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As global demand for green hydrogen rises, potential hydrogen exporters move into the spotlight. However, the large-scale installation of on-grid hydrogen electrolysis for export can have profound impacts on domestic energy prices and energy-related emissions. Our investigation explores the interplay of hydrogen exports, domestic energy transition and temporal hydrogen regulation, employing a sector-coupled energy model in Morocco. We find substantial co-benets of domestic climate change mitigation and hydrogen exports, whereby exports can reduce domestic electricity prices while mitigation reduces hydrogen export prices. However, increasing hydrogen exports quickly in a system that is still dominated by fossil fuels can substantially raise domestic electricity prices, if green hydrogen production is not regulated. Surprisingly, temporal matching of hydrogen production lowers domestic electricity cost by up to 31% while the effect on exporters is minimal. This policy instrument can steer the welfare (re-)distribution between hydrogen exporting firms, hydrogen importers, and domestic electricity consumers and hereby increases acceptance among actors.
A novel method for controlling the rebound behavior of small balls made of Al2O3 with a radius of 2.381 mm is presented. It uses different types of micro-structured surfaces of soft magnetoactive elastomers. These surfaces were fabricated via laser micromachining and include fully ablated surfaces as well as micrometer-sized lamellas with a fixed width of 90 µm, height of 250 µm and three different gap sizes (15, 60 and 105 µm). The lamellas can change their orientation from edge-on to face-on configuration according to the direction of the external magnetic field from a permanent magnet. The orientation of the external magnetic field significantly influences the rebound behavior of the balls, from a coefficient of restitution e of to < 0.1. The highest relative change in the coefficient of restitution between zero field and face-on configuration of is observed for lamellas with a gap of 60 µm. Other characteristics of the ball rebound such as the penetration depth into an Magnetoactive elastomer and the maximum deceleration are investigated as well. The proposed method does not require a constant power supply due to the use of permanent magnets. It may find novel applications in the field of impact engineering.
With the ongoing miniaturization of wireless devices, the importance of wearable textiles in the antenna segment has increased significantly in recent years. Due to the widespread utilization of wireless body sensor networks for healthcare and ubiquitous applications, the design of wearable antennas offers the possibility of comprehensive monitoring, communication, and energy harvesting and storage. This article reviews a number of properties and benefits to realize comprehensive background information and application ideas for the development of lightweight, compact and low-cost wearable patch antennas. Furthermore, problems and challenges that arise are addressed. Since both electromagnetic and mechanical specifications must be fulfilled, textile and flexible antennas require an appropriate trade-off between materials, antenna topologies, and fabrication methods—depending on the intended application and environmental factors. This overview covers each of the above issues, highlighting research to date while correlating antenna topology, feeding techniques, textile materials, and contacting options for the defined application of wearable planar patch antennas.
ntervention with motivational emails can have a positive effect on course retention in e-learning. It is, however, not yet clear whether different forms of emails affect course retention and how students make progress during the sending of emails. We therefore used a voluntary asynchronous online course with 206 students. Students were randomly divided into four groups: text–picture personalised email vs. text personalised email vs. generalised email vs. no email. Emails were sent weekly for 3 months. Results yield that more students made progress in the text–picture personalised email group than in the control group. Students in all email groups progressed by more units than students in the control group. Only students in email groups completed the course and only students in personalised email groups reacted to the emails. Emails were accepted by most students enrolled. The findings suggest that cost-effective and easily implemented emails can encourage students to progress from unit to unit.
Im Betrieb von Förderbändern auftretende Phänomene sind das Bandwandern in Achsrichtung der Walzen und die Bandverformung in der Bandebene [1, S. 142]. Das Bandwandern ist bisher detailliert untersucht [2–4], während die Bandverformung Gegenstand weniger Veröffentlichungen ist. Deshalb wird in diesem Artikel eine Methode zur Berechnung der sich im dynamischen Betrieb einstellenden Verformung eines elastischen Förderbandes vorgestellt. Dazu wird ein Mehrkörpersimulations-Modell basierend auf der Lumped-Mass-Modellierung verwendet. Als Untersuchungsbeispiel dient ein Zwei-Walzensystem mit flexiblen und zylindrischen Walzen, sowie ein flexibles Förderband. Es zeigt sich, dass mit der Lumped-Mass-Modellierung eine dynamische Bandverformung, die von der gewählten Diskretisierung abhängt, berechnet werden kann. Aufgrund dieser Abhängigkeit ist es notwendig eine Konvergenzanalyse durchzuführen. Zusätzlich ist darauf zu achten, dass die künstliche Anregung aufgrund der Lumped-Mass-Modellierung nicht mit einer Eigenfrequenz des Modells zusammenfällt.
Der Einbau mineralischer, granularer Lasttransferschichten (LTS) zwischen einem Bau-werk und einer Tiefgründung z. B. aus pfahlartigen Traggliedern (PTG) beeinflusst erheb-lich die Lastaufteilung auf die PTG und den zumeist weichen Boden dazwischen. Eine genaue Kenntnis über die Lastaufteilung ist für die Bemessung der LTS und PTG, aber auch des darauf gegründeten Bauwerks erforderlich. Für die Quantifizierung der Lastauf-teilung wurden eine Vielzahl an Berechnungsansätzen entwickelt, welche zumeist auf Modell- oder Großversuchen basieren, aus deren Ergebnissen analytische Ansätze entwi-ckelt wurden. Zunächst werden ausgewählte Berechnungsansätze zur Lastaufteilung vor-gestellt, dann mit Hilfe von Finite-Element-Berechnungen der Einfluss verschiedener Parameter auf die Lastaufteilung veranschaulicht und diskutiert. Für geringe Dicken der LTS ergibt sich dabei eine hohe Übereinstimmung der FE-Berechnungen mit einem Durchstanzmechanismus. Ab einer bestimmten Dicke der LTS, abhängig von ver-schiedensten Randbedingungen, bildet sich in den FE-Berechnungen ein Gewölbe aus. Das Einstanzen der PTG in die LTS kann dann durch einen Grundbruchmechanismus beschrieben und als obere Begrenzung der Lastumlagerung auf die PTG angesehen wer-den. Anhand zahlreicher Vergleichsberechnungen konnte gezeigt werden, dass die beiden Mechanismen Durchstanzen und Grundbruch in sehr vielen Fällen ausreichen, um die Lastaufteilung auch ohne FE-Berechnungen abzuschätzen.
Bei der Untersuchung und Optimierung der Getriebedynamik, im Sinne der Akustik und Lastverteilung, wird oftmals der Drehwegfehler als Bewertungskriterium herangezogen. Zahlreiche Forschungsvorhaben haben sich in diesem Zusammenhang intensiv mit dem Drehwegfehler als parametererregte Schwingungsanregung auseinandergesetzt, um diesen im Hinblick auf das Systemverhalten zu optimieren. Dabei bleibt der Zahnfußbereich, welcher außerhalb der Evolvente liegt, und dessen Einfluss auf den Drehwegfehler oftmals unberücksichtigt.
Dieser Artikel untersucht daher den Einfluss des Zahnfußbereiches auf den statischen Drehwegfehler, indem relevante Größen wie das spezifische Kopfspiel und der Fußrundungsradius an einer typischen und real eingesetzten Stirnradverzahnung variiert werden.
Ferner wird betrachtet, inwiefern der Drehwegfehler durch unterschiedliche Geometriedefinitionen (Fertigungssimulation, Zahngeometrie mit vereinfachter sowie stark vereinfachter Modellierung des Zahnfußbereichs) beeinflusst wird. Die Autoren nutzen hierfür zwei FE-Ansätze (adaptives und nicht-adaptives Netz) und bewerten diese hinsichtlich ihrer Gültigkeit, Modellgüte und Praktikabilität.
Die Ergebnisse der Untersuchungen werden hinsichtlich einer minimal erforderlichen Geometrie des Zahnfußbereiches interpretiert. Für den Praxistransfer wird einerseits der maximale Fehler bei Nichtberücksichtigung des gesamten Zahnfußbereichs beziffert.
Andererseits wird eine Empfehlung zur Anpassung einer vereinfachten Geometrie, an eine Geometrie, ähnlich der aus einer Fertigungssimulation, gegeben. Weiterhin wird der Artikel diskutieren, ob man ein vereinfachtes Zahnmodell zur Berechnung der Getriebedynamik nutzen kann, wenn der Fertigungsprozess noch nicht definiert ist.
Es gibt eine Vielzahl von „Wie-erstelle-ich-ein Finite-Element-Programm?“-Lehrbüchern, aber nur recht wenige Veröffentlichungen zur Frage „Wie wende ich ein Finite-Element-Programm an?“. Dieses Buch legt den Schwerpunkt auf die zweite Fragestellung. Es basiert auf den Vorlesungen zur Anwendung der Finite-Element-Methode, die der Autor seit 1998 an der OTH Regensburg hält. Deren Inhalte kommen aus seiner Tätigkeit als Prüfingenieur für Baustatik in einem großen Münchener Ingenieurbüro. Behandelt werden sowohl Fragestellungen, mit denen sich jeder Ingenieur konfrontiert sieht, wenn er Berechnungen mit einem Finite-Element-Programm erstellen will, als auch Problempunkte, die im Büro des Autors im Zuge einer Projektbearbeitung auftraten und auf den ersten Blick gar nicht so klar waren. Der 2.Teil des zweibändigen Werkes befasst sich mit Themen aus der nichtlinearen Statik.
Generative deep learning approaches for the design of dental restorations: A narrative review
(2024)
Objectives:
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
Data/Sources:
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.
Study selection:
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.
Conclusions:
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.
Clinical significance:
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.
The present paper takes a novel approach to production of fibre-reinforced thermoplastic tubes. The method begins with the raw materials, reinforcing fibre and thermoplastic granulate which are processed to tapes through a newly developed direct impregnation process. It is followed by consolidation of fibre-reinforced thermoplastic tubes using infrared (IR) emitters in the filament winding process. This process employs various angles and utilizes a rotatable consolidation axis. The winding process operates at a constant speed, addressing the challenge of bending the fibre-reinforced tapes in the angle reversal areas near the tube ends. Experiments have confirmed that the process can run at speeds reaching approximately 470 mm/min. The design of the impregnation line takes into account the properties of the thermoplastic and the roving, allowing for a speed of up to 1 m/s.
Background:
With the prevalence of burnout among surgeons posing a significant threat to healthcare outcomes, the mental toughness of medical professionals has come to the fore. Mental toughness is pivotal for surgical performance and patient safety, yet research into its dynamics within a global and multi-specialty context remains scarce. This study aims to elucidate the factors contributing to mental toughness among surgeons and to understand how it correlates with surgical outcomes and personal well-being.
Methods:
Utilizing a cross-sectional design, this study surveyed 104 surgeons from English and German-speaking countries using the Mental Toughness Questionnaire (MTQ-18) along with additional queries about their surgical practice and general life satisfaction. Descriptive and inferential statistical analyses were applied to investigate the variations in mental toughness across different surgical domains and its correlation with professional and personal factors.
Results:
The study found a statistically significant higher level of mental toughness in micro-surgeons compared to macro-surgeons and a positive correlation between mental toughness and surgeons' intent to continue their careers. A strong association was also observed between general life satisfaction and mental toughness. No significant correlations were found between the application of psychological skills and mental toughness.
Conclusion:
Mental toughness varies significantly among surgeons from different specialties and is influenced by professional dedication and personal life satisfaction. These findings suggest the need for targeted interventions to foster mental toughness in the surgical community, potentially enhancing surgical performance and reducing burnout. Future research should continue to explore these correlations, with an emphasis on longitudinal data and the development of resilience-building programs.
Digital Twins (DT) implementation in the Built Environment (BE) industry is still in its early stages. Aiming to increase the knowledge about DT, this study analyzes how DT can be understood in the BE sector and investigates its different potential benefits and expected challenges. To do so, the Systematic Literature Review (SLR) approach was employed. Using 228 publications, the current study presents a proposed definition and structure for DT systems. The proposed structure is based on four main layers: physical, digital, application, and user layers. The study also classified the applications of DT into six groups: sustainability and environmental, facility management, safety, health, and risk management, structural performance, construction management, and architectural and urban-related applications. The challenges of DT implementation were also grouped based on industry-related, social and organizational, economic, technological, and political and legal challenges. Based on the results, future research directions and practical recommendations were presented to support the successful deployment of the technology.
Künstliche 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?
Um den zunehmenden Anforderungen an die Beherrschung digitaler Techniken und an die Fähigkeit zur interdisziplinären Zusammenarbeit an Studierende aller Fachrichtungen zu begegnen wurde das interdisziplinäre Lehrformat Digitalisierungskollegs für Studierende entwickelt. Das in vielen Fachbereichen ausbaufähige Angebot von Digitalthemen in der Hoch- schullehre wird hiermit dauerhaft erweitert. Ein Digitalisierungskolleg besteht aus einer Vorlesungs- reihe mit angrenzendem Seminar, in denen Studierende interdisziplinäre Lösungen für Fragen der digitalen Transformation entwickeln. Geleitet werden sie von etablierten Wissenschaftlerinnen und Wissenschaftlern, aktiv betreut und ausgestaltet von ein bis zwei Coaches. Kernelement sowohl des Kollegs als auch der einzelnen Projekte ist die Interdisziplinarität. Eine*r der beteiligten Projekt- leiter*innen hat einen direkten Bezug zur Technik und kommt aus der Informatik, der Wirtschafts- informatik, der Elektrotechnik oder vergleichbaren Disziplinen. Zielgruppe der Projekte sind Studierende verschiedener Disziplinen im Masterstudium oder in den letzten Semestern eines Bachelorstudiums. Durch die Teilnahme erwerben auch Studierende aus digitalisierungsfernen Fächern frühzeitig umfangreiche IT-Kenntnisse. Als Begleiteffekt der umfangreichen Vernetzung zwischen den Digitalisierungskollegs (Studierende, Coaches und Projektleitende) entsteht bereits zu Beginn einer wissenschaftlichen Karriere eine große digitale Community. Alle Teilnehmenden lernen frühzeitig die interdisziplinäre Zusammenarbeit und verbessern erheblich ihre Karriere- chancen innerhalb und außerhalb der Wissenschaft.
Automated use of wired measures in grid planning for solving current and voltage band problems
(2019)
This work deals with the use of cable exchange and parallel cabling as network expansion measures in the context of an automated grid planning for the elimination of current and voltage band problems in a distribution network. Inter alia, reference is made to a higher-level program, which is being developed in the EU project "CrossEnergy", and the procedures for remedying the limit value violations are presented.
Background
This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett’s esophagus (BE).
Methods
96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett’s esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level.
Results
AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%–74.2%] to 78.0% [95%CI 74.0%–82.0%]; specificity 67.3% [95%CI 62.5%–72.2%] to 72.7% [95%CI 68.2%–77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI.
Conclusion
BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists’ decisions to follow or discard AI advice.
Aims
Recent evidence suggests the possibility of intraprocedural phase recognition in surgical operations as well as endoscopic interventions such as peroral endoscopic myotomy and endoscopic submucosal dissection (ESD) by AI-algorithms. The intricate measurement of intraprocedural phase distribution may deepen the understanding of the procedure. Furthermore, real-time quality assessment as well as automation of reporting may become possible. Therefore, we aimed to develop an AI-algorithm for intraprocedural phase recognition during ESD.
Methods
A training dataset of 364385 single images from 9 full-length ESD videos was compiled. Each frame was classified into one procedural phase. Phases included scope manipulation, marking, injection, application of electrical current and bleeding. Allocation of each frame was only possible to one category. This training dataset was used to train a Video Swin transformer to recognize the phases. Temporal information was included via logarithmic frame sampling. Validation was performed using two separate ESD videos with 29801 single frames.
Results
The validation yielded sensitivities of 97.81%, 97.83%, 95.53%, 85.01% and 87.55% for scope manipulation, marking, injection, electric application and bleeding, respectively. Specificities of 77.78%, 90.91%, 95.91%, 93.65% and 84.76% were measured for the same parameters.
Conclusions
The developed algorithm was able to classify full-length ESD videos on a frame-by-frame basis into the predefined classes with high sensitivities and specificities. Future research will aim at the development of quality metrics based on single-operator phase distribution.
Aims
While AI has been successfully implemented in detecting and characterizing colonic polyps, its role in therapeutic endoscopy remains to be elucidated. Especially third space endoscopy procedures like ESD and peroral endoscopic myotomy (POEM) pose a technical challenge and the risk of operator-dependent complications like intraprocedural bleeding and perforation. Therefore, we aimed at developing an AI-algorithm for intraprocedural real time vessel detection during ESD and POEM.
Methods
A training dataset consisting of 5470 annotated still images from 59 full-length videos (47 ESD, 12 POEM) and 179681 unlabeled images was used to train a DeepLabV3+neural network with the ECMT semi-supervised learning method. Evaluation for vessel detection rate (VDR) and time (VDT) of 19 endoscopists with and without AI-support was performed using a testing dataset of 101 standardized video clips with 200 predefined blood vessels. Endoscopists were stratified into trainees and experts in third space endoscopy.
Results
The AI algorithm had a mean VDR of 93.5% and a median VDT of 0.32 seconds. AI support was associated with a statistically significant increase in VDR from 54.9% to 73.0% and from 59.0% to 74.1% for trainees and experts, respectively. VDT significantly decreased from 7.21 sec to 5.09 sec for trainees and from 6.10 sec to 5.38 sec for experts in the AI-support group. False positive (FP) readings occurred in 4.5% of frames. FP structures were detected significantly shorter than true positives (0.71 sec vs. 5.99 sec).
Conclusions
AI improved VDR and VDT of trainees and experts in third space endoscopy and may reduce performance variability during training. Further research is needed to evaluate the clinical impact of this new technology.
Aims
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.
Aims
Endoscopic retrograde cholangiopancreaticography (ERCP) is the gold standard in the diagnosis as well as treatment of diseases of the pancreatobiliary tract. However, it is technically complex and has a relatively high complication rate. In particular, cannulation of the papillary ostium remains challenging. The aim of this study is to examine whether a deep-learning algorithm can be used to detect the major duodenal papilla and in particular the papillary ostium reliably and could therefore be a valuable tool for inexperienced endoscopists, particularly in training situation.
Methods
We analyzed a total of 654 retrospectively collected images of 85 patients. Both the major duodenal papilla and the ostium were then segmented. Afterwards, a neural network was trained using a deep-learning algorithm. A 5-fold cross-validation was performed. Subsequently, we ran the algorithm on 5 prospectively collected videos of ERCPs.
Results
5-fold cross-validation on the 654 labeled data resulted in an F1 value of 0.8007, a sensitivity of 0.8409 and a specificity of 0.9757 for the class papilla, and an F1 value of 0.5724, a sensitivity of 0.5456 and a specificity of 0.9966 for the class ostium. Regardless of the class, the average F1 value (class papilla and class ostium) was 0.6866, the sensitivity 0.6933 and the specificity 0.9861. In 100% of cases the AI-detected localization of the papillary ostium in the prospectively collected videos corresponded to the localization of the cannulation performed by the endoscopist.
Conclusions
In the present study, the neural network was able to identify the major duodenal papilla with a high sensitivity and high specificity. In detecting the papillary ostium, the sensitivity was notably lower. However, when used on videos, the AI was able to identify the location of the subsequent cannulation with 100% accuracy. In the future, the neural network will be trained with more data. Thus, a suitable tool for ERCP could be established, especially in the training situation.
One major goal of production system design is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements and the capital investment in raw materials and should therefore be considered in factory and production planning. This research article proposes a simulation approach for a production simulation in which raw material replenishment is integrated to evaluate the capital investment and space requirements for raw materials.
Transient surrogate modeling of modally reduced structures with discontinuous loads and damping
(2024)
In this work, a surrogate model for structural, transient and discontinuously excited finite element method simulations is developed. This allows to reduce the computational effort of repeated calculations of identical models under different load cases. The architecture of the surrogate combines fully connected neural network layers with long short-term memory layers. For the reproduction of different damping ratios, a categorical variable is added to the continuous input data. Based on a recursive flow of the predicted data back to the input layer, long-term dependencies do not vanish due to short-input sequences. The system dimension is reduced by applying the model-order reduction technique for modal decomposition. The high accuracy of the surrogate and the reduction of computational costs are shown on an academic example of a cantilever beam and a real-world example of a robot. The advantages of our approach are illustrated in comparison with state-of-the-art surrogates for transient finite element analysis. By using the surrogate proposed in this study, oscillations due to discontinuous excitation of mechanical structures can be reproduced. For this purpose, only short-input sequences are necessary since the excitation of the oscillations does not have to be part of the input sequence during the whole duration of the oscillations. Due to the categorical variable for the damping ratio, the surrogate can account for the influence of different damping in parameter studies.
Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.
Weibliche Soldaten
(2005)
Seit Ende der 80er Jahre wird in der amerikanischen Militärsoziologie die Frage diskutiert, ob der Beruf des Soldaten eine „profession of arms“ ist, die in einer Institution mit besonderen Merkmalen ausgeübt wird, oder sich vielmehr zu einem „Job wie jeder andere“, also einer „occupation“ entwickelt habe. Versucht man den Unterschied auf einen knappen Nenner zu bringen, so legitimiert sich eine Institution durch spezifische Normen und Werte und verfolgt einen Zweck, der das individuelle Eigeninteresse transzendiert; ein „Job“ oder ein „Beruf wie jeder andere“ folgt demgegenüber dem Prinzip von Angebot und Nachfrage, beruht auf einer Fixierung von Rechten und Pflichten und wird von den Individuen auf der Grundlage utilitaristischer Erwägungen gewählt (vgl. Moskos 1988).
Die hohe Rate an Geburtsverletzungen unter Spontangeburt mit teilweise gravierenden Kurz- und Langzeitfolgen stellt eine physische und psychosoziale Belastung für Gebärende dar, sodass der Wunsch nach vollständig erhaltenem Damm und erhaltener Beckenbodenmuskulatur stark vorhanden ist. Als Erhaltung der Gesundheit von Mutter und Kind stellt der Schutz des Dammes und Beckenbodens eine Kernkompetenz der Hebammen dar. Um dieser nachzukommen, wird die Pathophysiologie des Dammrisses erörtert. Nachdem der weibliche Beckenboden anatomisch und histologisch aufgearbeitet wurde sowie die Pathophysiologie der Wundentstehung, konnte unter Einbeziehung des Verhaltens des Beckenbodens unter Spontangeburt die Pathophysiologie des Dammrisses aufgezeigt werden. Diese ist ein Zusammenspiel aus Druck auf das Gewebe durch das tiefertretende Kind und zum Teil aus den wirkenden Scherkräften der stattfindenden Dehnung. Der entscheidende Mechanismus liegt jedoch beim Druck, da dieser eine Minderversorgung der Zellen verursacht, sodass es zur hypoxischen Ischämie und schließlich zum nekrotischen Zelltod kommt. Es konnte aufgezeigt werden, dass zusätzliche Anspannung einen stark negativen Faktor für Geburtsverletzungen darstellt, weshalb die genetische Disposition sowie das maternales Alter valide Risikofaktoren sind und aus welchen Gründen eine trainierte Beckenbodenmuskulatur von Vorteil sein dürfte.
It remains difficult to segregate pelagic habitats since structuring processes are dynamic on a wide range of scales and clear boundaries in the open ocean are non-existent. However, to improve our knowledge about existing ecological niches and the processes shaping the enormous diversity of marine plankton, we need a better understanding of the driving forces behind plankton patchiness. Here we describe a new machine-learning method to detect and quantify pelagic habitats based on hydrographic measurements. An Autoencoder learns two-dimensional, meaningful representations of higher-dimensional micro-habitats, which are characterized by a variety of biotic and abiotic measurements from a high-speed ROTV. Subsequently, we apply a density-based clustering algorithm to group similar micro-habitats into associated pelagic macro-habitats in the German Bight of the North Sea. Three distinct macro-habitats, a “surface mixed layer,” a “bottom layer,” and an exceptionally “productive layer” are consistently identified, each with its distinct plankton community. We provide evidence that the model detects relevant features like the doming of the thermocline within an Offshore Wind Farm or the presence of a tidal mixing front.
Aims
Human-computer interactions (HCI) may have a relevant impact on the performance of Artificial Intelligence (AI). Studies show that although endoscopists assessing Barrett’s esophagus (BE) with AI improve their performance significantly, they do not achieve the level of the stand-alone performance of AI. One aspect of HCI is the impact of AI on the degree of certainty and confidence displayed by the endoscopist. Indirectly, diagnostic confidence when using AI may be linked to trust and acceptance of AI. In a BE video study, we aimed to understand the impact of AI on the diagnostic confidence of endoscopists and the possible correlation with diagnostic performance.
Methods
22 endoscopists from 12 centers with varying levels of BE experience reviewed ninety-six standardized endoscopy videos. Endoscopists were categorized into experts and non-experts and randomly assigned to assess the videos with and without AI. Participants were randomized in two arms: Arm A assessed videos first without AI and then with AI, while Arm B assessed videos in the opposite order. Evaluators were tasked with identifying BE-related neoplasia and rating their confidence with and without AI on a scale from 0 to 9.
Results
The utilization of AI in Arm A (without AI first, with AI second) significantly elevated confidence levels for experts and non-experts (7.1 to 8.0 and 6.1 to 6.6, respectively). Only non-experts benefitted from AI with a significant increase in accuracy (68.6% to 75.5%). Interestingly, while the confidence levels of experts without AI were higher than those of non-experts with AI, there was no significant difference in accuracy between these two groups (71.3% vs. 75.5%). In Arm B (with AI first, without AI second), experts and non-experts experienced a significant reduction in confidence (7.6 to 7.1 and 6.4 to 6.2, respectively), while maintaining consistent accuracy levels (71.8% to 71.8% and 67.5% to 67.1%, respectively).
Conclusions
AI significantly enhanced confidence levels for both expert and non-expert endoscopists. Endoscopists felt significantly more uncertain in their assessments without AI. Furthermore, experts with or without AI consistently displayed higher confidence levels than non-experts with AI, irrespective of comparable outcomes. These findings underscore the possible role of AI in improving diagnostic confidence during endoscopic assessment.
Academic self-regulation is a key factor for motivation and learning achievement. Yet with the large range of individual factors, this is not a one-size-fits-all proposition. This study of L2 Technical English students at two German universities explored learners’ expectations and motivations, in particular regarding self-regulation and self-efficacy via the individual’s time investment in self-led study. In an initial survey, learners (N=1646) reported on their English skill levels and anticipated learning habits. Complementarily, the retrospective survey investigated learners’ (N=796) actual behavior during the course, their perceptions of language skill improvement, and their satisfaction. The initial survey indicates a clear understanding that time investment in self-regulated study will lead to greater improvement, an outcome confirmed in the retrospective survey. Additionally, students who invested more time in their coursework were more satisfied with their achievement, although most learners acknowledge they should have studied more. The results verify that learners recognize the nexus between self-regulation and language skill improvement, yet university students are not satisfied with their capacity to self-regulate their language learning strategies. While differences in students’ skill levels and academic self-efficacy result in divergent degrees of progress, students of all types report benefits to their language skills when motivated to self-regulated study.
Effect of AI on performance of endoscopists to detect Barrett neoplasia: A Randomized Tandem Trial
()
Background and study aims
To evaluate the effect of an AI-based clinical decision support system (AI) on the performance and diagnostic confidence of endoscopists during the assessment of Barrett's esophagus (BE).
Patients and Methods
Ninety-six standardized endoscopy videos were assessed by 22 endoscopists from 12 different centers with varying degrees of BE experience.
The assessment was randomized into two video sets: Group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level.
Results
AI had a standalone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.6%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1 and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.7% (95% CI, 65.2% - 74.2%) to 78.0% (95% CI, 74.0% - 82.0%); specificity 67.3% (95% CI, 62.5% - 72.2%) to 72.7% (95 CI, 68.2% - 77.3%). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI.
Conclusion
BE nonexperts benefitted significantly from the additional AI. BE experts and nonexperts remained below the standalone performance of AI, suggesting that there may be other factors influencing endoscopists to follow or discard AI advice.
In the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions.
Complementing this process with a global sensitivity analysis can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component.
Here, we show in an exemplary application problem, that both a standard kriging model and an ensemble model can provide commendable results at a fraction of the computational cost. Moreover, we compare our surrogate models to existing approaches in the field. Furthermore, we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase.
Control and Automation of services of the urban infrastructure offered to citizens and tourists are elementary parts of a smart city. But both rely on a stable supply of data from sensors spread across the whole city, e. g., the fill level sensors of waste bins needed for a waste management tool which we developed in a collaboration with the Regensburg city council for the on-demand collection of waste bins. Europe has a lot of historic cities like Regensburg with narrow streets and huge building walls, some made from granite and fieldstones, which often represents an insurmountable obstacle to wireless data transmission. The reduction of the road traffic volume poses an additional challenge for city planners. By means of networked planning and simulation software, the situation, state and efficiency of citywide logistic services can be monitored and optimized. In the course of such optimizations, we propose the combination of digital and logistic services. As an example, we show that monitoring state information, such as the waste bin fill levels, can be accomplished using the same vehicles and the same planning software, that is used for luggage transportation. Moreover, we describe how we adapted a solver for a variant of the TSP, namely the prize-collecting traveling salesman, to optimize the route planning dynamically.
Dieses essential erläutert, wie B2B-Unternehmen Inbound-Marketing erfolgreich umsetzen können, um ihre Vertriebsergebnisse nachhaltig zu verbessern. Der Schlüssel liegt in kontinuierlich generiertem Content, der über geeignete Kanäle ausgespielt wird – mit dem Ziel, dass Potenzialkunden von sich aus auf das verkaufende Unternehmen zukommen. Marketing-Automation-Software ermöglicht dabei eine individuelle und gleichzeitig kostengünstige Interaktion mit potenziellen Kunden. Ist ein Lead „Sales Ready“, kann der Vertrieb den bereits interessierten und qualifizierten Kontakt übernehmen.
We investigate the influence of the geometry and doping level on the performance of n-type silicon nanowire field emitters on silicon pillar structures. Therefore, multiple cathodes with 50 by 50 pillar arrays (diameter: 5 μm, height: 30 μm, spacing: 50 μm) were fabricated and measured in diode configuration. In the first experiment, we compared two geometry types using the same material. Geometry 1 is black silicon, which is a highly dense surface covering a forest of tightly spaced silicon needles resulting from self-masking during a plasma etching process of single crystal silicon. Geometry 2 are silicon nanowires, which are individual spaced-out nanowires in a crownlike shape resulting from a plasma etching process of single crystal silicon. In the second experiment, we compared two different silicon doping levels [n-type (P), 1–10 and <0.005 Ω cm] for the same geometry. The best performance was achieved with lower doped silicon nanowire samples, emitting 2 mA at an extraction voltage of 1 kV. The geometry/material combination with the best performance was used to assemble an integrated electron source. These electron sources were measured in a triode configuration and reached onset voltages of about 125 V and emission currents of 2.5 mA at extraction voltages of 400 V, while achieving electron transmission rates as high as 85.0%.
AbstractRegularized static friction models have been used successfully for many years. However, they are unable to maintain static friction in detail. For this reason, dynamic friction models have been developed and published in the literature. However, commercial multibody simulation packages such as Adams, RecurDyn, and Simpack have developed their own specific stick-slip models instead of adopting one of the public domain approaches. This article introduces the fundamentals of these commercial models and their behavior from a practical point of view. The stick-slip models were applied to a simple test model and a more sophisticated model of a festoon cable system using their standard parameters.
An inexpensive UV-LED photoacoustic based real-time sensor-system detecting exhaled trace-acetone
(2024)
n this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) – providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm 1Hz 0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.
An Inexpensive Uv-Led Photoacoustic Based Real-Time Sensor-System Detecting Exhaled Trace-Acetone
(2024)
In this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) – providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 6.4 ppbV and an NNEA of 1.1E-9 Wcm-1Hz-0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.
Small and medium-sized enterprises (SMEs) increasingly need to manage nformation technology (IT) effectively in order to remain competitive. However, compared to larger organizations, SMEs often face challenges in terms of resources and employer attractiveness, and regularly do not have the need to employ a Chief Information Officer (CIO) on a full-time basis. To address this issue, a growing number of global experts have begun to provide CIO services on a part-time basis for multiple clients simultaneously. This approach allows SMEs to tap into the expertise of experienced IT leaders at a fraction of the cost and without committing to long-term arrangements. While these professionals, known as “Fractional CIOs”, have proven their value in the field, there has been a lack of academic research on this emerging trend. Therefore, we carried out a comprehensive research project between 2020 and 2023, involving 62 Fractional CIOs from 10 countries. The research produced a definition, different types of engagements, and success factors for Fractional CIOs and their engagements. This paper summarizes these findings for a wider audience of academics and practitioners.
Der zweite ITG-Workshop „Sprachassistenten – Anwendungen, Implikationen, Entwicklungen“ fand am 5. März 2024 in Regensburg statt. Er bot eine organisatorische und inhaltliche Fortführung des ersten Workshops vor vier Jahren in Magdeburg 2020. Auch in diesem Jahr war er wieder der Konferenz Elektronische Sprachsignalverarbeitung angegliedert. Auf dem Workshop wurden vielfältige und interdisziplinäre Beiträge in eingeladenen Vorträgen und als eingereichte Poster präsentiert. Durch die gute Mischung von Beitragenden sowohl aus der Hochschullandschaft als auch aus der Industrie wurden die verschiedensten Aspekte anwendungsnah diskutiert.
One of the tasks PAULE[1, 2] solves is finding suitable control parameter (cp-)trajectories for a given target acoustic. These cp-trajectories can be used to synthesize speech with the articulatory speech synthesizer of the VocalTractLab (VTL) [3]. If the target acoustic contains substantial microphone noise or other background noises, occasionally PAULE optimizes not for the speech in the target, but for this background noises. By adding a speech/non-speech classifier to the feedback and planning-loop in PAULE this resynthesis of background noises should be mitigated. Unfortunately, the improvements were minor, which might be due to uninformative gradients of the classifier. The importance of informative gradients and the use classifiers to adapt PAULE to different tasks are explained and discussed.
There is high confidence for the hypothesis that in speech perception the cycles of a θ-oscillation segment the auditory signal into syllables [8]. Yet the functionality of the oscillator generating the θ-oscillation is unknown. We follow the finding that, within an auditory scene, speech is perceived as a stream given by temporal coherence [12]. We work with the hypotheses that the θ-oscillator is driven by temporal features providing this coherence. We propose a new temporal feature called O-distance, which detects the onset of a syllable - the starting point to of a θ-cycle–triggered by the temporal distance from to to the instance of the maximal rise of the loudness curve of the vowel. To extract to from the auditory signal, we use the statistical properties of this distance based on the C-center hypothesis [25], which predicts a close temporal relation of the onset consonants to the onset of a vowel. The statistics are derived from reference O-distance extracted from an articulatory database, where the minima and maxima of the loudness are related to maxima and minima of the lower incisor and tongue tip. To judge the quality of the O-distance extracted from the auditory signal, we regard the temporal deviation of the O-distance to the reference O-distance. Currently we achieve a mean deviation of 34ms.
This paper addresses the challenges and advancements in speech recognition for singing, a domain distinctly different from standard speech recognition. Singing encompasses unique challenges, including extensive pitch variations, diverse vocal styles, and background music interference. We explore key areas such as phoneme recognition, language identification in songs, keyword spotting, and full lyrics transcription. I will describe some of my own experiences when performing research on these tasks just as they were starting to gain traction, but will also show how recent developments in deep learning and large-scale datasets have propelled progress in this field. My goal is to illuminate the complexities of applying speech recognition to singing, evaluate current capabilities, and outline future research directions.
Für den sicheren Betrieb eines Brennstoffzellenfahrzeugs sind Sensoren wichtig, welche die Wasserstoffkonzentration im Fahrzeuginterieur kontinuierlich überwachen, um möglichen Entzündungen bzw. Explosionen vorzubeugen. Dafür wurden von verschiedenen nationalen und internationalen Instanzen bestimmte Kriterien definiert, die Wasserstoffsensoren für einen Einsatz in Brennstoffzellenfahrzeugen erfüllen müssen. Im Jahr 2020gab es noch keinen einzigen kommerziell erhältlichen Sensor, der alle diese Kriterien bedienen konnte.
In der vorliegenden Arbeit wurde für zwei verschiedene Sensoren die Eignung des thermischen Wirkprinzips für die Detektion von Wasserstoff im Kontext dieser Sicherheitskriterien untersucht. Dabei wurden mit einem MEMS-Sensor und einem Mikrodrahtsensor zwei vergleichsweise einfache Geometrien herangezogen, welche den eindimensionalen und zylindersymmetrischen Fall abbilden sollen. Für zwei dynamische Operationsmodi (gepulste Ströme und 3ω-Methode) wurden darauf aufbauend analytische Modelle basierend auf vereinfachten Sensorgeometrien entwickelt, um die Temperaturentwicklung des
Sensorelements in Abhängigkeit der Zeit zu charakterisieren. Diese Studien wurden anschließend anhand von detaillierteren Modellen mithilfe von FEM-Simulationen ergänzt und spezifiziert. Darin konnte gezeigt werden, dass für den gepulsten Betrieb sowohl die Pulshöhe als auch die Zeitkonstante des gemessenen Widerstands- bzw. Temperaturpulses als Messgröße herangezogen werden kann. Beim 3ω-Betrieb konnte zudem eine starke Frequenzabhängigkeit dieser Widerstands- bzw. Temperaturcharakteristik beobachtet werden. Die analytischen und numerischen Studien wurden anschließend durch experimentelle Ergebnisse bestätigt. Im Hinblick auf die zuvor definierten Kriterien stellte sich heraus, dass die Auswertung der Zeitkonstante beim gepulsten Betrieb im Hinblick auf Sensitivität und Auflösungsgrenze der Auswertung der Pulshöhe deutlich unterlegen ist. Beim 3ω-Betrieb können durch die hochintegrierte Messumgebung deutlich höhere Genauigkeiten und Auflösungsgrenzen erzielt werden. Für den MEMS-Sensor wurde bei einer Anregungsfrequenz von 1 Hz mit 55 ppm die bislang geringste in der Literatur berichtete Auflösungsgrenze für einen thermischen Wasserstoffsensor erreicht. Der Leistungsverbrauch ist dabei im Vergleich zum Pulsbetrieb entsprechend des jeweiligen Tastgrads leicht bis mäßig erhöht, die Ansprechzeit wird bei beiden Operationsmodi maßgeblich durch die Sensorgeometrie beeinflusst. Die geometrischen Parameter der jeweiligen Sensoren haben ferner einen starken Einfluss auf die Sensitivität, womit die Divergenz in der
Performance von MEMS-Chip und Drahtsensor erklärt werden kann. Es besteht ferner eine starke Temperatur- und Feuchtigkeitsabhängigkeit des Sensorsignals, welches bei beiden Operationsmodi indirekt proportional zur Wärmeleitfähigkeit des betrachteten Gasgemischs ist. Die Abhängigkeit vom definierten Umgebungsdruck ist lediglich für die
Zeitkonstante relevant, bei der Pulshöhe besteht keine Druckabhängigkeit. Es konnte eine grundsätzliche Eignung des thermischen Messprinzips für den Einsatz eines Wasserstoffsensors im Automobilbereich festgestellt werden. Für eine Anwendung müssen allerdings Referenzsensoren für Temperatur, Feuchtigkeit und Druck implementiert werden und eine Kalibrierung in Abhängigkeit dieser Größen erfolgen. Der 3ωBetrieb ist aufgrund der höheren Genauigkeit zu bevorzugen. Während der MEMSSensor im Hinblick auf Sensitivität, Ansprechzeit und Leistungsaufnahme gut abschneidet, liegen die Vorteile des Mikrodrahtsensors in seiner Robustheit und der vergleichsweise einfachen Realisierbarkeit
Als Alternative zur konventionellen Bettung von Leitungen und Rohren in Sand werden immer häufiger Zeitweise fließfähige, Selbstverdichtende Verfüllbaustoffe (ZFSV) verwendet. Die Bettung in ZFSV kann zu einer Zeit- und Kostenersparnis führen. In diesem Beitrag wird gezeigt, dass die Verwendung von ZFSV zur Rohrgrabenverfüllung gegenüber der konventionellen Sandbettung neben der Schonung der natürlichen Ressourcen zusätzlich ein Einsparpotenzial bezüglich der Treibhausgasemissionen aufweist.
Nach Einführung in die Thematik werden unter Berücksichtigungen der EBGEO und des aktuellen Entwurfs des neuen Eurocodes 7, Teil 3 numerische Berechnungen zur Beanspruchung unbewehrter pfahlartiger Tragglieder / Stabilisierungssäulen aufgrund von Spreizverformungen von Erddämmen durchgeführt. Dabei werden u.a. die Materialeigenschaften der Tragglieder und die Dehnsteifigkeiten eingelegter Geogitter variiert, sowie deren Einflüsse auf die Verdrehung und Verbiegung der Tragglieder aufgezeigt.
Real-time computational speed and a high degree of precision are requirements for computer-assisted interventions. Applying a segmentation network to a medical video processing task can introduce significant inter-frame prediction noise. Existing approaches can reduce inconsistencies by including temporal information but often impose requirements on the architecture or dataset. This paper proposes a method to include temporal information in any segmentation model and, thus, a technique to improve video segmentation performance without alterations during training or additional labeling. With Motion-Corrected Moving Average, we refine the exponential moving average between the current and previous predictions. Using optical flow to estimate the movement between consecutive frames, we can shift the prior term in the moving-average calculation to align with the geometry of the current frame. The optical flow calculation does not require the output of the model and can therefore be performed in parallel, leading to no significant runtime penalty for our approach. We evaluate our approach on two publicly available segmentation datasets and two proprietary endoscopic datasets and show improvements over a baseline approach.
Limitations 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.
Heavy smoke development represents an important challenge for operating physicians during laparoscopic procedures and can potentially affect the success of an intervention due to reduced visibility and orientation. Reliable and accurate recognition of smoke is therefore a prerequisite for the use of downstream systems such as automated smoke evacuation systems. Current approaches distinguish between non-smoked and smoked frames but often ignore the temporal context inherent in endoscopic video data. In this work, we therefore present a method that utilizes the pixel-wise displacement from randomly sampled images to the preceding frames determined using the optical flow algorithm by providing the transformed magnitude of the displacement as an additional input to the network. Further, we incorporate the temporal context at evaluation time by applying an exponential moving average on the estimated class probabilities of the model output to obtain more stable and robust results over time. We evaluate our method on two convolutional-based and one state-of-the-art transformer architecture and show improvements in the classification results over a baseline approach, regardless of the network used.
Training data for Neural Networks is often scarce in the medical domain, which often results in models that struggle to generalize and consequently showpoor performance on unseen datasets. Generally, adding augmentation methods to the training pipeline considerably enhances a model’s performance. Using the dataset of the Foot Ulcer Segmentation Challenge, we analyze two additional augmentation methods in the domain of chronic foot wounds - local warping of wound edges along with projection and blurring of shapes inside wounds. Our experiments show that improvements in the Dice similarity coefficient and Normalized Surface Distance metrics depend on a sensible selection of those augmentation methods.
Seit mehr als 25 Jahren ist der Workshop "Bildverarbeitung für die Medizin" als erfolgreiche Veranstaltung etabliert. Ziel ist auch 2024 wieder die Darstellung aktueller Forschungsergebnisse und die Vertiefung der Gespräche zwischen Wissenschaftlern, Industrie und Anwendern. Die Beiträge dieses Bandes - viele davon in englischer Sprache - umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz.
Significant deformations of bodies made from compliant magnetoactive elastomers (MAE) in magnetic fields make these materials promising for applications in magnetically controlled actuators for soft robotics. Reported experimental research in this context was devoted to the behaviour in the quasi-static magnetic field, but the transient dynamics are of great practical importance. This paper presents an experimental study of the transient response of apparent longitudinal and transverse strains of a family of isotropic and anisotropic MAE cylinders with six different aspect ratios in time-varying uniform magnetic fields. The time dependence of the magnetic field has a trapezoidal form, where the rate of both legs is varied between 52 and 757 kA/(s·m) and the maximum magnetic field takes three values between 153 and 505 kA/m. It is proposed to introduce four characteristic times: two for the delay of the transient response during increasing and decreasing magnetic field, as well as two for rise and fall times. To facilitate the comparison between different magnetic field rates, these characteristic times are further normalized on the rise time of the magnetic field ramp. The dependence of the normalized characteristic times on the aspect ratio, the magnetic field slew rate, maximum magnetic field values, initial internal structure (isotropic versus anisotropic specimens) and weight fraction of the soft-magnetic filler are obtained and discussed in detail. The normalized magnetostrictive hysteresis loop is introduced, and used to explain why the normalized delay times vary with changing experimental parameters.
The 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.
The use of chatbots based on large language models (LLMs) and their impact on society are influencing our learning experience platform Hochschul-Assistenz-System (HAnS). HAnS uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes [1]. This paper introduces LLM-based features available in HAnS which are using the transcript of our improved Automatic Speech Recognition (ASR) pipeline with an average transcription duration of 45 seconds and an average word error rate (WER) of 6.66% on over 8 hours of audio data of 7 lecture videos. A LLM-based chatbot could be used to answer questions on the lecture content as the ASR transcript is provided as context. The summarization and topic segmentation uses the LLM to improve our learning experience platform. We generate multiple choice questions using the LLM and the ASR transcript as context during playback in a period of 3 minutes and display them in the HAnS frontend.
In this study, we address the complex dynamics of emotional speech and
comprehensively examine the integration of rhythmic and vocal features to recognize emotional patterns. Our exploration is conducted using two German emotional corpora: VMEmo and EmoDB. Employing a combination of supervised methods (here linear discriminant analysis, LDA) and unsupervised techniques (here k-means clustering), we aim to uncover nuanced patterns within the emotional speech in these corpora. The application of LDA highlights salient patterns across different feature sets and focuses on the classification of speakers and prosodic characteristics. In addition, k-means clustering uncovers latent structures that reveal subtle mapping between emotions and speech behavior. Our results suggest that it is possible to cluster data based on prosodic behaviors that are influenced by emotional changes. Although precise mapping to the actual clusters derived from emotional labels could not be fully achieved, the results nonetheless reveal a moderate level of success in this investigation.
This study investigates the effects of speech segmentation methods on speaker recognition models, particularly with regard to the use of rhythmic feature sets. Using three automatic methods and one manual method on the German database of Kiel corpus, segmentation was performed based on the identification of vowel onsets. Subsequently rhythmic variability indices derived from these intervals were calculated and used for principal component analysis and support vector machine model in order to investigate the variation among speakers. The results underline the influence of signal segmentation methods on speaker recognition models.
PROM surveys, used to measure the effect of rehabilitation treatments, are typically filled out on paper, and often suffer from low response rates. Replacing it with a multimodal survey system, supporting touch and speech interaction, could lead to lower hurdles and therefore more data quantity. To do this, it requires task-specific training samples for the Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) to classify spoken answers into one of the standardized PROM answer options.
Due to the lack of training data for medical PROM surveys, we created augmented text samples with each answer option description, combined with different templates. To improve training capabilities, introduce a proper test set, and evaluate the ASR, we also collected 1,797 real voice samples within an empirical study. Further, we incorporate the contextual knowledge of the current question into our NLU architecture by implementing one classifier for every question scale.
Our results reveal that training with empirical data leads to better results than augmented data from templates and original answer option descriptions. Because of participant mislabeling of 33% due to the ambiguity of the task, we receive overall low NLU performances with up to 51.1% accuracy, and rank-1-accuracy up to 79.3%. We also find that our implementation of many scale-specific NLU classifiers significantly outperforms one NLU classifier for all labels, that incorporates the same contextual knowledge after the prediction, by 8 percent points.
Speech Recognition Errors in ASR Engines and Their Impact on Linguistic Analysis in Psychotherapies
(2024)
Modern intervention planning in psychotherapies may benefit from predicting process relevant psychotherapy constructs by automated speech analysis. One essential step is the extraction of relevant linguistic speech markers by ASR engines, which because of highly sensible data, work offline. We analyze transcription errors from NeMo, Whisper, and Wav2Vec2.0, focusing on their impact on linguistic markers usually requiring high quality transcripts. By utilizing part-of-speech tagging, we examine error occurrences among different word types. The Linguistic Inquiry and Word Count (LIWC) software aids in extracting markers. We highlight challenges in transcribing spontaneous speech, prevalent in therapy, and compare results with the Mozilla CommonVoice dataset, which features read speech.
This paper investigated whether predictability-based adjustments in production have listener-oriented consequences in perception. By manipulating the acoustic features of a target syllable in different predictability contexts in German, we tested 40 listeners’ perceptual preference for the manipulation. Four source words underwent acoustic modifications on the target syllable. Our results revealed a general preference for the original (unmodified) version over the modified one. However, listeners generally favored the unmodified version more when the source word had a higher predictable context compared to a less predictable one. The results showed that predictability-based adjustments have perceptual consequences and that listeners have predictability-based expectations in perception.
Recent neural text-to-speech (TTS) models are able to synthesize highly
natural speech signals using deep learning techniques. In practical applications, it can be desirable to have explicit control over the prosody (speech rate, fundamental frequency, and energy) of the synthesized speech. Such controllability can be achieved by adding prosody prediction modules, whose main purpose is to estimate plausible prosody features for each phoneme in the text input. This explicit modeling also allows for changing prosody features at inference time, consequently enabling the adjustment of the prosody in the synthesized audio. In this paper, we evaluate to which extent deliberate manipulation of such prosody features is reflected in the resulting speech audio. We focus particularly on changing the pitch (i.e., fundamental frequency) while applying different normalization strategies.
Generative models for audio are commonly used for music composition, sound effects generation for video game development, audio restoration, voice cloning, etc. The ease of generating indistinguishable fake audio with deep learning poses a major threat to personal privacy, online security, and political discourse. Evaluating the quality and realism of these synthetic utterances is crucial for mitigating the potential for misinformation and harm. To assess this threat, this paper conducts a systematic review, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), on how these deepfake models are currently evaluated. The analysis of 86 papers shows that the majority of the evaluation is conducted on a machine level and highlights a research gap regarding the human perception of deepfakes. This paper explores various methods and perceptual measures employed in assessing audio deepfakes and evaluating their strengths, limitations, and future directions.
In this work we assess whether there is information in pauses in-between utterances of the same or different speakers that are predictive of the following speaker’s utterance. We present models that connect a person’s visual features before they speak to their upcoming utterance. In our experiments we find that outof-the-box pre-trained models can already reach a better-than-chance performance in correlating video embeddings to utterance embeddings. In contrast, models that attempt to predict the first word after the pause do not outperform a unigram model, indicating that our models do not read lips (based e.g. on co-articulation effects) but rather capture more fundamental aspects of the upcoming utterance.
Wine making is usually considered a domain being far off the processing of speech and language. But in a particular aspect, the domains of speech processing and wine making are related, namely, in the description of wine aromas. These descriptors are used for creating wine expertise as well as more general (advertisement-like) textual representations. In the current paper, we use Natural Language Processing techniques, especially Named Entity Recognition, to identify Aspects and Opinions, reflecting wine characteristics. These are combined with analyses of respective relations (triplet extraction) building Aspect-Opinion-Pairs to establish indicative aroma descriptors, also trying to approach the complex interplay amongst these individual statements. In our experiments, we rely on the Falstaff corpus comprising a huge set of wine descriptions. This results in an average F1 score of around 0.85 for Aspect-Opinion classification. For triplet generation multiple strategies were compared, resulting in an average F1 score of 0.67 in this challenging task. For both tasks we rely only on a handful of manually annotated samples, applying pseudo-labeling methods from seed data to achieve automatic labeling.
Despite the potential of AI, only a small percentage of small and medium-sized enterprises (SMEs) are adopting it due to data issues, expertise gaps, and implementation barriers. Zero-shot learning offers a promising approach for SMEs by minimizing these obstacles. This paper explores the use of zero-shot learning in a real-world NLP classification task on online comments (comparable with intent classification tasks) from the e-learning platform Sofatutor. While finetuning has achieved high accuracy (82.3–86.5%), zero-shot models have shown lower performance (39.3–61.4%) due to different label selection, grouping of different scenarios in one class and the type of classification task. Even if the current accuracy is not sufficient for practical application, pre-filtering the data using zeroshot learning might be a promising option for SMEs.
Wines are complex beverages whose taste can be described either numerically or textually, with the former involving the rating of the intensities of different aroma characteristics often with the help of a wine tasting wheel, and the latter with the help of crisp terms often in a poetic fashion. These are often done with the help of wine sommeliers who with one sniff can describe the wine. Usually, each sommelier has a unique style when it comes to textually describing a wine, research has shown that such differences have no negative impact in correctly classifying wines on the basis of their color, grape variety, region etc. Given the recent advancements in the field of Natural Language Processing, especially with the emergence of Large Language Models, we aim to check the capability of Llama 2 in its ability to generate texts pertaining to a specific color of a wine, given a list of aroma intensities as input prompts. In our experiments, we relied on data from Meininger and Falstaff, and on a combination of domain adaptation and pseudo-labeling techniques to create the corpus to train the Llama 2 model on. Also, we relied on a voting scheme of three differently trained classifiers to evaluate the wine-color specific text generation capabilities of Llama 2. Additionally, we employed the services of domain experts to evaluate the quality of a sample set of texts that was generated by Llama 2.
Our paper introduces a new technology for posture research and training:
the INteractive POsture COrrector, IN-POCO. The device warns its users about unfavorable postures when speaking (e.g., sitting in video conferences) and is thus suitable as an aid for rhetoric trainers. In addition, IN-POCO can also collect time-aligned posture and speech signals for researching prosody-posture relationships in the speech sciences. We outline the motivation for the development of IN-POCO and describe the key technical specifications and operational characteristics. The paper concludes
with a pilot experiment in which we provide initial evidence that, for a communicative (public) speaking task, posture does indeed affect speech prosody in gender-specific ways – in line with claims of rhetoric trainers and guidebooks, and such that an unfavorable (e.g,, humped) posture can be assumed to reduce the speaker's vocal charisma.
Das 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.
The growing prevalence of voice assistants has sparked privacy concerns with respect to content privacy and potential human-based attacks such as eavesdropping which make users feel uncomfortable utilizing them in public. To address these challenges, understanding human privacy perceptions in acoustic environments becomes paramount. This understanding can empower voice assistants to accurately quantify privacy perceptions, adapt conversational patterns, and ultimately enhance human-machine interaction. This study draws inspiration from human-tohuman interactions and previous research on acoustic privacy, to quantify privacy perceptions in environments characterized by babble noise. The primary objective is a comprehensive evaluation of both objective and subjective measures to quantitatively capture privacy perceptions in acoustic environments.
Speaker recognition systems often use mel-scaled cepstral coefficients (MFCCs) as main features. In contrast to MFCCs, Godoy et al. (2015) proposed a different type of short-term spectral analysis that provides features related to the lower vocal tract (LVT). They are calculated as the ratio of the acoustic shorttime spectra during the closed and open phases of the glottal oscillation cycles based on a pitch-synchronous analysis. These features were suggested to be particularly speaker-specific and might therefore be suitable to substitute or complement MFCCs in speaker recognition systems. The present study investigated the benefit of these features in an i-vector-based speaker recognition system. Using the LVT features alone, the system achieved a speaker recognition rate of 92.3% with 63 enrolled speakers. When the LVT features were fused with conventional MFCC features, the recognition rate was about equal to the recognition rate using MFCC features alone (> 98%).
Octra Backend ist eine portable web-basierte Infrastruktur für Transkriptionsprojekte, die lokal im Feld oder geschützten Bereichen, im begrenzten Intranet oder weltweit erreichbar im Internet eingesetzt werden kann. Entwicklungsziele waren die Gewährleistung möglichst hoher Sicherheitsanforderungen, eine gute Skalierbarkeit sowie eine einfache Installation auch ohne Administratorrechte. Octra Backend ist in Node.js implementiert und für MacOS, Windows und Linux verfügbar.
Wir repräsentieren eine Bedeutung als Liste von Mustersignalen, und unser Ziel ist es, ein weiteres ankommendes Signal damit zu vergleichen. Die Quantenlogik motiviert die Verwendung von Orthogonalprojektoren, um die gesuchte Ähnlichkeit als Projektionswahrscheinlichkeit darzustellen. Die Ergebnisse des quantenlogischen Verfahrens hängen davon ab, in welcher Weise die Signale vorverarbeitet werden. In diesem Aufsatz untersuchen und diskutieren wir vier verschiedene Möglichkeiten der Vorverarbeitung.
NoiSLU: a Noisy speech corpus for Spoken Language Understanding in the Public Transport Domain
(2024)
The use of local public transport requires the barrier-free purchase of a ticket. Travellers who are not proficient in the local language benefit from a multilingual human(ticket)machine voice interaction. This paper presents a nearly parallel audio dataset with 13218 annotated user queries from 20 speakers for English, German and Dutch. The domain-specific speech corpus can be understood as an evaluation dataset for future research in Spoken Language Understanding (SLU) and thus, it enables researches to improve the quality of human-machine interaction applications. Furthermore, we compare the SLU performance of different compositions of Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) models in baseline experiments on different test datasets.
Um bei Minimalistischen Grammatiken (MG) Übergenerierung zu vermeiden, kann man Einträge mit leeren Exponenten (ε-Einträge) verwenden. Ein Eintrag besteht aus einem Exponenten, der die Äußerung oder Schrift eines Wortes repräsentiert, einer Merkmalsliste, welche die Syntax kodiert und einem λ-Ausdruck, der die Semantik repräsentiert. Leere Einträge führen allerdings zu einer schlechteren Verwendbarkeit der Grammatik für das Parsen. Die vorliegende Arbeit wird ein Umformungsalgorithmus für MGs vorstellen, sodass die Anzahl der ε-Einträge verringert werden kann, um sie wieder für Parser verwendbar zu machen. Hierzu werden die ε-Einträge mit den anderen Einträgen vorverarbeitet und dadurch neue Einträge geschaffen. Die nun überflüssigen ε-Einträge können dann problemlos entfernt werden. Der Algorithmus wurde anhand von über 260 Zahlwortgrammatiken getestet.
Die vorliegende Studie untersucht, inwiefern sich die in der Phonetik verbreiteten Skripte zur automatisierten Feststellung von verschiedenen Aspekten des Sprechflusses von de Jong et al. zur Beurteilung des Sprachstands bei Kindern eignen und in welcher Art und Weise die Methodik angepasst werden könnte. Dazu wurden Sprachdaten von Vorschulkindern mit Deutsch als Muttersprache bzw. als Zweitsprache mithilfe eines Serious Game zur Sprachstandserhebung elizitiert. Die Audiodaten wurden bezüglich Artikulationsrate, Pausen und Füllpartikeln sowohl automatisiert durch die Skripte als auch manuell annotiert. Die Ergebnisse zeigen, dass sich die Skripte zur Ermittlung der Artikulationsrate mit einer relativ hohen Übereinstimmung mit der manuellen Ermittlung zur Verwendung in Sprachstandserhebungsverfahren eignen. Auch die automatische Erkennung von Sprechpausen weist einen hohen Precision-Wert auf und könnte als Instrument in Sprachstandserhebungen verwendet werden. Eine solche Verwendung würde mit
einer Erweiterung um die hier vorgestellte manuelle Methode zum Annotieren von disfluenten und nicht disfluenten Pausen profitieren. Bei den Füllpartikeln stellte sich die automatisierte Klassifizierung hingegen als weniger geeignet heraus. Hier wurde keine hohe Übereinstimmung mit der menschlichen Annotation festgestellt. Um in der Praxis Verwendung finden zu können, muss diese Methode noch erweitert werden, zum Beispiel durch Miteinbeziehen der Pausenerkennung.
Synchrony of Glottal Area Waveform Parameters During the Production of Obstruents in Vowel Context
(2024)
Obstruents are phonemes which require partial or total obstruction of airflow through the vocal tract. Their articulation also requires adjustments of the laryngeal settings, e. g., an abduction gesture to stop vocal fold vibration for voiceless obstruents. This study investigated the laryngeal settings during the production of voiced and voiceless obstruents in vowel context to analyze the degree of synchrony of the involved glottal gestures. High-speed laryngoscopy images were used to determine the glottal area waveform, from which the time functions of the parameters open quotient (OQ), fundamental frequency (f0), and AC and DC amplitude (ACA and DCA) were calculated and analyzed. Significant correlations were found between all pairs of parameters, with strong correlations between some of them, e.g. Open Quotient and AC Amplitude. Correlations were also either consistently positive or negative for specific pairs of parameters across all investigated phonemes. These results could point to consistent patterns in laryngeal gestures that could enhance articulatory speech synthesis.
Concatenative text-to-speech (TTS) systems remain a widely used cheaper alternative to neural TTS systems. Yet concatenation of prerecorded units entails some drawbacks, such as spectral distortion, the perceptual consequences of which remain unclear. In an attempt to bridge this gap, our study focused on the effect of spectral distortion in vowel formants on perceived speech quality in
naturally-read manipulated German words as well as non-words. More specifically, we explored the distortion effect on a varying number of affected formants, at different magnitude and directionality in two corner vowels /a:/ and /i:/. The results indicate that single formant manipulations have a less pronounced effect on the listeners’ perception compared to multiple formant perturbations. The threshold at which the distortion became generally audible was estimated to lie between 0.4 and 1.0 bandwidth. The directionality of the distortion was not found to be significant.
The 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.
Die Therapie-App aphaDIGITAL wird im Rahmen eines Forschungsprojekts entwickelt, um Menschen mit Aphasie im häuslichen Umfeld zu
unterstützen. Das Projekt kombiniert bewährte Therapiemethoden mit digitalen Technologien, einschließlich künstlicher Intelligenz und einem interaktiven Avatar namens Eva. Dieser Artikel betrachtet die Analyse und Entwicklung der Interaktionsmechanismen, die für eine digitale Assistenz von Aphasietherapie den größten Einfluss haben. Es wurden dazu reale Therapiesitzungen zwischen sprachgeschädigten Menschen und ihren Therapeuten konversationsanalytisch nach spezifischen Merkmalen untersucht. Darauf aufbauend wurde durch manuelle Animation ein prototypisches Mundbild modelliert und ein eigenes Artikulationssystem konzipiert, um ein entsprechend authentisches Modell der deutschen Artikulations- und Koartikulationsmuster abzubilden.
This paper describes the usability evaluation of the parts of the CHATU chatbot. The evaluation was conducted with 21 participants. A focus of this paper is the description of the carefully designed evaluation procedure, which aims to avoid textual priming of the participants. The general evaluation procedure can be applied to other speech- or text-based conversational systems, and additional material is provided. The evaluation results show that the usability and user experience of CHATU are positively rated. However, the naturalness and novelty of the interaction are not optimal, and the potential influence of users’ experience with LLMs on the evaluation is discussed.
The article presents the process of developing a silicon electron source designed for high-vacuum microelectromechanical system (HV MEMS) devices, i.e., MEMS electron microscope and MEMS x-ray source. Technological constraints and issues of such an electron source are explained. The transition from emitters made of carbon nanotubes to emitters made of pure silicon is described. Overall, the final electron source consists of a silicon tip emitter and a silicon gate electrode integrated on the same glass substrate. The source generates an electron beam without any carbon nanotube coverage. It generates a high and stable electron current and works after the final bonding process of an HV MEMS device.
This paper describes a field study conducted with a museum chatbot at the Städel Museum Frankfurt. The chatbot uses the BERT language model for natural language processing and can be operated via touchscreen as well as via speech input. Prior to the study, hypotheses regarding the user experience of the system were formulated and a system-specific questionnaire was designed, which was used to inquire (among other things) about the perceived quality of the speech output and the frequency of audio guide use in museums. During the interaction with the chatbot, log data was collected and stored in the back-end system. The results show a significant correlation between perceived speech quality and user experience. An exploratory data analysis revealed that participants who used only speech input rated the system as significantly more stimulating than participants who used only touch input. Touch input turned out to be the most efficient input modality in terms of answer correctness and was rated highest regarding pragmatic quality. Interestingly touch input was preferred by younger participants. We discuss our findings and conclude that speech interaction should be seriously considered to create engaging conversational user experiences in museums.
Test Setup for Investigating the Impact Behavior of Biaxially Prestressed Composite Laminates
(2024)
Instrumented 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.
Measured building survey
(2022)
The precisely measured documentation of historical buildings by means of drawings – the building survey – serves several purposes: it is not only an important part of the scientific study of architecture, but also an essential basis for building measures in existing contexts. It is the only way to undertake well-founded research respectively detailed architectural planning, from structural improvement to conservation and restoration measures. In addition to a historical overview of building surveying as a method, this volume mainly offers practical assistance regarding the different methods for documenting historic buildings – starting from manual methods of measuring up to electronic and computer-aided utilities available today. The content is complemented by many illustrations and examples as well as an introduction to methods, that go beyond the mere graphic documentation and thus represent ways of conducting thorough building examinations. The book offers easy access to the topic of building surveying, both for students as well as for anyone dealing professionally with historic buildings
Utility of Smartphone-based Three-dimensional Surface Imaging for Digital Facial Anthropometry
(2024)
Background
The utilization of three-dimensional (3D) surface imaging for facial anthropometry is a significant asset for patients undergoing maxillofacial surgery. Notably, there have been recent advancements in smartphone technology that enable 3D surface imaging.
In this study, anthropometric assessments of the face were performed using a smartphone and a sophisticated 3D surface imaging system.
Methods
30 healthy volunteers (15 females and 15 males) were included in the study. An iPhone 14 Pro (Apple Inc., USA) using the application 3D Scanner App (Laan Consulting Corp., USA) and the Vectra M5 (Canfield Scientific, USA) were employed to create 3D surface models. For each participant, 19 anthropometric measurements were conducted on the 3D surface models. Subsequently, the anthropometric measurements generated by the two approaches were compared. The statistical techniques employed included the paired t-test, paired Wilcoxon signed-rank test, Bland–Altman analysis, and calculation of the intraclass correlation coefficient (ICC).
Results
All measurements showed excellent agreement between smartphone-based and Vectra M5-based measurements (ICC between 0.85 and 0.97). Statistical analysis revealed no statistically significant differences in the central tendencies for 17 of the 19 linear measurements. Despite the excellent agreement found, Bland–Altman analysis revealed that the 95% limits of agreement between the two methods exceeded ±3 mm for the majority of measurements.
Conclusion
Digital facial anthropometry using smartphones can serve as a valuable supplementary tool for surgeons, enhancing their communication with patients. However, the proposed data suggest that digital facial anthropometry using smartphones may not yet be suitable for certain diagnostic purposes that require high accuracy.
Progress in additive manufacturing is leading to the emergence of new areas of application. Laser Powder Bed Fusion (L-PBF) is increasingly used for the development of metallic medical implants, but for high-risk implants like vascular support structures (stents), surface quality is critical to ensure successful implantation without harming the surrounding tissue and ensure the patients’ health. Therefore, enhancing the surface quality is crucial. Electropolishing is a method for removing surface roughness by smoothing out micro-peaks and valleys. However, L-PBF structures have a high surface roughness due to metal particles adhering on the surface. To achieve a smooth surface for additively manufactured implants like stents using electropolishing, the removal of these particles needs to be studied in more detail.
The objective of this study is to examine the electropolishing mechanism of 316L stainless steel samples additively manufactured through Laser Powder Bed Fusion (L-PBF). The main objective is to investigate the removal properties and surface characteristics during electropolishing. To achieve this, various surfaces were characterized for morphology and roughness during Hull cell experiments. Markings are utilized on the Hull cell sample surfaces to identify points of interest during quasi-in-situ measurements. The surfaces are then analyzed after multiple time steps, applying different currents to investigate particle dissolution. The surface characteristics are analyzed through scanning electron microscopy, and surface roughness is analyzed using laser scanning microscopy.
The results show that the electropolishing process preferentially removes the adhering particles present on the surface of the samples. Increasing the current density results in faster particle dissolution and a smoother surface (see Figure 1a and b). The mechanism of material removal of various surface features, as shown in Figure 1 (red circle, yellow arrow and red square), was assessed based on the experimental results of the surface structures seen on the SEM images. It was found that different surface features were removed during the experiment at different polishing times and current densities. The amount of charge flowed was found to correlate with surface morphology.
Based on the obtained results, various surface features (such as large adherent particles, agglomerates of smaller particles, and valleys) and their changes with increasing test duration and current density were observed by quasi-in situ analyses. A reduction in the diameter of round particles adhering to the surface was observed at both low and higher current densities (see Figure 1a red circle a). Increasing the polishing time resulted in leveling of both large particles and valleys (see Figure 1b red square). Also, dissolution of agglomerates of smaller particles occurred at different polishing times as a function of current density and polishing time (see Figure 1a yellow arrow) are observed.
Smoothed surface structures can be observed in regions with equivalent surface charge density (see Figure 2). As a result, comparable surface morphologies may appear at the same area charge density, irrespective of a specific current density. So, it may be adequate to only consider the amount of charge flowed to describe the electropolishing of additive materials.
In conclusion, comprehending the dissolution characteristics of particles on L-PBF surfaces is essential for attaining satisfactory surface finish in electropolishing. The results of this study offer valuable perspectives into the electropolishing mechanism of additively manufactured 316L stainless steel and can guide future investigations on surface finishing and polishing of additive manufactured implants like stents.
Figure 1
BACKGROUND
Differentiation of high-flow from low-flow vascular malformations (VMs) is crucial for therapeutic management of this orphan disease.
OBJECTIVE
A convolutional neural network (CNN) was evaluated for differentiation of peripheral vascular malformations (VMs) on T2-weighted short tau inversion recovery (STIR) MRI.
METHODS
527 MRIs (386 low-flow and 141 high-flow VMs) were randomly divided into training, validation and test set for this single-center study. 1) Results of the CNN's diagnostic performance were compared with that of two expert and four junior radiologists. 2) The influence of CNN's prediction on the radiologists' performance and diagnostic certainty was evaluated. 3) Junior radiologists' performance after self-training was compared with that of the CNN.
RESULTS
Compared with the expert radiologists the CNN achieved similar accuracy (92% vs. 97%, p = 0.11), sensitivity (80% vs. 93%, p = 0.16) and specificity (97% vs. 100%, p = 0.50). In comparison to the junior radiologists, the CNN had a higher specificity and accuracy (97% vs. 80%, p < 0.001; 92% vs. 77%, p < 0.001). CNN assistance had no significant influence on their diagnostic performance and certainty. After self-training, the junior radiologists' specificity and accuracy improved and were comparable to that of the CNN.
CONCLUSIONS
Diagnostic performance of the CNN for differentiating high-flow from low-flow VM was comparable to that of expert radiologists. CNN did not significantly improve the simulated daily practice of junior radiologists, self-training was more effective.
In this work, high-current field emission electron source chips were fabricated using laser-micromachining and MEMS technology. The resulting chips were combined with commercially available printed circuit boards (PCBs) to obtain a multichip electron source. By controlling the separate electron sources using an external current control circuit, we were able to divide the desired total current evenly across the individual chips deployed in the PCB-carrier. In consequence, we were able to show a decreased degradation due to the reduced current load per chip. First, a single electron source chip was measured without current regulation. A steady-state emission current of 1 mA with a high stability of ±1.3% at an extraction voltage of 250 V was observed. At this current level, a mean degradation slope of −0.7 μA/min with a nearly perfect transmission ratio of 99% ± 0.4% was determined. The measurements of a fully assembled multichip PCB-carrier electron source, using a current control circuit for regulation, showed that an even distribution of the desired total current led to a decreased degradation. This was determined by the increase in the required extraction voltage over time. For this purpose, two current levels were applied to the electron source chips of the PCB-carrier using an external current control circuit. First, 300 μA total current was evenly distributed among the individual electron source chips followed by the emission of 300 μA per electron source chip. This allows the observation of the influence of a distributed and nondistributed total current, carried by the electron source chips. Thereby, we obtained an increase in the mean degradation slope from +0.011 V/min (300 μA distributed) to +0.239 V/min (300 μA per chip), which is approximately 21 times higher. Moreover, our current control circuit improved the current stability to under 0.1% for both current levels, 300 μA distributed and 300 μA per chip.
In diesem Beitrag werden am Beispiel des berufsbegleitenden Bachelorstudiengangs Pflegemanagement der OTH Regensburg die Charakteristika dieses Studiums sowie die Merkmale der Studierenden und die daraus resultierenden methodischen, didaktischen und organisatorischen Spezifika dargestellt. Um Hintergrund und Relevanz aufzuzeigen, soll zunächst ein Blick auf die Akademisierung der Pflege geworfen und eine Standortbestimmung von Pflegemanagementstudiengängen innerhalb der verschiedenen inzwischen etablierten Pflegestudiengänge vorgenommen werden. Im zweiten Kapitel wird der Studiengang der OTH Regensburg vorgestellt und im dritten Kapitel werden die Erfahrungen, Herausforderungen und Lösungsstrategien beschrieben, die aus dem berufsbegleitenden Format mit „nicht-traditionell“ Studierenden resultieren. Abschließend finden sich eine kurze Zusammenfassung und ein Ausblick.
Im Rahmen der vier Kopernikus-Projekte – Ariadne, ENSURE, P2X und SynErgie – wird die Transformation des deutschen Energiesystems hin zur Klimaneutralität analysiert. Im vorliegenden Bericht werden hierbei robuste Kernaussagen und relevante Unsicherheiten derjenigen Szenarien der Kopernikus-Schwesterprojekte dargestellt, die zur Zielsetzung die Klimaneutralität 2045 haben. Hierbei liegt der Fokus auf Indikatoren, die eine besonders hohe klimapolitische Relevanz haben.
Analysis and Improvement of Engineering Exams Toward Competence Orientation by Using an AI Chatbot
(2024)
ChatGPT is currently one of the most advanced general chatbots. This development leads to diverse challenges in higher education, like new forms of teaching and learning, additional exam methods, new possibilities for plagiarism, and many more topics. On the other side with the development of advanced AI tools, pure knowledge will be less and less important, and demands from industry will change toward graduates with higher competencies. Education has therefore to be changed from knowledge-centered toward competence centered. The goal of this article is to use ChatGPT for analyzing and improving the competence orientation of exams in engineering education. The authors use ChatGPT to analyze exams from different engineering subjects to evaluate the performance of this chatbot and draw conclusions about the competence orientation of the tested exams. The obtained information is used to develop ideas for increasing the competence orientation of exams. From this analysis, it is visible that ChatGPT gives good performance mainly where knowledge is tested. It has, however, much more problems with transfer questions or tasks where students need creativity or complex insights for finding new solutions. Based on this result, exams and also lectures can be optimized toward competence orientation.
This article describes a contactless fiber-optic position sensor. It comprises a Polymer Optical Fiber that is grinded to form a D-shaped cross-section with an exposed fiber core. This sensor has two photodiodes at both fiber ends to measure the emitted light intensity. Light is coupled using a red LED at the side face into the exposed core of the fiber at a defined position. The position of the LED at the length of the fiber is measured by calculating the optical power quotient measured by both photodiodes. To test this sensor, Polymer Optical Fibers with different side surface roughness are produced and qualified. Measurements show that the optical power quotient is reproducible and nearly linear over the length of the fiber. It is also seen that the fiber attenuation increases when grinding the fiber side-face with rougher sandpaper. Position measurements show an absolute position error of this sensor in the range of a few millimeters. Microscope images show surface defects along the polished side face of the fiber that are expected to lead to a nonuniform attenuation along the fiber and cause the position errors. Overall, it is proven that this sensor principle works as a contactless low-cost position sensor for short distances with an absolute position standard deviation error lower than 1 mm.
Increasingly stringent emission limits have made particulate filters necessary for gasoline engines. Similar to diesel applications, gasoline particulate filters (GPFs) can be monitored by differential pressure measurement or by the radio-frequency-based filter diagnosis (RF sensor). In addition to measuring the soot loading, ash detection is critical for monitoring the GPF over the entire vehicle lifetime. Because the RF sensor detects the filter loading through a change in the dielectric properties of the GPF, it can detect not only soot but also ash. In diesel applications, the RF sensor has already demonstrated its potential for ash detection. To verify the feasibility of simultaneous ash and soot monitoring for GPFs, filters were loaded with ash on an engine test bench and measured on a lab test bench under defined synthetic exhaust gas conditions. By evaluating resonant modes, soot and ash could be clearly distinguished, as ash mainly affects the resonant frequency, while soot also changes the quality factor due to its high dielectric losses. However, higher soot loadings could not be detected by the resonant parameters, but instead by a frequency-averaged transmission signal. While the presence of ash caused an offset in this signal, its sensitivity to soot was not affected. Thus, the influence of ash can be corrected if the signal in the soot-free filter state is known, e.g., from the behavior of the resonant parameters. Therefore, even with a continuously increasing ash loading over the lifetime of a vehicle, an accurate soot detection is possible with the RF sensor.
As more energy systems from renewable sources and the electricity market becomes more volatile, new solutions to ensure the security of supply are tested. Microgrids offer a possibility to prevent construction downtime. In order to use the energy sources and storage facilities of the microgrid effectively, load forecasting algorithms are essential. Thus, in this study a short term load forecasting model for a construction company is designed, that will be used for managing a microgrid as well as the general load consumption. This paper proposes the use of the XGBoost algorithm for the 36-hour forecast including predictors based on past measurements as well as information extracted from the timestamp. Moreover, it is shown that including load profiles attained by conventional methods has the potential to improve the accuracy of the model.
Wer bekommt eine Verordnung zu welcher physiotherapeutischen Leistung und wie kommt die Entscheidung darüber zustande? Ist die Therapie leitliniengerecht? Dem Zusammenhang zwischen Heilmittel-Richtlinie, Heilmittelkatalog, ärztlicher Verordnung und durchgeführter Therapie wird in diesem Artikel am Beispiel chronischer Rückenschmerz auf der Basis von Routinedatenanalysen der BARMER nachgegangen.