Dynamical Component Analysis (DyCA) and Dynamic Mode Decomposition (DMD), both data-driven dimension reduction methods, are introduced. After application to multivariate simulated signals the techniques of mode selection and the resulting amplitudes are compared with respect to noise robustness and sampling periods. The results indicate that DyCA is a useful alternative to DMD and outperforms DMD under certain conditions. These conditions are based on the underlying dynamics in the terms of differential equations and on the noise ratios of the signals.

Geometric deviations play a crucial role in the quality of additive manufacturing, particularly in parts made with biodegradable resins. Accurately controlling dimensional and geometric variations in manufactured components is critical for achieving defect-free production and meeting functional standards. However, defining a final quality score can be challenging due to numerous dimensional and geometric deviations associated with a part. An innovative metric for evaluating geometric performance was created to measure dimensional precision in components produced through VAT photopolymerization. The index measures the dimensional and geometrical deviations, revealing that external surfaces exhibit greater precision than internal ones. This difference is likely due to internal surfaces overcoming heat dissipation challenges during the cooling process, resulting in less shrinkage for external surfaces. This index is essential in various stages of the manufacturing process, including part design, design for manufacturing and assembly, quality assurance, and process planning, helping to select the appropriate additive manufacturing technology and optimal process parameters.

This paper discusses the challenges and methods for source reconstruction of evoked potentials using deep learning in the context of electroencephalography (EEG). We propose the use of deep learning to address known challenges and improve traditional approaches. We explain the creation of a suitable dataset for solving the inverse problem, including the simulation of neural activity and the use of lead field matrices for the forward solution. Furthermore, we undertake a comparative analysis of some initial deep learning models with similar classical methods.

The manifold of essential matrices is equipped with a one-parameter family of (pseudo-)Riemannian metrics. For the whole family, explicit formulas for geodesics are derived. Moreover, specific curves, so-called quasi-geodesics, are studied and a closed form expression for a quasi-geodesic connecting two given essential matrices is obtained.

In this paper, we show an application of spatiotemporal persistence landscapes to real world time series. Spatiotemporal persistence landscapes are a recent extension of persistence landscapes to time series that capture features of the data that are persistent with respect to time and space. We perform our analysis on EEG data to detect absence epileptic seizures. Further, we compare two dimension reduction techniques (DyCA and PCA) with no dimension reduction and show that the combination of DyCA and persistent landscapes yields the best results.

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Ein Buch aus der Praxis für die Praxis – ein Buch für B2B-Marketers, die Ihre Marketing-Performance für alle sichtbar steigern wollen. Diese zweite Auflage wurde vollständig aktualisiert und erweitert.

Since October 2017, 3D audio recordings have been made every month at the Vogelinsel nature reserve on Lake Altmühlsee, Germany. The recordings start one hour before and end one hour after sunset. The microphone, position and recording device are identical for all recordings. Each recording is analysed by hand using the same analysis method, whereby the content of the recordings is annotated. The weather data for the recording days is collected. The recording and weather conditions at the recording location are documented photographically. This data set is highly unique due to the 3D recording technique and the long duration of the acoustic observation of nature at one spot. In addition to collecting further data, the project aims to compare the recordings with known algorithmic bioacoustic analysis methods in order to better understand the changes in nature at the recording site (possibly caused by climate/biodiversity change), to develop further algorithmic methods of soundscape analysis and to use the recordings and additional data for nature education, medical-therapeutic and wellness contexts. The article describes the data collection in detail, outlines the comparative results achieved so far, presents our additional analysis methods and gives an outlook on the future of the project.

The possibilities and limitations of auralizing an acoustic
laboratory with the “WWISE Spatial Audio” are discussed
by 3d modeling and auralizing a door test laboratory in VR,
designed for sound reduction index measurements. The
simulated results are compared to real measurements with
the acoustic parameters: Reverberation Time [T30] and
Sound Reduction Index [R], with three binauralization
methods compared and further optimizations conducted
with Dolby Atmos for Headphones. The results showed a
deviation and partly a correlation within the “Just
Noticeable Differences [JND]” for T30 values and a precise
reproduction of the sound reduction index within WWISE
was possible for test components with the lowest real R
values. The frequency dependent Rw values were subtracted
from the audio file using bandpass filters - before using
“WWISE” for sound playback in VR. The results of this
work can be a seen as a technical analysis and fundament
for future research regarding realtime auralization
capabilities of audio middlewares.

The positive effects that bioacoustic environments in nature can have on people raise the question of the extent to which bioacoustic recordings can have positive effects as well when played back via VR in the context of a hospital. This paper presents a study in which the effect of a 3D soundscape recording including a 360° still image on geriatric patients in a clinic was investigated. 28 test subjects each spent 15 minutes in a 360° VR room, surrounded by six projection screens and 14 loudspeakers. The study was conducted in a geriatric rehabilitation ward. The recording location was chosen in a nearby recreation and nature reserve. The subjects' state of mind was measured at four points in time using a standardised psychological measurement procedure. During the 15-minute stay in the virtual nature, biosignals of various vital parameters were recorded. In addition, the room was opened to patients and staff at defined times and their use was recorded. The staff were asked about their experiences. The results show that playback increased the well-being of most participants. The therapeutic possibilities of using 3D bioacoustic recordings are discussed and future research and application scenarios for the system are presented.

The interactive soundscape education project, ‘Klangwelt-Entdecker’, aims to raise awareness of the soundscape in a nature reserve. By scanning QR codes with their smartphones at six listening stations on a circular route along the bird island on Lake Altmühlsee, Germany, visitors can experience the diversity of the soundscape of the site throughout the year and obtain information about the bioacoustics of the local nature and birdlife via the project website. The initiative places a special focus on the effects of climate change on the soundscape and aims to convey a greater understanding of the acoustic facets of nature. Sounds are used from a database that has been fed with monthly 3D sound recordings of the bird island for more than 7 years. The impact of the project with regard to the soundscape educational objectives will be analysed by means of a study among participants of the circular walk. The results of the study will be presented and discussed in the context of existing similar projects. Based on this, well-founded demands are made on nature education and, in particular, bioacoustics and soundscape education.

The development of digital musical instruments is often pursued from a substance-ontological functional and physical perspective. However, many musicians talk less about music from a functional and physical perspective but more about the experienced phenomena they encounter when making music. In this article, instrumental playing and musical interaction will be considered from two perspectives: the broadly used substance-ontological view and the process-ontological approach as drafted by Alfred North Whitehead. The instrumental focus here is on digital and traditional bowed stringed instruments and varying descriptions of the interaction on the instrument. Related works and these descriptions are looked at from the two perspectives mentioned. It is shown why it often comes to a bifurcation of nature into primary and secondary qualities of musical interaction during the interface design process. Furthermore, it is shown that musicians' requirements for digital musical instruments are sometimes unfavourably excluded. These problems will be exemplified and suggestions for a process-ontological perspective on the design of musical interaction will be presented. Also an example of a highly market relevant design principle of a musical sound synthesis system fitting to the process-ontological understanding of musical interaction is presented and discussed.

Der Fachbeitrag beleuchtet die Herausforderungen des Wissensverlusts durch den Ruhestand von Babyboomern in kommunalen Verwaltungen. Die Stadt Nürnberg hat in Zusammenarbeit mit der Hochschule Ansbach verschiedene Instrumente entwickelt, um das Erfahrungswissen von ausscheidenden Mitarbeitenden zu sichern. Diese Methoden umfassen Seminare, Leitfäden, Erklärvideos und ein digitaler Assistent. Ein besonderes Augenmerk liegt auf dem Expert Debriefing, bei dem Wissenstragende durch moderierte Sitzungen unterstützt werden. Zudem wird die innovative Methode der Büro-Fika vorgestellt, die das Wissensteilen in einem informellen Rahmen fördert. Der Einsatz von KI-gestützten Assistenten zur Wissenssicherung zeigt die zukunftsorientierte Ausrichtung der Stadt Nürnberg in diesem Bereich. Der Beitrag verdeutlicht, wie durch gezielte Maßnahmen der Wissensverlust minimiert und die Qualität der Dienstleistungen aufrechterhalten werden kann.

Industrial parts often demand high dimensional accuracy and mechanical strength. This study introduces a method using Response Surface Methodology and composite desirability to simultaneously optimize these performances in components manufactured through additive processes. The approach was validated on Polylactic Acid parts fabricated through Fused Filament Fabrication. Optimized process parameters included a print speed of 80 mm/s, a layer height of 0.2 mm, a fan speed of 50%, and an extrusion temperature of 210 °C, yielding tensile strength of 53.27 MPa and dimensional deviations under 5%. Experimental validations showed less than a 5% deviation between predictions and outcomes. The findings provide valuable insights into improving the quality and performance of printed components in various industrial applications, such as gears, highlighting the significance of multi-response optimisation in 3D printing processes. This study ultimately contributes to more efficient and cost-effective manufacturing processes.

This study explores the unique operating behavior of an alkaline water electrolysis cell equipped with an ion-solvating membrane, operated with a diluted alkaline electrolyte, specifically 1-M potassium hydroxide (1M KOH), in anode feed mode. Our investigations reveal several key insights.