Throughout their life cycle, tree leaves are subject to colonization and degradation by microorganisms, including fungi, bacteria, and algae. These relationships co-evolved with chemical properties, leaf shape, and surface structures. Here we developed (i) a novel quantitative trait describing leaf surface texture complexity based on variables extracted from scanning electron microscopic images, resulting in a quantitative score of surface texture complexity on a tree species level. This complexity score was then used (ii) to test functional hypotheses, quantifying the contribution of leaf surface texture complexity in context of growth habitat preferences and colonization patterns by fungi and bacteria. We show that (iii) leaf surface texture complexity correlated with anatomical features such as stomatal density and leaf orientation as well as with Ellenberg temperature habitat indicator. Increasing leaf surface texture complexity was negatively correlated with leaf-associated fungal and bacterial specialists. Moreover, leaves with higher leaf surface texture complexity values showed reduced richness of colonization with plant pathogens (broad-leaved species) or lichenization (conifers), suggesting protection effects. Our results highlight leaf surface texture complexity as a previously underappreciated trait that may be a key to understanding microbial diversity between tree species and interaction patterns with leaf-associated microbes. This opens promising avenues for future research on plant-microbe co-evolution, trait-based ecosystem modeling, and the potential use of surface traits in forest management and disease resistance strategies.

Early detection of aseptic or septic loosening of hip endoprostheses is clinically crucial but still relies primarily on projection radiography, which lacks sensitivity for sub-millimetre interlayers at the bone–implant interface. Recent work by Lützelberger et al. demonstrated that ultrasound interferometry enables quantification of the width of this interface by analysing frequency-domain interference patterns of reflections from the soft-tissue gap. However, several open theoretical questions remained, particularly regarding the fundamental detection limits for small gap widths and the optimal choice of excitation frequency. This work provides a mathematical analysis of (i) the minimal resolvable gap width based on the Gabor limit, bandwidth constraints, and the signal-to-noise ratio (SNR) and (ii) the optimal operating frequency considering attenuation in soft tissue and the limited thickness of the cortical bone layer. The results show that an excitation frequency around 3 MHz represents an optimum here. Furthermore, closed-form expressions for the minimum detectable width are derived, predicting detection thresholds in the range of 156–212 µm for different SNR conditions – consistent with previously reported experimental results. These findings provide theoretical justification for system design choices in interferometric ultrasound for implant-integration monitoring.

Bone fractures represent an increasing medical and social challenge, particularly due to falls and osteoporosis in the elderly. Among these, tibial (shinbone) fractures are the most clinically significant. Proper clinical management requires assessing whether biological healing progresses well and determining when sucient stiness and stability are regained – for example, to decide when to remove artificial fixation. Currently, fracture healing is mainly monitored through functional tests and X-ray imaging, which involves radiation, is costly, and provides no direct information about the mechanical stiness of the healing bone. To overcome these limitations, we propose an ultrasonic approach for continuous monitoring the elasticity changes of the fracture regions during the healing process. By evaluating the propagation characteristics of guided acoustic Lamb waves travelling through the fracture region, our method aims to enable tracking of the healing progress and prediction of the remaining healing time. Using phantom materials that replicate various bone anatomies and fracture types, we obtained initial experimental results with a single-element transmitter–receiver ultrasonic setup, showing excellent agreement with theoretical predictions. In future work, we aim to increase the realism of our phantoms by including various soft-tissue layers and to advance both hardware and software toward an integrated medical device.

In this paper, a novel sensing structure to be used for real-time monitoring of soil movements in construction sites is proposed. The structure integrates an array of sensor nodes, to be deployed at different depths according to a tree-shaped structure. Each sensor node measures temperature, Volumetric Water Content (VWC) and soil movement, by exploiting the measurement of pressure variations exerted by a column of water on pressure sensors positioned in the soil. The structure manages the acquisition of data from each sensor node every 30 minutes and transmits it to a remote data management centre using the Long Range Wide Area Network (LoRaWAN) protocol. A prototype of the structure was designed, developed and installed at a test site in Coburg. The results acquired across several months of experimentation demonstrate the accuracy of the measurements as well as the reliability of the overall sensing structure.

We investigated the adaptation and performance of Masked Autoencoders (MAEs) with Vision Transformer (ViT) architectures for self-supervised representation learning on one-dimensional (1D) ultrasound signals. Although MAEs have demonstrated significant success in computer vision and other domains, their use for 1D signal analysis, especially for raw ultrasound data, remains largely unexplored. Ultrasound signals are vital in industrial applications such as nondestructive testing (NDT) and structural health monitoring (SHM), where labeled data are often scarce and signal processing is highly task-specific. We propose an approach that leverages MAE to pre-train on unlabeled synthetic ultrasound signals, enabling the model to learn robust representations that enhance performance in downstream tasks, such as time-of-flight (ToF) classification. This study systematically investigated the impact of model size, patch size, and masking ratio on pre-training efficiency and downstream accuracy. Our results show that pre-trained models significantly outperform models trained from scratch and strong convolutional neural network (CNN) baselines optimized for the downstream task. Additionally, pre-training on synthetic data demonstrates superior transferability to real-world measured signals compared with training solely on limited real datasets. This study underscores the potential of MAEs for advancing ultrasound signal analysis through scalable, self-supervised learning.

With the emergence of new technologies, numerous options for stress management are becoming available. One promising approach to support individuals in coping with stress is virtual reality (VR). A variety of relaxation techniques, such as guided imagery and breathing meditation, have recently been translated into virtual environments, providing easily accessible alternatives to traditional approaches. As studies suggest that VR interventions are effective in reducing stress, the consortium project AI4Coping aims to develop a toolbox for individually adaptive, digitally supported coping strategies. Furthermore, the project evaluates the efficacy of VR interventions across various application fields and addresses the societal, ethical, and economic dimensions that determine its use in the general population.