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Table 1 Computational values of Nux with respect to variations in n and Pr as compared with published data n Grubka & Bobba [51] Present study Pr=1 Pr=10 Pr=100 Pr=1 Pr=10 Pr=100 -2.0 -1.0000 -10.0000 -100.0000 -1.00000 -10.00000 -100.00000 -1.0 0.0000 0.0000 0.0000 0.00012 0.00000 0.00000 0.0 0.5820 2.3080 7.7657 0.58201 2.30800 7.76565 1.0 1.0000 3.7207 12.2940 1.00000 3.72067 12.29408 2.0 1.3333 4.7969 15.7120 1.33333 4.79687 15.71197 3.0 1.61534 5.6934 18.5516 1.61538 5.69338 18.55154 Table 2 Variations in m when other parameters are zero with respect to Cfx as compared with published data m Cortell [30] Fatunmbi et al. [32] Present 0.0 0.627547 0.627624 0.627563 0.2 0.766758 0.766901 0.766945 0.5 0.889477 0.889602 0.889552 1.0 1.000000 1.000052 1.000008 3.0 1.148588 1.148637 1.148601 10.0 1.234875 1.234913 1.234882 4 Presentation
Likewise, the reduction in the fluid flow owing to a rise in γ indicates that growth in γ compels a reduction in the transport field due to a fall in the yield stress as γ increases which in turn lowers the fluid flow.
Furthermore, growth in the Schmidt number Sc shrinks the solutal boundary layer structure which in turn dictates a reduction in the concentration profile as found in Fig. 8.
In this view, the Schmidt number Sc varies inversely to the coefficient of mass diffusivity and at such, a rise Sc propels a decrease in the nanoparticles concentration boundary layer and consequently compels a reduction in the nanoparticles concentration profiles.

These patterns represent that the grain size of commercial ZnO and synthesized Fe3O4 NPs are around 61.9 nm and 15.6 nm, respectively, in agreement with the size calculated from XRD data.
Fig. 2(a) reveals spherical Fe3O4 NPs with an average size of 10-20 nm, relation with according to calculate from XRD data.
Wu. [40] In this result, we can be to conclude that ZGF nanocomposite effectively enhanced visible light absorption, which led generate electron-hole pairs using photo-reduction and photo-oxidation for photodegradation improvement.
Długosz et al [41], which results in the reduction of Fe3+ ions and tends to result in photocatalytic Fe3+ /Fe2+ redox cycle, under the solar light irradiation.
Thus Fe2+ ions are rapidly oxidized to Fe3+ ions by oxidizing with the oxygen molecules in the photo-reduction process, leading to superoxide radical anions (•O2- ) as a strong oxidizing agent in photodegradation.

The data related to consumption are converted into Terra Watt Hour from other units to unify the units and get a clearer result in the same unit.
PARAMETER DESCRIPTION Parameter Name of the Parameter Value d Natural acid rain rate 0.018 α Acid rain caused for fossil fuel consumption 1.534592e-06 β Acid rain reduction rate for using hydro-power 1.455974e-05 Γ Acid rain reduction rate for using wind power 3.368984e-05 R Fossil fuel consumption rate 0.012 K Carrying capacity of fossil fuel 955000 Φ Fossil fuel consumption reduction rate for using hydro-power 5e-6 Ψ Fossil fuel consumption reduction rate for using wind power 5e-6 Hydro power usage rate for other than electricity 0.001 Wind power usage rate for other than electricity 0.005 Fossil fuel to electricity gain percentage 0.04 Hydro-power to electricity gain percentage 0.8 Wind power to electricity gain percentage 0.65 Fig. 2.
These data show the impact of hydro and wind power energy in today’s real life world.

Low sampling rate and missing data due to sensor or system errors are common reasons that yield incomplete data during acquisition.
These data are all with very low data rates.
The essence of compressed sensing is to acquire condensed data through dimensionality reduction.
The data rate is only 3%.
The major reason is that more data points are presented in this simulation while the previous one has much lower data rates.

The reduction in particle size leads to an increased surface area, expanding the contact interface between zircon and the alkali agents.
This reduction in strain lowers the energy stored in the form of dislocations and defects.
The main reactions occurring between ZrSiO₄ and NaOH during the alkali fusion process are as follows: ZrSiO₄ + 2NaOH → Na₂ZrSiO₅ + H₂O (1) ZrSiO₄ + 4NaOH → Na₂ZrO₃ + Na₂SiO₃ + 2H₂O (2) ZrSiO₄ + 6NaOH → Na₂ZrO₃ + Na₄SiO₄ + 3H₂O (3) The X-Ray Diffraction (XRD) data was measured by Rigaku miniflex 600 with Cu-Kα radiation (λ = 1.540598 Å) and a range of 2θ from 5º to 100º.
In contrast, the alumina crucible retained its color and integrity, indicating minimal degradation, with a moderate sample reduction from 17.31 grams to 15.80 grams, making it a more stable and inert choice for alkali fusion.
The use of an alumina crucible, coupled with milling and leaching, appears to strike a balance between crystal growth and strain reduction, resulting in a product with controlled microstructural properties.

Successive filtration of water through H-cationisation and OH-anionisation can lead to complete desalination of water or, depending on the ionic composition, to a reduction in its salt content.
The following methods were used to achieve this goal: - experimental measurements - a series of experiments were conducted using modified ion exchangers to analyse their impact on diffusion processes in water supply systems; - analysis of the quality indicators of industrial water - a comprehensive analysis of the quality indicators of water that passed through modified ion exchangers was carried out to assess the effectiveness of their work; - determination of modification parameters - a study was conducted on the parameters of ion exchangers modification, such as chemical composition, surface structure and other characteristics that could affect their efficiency; - statistical analysis of the results - the data obtained were subjected to statistical analysis to determine statistically significant differences in the effect of modified ion exchangers on ion exchange processes.
It also depends on hydration of functional groups, their number, on the intensity of interaction of counterions with fixed groups and some other factors, which is confirmed by experimental data [25, 26].
The amount of sorbed hardness salts at reduction of acid excess, in case of magnetic modification of cationite, is practically the same as in case of optimal regeneration mode at equivalence of acid in relation to cationite loading 2.18.
AN-22 337 g-eq/m3 (ammonia excess 3.66), at magnetic modification it is equal to 361 g-eq/m3 (ammonia excess 3.12), i.e. there is a reduction of regeneration solution consumption.

As the Decree includes only 12 derivatives among the Σ PAHs, it was necessary to additionally subtract the remaining values and to compare the newly obtained data of Σ PAHs again.
Comparison of the obtained data of sludge prior to and post biodegradation experiments and the limit concentrations of pollutants in Appendix 10 of Decree 294/2005 Coll. in order to assess the application of sludge as the feed material for the production of TAP.
Comparing the concentrations of EOXs in the samples after biodegradation and in the input sample, a reduction in the concentrations in PP+R and R samples was identified.
The most pronounced reduction occurred in the sample PP, namely of 66.3 wt %, in the sample PP+R of 57.7 wt %; the lowest decrease in the concentration of the sum of PAHs was in the sample R, i.e. of 47 wt %.
According to the analyses and comparison of the results of sludge samples prior to and post the biodegradation processes, there was a reduction in the concentrations of the following indicators: EOXs of about 35 % in the sludge sample labelled as R (using bacteria of Rhodococcus sp.) and in the sample labelled as PP+R (bacterial mixture); OCP shows a decrease in the concentration of individual substances of 30 to 75 % in all the three experiments; in the sum of PCBs there is a drop of 62 % in the sample post biodegradation labelled as PP (applying the bacteria of Pseudomonas putida), of 52 % in the sample labelled as PP+R; the sum of PAHs fell by 66 % in the post biodegradation sample labelled as PP and by 58 % in the post biodegradation sample labelled as PP+R.

Gray online prediction Grey prediction is based on sample data with uncertain poor condition; the accumulation make gray information turned white and build systems change trend model that predict future state.
The single input/output system, assuming that measure the input/output time sequence in e.q (1): (1) Because there are some random disturbances, the input/output time data can be regarded as gray data.
Generated accumulate sequence could greatly weaken the influence of random disturbance, and get a generation the first time accumulate data sequence
The control model uses the five continuous sampling data before the present moment k and obtained predict values of k+M moment through grey predict algorithm.
System adjustment time is not better than fuzzy PID except the reduction of maximum deviation.

With the low-carbon technology becoming more sophisticated, the trend of emission reduction increasingly clear, various types of low-carbon power (mainly renewable and distributed power) will continually enter the market, and the competitive with conventional power will also be a rising trend.
But there are some problems of China's demand side management system, such as less involvement of user, poor client real time data, and flexibility shortage of decision-making.
So there are needs of increasing modules of the client real-time data acquisition, visual query and price response of users, building demand-side intelligent management system that have strong real-time data, high participation of user.
In order to monitor real-time load data and electricity consumption data of electricity consumers, target to adjust the load in real time and electricity price in various time according to the demand and spread to the terminal user and detect abnormal electrical power users, make user know his own load and power consumption and make a adjustment.

In engineering, the general method of fatigue life prediction is based on lots of experimental data.
But there are still some problems in predicting fatigue life from experimental data of standard specimens[1].
(12) Substituting these data of Table 1 into Eq. (12), we get:
Substituting data of Table 2 and Eq. (13) into Eq. (10), and dong iterative calculation until .
We should calculate the average of these data as experimental result which is listed in Table 3.