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PCM’s incorporated into concrete pavements have shown significant temperature reduction effects, mitigating thermal stresses.
The reduction of urban heat island effects and energy demand ties into SDG 13, while improved energy efficiency supports SDG 7.
This data serves as the foundation for further calculations of temperature stresses within the scope of this current investigation.
Figure 5 reveals significant reductions in curling stress with increasing percentages of PCM.
Meanwhile, the P12 demonstrated significant thermal stress reduction but lacked balanced mechanical strengths compared to the P8 mix.

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

With the development of computer science and data mining technology, many scholars combine with different theories and industry backgrounds to discuss the importance measure method of attribute based on data.
Such as, Liu [3] according to the characteristics of failure data for nuclear power plant, using the powerful knowledge discovery capability of data mining method, proposed a new data standardization method, and using attribute reduction method using concept lattice, conducted a reduction process of attributes, then which can be drawn core attributes, so, we can accurately diagnose faults, Chen and Liang [4] showed the attribute reduction method based on rough set, and proved that it is a feasible reduction method in TCM syndrome data processing, the literature [5] processed one year flight test data of some flight test aircraft according to the data reduction method, and through practice, which shows that the method is useful and efficient to flight data reduction, and Qu [6] proposed the attribute reduction of the power big data pretreatment based on the cloud computing technology, and processed wind power measured data and grid fault diagnosis table on the Hadoop.
Therefore, how to combine with data mining technology and the accumulation data information, building an attribute importance measure which has the structural characteristics of fuzzy measure is an effective way to achieve information fusion.
This indicates that during the disease diagnosis process, the more aspects are considered, the more helpful for you to make the correct diagnosis; 2) there is an interaction among attributes, and the portion importance can not completely representative the sum importance (for example, when w1=0.8, w2=0.2, μ({a2})=μ({a3})=0.1<0.534=μ({a1}), but μ({a1, a2})<μ({a1,a3}); 3) μ({a1, a3})=μ(A)=1 indicates that headache or not has no effect on the diagnosis result when diagnosing whether a patient is suffering from pneumonia or flu (i.e. it only needs to consider the two attribute values that fever characterization and cough characterization); 4) μ(B) changes with w1 and w2 (for example, when w1=0.8 and w1=0.2, μ({a1, a2})=0.623, and when w1=0.2 and w1=0.8, μ({a1, a2})=0.691), this indicates that the established attribute importance measure mode not only has a good structural features, but also can take the decision consciousness into decision process simply; 5) though the amount of data in this

It was showed that there was no significant difference among data measured from samples treated with stabilizer or air at pH=12 in present of stabilizer.
The data revealed that Cr(3+) content increased greatly after the reduced soil (reduction with only reductant: sodium bisulfite or sodium hypophosphite) was oxidized with air at pH=12, it agrees with others researches[10,11], in contrast, the Cr(6+) content decreased after the stabilized soil (treated with chelate reagent or chelate reagent and reductant before) was oxidized with air at pH=12.
The data illustrated that Cr(3+) content increased greatly after the reduced soil (reduction with only reductant: sodium bisulfite or sodium hypophosphite) was oxidized with air at pH=12, in contrast, the Cr(3+) content almost kept unchanged after the stabilized soil (treated with chelate reagent or chelate reagent and reductant) was oxidized with air at pH=12.
The data revealed that Cr(6+) content increased greatly after the reduced soil (reduction with only reductant: sodium bisulfite or sodium hypophosphite) was oxidized with air at pH=12.
The data illustrated that Cr(3+) content increased greatly after the reduced soil (reduction with only reductant: sodium bisulfite or sodium hypophosphite) was oxidized with air at pH=12.

Microbial ferrous-oxidation by mesophilic microbewas studied in a recently reported novel packed-column bioreactor with a view to investigate the potential of using solution pH to manage jarosite accumulation in the bioreactor in addition to establishing a base case data for the bioreactor.Experiments were conducted in the bioreactor packed with glass balls (15mm diameter) at constant temperature of 38.6 oC, residence time of 18 hours, airflow rate of 20 mL.s-1 and pH values of 1.3, 1.5 and 1.7.
The results also showed that 33% and 52% precipitate reduction could be achieved by gradual decrease in the bioreactor solution pH to 1.5 and 1.3 after being continuously operated for 10 days at pH 1.7 respectively after an addition of 5 days.
Steady data were recorded only when the bioreactor achieved a steady state operation, i.e. when the pH and redox potential in the culture liquor was constant for at least three residence time.
The fit of Hansford model (Eq.1) to experimental data Table 1.
This reduction was linked to the chemistry of dissolution of jarosite precipitation at low pHsby Mazuelos et al.[3].The expected relationships between oxidation rate, conversion, solution potential and dilution rate were observed (Figure 3& 4).

The paper contains: problem definition, presentation of the measured data and the final analysis with the fuzzy regression approach.
As a result, the data are scarce and irregular.
The median is a better description for low amount samples with non-symmetric data.
Grzegorzewski, Trapezoidal Approximation of Fuzzy Numbers Based on Sample Data, Comm.
Gastaldi, An "orderwise" polynomial regression procedure for fuzzy data, Fuzzy Set Syst. 130 (2002) 1-19

The comparison with experimental data shows that this method can well forecast the absorbing properties of absorbing material.
Calculation based on Eq.2 were also carried out, and the obtained results are compared in Fig.2 with those data from reference 12.
In some cases, the exponent in Eq.2 can be treated as empirical data.
Fig.3 Real permeability comparing between the modified calculation and experimental data Fig.4 Imaginary permeability comparing between the modified calculation and experimental data Shown in Fig.5 is the reflection rate calculated from Eq.2 and the measured reflection line.
Fig.5 The reflection data of calculation and experiment.

Other reported preparation methods are mostly the conventional impregnation procedure associate with borohydride reduction.
Sun et al reported a formic acid reduction method to prepare carbon black suppored Pt nanowires at room temperature [14].
In this study, a modified formic acid reduction method was developed and applied to load Pt nanoparticles on carbon aerogel.
Electrochemical measurements were performed using an Autolabpotentiostat PGSTAT 30 (Eco Chemie, Utretch, The Netherlands) driven by the General Purpose Electrochemical Systems data processing software (GPES and FRA, version 4.9, Eco Chemie).
Conclusions Pt/CA catalyst with improved Pt dispersion was prepared by a mild formic acid reduction method.

In summer, gaseous fuel reduction by 10% could be achieved and the detail results are presented in this paper.
Fig. 6: Operation data of cooling (20th May) Fig. 7: Operation data of cooling (28th July) Evaluation Status and Results Operation conditions.
Figure 6 shows the operation data on May 20 and Figure 7 shows the operation data on June 28.
From the data, it was confirmed that the solar heat priority usage function and gas-based backup function operate properly and overall system functions normally.
Cooling operation starts in late May, therefore, the monthly reduction rate shows the data only in June, however, reduction by 10% could be achieved and the results as estimated were obtained.

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.