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The oxidation kinetic data were experimentally obtained at elevated temperature in dry O2/Ar gas and the temperature dependence of the reaction rates were determined with the Arrhenius-type equation.
In addition, the kinetic data, composition, and structure of oxide scales were examined by scanning electron microscopy(SEM), energy-dispersive X-ray spectroscopy(EDX), and X-ray diffraction(XRD) in an attempt to define the basic mechanism of Fe-36Ni oxidation and reduction. 2.
The specimen was reduced using pure hydrogen gas at each temperature of 450°C, 800°C, and 1000°C shown in Fig. 4 to fabricate metallic alloy powder and the reductive reaction might be taken place with 3 stage reactions as follows: The reduction ratio of the alloy powder increased with increasing temperature and finally reached up to 97% at 600°C for 80 minutes, and the measn particle size was found to be about 200μm.
Fig. 4 The reduction raio of Fe-36Ni oxide powder with time using pure hydrogen gas. 4.
The reduction ratio of metallic powder reached up to 97% and its mean particle size was obsered to be about 200μm.

Toward Sub-20 nm Magnetic Tunnel Junction for Embedded Cache Memory Toshihiro Sugii a *, Hideyuki Noshiro, Yuichi Yamazaki, Chikako Yoshida, Yoshihisa Iba Fujitsu Limited, 10-1 Morinosato-Wakamiya, Atsugi 243-0197, Japan asugii@jp.fujitsu.com Keywords: STT-MRAM, Magnetic Tunnel Junction, MTJ, Perpendicular, Cache memory, MgO, Shrink, Oxidation, Reduction, Plasma treatment Abstract.
From this figure, we found 1) longer O2 plasma treatment time shifts the cumulative probability data to higher resistance, which means further reduction in the electrical MTJ size, 2) the slopes in the cumulative probability data do not change between them, which means the variation in the MTJ size does not increase during the shrink process, 3) the shift in 1/(Rp)1/2 is proportional to the treatment time, which means the possibility in further shrink, 4) NH3 plasma treatment results in enlargement in the electrical MTJ size on the contrary.
From these results, NH3 plasma treatment results in the reduction of the sidewall surface and O2 plasma treatment results in the oxidation of it.
Owing to the size reduction, the current required for MTJ switching decreases with O2 plasma treatment time.
We clarified the mechanism of the shrink process as oxidation and reduction on the sidewall surface in MTJs, and optimized the process for higher density embedded MRAMs.

After making data correction for the background and intensity, the ODF was calculated to recalculate the complete pole figures of each sample using MTEX software.
A 50% reduction was achieved after 9 successive passes while the 75% reduction was achieved after 13 passes.
The 90% reduction was accomplished in a further two steps.
The evidence of RX was observed for alloy-2 to 3 at 50% reduction while for alloy-4 it only appeared after 75% reduction.
SEM/EDX analysis was employed to obtain more data about the second phase particles in alloy-4.

The engine data was implemented in the engine model developed in GSP (Fig. 1).
Assessment of deterioration effect on the turbofan was done according to information and data from the references.
The simulation result approximately matches the data given in Frith [4] and also Ioannis [7].
Comparison between GSP data with reference data (Frith’s and Ioannis’s data) ranges from 0.59% to 8.71% as shown in Table 4.
The result shows similar data trend and thus the obtained GSP result are well validated.

Introduction Metadata is data about data [1].
In meteorological scientific data，especially observational data can not be repetitive.
These data need long-term long term storage, and there is a great deal of meteorological data which need to be transferred and shared.
Meteorological metadata will be the key to data sharing.
Metadata element is basic information unit of data.

Discretization of decision table is the important step for pretreatment of data mining and machine learning, which related to the effect of learning.
However, there are a lot of continuous database data, in order to obtain from the database which contains the continuous attribute gets better data sample and concise and effective decision rules, often need to continuous attribute discretization.
Therefore, decision-making system of continuous attributes discretization for data mining has the extremely vital significance.
Therefore, this follow-up work will study the algorithm for which the distribution of the data set to meet.
New Algorithm for Data Discretization based on Rough set.

Abstract.Based on the remote sensing data and meteorological data of MODIS, this paper studied the water conservation capacity of different land use type of the ecotone of the forest and grassland in HulunBuir with the help of the ArcGIS software.
This type of soil’s fertility is poorer and appears the fixed, semi-fixed and flow state.[2] Research Method The meteorological data need to be rasterized in computation to make it match the remote sensing data in the space.
The pixel size and the projection are consistent with NDVI data
（1）The calculation method of vegetation coverage The evaluation of the main ecosystem service function of the study area is mainly based on the Modis—NDVI data.
c.The calculation method of the total quality of the water conservation Q=Q1+Q2（6） The total rainfall data used in the formula above is comes from the meteorological data of the meteorological stations.

Coal-fired power generation in thermal power is the most major method, data shows that the national power generation capacity was 966.413 million kilowatts by the end of 2010, of which thermal power was 704.1444 million kilowatts, accounting for 72.86% of the total installed capacity.
Therefore, the situation of energy conservation and emission reduction is very grim.
Because the coal consumption data is very poor, it will be carried out equal dimension new information to the model, when the forecast get a value ,added to the known data at the same time remove the oldest data, keep the dater series equal dimension, then established a new GM (1.1) model ,predict the next value, and then added to the known data at the same time remove the oldest data, such as metabolism, individually forecasts, followed by replacement, until complete of the prediction target and reach the prediction accuracy.
In addition, carbon capture and storage technology (CCS) has significant emission reduction.
In the calculation assumes that the proportion of thermal power units remain unchanged, taking the 2010 national data, that is thermal power units accounted for 73.68%; and assumes power grid net loss (3.9%) also remain unchanged.

It was found that homogenizing time at 400℃ for the cast strip was 4h and reduction per pass should be 8-10% for producing thinner (1-1.5mm) rolled AZ31 sheet at 350℃.
In the present, some cast strips of magnesium alloys have been prepared[1-4], but there are few published data on the rolling of cast strip.
Fig. 1 Magnesium alloy strips by vertical twin-roll casting During the rolling under various reduction per pass of 12, 10 and 8%, the times and rolling reduction when the obvious side-cracking occurred are listed in Table 3.
It was found that reduction per pass should be 8-10% for producing thinner (1-1.5mm) rolled AZ31 sheet.
Table 3 The times and rolling reduction when the obvious side-cracking occurred under various reduction per pass of AZ31 The homogenized specimens were rolled from 3 to 1.0 mm under reduction per pass of about 8% and other parameters of rolling process were the same as that forementioned, then annealed at 300℃ for 30 minute.

Internal defects of the concrete specimen were simulated and compressive strength reduction mechanism was studied.
From this study, it is shown that numerical analysis can effectively simulate internal defects and provide an efficient way to study compressive strength reduction.
Among the defect conditions considered in this paper, for the influence of weak layer direction, the maximum reduction in standard compressive strength is 61.8%; for the influence of weak layer thickness, the maximum reduction in standard compressive strength is 73.1%; for the influence of weak area, the maximum reduction in standard compressive strength is 89.9%.
Use result for every defect condition included in this research as a data point for the random event, the average value of compressive strength is -3.831MPa, standard deviation s=2.184MPa.