A new type seismic intensity meter

Abstract:A new type of seismic intensity meter based on MEMS accelerometer is introduced. It employs STM32F107 as the data processing core and detects the changes of acceleration with triaxial MEMS LIS344ALH and uses ADS1248 for 24 bit data sampling. The test on vibration table shows that the linearity of the meter is δL = ± 1.4%, and the sensitivity is Kc = 0.9671V/g with zero deviation of 0.0043g. The seismic intensity meter has the advantages of simple structure and stable performance and it is appropriate for intensive layout on a large scale

Comparison of the cumulative absolute velocity and acceleration peak value based on Wenchuan earthquake data

This paper discusses and presents the cumulative absolute velocity (CAV) parameters of the Wenchuan earthquake. Additionally, the CAV calculated from recorded data for the earthquake is compared to the peak ground acceleration (PGA), based on a brief analysis of background information. Accordingly, the paper studied the relationship between the CAV and PGA, and 3 CAV/PGA ratio charts were obtained in three different sub-directions. Linear and polynomial fitting operations were then used to analyze the potential discipline and characteristics in these directions. Finally, in the study, we investigated the applicability of using the CAV parameter for earthquake observation systems, and the CAV parameter was paired with the currently used PGA to provide earthquake observers and emergency responders with a theoretical basis

Research on Sparse Denoising of Strong Earthquakes Early Warning Based on MEMS Accelerometers

In view of the fact that the noise in the same frequency band as the useful signal in the MEMS acceleration sensor observation data cannot be effectively removed by traditional filtering methods, a denoising method for strong earthquake signals based on the theory of sparse representation and compressive sensing is proposed in this paper. This skillfully realized the separation of strong earthquake signals from noise by adopting a fixed dictionary and utilizing sparse characteristics. Furthermore, considering the weakness of the sparse denoising method based on the fixed dictionary in the high signal-to-noise ratio, a spare denoising method based on learning an over-complete dictionary is proposed. Through the initial given seismic data, the ideal over-complete dictionary is trained to achieve seismic data denoising. In addition, for the interference waves of non-seismic events, this paper proposes an idea based on sparse representation classification to remove such non-seismic interference directly. Combining the ideas of noise reduction and non-seismic event elimination, we can obtain a standard sparse anti-interference denoising model for earthquake early warning. It’s innovative that this model implements the sparse theory into the field of earthquake early warning. According to the experimental results, in the case of heavy noise, the denoising model based on sparse representation can reach average SNR of 8.73 and an average MSE of 29.53, and the denoising model based on compression perception can reach average SNR of 7.29 and an average MSE 41.34, and the denoising model based on learning dictionary can reach average SNR 11.07 and average MSE 17.32. The performance of these models is better than the traditional FIR filtering method (average SNR −0.73 and average MSE 260.37) or IIR filtering method (average SNR 4.73 and average MSE 73.95). On the other hand, the anti-interference method of the sparse classification proposed in this paper can accurately distinguish non-seismic interference events from natural earthquakes. The classification accuracy of the method based on the noise category of the selected test data set reaches 100% and achieves good results

The Oncogenic Role of Tribbles 1 in Hepatocellular Carcinoma Is Mediated by a Feedback Loop Involving microRNA-23a and p53

Hepatocellular carcinoma (HCC) is a common malignancy associated with a high risk of recurrence and metastasis and a poor prognosis. Here, we examined the involvement of the pseudokinase Tribbles 1 (TRIB1), a scaffold protein associated with several malignancies, in HCC and investigated the underlying mechanisms. TRIB1 was upregulated in HCC tissues and cell lines in correlation with low levels of p53. TRIB1 gain and loss of function experiments indicated that TRIB1 promoted HCC cell viability concomitant with the downregulation of p53, and induced HCC cell migration, invasion, and epithelial-mesenchymal transition. TRIB1 was identified as a target of microRNA-23a (miR-23a), and miR-23a overexpression downregulated TRIB1 and upregulated p53 in HCC cells. Ectopic expression of TRIB1 upregulated β-catenin and its effectors c-myc and MMP-7 in a p53-dependent manner. TRIB1 silencing inhibited tumor growth and promoted apoptosis in vivo via a mechanism that would involve the modulation of p53 and β-catenin signaling. The present results indicate that TRIB1 promotes HCC tumorigenesis and invasiveness via a feedback loop that involves the modulation of its expression by miR-23a with the likely downregulation of p53, and suggest the involvement of the β-catenin signaling pathway. These findings suggest potential targets for the treatment of HCC and therefore merit further investigation