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Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model. The analysis is conducted on dividing the continuous scalp electroencephalograms (sEEG) into 5 phases: Interictal, Preictal, Seizure Prediction Horizon (SPH), Seizure, and Postictal. The brain network of Phase-Locking Value (PLV) of 5 typical brain rhythms is constructed, and the mechanism of epileptic changes is analyzed by statistical methods. It is found that 1) the connections between the prefrontal, occipital, and central regions show a large variability at each stage of seizure transition, and 2) 4 sub-bands of brain rhythms (θ, α, β, γ) are predominant. Group and individual variabilities are validated by using the Resnet18 deep model on data from 25 patients with infantile spasms, where the consistent results to statistical analyses can be observed. The optimized model achieves an average of 79.78 %, 94.46%, 75.46% accuracy, specificity, and recall rate, respectively. The method accomplishes the analysis of the synergy between infantile spasms mechanism, model, data and algorithm, providing a guideline to build an intelligent and systematic model for comprehensive IS seizure prediction.

Plasma-assisted combustion is the frontier research in the field of energy and power. In order to reveal the mechanism of plasma-assisted combustion, the mechanism of plasma excitation on CH4/Air combustion reaction is studied. Using the CHEMKIN, a CH4/Air discharge model and a zero-dimensional dynamic calculation model of plasma-assisted CH4/Air combustion are established. Through the simulation calculation, the ignition delay time, the evolution law of the active particle concentration, the chemical reaction path and the consumption of OH, and the sensitivity of the elementary reactions are obtained. The results show that during the CH4/Air mixed gas discharge breakdown, a large number of excited-state particles are generated, in which N2(ap) reacts most strongly with CH4 to promote the reaction. The dissociation effect of N2(C3) on CH4 is most prominent in the combustion reaction. In addition, it is found that adding plasma, increasing ignition temperature and increasing reaction pressure can shorten the ignition delay time and increase the upper limit of temperature rise. In the combustion reaction, H + O2 = O + OH play a major role in promoting the reaction.

Fear of heights (FoH) analysis and its association to physiological signals can better help understand people’s emotion and quantify human’s behavior, which have been found important in many applications, such as disease analysis, affective computing, etc. Existing studies are mainly on how to alleviate FoH while little literature on FoH analysis has been reported in the past. In this paper, we present the studies of correlation on FoH to multi-modal physiological signals in the virtual reality (VR). To stimulate the FoH to participants, 4 types of VR scenarios that consist of the virtual scene of the VR game “Richie’s plank experience” and the realistic stimulus of hitting by basketball are adopted in the experiment. The synchronized eye movement (EMO), pupil, and electrocardiogram (ECG) of 17 healthy subjects with an even mix of men and women are recorded for FoH analysis. The observations on the FoH analysis carried out in the paper are two-fold: (1) The recognition ability of multi-modal physiological signals for HoF has been evaluated using ReliefF feature selection algorithm. The results show that pupil feature based model generally achieves the best classification performance for FoH. Multiple features consisted of pupil diameter, power spectral density (PSD) of EMO, pupil, ECG, and mean value of EMO can effectively quantify the variability. (2) The FoH analysis model built on the correlation analysis algorithms combined with multi-modal feature-level fusion and strategy-level fusion methods can effectively overcome the drawbacks of conventional statistical models.

This paper introduces a detection method that uses a white-light interference microscope to scan along the circle of latitude line to detect defects on the entire surface of microsphere with a diameter of 2mm. The surface of the microsphere is exposed under the white light interference microscope from the top to the equator in turn according to the annular belts of different latitudes to make the acquired image cover the entire surface of the ball. The topography data is processed, the defects are identified, and the number of defects of different horizontal and vertical sizes is counted, so as to achieve the quantitative evaluation of the surface defects of the small ball.