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The water curtain system is a vital part of an underground water-sealed oil reservoir. It is significant to evaluate the performance of the water curtain system. A field monitoring system was designed for realizing the performance assessment in the first underground water-sealed oil reservoir in Huangdao city, China. The system could monitor the operating water level of the water curtain system, groundwater level around the reservoir, and pore pressure between the caverns. This study systematically analysed the complete monitoring data of 2015. During the trial production operation, the operating water level of the water curtain system was above the 38 m minimum hydraulic boundary. Without considering other water replenishment, the ratio of the water inflow to the replenishment of the water curtain system was about 84.86%. Due to the effect of construction, the groundwater level of some wells was decreased. After starting the operation of artificial water replenishment, the water curtain system was able to supply a sufficient amount of water to these wells (OH-2, OH-4, OH-7, and OH-10) and recover the groundwater level. Most of the pore pressures at − 15 m and − 25 m were higher than or equal to the medium pressure; thus, the effective recharge depth of the water curtain system was between − 15 m and − 25 m, which encompassed the elevation of the cavern vault (− 20 m). Therefore, when the operating water level was between 38 and 40 m, the performance of the water curtain system could guarantee the project safety based on the analysis of water inflow, the groundwater, and the effective recharge depth of the water curtain system. These data could be used to check the effect of the cavern design and the employed construction method, which could further guide development and inventions related to the water-sealed technology.

As high-performing materials, bulk metallic glasses have attracted widespread attention for biomedical applications. Herein, the bacterial adhesion properties of femtosecond laser-nanostructured surfaces of four types of zirconium-based bulk metallic glasses are assessed. Laser-induced periodical surface structures and nanoparticle structures were fabricated by femtosecond laser irradiation under different energy intensities (0.23 and 2.3 J/mm2). Surface topography, roughness, wettability, and surface energy were investigated after femtosecond laser irradiation and the surface bacterial adhesion properties were explored using Escherichia coli and Staphylococcus aureus as respective representatives of Gram-negative and Gram-positive bacteria. 4′,6-Diamidino-2-phenylindole fluorescence staining was used to characterize and assess the bacterial surface coverage rate. The in vitro cytotoxicity of polished and laser-nanostructured surfaces was investigated using MC3T3-E cells. The obtained results demonstrate that femtosecond laser surface nanostructuring retained the amorphous structure of zirconium-based bulk metallic glasses and led to an obvious decrease in bacterial adhesion compared with polished surfaces. The inhibition of bacterial adhesion on laser-induced periodical surface structures was greater than on nanostructured surfaces after 24 h of bacterial incubation. In addition, femtosecond laser nanostructuring did not have an apparent effect on the cytotoxicity of zirconium-based bulk metallic glasses.

Site monitoring provides not only critical information for the safe operation of underground facilities but also important data for characterizing the groundwater flow within heterogeneous rock masses. Site monitoring and statistical analysis were performed to ascertain the groundwater response to oil storage in the Huangdao pilot large-scale underground oil storage facility. An all-sided monitoring scheme was designed and implemented for the assessment of the performance of the facility. The groundwater level and pressure along with rainfall, storage pressure, water curtain level were obtained with the monitoring system. The containment property was assessed according to the monitoring results, showing that the containment property of the facility can be maintained under operational conditions. Autocorrelation and cross-correlation analyses of the monitored data were performed to investigate the quantitative relations of the groundwater response to the rainfall amount, water curtain level and storage pressure. The correlation coefficients and time delays of the groundwater responses to the rainfall amount, water curtain level and storage pressure were obtained through statistical analysis. Evidently, the influence of rainfall on both the groundwater level and the groundwater pressure is insignificant. The groundwater pressures in the upper cavern pillars are influenced mainly by the water curtain system, while the groundwater pressures in the lower cavern pillars are influenced by the storage pressure. The hydrogeological conditions determine the time delays of the groundwater level and groundwater pressure responses to the water curtain level and storage pressure. Eventually, it was concluded that the groundwater response to oil storage in rock caverns with a water curtain system is related to both the geometrical layout and the hydrogeological conditions of the facility.

Motor imagery-based BCI-FES rehabilitation system has been proved to be effective in the treatment of movement function recovery. Common Spatial Pattern (CSP) and Support Vector Machine (SVM) are commonly used in the feature extraction and classification of Two-classes motor imagery. However, motor imagery signals of stroke patients are irregular due to the damage of the specified brain area. Traditional CSP is not able to detect the optimal projection direction on such EEG data recorded from stroke patients under the interference of irregular patterns. In this paper, an adaptive CSP method is proposed to deal with these unknown irregular patterns. In the method, two models are trained and updated by using different subsets of the original data in every iteration procedure. The method is applied on the EEG datasets of several stroke subjects comparing with traditional CSP-SVM. The results also provide an evidence of the feasibility of our BCI-FES rehabilitation system.

Traditional 2-class Motor Imagery (MI) Electroencephalography (EEG) classification approaches like Common Spatial Pattern (CSP) and Support Vector Machine (SVM) usually underperform when processing stroke patients' rehabilitation EEG which are flooded with unknown irregular patterns. In this paper, the classical CSP-SVM schema is improved and a feature learning method based on Gaussian Mixture Model (GMM) is utilized for depicting patients' imagery EEG distribution features. We apply the proposed modeling program in two different modules of our online BCI-FES rehabilitation platform and achieve a relatively higher discrimination accuracy. Sufficient observations and test cases on patients' MI data sets have been implemented for validating the GMM model. The results also reveal some working mechanisms and recovery appearances of impaired cortex during the rehabilitation training period.