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Pelvic floor dysfunction is a common problem in women and has a negative impact ontheir quality of life. The aim of this review was to provide a general overview of the current state oftechnology used to assess pelvic floor functionality. It also provides literature research of the phys-iological and anatomical factors that correlate with pelvic floor health. The systematic review wasconducted according to the PRISMA guidelines. PubMed, ScienceDirect, Cochrane Library andIEEE databases were searched for publications on sensor technology for the assessment of pelvicfloor functionality. Anatomical and physiological parameters were identified through a manualsearch. In the systematic review 115 publications were included. 12 different sensor technologieswere identified. Information on the obtained parameters, sensor position, test activities and subjectcharacteristics were prepared in tabular form from each publication. 16 anatomical and physiologi- cal parameters influencing pelvic floor health were identified in 17 published studies and rankedfor their statistical significance. Taken together, this review could serve as a basis for the develop-ment of novel sensors which could allow for quantifiable prevention and diagnosis, as well as par-ticularized documentation of rehabilitation processes related to pelvic floor dysfunctions.
Pelvic floor dysfunction is a common problem in women and has a negative impact on their quality of life. The aim of this review was to provide a general overview of the current state of technology used to assess pelvic floor functionality. It also provides literature research of the physiological and anatomical factors that correlate with pelvic floor health. This systematic review was conducted according to the PRISMA guidelines. The PubMed, ScienceDirect, Cochrane Library, and IEEE databases were searched for publications on sensor technology for the assessment of pelvic floor functionality. Anatomical and physiological parameters were identified through a manual search. In the systematic review, 114 publications were included. Twelve different sensor technologies were identified. Information on the obtained parameters, sensor position, test activities, and subject characteristics was prepared in tabular form from each publication. A total of 16 anatomical and physiological parameters influencing pelvic floor health were identified in 17 published studies and ranked for their statistical significance. Taken together, this review could serve as a basis for the development of novel sensors which could allow for quantifiable prevention and diagnosis, as well as particularized documentation of rehabilitation processes related to pelvic floor dysfunctions.
Extracorporeal membrane oxygenation (ECMO) was established as a treatment for severe cardiac or respiratory disease. Intra-device clot formation is a common risk. This is based on complex coagulation phenomena which are not yet sufficiently understood. The objective was the development and validation of a methodology to capture the key properties of clots deposed in membrane lungs (MLs), such as clot size, distribution, burden, and composition. One end-oftherapy PLS ML was examined. Clot detection was performed using multidetector computed tomography (MDCT), microcomputed tomography (μCT), and photography of fiber mats (fiber mat imaging, FMI). Histological staining was conducted for von Willebrand factor (vWF), platelets (CD42b, CD62P), fibrin, and nucleated cells (4′, 6-diamidino-2-phenylindole, DAPI). The three imaging
methods showed similar clot distribution inside the ML. Independent of the imaging method, clot loading was detected predominantly in the inlet chamber of the ML. The μCT had the highest accuracy. However, it was more expensive and time consuming than MDCT or FMI. The MDCT detected the clots with low scanning time. Due to its lower resolution, it only showed clotted areas but not
the exact shape of clot structures. FMI represented the simplest variant, requiring little effort and resources. FMI allowed clot localization and calculation of clot volume. Histological evaluation indicated omnipresent immunological deposits throughout the ML. Visually clot-free areas were covered with leukocytes and platelets forming platelet-leukocyte aggregates (PLAs). Cells were embedded in vWF cobwebs, while vWF fibers were negligible. In conclusion, the presented
methodology allowed adequate clot identification and histological classification
of possible thrombosis markers such as PLAs.
The use of intrinsically compliant tensegrity structures in manipulation systems is an attractive research topic. In this paper a 3D compliant robotic arm based on a stacked tensegrity structure consisting of x-shaped rigid members is considered. The rigid members are interconnected by a net of prestressed, tensioned members with pronounced intrinsic elasticity and by inelastic tensioned members. The system's motion is achieved by length-change of the inelastic tensioned members. The operating principle of the system is discussed with the help of kinematic considerations and verified by experiments.
Von den modelltheoretischen Grundlagen hin zu konkreten diagnostischen Möglichkeiten, diese komplexe Symptomatik zu erfassen; Zahlreiche Therapieansätze – verständlich, präzise und gezielt in der Praxis anwendbar; Wertvoller Begleiter für Lehrende, Studierende sowie praktisch tätige Therapeutinnen und Therapeuten
The recent REACH regulations require the elimination of bisphenol-A and titanium dioxide from commercially available boron-based polymers. This has led to changes in some of the mechanical characteristics, which strongly influence the properties of magnetoactive borosilicate polymers. This work delivers results on the electrical properties and discusses some implications for future research using bisphenol-A and titanium-dioxide-free substitutes.
Hintergrund und Fragestellung
Nichtregierungsorganisationen (NRO) sind ein wichtiger Bestandteil der Zivilgesellschaft und interagieren auch mit Regierungen, Unternehmen und anderen gesellschaftlichen Akteuren. Aufgrund der komplexer werdenden Arbeit von NROs scheint Künstliche Intelligenz (KI) Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Jedoch ist wenig über die Arbeit von NROs mit KI bekannt.
Methodik
Es wurden fünf explorative Interviews mit Vertreter*innen von Nichtregierungsorganisationen (NROs) zum Thema Wissen, Akzeptanz, Bedarfe und Risikoeinschätzungen geführt und ausgewertet. Dabei sind NROs aus verschiedenen Handlungsfeldern und Größe interviewt. Es wurden informelle Vorgespräche geführt, um eine erste Orientierung im Forschungsfeld zu generieren. Aus den Erkenntnissen der Vorgespräche
und den Ergebnissen des Scoping Reviews ist ein Leitfaden erstellt worden, der zur Orientierung für die Expert*inneninterviews dient. Im Anschluss wurden dann fünf explorative leitfadengestützte Expert*inneninterviews durchgeführt und qualitativ ausgewertet.
Ergebnisse
Ein zentraler Befund ist, dass das Thema KI gerade in den NROs ankommt und es noch keine gefestigten Strukturen und Vorstellungen zum Einsatz von KI gibt. KI wird in einzelnen spezifischen Projekten eingesetzt, ohne dass diese umfassend in Arbeitsabläufe integriert ist. Die Akzeptanz von KI ist generell positiv; die Technologie wird als potenzielle Lösung für strukturelle Herausforderungen und Unterstützung im Alltag gesehen. Die Nutzung von KI-Anwendungen beschränkt sich jedoch mit Ausnahme von Large Language Models auf Pilotprojekte. Mit jüngerem Alter und Technikaffinität ist eine höhere Akzeptanz verbunden. Besonders kritisch werden Anwendungen von KI im Sozial- oder Gesundheitsbereich als Ersatz für menschliche Interaktionen gesehen. Betont werden auch ethische Bedenken und eine hohe Bedeutsamkeit von Datenschutz.
Schlussfolgerung
Künstliche Intelligenz in Nichtregierungsorganisationen ist ein aufkommendes und sich entwickelndes Forschungsthema. Die Interviews unterstreichen den Bedarf an mehr Wissen, ethischen Richtlinien und finanziellen Ressourcen für eine effektive Nutzung von KI in NROs. Ein umfassendes Verständnis von KI und eine tiefergehende, systematische Integration in Arbeitsabläufe in diesen Organisationen müssen noch entwickelt werden.
Lumped-Mass-Modellierung von Förderbändern am Beispiel eines Zwei-Walzensystems mit flexiblen Walzen
(2024)
Background:
Stroke as a cause of disability in adulthood causes an increasing demand for therapy and care services, including telecare and teletherapy.
Objectives: Aim of the study is to analyse the acceptance of telepresence robotics and digital therapy applications. Methods: Longitudinal study with a before and after survey of patients, relatives and care and therapy staff.
Results: Acceptance of the technology analysed is high in all three groups. Although acceptance among patients declined in parts of the cases in the second survey after having used telerobotics, all in all approval ratings remained high. With regard to patients no significant correlation was found between the general technology acceptance and the acceptance of use of telerobotics.
Conclusion:
Accepted new telecare and teletherapies can be offered with the help of telepresence robotics. This requires knowledge of and experience with the technology.
Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.
Determinants of household electricity consumption measured by smart meters found by the authors in a scoping review were analyzed for the example of Germany utilizing the 2018 Survey of Income and Expenditure. All variables identified in the scoping review were covered in the survey (number and type of appliances, sociodemographic, and dwelling-related aspects). One can therefore use this large representative data set to test these relationships for German households. Expenditure on electricity is considered an indicator of household electricity consumption. The determinants show weak to moderate correlations with energy expenditure in bivariate analyses. The multivariate analysis shows effects of household-specific, dwelling-related, and appliance-specific factors. Models considering only one aspect overestimate this effect. Thus, all three aspects should be considered simultaneously when explaining residential electricity consumption. The largest effects are found for electricity as the main energy source for heating, the number of household members, as well as their presence at home. While household structure plays an important part in explaining residential energy consumption, dwelling and appliance-related aspects influence it as well. The latter aspects may be influenced by appropriate policy measures.
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.For this task, the deep learning techniques’ black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett’s esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett’s esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem.
Hintergrund und Fragestellung
Nichtregierungsorganisationen (NRO) sind ein wichtiger Bestandteil der Zivilgesellschaft und interagieren auch mit Regierungen, Unternehmen und anderen gesellschaftlichen Akteuren. Aufgrund der komplexer werdenden Arbeit von NROs scheint Künstliche Intelligenz (KI) Möglichkeiten zur Bewältigung aktueller und zukünftiger Herausforderungen zu bieten. Jedoch ist wenig über die Arbeit von NROs mit KI bekannt. Daher beschäftigt sich das Projekt KINiro in diesem ersten Working Paper mit der Frage, welche (nicht-)wissenschaftlichen Erkenntnisse zum Themenkomplex NROs und KI bereits vorliegen.
Methodik
Es wurde ein Scoping Review zur Erfassung (nicht-)wissenschaftlicher Texte zu NROs und KI durchgeführt. Die systematische Literaturrecherche wurde in den Datenbanken Web of Science, Science Gate und WISO durchgeführt. In den Review wurden schließlich 14 Titel eingeschlossen und qualitativ analysiert.
Ergebnisse
Die Mehrheit der gefundenen Treffer sind Pressemitteilungen. Unter den Treffern befinden sich lediglich zwei (wissenschaftliche) Studien. Die NROs setzen sich auf verschiedenen Ebenen mit der Thematik von KI auseinander, wobei sich zwei Herangehensweisen unterscheiden lassen. Einige NROs nehmen am gesellschaftlichen Diskurs über den Einsatz von KI teil und treiben diesen in theoretischer Hinsicht voran, ohne die Technik dabei selbst zu nutzen. Andere NROs integrieren die KI-Systeme praktisch in ihre Arbeitsabläufe oder führen Projekte zum Zweck der NRO mit KI-Unterstützung durch. Für die Entwicklung von KI-Anwendungen wird mit For-Profit-Unternehmen kooperiert und die Expertise der Unternehmen mit Daten der NROs kombiniert. Durch den Einsatz von KI erhoffen sich NROs einen gezielteren Einsatz von Ressourcen. Hierbei zeigt sich, dass für die Nutzung in KI-Systemen ein interdisziplinärer Konsens über Standards in Datenerhebung und Speicherung als notwendig angesehen wird.
Schlussfolgerung
Aus der geringen Anzahl an gefundenen Texten, insbesondere (wissenschaftlichen) Studien, und dem Veröffentlichungszeitraum, der in den vergangenen sieben Jahren liegt, lässt sich schließen, dass es sich um einen jungen Forschungsbereich handelt. Die ausgeschlossenen Titel zeigen auf, dass NROs aktuell noch häufiger mit der Digitalisierunge allgemein beschäftigt sind und die Auseinandersetzung mit KI erst noch am Anfang steht.
A magnetic levitation system is a perfect educational example of a nonlinear unstable system. Only with suitable control, a small permanent magnet can be held floating stable below a coil. After modeling and simulation of the system, control of the system can be developed. At the end, the control algorithm can be coded on a microcontroller, connected to a pilot plant.
The RILEM TC 281–CCC ‘‘Carbonation of concrete with supplementary cementitious materials’’ conducted a study on the effects of supplementary cementitious materials (SCMs) on the carbonation rate of blended cement concretes and mortars. In this context, a comprehensive database has been established, consisting of 1044 concrete and mortar mixes with their associated carbonation depth data over time. The dataset comprises mix designs with a large variety of binders with up to 94% SCMs, collected from the literature as well as unpublished testing reports. The data includes chemical composition and physical properties of the raw materials, mix-designs, compressive strengths, curing and carbonation testing conditions. Natural carbonation was recorded for several years in many cases with both indoor and outdoor results. The database has been analysed to investigate the effects of binder composition and mix design, curing and preconditioning, and relative humidity on the carbonation rate. Furthermore, the accuracy of accelerated carbonation testing as well as possible correlations between compressive strength and carbonation resistance were evaluated. The analysis revealed that the w/CaOreactive ratio is a decisive factor for carbonation resistance, while curing and exposure conditions also influence carbonation. Under natural exposure conditions, the carbonation data exhibit significant variations. Nevertheless, probabilistic inference suggests that both accelerated and natural carbonation processes follow a square-root-of-time behavior, though accelerated and natural carbonation cannot be converted into each other without corrections. Additionally, a machine learning technique was employed to assess the influence of parameters governing the carbonation progress in concretes.
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.