A modified technique for thymectomy in adult mouse with no ventilation support

Thymectomy in adult mice, a method to eliminate newly produced n

On the talent training mode of “new engineering” in local undergraduate colleges

Funding  source: Research on talent training mode of "new engineering" in local undergraduate colleges and universities (project code: 2019SJA2197) Abstract: With the development of China's new economy and emerging industries, new engineering construction comes into being. Facing the construction of new engineering, local undergraduate colleges and universities should deeply understand their own development trend, clarify the training mode of new engineering talents, and take the adjustment of specialty setting guided by regional industrial demand, the innovation of talent training mode based on the consensus of coordinated development, and the improvement of talent training quality as the main measures for local undergraduate colleges and universities to cultivate new engineering talents Key words: local; New engineering; Talent training mode DOI: 10.7176/JEP/12-33-15 Publication date: November 30th 202

Research on consumption prediction of spare parts based on fuzzy C-means clustering algorithm and fractional order model

In order to achieve the non-stationary de-noising signal effectively, and to solve the prediction of less sample, a hybrid model composed of FCCA (Fuzzy C-means clustering algorithm) and FOM (Fractional Order Model) was constructed. The degree of each data point was determined by FCCA to de-noise and the p order cumulative matrix was extended to r fractional cumulative matrix, so that the fractional order cumulative grey model was established to make forecasting. The results of numerical example showed that the hybrid model can obtain better prediction accuracy

Consumption prediction of bearing spare parts based on a hybrid model

Aiming at improving the accuracy of consumption prediction, a hybrid model was constructed, which designs an empirical wavelet filter bank to remove noise factors in original data. Besides the value prediction, the EWT-PGPR model can also give a certain credible interval, which effectively improves the practicability of the model