Cerebral hemodynamics is fundamentally regulated through the Circle of Willis (CoW), which redistribute flow via communicating arteries to stabilize perfusion under anatomical variations and vascular stenosis. In this study, a multiscale circulation model was developed by coupling a multiscale systemic hemodynamic framework with cerebral arterial network reconstructed from medical imaging. The model integrates a cerebral autoregulation mechanism (CAM) and enables quantitative simulation of flow redistribution across the CoW under normal, anatomically varied, and pathologically narrowing (stenosis) conditions. Baseline simulations at normal states reproduced physiological flow distributions in which the communicating arteries remained nearly inactive, showing negligible across-flow and agreement with clinical measurements, while two anatomical variations revealed distinct collateral activation patterns: the anterior communicating artery (ACoA) acted as the earliest and most sensitive functional collateral pathway, whereas the posterior communicating arteries (PCoAs) exhibited structure-dependent engagement. Progressive stenosis modeling further demonstrated the transition from a complete CoW to a fetal-type posterior cerebral artery (PCA) configuration, with early ACoA flow reversal followed by the ipsilateral PCoA activation, in agreement with experimental and transcranial Doppler observations. We further present a path-based quantitative analysis of source-to-sink flow contributions to quantitatively illustrates how the cerebral vascular network dynamically reconfigures collateral pathways in response to structural changes. Overall, the proposed framework provides a physiologically interpretable and image-informed tool for investigating cerebral flow regulations through the functional collaterals within the CoW, offering potential for clinical applications in the diagnosis, prognosis, and treatment planning of cerebrovascular diseases.

Marine controlled source electromagnetic (CSEM) is an efficient method to explore ocean resources. The amplitudes of marine CSEM signals decay rapidly with the measuring offsets. The signal is easily contaminated by various kinds of noise when the offset is large. These noise include instrument internal noise, dipole vibration noise, seawater motion noise and environmental noise Suppressing noise is the key to improve data quality and interpretation accuracy. Sparse representation based denoising method has been used for denoising for a long time. provides a new way to remove noise. Under the framework of sparse representation, the denoising effect is closely related to the chosen transform matrix. This matrix is called dictionary and its column named atom. In general, the stronger the correlation between signal and dictionary is, the sparser representation will be, and further the better the denoising effect will be. In this article, a new method based on dictionary learning is proposed for marine CSEM denoising. Firstly, the signal segments suffering little from noise are captured to compose the training set. Then the learned dictionary is trained from the training set via K-singular value decomposition (K-SVD) algorithm. Finally, the learned dictionary is used to sparsely represent the contaminated signal and reconstruct the filtered one. The effectiveness of the proposed approach is verified by a synthetic data denoising experiment, in which windowed-Fourier-transform (WFT) and wavelet-transform (WT) denoising methods and three dictionaries (discrete-sine-transform (DST) dictionary, DST-wavelet merged dictionary and the learned dictionary) under a sparse representation framework are tested. The results demonstrate the superiority of the proposed dictionary-learning-based denoising method. Finally, the proposed approach is applied to field data denoising process, coupled with DST and DST-wavelet dictionaries based denoising methods. The outcomes further proves that the propsoed approach is effective and superior for marine CSEM data denoising.

Hot dry rock (HDR) is a geothermal resource with a high temperature that is widely distributed and has good potential as a clean and renewable energy source. To determine underground electrical structures and to predict granite reservoir distributions, the wide-field electromagnetic (WFEM) method has been applied to explore deep mineral resources and has advantages such as explorations at greater depths and at high resolutions. In this study, a WFEM investigation was carried out for HDR exploration in Gonghe Basin within Qinghai Province. Six parallel survey lines, each spaced apart by 1 km, were designed for WFEM data acquisition. After data processing and inversion, we mapped the subsurface resistivity distribution and divided the inversion resistivity of HDR in the Qiabuqia area into four layers. From the WFEM results, we inferred the location of HDRs, which was verified using drilling wells. HDRs were found at a depth between 3200 m and 3705 m in the well. Furthermore, with the calibration of drilling well GR1, we provided the relationship between temperature and inversion resistivity. From this relationship, the exploration areas with mining potential can be determined.