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The substitution of trivalent rare earth cation such as La 3+ for Ca2+ in the parent CaMnO3 compound causes the reduction of Mn 4+ to Mn3+ [4].
Ca1-xLaxMn1-yCeyO2 composition has not been studied before, and according to literature data Ce ions can enter both A and B positions in different perovskites [6,7].
To derive relevant structural parameters, the experimental data for the Rietveld refinement were taken afterwards in the angular range 20 - 90 º2θ, with a step width of 0.025º and 5 s per step.
Table 1 Crystallographic data and the results of Rietveld refinement for perovskites in CLM and CLMC powders.
Independent of relatively large difference in ionic radii between Mn and Ce ions in coordination VI the literature data claim that Ce gets incorporated into the position B [6].

GANs are designed to generate new, realistic data based on existing data, such as images, text or sounds.
The discriminator is responsible for distinguishing real data from fake data produced by the generator.
The discriminator tries to decide whether a given data point is "real" (from the original data set) or "fake" (created by the generator).
Applications of the autoencoder: - Dimension reduction: autoencoders can reduce the dimension of image data by finding the most important features.
Deleted Journal, 5(1), 50–62. https://doi.org/10.60087/jaigs.v5i1.163 [10] Cheng-Yu Chen, Jenq-Shiou Leu and Setya Widyawan Prakosa Using Autoencoder to Facilitate Information Retention for Data Dimension Reduction, IEEE, pp. 1-5, 2018

In this situation, some enterprise energy conservation and emission reduction task become more difficult, the set of laws and regulations become more strictly.
As a large-scale and energy intensive industrial enterprise, it tries hard to hold the work of energy conservation and emission reduction, and to explore the production process low energy method.
The Compressed Air is Predicted By The Model of WNN At present people often exploit the neural network to predict the compressed air, but this paper used WNN to predict.With the historical data characteristics for an enterprise in August for a week of compressed air data as the predicted data, we can predict the compressed air, the methods are shown below: From the data of August,choosing seven days’data to as the input and output of the predicted model, first using the first six days of data as the training sample, the compressed air of every two days as the input vector, the compressed airt of the third day as the target vector, which can get three set of training samples, finally using the trained wavelet neural network to predict the compressed air of the seventh day.
Owing to the large data range in the predicted process, the neural network input and output data within a certain range, the bigger of the input data can also fell on the conversion of gradient is larger for neurons function , so can make the training speed is accelerated, therefore, to deal with input data normalization processing.
The prediction of WNN Table I Experimental Result Actual data 13554 7828 8268 7430 5298 3572 13338 17126 17754 18068 18248 18206 18082 17918 17814 17744 17976 17958 17736 17348 16968 17364 15526 13278 The predicted data of BP 13550 7551 8217 6515 5521 3137 13103 17004 17648 17687 17497 17646 17981 18045 17973 17805 18034 16809 17581 17425 17124 16587 15429 14122 Relative error of BP 0.03% 3.54% 0.62% 12.31% 4.21% 12.18% 1.76% 0.71% 0.60% 2.11% 4.12% 3.08% 0.56% 0.71% 0.89% 0.34% 0.32% 6.40% 0.87% 0.44% 0.92% 4.47% 0.62% 6.36% The predicted data of WNN 13713 7855 8666 6959 5950 3816 13203 17121 18006 18508 18520 18360 18292 18348 18225 17973 18151 18132 17852

The second stage is called data post-processing, by reverse engineering software and 3D-moulding software process the point cloud data, then reconstructing CAD model.
So-called "point cloud" data refers to during the reverse design process, through various methods to obtain 3D data sets of objects surface, when the data display on a computer screen, it looks like clouds, so vividly called it "point cloud".
The measured point cloud data is shown in figure 2.
Main process includes point cloud data alignment, noise removal, reduction, polygonization.
Figure.2 Point Cloud Data Fig.3 Point cloud data processing model In the process of reverse modeling, solid modeling is the most important step.

When the concentration of HBP-NH2 and zinc nitrate were 24 g/L and 17.85g/L respectively, the UPF value of treated silk fabric was 131 and the bacterial reduction rates against Staphylococcus aureus and Escherichia coli both exceeded 99%.
The output data clearly state the formation of nanoparticles of zinc oxide on treated silk fiber surface as shown in Figure 4.
The bacterial reduction rates all exceeded 99.9%.
When the concentration of HBP-NH2 and zinc nitrate were 24 g/L and 17.85g/L respectively, the UPF value of treated silk fabric was 131 and the bacterial reduction rates exceeded 99.9%.

The size reduction and structural evolutions of the milled samples were investigated as a function of the milling time by means of X-ray diffraction, and field emission scanning electron microscopy.
Aside from size reduction, the advantage of HEBM is that the structural evolution as well as phase transformation of the grinded materials could be entirely controlled through milling parameters.
It should be noticed that structural alteration of diamond powder, as a result of crystal deformation during milling process, has great influence on performance of the size reduction process and quality of as-prepared ND particles.
The graphitization as well as forming amorphous phase of diamond in HEBM process is not understood well yet; our data, however, suggests that non-diamond carbon phase were produced and attributed to the formation of iron carbide composite under milling conditions.

The results show that by optimizing orifice diameter and orifice layouts, good effect on reduction of self-excited vibration can be achieved, with the time of vibration attenuation reduced from 1.8s to 0.9s.
Because of the compressibility of the air working as the medium of aerostatic stage, it is easy to cause small self-excited vibration, which results in reduction of guiding and positioning accuracy.
Thus, this paper studied on the reduction of self-excited vibration of aerostatic stage through decreasing the diameter of orifice and changing the structure of orifice, by both theoretical and experimental means.
Data of the flow coefficient of orifice and differential pressure are obtained through this experiment, then using the method mentioned in the previous section to acquire function expressions between flow coefficient and differential pressure under the circumstance that orifice diameter is 0.3mm (see Fig.4 for reference):

Impact of N Doping on 3C-SiC Defects Cristiano Calabretta1,a*, Viviana Scuderi1,b, Annalisa Cannizzaro1,c, Ruggero Anzalone2,d, Marco Mauceri3,e, Danilo Crippa4,f, Simona Boninelli1,g, and Francesco La Via1,h 1CNR-IMM, VIII Strada, 5, 95121 Catania, Italy 2STMicroelectronics, Stradale Primosole 50, 95121 Catania, Italy 3LPE, Strada XVI, Catania, Italy 4LPE, via Falzarego 8 Baranzate (MI), Italy a*cristiano.calabretta@imm.cnr.it, bviviana.scuderi@imm.cnr.it, cannalisa.cannizzaro@imm.cnr.it, druggero.anzalone@st.com, emarco.mauceri@lpe-epi.com, fdanilo.crippa@lpe-epi.com, gsimona.boninelli@imm.cnr.it, hfrancesco.lavia@imm.cnr.it Keywords: 3C-SiC; Stacking Faults; Dislocations; Molten KOH; Defect reduction; nitrogen doping; SEM.
These characteristics, along with 2.5 eV bandgap, make 3C-SiC suitable for power electronic applications, due to several benefits in MOS devices such as a notable Ron reduction for medium voltage applications working under 1200 V [1].
Figures 2a and b show, in particular, the 95% confidence curves of the exponential fits extracted from the experimental densities data with respect to the grown 3C-SiC layer thickness, from interface to 40 µm thickness.
The 95% confidence curves of the decaying exponential fits plotted by the density data of SFs a) and etch pits b) as a function of the growth 3C-SiC thickness.

Experimental data show that a gallon of gas burned produces about 20 pounds of CO2.
Firsthand preliminary data were obtained.
For the convenience of using filed data, discrete form based on average numbers may be used,
Firsthand field data, such as vibration, noise, and environmental impact, can be obtained.
Those data are expected to lead to further insights of the relevant applications.

Manual investigation of analytical data for profiling studies is time-consuming, complicated and can be inefficient at times [21].
Various chemometric methodologies including principal component analysis (PCA) was efficiently used to compare and interpret profiling data promptly.
The impurity profiles were scrutinized using similar data analysis software and the compounds were identified based on previous literature, and the NIST library (version 2.0).
Total ion chromatograms (TIC) data matrix was normalised prior to PCA analysis.
Furthermore, the analytical data combined with statistics and machine learning model such as PCA has enabled objective interrogation for precursor origin study.