Cellular Automata (CA) models have become the most commonly used tool for simulating urban expansion. To improve the accuracy of CA models, various driving factors like spatial proximity and neighbourhood effects have been explored in previous studies, but the inclusion of these factors does not address the directional differences in urban expansion. To address this issue, this study develops a method to measure urban spatial anisotropy (SA) with respect to 18 variables at both the global and local scales, and integrates all these SA variables into a logistic regression-based CA model. The revised CA model is evaluated with a case study for Huizhou, China. The case study shows that the simulation results for the CA model with SA exhibit 89% overall accuracy; compared to CA models that do not consider SA, the revised CA model can improve precision by 5% on newly developed cells. The consideration of SA in CA models proves promising in improving the accuracy of urban expansion simulations.

Epilepsy is one of the most common neurological disorders, and there exists a subset of patients with refractory epilepsy that require surgical removal of the epileptogenic foci (EF) area. Studies have shown that high-frequency oscillations (HFOs) in epileptic electroencephalogram signals can be used as an essential biomarker for locating EF. This paper proposes a new method for rapid localization of EF based on the automatic detection of HFOs by waveform feature templates (WFTs). First, the initial screening of HFOs based on Hilbert transform and subsequent rescreening with short-time energy and short-time Fourier transform is performed, and the two screening results are used as the template data set of HFOs. Then, a coarse-grained and fine-grained screening method for detecting HFOs using autocorrelation coefficients and interrelation coefficients as WFT detectors, respectively. Compared with the Hilbert transform detector and other HFOs detector methods proposed at abroad in recent years, the experimental simulations showed that the automatic detector based on WFT could detect HFOs more rapidly, accurately, and efficiently. Our proposed WFT detector has the advantages of high specificity, high sensitivity, and high accuracy in locating EF and has a high clinical utility.

Epilepsy is a disease caused by abnormal discharges in the central nervous system. Automatic detection and accurate identification of epileptic seizures based on electroencephalography (EEG) are significant in the clinical diagnosis and treatment of epilepsy. In this paper, we first decompose the patient's EEG signal into multiple intrinsic modal functions (IMFs) using empirical modal decomposition, then compute the mean, standard deviation, fluctuation index, and sample entropy of IMF1, and finally classify them using a fusion algorithm of support vector machine and K-nearest neighbor optimized by particle swarm algorithm. The results of validation using the epileptic EEG data set from Bonn University show that the auto-detection and fast recognition method proposed in this paper can achieve a high seizure accuracy recognition rate (≥95%) with only a small number of training samples, which has a good clinical application value.