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SIMULATION-BASED DESIGN STRATEGY AND EXPERIMENTAL VALIDATION OF AN ACTIVE NOISE CONTROL SETUP
No full textDuring the pandemic, mobile ventilation systems became increasingly popular. These systems filter the air in a room by moving it through a filtration system. This process generates noise, primarily at the air inlet and outlet of the unit. To meet regulatory requirements and design goals this noise must be minimized. Active Noise Control (ANC) is an effective way to influence airborne sound paths. To mitigate broadband stochastic noise, reference microphones within the ventilation system detect the noise in advance. By using an array of multiple loudspeakers, the propagation of noise into the free field can be controlled. Such an ANC system can also reduce noise in many other applications, such as heat pumps, industrial ventilation, and residential air conditioning. Setting up an acoustic simulation model and a virtual test bench can significantly increase design efficiency. This improves the quality of the solution in advance without relying on a physical prototype. Such a setup allows for efficient numerical investigations on appropriate positioning of ANC components, how to parameterize the controller, and what to expect in the real world. Free-field simulations are performed using the finite element (FE) method and transferred to a time-domain system simulation to evaluate component placement and controller design
FutureHotel - 360° Hotel Service Ecosystem Study
No full textThe study explores a paradigm shift in the hospitality industry by redefining hotels as multifunctional service hubs embedded in their local environments. A central innovation lies in the ecosystem-based perspective: Hotels are conceptualized not merely as isolated providers but as integral nodes in a broader value creation network involving guests, employees, local communities, and strategic partners. The study introduces a new framework that classifies hotel service innovations into ten service categories while mapping demand patterns across stakeholder groups. Using a mixed-methods approach combining literature analysis, expert workshops, and empirical survey data of 4,880 respondents, the study identifies and systematizes emerging service potentials beyond traditional accommodation functions. This approach enables a differentiated understanding of service expectations and highlights underutilized business potential. By aligning hotel offerings more closely with community needs and urban development goals, the study provides a future-oriented model for sustainable hospitality transformation and hybrid business model innovation
Modulation of Mast Cell Activation via MRGPRX2 by Natural Oat Extract
No full textThe Mas-related G protein-coupled receptor (MRGPR) X2 is expressed on skin mast cells and can be stimulated by an unusually broad spectrum of ligands, including specific drugs and even endogenous peptides. MRGPRX2 activation can induce mast cell degranulation and consequently mediator release, leading to inflammatory and hypersensitivity reactions. In addition, MRGPRX2 mediates pain and itching sensations, leading to increased efforts to identify MRGPRX2 inhibitors, including plant-derived compounds. Components within oat extracts have been shown to mediate anti-inflammatory and itch-relieving properties, but a possible inhibitory effect on MRGPRX2 activation has not yet been investigated. We aimed to fill this gap and explored whether an oat kernel extract can modulate MRGPRX2 activation. For this purpose, we established a mast cell model with the human LAD2 cell line and used it to investigate the consequences of exposure to oat extract. While we did not observe any influence on cell viability, we analyzed the impact of oat extract on MRGPRX2-mediated mast cell activation and degranulation initiated by the three confirmed MRGPRX2 ligands c48/80, substance P, and cortistatin 14. Exposure to oat extract resulted in a significant reduction in mast cell degranulation for all three ligands, as assessed by the release of β-hexosaminidase, tryptase, cell surface expression of CD63 and CD107a, and phosphorylation of ERK. All results were confirmed with primary human mast cells. Thus, we demonstrated for the first time that oat extract leads to a significant reduction in MRGPRX2 activation, pointing to a previously unrecognized capacity of natural compounds to modulate this pathway.27
Partial Discharge Characterization of Ceramic Power Electronics Circuit Carriers Assisted by Machine Learning
No full text94103This paper presents an approach for transferring knowledge about partial discharges in polymer insulators to ceramic insulators with the aid of machine learning. It is shown how various machine-learnable features can be generated from partial discharge measurement data and processed in varying artificial neural networks for classification. It is found that polymer-based partial discharges can be classified using this method. In addition, the Long Short-Term Memory based artificial neural network enables partial discharge cause finding and thus fault detection in ceramic power electronics substrates
Recyclability Assessment and Design for Recycling Recommendations of End-of-Life NMC811 Lithium -Ion Batteries
No full text623637This case study provides design for recycling (DfR) recommendations for NMC811 batteries, leveraging a detailed quantitative model of a recycling process optimized during the HydroLiBRec Research project. The adopted methodology integrated comprehensive empirical and analytical measurements to construct an accurate thermodynamic simulation of material and substance flow throughout the entire recycling process. This approach involved stages of manual dismantling, followed by hydro-mechanical, thermal treatment and hydrometallurgical processing of the end-of-life (EoL) NMC811 battery cells. Thermodynamic simulations were conducted using FactSage™ version 8.2 and HSC Chemistry 10 version 10.3.7.1 software to model the recovery of lithium present in the black mass via metallurgical processing methods. The results provided a detailed breakdown of the material composition post-dismantling, revealing that separated cells containing the active NMC811 material constituted 63.7% of the total weight. The study computes the recyclability index and quantify the recovery rates of individual elements, highlighting a recovery rate of 59.7% for lithium and an overall recycling index of 59.4%. The formatted data clearly supported material flow analysis and a product-centric approaches, which are crucial for optimizing DfR strategies and improving overall recycling efficiency. Recommendations included optimizing the dismantling process to achieve a 19% reduction in environmental impacts, such as Global Warming Potential (GWP), and avoiding the use of materials that would be lost in the recycling route. Overall, the measures are projected to increase the recycling index to 90%, thereby significantly improving the overall sustainability of the recycling process for NMC811 batteries. Moreover, a key finding highlighted the necessity of aligning product design with recycling process design in order to maximize recovery while minimizing impacts and losses.43
Coordinated Control of Load Tap Changer Transformers for Voltage Regulation and Voltage Hunting Prevention: A Switched Systems Approach
No full text85538558We propose a coordinated control strategy for load tap changer (LTC) transformers in high voltage radial transmission systems that are connected to higher voltage grids and active distribution networks. We utilize switched systems modeling tools to capture the non-smooth characteristics of the LTCs. Our approach employs a state- and time-dependent switching logic to regulate the voltage at a specific node while preventing stability issues produced by the voltage hunting phenomenon. Then, we derive sufficient tuning conditions for the LTC control parameters, namely, deadband widths and time delays, as a function of the tap magnitude of the LTCs. These conditions ensure the existence of an exponentially stable equilibrium point of the closed-loop switched system and the exponential convergence of its output, i.e., the regulated voltage, to a desired set. Finally, a numerical example shows the proposed strategy's superior performance over an uncoordinated scenario
Machine Learning Driven Design Of Experiments For Predictive Models In Production Systems
No full text110118Machine learning (ML) describes the ability of algorithms to structure and interpret data independently or to learn correlations. The use of ML is steadily increasing in companies of all sizes. However, insufficient market readiness of many ML solutions inhibits their application, especially in production systems. Predictive models apply ML to understand the complex behavior of a system through regression from operational data. This enables determining the relationship between factors and target variables. Accurate predictions of these models for production systems are essential for their application, as even minor variations can significantly affect the process. This accuracy depends on the available data to train the ML model. Production data usually shows a high epistemic uncertainty, leading to inaccurate predictions unfit for real-world applications. This paper presents ML-driven, data-centric Design of Experiments (DoE) to create a process-specific dataset with low epistemic uncertainty. This leads to improved accuracy of the predictive models, ultimately making them feasible for production systems. Our approach focuses on determining epistemic uncertainty in historical data of a production system to find data points of high value to the ML model in the factor space. To identify an efficient set of experiments, we cluster these data points weighted by feature importance. We evaluate the model by running these experiments and using the collected data for further training of a prediction model. Our approach achieves a significantly higher increase in accuracy compared to continuing the training of the prediction model with the same amount of regular operating data
The human being as a driver
No full text63159In order to adapt the vehicle to human being, knowledge of some of his characteristics and abilities is essential. Specifically, the characteristics of the individual sensory organs are presented (information reception), the way in which the experienced driver's well-practiced activity is to be understood and how decisions are made in changed situations, and in particular, how they are limited (information processing). In addition, the structures underlying the innervation of the musculature are reproduced (information implementation). This includes in particular the feeling for time, the eye behaviour when driving, but also emotional aspects, such as the feeling of comfort and discomfort and the individual reaction of the driver to the load given by the driving task. The resulting driving errors and their causes are explained in detail
NLyticsFKIE at SemEval-2021 Task 6: Detection of Persuasion Techniques In Texts And Images
No full text10371044The following system description presents our approach to the detection of persuasion techniques in texts and images. The given task has been framed as a multi-label classification problem with the different techniques serving as class labels. The multi-label classification problem is one in which a list of target variables such as our class labels is associated with every input chunk and assumes that a document can simultaneously and independently be assigned to multiple labels or classes. In order to assign class labels to the given memes, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) as a neural network architecture for token and sequence classification. Starting off with a pre-trained model for language representation we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps. To incorporate image features in the multi-modal setting, we rely on the pre-trained VGG-16 model architecture
Experimental Characterization Method for Passive Microvalves in Diaphragm Pumps for Medical Applications
No full text204207Microfluidic components can improve medical devices or even enable new applications. Medical use often comes along with complex requirements, leading to intricate specifications for all microfluidic components. Key devices are micropumps that can be used as actuators, e.g., for drug delivery or hydraulic application [1, 2]. They basically consist of an actuator, a pump chamber and valves [1, 3]. In this work, we focus on the optimization of the passive valves, where we always face the tradeoff between blocking capability (high fluidic resistance in off phase) and low flow resistance when opened. Especially, hydraulic medical applications such as a penile prosthesis require high flow rates and a high-pressure generation, hence very tight valves [1, 4]. To further improve the fluidic performance, it is obligatory to analyze the valve behavior in detail. Literature research shows deficits in the investigation of microvalves with a view to experimental data using liquid as a medium. However, measurements are inevitable for the validation of derived valve models and, hence, for the optimization of the valves itself and the entire fluidic system. To this end, we implemented and calibrated a measurement setup, which enables to determine the deflection of the valve diaphragm operating in liquids. This provides a deeper understanding of the valve operation, thus enabling further optimization with respect to the fluidic requirements on the whole system. We first discuss measurements comparing the valve behavior in water and air and hence provide additional design input derived from the extracted data