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Concrete Face Strain and Stress Observation Data Analysis.
Based on the Duncan E-B model and the deformation monitoring data, the construcion process were simulated accurately.
Generally speaking, all these results above are basically consistent with the observed data of strain gauges.
Compared with the observation data, the simulated results are close to the actual operation situation.
J., Analysis of Monitoring Data of Stress and Deformation for Shuibuya Concrete Face Rockfill Dam.

The data from experimental and calculation were obtained and investigated.
SAC Diagram and Testing Apparatus Noise Reduction Coefficient.
The single-number rating, noise reduction coefficient, compares the sound absorber ability with different materials.
The noise reduction coefficient was rounded off to the nearest 0.05 after calculating.
Effect of Alkali Treatment on Sound Absorption Coefficient (SAC) Noise Reduction Coefficient (NRC).

In this context, analytic quantification of strength and deflection characteristics of structural components with web openings must be developed through research data collected through controlled laboratory testings.
A reduction factor 1α of 35% was found to be an effective tuning parameter for the deflection prediction as seen in Fig.6 to Fig.10.
A reduction factor 1β of 25% was found to be an effective tuning parameter for the deflection prediction as seen in Fig.6 to Fig.10.
A reduction factor, 1γ of 15% was found to be an effective tuning parameter for the deflection prediction as seen in Fig. 6 to Fig. 10.
Thanks are extended to Hume Concrete Product Research Centre (HCPRC) for several useful discussions and generously provided large collection of prestressed beam tests data.

Including data cleaning, transformation, reduction, delete the useless content, check the information completeness and consistency.
¿User behavior information storage based on HBase: storage of user behavior information from the client and the server, the dynamic and static user behavior data, results and analysis.
As the following methods used: 1)Data cleaning: removal of the incomplete data, delete duplicate data, delete access to pictures, delete pages of animation, the user behavior analysis of useless data[8]. 2) Data conversion: the pages print, collection, preservation, download operation, in the acquisition, will be converted into the corresponding data format in the database. 3) Data reduction: the user behavior data in large quantity, to standardize the data quantity, reduce the very necessary, but must maintain the integrity of the data.
Because of the time and energy constraints, this work is currently only integrates three cloud platform of the data mining model, looking for more scene data mining model, and transformation, in the cloud platform integrated, make the system more universal, universal, is the next step of work to do.
Proc ACM SIGMOD Int’l Conf Management of data [C].Washington DC, May 1993. 207~216 [3] Agrawal R, Srikant R.

The studies of intersection safety evaluation mainly rely on accidents and conflict data.
Data Description These intersections cover four cities from East China to West China, major cities and some towns, therefore, these safety data can represent widely.
Besides that, these accident data also contain the crash type of each accident.
The third type of intersection (called T-3 intersection for short) has some accident data, but these data is not full in recently three year.
IDP is weights by IADP and ICDP with different weights, which determined by the relationship and quantity between accident data and conflict data.

The comparative estimation of the runtime reduction in the second stage of CWT calculation is deduced.
This calculations must be repeated with each new data packet, because a delay in the calculation leads to detection delay enemy boats that in the event of hostilities could have disastrous.
Then we can reduce volume of calculations in the second stage CWT when, e.g., solving the problem of detecting and calculating the CWT that must be repeated with each new data packet.
Dividing (9) by (8) we get the comparative estimation of the computation time reduction C in the second stage of CWT calculation:
Gain in computation time increases with the number of samples of the input signal N in the treated data packet.

After that, selected samples were cold rolled to 30 and 75 % reduction.
Figure 2a shows the microhardness data of PA samples measured after aging at 100 °C versus aging time up to 6 days.
Figure 3 shows the DSC curve obtained at the heating rate of 10 °C/min for the cold rolled (to 30 and 75 % reductions) materials.
Figure 4 shows the microhardness data of both the deformed materials measured after ageing at 100 °C versus aging time up to several days.
Figure 4 The microhardness data of both the deformed materials measured after ageing at 100 °C versus aging time.

A356 Al-alloys play an important role in achieving vehicle weight reduction and improving fuel economy in the automotive industry.
Forging is performed with the help of 150 tons of hydraulic press with different reduction in the thickness of each sample as per Taguchi design data sheet of (L9 34) OA for each trial condition.
Grey Relational Analysis: Grey data processing is performed before calculating the grey correlation coefficients.
A linear data preprocessing method for the S/N ratio can be expressed as follows: Zij= yij-min(yij,i=1,2,…..n)max(yij,i=1,2,…..n)-min(yij,i=1,2,…..n) Eq. 1 The grey relational coefficient is calculated to express the relationship between the ideal (best) and actual normalized experimental results.
The grey relational coefficient can be expressed as: γ k, yi k= ∆min+ ξ∆max∆oj k+ξ∆max Eq.2 Where; j = 1, 2...n; k = 1, 2...m, n is the number of experimental data items and m is the number of responses. yo(k) is the reference sequence (yo(k) = 1, k = 1, 2...m); yj(k) is the specific comparison sequence.

It studies the key technology in the process of data acquisition, data pre-processing and model reconstruction, and put forward a model reconstruction method to improve the blades design efficiency.
Data acquisition and pre-processing Data acquisition In reverse engineering, data acquisition method for object surface subdivided into contact and non-contact[3].
Pre-processing of point cloud includes noise point remove, data compaction and data extract.
Data compaction can reduce total data amount and improve the data pre-processing speed, but too much data reduction will effect the precision of model construction, so the point cloud is reduced in the condition of guaranteed precision The separation parts should be kept after point cloud reduction, it is useful for making surface shape when surface is designed, and also can provide precision data for subsequent surface modeling, Importing point cloud in Geomagic software for pre-processing, in order to indentify the characteristics of the model, the point cloud display style is changed to grid establishment style, as shown in Fig. 2.
British library Cataloguing in Publication Data.2008:05-120

Developed in Google Colab with Python, it ensures flexibility and is linked to Google Sheets for real-time data input without additional software.
The model was tested on production data from the Southern section of the Mezhyrichchia quarry using a CAT 980H wheel loader and a KrAZ-65055 dump truck.
Experimental data indicate that on road sections with a gradient exceeding 8 %, fuel consumption may increase by 1.5 to 2 times.
To carry out simulations based on real production data and compare the efficiency of using a wheel loader versus a dump truck, justifying the feasibility of each option depending on operational scenarios.
Dimensions, volume, and mass of blocks (empirical data collected by the author at the Mezhyrichchia deposit) (developed by the author based on the research by [18] and modeling results) Fig. 3.