Comparison of Perturbation Strategies for the Initial Ensemble in Ocean Data Assimilation with a Fully Coupled Earth System Model

It is widely recognized that the initial ensemble describes the uncertainty of the variables and, thus, affects the performance of ensemble-based assimilation techniques, which is investigated in this paper with experiments using the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. Five perturbation strategies involving adding noises of different patterns and with/without extra integration are compared in the observation system simulation experiments framework, in which the SST is assimilated with the ensemble adjustment Kalman filter method. The comparison results show that for the observed variables (sea surface temperature), the differences in the initial ensemble lead to different rate of convergence in the assimilation, but all experiments reach convergence after three months. However, other variables (sea surface height and sea surface salinity) are more sensitive to the initial ensemble. The analysis of variance results reveal that the white-noise perturbation scheme has the largest RMSE. After excluding the effect of the white noise perturbation scheme, it can be found that the difference in the effect of different initial ensembles on the SSH with only assimilated SST is concentrated in the region of the Antarctic Circumpolar Current, which is related to the spread of the covariance between the SSH and the SST

Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition.

The prediction of energy consumption is of great significance to the stability of the regional energy supply. In previous research on energy consumption forecasting, researchers have constantly proposed improved neural network prediction models or improved machine learning models to predict time series data. Combining the well-performing machine learning model and neural network model in energy consumption prediction, we propose a hybrid model architecture of GRU-MMattention-LightGBM with feature selection based on Prophet decomposition. During the prediction process, first, the prophet features are extracted from the original time series. We select the best LightGBM model in the training set and save the best parameters. Then, the Prophet feature is input to GRU-MMattention for training. Finally, MLP is used to learn the final prediction weight between LightGBM and GRU-MMattention. After the prediction weights are learned, the final prediction result is determined. The innovation of this paper lies in that we propose a structure to learn the internal correlation between features based on Prophet feature extraction combined with the gating and attention mechanism. The structure also has the characteristics of a strong anti-noise ability of the LightGBM method, which can reduce the impact of the energy consumption mutation point on the overall prediction effect of the model. In addition, we propose a simple method to select the hyperparameters of the time window length using ACF and PACF diagrams. The MAPE of the GRU-MMattention-LightGBM model is 1.69%, and the relative error is 8.66% less than that of the GRU structure and 2.02% less than that of the LightGBM prediction. Compared with a single method, the prediction accuracy and stability of this hybrid architecture are significantly improved

Kinetics of lipase-catalyzed hydrolysis of olive oil in AOT/isooctane reversed micelles

The kinetics of lipase-catalyzed hydrolysis of olive oil in AOT/isooctane reversed micellar media was studied. It was shown that the deactivation of lipase had a great influence on the reaction kinetics. Based on whether the enzyme deactivation and influences of both product and substrate on enzyme stability were included or not, four different kinetic models were established. The simulating results demonstrated that the kinetic model, which including product inhibition, enzyme deactivation and the improvements of lipase stability by both product and substrate, fit the experimental data best with an overall relative error of 4.68%. (c) 2005 Elsevier B.V. All rights reserved

Numerical Study on the Disturbance Effect of Short-Distance Parallel Shield Tunnelling Undercrossing Existing Tunnels

The construction of new tunnels poses a threat to the operational safety of closely existing tunnels, and the construction mode of parallel undercrossing over short distances has the most significant impact. In this study, a new double-line shield tunnel parallel undercrossing of existing tunnels in Hefei, China, is taken as an example. A three-dimensional (3D) numerical model using FLAC3D finite difference software was established. The dynamic construction of the new double-line shield tunnel undercrossing the existing subway tunnel over a short distance and in parallel was simulated. The pattern of existing tunnel settlement and change in lining stress caused by the shield tunnelling process were analyzed. The reliability of simulation was verified through field-monitoring data. Finally, based on the numerical model, the effects of change in stratum sensitivity on the settlement of existing tunnel, lining internal force, and surface settlement are discussed. The results show that during shield tunnelling, the maximum ground settlement is 3.9 mm, the maximum settlement at the arch waist of existing tunnel near the new tunnel is 7.75 mm, and the maximum vault settlement is 5.38 mm. The maximum stress of lining of existing tunnel before the excavation is 7.798 × 105 Pa. After the construction of double-line shield tunnel, the maximum stress of lining is 1.124 × 106 Pa, an increase of 44% than that before the construction. The surface settlement and tunnel settlement are sensitive to the weakening of soil layer strength, and lining stress is not affected by the weakening of soil layer strength. The field-monitoring results are consistent with the numerical simulation results, and the model calculation is reliable. This study plays an important role in ensuring construction safety and optimizing the construction risk control of a tunnel

The prediction results in the test set.

(a) GRU; (b) GRU-MMattention.</p

The MAPE of the noise experiment on all methods.

The MAPE of the noise experiment on all methods.</p

Each feature after Prophet decomposition.

Each feature after Prophet decomposition.</p

Schematic diagram of the GRU structure.

Schematic diagram of the GRU structure.</p

GRU-MMattention-LightGBM model prediction process.

GRU-MMattention-LightGBM model prediction process.</p

PlightGBM prediction results.

PlightGBM prediction results.</p