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Direct data on the mechanisms of the diffusion of atoms in amorphous alloys are almost non-existent, so the discussion in this section is somewhat abstract in nature.
Analyzing the experimental data in Fig. 2, it can be seen that such an overestimation of the activation energy was facilitated by the absence of data on experiments at low temperatures.
Secondly, we should note the tendency to decrease the exponent m in the Gm factor during accumulation of new experimental data in various reactors.
Unfortunately, in order to determine which correlation is closer to the truth, it is necessary to conduct long-term and long-term pilot programs for irradiating fuels with different enrichment (especially low, which will lead to a reduction in the fission rate) in different reactor installations.
The authors of this paper hope that such data will appear in the next decade due to the successful transfer to a dispersed uranium-molybdenum fuel of a large number of research reactors in the world.

Method Portion II: (post tree development techniques) Partition the data into training dataset and testing dataset.
Pei, Data mining concepts and techniques, third ed., Elsevier, Morgan Kaufmann, USA (2012) 17-27, 248-273, 461-488
Yin, Mining frequent patterns without candidate generation, Proceeding of the 2000 ACM SIGMOD International Conference on Data Mining, New York, USA, CM Press, (2000) 1-12
Knowledge Data Eng. 15(3) (2003) 642-658
Mitra, Pattern recognition algorithms for data mining, Chapman & Hall, Florida, USA, CRC Press LLC, 2004, pp. 165-168, 170-174

qt=qe2k2t(1+qek2t) (11) qt,des = qe,des +(qe,ads-qe,des)(1+qe,ads-qe,desk2,dest) (12) Non-linear pseudo-second-order fittings are performed by plotting qt data (mg/g) to the time (minute) and fitting process using application origin.
The area under the XRD curve shows the proportion of material structure composition (%), based on the results of data fitting (decomposition) of the equation (1).
The suitability of the data point to the fitting model (R2) indicates that the experimental data corresponds to the curve of the second-order pseudo-kinetic study model by the presence of active sites [16].
Table 5 show data tabulated parameters of MB adsorption and desorption for H-CS3.1 and H-CS3.2 material.
The suitability of the data points to the fitting model indicates that the experimental data correspond to the curves of the pseudo-1st order kinetic study model.

Good agreement can be found from the comparison of the analytical results obtained in this paper and experimental data.
The results were also compared with experimental data and good agreement can be observed.
Based on the specimen size and material parameters used in literature [6] and [10], the comparison of ultimate bearing capacity obtained in this paper with the experimental data is shown in the table below.
According to the data in Table 2, the relationship between bearing capacities and parameter can be obtained as shown in Figure 3.
Good agreement can be found from the comparison with the analytical results and the experimental data

A SIMULATION MODEL BUILT UP IN MODELING SOFTWARE-WITNESS BY CONSIDERED ENTIRE SET OF DATA.
However the collection and analysis of downtime data is important to this study and required more effort and time.
Since “percent down” has inadequate to characterize the performance of various machines both mean time between failure (MTBF) and mean time to repair (MTTR) data are collected, both by observation and study of operating system.
Before specific data collection began, a team of plant, process and controls engineers and simulation consultants to specify precisely the scope of the project as described .The project scope understanding of which process data, such as cycle times, travel times, location, capacities and percentage and duration of downtime would be required.
Besides the operation time, the following schedule and data of the assembly line are also obtained for the simulation inputs: a) There are 300 working days per annum.

According to the rising requirement for energy saving and emission reduction, forest biomass power generation will be the important way to reduce CO2 emission in the future.
This article extracts basic data from forest biomass direct-fired power generation projects of Naiman Banner in Inner Mongolia, and the environmental cost incurred during the power generation process is calculated.
According to the data information from the demonstration project of forest biomass power generating station,its main pollutants emission situation is reflected, as the following Table 3 shows: Table 3 Emission results of ambient air pollutants in the projects Projects Units 2×12MW SO2 Emission （t/h） 0.0328 NOX Emission （t/h） 0.078 Fume Emission （t/h） 0.000267 SO2 Emission Concentration （mg/Nm3 dry flue gas） 253 NOX Emission Concentration （mg/Nm3 dry flue gas） 200 Fume Emission Concentration （mg/Nm3 dry flue gas） 2.1 Data sources: Chinese Forest Biomass Energy Generation Special Research Reports FOR Mid-And-Long Term Plans After the treatment of dust removal system, all emitted pollutants can meet the emission requirements of “emission standards for thermal power plants on atmospheric pollutants”.
Table 4 Emission allowance for ambient air pollutants Projects Units Actual Emission Allowance Proportion（%） SO2 Emission （t/h） 0.0328 1.169 2.8 本 Q SO2 Emission Concentration （mg/Nm3 ） 253 800 31.6 NOX Emission Concentration （mg/Nm3 ） 200 450 44.5 Fume Emission Concentration （mg/Nm3 ） 2.1 200 1.1 Data sources: Chinese Forest Biomass Energy Generation Special Research Reports For Mid-And-Long Term Plans It can be seen from the above table that, all emission concentration data in the demonstration project of forest biomass power generating station can meet the requirements.
Moreover, the emission concentration data of SO2 and fume are far below the allowance, and the emissions are also at low level.

Table 1 Index system of risk measurement for IT project target strata factors layer index layer Risk measurement for IT construction project A Design risk A1 Risks of scheme and designing errors A11 Risks of multi disciplinary A12 Risks of importance degrees A13 Risks of data management A14 Risks of designing changes A15 Risks of creativity A16 Personnel technical force risk A2 Risks of techniques ability of crew A21 Risks of crew size A22 Actual experience risks A23 Risk management A3 Risks of systems A31 Risks of organization structure A32 Risks of controlling projects A33 Risks of quality
3.03 2.13 2.9 2.05 2.34 Medium 7 Risks of techniques ability of crew 1.93 1.78 3 2.7 2.35 2.05 2.29 Medium 8 Risks of controlling projects 2.08 2.23 3.18 2.28 1.95 2 2.27 Medium 9 Risks of multi disciplinary 2.43 1.65 2.65 2.35 1.95 2.88 2.21 Medium 10 Risks of designing changes 1.95 1.8 1.5 1.8 2.83 1.95 2.06 Medium 11 Risks of a lack of early warning mechanism. 2.2 1.8 1.68 2.45 1.95 1.83 2.04 Medium 12 Risks of resources management 2.13 1.68 1.68 1.68 1.95 1.85 1.88 Slight 13 Risks of measures of managing and controlling 2.35 1.6 1.98 1.98 1.08 1.95 1.81 Slight 14 Risks of systems 2.3 1.55 1.93 1.63 1.08 2.03 1.73 Slight 15 Risks of organization structure 1.93 1.33 1.33 1.33 1.95 2.03 1.67 Slight 16 Risks of experience accumulation 2.28 1.58 1.58 1.58 1.08 1.18 1.67 Slight 17 Risks of creativity 1.85 1.33 1.33 1.33 1.95 2.23 1.64 Slight 18 Risks of sharing information 1.5 1.45 1.33 1.33 1.95 2.73 1.55 Slight 19 Statement risks 1.88 1.43 1.43 1.43 1.08 1.83 1.48 Slight 20 Risks of data
The expert evaluation group evaluate sub-factors under factors layer, then the weight set of comprehensive evaluation factors level for all sub-factors can be gotten, such as table 10 to 14: Table 14 Risks response planning Sub-factors of risks Priority Risk response methods Measures adopted Main stages Risks of scheme and designing errors 1 Risk avoidance Risks must be aversed and the verification dimensions must be strengthened and meetings must be organized to discuss problems Proposal stage Risks of quality controlling 2 Risk reduction Pay attention to the quality controlling and details, adopting the methods of combining the regular maintenance examinations and strengthen regulation Implementation phase and starting stage Risks of coordination 3 Risks solution Strengthen communication and adopt forms of informal communication Proposal stage, implementation stage and acceptance stage and production stage Risks of importance degrees 4 Risks solution Members in the project group
should report the importance of the program at the beginning of the report, and encourage those involved in this work to pay much attention to the program from the perspective of evaluating the performance of individuals Proposal stage, implementation stage and acceptance stage and production stage Actual experience risks 5 Risks reduction Arrange proper work based on the amount of actual experience, avoid those inexperienced staff finishing important operations, and eliminate thoughts of luck Proposal stage Risks of safe measures 6 Emergency measures Carry out emergency measures and test them Proposal stage and acceptance and production stage Risks of crew size 7 Risk Contained Contain risks and add personnel Implementation stage Risks of techniques ability of crew 8 Risks reduction Every technical member must do the job related to his own ability to avoid the malposition of being arranged with jobs Implementation stage Risks of controlling projects 9 Risks contained The project manager
Implementation stage, acceptance and production stage Risks of data management 21 Risk contained.

The “Safe Hydrogen Fuel Handling and Use for Efficient Implementation 2 (SH2IFT-2)” [5] is just one of the numerous research and more applied projects regarding hydrogen-steels compatibility that will provide new experimental data, helping to bridge crucial knowledge gaps in the following years.
The following table provides relevant data using the SI units for some steel grades.
Hydrogen Compatibility with Steels As mentioned, hydrogen exposure can determine the loss of ductility of a steel, it can lead to reduction in fracture toughness and in general it triggers degradation mechanisms which may severely affect the performance of metallic materials.
More specifically, among the most relevant consequences of HE, the reduction of the following parameters is deemed to be critical: elongation to failure, area reduction to failure, strain hardening rate, tensile strength, fracture toughness and fatigue performances are crucial mechanical properties severely affected by hydrogen embrittlement [7,11,12].
Future experiments, conducted within the SH2IFT-2 project [5], will aim to partially bridge those gaps, thus providing new experimental data towards more reliable and safe hydrogen technologies development and utilization.

(5) The task of energy conservation and emissions reduction is heavy.
So it is great meaningful for energy conservation and emissions reduction to focus on developing energy saving transformation of existent public buildings.
(3) Strengthen the analysis and application of building energy consumption data.
The operational data of construction for building owners of the building units (including the owner individual), real estate development and construction units, design units, construction units, the property management unit is very meaningful.
It can make the property management unit get scientific data, handle and analysis the service condition of construction better, optimize the use of building energy, in order to maintain long-term effective energy saving building system.

From the XRD data, the crystallite size was also estimated to be in the range of nanometers (nm).
Several methods like carbothermal reduction of silica carbon mixture, self-propagating high temperature synthesis, microwave radiation, sol-gel, plasma etc. have been reported for the preparation of SiC powder.
This result is in fairly close agreement with other published data [7] and it confirms that the sample is crystalline.