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The lubricant presence in the cutting area may contribute to the friction and thermal field values reduction, affecting the wear processes.
The coating layers can act as a chemical and thermal barrier between the tool and the workpiece, increasing the tool wear resistance, improving the chemical inertia of the cutting material, reducing the volume of the cutting edge deposition, decreasing the friction between tool and splinter, contributing in this way to the cutting forces reduction.The thin layers deposition using as environment of deployment of the deposition process, the vacuum, had a big ascension in the last period.
The data indicate getting the highest values of durability for the type SPUN 120412-B and SPUN 120312-B cutting inserts coated with titanium, case in which can be processed the highest volume of manufactured parts.

Cost reduction. 2.
Some critical aspects need to be improved to make the rheo-diecast process more and more successful. (1) Cost reduction.
The production data online acquisition and monitoring, the development and application of intelligent manufacturing systems are necessary. (3) New rheo-diecast alloys development.

According to the results of the experimental data, the relationship of nucleus particle-number density n0 and crystal growth rate G with the residence time, temperature and the stirring speed is summarized.
And the calcium sulfate will produce some adverse consequences such as flow area reduction, heat transfer coefficient decrease, energy consumption increase and so on.
Temperature [ºC] Stirring Rate [r/min] Residence Time [h] Suspended Density [g/cm3] Average Diameter [μm] 1 30 120 0.33h 0.0295 156.6 2 30 120 0.67h 0.0334 169.4 3 30 120 1.0h 0.0254 145.3 4 30 200 0.33h 0.0203 126.1 5 30 200 0.67h 0.0215 123.9 6 30 200 1.0h 0.0225 121.3 3.3 Nucleus particle-number density n0 and crystal growth rate G: Using a Laser particle size analyzer to analyze and determine the crystal particle distribution, and the distribution graphs are showed in Fig.2: (a) 30 ºC 120r/min 0.33h (b) 30 ºC 120r/min 0.67h (c) 30 ºC 120r/min 1.0h (d) 30 ºC 200r/min 0.33h (e) 30 ºC 200r/min 0.67h (f) 30 ºC 200r/min 1.0h Fig.2 Particle Size Distribution Graph The crystal particle-number density n is calculated by Eq.4 according to the data of the particle size distribution.
(2) At the same temperature, the nucleus particle-number density n0 and nucleation rate coefficient k2 is proportional to stirring speed, as well as crystal growth rate G is inversely proportional to stirring speed; (3) At the same stirring speed, the nucleus particle-number density n0 and nucleation rate coefficient k2 is proportional to temperature, as well as crystal growth rate G is inversely proportional to temperature; (4) According to the results of the experimental data, there are two crystallization kinetics formula is given in this paper: under 30ºC and 120r/min the crystallization kinetics formula is derived to be: B0=1.16×104G0.16, as well as under 30ºC and 200r/min the crystallization kinetics formula is derived to be: B0=1.30×104G0.73 Acknowledgements This work was financially supported by “Public science and technology research funds projects of ocean (201005021) and (201405008)”.

Based on the data sources collected by traditional data acquisition methods and modern information capture means, key techniques such as modeling of spillway, dynamic simulation of the flow and texture mapping and other corresponding methods are put forward.
The data sources are obtained both by traditional data acquisition methods such as engineering design drawings, charts documentation and field control measurement and by modern information capture means such as satellite remote sensing images, high-precision dynamic positioning technology and so on.
First of all, the disorder text data needs to be digitized and input; then depending on the important degree of the information, classification and summary are carried out, in order to reduce the redundancy of the data and guarantee the comprehensiveness of the information; next the information is checked in all respects to ensure the authenticity and accuracy of the original data; and then a unified data coordinate system is achieved; finally effective coupling and inventory analysis of the multi-source data is completed for further application of 3D solid modeling for the reservoir project.
A graphical flowchart illustrating the data acquisition and processing is shown in Fig.1.
According to the data source based on the traditional data acquisition methods and modern information capture means, the 3D visualization technology of the spillway flood discharge is in-depth explored.

Predicting the children's blood lead level in a lead and zinc smelting area based on IEUBK model Fei Chen1,a, Hongguang Cheng1,b, Xiangfen Cui1,c 1School of environment, Beijing Normal University, China a15510492759@163.com,bchg@bnu.edu.cn,ccui1987rainny@163.com Keywords: Lead and zinc smelting area,Lead exposure,Children's blood lead level,IEUBK model Abstract.According to environmental lead exposure data and dietary survey data of lead and zinc smelting area,blood lead levels of children (2~7years old) in study area were predicted with IEUBK model.
A total of 123 people, included 42 cases the measured blood lead data.
Method Dietary exposure, lifestyle and behavior patterns of the 123 children were investigated through the form of questionnaire, the atmosphere, soil, water, crops samples in children living areas were collected and measured their lead concentration,estimated children's dietary lead exposure data, we used these data predicted children's blood lead concentration by IEUBK model, the lack of necessary data were added by the model default parameters.
IEUBKwin32 model was used to predicted children's blood lead levels in the study area,this model involved the various parameters and data source as shown in Table 1: Table 1 IEUBK model involved indicators The index type Parameters The data source The concentration of pollutants in environmental media Outdoor dust lead levels According to the survey data interpolation Indoor dust lead levels According to the survey data interpolation Outdoor air lead levels According to the pollution sources calculated the health protection distance, atmospheric environmental protection distance and took standard limit The concentration of pollutants in exposed medium Lead contents in food According to the survey data from village average Lead contents in drinking water According to the survey data from village average Exposure parameters Dietary structure According to the average of children's dietary questionnaires Behavior patterns According to the average of the children's behavior pattern survey
Effect of smelter emission reductions on children's blood lead levels[J].

The data obtained from this work can be used for subject specific therapeutic methods.
The findings indicate that gait velocity data reported from long walkways tend to be higher than data obtained a short walkway also the gait pattern is different in indoor and outdoor gait.
These data can be further used for the therapeutic purposes.
The data was transmitted to PC via bluetooth at baud rate of 9600 bps.
The filtered data showed reduced peak amplitude and noise.

On the basis of data of adiabatic thermometry, IR and 1H NMR spectroscopy we revealed possibilityto synthesize polymeric and oligomeric products.These products basically correspondto the industrial petropolymericresinsobtained from C9 fraction.
Both ways inevitably lead to critical reduction of amount of pyrolysis liquid products as raw source for production of petropolymeric resins.
It is confirmed by the data of chromatography and thermogravimetric analysis of the respective terpolymer samples [10].
The conclusions are confirmed by data of IR and 1H NMR spectroscopy.

Filler materials are used extensively with polyester resin for a variety of reasons like cost reduction, and more importantly to enhance some physical and/or mechanical properties.
This is a general filler commonly used for cost reduction in many thermoset resins, while keeping an appropriate level of mechanical properties and without much drop in viscosity.
Anyhow, other organic and inorganic fillers were also employed mainly for the cost reduction and favorably for enhancing some of the composite properties.
These results are in well agreement with other published data [8].

We present a simple and rapid production of Ag-NPs made by a sol-gel synthesis in flow mode by means of microreactor tecnology through a chemical reduction of AgNO3 with NaBH4 in presence of two different organic ligands: sodium/potassium tartrate and trisodium citrate.
Silver nanoparticles (Ag-NPs) were synthesized employing sodium borohydride as reduction agent, and two organic ligands, Na3Citrate, and NaK Tartrate, as stabilizers.
The reduction process, in batch and flow mode, always carried out at room temperature with a molar ratio NaBH4 : AgNO3 of 2:1.
From 48-72hs all the plates present a similar sporulation pattern (data not shown) .

In the application, the data of deflection deviation has generated through the cast-in-place segmental cantilever to train the BP neural network.
Judging from experience, the Sigmoid function curve considerably near 0 and 1,and it is bad for the convergence of the network self-learning, the suggestion is the processed output values between 0.05-0.95 is advisable, and normalize the input data for processing and anti-normalize the output processing data.
Input data of the network are normalized [1].Without loss of generality, the sample data is, definition,, which was normalized by follows to sample data into 0~1 interval data: (1) After network learning, then the output of the network data reduction, which is normalized inverse transform [1].The normalized inverse transform formula is given by follows: (2) (4)The BP neural network prediction program introduced According to the above analysis, compiled by FORTRAN used in the construction control of adjustment calculation program, the program interface friendly, easy to operate.
In the program uses the type (1) changes of the input and output data of the processing, which is no longer needed input and output data normalization, after the measure data inputting and network learning of the program, then get the reliable results after the forecast the rest of the samples if you find the law between the input vector and output vector.
The input sample data are shown in table 0.