Open Access

Background Military and law enforcement personnel are routinely exposed to shock waves from weapon systems and explosive devices during training and operations. Even when such shock wave exposure results in mild traumatic brain injury (mTBI) without abnormalities on routine imaging, persistent neurological symptoms may occur. Yet, the biological processes that link an acute blast wave insult to progressive neuronal dysfunction remain poorly defined. Methods We established Caenorhabditis elegans nematodes as a genetically accessible animal model for blast-related mTBI (br-mTBI). Animals were exposed in gelatin to shock waves generated by a custom-built shock wave generator (SWG), which produces explosive shock waves with an abrupt overpressure peak followed by a negative pressure phase. The resulting pressure-time curves are very similar to the profiles of conventional explosives in free-field setups. To enable mechanistic studies under controlled laboratory conditions, we developed a complementary platform using a medical radial extracorporeal shock wave therapy (rESWT) device. Motor behavior, mechanosensory function, neuronal morphology and cytoskeletal integrity were analyzed during aging. Results SWG-derived shock waves induced immediate but reversible motor and sensory deficits, followed by an accelerated age-dependent decline in movement and touch sensitivity. The rESWT platform accurately reproduced these phenotypes. Touch receptor neurons showed progressive structural abnormalities, including degeneration, alongside acute PTL-1/Tau mislocalization consistent with cytoskeletal injury. Conclusions Defined shock wave exposure is sufficient to provoke long-term neuronal functional decline in C. elegans, accompanied by structural deterioration and premature neurodegeneration. This tractable model enables lifelong phenotyping and mechanistic dissection of how an acute shock wave insult progresses to chronic neuronal dysfunction, and provides a scalable platform for identifying molecular and pharmacological modifiers that promote resilience.

Halide double perovskites (ABB'X) are a long-known class of materials that has recently been rediscovered for diverse applications, including photovoltaics, photocatalysis, and radiation detection. Their doubled unit cell provides immense chemical tunability, allowing the incorporation of magnetic ions and enabling access to a wide range of electronic-structure features, including different band-edge characters, alignments, and symmetries. Magnetic elements may further introduce spin degrees of freedom and magnetic behaviour, thereby broadening the functional landscape of these compounds. Here, we present the first comprehensive database of spin-polarised electronic-structure data for all halide double perovskites predicted to be stable by the recently introduced tolerance factor by Bartel et al. The dataset focuses on the CsBB'X family, with X = I, Br, Cl, and F, and includes density of states (DOS) for 2,500 compounds, calculated using hybrid-functional density functional theory. Among these, 719 compounds exhibit band gaps in the visible range and 118 display half-metallic character. In addition, we provide chemical-bonding analysis using \textsc{lobster}, which provides insights into orbital interactions across the dataset. To facilitate exploration, we further offer UMAP-based visualisations and an interactive app for systematic investigation of chemical composition, electronic structure, and magnetic properties.

Machine-learning interatomic potentials are widely used as computationally efficient surrogates for density functional theory in atomistic simulations, enabling large-scale, long-time modeling of materials systems. We investigate how different fine-tuning strategies influence the prediction of harmonic phonon band structures, thermal properties, and the potential energy surface along imaginary phonon modes. We achieve substantial accuracy improvements with minimal additional data, with as few as 10 additional training structures already yielding significant gains. In addition to existing approaches, we introduce Equitrain, a finetuning framework that implements LoRA-based adaptation. Across 53 materials systems, we show that fine-tuned models consistently outperform both the underlying pretrained model and models trained from scratch. Equitrain achieves the best overall performance, and our results demonstrate that fine-tuning enables accurate phonon predictions.

Modeling complex physical systems such as they arise in civil engineering applications requires finding a trade-off between physical fidelity and practicality. Consequently, deviations of simulation from measurements are ubiquitous even after model calibration due to the model discrepancy, which may result from deliberate modeling decisions, ignorance, or lack of knowledge. If the mismatch between simulation and measurements are deemed unacceptable, the model has to be improved. Targeted model improvement is challenging due to a non-local impact of model discrepancies on measurements and the dependence on sensor configurations. Many approaches to model improvement, such as Bayesian calibration with additive mismatch terms, gray-box models, symbolic regression, or stochastic model updating, often lack interpretability, generalizability, physical consistency, or practical applicability. This paper introduces a non-intrusive approach to model discrepancy analysis using mixture models. Instead of directly modifying the model structure, the method maps sensor readings to clusters of physically meaningful parameters, automatically assigning sensor readings to parameter vector clusters. This mapping can reveal systematic discrepancies and model biases, guiding targeted, physics-based refinements by the modeler. The approach is formulated within a Bayesian framework, enabling the identification of parameter clusters and their assignments via the Expectation-Maximization (EM) algorithm. The methodology is demonstrated through numerical experiments, including an illustrative example and a real-world case study of heat transfer in a concrete bridge.

Terahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional image processing techniques cannot simultaneously address both issues, and manual intervention is often required due to the unknown boundary between denoising and deblurring. To tackle this challenge, we propose a principal component analysis (PCA)-based THz self-supervised denoising and deblurring network (THz-SSDD). The network employs a Recorrupted-to-Recorrupted self-supervised learning strategy to capture the intrinsic features of noise by exploiting invariance under repeated corruption. PCA decomposition and reconstruction are then applied to restore images across both low and high frequencies. The performance of the THz-SSDD network was evaluated on four types of samples. Training requires only a small set of unlabeled noisy images, and testing across samples with different material properties and measurement modes demonstrates effective denoising and deblurring. Quantitative analysis further validates the network’s feasibility, showing improvements in image quality while preserving the physical characteristics of the original signals.