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However, from the data we retrieved, from board to our country, at present the reinforcement arrangeed method adopted in the prestressed concrete slab is uniform, and for other arrangement of prestressed plate has not much discussion.One disadvantage of uniformly arrangement method is too much amount of prestressed reinforcement .This then causes high cost.
To change this situation, on the one hand, ask for an increase in steel production, an improvement of production technology and the reduction of its cost.
Relationship of limited load with the side length ratio a/b 1.0 1.2 1.4 1.6 1.8 2.0 Pycr( ) 1.000 1.1276 1.3038 1.5233 1.7835 2.0833 From above data, the bigger the side length ratio,the bigger the limited load.
Relationship of limited load with the distribution of reinforcement N 1 2 3 4 Pycr( ) 1.00 0.67 0.50 0.40 From above data of table 2, the more concentrated prestressed tendons layout, the greater the limited load.

For general, closed form, material LCF data base establishment, the specimen FEA and testing is performed at several force loading points.
Therefore, for convenience, equations and data are given in terms of range and not amplitude, as is more common in full reverse studies.
This data is still widely used for thread strength assessment under different loading conditions.
Comparison with the reference experimental specimen testing data is provided in table 5.
Specimen geometry and loading waveform may be further adjusted to specific part requirements, thus improving experimental data customisation.

The main processing parameters were compared, analyzed and ranked after their effect on extruded flow as output data.
The 3 factorial experiment with the data from the extrusion of LDPE, with this device and without ultrasonic activation (UA=0 and the limits min/max for the parameters were: melt temperature T=140...180 °C, injection pressure P = 2...5 daN/cm2, and the flowing interstice I=0.5...3 mm), revealed that the temperature of the melt is the most important parameter, with the most significant influence on the output parameter of the study, the extruded discharge.
The data from the first 8 lines of the Table 1 were grouped in a 3 factorial experiment: pressure = factor A, melt temperature Ti = factor B, mould temperature Tm = factor C, without ultrasonic activation.
The sstatistical data processing, Figure 5, shows that none of the factors has a decisive role in influencing the output parameter of the study, the length of the injected spiral, "LSp" from the Table 1.
Overall, the data from the Table 1 were used in a 4 factorial experiment (3 above mentioned factors plus the ultrasonic activation as the 4th influence factor, and the same LSp as output parameter).

At last, the numerical results of the third scheme were compared with test data, and the reliability of finite element models was confirmed.
Comparison of FEM Results and Test Data According to the comparison of three schemes above, scheme 3 was selected for stator transportation.
Comparison of FEM results and test data at arc of ear plate (DW1 in Fig. 7(a), there four data due to symmetry of structure) and 5 bolts at vicinity of ear plate(bolt L1~L5 in Fig.7(b)) are shown in Fig.8 Fig. 8 Comparison of FEM results and strength test Good consistent could be found between FEM results and test data in Fig.8, which indicates that FEM results could be reference for evaluation of transportation safety.
At the meanwhile, numerical results are slightly lower than test data for bolts, the bolt with maximum axial stress 450MPa is labeled as L3, axial stress is 465MPa if dynamic coefficient is included, and is still lower than permissible stress of bolt 506MPa, which indicates the reliability of connection between end shield and frame during transportation.
Then, numerical results and strength test data were compared, and precision and reliability of finite element model were confirmed, which indicates that FEM results could be an important reference for evaluation of transportation safety.

The simulation data show that the Multi-objective optimum method can get more effective results compared to the weight coefficient method.
However, SVM training is time consuming when analyzes huge data.
Fig.2 LSSVM characteristic models of demister Multi-objective optimization model of the demister Multi-objective optimization means to search the best design data within the range of design variables under all constraint conditions.
However, there is no exist of the best design data which can make all the targets reach optimal values at the same time, as these targets are always in conflict situations.
As a result, the best design data which can make all the targets reach optimal values at the same time don’t exist in Multi-objective optimal model of demister.

One of aluminum's key characteristics is its low density, approximately one-third that of steel, making it an ideal option for transportation applications where weight reduction is essential for enhancing fuel efficiency and performance [2,3].
The increased deformation resistance in the A6063 alloy is accompanied by a reduction in plasticity and corrosion resistance [18-22].
This indicates that the model is responsible for at least 81.63% of the variability in the data for the response.
Conversely, a reduction in Si content below 0.42%, a decrease in temperature below 170 °C, or an increase above 200 °C results in lower HRB values.
A reduction in either component limits the formation of Mg2Si particles, which reduces the barriers to dislocation movement, thereby diminishing hardness.

But very soon new data appeared [5, 6] showing that the answer depends on the growth conditions.
The point defect parameters in the high-T range relevant for defect incorporation and subsequent agglomeration were deduced [12] using reported data on self-diffusion (controlled by both I and V), metal in-diffusion (controlled mostly by I), metal out-diffusion (controlled mostly by V), quenched-in vacancy profiles installed into wafers by Rapid Thermal Annealing, as well as some data on the grown-in microdefects themselves.
Some data indicate [13] a remarkably strong vacancy drift in the uphill direction (towards a higher T), with aV as high as 0.06 mm2/(min K).
These data (shown by circles) are compared in Fig.2 to CV calculated by Eq.(2).
TEM data on B-defects are scarce; most likely these are small globular agglomerates of self-interstitials.

Hardness and elastic modulus were calculated from the load-displacement data obtained by nanoindentation using a Nano Indenter XP (MTS Systems Corp.) equipped with the CSM option and the LFM option.
This effectively sets limit on the minimum indentation depth that is necessary to obtain reliable elastic modulus and hardness data, which is believed to be about 20 nm [3], so data at contact depth less than 20 nm are not considered reliable.
For Vickers indenters, αwas empirically found based on experimental data and is equal to 0.016 [5].
Both E and H values were obtained from the nanoindentation data (Fig. 1).
For the Si(100), there is a large scatter in the scratch depth data after the load of 138.64 mN, which is associated with the generation of cracks, material removal and debris.

Taro Production Phenomenon According to BPS North Sulawesi, taro growth data from 2013 was 86.77%, an increase in 2014 of around 93.06%, and 95.40% in 2015 although in 2016 it fell to 94.24%, based on these data, it can be seen that the prospect Taro sweet potato in North Sulawesi can be said to be abundant as well as South Minahasa Regency, especially West Motoling District which was recorded by the Center for the Study of Agricultural Growth in 2014 the harvested area was 78ha with production of 5700 tons, while 2016 harvested area was 174ha with production of 10,000 tons (BPS North Sulawesi, 2016).
This research purposes, (1) Develop proper processing technique skills in manufacturing taro into taro flour. (2) Developing a Taro Brownies product by using taro flour as a substitute for wheat flour or flour reduction wheat. (3) Develop a Pancake product using basic ingredients taro flour as a substitute for wheat flour or flour reduction wheat. (4) Knowing consumer acceptance of Brownies Taro products. (5) Analyzing the nutritional value contained in Taro Brownies. (6) Analyzing the nutritional value contained in Taro Brownies [3].
The data sources taken were 15 panelists.
According to Anonymous, 2013 Data analysis techniques in the product development process use organoleptic tests by providing product samples to be tasted, after which the panelists are given a questionnaire to fill out.
The data that has been collected is tabulated and analyzed and presented with a diagram using the analysis of variance formula (analysis of variance).

The advantage of using RSM method includes reduction in the number of experiments, identification of interaction between factors, detection of the optimal response within the experimental region, and empirical modelling of the data.
RSM is also defined as a statistical method, which employs quantitative data from proper researches, to establish and concurrently resolve multi-variable equations.
Moreover, there is a very high concurrence between the predicted values and the observed data, as seen in Table 2.
The model must be adequate enough to play a significant role in the data analysis process.
Furthermore, the normality assumptions made earlier could be confirmed and the predictive regression model has extracted all information available from the experimental data.