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Based on the data depicted in Figs. 3 and 4, one can conclude that the Froude number is approximately constant and equal to about 0.33 (standard deviation=0.01).
In this last domain, he identify two sub-domains: (i) the interpersonal distance between the pedestrians is not enough to walk at their comfortable walking speed, but is large enough to avoid contacts between them by a small reduction of walking speed; and (ii) region of a more pronounced reduction in walking speed because the space available around each pedestrian is very small (i.e., repulsive forces guiding the behavior of pedestrians).
Fitting the empirical data presented by [27] to the Eqs. (10) and (11), we obtain: - St=17; Pe=1.2; urp*=0.73 (unidirectional pedestrian traffic), - St=54; Pe=1.2; urp*=0.76 (multi-directional pedestrian traffic).
This result shows that the reduction of interpersonal distances favors the occurrence of streams of people, and self-organized streams start to occur since the interpersonal distances between pedestrians is less than about 1 m.

The hydraulic losses influence uneven flow velocity and pressure distribution of the working fluid, which eventually leads to the reduction of the total head [1].
The up to date methods that are effective in preventing cavitation includes the reduction of the NPSHr, lowering the temperature of the fluid, or increasing the suction head [19]. 3.
These geometric parameters were altered by the group method of data handling (GMDH)-type algorithms and CFD methods.
There was an improvement in efficiency and a reduction in NPSHr of the pump.
Table 1: Findings of the previous research on direct design optimization methods Reference of the investigations Altered geometric parameters Applied numerical methods Strengths Weaknesses [8,20,21] [22] [23] Hub angle, chord angle, cascade solidity of chord and the blade thickness Cascade solidity of chord and chord angle Blade thickness, hub length, hub angle, chord length, and the chord angle Group method of data handling (GMDH)-type algorithms and a CFD methods Isight numerical optimization software and CFD methods Surrogate model and a CFD methods The pump efficiency and NPSHr were successfully improved at BEP.

In some countries sludge reduction and utilization are the preferred strategies for sewage treatment and disposal.
From the foregoing data, it can be concluded that 1000 W microwave pretreatement does benefits the dewatering ability of activated sludge in a laboratory scale possibly by disrupting its stable floc structure and releasing cell components into the surrounding medium. 3.2 Characterization of EPS during microwave pretreatment It is well known that one reason for the difficulty in activated sludge dewatering is the presence of EPS [20].
The optimum microwave pretreatment time for sludge dewatering improvement corresponds with the initial stage of the microwave radiation in this study, which caused a reduction of EPS and the breach of microbial cell walls.
The microwave power absorption is determined using the Equation (3) according to the temperature data in figure 6.
The initial stage of the microwave irradiation caused a reduction of EPS and the breach of microbial cell walls, and is also the optimum time for improving sludge dewatering.

Count-method block diagram The data of diameter calculation always appear abnormal wave.
Experimental data is shown in Figure 5.
It can be seen by the data of Figure 5, the improved Count-method for the uncoiler applications is more accurate.
Linear/ Angular Velocity data curve With the speed of line becoming fast, roll diameter changes quickly.
Experimental data curve is shown in Figure 7.

The impacts of the estimation by noise, harmonics and data window length are investigated.
Frequency estimation of model (2) with different data window length when SNR is 60dB.
Frequency estimation of model (3) with different data window length when SNR is 60dB.
Relationship of MSE, noise and data window length.
By lengthening the data window can partly suppress the noise interference.

To the best of our knowledge no such data are available in literature.
The residence times were calculated using only the volume data of the hot runner.
The residual acetaldehyde data exhibits a non-linear behavior with respect to the residence time, for each temperature.
The model was the fitted to the experimental data.
In fact it is sufficient to change only the process temperature for evaluate the generation of acetaldehyde to vary the residence time, as confirmed by the experimental data.

The former only need the past water usage data, and the operation process is simple, which can be suitable for the swing situation, but the results precision from this method is difficult to guarantee.
If the training data with large-scale and much field, these disadvantages become more obvious.
From the mathematical points, this belongs to the process of dimension reduction technology [3].
The data samples from 1995 to 2002 are selected for network training, the given samples from 2003 to 2005 are selected to inspect the network.
[3] Yu Jianying, He Xuhong, in: Data Statistical Analysis and the Application of SPSS, chapter, 9, 291-310, the People’s Postal & Telecommunications Press (2003)

Weight and time/temperature data were recorded, yielding the weight loss (TG) and differential weight loss (DTG) curves.
Weight and time/temperature data were recorded using TGA software, yielding the weight loss (TG) and differential weight loss (DTG) curves.
The data on the first 30 min of reaction were not processed, so the mass variations due to water loss were not considered.
Kissinger- Akahira-Sunose (K–A–S) and (7) Friedman, respectively, for the DTG data of the malt waste.
The data used in the regressions correspond to conversions of 15, 20, 30, 40, 50 and 65 %.

Natural disasters(C1)= reduction/halt days of of mine production /(30*3)+ waiting days of ships /(30*3) economic growth rate(C2)=quater growth rate of GDP shipping market conditions (C3)=quater growth rate of CCBFI production equipment failure (C8)=days of production equipment failure/(30*3) Railway equipment failure rate(C10)=days of railway equipment failure/(30*3) Rate of press habor(C13)=number of press vessels because of vessel operation/total vessels in a quater Transportation accident rate(C14)=railway transportation accident rate+shipping transportation accident rate inventory shortage(C16)=days of the inventory below the red warning line /(30*3) Vulnerability of network structure(C17)=min(number of Organization involvement in the supply chain) Other qualitative indicators can be quantified processing with the expert scoring method.
Evaluation model based on SVM Data collection.
Table 2 Five samples of data collection sample C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 1 0.26 16.27% 1497.16 2.5 2.5 2.5 2.5 0.16 2.5 0.60 2 0.33 23.48% 1745.65 2.1 1.9 2.4 2.0 0.23 2.2 0.65 3 0.31 16.27% 1210.37 1.8 1.9 2.3 2.1 0.21 2.3 0.69 4 0.29 19.42% 1569.88 1.9 1.7 1.5 2.0 0.16 2.0 0.68 5 0.26 18.23% 1487.13 1.5 2.3 1.9 2.2 0.19 2.1 0.75 sample C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 1 2.5 2.5 0.062 0.042 2.5 0.0556 5 2.5 2.5 2.5 2 1.6 1.9 0.065 0.045 2.4 0.0598 3 2.3 2.4 2.0 3 2.4 1.9 0.072 0.052 2.3 0.0591 4 2.3 2.0 1.9 4 1.9 2.3 0.069 0.055 2.0 0.0583 5 1.2 1.9 2.0 5 2.3 2.0 0.067 0.057 1.6 0.0641 3 2.0 2.1 1.8 Data Normalization.Two kinds of indice which are positive index and negative index in the thermal coal supply chain index system.
To normalize the data, scale conversion method is used.
(1) Here, X is the initial data; is the minimum of initial data; is the maximum of initial data; T is the conversion data.

In most cases very complex microstructures emerged from AFM data.
In this work, it is presented for the first time a comparative study of AFM and PDM data from the same bitumen either unmodified or after modification with a reactive polymer, as well as after thermal ageing.
AFM data strongly suggests that the true picture is much more complex.
After modification of the S bitumen, an increase of para-phase content and a decrease of the peri-phase content are observed, as well as a decrease of the number of branches in catana phase; AFM data strongly suggests that the reduction of the area around catana (periphase) was a consequence of adding a reactive polymer to the bitumen.
Conclusion We presented for the first time, AFM data (both topographic and phase detection data) of raw bitumen and bitumen modified by reactive polymers.