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The associated parameters of membership functions change through the learning algorithm by a gradient vector modeling the input output data in case of given parameters.
Optimization method will be investigated to adjust the parameters according to error reduction computed by sum of the squared variation from actual outputs to the desired ones.
This adjustment leads the fuzzy systems to learn about the data modeling.
Its architecture is based upon an open communication structure in combination with the flexible electrical construction elaborating the moderated technology of future based on a new platform for dynamic exchange of data and information.
ANFIS membership functions are optimized automatically trough training so it could be more effective for various applications in data prediction which usually are constructed with complicated pattern.

The result is the inclusion of the remaining best practices into one of four categories: (1) Ideal – high reduction of emissions can be achieved at low costs or even cost savings; (2) Economic – only limited reduction of emissions can be achieved at low costs or even cost savings; (3) Ecological – incurring high costs will achieve a high reduction of emissions; (4) Ineffective – incurring high costs brings only a limited reduction of emissions.
The information and the data necessary for determining the indicators are collected during this stage.
Given the extent of the assessment and the sensitivity of the used data, this section will present only a simplified version of the performed evaluation.
There are the following main outcomes of the preliminary analysis: (1) Purpose of evaluation – the objective of the case study is to evaluate the current GT level and to determine the potential for its improvement; (2) Scope of evaluation – inbound transport was selected as the system with the highest occurrence of emissions; (3) Availability of information – the data were collected by means of questionnaires and interviews with the employees of the company involved in this issue.
The main objective is Emission Reduction.

In the research [7], a scientifically-substantiated sequence of procedures was created, with a detailed selection of equipment and test samples, in order to provide reliable experimental data when studying the temperature regime of a fire.
Nomogram for determining the coefficients of tensile strength (a) reduction, compressive strength (b) reduction and tensile modulus (c) reduction to design fire-resistant steel building structures at Am/V = 150 [m-1].
I-beam profile according to DSTU 8768:2018 Geometric characteristics of cross-sections Cross-section coefficient Am/V, [m-1] Resistance moment, Wx, [m3] Profile № 55 157.842 2000×10-6 Profile № 40 123.246 947×10-6 Profile № 27а 93.684 407×10-6 Using the data in tab. 2, we can determine the boundary moment using the formula [1]: Mc,Rd=Mpl,Rd=WplfyγM0
Fig. 9 shows that the obtained data can be used to build nomograms to determine the corresponding beam boundary moment.
Analyzing the data shown in fig. 11, it can be observed that in almost all the area of the possible values of the opening coefficients and fire load density, the time of reaching the boundary state of the loss of load-bearing capacity is much greater for the temperature regimes determined by the proposed mathematical models than the values obtained using the standard temperature regime of fire.

The value of the numerical prefactor was determined by fitting Eq. 1 to the strength data of isotropic foams, for which R=1 and f1=1, resulting in C=0.3 for plastic foams [12].
Using the data presented in [4], the reinforcement efficiency factor Γ was evaluated by Eq. 5 and plotted in Fig. 1 as a function of CNC volume fraction νc in the monolithic PU.
The presence of the filler led to a reduction in the cell size without apparent changes in their geometrical anisotropy, as in [4].
It is seen in Fig. 3 that most of strength data are close to the prediction employing the values of ηo based on Eqs. 9 and 10.
Reinforcement efficiency factor of strength of nanocomposite PU foams vs CNF volume fraction as derived from experimental data [8] (markers) and predicted by Eq. 5 (lines) Summary With the aim to separate the nanofiller reinforcement effect from the foam strength changes caused solely by variation in foam density, a reinforcement efficiency factor of foam strength is introduced.

For estimation of the effective mass diffusion coefficient, experiment data of average moisture content of cassava cubes (fresh and osmotically dehydrated) was fitted to the simplified Fick model and a good agreement was obtained.
The effect of water is related to large waste, reduction in quality, and shelf life of these materials.
The average final moisture content data were subjected to variance analysis at a significance level of 5% and Tukey test means comparison by using STATISTICA® software [17-19].
Experimental drying data were adjusted to the simplified Fick´s model [20] to predict the drying process and to estimate the water effective diffusivity, considering uniform initial moisture distribution, absence of thermal effect in mass transfer, and applied to an infinite flat plate and long drying time.
Osmotic pretreatment resulted in a reduction of free water in these samples, contributing to the reduction of the mass transfer rate in convective drying.

Data Definition: the initial data are defined, such as the geometry, masses and sections of the structure, as well as the elastic spectrum in acceleration Sae (demand) corresponding to a specified limit state, TC (constant acceleration phase limit) and duration of ground motion TD. 2.
The point which has a 15% reduction of maximum capacity is assumed represents the end of the curve.
(a) Stress-strain relationship for carbon steel at elevated temperatures; (b) reduction factors for the stress-strain relationship of carbon steel at elevated temperature [15].
The elastic spectrum for the damaged structure is reduced by a greater reduction factor compared to that of the undamaged structure: in reality the spectrum for the damaged structure is reduced by a greater reduction factor as the demand in ductility turns out to be greater, since the structure turns out to be less rigid as it damages.
International Journal of Disaster Risk Reduction 98 (2023) 104124 [8] Covi, P., Tondini, N., Sarreshtehdari, A., Elhami-Khorasani, N. 2023.

Based on literature data and the performance-based approach of “Model Code for Service Life Design” published by the International Federation for Structural Concrete (fib), risks associated with the use of using seawater in relation to the service life a reinforced concrete element in a marine environment are investigated.
During the development of the project, laboratory and field tests will allow for the collection of experimental data to better define the durability evaluation and analyse advantages of the proposed approach in terms of life cycle assessment.
For the types of concrete considered in this work, experimental data on DRCM published in a previous work were considered: DRCM of 6.5∙10-12 m2/s and 4.5∙10-12 m2/s for OPC and FA concretes were assumed, respectively [3].
Thus, the values of Clth for the stainless steel reinforcement were determined through literature data.
As far as XM-28 stainless steel is concerned few data are reported in the literature and a mean value and a standard deviations respectively equal to 3.4 and 0.6% mass of cement was taken into account as suggested in [9].

The experiment data show 1-1.5V/cm is cost effective voltage range for the soils studied.
Current reduction may be mainly caused by two reasons.
Studies showed that electric current is proportional to concentration of dissolved ions in soil media solution [10], thus electric current decreased because of the reduction of the concentration of dissolved ions.
According to the test data, removal efficiencies and energy consumption were obtained as shown in Tab.2.
According to the data fitting, the energy consumption equation of the soil contaminated with chromium in concentration of 100mg·kg-1.

K-means algorithm Theory K-means algorithm is a clustering method in data mining technology.
The peak time data in the following table ,the data of 1-20 is the peak times of ginsenoside Rb1 and ginsenoside Rg1 in radix notoginseng,the data of 21-40 is the ginsenoside Rb1 and ginsenoside Rg1 in American ginseng.
Using the aiNet algorithm on the 40 sample data in Table 1 for simulation.
Putting the forty set of data as the test sample data set, using k-means algorithm to cluster this data .
R. (1992), Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data, New York: IEEE Press

Impregnation process in SC CO2 and reduction.
The reduction gas was a mixture of 5% H2 in N2 flowing at 60 ml min-1.
Table 2 shows the detailed data of the BET surface area, pore volume and mean pore diameter of selected Ru/AC catalyst samples prepared under SC CO2, and traditional aqueous condition, respectively.
Both curves exhibit almost the same main reduction peaks assigned to the reduction of Ru3+.
However, the 11# sample impregnated with SC CO2 shows an increased reduction temperature.