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The obtained results of thermodynamic modeling and experimental data showed that it is possible in principle to directly microlite boron steel by reducing it with aluminum and silicon contained in the metal.
The obtained TM results showed good convergence with the experimental data [21].
There is no data on the processes of boron reduction from slags containing B2O3, reducing elements that are part of the processed steel (aluminum, silicon, carbon).
In table 1 presents TM data on the degree of reduction of boron depending on the aluminum content in steel in the temperature range 1400–1700 0С, which indicate its decrease with increasing process temperature for all initial concentrations of aluminum and increasing the degree of boron reduction with increasing aluminum concentration in the metal.
An experimental study of the distribution of boron between the oxide and metallic phases confirms the data of TM on the possibility of boron reduction from slag containing 4% B2O3, aluminum and silicon in the steel, and the influence of the initial aluminum content in the melt on the degree of boron reduction.

The Investigation of Pipe Ends Formation under Reduction Mill Rolling D.
It is established that the greatest "contribution" to wall thickness data spread is made by the pipe facets, which is caused by the influence of the reduction regimes and the rolls calibration due to the metal flow into the tapers of groove.
The reduction regime is chosen so that the wall thickness increases from 4.5 to 4.8 mm.
Thus, at the rear end the average wall thickness is greater than at the front end, which corresponds to the literature data [2-8].
Summary In the course of the investigation of the longitudinal and transverse wall thickness variation of the oil-well tubing with the size of 60 × 5 mm rolled at EWPM 20-102 it was established that the greatest "contribution" to the wall thickness data spread is the pipe facetedness that is caused by the influence of the reduction regimes at the reduction mill and the rolls calibration due to the metal flow into the tapers of groove.

H2: Waste reduction subjective norm has a positive influence on RBI.
In general the response rate was 100%, but since ten questionnaires later were discarded because of missing data, the effective response rate was approximately 98%.
Findings and Discussion SEM using AMOS version 20 [11] was applied to the data in order to test the hypothesised saturated model.
Overall fit indices showed that the hypothesis model fit the data well: x2 (df = 184, N = 470) = 411.056 p < .05, CFI = .924, PGFI = .737, RMSEA = .051 (Low = .045, high = .058).
H3: Predicts that, waste reduction perceive behaviour control has a positive influence on reduce behavioural intention, to increase the waste reduction practice.

A private cloud is a proprietary network or a data center that supplies hosted services to a limited number of people.
Through the enterprise private cloud, a factory can obtain the device service, the software service, model service, the data service, the information service and knowledge service from the public cloud.
In addtion, the service cloud should supply safe data storage for the users to record, look up, manage, and reuse their manufacture process data.
According to the business requirements from energy-saving and emission reduction and the new related technologies in this area [7-9], these key technologies are: pinch technology for device improvement in energy-efficiency, the coupling modeling technology to describe the nature of the coupling relation between production and energy consumption, computer simulation for calculate the possible energy-consumption value by reproduction in a virtual environment, dispatch optimization technology, evaluation technology for energy-consumption efficiency, data mining technology for energy-saving oriented etc.
Conclusions Both of global climate improvement and manufacture cost-reduction require the people to make endeavor in work of energy-saving and emission-reduction.

Crash Reduction Factor(CRF) CRF (Crash Reduction Factor), also known as Crash Reduction Rate, refers to the percentage of reduction in the number of traffic accidents when taking a particular security measures in transport system[1].
The data required is: the average number of accidents in many years before the implementation of safeguard measures which is called aB, the average number of accidents in several years after the implementation of safeguard measures which is called aA.
A certain improvement measures have classified the value of CRF to different type of accidents, which not only improves the accuracy but also provides a variety of research required data.
General types of accidents are: All, Fatality or Injury, Property Damage Only, Head On, Rear End, Side Swipe, Left Turn, Right Turn, Right Angle, Fixed Object, Pedestrian, Red Light Run, Run Off Road, Wet , Night[3].Table1 shows the Statistic data of CRF channelization portion in Missouri State，U.S.A.[4] Table.1 Statistic Data of CRF Channelization Portion in Missouri State，U.S.A.
If it is got that the historical traffic accident data of the road which has been implemented a certain safeguard measure, the number of reduction of accidents can also be calculated after the implementation of a certain safeguard measure, and the calculation formula(Eq.  2) is used for caculating the number of reduction of accidents.

Introduction The original features extracted from test data in mechanical fault diagnosis can characterize device state[1].
is the number of testing data points.
represents the kernel matrix for the testing data points.
Acceleration signals were measured using the Dewetron 16 channels data acquisition system and the IMI 603C01 accelerometers.
The data was stored in .mat format for further Matlab operation.

The reported mechanism does not provide information for the reduction kinetics; however, it gives certain suggestions how reduction might occur in complex ore minerals.
After reduction the crucible was cooled down with a furnace to room temperature.
The captured micrographs were analysed using ImageJ 1.8.0_60 software enabling to obtain the data about quantity, size and distribution of the metal particles, Fig. 4.
The data was sorted and entered in RStudio 1.0.143 program, where the normal distribution and homogeneity of variance of the metal particles were estimated.
Vinters, Gaseous Reduction of Iron Oxides: Part III.

For NIR data has the character of high dimension, nonlinear, and high noise, we often confront the problem of dimensionality reduction when building the classification model on Near-Infrared spectra data.
Recently, nonlinear dimensionality reduction methods are applied in the spectra data like Isomap [5] and LLE [3].
Firstly, preprocess the training data and do nonlinear dimensional reduction by using S-Isomap.
Table 1 NIR spectra data of five brands’ cut tobacco Dataset No. classes No. variables No.samples NIR data 5 1609 80 Dimensions Reduction.
After KLLE dimension reduction process, the information of embedded data has a little loss.

Technological Transformation for Loss Reduction in Power Networks in Low-carbon Economy Energy Saving and Consumption Reduction.
Technical transformation for loss reduction could help optimize, refine and quantify loss reduction measures.
Tab. 1 Projected National Percentages of Thermal Power and Average Coal Consumption of Electricity Generating (×10-6) Year 2013 2014 2015 2016 2017 2018 Percentage/% 80.58 79.81 78.93 77.95 76.84 76.12 Coal Consumption/t/kWh 335 332 329 326 322 320 Data from: http://news.bjx.com.cn, http://www.lib.hust.edu.cn/xueke/slsd/wszy/dl.htm, etc.
Tab. 2 Predicted Electricity Saving Amount (×10-6) and Transmission Loss in Distribution Network of the Studied Company Year 2014 2015 2016 2017 2018 2019 Electricity Saving Amount/kWh 32.28 40.56 45.34 49.76 52.18 54.45 Overall Transmission Loss/% 4.8 4.6 4.5 4.4 4.3 4.2 Data from: the studied power supply company.
With the above data and assumptions substituted in the emission reducing model introduced in the last section, the developing trend of low-carbon benefit of the company, by the implementation of technical transformation projects for loss reduction, in the next five years was plotted, as shown in following Figure 1.

The dimentional data then will be used to model a steel wheel rim model by using CATIA V5.
The shape optimization process will be applied when the data has be transferred to FEM data.
Each optimization is done beginning at the datum and not continuous from previous reduction to allow comparable results.
Static structural analysis had been done to the datum and optimized design to obtain the results of maximum stress, total deformation and mass reduction.
Eventhough the maximum stress of optimal design selected is not as lower as datum, but if compared to the higher reduction target, 15% of reduction target is the better option.