Publikationsserver der Ostbayerischen Technischen Hochschule Regensburg
Not a member yet
6172 research outputs found
Sort by
Corporate Citizenship als strategisches Kommunikationsinstrument unternehmerischer Krankenhäuser
No full textTest Setup for Investigating the Impact Behavior of Biaxially Prestressed Composite Laminates
No full textInstrumented impact testing and compression-after-impact testing are important to adequately qualify material behavior and safely design composite structures. However, the stresses to which fiber-reinforced plastic components are typically subjected in practice are not considered in the impact test methods recommended in guidelines or standards. In this paper, a test setup for investigating the impact behavior of composite specimens under plane uniaxial and biaxial preloading is presented. For this purpose, a special test setup consisting of a biaxial testing machine and a specially designed drop-weight tower was developed. The design decisions were derived from existing guidelines and standards with the aim of inducing barely visible impact damage in laminated carbon fiber-reinforced plastic specimens. Several measurement systems have been integrated into the setup to allow comprehensive observation of the impact event and specimen behavior. A feasibility test was performed with biaxially prestressed carbon fiber-reinforced plastic specimens in comparison with unstressed reference tests. The compressive-tensile prestressing resulted in lower maximum contact forces, higher maximum deflections, higher residual deflections and a different damage pattern, which was investigated by light microscopic analysis. Finally, the functionality of the experimental setup is discussed, and the results seem to indicate that the test setup and parameters were properly chosen to investigate the effect of prestresses on the impacts behavior of composite structures, in particular for barely visible subsequent damages
Tomo-PIV in a patient-specific model of human nasal cavities: a methodological approach
No full textThe human nose serves as the primary gateway for air entering the respiratory system and plays a vital role in breathing. Nasal breathing difficulties are a significant health concern, leading to substantial healthcare costs for patients. Understanding nasal airflow dynamics is crucial for comprehending respiratory mechanisms. This article presents a detailed study using tomo-Particle Image Velocimetry (PIV) to investigate nasal airflow dynamics while addressing its accuracy. Embedded in the OpenNose project, the work described aims to provide a validation basis for different numerical approaches to upper airway flow. The study includes the manufacturing of a transparent silicone model based on a clinical CT scan, refractive index matching to minimize optical distortions, and precise flow rate adjustments based on physiological breathing cycles. This method allows for spatial high-resolution investigations in different regions of interest within the nasopharynx during various phases of the breathing cycle. The results demonstrate the accuracy of the investigations, enabling detailed analysis of flow structures and gradients. This spatial high-resolution tomo-PIV approach provides valuable insights into the complex flow phenomena occurring during the physiological breathing cycle in the nasopharynx. The study's findings contribute to advancements in non-free-of-sight experimental flow investigation of complex cavities under nearly realistic conditions. Furthermore, reliable and accurate experimental data is crucial for properly validating numerical approaches that compute this patient-specific flow for clinical purposes
Review of Usage and Potentials of Conversational Interfaces at Universities and in Students Daily Lifes
No full textThe continuous advancement of digitization extends beyond educational institutions, giving rise to numerous innovations, particularly in the realm of study information [1]. One avenue for incorporating digital methodologies involves leveraging conversational agents (CAs) [2], serving as interactive interfaces bridging the gap between humans and computers. In the broader context, conversational agents are gaining prominence, offering several benefits to their users. The overarching goal is to comprehensively assist users through these intelligent systems. Consequently, exploring existing university chatbots becomes imperative to discern the areas where they excel. This research aims to scrutinize diverse chatbot systems, delving into their use cases and the challenges they encounter, employing a systematic review. Here it turns out that chatbots support universities the most in the fields of administration, e-learning and mental health. Furthermore, the study will investigate practical experiences on the potential applications and implementation of these systems in university settings, incorporating insights from an online survey and interviews, both made with experts. Here it comes to conclusion that preparation in relation to a chatbot implementation is the key factor to success. Otherwise, a failed system is nearly impossible to be saved, once users lost trust in the system. Therefore, carefully made preparations in the technical and organisational field are necessary to provide a helpful assistant
Konzept und Evaluation eines Softwaresystems zur Unterstützung der CRM-basierten Sprechwirkungsuntersuchung
No full textDas Continuous-Response-Measurement-Verfahren bildet durch die kontinuierliche Bewertungsmöglichkeit eine wichtige Ergänzung zu den gängigen Methoden im Repertoire der Wirkungsforschung. Um diesen Mehrwert voll ausschöpfen zu können, wird als Verfahrensoptimierung die Entwicklung einer Softwarelösung vorgestellt. Die Überprüfung des optimierten CRM-Verfahrens erfolgt mittels eines Anwendungsfalls aus der sprechwissenschaftlichen Telekommunikationsforschung im Rahmen eines User-Acceptance-Tests. Dabei wird die Funktionalität und Bedienerfreundlichkeit der entwickelten CRM-Softwarelösung unter Beachtung der für die Sprechwirkungsforschung relevanten Kriterien in Form einer A-BStudie getestet.
Das Gesamtergebnis des User-Acceptance-Tests fällt für die Software Evalue positiv aus. Mit Hilfe der Verfahrensoptimierung des CRM-Verfahrens ist eine variabel einsetzbare und damit vielfältig nutzbare CRM-Softwarelösung entstanden
DeepCraftFuse: visual and deeply-learnable features work better together for esophageal cancer detection in patients with Barrett’s esophagus
No full textLimitations in computer-assisted diagnosis include lack of labeled data and inability to model the relation between what experts see and what computers learn. Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. While deep learning techniques are broad so that unseen information might help learn patterns of interest, human insights to describe objects of interest help in decision-making. This paper proposes a novel approach, DeepCraftFuse, to address the challenge of combining information provided by deep networks with visual-based features to significantly enhance the correct identification of cancerous tissues in patients affected with Barrett’s esophagus (BE). We demonstrate that DeepCraftFuse outperforms state-of-the-art techniques on private and public datasets, reaching results of around 95% when distinguishing patients affected by BE that is either positive or negative to esophageal cancer
AI for decision support: What are possible futures, social impacts, regulatory options, ethical conundrums and agency constellations?
No full textAlthough artificial intelligence (AI) and automated decision-making systems have been around for some time, they have only recently gained in importance as they are now actually being used and are no longer just the subject of research. AI to support decision-making is thus affecting ever larger parts of society, creating technical, but above all ethical, legal, and societal challenges, as decisions can now be made by machines that were previously the responsibility of humans. This introduction provides an overview of attempts to regulate AI and addresses key challenges that arise when integrating AI systems into human decision-making. The Special topic brings together research articles that present societal challenges, ethical issues, stakeholders, and possible futures of AI use for decision support in healthcare, the legal system, and border control.Obwohl künstliche Intelligenz (KI) und automa‑tisierte Entscheidungssysteme schon länger existieren, haben sie erst in jüngster Zeit stark an Bedeutung gewonnen, da sie nun tatsäch‑lich eingesetzt werden und nicht mehr nur Gegenstand der Forschung sind. KI zur Unterstützung von Entscheidungen betrifft somit immer grö‑ßere Teile der Gesellschaft, wodurch technische, vor allem aber ethi‑sche, rechtliche und soziale Herausforderungen entstehen, da nun Ent‑scheidungen von Maschinen getroffen werden können, die bisher in der Verantwortung von Menschen lagen. Diese Einführung gibt einen Über‑blick über die Versuche, KI zu regulieren, und geht auf zentrale Heraus‑forderungen ein, die sich aus der Integration von KI‑Systemen in die menschliche Entscheidungsfindung ergeben. Das Special topic versammelt Forschungsartikel, die gesellschaftliche Herausforderungen, ethi‑sche Fragen, Akteur*innen sowie mögliche Zukünfte des KI‑Einsatzes zur Entscheidungsunterstützung in der Gesundheitsversorgung, dem Rechtssystem und bei der Grenzkontrolle präsentieren
AI‑based decision support systems and society: An opening statement
No full textAlthough artificial intelligence (AI) and automated decision-making systems have been around for some time, they have only recently gained in importance as they are now actually being used and are no longer just the subject of research. AI to support decision-making is thus affecting ever larger parts of society, creating technical, but above all ethical, legal, and societal challenges, as decisions can now be made by machines that were previously the responsibility of humans. This introduction provides an overview of attempts to regulate AI and addresses key challenges that arise when integrating AI systems into human decision-making. The Special topic brings together research articles that present societal challenges, ethical issues, stakeholders, and possible futures of AI use for decision support in healthcare, the legal system, and border control
Normierung, Regulierung, Governance: Wie, von wem und mit welchen Mitteln kann der Einsatz Künstlicher Intelligenz gesellschaftlich gestaltet werden?
No full textKünstliche Intelligenz (KI) stellt eine Schlüsseltechnologie des gesellschaftlichen Wandels im 21. Jahrhundert dar. Mittlerweile werden zahlreiche technologische Anwendungen genutzt, die auf maschinellem Lernen und den damit verbundenen Möglichkeiten der Datensamm¬lung, -nutzung und -verwertung aufbauen. Indem KI große Datenmengen beherrschbar und verborgene Muster und Zusammenhänge sichtbar macht, wird vieles schneller, einfacher und effizienter – sei es im Alltag, in der Arbeit oder in Organisationen. Offen bleibt jedoch nach wie vor die Frage, welche tiefgreifenden und teilweise latenten Folgen für den Menschen als soziales Wesen und das gesellschaftliche Zusammenleben mit dem Einsatz und der Entwick¬lung von KI verbunden sind. Wie wandelt sich das Verhältnis von Mensch und Technik durch KI und wie ist dieser Wandel zu bewerten? Welche Chancen, aber auch Risiken eröffnen sich durch den Einsatz und die Entwicklung von KI für Mensch und Gesellschaft? Welchen Grenzen unterliegt der Wandel und welche Gestaltungsmöglichkeiten bieten sich? Und nicht zuletzt: Was und wer bestimmt die Entwicklungspfade, die KI nimmt – mit welchen Folgen und für wen
Early Esophageal Cancer and the Generalizability of Artificial Intelligence
No full textAims
Artificial Intelligence (AI) systems in gastrointestinal endoscopy are narrow because they are trained to solve only one specific task. Unlike Narrow-AI, general AI systems may be able to solve multiple and unrelated tasks. We aimed to understand whether an AI system trained to detect, characterize, and segment early Barrett’s neoplasia (Barrett’s AI) is only capable of detecting this pathology or can also detect and segment other diseases like early squamous cell cancer (SCC).
Methods
120 white light (WL) and narrow-band endoscopic images (NBI) from 60 patients (1 WL and 1 NBI image per patient) were extracted from the endoscopic database of the University Hospital Augsburg. Images were annotated by three expert endoscopists with extensive experience in the diagnosis and endoscopic resection of early esophageal neoplasias. An AI system based on DeepLabV3+architecture dedicated to early Barrett’s neoplasia was tested on these images. The AI system was neither trained with SCC images nor had it seen the test images prior to evaluation. The overlap between the three expert annotations („expert-agreement“) was the ground truth for evaluating AI performance.
Results
Barrett’s AI detected early SCC with a mean intersection over reference (IoR) of 92% when at least 1 pixel of the AI prediction overlapped with the expert-agreement. When the threshold was increased to 5%, 10%, and 20% overlap with the expert-agreement, the IoR was 88%, 85% and 82%, respectively. The mean Intersection Over Union (IoU) – a metric according to segmentation quality between the AI prediction and the expert-agreement – was 0.45. The mean expert IoU as a measure of agreement between the three experts was 0.60.
Conclusions
In the context of this pilot study, the predictions of SCC by a Barrett’s dedicated AI showed some overlap to the expert-agreement. Therefore, features learned from Barrett’s cancer-related training might be helpful also for SCC prediction. Our results allow different possible explanations. On the one hand, some Barrett’s cancer features generalize toward the related task of assessing early SCC. On the other hand, the Barrett’s AI is less specific to Barrett’s cancer than a general predictor of pathological tissue. However, we expect to enhance the detection quality significantly by extending the training to SCC-specific data. The insight of this study opens the way towards a transfer learning approach for more efficient training of AI to solve tasks in other domains