechnik und Informationstechnik (11)
- IfB - Institut für Bioengineering (11)
- Fachbereich Luft- und Raumfahrttechnik (9)
- Fachbereich Chemie und Biotechnologie (8)
- INB - Institut für Nano- und Biotechnologien (7)
- ECSM European Center for Sustainable Mobility (6)
- Fachbereich Maschinenbau und Mechatronik (6)
- Fachbereich Wirtschaftswissenschaften (6)
- Fachbereich Energietechnik (3)
Language
- English (75) (remove)
Document Type
- Article (49)
- Conference Proceeding (16)
- Part of a Book (4)
- Book (3)
- Preprint (2)
- Working Paper (1)
Keywords
- Additive Manufacturing (2)
- Additive manufacturing (2)
- Coal (2)
- Engineering education (2)
- Hot S-parameter (2)
- Industry 4.0 (2)
- LPBF (2)
- SLM (2)
- 1P hub loads (1)
- 3-D printing (1)
- 316L (1)
- A. succinogenes (1)
- ABE (1)
- AI ethics (1)
- AM implementation (1)
- Accessibility (1)
- Accuracy (1)
- Acid crash (1)
- Active humidity control (1)
- Acyl-amino acids (1)
- Acylation (1)
- Aeroelasticity (1)
- Ageing (1)
- Agent-based modeling (1)
- Aminoacylase (1)
- Analytical models (1)
- Anatomy (1)
- Automation (1)
- Bio-inspired systems (1)
- Biobeneficiation (1)
- Biocatalysis (1)
- Biosolubilization (1)
- Biosurfactants (1)
- Boundary integral equations, (1)
- Business understanding (1)
- Butanol (1)
- C. acetobutylicum (1)
- CO2 (1)
- Capacitive model (1)
- Carrier solvents (1)
- Centrifugal twisting moment (1)
- Chaperone (1)
- Circuit simulation (1)
- Collagen fibrils (1)
- Comparative simulation (1)
- Complex-valued eigenvalues (1)
- Connective tissues (1)
- Constitutive model (1)
- Control engineering (1)
- DAC (1)
- DC machines (1)
- Damage mechanics theory (1)
- Data analysis (1)
- Data visualization (1)
- Database (1)
- Discontinuous fractures (1)
- Drugs (1)
- Dynamic modeling (1)
- E. coli detection (1)
- ESATAN-TMS (1)
- Easy read (1)
- Eigenvalue trajectories (1)
- Electrochemical impedance spectroscopy (1)
- Electronic cigarettes (1)
- Electronic learning (1)
- Energy dispatch (1)
- Energy market (1)
- Energy-efficient ventilation (1)
- Enzyme coverage (1)
- Exponential Euler scheme, (1)
- Extension–twist coupling (1)
- Extracellular matrix (ECM) (1)
- Factory Planning (1)
- Fault detection (1)
- Fiducial marker system (1)
- Field devices (1)
- Field-effect biosensor (1)
- Flutter (1)
- Focusing (1)
- Free-base nicotine (1)
- Fuel cell (1)
- Furnace (1)
- Fused Filament Fabrication (1)
- Fusion (1)
- German (1)
- Germany (1)
- H2 (1)
- Health management system (1)
- Hybrid Manufacturing (1)
- Hydrogenotrophic methanogens (1)
- IO-Link (1)
- Im-plementation of AI-systems (1)
- Impedance analysis (1)
- Indoor air quality (1)
- Indoor environmental quality (1)
- Instrumental analysis (1)
- Instruments (1)
- Interior transmission problem (1)
- Interoperability (1)
- LQR (1)
- Large language models (1)
- Li7La3Zr2O12 (1)
- LiDAR (1)
- LiGaO2 (1)
- Lifting propeller (1)
- Liquid chromatography (1)
- Low field NMR (1)
- Low voltage (1)
- Lunar Surface (1)
- MOS (1)
- MPC (1)
- Manifolds (1)
- Manufacturing Process Chains (1)
- Master stamp (1)
- Matlab (1)
- Measurement (1)
- Mechanical stability (1)
- Metabolic shift (1)
- Methane (1)
- Methanogenesis (1)
- Microorganisms (1)
- Mode converter (1)
- Modeling (1)
- Monitoring (1)
- Multianalyte detection (1)
- Muscle (1)
- Musculoskeletal system (1)
- Natural language processing (1)
- Non-model-based Evaluation (1)
- Non-parallel fissures (1)
- Nonlinear eigenvalue problems (1)
- Obstacle avoidance (1)
- Online diagnostic (1)
- Online services (1)
- Ontology (1)
- Open Data (1)
- Open source (1)
- Operational Control (1)
- P2G (1)
- PEM fuel cell (1)
- PIV calibration (1)
- PM2.5 (1)
- PPO (1)
- Parabolic SPDEs (1)
- Participation (1)
- Path planning (1)
- Penicillin (1)
- Photolithographic mimics (1)
- Physiology (1)
- Plasma (1)
- Plasma diagnostics (1)
- Polyetheretherketone (PEEK) (1)
- Power dissipation (1)
- Pre-culture (1)
- Process Parameters (1)
- Process model (1)
- Propeller whirl flutter (1)
- Qualitative and quantitative determination (1)
- Quartz crystal microbalance (1)
- RANSAC (1)
- Raman spectroscopy (1)
- Rapid Tooling (1)
- Recognition algorithms (1)
- Refining (1)
- Reinforcement Learning (1)
- Relative air humidity (1)
- Renewable energy sources (1)
- Requirements (1)
- Residual Stresses (1)
- Robotics (1)
- Rotatory Inverted Pendulum (1)
- Simulation (1)
- Software packages (1)
- Spectroscopy (1)
- Surface imprinted polymer (1)
- Surfactants (1)
- Synchronous machines (1)
- Tapered ends (1)
- Technology Planning (1)
- Tensile Strength (1)
- Thermal Model (1)
- Thermal analysis (1)
- Thermal conductivity (1)
- Throughput (1)
- Time-series (1)
- Tool Making (1)
- Training (1)
- Trapeze effect (1)
- Uniaxial compression test (1)
- Unmanned aerial vehicles (1)
- Unsteady aerodynamics (1)
- Weak organic acids (1)
- Welding (1)
- Wellenausbreitung (1)
- analytical approach (1)
- antennas (1)
- aquaculture (1)
- artificial olfactory image (1)
- automated parking (1)
- bio-based economy (1)
- biodegradable polymers (1)
- biological hydrogen (1)
- biomethane (1)
- bubble column (1)
- carbon dioxide removal (1)
- catalytic metal (1)
- chip-based sensor setup (1)
- climate neutrality (1)
- concentrated solar power (1)
- coupled Néel–Brownian relaxation dynamics (1)
- dark fermentation (1)
- design factor (1)
- direct air capture (1)
- economics (1)
- electrical circuits (1)
- electrical conductivity of liquids (1)
- electrical engineering (1)
- electro-bioreactor (1)
- electrofermentation (1)
- electronic nose (1)
- ensiling (1)
- fermentation (1)
- field simulation (1)
- field-effect structure (1)
- frequency mixing magnetic detection (1)
- ga-doping (1)
- garnet solid electrolyte (1)
- gas sensor (1)
- glycine (1)
- high-frequency technology (1)
- hybrid solar power plants (1)
- hydrogels (1)
- hydrogen peroxide (1)
- hydrolysis (1)
- impedance spectroscopy (1)
- interviews (1)
- key performance indicators (1)
- lactic acid (1)
- lactobacillus (1)
- large language models (1)
- light-addressing technologies (1)
- line detection (1)
- magnetic biosensing (1)
- magnetic nanoparticles (1)
- magnetic relaxation (1)
- manufacturing management (1)
- metal-oxide-semiconductor structure (1)
- methanation (1)
- microfluidics (1)
- micromagnetic simulation (1)
- micronutrients (1)
- microwave technology (1)
- negative emissions (1)
- optical spore trapping (1)
- optimization model (1)
- organic waste (1)
- parking slot detection (1)
- perennial ryegrass (1)
- plasma technology (1)
- plug flow reactor (1)
- point cloud processing (1)
- polyaspartic acid (1)
- power-to-X (1)
- pretreatment (1)
- production systems (1)
- renewable resources (1)
- rollout (1)
- scanned light pulse technique (1)
- semantic role labeling (1)
- solar multiple factor (1)
- solid-state battery (1)
- speaker attribution (1)
- sterilization conditions (1)
- succinate (1)
- superabsorbent polymers (1)
- swelling properties (1)
- temperature (1)
- thematic analysis (1)
- thermal storage (1)
- thermometry (1)
- visualization (1)
- wave propagation (1)
The book covers various numerical field simulation methods, nonlinear circuit technology and its MF-S- and X-parameters, as well as state-of-the-art power amplifier techniques. It also describes newly presented oscillators and the emerging field of GHz plasma technology. Furthermore, it addresses aspects such as waveguides, mixers, phase-locked loops, antennas, and propagation effects, in combination with the bachelor's book 'High-Frequency Engineering,' encompassing all aspects related to the current state of GHz technology.
To successfully develop and introduce concrete artificial intelligence (AI) solutions in operational practice, a comprehensive process model is being tested in the WIRKsam joint project. It is based on a methodical approach that integrates human, technical and organisational aspects and involves employees in the process. The chapter focuses on the procedure for identifying requirements for a work system that is implementing AI in problem-driven projects and for selecting appropriate AI methods. This means that the use case has already been narrowed down at the beginning of the project and must be completely defined in the following. Initially, the existing preliminary work is presented. Based on this, an overview of all procedural steps and methods is given. All methods are presented in detail and good practice approaches are shown. Finally, a reflection of the developed procedure based on the application in nine companies is given.
Drought and water shortage are serious problems in many arid and semi-arid regions. This problem is getting worse and even continues in temperate climatic regions due to climate change. To address this problem, the use of biodegradable hydrogels is increasingly important for the application as water-retaining additives in soil. Furthermore, efficient (micro-)nutrient supply can be provided by the use of tailored hydrogels. Biodegradable polyaspartic acid (PASP) hydrogels with different available (1,6-hexamethylene diamine (HMD) and L-lysine (LYS)) and newly developed crosslinkers based on diesters of glycine (GLY) and (di-)ethylene glycol (DEG and EG, respectively) were synthesized and characterized using Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) and regarding their swelling properties (kinetic, absorbency under load (AUL)) as well as biodegradability of PASP hydrogel. Copper (II) and zinc (II), respectively, were loaded as micronutrients in two different approaches: in situ with crosslinking and subsequent loading of prepared hydrogels. The results showed successful syntheses of di-glycine-ester-based crosslinkers. Hydrogels with good water-absorbing properties were formed. Moreover, the developed crosslinking agents in combination with the specific reaction conditions resulted in higher water absorbency with increased crosslinker content used in synthesis (10% vs. 20%). The prepared hydrogels are candidates for water-storing soil additives due to the biodegradability of PASP, which is shown in an exemple. The incorporation of Cu(II) and Zn(II) ions can provide these micronutrients for plant growth.
N-Acyl-amino acids can act as mild biobased surfactants, which are used, e.g., in baby shampoos. However, their chemical synthesis needs acyl chlorides and does not meet sustainability criteria. Thus, the identification of biocatalysts to develop greener synthesis routes is desirable. We describe a novel aminoacylase from Paraburkholderia monticola DSM 100849 (PmAcy) which was identified, cloned, and evaluated for its N-acyl-amino acid synthesis potential. Soluble protein was obtained by expression in lactose autoinduction medium and co-expression of molecular chaperones GroEL/S. Strep-tag affinity purification enriched the enzyme 16-fold and yielded 15 mg pure enzyme from 100 mL of culture. Biochemical characterization revealed that PmAcy possesses beneficial traits for industrial application like high temperature and pH-stability. A heat activation of PmAcy was observed upon incubation at temperatures up to 80 °C. Hydrolytic activity of PmAcy was detected with several N-acyl-amino acids as substrates and exhibited the highest conversion rate of 773 U/mg with N-lauroyl-L-alanine at 75 °C. The enzyme preferred long-chain acyl-amino-acids and displayed hardly any activity with acetyl-amino acids. PmAcy was also capable of N-acyl-amino acid synthesis with good conversion rates. The best synthesis results were obtained with the cationic L-amino acids L-arginine and L-lysine as well as with L-leucine and L-phenylalanine. Exemplarily, L-phenylalanine was acylated with fatty acids of chain lengths from C8 to C18 with conversion rates of up to 75%. N-lauroyl-L-phenylalanine was purified by precipitation, and the structure of the reaction product was verified by LC–MS and NMR.
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.
The emergence of automotive-grade LiDARs has given rise to new potential methods to develop novel advanced driver assistance systems (ADAS). However, accurate and reliable parking slot detection (PSD) remains a challenge, especially in the low-light conditions typical of indoor car parks. Existing camera-based approaches struggle with these conditions and require sensor fusion to determine parking slot occupancy. This paper proposes a parking slot detection (PSD) algorithm which utilizes the intensity of a LiDAR point cloud to detect the markings of perpendicular parking slots. LiDAR-based approaches offer robustness in low-light environments and can directly determine occupancy status using 3D information. The proposed PSD algorithm first segments the ground plane from the LiDAR point cloud and detects the main axis along the driving direction using a random sample consensus algorithm (RANSAC). The remaining ground point cloud is filtered by a dynamic Otsu’s threshold, and the markings of parking slots are detected in multiple windows along the driving direction separately. Hypotheses of parking slots are generated between the markings, which are cross-checked with a non-ground point cloud to determine the occupancy status. Test results showed that the proposed algorithm is robust in detecting perpendicular parking slots in well-marked car parks with high precision, low width error, and low variance. The proposed algorithm is designed in such a way that future adoption for parallel parking slots and combination with free-space-based detection approaches is possible. This solution addresses the limitations of camera-based systems and enhances PSD accuracy and reliability in challenging lighting conditions.
The connective tissues such as tendons contain an extracellular matrix (ECM) comprising collagen fibrils scattered within the ground substance. These fibrils are instrumental in lending mechanical stability to tissues. Unfortunately, our understanding of how collagen fibrils reinforce the ECM remains limited, with no direct experimental evidence substantiating current theories. Earlier theoretical studies on collagen fibril reinforcement in the ECM have relied predominantly on the assumption of uniform cylindrical fibers, which is inadequate for modelling collagen fibrils, which possessed tapered ends. Recently, Topçu and colleagues published a paper in the International Journal of Solids and Structures, presenting a generalized shear-lag theory for the transfer of elastic stress between the matrix and fibers with tapered ends. This paper is a positive step towards comprehending the mechanics of the ECM and makes a valuable contribution to formulating a complete theory of collagen fibril reinforcement in the ECM.
Easy-read and large language models: on the ethical dimensions of LLM-based text simplification
(2024)
The production of easy-read and plain language is a challenging task, requiring well-educated experts to write context-dependent simplifications of texts. Therefore, the domain of easy-read and plain language is currently restricted to the bare minimum of necessary information. Thus, even though there is a tendency to broaden the domain of easy-read and plain language, the inaccessibility of a significant amount of textual information excludes the target audience from partaking or entertainment and restricts their ability to live life autonomously. Large language models can solve a vast variety of natural language tasks, including the simplification of standard language texts to easy-read or plain language. Moreover, with the rise of generative models like GPT, easy-read and plain language may be applicable to all kinds of natural language texts, making formerly inaccessible information accessible to marginalized groups like, a.o., non-native speakers, and people with mental disabilities. In this paper, we argue for the feasibility of text simplification and generation in that context, outline the ethical dimensions, and discuss the implications for researchers in the field of ethics and computer science.
Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled Néel–Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.