Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

Analysis of Traffic Accidents Based on the Integration Model

To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.

Live-Cell Imaging of Dual-Labeled Golgi Stacks in Tobacco BY-2 Cells Reveals Similar Behaviors for Different Cisternae during Movement and Brefeldin A Treatment

In plant cells, the Golgi apparatus consists of numerous stacks that, in turn, are composed of several flattened cisternae with a clear cis-to-trans polarity. During normal functioning within living cells, this unusual organelle displays a wide range of dynamic behaviors such as whole stack motility, constant membrane flux through the cisternae, and Golgi enzyme recycling through the ER. In order to further investigate various aspects of Golgi stack dynamics and integrity, we co-expressed pairs of established Golgi markers in tobacco BY-2 cells to distinguish sub-compartments of the Golgi during monensin treatments, movement, and brefeldin A （BFA）-induced disassembly. A combination of cis and trans markers revealed that Golgi stacks remain intact as they move through the cytoplasm. The Golgi stack orientation during these movements showed a slight preference for the cis side moving ahead, but trans cisternae were also found at the leading edge. During BFA treatments, the different sub-compartments of about half of the observed stacks fused with the ER sequentially; however, no consistent order could be detected. In contrast, the ionophore monensin resulted in swelling of trans cisternae while medial and particularly cis cisternae were mostly unaffected. Our results thus demonstrate a re- markable equivalence of the different cisternae with respect to movement and BFA-induced fusion with the ER. In addi- tion, we propose that a combination of dual-label fluorescence microscopy and drug treatments can provide a simple alternative approach to the determination of protein localization to specific Golgi sub-compartments.