Machtstrukturen und kulturelle Dynamiken in Mathilde Vaertings Soziologie und Psychologie der Macht

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Lernen, Kompetenzen und Beruflichkeit in der digitalen Transformation

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Technical Memorandum 527249322-3

Commonly, the sound power of an acoustic source in free field or in half space can be determined by integrating the time-averaged sound intensity over a surface which encloses the source. Therefore, the acoustic quantities particle velocity and sound pressure need to be measured, which can be troublesome. In a different approach just one acoustic quantity is measured, and the other is calculated using an acoustic mathematical model. The easiest model is the plane-wave model, which can be applied to sound-pressure measurements in the far field to obtain the particle velocity. This technique requires an appropriate facility like an anechoic chamber. For a vibrating plate in a rigid baffle, the structural velocity—assumed equal to the normal surface particle velocity—is measured, and the sound pressure is determined using Rayleigh's velocity-based integral equation. The main advantage of this technique is that measurements can be conducted in the near field of the planar sound-radiating structure without the need of any microphone. Nevertheless, free-field conditions are not necessary, if the effect of the fluid on the structure is negligible. In a further step, Rayleigh’s integral, as the acoustic model in use, can be replaced by its Fourier-transform representation. This requires the free-field Green’s function to be reformulated. For a two-dimensional source the resulting integral equation is fourfold. One may wonder why this method is preferred over the traditional Greens function in this context. The reasoning behind this choice is the connection to the Fraunhofer diffraction. The Fourier transform of a source function of a plane source field is proportional to the sound pressure radiated into the far field. When the radiated sound pressure in the far field is known, the sound power can be determined. Thus, an implementation using a Fast Fourier Transform algorithm to determine the radiated sound pressure and the radiated sound power is possible.For the project Near-field Acoustical Holography - a new sensor concept for methods of active noise reduction, it is necessary to formulate the radiated sound power of a planar source in the transform domain. The transform representation is commonly derived by applying the Fourier transform to the inhomogeneous Helmholtz equation. After all, one ends up with Weyl’s identity, which is derived here first. Second, sound-power formulations based on Fourier transformed particle velocities and sound pressures are derived.A

A review on machine learning approaches for the prediction of glucose levels and hypoglycemia

Type 1 Diabetes (T1D) is an autoimmune disease leading to insulin insufficiency. Thus, patients require lifelong insulin therapy, which has a side effect of hypoglycemia. Hypoglycemia is a critical state of decreased blood glucose levels (BGL) below 70 mg/dL and is associated with increased risk of mortality. Machine learning (ML) models can improve diabetes management by predicting hypoglycemia and providing optimal prevention methods. ML models are classified into regression and classification based, that forecast glucose levels and identify events based on defined labels, respectively. This review investigates state-of-the-art models trained on data of continuous glucose monitoring (CGM) devices from patients with T1D. We compare the models' performance across short-term (15 to 120 min) and long term (3 to more than 24 hours) prediction horizons (PHs). Particularly, we explore: 1) How much in advance can glucose values or a hypoglycemic event be accurately predicted? 2) Which models have the best performance? 3) Which factors impact the performance? and 4) Does personalization increase performance? The results show that 1) a PH of up to 1 hour provides the best results. 2) Conventional ML methods yield the best results for classification and DL for regression. A single model cannot adequately classify across multiple PHs. 3) The model performance is influenced by multivariate datasets and the input sequence length (ISL). 4) Personal data enhances performance but due to limited data quality population-based models are preferred.SMU

Investigating the analytical robustness of the social and behavioural sciences

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Berufliche Sozialisationstheorie und -forschung – überholt?

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Haben Menschen mit Hochsensibilität auch eine innere Widerstandskraft?

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Ökonomische Kompetenz in Crowdwork

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Jetzt erst recht

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Information distribution in decentralized context models of autonomous robot teams

This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).Collaborating autonomous robots provide significant advantages, particularly when compared to single, multifunctional robots or multiple uncoordinated and isolated robots. This includes enhanced spatial coverage, flexibility, and cost-efficiency through specialization. Effective information distribution is essential for such collaboration, especially in scenarios with communication constraints, such as disaster response. Based on a review of existing approaches, this paper introduces a decentralized approach to optimize information sharing among autonomous robots, focusing on when, what, and with whom to communicate. By prioritizing relevant and timely information, the approach reduces communication effort while maintaining mission efficiency. Simulations with homogeneous and heterogeneous robot teams demonstrate the effectiveness of this approach, highlighting its potential to enhance multi-robot collaboration in communication constrained environments. The findings establish a foundation for efficient information distribution, offering promising applications in disaster response and other scenarios requiring autonomous robot teams.Vo