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These plates were hot rolled at 360°C to final thicknesses of ~ 10 mm with a total reduction of 75%.
Part of these samples was subjected to subsequent cold rolled with reductions of 50 and 80% to final thicknesses of 5 and 2 mm, respectively.
The fatigue stress-number of cycles to failure curves were plotted with the data from the above tests.
The increase in the reduction of the cold rolling promotes the shear banding.
As expected, the combination of hot and cold rolling leads to a significant increase in the strength and a reduction in ductility (Fig. 2a, Table 2).

Bacterial reduction of at least 88.5% against both, Staphylococcus aureus and Escherichia coli, has been obtained.
The samples were pressed on a diamond and the absorbance data was recorded over the range between 400 and 4000 cm-1 with a spectral resolution of 1 cm-1 and averaged over 64 scans.
According to literature data [15] the plasmon maxima at 395 nm and 405 nm match silver nanoparticles size of ~10 nm and 14 nm, respectively.
The values of microbial reduction of cotton fabrics loaded with Ag nanoparticles are given in Table 4.
From the data it is evident that the reduction of bacterial colonies was always at least 88.5% or higher against both S. aureus and E. coli.

The forecast model is built on a grey system with few data, ie, small statistics, and through initial data conversing by adequate measures to seek the obvious and latent information, and to study the inherent regularity from the disorder initial data [3, 4].
That is to say, the followed abrasion data can be obtained through the forecast model founded on the initial measurement data.
Regarding the thickness reduction as the corresponding abrasion data, the measurement values of point 1 are shown in table 1.
The train of thought of the grey forecast modeling is based on the measurement abrasion data.
Consequently, it is only suitable for non-negative monotone exponential initial data or data close to exponential distribution.

The fatigue data were statistically elaborated by using a log-normal distribution and are plotted in a double log scale.
Figure 2 shows the fatigue obtained fatigue data.
Cu-Be fatigue data: (a) hour-glass shaped specimens; (b) plate with central hole specimens.
Since high temperature data from the cracked material under investigation were not available (e.g.
The fatigue data are plotted in terms of averaged SED range over a control volume in Figure 3.

Result and Discussion Beer’s Law equation was applied in this experiment to analyse the absorbance data obtained from UV-VIS spectrophotometer.
(1) Figure 1: Standard calibration curve The standard calibration curve using ammonium stock solution was prepared before undergoing the inhibition and data was recorded at 640 nm.
Meanwhile, urea solution with mixture of urease-guava leaves extract showed reduction in NH3 concentration release.
The reduction happened in small value with different around 0.1 mol/L and the releasing also was almost equal till the end of incubation time.
This small reduction might be because of the concentration of guava leaves extract that been used is slightly low.

Methodology This research is a statistical analysis of trends in crashes and injuries using time series data obtained from Federal Road Safety Corps and Federal Office of Statistics, Nigeria.
The data were obtained directly from the primary records of the institutions referred to above: An intervention model was adopted and it is expressed as: Intervention Model, Yt = Intervention Function, F(It) + ARIMA Noise Model, Nt () tt t NIFY +=
From table 1 and also Fig. 1, first abrupt reduction in accident rate occurred in period 4.
Table 1: Actual yt vs tyˆ Estimated Data For all practical purposes, the values estimated with our intervention model compare favourably (see Fig.1).
Specifically, the increased presence of staff of the Federal Road Safety Corps, resulting from the mass intake of staff in 1995 - 1996, led to a very significant reduction in the number of road traffic reduction nation-wide.

The LCI mainly includes the steps of data collection, data calculation, allocation of flows and releases.
The data need to be associated with the functional unit by calculation, and describe the calculation program in writing.
The inventory data of building materials are collected according to the unit process within the boundary of the system.
The data come from spot investigation, database and literature.
In addition, according to the purpose and scope, some optional elements are also include: normalization, groping, weighting, data quality analysis.

Lead, Mercury and Cadmium etc as the main evaluation index of heavy metal pollution established relational data model.
In fact, there are many uncertainty factors of heavy metal pollution, Rough set is a new mathematical tool to deal with uncertainty, inconsistent, incomplete data.
The evaluation model based on Rough Set Theory Establish the relation data model.
Table1 Heavy metal pollution index and pollution levels Sampling point Hg Cr6+ Cd As Pb Cr The comprehensive pollution index value 1 Ⅰ Ⅱ Ⅰ Ⅲ Ⅲ Ⅱ Good 2 Ⅱ Ⅱ Ⅰ Ⅱ Ⅳ Ⅱ Terrible 3 Ⅱ Ⅲ Ⅰ Ⅲ Ⅳ Ⅱ Terrible 4 Ⅱ Ⅱ Ⅱ Ⅳ Ⅳ Ⅲ Terrible 5 Ⅱ Ⅱ Ⅱ Ⅳ Ⅳ Ⅱ Terrible 6 Ⅲ Ⅲ Ⅱ Ⅳ Ⅳ Ⅲ Terrible 7 Ⅱ Ⅱ Ⅰ Ⅳ Ⅳ Ⅲ Terrible 8 Ⅳ Ⅳ Ⅲ Ⅳ Ⅳ Ⅳ Terrible 9 Ⅳ Ⅳ Ⅳ Ⅳ Ⅳ Ⅳ Terrible 10 Ⅳ Ⅲ Ⅳ Ⅳ Ⅳ Ⅳ Terrible 11 Ⅳ Ⅳ Ⅳ Ⅳ Ⅳ Ⅳ Terrible 12 Ⅲ Ⅲ Ⅱ Ⅳ Ⅳ Ⅳ Terrible 13 Ⅳ Ⅲ Ⅳ Ⅳ Ⅳ Ⅳ Terrible 14 Ⅲ Ⅱ Ⅱ Ⅳ Ⅲ Ⅲ Terrible 15 Ⅰ Ⅱ Ⅰ Ⅱ Ⅰ Ⅱ Good 16 Ⅱ Ⅲ Ⅰ Ⅳ Ⅲ Poor Data preprocessing: We got the information table is not a complete information table, as show the NO.16 data in table 1, there are missing values, according to the data the indiscernibility relation in Rough set theory to complete the missing data.
This is for the purpose of reduction data completion method, can get good result reduction.

Data acquisition logic control unit is designed by FPGA.
The module is reset to the system " Asynchronous reset synchronization release " Reduction of reliability.
The module drivers LCD and reads the controlled logical WRFIFO data.
Our FPGA decode the video data collection based on this data stream protocol, finally display 3.5 inch 320*240 LCD.
In this way, we will continually get cache data of video stream to the FIFO.

The kinetic process of the reduction reaction between phosphorous acid and adamsite has been studied to determine the reaction rate equation and to deduce the reaction mechanism, providing the theoretical basis for destroying adamsite by phosphorous acid.
At last, reaction is conducted under the best reaction conditions and the process is followed by test; on the basis of trial and error method, try to model experimental data through several potential reaction rate equations and finally determine the reaction rate equation, and furthermore to deduce the possible reaction mechanism according to literatures[7,8].
Results and discussions According to the experiment, it is found that during the reduction reaction course between phosphorous acid and adamsite, the reaction rate accelerates with the increasing quantity of phosphorous acid, which illustrates that the overall reaction rate not only relates to the quantity of adamsite, but also relates to the quantity of phosphorous acid.
Based on the theory that the trimolecular or tetramolecular collision probability at the same time is very low, and there is hardly any elementary reaction concerning larger molecular than trimoleculars, trial and error method can be considered; firstly, assume the reaction orders of phosphorous acid and adamsite are all order 1, and the experimental data is modeled with rate equation; if the model analysis results do not conform to experimental data, it is better to adopt other reaction rate equations to model data, eg, take the reaction order of phosphorous acid as order 1 and adamsite as order 2, or take the reaction order of adamsite as order 2 and take phosphorous acid as order 1.
Make analysis based on the chromatographic conditions mentioned in foregoing statement, and model the analysis data to validate the above reaction rate equation, and finally determining the rate equation and obtaining the reaction rate constant k.