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Experimental data show that MSW's shear failure still satisfies the Mohr-Coulomb criterion.
Test Data Shear stress τ and displacement w curves (τ-w curve) was recorded of MSW specimens of different densities.
Age during the 4-7d, it can be observed that the samples with lower density have a more significant expansion, accompanied by the gas (smell) ejects, liquid discharges and heat releases, which could result in the initial structure weakening and the porosity increasing, eventually leading to the apparent phenomenon of reduction in shear strength.
As for the specimen with larger density, whether there will still be a phenomenon of partial reduction in intensity after the age of more than 7d, and with the age of the sample increasing, whether the magnitude of shear strength will recovery and its process description, more complex tests need to be done.
From the experiment data available, for age and density fixed, normal stress and shear strength of MSW samples are approximate to linear relationship.

From air pollution recapitulation data, gas emissions from motor vehicles increase every year.
In the selective reduction of nickel laterite ore, Fe, and Ni, the selective reduction is the opposite.
Luís, “LI-NMC batteries model evaluation with experimental data for electric vehicle application,” Batteries, vol. 4, no. 1, pp. 1–16, 2018, doi: 10.3390/batteries4010011
Ding, “Thermodynamic of selective reduction of laterite ore by reducing gases,” Trans.
Smelting and selective reduction of limonitic laterite ore in mini blast furnace.

This result corresponded to a flattening of the resistivity distribution, and a ~34% reduction in on- and off-facet resistivity differential.
Results and Discussion Composite maps of TEDs, TSDs, and BPDs were constructed based on etch pit data for ≥20 wafers from each of the three tested process conditions.
Further work is required to understand the observed reduction in the local variation of on-wafer resistivity corresponding to global changes in the partial pressure of nitrogen.

Based on statistical data, the main sorts are as follows: water-inrush, slag, ground subsidence, landslide collapse, and ground crack et.al.
Based on the sequence forecast theory of grey system GM (1, 1), a forecast model is established for grey data sequence.
GM (1, 1) shows a differential equation involving a function of one variable, which basic thinking is that builds dynamic or static white module based given data sequence according to a kind rule, then depends on some change or solution to calculate grey module in the future; in the grey module, according to a kind rule, find gradually dynamic variation and forecast its future development trend. [1] Building model step as follows: Given original data sequence X(0)={x(0)(1), x(0)(2),…x(0)(t),…x(0)(n)} (1) Meanwhile, X(0)(t) represents the data of time,n≥4 Based on sequence X(0),in order to overcome the defect of original fluidity and randomcity,then generate a new sequence X(1),by accumulation generation operator, build a middle information model.
Table 1 Forecast Accuracy Grade [1] P C Forecasting Accuracy Grade > 0.95 < 0.35 Excellence > 0.80 < 0.50 Eligibility > 0.70 < 0.65 Reluctant eligibility 0.70 ≥ 0.65 Disqualification Application of Grey Model By analysis of these hazards including water-inrush, slag, ground subsidence, landslide collapse, ground crack in XXX mining of Pinglu District, Shanxi Province during last eight years, the output data have been chosen as the original data sequence since 2003, and GM (1, 1) adopted as the grey prediction model[3-6].
Improve mining geohazard prevention and reduction laws, and promote mining geohazard management system.

MacGregor and Kourti [1] established a PCA model from the training data and detected the abnormal behavior of online processes.
Monitoring based PCA-ICA algorithm PCA can handle high dimensional, noisy, and correlated data by projecting the data onto a lower dimensional subspace which contains most of the variance of the original data [4].
After applying PCA on the observed data, the PCs matrix can be expressed as linear combination of unknown independent components, that is,
The training and test data set can be downloaded from http://brahms.scs.uiuc.edu.
E.: Pattern matching in historical data.

Soft clay containing high water content and if not planned properly, water from the soft clay will seep into the slope and causes reduction in soil strength.
Data from the experiments will be used in PLAXIS software to obtain the factor of safety.
Data from the experiments were inserted in PLAXIS to run slope simulations.

The best mix of the RPC component materials is determined on the basis of test data got through testing with the four batches of 19 specimens.
The compressive strength fell down not obviously when the dosage is between 0-0.2, and the maximum reduction of that is just 4.9Mpa.
Although the compressive strength decreases when the dosage is 0.2, the reduction is not obvious.
To improve these shortcomings, the optimal RPC water-cement ratio and dosage of fly ash and tailings powder are analyzed through test data.

Derivation of differential equation of crowding system For the sake of detailed dynamic calculation and analysis of crowding system [1], it was assumed as following: 1）The belt between crowding motor and reduction gearbox, transmission shafts of reduction gearbox, gears and boom were rigid components; 2）The final crowding shaft was elastic shaft and the effect of damping was neglected.
（9） where （10） （11） Dynamic loads calculation of the crowding system 2.1Calculation of exciting loads response The eigenvalue question of the Eq.（6）is: （12） where is the natural frequency; is the natural vibration mode; The eigenvalue equation is: （13） For the substitution of the related data, the natural frequency was obtained as follow: 13.1473 rad/s， 187.6364 rad/s , was substituted into eigenvalue Eq.（12）and the natural vibration mode was under normalization process.
The steady-state response of the system acted on the stimulating vibrationforce was obtained: （14） 2.2 Calculation of crowding shaft loads Under the locked rotor condition when the bucket rod was perpendicular to the boom [8,9], the expression of dynamic load was : （15） The equation（13）was substituted into the Eq.（13） and the further result was obtained as: （16） The original data was substituted into the Eq.（15） and the change law with time of dynamic loads of the crowding shaft was calculated as illustrated in Fig.2.

A—compressor B—condenser C—fractionating tower D—cold-trap evaporator E—Shelf evaporator F—drying chamber G—electronic balance weighing rack H—electromagnet I—sample bottle J—vacuum pump K—digital vacuum gauge L—data monitoring system V1～V4—throttle valve EX1～EX3—heat exchanger Figure.1 The diagram of the freeze drying equipment Among them, the equipments outputting cold capacity are shelf evaporator and cold trap evaporator, which are connected in parallel and adjusted through needle throttle valves V1, V2 [4]. 2 Application Research of the Dual-evaporator In this paper, from three aspects, the experimental research on the dual evaporator system for the freeze-drying equipment is put forward.
When the valve opening gets smaller, because of the reduction of the refrigerant flow into the evaporators, refrigeration capacity is reduced, which causes the increasing of the temperatures.
Table 1 The Influence of the Surface Temperature of the Dual-evaporator on the First Drying Rate Cold trap Temp（℃） Shelf Temp（℃） Average rate（g/h） -80 -60 5.1 -80 -55 5.3 -80 -50 5.4 -70 -50 5.0 -65 -50 4.3 From the data in Table 1, it can be seen that when the cold trap is at low temperature (about -80℃), the surface temperature of the shelf will increase, which can obviously improve the drying rate.
When the surface temperature of cold trap is raised further (not higher than the glass transition temperature), the reduction of the average drying rate will be magnified. 3 Conclusions Therefore, in this paper, experimental study on the dual-evaporator refrigeration system in the process of freeze-drying is carried out from three aspects: (1) First, the two valves’ opening will influence the evaporating temperature of the dual-evaporator system.

Table 1 Core Samples Data[×10-3μm2] effective pressure K1-dry K1-wet K2-dry K2-wet [Mpa] step up step down step up step down step up step down step up step down 3 0.8317 0.3356 0.2587 0.1589 0.3236 0.1866 0.1041 0.0538 5 0.5543 0.2343 0.1578 0.0732 0.2123 0.1351 0.0832 0.0324 8 0.3312 0.1367 0.0785 0.0345 0.1612 0.0942 0.0545 0.0179 10 0.2367 0.0854 0.0462 0.0157 0.1394 0.0707 0.0357 0.0113 15 0.1467 0.0631 0.0234 0.0073 0.0965 0.0574 0.0163 0.0069 20 0.0945 0.0512 0.0135 0.0052 0.0594 0.0438 0.0112 0.0049 30 0.0532 0.0212 0.0057 0.0032 0.0312 0.0321 0.0069 0.0045 40 0.0293 0.0195 0.0034 0.0025 0.0235 0.0269 0.0045 0.0035 Results and Discussion From the Table 1, with the increasing of effective pressure, both the permeability value in condition of saturated with irreducible water and the permeability value in the condition of dry environment declined.
It led to the reduction of the permeability of core samples.
Due to the data normalization, the variation curves of normalized permeability of K1 and K2 core samples with effective pressure are showed in Fig.4.
It led to the reduction of the permeability of core samples.