Search results

A monoclinic crystalline structure factor was then modified in terms of structural parameters, e. g. fractional coordinates of atoms in the unit cell, and microstructural parameters, e.g. reduced crystallite size and lattice disorder, until a satisfactory agreement with the experimental data was obtained.
Data points are from the experiment, full lines refer to the Rietveld refinement.
The agreement factor Rwp between experimental data points and calculated curve is also reported.
From the technical point of view, the evaluation of the lattice parameter is made very precisely with the Rietveld method because the programme accounts for possible misalignment of the specimen in the Bragg-Brentano geometry, taking into consideration all the hkl peaks in the data collection range simultaneously.
Comparing micrographs A and C it is evident that the milling treatment causes a reduction in the size of the micrograin.

Extraction of Diffusion Correlation Information from Tracer, Interdiffusion and Ionic Conductivity Data I.V.
In terms of tracer correlation factors and atom-vacancy exchange frequencies we have the following reduction for the vacancy-wind factor: B ABBA B ABBA B A BAAB A BAAB A w)ww(cf w)ww(cf)f/f( w)ww(cf w)ww(cf)f/f( S −− −− = −− −− = 0 0 (6) There are also closely related expressions relating the two intrinsic diffusion coefficients D I A and DI B and the corresponding tracer diffusion coefficients: * * 1)( AA A A B A ABAAA I A DrD f cfcf D j j =        − = − ; * * 1)( BB B B A B ABBBB I B DrD f cfcf D j j =        − = − (7) where the r factors are also loosely described as vacancy-wind factors.
While it is tempting to apply Eq. 16 to glasses where much data have been measured, it is nonetheless much more appropriate to apply it to crystalline systems such as alkali earth silicates.

GM (1,1) model Grey Series GM (1,1) model prediction is a realistic and dynamic analysis prediction mehod, if given a raw data sequence ,respectively from x sequence, select a different length of the data as a sub-sequence. for the sub-sequence building the GM (1,1) model. determined any sub-sequence data as (2) To the sub-sequence data for an accumulative generation,attained (3) Where ，.
After the repeated calculation ,the equation for exponential smoothing value (14) Where,—the starting year's corresponding raw data.
According to the historical data of agriculture machinery total power in Heilongjiang province from 1995 to 2008 show in Table I,using the equation (10) ~(14) to calculate,the prediction results shown in Table II.
Combination forecasting method based on rough set made weight coefficient transformed into the attribute importance evaluation of rough set , regarded the various forecasting methods composition set as a condition attribute of decision table ,took the observation value of forecast object as decision-making attributes, calculated the importance that the various attributes (forecast method) to the decision-making set (forcast indexes) ,finally ,determining the weights of the various forecasting methods according to the importance.The method completely analyzed the importance of different forecasting methods from the data analysis,overcome the subjectivity of combination forecasting method. 1) Build the relationship data model In order to apply rough set theory to determine the weight cofficients of combined forecasting model , we first need to establish the relational data model.Took the various forecasting methods as condition attributes, then the condition attribute set,took the
Today there are many mathematical methods on discretization of continuous data, the typical practical methods mainly were: the discrete approach based on hierarchical clustering method , the discrete method based on genetic algorithm, the discrete method based on conditional information entropy and based on self-organizing neural network SOM and so on[7].

For the fixed sampling parameters used in the special test system may cause the loss or redundancy of the data; and the simple data treatment is fixed, it is not easy to expand.
Tab 1 Data Acquisition Board Parameter PXI-6251- High-Speed M Series Multifunction DAQ Resolution 16-bit Analog inputs 32 channel with Data Transfer Rate 1.25 MS/s Analog outputs 2 channel with Data Transfer rate 2.5Ms/s Range 7 programmable input ranges(±100 mV to ±10 V) per channel 4.
LabVIEW integrates almost all of the features of hardware communications such as data acquisition board.
In the central processing system, the DAQ (Data Acquisition) system could acquire the electric signal data from the transducers according to the sample parameter.
The computer receives raw data though the DAQ device and the data was read and analyzed by the program.

Introduction Accurate prediction of the reduction in strength of composite laminates containing notches such as holes, corners or cracks is a major concern in practice, especially in the design of critical components for aerospace applications [1].
A reduction in stiffness of components is accompanied by the growth of damage driven by the presence of notches, and ultimate fracture is induced at a lower level of remote stress.
The mechanisms that govern the reduction in strength of composite laminates with notches are more complicated than those of conventional metallic materials.
These unnotched and notched strengths are the average of two data each.

., TiO2, Fe2O3, Al2O3, SiO2) substrates, demonstrate unique catalytic activity for many reactions, such as low-temperature CO oxidation, propylene epoxidation, NO gas reduction, hydrogenation, and SO2 dissociation.
For each simulation, data were collected at the last 50 ps for further analysis.
During the fabrication of gold-TiO2 catalysts, metal hydroxide and/or citrates are used to control the pH and the reduction reaction of Au3+ to metallic Au.
As a result, the AuCl3(OH)- ions from the solution are adsorbed onto the positive sites of the network and then form the gold nanoparticle network upon the reduction reaction.

If the sequence completion time is less than , then directly transferred to the first vehicles to get a new division, and a new division of completion time is strict reduction.
In the first case the transfer can be achieved, and get a new reduction of the maximum completion time strictly.
Table 1 Calculation result It contains: [i1-i2-i3…] represent the path., after [i1-i2-i3…] the data is the arrival time of the last point of the path.
Points From 0 to 4 the shortest time is 46, so regardless of the number of vehicles the balance time of the problem will not be less than 46, and with the reduction of the number of vehicles equilibrium time will be strictly increasing.

The actuator patches are applied to the structural surface and the introduced curvature reduction of the shell is measured.
Fig. 2: Left: cross-section shape contour of sample before and after SMP patch actuation; right: summary of deformation reduction results for different specimen with different number (max. 4) and positions of actuators A considerable improvement in shape accuracy could be achieved.
On the other side, possible creep of the SMP and with that a reduction of actuation forces might be a problem possibly to be overcome by the inclusion of fillers and nano-particles.
These might come from changing communication data density or redirecting radiating beams between different parts of earth.

A reduction in mobility leads to increases in costs to society.
While the State pays a construction premium in advance, the cost savings from reductions in delays, fuel and travel time would apply directly to the traveling public [1].
(4) Calculate Savings by ABC (SAV) -Traffic Delay Reduction SAV (days) = (BCI-C)SAV – (BCI-A)SAV Convert SAV (in days) into $ amount savings
In the absence of actual vehicle count data, the VOC can be estimated conservatively by considering only commercial vehicles (light and heavy trucks) traveling the designated route due to weight restrictions on other county roads in the area.

See table 1 about the tested data.
The content of air entraining agent can reach the air content (≤20%) and also have fine keeping property, i.e. the air content reduction after one hour’s standing can not be more than 4%; at the same time, the compressive strength must reach more than 75% of the reference building mortar.
The mixing of air entraining agent helps reduce the water consumption of building mortar; the increase of air entraining agent means the increase of water reduction. 1.1.3 Relations between the air entraining agent content and compressive strength of mortar As the in crease of air entraining agent content, the compressive strength of mortar goes down. when the air entraining agent content is more than 7.98/100,000, the compressive strength ratio of mortar will be less than 75％.
From the above analysis we can find that the air entraining agent’s influence on mortar performance is: as the increase of air entraining agent, the mortar’s air content will increase and the water consumption will reduce and at the same time the bleeding water will reduce too; while as the reduction of the mortar density, the compressive strength will reduce too. 1.2 Thickening agent’s influence on building mortar performance Another important element in mortar plasticizer is the thickening agent methyl cellulose ester.