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Spectroscopy study of silver nanoparticles produced by chemical reduction.
The chemical reduction process was carried out at room temperature.
The absorbance data collected were prepared with AgNO3 reagent grade.
In the case of the reduction performed by using NaBH4, the reduction of Ag ions is carried out instantaneously.
Also in this graph the absorption data of Ag NPs in solutions can be observed.

Table 1.Summary of performance of each reduction technique.
Method Number of Species,NS Number of Reactions,NR Overall % of reduction in NS [%] Avg. % of reduction in tc [%] εID,MAX [%] DRG 431 1788 79.62 72.73 15.51 DRGASA 376 1501 82.22 78.18 13.91 DRGEP 375 1454 82.87 81.82 34.46 DRGEPSA 363 1404 82.84 83.33 34.47 Mechanism Reduction Scheme A novel five-stage mechanism reduction scheme which was applicable for large-scale mechanismreduction such as the n-hexadecane mechanism was established and presented in this section.
Superior performance of the new reduction scheme is observed with higher reduction scale and shorter computational runtime of CHEMKIN-PRO simulationsas compared to the reduced mechanisms generated from the DRG-based mechanism reduction techniques.
In this section, the reduced mechanism for diesel surrogate was denoted as NHX.ID timing of the NHX combustion was compared to the experimental data provided by Sandia National Laboratories which served as the benchmark for validation of the developed numerical models. #2 diesel (D2) fuel [11] with a CN of 46 and ID timing of 0.34 mswas selected as the reference fuel in this work.
[3] P.Pepiot and H.Pitsch: Systematic Reduction of Large Chemical Mechanisms (2005)

Size of pore after reduction determined by H2 concentration used during reduction where the higher the H2 concentration resulted in large pore size.
However, to perform reduction of NiO, several parameters must be considered namely gas type, gas concentration, gas flow rate, reduction process temperature, reduction process holding time and many more.
The bulk density before and after reduction was measured.
While after reduced under different H2 concentration, XRD data show that all peaks that belong to NiO disappeared and only Ni (ICDD file no: 01-088-2326) peak developed for all samples.
Ghosh, Reduction of nickel oxide powder and pellet by hydrogen, Trans.

Carbothermal Reduction Synthesis Of Superfined TiC Powders From TiOSO4 Jie Li1, a, Yi Wu2,b , Pengfei Li3,c 1,2,3Key Laboratory of Nonferrous Materials and New Processing Technology of Ministry of Education, Guilin University of Technology, Guilin 541004, Guangxi, China a43450744@qq.com, b1390209551@qq.com, c376854620@qq.com Keywords: Carbothermal reduction; TiC; Superfine Powders ; TiOSO4 Abstract.
Thermal decomposition of TiSO4, and carbothermal reduction of the mixtures were investigated by using thermogravimetry-differential thermal analyzer and x-ray diffraction analyzer.
Nanostructured TiC was synthesized by methods such as liquid-magnesium reduction of vaporized mixture of TiCl4 and CCl4 solution.
Through solution phase mixing under moderate conditions and subsequent heat treatment at a temperature which was still considerably lower than compared to that used in conventional carbothermal reduction synthesis of TiC.
However, the DSC data indicate that the mixed powder experiences two complete reactions up to 1500˚C.

The state value of energy structure evolution (EUSD) greater than 1, if EUSD value increases over time, the energy consumption structure optimization or energy consumption structure will present normal evolution; otherwise, energy consumption structure will appear inferior or negative evolution. 2.2 Data processing and consequence analysis Choosing time-series data which various types of energy consumption and CO2 emission of energy consumption in nationwide or Hebei province between 1980 and 2012, the calculated CO2 emission intensity correlate value ESCE and the state value EUSD of energy consumption structure in nationwide and Hebei province are shown in table 1.
However, the data dropped period again from 2004 to 2007, finally the statistic fluctuating rose between 2008 and 2012; the state value of Hebei’s energy consumption evolution fluctuated between 1.07 and 1.20, inconspicuous interim of evolution process.
According to energy structure—CO2 emission intensity correlate value (ESCE) , first, ESCE value of nationwide and Hebei province appeared general falling trend, and the main driving force isn’t the evolution of energy consumption structure, but the improvement of energy utilization efficiency led by technology progress; Second, compared to the nationwide data Hebei’s ESCE was much greater and decreasing speed was lower than the nationwide average level.
That is a key link of energy-saving and emission-reduction and low carbon economy development.
Goals of energy structure adjustment should be imbedded through mandatory mechanism in low carbon reduction framework, and then through the incentive mechanism, pressure mechanism and supporting mechanisms to ensure the overall realization of the goals that energy saving and emission reduction and structure upgrade, Low carbon policy in Hebei province are basically center on "to achieve energy-saving and emission-reduction targets", relying on administrative commands and rewards and punishment mechanism to urge the achievements of the goals.

Data and model Data sources and processing.
Historical data include construction value added and energy (fossil fuels listed in Table 1) end-use of the seven regions in East China from 2000 to 2010, sourced from [5][6] and [7] over the years.
Construction value added of each regions in 2020 are predicted with Grey Prediction Model GM(1,1) based on historical data[8].
Especially Shandong would become the focus of emission reduction in East China, since the proportion of its CO2 intensity reduction was close to 60%.
The Preferring potential case stimulates decision makers take differences among regions’ emission reduction space into account, and expect provinces with more reduction space to undertake greater reduction burden.

Basic Theory of Strength Reduction Finite Element Method Strength reduction FEM combines strength reduction technique with elastic-plastic finite element method.
(2) Where, andare cohesion and internal friction angle in front of the reduction; andare cohesion and internal friction angle after reduction; is strength reduction factor, also known as strength reserve safety factor or slope stability safety factor.
Analysis data of land slope is numerous.
It’s based on the single- channel seismic data and shallow strata data collected from the slope zone of northern South China Sea to obtain a more accurate submarine slope form and shallow strata characteristics and to establish the simplified theoretical slope model (see 2.2).
Therefore, the parameters are obtained by making full use of existing research results of physical and mechanical characteristics of submarine soil in South China Sea, and trough the statistical regression analysis of static sounding data (see 2.3).

Before commencing rule discovery, several data mining pre-processing tools are applied to reduce data complexity and controversy as well.
The figure corresponds to sampling rate used in the data acquisition.
To avoid excessive learning time, redundant samples were removed using a cluster-based data reduction method.
We note that the data reduction task was conducted separately for each of the histories.
Of the 393 filtered samples (i.e. outputs of k-means data reduction method), 209 were low damage segments and 184 were high damage segments.

Methodology and Framework Firstly, we estimate the CO2 emission reduction data of the HFCVs through the GREET model developed by Argonne National Laboratory [5].
U.S. had gotten great progress in its commercialization so the experiment data comes from DOE reports.
All the data of the CO2 emission reduction of HFCVs come from four kinds of hydrogen sources [6].
The data obtained by this method are consistent with the recent international study model results.
According to the GREET model and the data in Table 1, combined with existing data and formulas (1), (2) and (3), we can get the data in Table 2.

With the development of the computer science and technology, especially of computer network, there are a mass of data provided for people each moment.
The requirements of data analysis are more and more high due to the rapidly increase of the data.
However various approaches using rough set theory have been proposed to induce decision rules from data sets taking the form of decision systems.
Output: an attribute relative reduction B.
Rough Sets: Theoretical Aspects of Reasoning about Data.