Phasen hoher wirtschaftlicher Unsicherheit können das Interesse an Weiterbildung spürbar verringern. Dies zeigte sich während der Covid-19-Pandemie sehr deutlich. Zugleich nahm aber das Interesse an Online-Weiterbildungsmöglichkeiten in dieser Zeit stark zu.  Zu diesem Ergebnis kommt eine Auswertung einschlägiger Suchanfragen bei Google.

School Shootings sind gezielte Gewalttaten an Schulen bzw. Bildungseinrichtungen, die langfristig geplant sind. Obwohl der Begriff „Amoklauf“ häufig verwendet wird, trifft er nicht den Kern dieser Taten, da sie nicht impulsiv, sondern strategisch durchgeführt werden und daher Ähnlichkeit mit Terroranschlägen haben. Die Täter sind überwiegend männlich und Schüler oder ehemalige Schüler der betroffenen Bildungseinrichtung, oder es liegt ein direkter Bezug dazu vor. In den letzten Jahrzehnten hat die Zahl der School Shootings stark zugenommen, insbesondere seit den 1990er-Jahren. Das Columbine-Shooting von 1999 gilt als eine Art Vorbild für viele spätere Täter und war das erste School Shooting, über das die Medien weltweit live berichteten und dessen Bilder eine Art ikonischen Status erreichten. Die Täter von School Shootings haben oft wenig soziale Bindungen, aber ein starkes Interesse an Waffen und gewalthaltigen Medieninhalten. Sie bereiten ihre Taten über Monate, teilweise bis zu einem Jahr hinweg vor. Viele haben schulische Probleme, erleben soziale Zurückweisung und zeigen psychische Auffälligkeiten wie narzisstische oder depressive Züge. Ein entscheidender Faktor ist die Fantasie der Täter, die sich oft in Rachegedanken oder dem Wunsch nach Ruhm äußert. Zugang zu Waffen ist ein weiterer Risikofaktor. Medien tragen zur Verbreitung des Phänomens bei, da Täter sich an früheren Fällen orientieren und ihre eigene Tat medial inszenieren. Prävention erfordert Maßnahmen auf verschiedenen Ebenen: Ein frühzeitiges Erkennen von Warnsignalen, strengere Waffengesetze, eine gezielte medienpädagogische Arbeit und Veränderungen an Schulen können das Risiko senken. Schutzfaktoren auf individueller Ebene liegen in stabilen sozialen Beziehungen, emotionaler Unterstützung und sinnvollen Freizeitaktivitäten.

Maintaining the quality and reliability of selective soldering processes in printed circuit board (PCB) manufacturing is crucial for economic efficiency and sustainability. This study explores the application of autoencoders, a neural network-based approach, for automatic anomaly detection in soldering nozzles, emphasizing the economic benefits of predictive maintenance. Traditional methods, such as visual inspections and rule based algorithms, are limited by subjectivity, delay, and susceptibility to false readings, leading to increased costs and production downtime. Autoencoders, on the other hand, use unsupervised learning to identify deviations from normal operational states by reconstructing input data and detecting anomalies based on reconstruction errors. This predictive maintenance approach can significantly reduce unexpected failures and maintenance costs, ensuring continuous production and enhancing sustainability. This research highlights the potential of autoencoder-based systems to automate and enhance the reliability of selective soldering processes, ultimately leading to significant economic benefits. The findings pave the way for real-time monitoring solutions, reducing dependency on manual inspections and rule-based algorithms, and improving production efficiency and sustainability in the electronics manufacturing industry.

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.

Influencer-Marketing ist eine etablierte Strategie der digitalen Markenkommunikation, die auf die Authentizität und Überzeugungskraft von Influencern setzt. Mit der zunehmenden Verbreitung von Künstlicher Intelligenz (KI) verändert sich dieser Bereich durch den Einsatz von KI-Influencern. Immer häufiger vermitteln virtuelle, computergenerierte Persönlichkeiten Markenbotschaften und beeinflussen Kaufentscheidungen. Ein zentrales Merkmal von KI-Influencern ist ihre digitale Perfektion, die durch idealisierte ästhetische Merkmale und makellose Darstellung charakterisiert ist. Dieser Beitrag untersucht die Wirkung dieser digitalen Perfektion auf die Wahrnehmung von KI-Influencern. Basierend auf dem Uncanny-Valley-Effekt und der Theorie der para-sozialen Interaktion wird ein konzeptionelles Wirkungsmodell abgeleitet und empirisch überprüft. In einer quantitativen Studie (n=100) wurden Konsumenten KI-Influencer mit unterschiedlichem Perfektionsgrad (unperfekt, moderat perfekt, perfekt) präsentiert. Die Ergebnisse zeigen, dass eine höhere digitale Perfektion zu einer geringeren wahrgenommenen Kompetenz, Menschlichkeit und Glaubwürdigkeit führt. Diese Befunde unterstreichen die Bedeutung gezielter Imperfektion bei der Gestaltung von KI-Influencern, um Konsumentenvertrauen zu stärken und eine positive Markenwahrnehmung zu fördern. Abschließend werden Empfehlungen und Implikationen für Forschung und Praxis aufgezeigt.

Media organizations, journalism, and social media play a crucial role in the coverage of terrorism attacks including lone wolf/school shootings. Those crisis events involve strategic communication by perpetrators who seek (media) attention and public awareness and lead to widespread international impact. This chapter analyzes how media organizations, journalism, and social media respond to these events and how journalists shape their coverage. It reviews extant research and guidelines on how media can deal with the ethical and professional challenges in this field. It also discusses the challenges and dilemmas that media organizations face, introducing concepts such as labeling, framing, censorship, and contagion effects. The chapter concludes with recommendations for future research and practice in this field.

Splined assemblies ensure precise torque transmission and alignment in mechanical systems. Three-dimensional printing, especially FDM, enables fast production of customized components with complex geometries, reducing material waste and costs. Optimized printing parameters improve dimensional accuracy and performance. Dimensional accuracy is a critical aspect in the additive manufacturing of mechanical components, especially for splined shafts and hubs, where deviations can impact assembly precision and functionality. This study investigates the influence of key FDM 3D printing parameters—layer thickness, infill density, and nominal diameter—on the dimensional deviations of splined components. A full factorial experimental design was implemented, and measurements were conducted using a high-precision coordinate measuring machine (CMM). To optimize dimensional accuracy, artificial neural networks (ANNs) were trained using experimental data, and a genetic algorithm (GA) was employed for multi-objective optimization. Three ANN models were developed to predict dimensional deviations for different parameters, achieving high correlation coefficients (R2 values of 0.961, 0.947, and 0.910). The optimization process resulted in an optimal set of printing conditions that minimize dimensional errors. The findings provide valuable insights into improving precision in FDM-printed splined components, contributing to enhanced design tolerances and manufacturing quality.

Over the last four decades, the shared service centres idea has emerged as a predominant solution to reorganise the support processes of companies worldwide. A stream of academic literature emphasises their potential for ambitious cost savings, shaping the impression that the 'holy grail' for managing companies' administrative costs has been found. However, quantitative evidence based on publicly available financial data is scarce. With the assistance of an umbrella review, this article aims for a structured evaluation of secondary and tertiary SSC literature in that regard. The analysis of 22 publications confirmed initial perceptions. Quantitative data evaluating the impact of SSC organisations on firms administrative cost structure are absent. Furthermore, heterogeneous research procedures yield diverse outputs, deviating from established standards for structured literature reviews. The gathered results substantiate the need for additional investigations at the primary research level, to finalise evidence search and devise a research agenda to address identified gaps.

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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 realtime-auralizations of an acoustic laboratory with the “WWISE Spatial Audio” are discussed by 3D-modelling and auralizing a test rig designed for sound reduction index measurements in VR. The simulated results were compared to real measurements with the acoustic parameters: Reverberation Time [T30] and Sound Reduction Index [SRI], each with three binauralization methods used and further optimizations done with Dolby Atmos for headphones. The results showed a deviation and partly a correlation within the “Just Noticeable Differences [JND]” and a precise reproduction of the sound reduction index within WWISE was possible for test components with the lowest real SRI-values. The frequency dependent SRI-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.