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.

Nonlinear optimisation techniques are commonly employed to minimise complex cost functions, with their effectiveness determined largely by the structure of the underlying error landscape. These methods require initial parameter values, and in the presence of multiple local minima, they are prone to becoming trapped in suboptimal regions. The likelihood of locating the global minimum increases substantially when the initialisation lies within its corresponding basin of attraction. Consequently, high-quality initial parameters are critical for successful optimisation. This technical report outlines a new strategy for selecting suitable initial parameters for a trigonometric model and unevenly sampled data, ensuring that the optimisation procedure starts sufficiently close to the global minimum. The proposed parameter estimation approach is strictly NI-based, interpretable, and explainable. It targets at complicated cases which include: samples with strong random noise, samples with only few covered periods, and samples which cover only a fraction of one period. Special attention is put on the frequency estimation. It can be shown that an estimation of initial parameters with sufficient accuracy is possible down to a signal-noise-ratio of 1.4 dB at much lower computational costs than the Lomb-Scargle-periodogram method requires.

Progress in artificial intelligence research, caused by the volume of available data on the World Wide Web, the development of affordable yet extremely efficient mathematical processors ("GPUs", graphical processing units), and the discovery of more effective training algorithms for very large models (such as "transformer" neural networks like Google's BERT and OpenAI's GPT, the technology behind ChatGPT.com) has recently led to a technological convergence that has begun to disrupt many other areas of scientific research, business and life. In this work, we explore some methodological concerns and boundary conditions when aspiring to apply such advanced technologies in order to advance the state of the art in software-implementable models for risk intelligence. We look at the potential of these technologies to assist open-ended 360˚ risk profiling, ethical and government questions such as dealing with the inherent bias in data, potentially unknown status of of information’s factuality of datasets, questionable provenance of datasets and other factors, such as sabotaging models. Borrowing from security engineering, we adopt the concept of the ‘attack surface‘ and introduce a variant of it as ‘risk surface‘: we posit that a good risk model should be supplemented by a model of its own risks in the form of making limitations like blind spots and known questionable behavior explicit. Model cards a proposed as a standard type of document to capture the risk profile of the risk model itself.