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Reduction of glass defects.
Cost reduction of refractory.
The matching of each refractory's service life is also achieved with this inspection because the inner structure of each block is recognized and installed at the proper position based upon these data.
Thus, the inner structure inspection of the sidewall blocks can be performed and the data of the blocks are available for the installation positioning of the blocks.
Such data are also available for the determination of the electrode hole position after the campaign started.

General idea about the STEDS The Fig.1 shows the technical process of STEDS, its main content as follows: Data preparation.
They include RS data, elevation data, basic data of buildings and so on.
The 3D terrain is consisted of RS and elevation data, which is the foundation of STEDS.
The color RS image can be formed by cutting and fusion of RS data of different bands.
The 3D terrain can be reproduced by addition of fused RS data and adjusted elevation data (Fig.2~3), really degree of which depends on their precision.

In accordance with the XRD data, the unmodified sample matrix is austenite (γ- (Fe,C) phase) having a FCC lattice with an Fm3m spatial group.
The unmodified sample: а – optical metallography; b – histogram of grain size distribution As shown by the data of SEM there are nonmetallic inclusions inside the grains and along their boundaries observed in all unmodified samples at the horizontal section at a distance 10–15 mm off the lower boss surface (Fig. 2 а-c).
This is proved by the XRD data.
It was established by the XRD data that the introduction of the MM based on ultrafine particles of refractory metal oxides and cryolite did not lead to the formation of new phases.
It was established that the grain size reduction and decrease in the amount of nonmetallic inclusions results in the insignificant enhancement of mechanical properties.

The first stage is for the dimension reduction, where the high-dimensional spatio-temporal output data can be reduced to the low-dimensional time series data .
The second stage is for the dynamic modeling, where a low-order temporal model is identified using the time series data.
After measurement, a series time data of thermal crown are obtained from an aluminum company, as shown in Fig.1.
Thermal crown from measurement KL decomposition is implemented for the dimension reduction, where the high-dimensional spatio-temporal measured output data can be reduced to the low-dimensional time series data.
For the data shown in Fig.1, only 5 principal spatial structures are enough to capture the 99% of the system“energy”.

Data processing in dynamic traffic capacity of urban road based on squatted lane Hequan Zhang North China Electric Power University, Huadian-Lu 689, 071000 Baoding, china 1826086226@qq.com Keywords: Traffic Urban road Dynamic traffic capacity Mechanical mechanics Abstract.
Data Processing Ingredients of traffic capacity are very complex.
Pointing to these data, we calculate them in actual time interval.
Combined with the actual meaning of the expressionand these data, we can find that the definition of is feasible and has a practical significance, which can verify the correctness and feasibility of the model.
Meanwhile, the actual data that have tested basically reached the desired level, so the model is more suitable for the calculation of road capacity in the process of the actual operation.

Application of Rough Set Feature Selection in the Hazard Assessment of Debris Flow LI Ju1, a, CHANG Jinyi1,b , WANG Xing2, WU Xiaobiao1 1 School of Computer Science and Engineering, Chang Shu Institute of Technology, Changshu, Jiangsu, China 2College of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China aliju@284532@163.com, bchangjy@cslg.edu.cn Key words: rough set; debris flow,attribute reduction,value reduction Abstract.
The importance of property In decision-making system , is defined as In decision-making system , the attribute importance of is defined as Application of Rough Set Theory in Debris Flow Hazard Assessment The selection of data and discrete data This attributes were selected from a debris flow, a: The maximum amount out of; b: Drainage area;c: Main channel length;d:Watershed relative height; f: Cut density;g: Maximum rainfall; h: Loose solid material reserves; D: Risk Level Table 1 No. a b c d f g h D 1 8.99 50.0 9.00 2.82 15.61 100.41 7000 1 … … … … … … … … … 4 0.17 21 1.92 1.06 5.11 40.21 11.11 2 Table 2 No. a b c d f g h D 1 1 1 1 2 1 2 1 1 … … … … … … … … … 4 0 0 0 0 0 0 0 2 Calculate posC(D): U/{a}={{1,3},{2,4}},U/{D}={{1,3},{2,4}},U/{b}={{1},{2,3,4}},U/{c}={{1},{2,3,4}},U/{d}={{1},{2,3,4}},U/{f}={{1},{2,3,4}},U/{g}={{1,2},{3},{4}}U/{h}={{1,3},{2,4 }},U/{C-a}={{1},{2},{3,{4}},U/{C-b}={{1},{2},{3},{4}},U/{C-c}={{1},{2},{3},{4}},U/{C-d}={{1},{2},{3},{4}},U/{C-g}={{1},{3
Attribute reduction can be achieved through the following two steps: (1) in accordance with this sort of binary groups, delete the property, because its classification does not work

We have tried to develop a silver nitrate reduction method, with which a certain reducing agent has played an important role in the reduction of silver ions in an aqueous solution.
The chemical reaction includes commonly reduction process of silver ions.
The reflection peaks are indexed as the fcc (111), (200), (220), and (311) planes which are in accordance with the standard data, indicating that silver nanoparticles are well crystallized.
It is found that the dropping rate of reduction agent solution is very important.
When the reduction agent solution was added quickly, the size of particles grew larger.

It is reported that a decreasing relative density causes a reduction of incubation and overall isothermal transformation time.
The isothermal time temperature transformation diagrams for this powder-metallurgical alloy are presented and Avrami-type equations are fitted to the measured data.
In contrast, with a growing porosity a reduction of incubation and transformation end is detected.
By this default, Eq. 2 was fitted to the measured data by a least square method.
The resulting functions are given in Eq.4 to 7. . (4) . (5) . (6) . (7) The results of the model are compared with the experimental data in Fig. 5.

Sustainable Approach and Practice on Environmental Management and Carbon Emission Reduction in the Tourism Industry Yan Ke ¹,a, Peiyi Ding 2,b, Peiyu Ren3,c 1 Regional director of EarthCheck Pty Ltd，PO Box 12149，George Street，Queensland 4003，Australia 2 School of Tourism，The University of Queensland，St.
This paper introduces a holistic approach to environmental management and carbon emission reduction with practical application in the tourism industry by EarthCheck program.
The three most complete global temperature records available – from the UK Hadley Centre, NASA, and the US National Climate Data Centre – all show a clear upward trend in global average temperatures over the last 150 years (calculated using an 11 year running average).
A Holistic Approach to Environmental Management and Carbon Emission Reduction Based on 13 years of research by the Sustainable Tourism Cooperative Research Centre, the world’s largest dedicated research centre specialising in sustainable tourism and research funded by Australian federal government and its successor, the EarthCheck Research Institute, EarthCheck benchmarking program was developed and now operates in over 70 countries and services in excess of 1,300 tourism businesses.
The holistic approach to environmental management and carbon emission reduction provides a comprehensive framework for the business to improve resource consumption efficiency and environmental and social performance.

Critical potentials exist for the formation and reduction of oxide films in high temperature water.
The film reduction process is relatively slower than the film formation process.
Normalized CER (NCER=CER/CERlow DO) values calculated from the original CER data in figure 4a are shown in figure 5.
The time scale (in seconds) is also adjusted for easier data treatment.
The reduction process is more complicated.