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A new fitting parameter shows excellent agreement with the mean flow stress data from industrial processing of advanced high strength microalloyed steels.
The industrial processing of AHSS is a new challenge in steelmaking and rolling because of nontraditional chemistry design and the lack of experience and data.
To understand the mechanisms behind these MFS drops, more data and further tuning of the model are highly desirable.
Note that the f factor was derived with the data from the first three passes that follow the conventional MFS behavior.
Acknowledgemets: The authors are indebted to Mittalsteel Inland for the opportunity to work with hot rolling data.

So the strength reduction of soil caused by disturbing soil should be reduced in construction.
Too great slump of mixture not only can cause too much pile-head laitance, segregation and bleeding of concrete and the reduction of pile-body strength, but also may make fluidity of mixture reduce.
(3) Data synchronization arrangement The construction data of the site including working diary and construction records should be arranged and reserved well to make the record and original data really reflect the actual condition of construction site.

Available data shows that the effective electric energy demand level at peak utility is in the region of 15,000 MW, whereas current energy output level averages at around 2,500 MW (World Bank world development indicator, 2006) [10].
Materials and Method Data were obtained from Sapele thermal power station’s logbook.
In processing the data, percentage shortfall from target energy, load factor, capacity factor and utilization factor were obtained.
A reduction in shortfall indicates better performance of the plant.
The reduction in cost with good load factor is due to the fact that overall working cost per unit becomes low, the fixed charges having been distributed over more units of energy generated.

Chemical reduction and electrochemical reduction are the most popular methods.
At least 500 particles of every metal were used to calculate the distribution data.
Calibration curves (current-concentration graphs) were obtained from CVs data.
SEM data for nickel on GC surface have not been obtained because of the extremely small size and low contrast (low atomic number) of Ni nanoparticles.

Doi et al. [2] found that the {411} component, which is a favorable orientation for magnetic flux density, increases with large grain size in the hot-band and higher cold reduction.
Higher cold-rolling reduction enhances the volumes fractions of deformation bands.
In Sample A and B annealed at 800℃ without soaking (0 second), the EBSD data was used as the input for the texture fitting process.
Fig. 7 shows comparisons of ODF (f2=45°) between the measured data and the data assigned by the simulation code [7].

Fe has very little solid solubility in Al, therefore during solidification Fe easily develops into highly faceted Fe-rich intermetallics (IMC), leading to a reduction in mechanical properties [1,2].
A Type-K thermocouple attached to the StrainSmart8000 data acquisition system is used to collect data, as the thermocouple is submerged into the crucible containing the Al melt.
The extraction of heat is measured through temperature vs time, where the StrainSmart8000 system automatically collects and process the data.
When there is a reduction in the dendritic arms, there is little lateral diffusion of rejected solute elements into the intergranular regions, hence this influences the development of the Fe IMC, minimising the IMC growth [17].

The latter is evident from the reduction of the descending branch inclination (correlated to the fracture strength) [11].
In other words, the degradation of CSSD parameters reflects the complex reduction of mechanical characteristics of steels predicted by the modeling.
One needs both experimental surfaces of the available plasticity in their initial state and after cycling, along with the data on how material degradation intensity changes with a transfer from one level of stress to another.
The lack of the necessary experimental data constrains one to build simplified models of degradation based on the available data on equivalent stresses.

Introduction A large bandgap reduction caused by the incorporation of bismuth into III-V semiconductor material systems creates a lot of potentials in different areas of technology such as telecommunications, spintronics and photovoltaics applications.
This incomplete exctraction process from GaAsBi based devices also has been seen by Zhou et. al.[13] A similar case has been previously shown for GaInNAs/GaAs MQW p-i-n structures where electrons are trapped in the wells.[14] For GaAsBi based material, as opposed to N, the bandgap reduction of GaAs due to Bi incorporation is due to a raising of the valence band energy.[15], [16] Therefore, it is suggested that holes are being trapped in the wells instead of electrons due to large valence band offset.
(Data extrapolated from [5], [17] ) (b) Absorption coefficients of QW20 at 0V and -5V.
The solid lines are the Urbach effect fitted to the data.
Although the data taken from Kakuyama et al. is in terms of external quantum efficiency, (EQE), the line gradient shown in the figure is directly comparable with value of α of the devices.

Large increases in tensile properties may be achieved; for example, 0.2% proof stress levels may be doubled with little reduction in ductility, although these parameters can vary depending on the precise thermal treatments used [1,6,9].
What is available is a limited amount of data on Charpy impact toughness [11].
In addition to the measure of notch sensitivity (tear to yield strength ratio, TYR) and tear strength data generated directly by the test procedures, both the unit propagation energy (UPE) and unit total energy (UTE) (see Figure 1) are proportional to the critical strain energy release rate (Gc) through UPE = kGc and UTE = mGc where k and m are constants [13,15].
Underaged (UA) 360 alloy, when compared to the as-cast condition, showed an increase of 59% to the 0.2% proof stress, an increase of 14% to the UTS, and a slight reduction in ductility from 2.8% to 2%.
When comparing the data from Table 2 for the as-cast, T4, T6 and UA tempers, several interesting trends are apparent.

According to the principle of heat transfer: (1) Where is the variation of temperature in the fire field, is the variation of temperature in the leeward place where is far from the fire field (The increment of the initial airflow temperature in tunnel while fire is at development and stable stage , the reduction of the roadway airflow temperature at the decay stage ), ℃; dQ is the quantity of heat that the leeward tunnel wall where is far away from the fire field absorbed (at the developing and stable stage) or released (at the failing stage),kilocalorie; Kx is heat conduction coefficient of tunnel wall, it could be calculated by the experiential equation[6] , kilocalorie/(h·m2);is experimental coefficient, determined by experiment; vx is the wind speed in tunnel, m/s; Ux is the perimeter of tunnel, m.
That is: (2) Where G is the hot air flow of the leeward tunnel section that far away from the fire fieldm, kg/s; Cp is specific heat of hot airflow, kilocalorie/(K.kg); dTx is the reduction of the hot airflow temperature in dx distance(at the developing and stable stage), or the increment of it (at the failing stage), ℃.
The data can be seen in the Table 1.
The data can be seen in the Table 1.
Fig. 2 Temperature trends for each measuring point The historical data was inquired by temperature measurement software, and then determined the temperature when dynamic pressure measured.