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The temperature increase to 700°C, has been transformed a part of the orthorhombic ZrO2 to a monoclinic phase, contributing to a surface area reduction of the samples, showing irregular agglomerates in morphology, with adsorption/desorption isotherms type IV and mesoporosity characteristic.
It can be seen on Fig. 1 that main peaks of tetragonal and orthorhombic phases are very close to each other, with a difference of just dq = 0,09o between their angles, according to the crystallographic chips JCPDF 79-1769 JCPDF 79-1769 (tetragonal zirconia) and JCPDF 79-1796 (orthorhombic zirconia) from data package of Shimadzu program.
The data of X-rays diffraction collected were used for phase identification and crystallite size calculation from the extension line of X-ray (d111) through the deconvolution of secondary diffraction line of polycrystalline cerium (used as standard), using the Scherrer equation [12].
For these calculations, it was used the major phase density identified in the XRD and it was considered theoretical density (ρ) of 5.606 g/cm³ for monoclinic ZrO2 and 6.047 g/cm³ for orthorhombic ZrO2, obtained according to the crystallographic chips JCPDF 65-2357 and JCPDF 79-1796 from data package of Shimadzu program.
It was also found that all samples have shown peaks displacement to the orthorhombic and monoclinic phase to smaller angles, when compared with the crystallographic chips JCPDF 65-2357 and JCPDF 79-1796 from data packet of Shimadzu program and the displacement value was less evident in the sample calcined at 700°C.

Outdoor Test of Wind-Drifted Snow Distribution around a Cube Keqin Yan1,2, a Xuanyi Zhou1,b Ming Gu1,c 1 State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China 2 School of Civil Engineering, Huangshi Institute of Technology, Huangshi 435003, China a drkqyan@yahoo.com.cn , b zhouxytj@tongji.edu.cn , cminggu@tongji.edu.cn Keywords: outdoor test, wind-drifted now distribution, CFD simulation, Navier-Stokes equations Abstract: This paper presents the results of an outdoor investigation of wind-drifted snow distribution around a cube.
The inlet profiles of the simulation adopted data got from wind tunnel.
Due to the complexity of the special material, only few researchers get the data from field investigation though lots of job carried out in simulating.
Scan valve and DSM3000 are adopted as data collection dealt with program.

Test platform software controls motor load , sends data after collecting, protects overrun, and so on.
PC software stores data storage and displays various results including all the parameters and electromotor state.
Then it begins to control load and collect data.
Data are sent once per second.
Under the condition of no overrun protection, according to the data received, it displays contents.

The technologies of reverse design such as the measure method of points cloud, points cloud data treatment, and curve modeling were studied.
Introduction Reverse design is a process in which the product sample or image data is used as the research object, and the modern design methods and techniques are used to design and development of new products.
The reverse design precision of slicing method mainly depends on the cutting thickness, the measuring accuracy of point cloud data scanning equipment, curve and curved surface fitting error.
The 3D CaMega optical scanner is used as the measurement equipment to get the point cloud data in this article, scanner measurement precision is 0.03 mm.
Processing the Point Cloud Data The point cloud data is processed preliminary by using the scanner software, such as data noise reduction and interpolation; the further processing is done by using Imageware.

Crystallization with low cooling rate leads to the reduction of the amount of carbon into g-phase.
Table 1 contains the statistical data of analysis including a scatter between minimum and maximum diameters of particles and also the calculated volume fraction of graphite.
On the basis of the obtained data average CTE of alloys in the set temperature intervals have been calculated: 20-100 °C; 20-200 °C; 20-300 °C.

Finally, Gleeble specimens were subjected to hardness tests to compare FE prediction of hardness with experimental data.
The available data set has been divided into three parts: training, validation and testing data.
The ANN model has been trained using 80% of the total data randomly selected, while the remaining data 10% has been used for validation and the other 10% for testing.
A measurement of how well neural network has fit the data is the regression analysis and the error histogram.
The blue bars represent training data, the green bars represent validation data, and the red bars represent testing data.

For this purpose, KPCA [12] was conducted to the input data.
Afterwards the training data were projected onto these selected components.
For a simplest linear 2-class issue, if some given training data points each belong to one of two classes, the machine can separate the two class data by a hyperplane which has the largest margin to the nearest data point of each class.
In that space, the maximum-margin hyperplane can be constructed easily to discriminate the mapped data points.
Data mining and knowledge discovery, 1998, 2(2): 121-167

Maximum Aggregate Size 12 mm Figure 6: Effect of water cement ratio on the compressive strength of concrete Deductions from the presented data in Figures 6a showed that the compressive strength of concrete at 0% RISS aggregate replacement decreases by 2.95% between WCR 0.65 and 0.60; decreases by 0.80% between WCR 0.60 and 0.55 while at 20% RISS aggregate replacement the values of compressive strength decreases by 3.12% between WCR 0.65 and 0.60 and decreases by 0.66% between WCR 0.60 and 0.55; at 60% RISS aggregate replacement the values of compressive strength decreases by 4.71% between water cement ratios of 0.65 and 0.60, and decreases by 0.60% between WCR 0.60 and 0.55; deductions from Figure 6b showed that the compressive strength of concrete at 0% RISS aggregate replacement decreases by 0.98% between WCR of 0.65 and 0.60 while 0.27% reduction in compressive strength was observed between WCR 0.60 and 0.55.
At 20% RISS aggregate replacement reduction in compressive strength observed was 1.06% between WCR 0.65 and 0.60 and 0.79% between WCR 0.60 and 0.55 while at 60% RISS aggregate replacement reduction in compressive strength observed was 0.82% between WCR 0.65 and 0.60 and 0.98% between WCR 0.60 and 0.55.
Similar trend was observed in Figure 6c a reduction of 1.70% was observed between WCR of 0.65 and 0.60, while a reduction of 0.38% in compressive strength was observed between WCR 0.60 and 0.55 at 0% RISS aggregate replacement; while at 20% RISS aggregate replacement a decreases of 1.91% reduction in compressive strength was observed between WCR 0.65 and 0.60 and a reduction of 1.90% was observed between WCR of 0.60 and 0.55 and at 60% RISS aggregate replacement the compressive strength reduces by 1.30% between WCR of 0.65 and 0.60 and a reduction of 1.48% in compressive strength between WCR 0.60 and 0.55.
Shamsai [32] showed that reduction of water cement ratio by 0.33 and 0.50 improves the strength by 34.4% and 35.2%.
The coefficient of determination, R2 obtained for the three mix ratios are 0.777, 0.681 and 0.791 indicating that the deductions from the analysis are reliable and can be used to interpret the data generated to arrive at a valid conclusion and inferences.

This paper attempted to give the answer on the basis of GPS data and the local water report. 1 Landslide Introduction Xiangxi River is a branch of north Yangtze River.
Geological survey data shows that the primary gully is located in the direction of the landslide main axis, and the counter-slope is in forepart of the primary gully.
Fig.2 Bazimen landslide deformation monitor data Fig.3 Relationship among GPSC3 monitor data, water level and rainfall As shown in Fig.3, the deformation of the landslide presents a saltatory development.
As the deformations of the monitoring points in early-warning region are in same pace, GPSC3 is selected as typical deformation data.
By using data fitting tools, the unknown numbers in the exponential term are fixed as a=－3.795,b=10.883,c=－3.901.

Analysis can be conducted on real-valued continuous data, discrete numeric data, or discrete symbolic data such as characters.
The seven basic QC tools are employed mainly to achieve process stability; to create an understanding of and to determine guidelines for process improvement by means of reduction, control, and elimination of variation; to assess process efficiency; and to collect data for making management decisions.
The data were tranformed until mean = 0 and variance = 1.
(b) Time series data during January and April from 1996 to 2016.
Smit, Shewhart-type control charts for variation in phase I data analysis, Computational Statistics & Data Analysis. 54.4 (2010) 863-874.