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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.