High quality gallium oxide (Ga2O3) thin films are deposited by remote plasma-enhanced atomic layer deposition (RPEALD) with trimethylgallium (TMG) and oxygen plasma as precursors. By introducing in-situ NH3 plasma pretreatment on the substrates, the deposition rate of Ga2O3 films on Si and GaN are remarkably enhanced, reached to 0.53 and 0.46 Å/cycle at 250 °C, respectively. The increasing of deposition rate is attributed to more hydroxyls (–OH) generated on the substrate surfaces after NH3 pretreatment, which has no effect on the stoichiometry and surface morphology of the oxide films, but only modifies the surface states of substrates by enhancing reactive site density. Ga2O3 film deposited on GaN wafer is crystallized at 250 °C, with an epitaxial interface between Ga2O3 and GaN clearly observed. This is potentially very important for reducing the interface state density through high quality passivation.

The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.

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The stress–dilatancy of granular soil is highly dependent on its material state. To understand and model such behaviour, a variety of state-dependent stress–dilatancy equations have been proposed by empirically incorporating different state parameters. Even though a good performance was often observed when using these equations, the basic mathematical origins were missing. The purpose of this note is to provide one possible mathematical interpretation of the state-dependent stress–dilatancy. A novel state-dependent stress–dilatancy equation without using any state parameters is derived step by step based on fractional stress operators. As mathematically proved, the plastic flow of granular soil is determined not only by the current load state but also by the memory distance from the current state to the corresponding critical state, which conforms to the concept indicated by state parameters. Possible mathematical and physical meanings of the fractional order are also discussed. To verify the proposed approach, a series of stress–dilatancy data of granular soils with different material states from the literature are simulated and compared, from which a good agreement can be observed. To further demonstrate the capability of the approach, the developed state-dependent stress–dilatancy equation is then incorporated into the well-known model developed by X. Li and Y. Dafalias in 2000; here also a good performance is observed.

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