Research on Random Drift Model Identification and Error Compensation Method of MEMS Sensor Based on EEMD-GRNN.

Random drift error is one of the important factors of MEMS (micro-electro-mechanical-system) sensor output error. Identifying and compensating sensor output error is an important means to improve sensor accuracy. In order to reduce the impact of white noise on neural network modeling, the ensemble empirical mode decomposition (EEMD) method was used to separate white noise from the original signal. The drift signal after noise removal is modeled by GRNN (general regression neural network). In order to achieve a better modeling effect, cross-validation and parameter optimization algorithms were designed to obtain the optimal GRNN model. The algorithm is used to model and compensate errors for the generated random drift signal. The results show that the mean value of original signal decreases from 0.1130 m/s2 to -1.2646 × 10-7 m/s2, while the variance decreases from 0.0133 m/s2 to 1.0975 × 10-5 m/s2. In addition, the displacement test was carried out by MEMS acceleration sensor. Experimental results show that the displacement measurement accuracy is improved from 95.64% to 98.00% by compensating the output error of MEMS sensor. By comparing the GA-BP (genetic algorithm-back propagation) neural network and the polynomial fitting method, the EEMD-GRNN method proposed in this paper can effectively identify and compensate for complex nonlinear drift signals.

Recent advances in MOF-based nanoplatforms generating reactive species for chemodynamic therapy.

Still today, cancer remains a threat to human health. Possible common treatments to cure this disease include chemotherapy (CT), radiotherapy (RT), photothermal therapy (PTT), and surgical resection, which give unreasonable results because of their limited efficiency and also lead to side-effects. Hence, different strategies are now being exploited to not only enhance the efficiency of these traditional therapeutic methods or treat the tumor cells but also curtail the side effects. A latest method with authentic proof of chemodynamic therapy (CDT) utilizing the Fenton reaction is now gaining importance. This approach, which is developed based on the high level of hydrogen peroxide (H2O2) in a tumor microenvironment (TME), can be used to catalyze the Fenton reaction to generate cancer cell-killing reactive oxygen species (ROS). The selection of materials is extremely important and nanomaterials offer the most likely method to facilitate CDT. Among various materials, metal-organic frameworks (MOFs) which have been extensively applied in medical areas are regarded as a promising material and possess potential for the next generation of nanotechnology. This review focuses on summarizing the use of MOFs in CDT and their synergetic therapeutics as well as the challenges, obstacles, and development.