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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.
The “1. Stokes’ problem”, the “suddenly accelerated flat wall”, is the oldest application of the Navier-Stokes equations. Stokes’ solution of the “problem” does not comply with the mathematical theorem of Cauchy and Kowalewskaya on the “Uniqueness and Existence” of solutions of partial differential equations and violates the physical theorem of minimum entropy production/dissipation of the Thermodynamics of Irreversible Processes. The result includes very high local shear stresses and dissipation rates. That is of special interest for the theory of turbulent and mixed turbulent/laminar flow. A textbook solution of the “1. Stokes Problem” is the Couette flow, which has a constant sheer stress along a linear profile. A consequence is that the Navier-Stokes equations do not describe any S-shaped part of a turbulent profile found in any turbulent Couette experiment. The paper surveys arguments referring to that statement, concerning the history of >150 years. Contrary to this there is always a Navier-Stokes solution near the wall, observed by a linear part of the Couette profile. There a turbulent description (e.g. by the logarithmic law-of-the-wall) fails completely. That is explained by the minimum dissipation requirement together with the Couette feature τ = const. The local co-existence of a turbulent zone and a laminar zone near the wall is stable and observed also at high Reynolds-Numbers.
Positionssensorvorrichtung
(2024)
Die Erfindung betrifft eine Positionssensorvorrichtung zur Bestimmung einer Absolutposition eines beweglichen ersten Teils relativ zu einem ortsfesten zweiten Teil mit einem mit dem beweglichen ersten Teil gekoppelter Codekörper, der dazu eingerichtet ist, eine Codespur mit einer Mehrzahl von in Spurrichtung aufeinanderfolgenden Codeelementen zu enthalten zur Bildung eines Codewortes, mit einer magnetischen Detektionseinrichtung zur Detektion der Codespur, wobei die Detektionseinrichtung zum einen an dem Codekörper befestigte und entlang der Spurrichtung in einem solchen Abstand gegenpolig zueinander angeordnete Permanentmagneten aufweist, dass der Abstand mit einer vorgegebenen Länge der jeweiligen Codeelemente übereinstimmt, und zum anderen eine Anzahl von ortsfest und quer zu dem Codekörper versetzt
angeordnete Wiegandsensoren aufweist, wobei der Abstand des Wiegandsensors zu einer Erstreckungsebene der Permanentmagneten derart gewählt ist, dass bei Überdeckung des Wiegandsensors durch den Permanentmagneten ein Wiegandpuls in dem Wiegandsensor induziert wird.
The deformation and damage laws of non-homogeneous irregular structural planes in rocks are the basis for studying the stability of rock engineering. To investigate the damage characteristics of rock containing non-parallel fissures, uniaxial compression tests and numerical simulations were conducted on sandstone specimens containing three non-parallel fissures inclined at 0°, 45° and 90° in this study. The characteristics of crack initiation and crack evolution of fissures with different inclinations were analyzed. A constitutive model for the discontinuous fractures of fissured sandstone was proposed. The results show that the fracture behaviors of fissured sandstone specimens are discontinuous. The stress–strain curves are non-smooth and can be divided into nonlinear crack closure stage, linear elastic stage, plastic stage and brittle failure stage, of which the plastic stage contains discontinuous stress drops. During the uniaxial compression test, the middle or ends of 0° fissures were the first to crack compared to 45° and 90° fissures. The end with small distance between 0° and 45° fissures cracked first, and the end with large distance cracked later. After the final failure, 0° fissures in all specimens were fractured, while 45° and 90° fissures were not necessarily fractured. Numerical simulation results show that the concentration of compressive stress at the tips of 0°, 45° and 90° fissures, as well as the concentration of tensile stress on both sides, decreased with the increase of the inclination angle. A constitutive model for the discontinuous fractures of fissured sandstone specimens was derived by combining the logistic model and damage mechanic theory. This model can well describe the discontinuous drops of stress and agrees well with the whole processes of the stress–strain curves of the fissured sandstone specimens.
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.
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 growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.
Im Hinblick auf die Klimaziele der Bundesrepublik Deutschland konzentriert sich das Projekt Diggi Twin auf die nachhaltige Gebäudeoptimierung. Grundlage für eine ganzheitliche Gebäudeüberwachung und -optimierung bildet dabei die Digitalisierung und Automation im Sinne eines Smart Buildings. Das interdisziplinäre Projekt der FH Aachen hat das Ziel, ein bestehendes Hochschulgebäude und einen Neubau an klimaneutrale Standards anzupassen. Im Rahmen des Projekts werden bekannte Verfahren, wie das Building Information Modeling (BIM), so erweitert, dass ein digitaler Gebäudezwilling entsteht. Dieser kann zur Optimierung des Gebäudebetriebs herangezogen werden, sowie als Basis für eine Erweiterung des Bewertungssystems Nachhaltiges Bauen (BNB) dienen. Mithilfe von Sensortechnologie und künstlicher Intelligenz kann so ein präzises Monitoring wichtiger Gebäudedaten erfolgen, um ungenutzte Energieeinsparpotenziale zu erkennen und zu nutzen. Das Projekt erforscht und setzt methodische Erkenntnisse zu BIM und digitalen Gebäudezwillingen praxisnah um, indem es spezifische Fragen zur Energie- und Ressourceneffizienz von Gebäuden untersucht und konkrete Lösungen für die Gebäudeoptimierung entwickelt.
In this paper, the use of reinforcement learning (RL) in control systems is investigated using a rotatory inverted pendulum as an example. The control behavior of an RL controller is compared to that of traditional LQR and MPC controllers. This is done by evaluating their behavior under optimal conditions, their disturbance behavior, their robustness and their development process. All the investigated controllers are developed using MATLAB and the Simulink simulation environment and later deployed to a real pendulum model powered by a Raspberry Pi. The RL algorithm used is Proximal Policy Optimization (PPO). The LQR controller exhibits an easy development process, an average to good control behavior and average to good robustness. A linear MPC controller could show excellent results under optimal operating conditions. However, when subjected to disturbances or deviations from the equilibrium point, it showed poor performance and sometimes instable behavior. Employing a nonlinear MPC Controller in real time was not possible due to the high computational effort involved. The RL controller exhibits by far the most versatile and robust control behavior. When operated in the simulation environment, it achieved a high control accuracy. When employed in the real system, however, it only shows average accuracy and a significantly greater performance loss compared to the simulation than the traditional controllers. With MATLAB, it is not yet possible to directly post-train the RL controller on the Raspberry Pi, which is an obstacle to the practical application of RL in a prototyping or teaching setting. Nevertheless, RL in general proves to be a flexible and powerful control method, which is well suited for complex or nonlinear systems where traditional controllers struggle.