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The data sheet was prepared and the instruments were kept ready for conducting the experiment.
Table 3 Performance Parameters Description Existing FTL Energy efficient FTL Model TL-D 36 W/54 TL-5 28 W/865 Measured light output [lux] 78.7 69.9 Power consumption [W] 53 W/FTL 32 W/FTL It is observed from the data that the difference in lighting output for the TL-D 36 W/54 and TL-5 28 W/865 FTL is only 8.8 lux which accounts for a reduction of 11.1 %.
This reduction is insignificant compared with the energy saving.
As per the energy tariff data collected from the selected textile industry, the annual bill saving is calculated by applying INR 8 per kWh.
ROI can be calculated using Eq. 7 CO2 Emission Reduction.

First the data on different antennas is divided into blocks.
We can obtain multiple sets of data, and finally choose the optimal PAPR performance of a group of data to be transmitted.
Firstly, the first sub-block of data is inverted and replaced, while maintaining the same data block of the other sub, thereby generating 4 new set of data sequences.
Then transform the third sub-block of data, turn to operation, finally we will obtain the set of data with optimal PAPR performance[4].
When ai,j is 1, the data on the two antennas is kept to the same, when ai,j is 2, the data of the j-th data blocks on the two antennas is simultaneously negated; When ai,j is 3，the data of the j-th data blocks on the two antennas is replaced; When ai,j is 4, the data of the j-th data blocks on the two antennas is replacement negated.

Material parameters and loading conditions In this project, the relative data is as follows: the grade of concrete intensity is C40; the elasticity modulus ; Poisson’s ratio ; linear expansibility ; the density ; proportional elastic limit ; collapsing strength the concrete could obtain .
If the structure load is relative with the temperature reduction, the calculation mode is.
At the beginning, the effect of load and the temperature reduction is undertaken, so the referenced temperature is T0=0.
At the same time the strain measuring point in the location of pouring span in the axes is compared with the calculation result under the condition of the temperature reduction.
In these data, the test element in the surface plate spot keeps well and the tested data conforms to the theory calculation result.

Respectively on the basis of experimental data, using graphic method, standard algorithm and the width of the folding subtraction, determine the equivalent stiffness beam after fire and the damage degree of mechanical properties.
Test observation data and failure criteria.
The first step: the elastic modulus of steel bar bending modulus of elasticity coefficient of reinforced the reduction coefficient is defined as the reinforced after the temperature of T and the elastic modulus and the elastic modulus at room temperature after cooling ratio; The second step: cross-sectional width reduction coefficient calculation section first on the grid, and each grid (unit) the highest temperature center experience as the cell temperature, calculate the corresponding strength reduction coefficient.
Table2-3 The calculation of stiffness according to the width reduction method 　 Stiffness （N·mm2） reduce degree (%) Stiffness （N·mm2） reduce degree(%) A-0-01(K1） 6.48×1012 　 B-0-01(K1） 3.01×1012 　 A-2-02(K2) 5.61×1012 13.43 B-2-02(K2） 2.61×1012 13.29 A-3-03(K3) 5.23×1012 19.29 B-3-03(K3） 2.45×1012 18.60 A-3-04(K4) 5.21×1012 19.60 B-3-04(K4） 2.48×1012 17.61 Table2-4 The calculation of stiffness according to the width reduction method 　 width reduction method(K1） illustration method(K2) error% specification algorithm(K3) error% A-2-02 5.61×1012 5.45×1012 2.85 5.74×1012 2.32 A-3-03 5.23×1012 4.63×1012 11.47 5.35×1012 2.29 A-3-04 5.21×1012 5.31×1012 -1.92 5.18×1012 -0.58 B-2-02 2.61×1012 2.57×1012 1.53 2.72×1012 4.21 B-3-03 2.45×1012 2.18×1012 11.02 2.55×1012 4.08 B-3-04 2.48×1012 2.34×1012 5.65 2.58×1012 4.03 Conclusion Specimens in the experiment group, selection, production and lab equipment, experimental arrangement and so on all strictly follow the design principle

The 2nd cold rolling, with a reduction ratio of 70%, was done under lubricated conditions and the annealing heat treatment was executed in an infrared image furnace, ULVAC (Model CCT-AY).
It is well known [9, 10] that the {112}<110> component is one of the most stable orientations at the applied strain and {111}<110> component is more developed than {111}<112> component by heavy rolling, which agrees well with the data of Fig. 2.
The change of Vickers hardness is shown with the increase of temperature in Fig. 4, and the average stored energy of the specimens determined according to the method presented by the present authors [8] is shown in Fig. 5, which is well in agreement with the Vickers hardness data of Fig. 4.
Rajmohan et al. [11] reported the stored energy distribution along the γ fiber of 80% cold rolled IF steel based on neutron diffraction line broadening data.
These observations, together with the stored energy distribution data (Fig.6) strongly support the evidence for the hypothesis of an orientation pinned growth.

The oil-water IFT presents a reduction at both high and low pH.
At least three data points were taken for every experiment and the calculated IFT results were averaged.
Table2 shows the extent of IFT reduction is higher in oil-water compared to oil-gas.
The rule can be confirmed by experimental data.
The oil-water IFT presents a reduction at both high and low pH.

Empirical analysis on bidding data The analysis uses 195 data collected from PWD of Malaysia.
The data is restricted to additional public school buildings because they are the highest number of data available.
Natural logarithm transformation improves the linearity of the data (denoted as ln or LN).
This test is more applicable when examining the normality of data for a small sample size [20].
Levene test shows that (p>0.05) the data variances are equal, thus meeting the model assumption.

The existing research data shows that diseases of roadbed in permafrost and seasonally frozen ground areas is mainly caused by frost heaving and thaw collapse.
The final result was determined by the average of the datum from 3 parallel tests.
In a word, the reduction of the resilient modulus was realized conformably.
The curve of shear strength with freezing and thawing cycle Above datum indicated the reduction of cohesion from 118.49KPa to 38.02Kpa after 6cycles, while the slight increase of internal friction angle from original 27.1°to the final 39.8°for the roadbed fillings in Songbei region.
Comprehensively, the reduction of shear strength could be obtained with the numbers of freezing and thawing cycle.

Reaction rate constant and thermodynamic data in elementary chemical reaction equations come from Leeds model and NSAS data base.
At 900˚C, the reaction of NO reduction by NH3 is almost finished in 0.27s; at 1000˚C or above, the reaction of NO reduction by NH3 is already finished in 0.006s.Therefore, with temperature rising, reaction rate of NO reduction by NH3 is increasing and reaction is finished in a short time.
Based on total reactions of NO reduction by NH3 and NH3 oxidation, O2 is necessary in the process of NO reduction by NH3.Effect of NO reduction by NH3 is going to be influenced by lack of O2.The simulating results are shown in Fig. 3 and Fig. 4.Obviously, when the volume fraction of O2 is 0.2%, reaction rate of NO reduction by NH3 is very small and the decline of NO concentration is limited.
At 0.012s, reduction rate of NO is the maximum of -5.49E-7mole/cm3·s.
Then reduction rate declines gradually.

This is related to the data available for those channels in addition to the data available from other channels, excluding channel 8[3]
Strategy is based on data collection where past data associated with water quality parameters is collected.
System Architecture Diagram Fig. 1: Architecture flow Workflow Diagram Fig. 2: Workflow Diagram Technical Modules Used Data Pre-processing Pre-data processing is preparing raw data to process and adapting the data to a ML technique.
Phases of pre-processing data: Data Purification, Data Integration, Data Reduction, and Data Modification.
Fig. 3: Data Pre-processing Flow chart Data Visualization With the help of data recognition, we can see what the data looks like and what kind of relationship the data features hold.