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The pore wall morphology of carbon fiber reinforced composite (CFRP) laminates is one of the key factors to determine their service performance. However, due to the direction angle of the material fibers, the original micro surface morphology of the pore wall is complex and difficult to characterize. In this paper, a method to reconstruct the micro surface of the hole wall by fractal interpolation is proposed. By using a small number of data points extracted, the high-precision equivalent micro surface by fractal interpolation can be reconstructed, which can effectively characterize the composite joint surface. Firstly, the geometric features of micro surface are extracted by using the experimental measurement. Then, based on the model for fractal interpolation, an improved self affine fractal interpolation algorithm is used to reconstruct the original measured surface. Finally, the geometric and mechanical validity of fractal interpolation surface reconstruction is verified by combining geometric model and finite element model. The results show that when the sampling interval is 3~5, the geometric error and mechanical property error of the fractal interpolation method are below 10%, which can effectively characterize the original composite joint surface.

The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals. [ABSTRACT FROM AUTHOR]

Nocardia primarily affects immunocompromised individuals, and Infection with Nocardia is uncommon and primary cutaneous nocardiosis caused by percutaneous inoculation is even rarer. Primary cutaneous nocardiosis remains a diagnostic challenge and should be considered in the differential diagnosis for any superficial cutaneous infection that arises in patients with normal immune function. We report a case that was diagnosed with primary cutaneous Nocardia by metagenomic next‐generation sequencing technology. [ABSTRACT FROM AUTHOR]

In this study, we focus on the ultra-long integration of orbital perturbations of geosynchronous Synthetic Aperture Radar (SAR) for imaging. By deriving mathematical expressions for the Doppler rate and quadratic phase from orbital elements perturbated by oblateness or the J2 term of the non-spherical gravitational force of the Earth, we analyze the impact on SAR data focusing. Based on our results, we conclude that the quadratic phase will exceed 45°, which is the defocusing threshold for imaging, after accumulation during a long integration time at the minute level. Because the potential for defocusing exists throughout nearly the entire satellite motion cycle, the SAR processor must carefully manage and compensate for the quadratic phase to avoid image degradations.