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Structural and Optical Anomalies in Thin Films Grown in a Magnetic Field by Electron-Assisted Vacuum Deposition of PTFE
No full textPolytetrafluoroethylene (PTFE) films are deposited in parallel and perpendicular magnetic fields (MF) by electron-enhanced vacuum deposition (EVD) and EVD + low-temperature plasma (LTP) methods. The structure, morphology, and nanomechanical properties of the films are studied by infrared spectroscopy (IRS), atomic force microscopy (AFM), and spectroscopic ellipsometry. The structure of the thicker films is closer to that of bulk PTFE than that of thin films. The films' crystallinity and surface roughness are higher than those deposited without MF. The birefringence of the refractive index (n) of the films deposited in the MF is inverse to the anisotropy of the n of the films deposited without MF. The hardness of the films is close to that of bulk PTFE
Künstliche Intelligenz: Grundlagen für das Handeln in der Hochschullehre
No full textWie verändert Künstliche Intelligenz Studium, Lehre und Prüfungen? Hochschulen stehen vor der Herausforderung, KI nicht nur zu integrieren, sondern auch reflektiert mit ihren Auswirkungen umzugehen. Das neue Arbeitspapier der HFD-AG “Künstliche Intelligenz: essentielle Kompetenzen an Hochschulen” beleuchtet diese Fragen mit einem szenariobasierten Ansatz – und leitet daraus zentrale Kompetenzfelder für Hochschulakteure ab.
Die Publikation skizziert drei Szenarien zu KI-Herausforderungen an Hochschulen: Prüfungen und akademische Integrität thematisieren den Einsatz von KI in Hausarbeiten und die Notwendigkeit neuer Prüfungsformate. Lernprozesse und Autonomie beleuchten die Balance zwischen personalisiertem KI-Feedback und der Förderung studentischer Selbstständigkeit. Forschung und ethische Verantwortung zeigt die Gratwanderung zwischen KI-gestützter Hassrede-Erkennung und Datenschutz. Zu den zentralen Learnings gehört, dass Hochschulen Prüfungen als Reflexionsräume gestalten sollten, in denen Analyse, kreative Anwendung und Entscheidungsbegründung im Fokus stehen, statt reiner Reproduktion. Zudem gilt es, Studierende zur kritischen KI-Nutzung zu befähigen und KI-Strategien hochschulweit zu verankern, um technische, didaktische und ethische Herausforderungen gezielt anzugehen
Predictive Maintenance in Tree Care - TreeAngel
No full textEnsuring the traffic safety of trees poses a significant challenge for urban authorities. Conventional manual inspection methods are both time-consuming and resource-intensive, and they are subject to human error. The following paper presents an innovative system for automated tree condition assessment using modern camera technologies and artificial intelligence (AI). As part of a feasibility study, image data generated by various camera systems was analyzed. Based on this data, a YOLOv8 model was trained, which enables precise detection of trees and damage, such as deadwood. The results of the prototype system presented are promising in terms of accuracy and efficiency, suggesting the potential to supplement or replace manual spections with automated procedures.
The results of this study lay the foundation for sustainable and scalable approaches in tree care and can contribute to increasing public safety and efficiency in urban management
KI-Kooperationssysteme: Ein Forschungsansatz zur Analyse von Kooperationsnetzwerken in der Anwendung Künstlicher Intelligenz
No full textDie digitale Transformation und das Potenzial Künstlicher Intelligenz (KI) stellen insbesondere kleine und mittlere Unternehmen (KMU) vor komplexe Herausforderungen. KI-Technologien bieten einerseits erhebliche Potenziale für Innovationen und Wettbewerbsfähigkeit. Andererseits ist die Nutzung dieser Potenziale voraussetzungsreich und erfordert die Verfügung bzw. den Aufbau von Ressourcen, wie Fachwissen, Daten, finanziellen Ressourcen und IT-Infrastruktur. KI-Kooperationen sind ein zunehmend relevantes strategisches Instrument für KMU, um diesen strukturellen Defiziten zu begegnen und den Unternehmen Zugänge zu benötigten Ressourcen zu ermöglichen. Darüber hinaus können sie im Sinne der organisationalen Ambidextrie die Balance zwischen Exploration, der Suche und Erschließung neuer Handlungsfelder und Exploitation, dem Ausbau etablierter Wertschöpfungspotenziale im Unternehmen und damit langfristig die Wettbewerbsfähigkeit des Mittelstandes unterstützen. Der vorgestellte Forschungsansatz erweitert das Konzept der organisationalen Ambidextrie um eine systematische Analyse von KI-Kooperationssystemen an der Schnittstelle von KI-Technologie-Anwendung, Organisations- und Innovationsentwicklung sowie der menschenzentrierten Produktion
Studies of ultrafast dynamics in substrate-free nanoparticles at ELI using Timepix3 optical camera
No full textWe present a novel application of the Timepix3 optical camera (Tpx3Cam) for investigating ultrafast dynamics in substrate-free nanoparticles at the Extreme Light Infrastructure European Research Infrastructure Consortium (ELI ERIC). The camera, integrated into an ion imaging system based on a micro-channel plate (MCP) and a fast P47 scintillator, enables individual time-stamping of incoming ions with nanosecond timing precision and high spatial resolution. The detector successfully captured laser-induced ion events originating from free nanoparticles disintegrated by intense laser pulses. Owing to the broad size distribution of the nanoparticles (10-500 nm) and the variation in laser intensities within the interaction volume, the detected events range in occupancy from near-zero to extremely high, approaching the readout limits of the detector. By combining time-of-flight and velocity map imaging (VMI) techniques, detailed post-processing and analysis were performed. The results presented here focus on the performance of Tpx3Cam under high-occupancy conditions, which are of particular relevance to this study. These conditions approach the limitations imposed by the camera readout capabilities and challenge the effectiveness of standard post-processing algorithms. We investigated these limitations and associated trade-offs, and we present improved methods and algorithms designed to extract the most informative features from the data
Recommender-Systeme in Bibliothekssuchmaschinen : Ein KI-basierter Ansatz zur Empfehlung von Fachdatenbanken
No full textDiese Masterarbeit beleuchtet die Entwicklung eines dialogbasierten Recommender-Systems, das Studierenden die Auswahl und Nutzung von Fachdatenbanken erleichtern soll. Der Fokus liegt auf der Integration von Large Language Models (LLMs) und der Retrieval-Augmented Generation (RAG)-Technik, um kontextbezogene Antworten aus einer Wissensdatenbank zu generieren. Diese Wissensdatenbank wurde aus den Metadaten des Bibliotheks-Management-Tools erzeugt. Die Arbeit analysiert die Anforderungen an einen solchen Chatbot hinsichtlich organisatorischer und rechtlicher Vorgaben, wie z.B. Datenschutz, sowie die Anforderungen an die technische Infrastruktur und die Auswahl geeigneter LLMs. Zudem wird ein modellhafter Workflow skizziert, der als Grundlage für ähnliche Projekte dienen kann. Die Ergebnisse zeigen, dass durch die Kombination von LLMs und RAG-Technik durchaus ein lokaler Chatbot erstellt werden kann, der Benutzer*innen bei der Suche nach speziellen Bibliotheksressourcen unterstützen kann.The master’s thesis examines the development of a dialog-based recommender system designed to facilitate students’ selection and use of scientific databases. The focus is on the integration of Large Language Models (LLMs) and the Retrieval-Augmented Generation (RAG) technology to generate context-specific responses from a knowledge base. This knowledge base was created by extracting metadata from the library management system. The master’s thesis analyses the requirements for such a Chatbot in terms of organisational and legal guidelines, such as data protection, as well as the technical infrastructure and the selection of suitable LLMs. Additionally, a model workflow is given, which can serve as a guideline for similar projects. The findings indicate that by combining LLMs and RAG techniques, a local run Chatbot has the potential to support patrons search for special library resources
Electron thermalization and ion acceleration in XUV-produced plasma from nanoparticles in He gas environment
No full textWe use intense femtosecond extreme ultraviolet (XUV) pulses with a photon energy of 92 eV from the FLASH free electron laser to irradiate substrate-free CsCl nanoparticles surrounded by a He gas with a number density of around 1015 cm−3. By simultaneously detecting electrons and energetic ions from the laser-irradiated micron-size target we study the acceleration mechanism of light ions at the microplasma-vacuum boundary as well as at the layer close to the nanoparticle surface. When the XUV pulse interacts with the gas alone, helium ions are accelerated to energies exceeding 100 eV. In the presence of the nanoparticle, light ions gain additional energy in the electric field around the ionized nanoparticle and their energy spectrum changes considerably. We present an electrostatic model to explain the ion acceleration mechanisms both with and without the nanoparticle and discuss the role of the gas environment in experiments
Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models
No full textMicrobiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward
Embracing the third mission through art: Pathways to address climate change
No full textAddressing wicked problems of our society demands fundamental changes in the way we live. Universities play a pivotal role in this process through the creation and dissemination of knowledge, engaging broad sectors of society to improve understanding and to inspire action. However, universities face barriers in fulfilling their role in addressing complex societal challenges such as climate change. This contribution explores the potential of the arts to foster public engagement and stimulate responsible pathways of research at universities within this context. Using an integrative literature review, the paper provides an overview of conceptual contributions aimed at understanding different modes of the interplay between the arts, science, and society, or, more specifically, of integrating the arts into the university’s third mission. The overview is organized around three pillars: (i) arts in knowledge creation, (ii) arts in the communication of scientific research, and (iii) arts in the alignment of research with societal needs. Within each pillar, specific modes of interplay between the arts, science, and society are identified and exemplified with selected climate-related artworks: i) artistic research, arts-based research, arts-science collaboration, ii) arts-based science communication and iii) arts-based responsible research and innovation. The examples hihlight resources, processes and activities enabling different modes of interplay between arts, science and society. The value of defining such modes of interplay is their use as conceptual tools for deriving arts-based strategies for university’s third mission. They can help understanding the integrative potential of arts in third mission activities around diverse pillars and relevant conditions for impact assessment
Monolithically Integrated Optical Through-Silicon Waveguides for 3D Chip-to-Chip Photonic Interconnects
No full textThe scaling limitations of electrical interconnects are driving the demand for efficient optical chip-to-chip links. We report the first monolithic integration of air-clad optical through-silicon waveguides in silicon, fabricated via Bosch and cryogenic deep reactive-ion etching. Rib, single-bridge, and double-bridge designs with 50 μm cores and up to 150 μm propagation lengths have been evaluated. Cryogenic-etched rib waveguides achieve the highest median transmission (66%, −1.80 dB), compared to Bosch-etched ribs (62%, −2.08 dB). Across all geometries, 3 dB alignment windows range from 9.3 μm to 49.2 μm, with Bosch-etched double-bridge waveguides providing the broadest tolerance. We show that geometric fidelity outweighs sidewall roughness for transmission and alignment in these large-core, multimode optical through-silicon waveguides. This technology provides a scalable, complementary metal-oxide semiconductor-compatible pathway toward 3D photonic interconnects