Abstract
Subject-independent seizure detection algorithms are typically grounded in scalp electroencephalogram (EEG) databases, due to standardized channels and locations of EEG electrodes. Intracranial EEG (iEEG) has the characteristics of low noise and high temporal resolution compared with scalp EEG. However, it is still a big challenge for seizure detection using iEEG, because of the inconsistent number and locations of implanted electrodes in different patients, which results in a lack of unified algorithms. This study introduces an innovative approach for subject-independent seizure detection using iEEG, combining channel-wise mixup, transformer networks, and multi-task learning. Channel-wise mixup enhances data utilization by effectively leveraging information from different subjects, while multi-task learning improves the generalization of the model by concurrently optimizing both the seizure detection and the subject recognition tasks. 2983 files from two well-known epilepsy databases, i.e. SWEC-ETHZ and HUP were used in our study and the result showed that our approach surpasses currently existing methods. In terms of accuracy and generalization of seizure detection, our method achieved an area under the receiver operating characteristic curve (AUC) of 0.97 and 0.95 on the two databases respectively, which are significantly higher than the result of the currently existing methods. This study proposed anew method with great potential for surgery planning of epilepsy patients.

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[No abstract available]

Abstract
Accurate surgical planning is critical in mandibular reconstruction to restore the oncology patient's function and aesthetics. However, the use of physical three-dimensional (3D) models is often limited by time-consuming manual segmentation procedures or the high cost of commercial solutions. This work addresses the need for an accessible, quick, and low-cost pipeline to obtain a 3D printed model of the segmented mandible from a Computed Tomography (CT) scan. The automatic segmentation stage relied on the two-dimensional U-Net architecture, which was trained and validated with slices across two public datasets (PDDCA, HaN-Seg) and tested with the other two public datasets (TCIA RT, Austrian). The best model achieved an average dice similarity coefficient (DSC) of 0.912 ± 0.077 across all test sets. The segmentation output was reconstructed into a 3D volume, improved through a post-processing method (with morphological closing, upsample, smoothing, and mesh reduction), and 3D printed through fused deposition modelling. The assessment of a stomatologist confirmed overall high anatomical fidelity to the CT and clinical utility, even though further improvements in important fine anatomical elements were suggested. This solution contributes to a promising alternative to producing 3D personalised mandibles for surgical planning, reducing time and manual effort while improving the quality and accessibility. Future work may explore the use of 3D DL architectures and a broader evaluation of the 3D mandible models.

Abstract
Recent work by Oberti et al, (Astron. Astrophys., 667, 48, 2022) argued and made a compelling case that classical astronomical adaptive optics (AO) tomography performance can be further enhanced by carefully designing and optically configuring the system to leverage inherent super-resolution (SR) capabilities. Our goal here is to further materialise the concept by providing the means to compute SR-enabling tomographic reconstructors for AO and showcase its broad uptake on soon every 10 m-class VIS/NIR telescopes and Giant Segmented Mirror Telescopes of up to 40 m in diameter. To that end we indicate the necessary tomography generalisations where we: (i) clarify how model-and-deploy is a generic methodological umbrella for linear minimum-mean-squared-error (LMMSE) tomographic reconstructors arising naturally from the solution of the tomographic inverse problem, thus unifying various solutions presented as distinct in the literature within a single framework, (ii) recall how such solutions are found as limiting cases of a model-based optimal control problem, thus elucidating how pseudo-open-loop control is a feature of the latter that allows LMMSE reconstructors to be adapted to closed-loop systems, (iii) review the two forms of the LMMSE tomographic reconstructors, highlighting the necessary adaptations to accommodate super-resolution, (iv) review the implementation in either dense-format vector-matrix-multiplication or sparse iterative forms and (v) discuss the implications for runtime and off-line real-time implementations, anticipating widespread adoption. We illustrate our examples with physical-optics numerical simulations for 10 m and 40 m-scale systems showing the performance benefits of super-resolution in the order of several tens of nm rms and the computational burden associated.

Abstract
In the study, a new method for analyzing Electrocardiogram (ECG) signals is suggested, which is vital for detecting and treating heart diseases. The technique focuses on improving ECG signal classification, particularly in identifying different heart conditions like arrhythmias and myocardial infarctions. An enhanced version of the differential evolution (DE) algorithm integrated with neural networks is leveraged to classify these signals effectively. The process starts with preprocessing and extracting key features from ECG signals. These features are then processed by a multi-layer perceptron (MLP), a common neural network for ECG analysis. However, traditional MLP training methods have limitations, such as getting trapped in suboptimal solutions. To overcome this, an advanced DE algorithm is used, incorporating a partition-based strategy, opposition-based learning, and local search mechanisms. This improved DE algorithm optimizes the MLP by fine-tuning its weights and biases, using them as starting points for further refinement by the Gradient Descent with Momentum (GDM) local search algorithm. Extensive experiments demonstrate that this novel training approach yields better results than the traditional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract