Humidity, salt content, and migration in building materials lead to weathering and are a common challenge. To understand damage phenomena and select the right conservation treatments, knowledge on both the amount and distribution of moisture and salt load in the masonry is crucial. It was shown that commercial portable devices addressing moisture are often limited by the mutual interference of these values. This can be improved by exploiting broadband radar reflectometry for the quantification of humidity in historic masonry. Due to the above-mentioned limitations, today’s gold standard for evaluating the moisture content in historic buildings is still conducted by taking drilling samples with a subsequent evaluation in a specially designed laboratory, the so-called Darr method. In this paper, a new broadband frequency approach in the range between 0.4 and 6 GHz with improved artificial-intelligence data analysis makes sure to optimize the reflected signal, simplify the evaluation of the generated data, and minimise the effects of variables such as salt contamination that influence the permittivity. In this way, the amount of water could be determined independently from the salt content in the material and an estimate of the salt load. With new machine learning algorithms, the analysis of the permittivity is improved and can be made accessible for everyday use on building sites with minimal intervention by the user. These algorithms were trained with generated data from different drying studies on single building bricks from the masonries. The findings from the laboratory studies were then validated and evaluated on real historic buildings at real construction sites. Thus, the paper shows a spatially resolved and salt-independent measurement system for determining building moisture.

The loosening of an artificial joint is a frequent and critical complication in orthopedics and trauma surgery. Due to a lack of accuracy, conventional diagnostic methods such as projection radiography cannot reliably diagnose loosening in its early stages or detect whether it is associated with the formation of a biofilm at the bone–implant interface. In this work, we present a non-invasive ultrasound-based interferometric measurement procedure for quantifying the thickness of the layer between bone and prosthesis as a correlate to loosening. In principle, it also allows for the material characterization of the interface. A well-known analytical model for the superposition of sound waves reflected in a three-layer system was combined with a new method in data processing to be suitable for medical application at the bone–implant interface. By non-linear fitting of the theoretical prediction of the model to the actual shape of the reflected sound waves in the frequency domain, the thickness of the interlayer can be determined and predictions about its physical properties are possible. With respect to determining the layer’s thickness, the presented approach was successfully applied to idealized test systems and a bone–implant system in the range of approx. 200 µm to 2 mm. After further optimization and adaptation, as well as further experimental tests, the procedure offers great potential to significantly improve the diagnosis of prosthesis loosening at an early stage and may also be applicable to detecting the formation of a biofilm.

Introduction: Distributed ledger networks, chiefly those based on blockchain technologies, currently are heralding a next-generation of computer systems that aims to suit modern users’ demands. Over the recent years, several technologies for blockchains, off-chaining strategies, as well as decentralised and respectively self-sovereign identity systems have shot up so fast that standardisation of the protocols is lagging behind, severely hampering the interoperability of different approaches. Moreover, most of the currently available solutions for distributed ledgers focus on either home users or enterprise use case scenarios, failing to provide integrative solutions addressing the needs of both.

Methods: Herein, we introduce the OpenDSU platform that allows to interoperate generic blockchain technologies, organised–and possibly cascaded in a hierarchical fashion–in domains. To achieve this flexibility, we seamlessly integrated a set of well conceived components that orchestrate off-chain data and provide granularly resolved and cryptographically secure access levels, intrinsically nested with sovereign identities across the different domains. The source code and extensive documentation of all OpenDSU components described herein are publicly available under the MIT open-source licence at https://opendsu.com.

Results: Employing our platform to PharmaLedger, an inter-European network for the standardisation of data handling in the pharmaceutical industry and in healthcare, we demonstrate that OpenDSU can cope with generic demands of heterogeneous use cases in both, performance and handling substantially different business policies.

Discussion: Importantly, whereas available solutions commonly require a pre-defined and fixed set of components, no such vendor lock-in restrictions on the blockchain technology or identity system exist in OpenDSU, making systems built on it flexibly adaptable to new standards evolving in the future.

Kulturelle Bildung – ein Containerbegriff, welcher mit verschiedensten Merkmalen assoziiert ist. Wie verstehen lokale Akteur*innen Kulturelle Bildung, und wie kann dies zur Begriffsschärfung bezüglich der Strukturen, Angebote, Dimensionen und Wirkung Kultureller Bildung beitragen?
Für eine erste Annäherung an diese Frage wurden 34 Akteur*innen aus verschiedenen Sektoren Kultureller Bildung aus der Region und Stadt Coburg in leitfadengestützten Expert*inneninterviews befragt und die Interviews mittels qualitativer Inhaltsanalyse ausgewertet. Die Ergebnisse geben einen Überblick über die Perspektive lokaler Akteur*innen auf die aktuelle Lage Kultureller Bildung in der Stadt und Region Coburg (Oberfranken), auf Wirkpotenziale sowie Vernetzungs- und Förderbedarfe. Der besondere Zeitpunkt der Erhebung (August – Dezember 2020) ermöglicht darüber hinaus Einblicke in die Kulturelle Bildung während der COVID-19 Pandemie, beschreibt diesbezügliche Veränderungen und hinterfragt diese kritisch.  Darüber hinaus setzt die differenziert kategorisierende Analyse von Begriffsverständnissen, Strukturen und Dimensionen aus der Perspektive lokaler Akteur*innen Impulse für zukünftige, auch konturiert quantifizierende Forschung im Feld der Kulturellen Bildung.

Due to increasingly ageing populations and a rising demand on quality of life hip replacement became one of the most common operations in orthopedics and trauma surgery over the last decades and even continues to gain importance. However, in more than 10 percent of cases, loosening of the implanted prosthesis occurs within the first 15 years after surgical implantation. As a result, the prosthesis usually has to be completely replaced in a complex operation which often leads to complications. The later the prosthesis loosening is detected, the more difficult the initial situation for a successful and complication-free prosthesis replacement is. In addition to early diagnosis, it is essential to distinguish between purely mechanical (aseptic) loosening and loosening caused by bacterial infection (septic). Due to a lack of accuracy, conventional diagnostic methods such as projection radiography cannot reliably diagnose loosening in its early stages or detect whether it is associated with the formation of a biofilm in the bone-implant interface.

In this work, we developed a non-invasive ultrasound-based measurement procedure for quantifying the thickness of the layer between the bone and the stem of a hip prosthesis as a correlate to loosening. In principle, it also allows for material characterization of the interface.

The introduced method is based on a well-known analytical model of the reflection of sound waves in a three-layer system being mainly used for characterizing the thickness of thin lubricant films. For the desired medical application of characterizing the bone-implant interface which comes along with rough surfaces, inhomogeneous materials and the impossiblity of reference measurements, we adapted and extended this model and developed suitable data processing algorithms for analyzing the interface.

With respect to determining the layer thickness, the procedure was experimentally validated at different idealized test systems and a more realistic bone-implant system within in the range of approx. 200 µm to 2 mm. Consequently, our procedure is able to quantify the thickness of thin interlayers even at rough, porous and inhomogeneous materials and without a reference measurement.

After further optimization and adaptation, as well as further experimental tests, the procedure offers great potential to significantly improve the diagnosis of prosthesis loosening at an early stage and may also bei applicable to detect the formation of a biofilm.

Objective
Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs.
Methods
We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers.
Results
For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability.
Conclusion
If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high.
Clinical Significance
This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.