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Materials and Method This research employed experimental techniques to assess the water's quality and compare the findings to data on the standards for quality established by laws and regulations.
The data that was gathered consisted of both primary and secondary data.
The primary data collected for this study consists of direct measurements obtained through assessing the physical and chemical properties of healthy water in the Fitu sub-district.
Secondary data refers to information about water quality standards derived from existing sources such as references, legislation, and regulations.
Chemical Properties Fe Mg/L 1,3 0,91 30 Mn Mg/L 0,6 0,45 25 pH - 9 7,90 12 According to the data presented in Table 3, the most notable improvement in filtering efficiency is the reduction of Fe concentration in healthy water.

First method imposes the reduction of the ANN filter structure.
Combining the selection matrix (7) with the adaptation equations of neural weights (3) and (4), the following update equation is obtained: w(k+1) = w(k) – ηIM(k)∆w(k). (10) It is now possible to accumulate the block of data for the adaptation purpose, each Nlayers+1 samples.
Experimental Verification The proposed algorithm has been verified using data from the real plant, in the software in the loop simulation.
The algorithms have been based on real data acquired from the active casing.
Both of them result in the same reduction level.

The energy data was collected using Grant SquirrelView 2020-1F8 Data Logger, a PC-linked data acquisition system.
Figure 5 presents the average value of CIS response data with one standard deviation.
SEC response data with average value Figure 5.
Distribution of CIS response data with one standard deviation RFM Analysis.
In Figure 4 and Figure 5, it is observed that linear lines can satisfactorily fit into both SEC and CIS response data.

These results suggested that vermicomposting was a better way for Chinese medicine residue treatment, the rate of quantitative reduction and the treatment efficiency were 73.54% and 44.52%, respectively.
The data in Table 4 showed that TOC in all treatments declined drastically compared with CK, and the maximum TOC loss was observed in T2.
Moreover, earthworm activities in substrate materials accelerated the mineralization of materials, and therefore contribute effciently in reduction of the wastes materials[19].
The reduction and biodegradability of Chinese medicine residue The reduction of Chinese medicine residues in different treatments were presented in Table 5.
The reduction work best reached 73.54%, and the optimal treatment efficiency was up to 44.52%.

Introduction To encounter the global warming and CO2 emission problems, save energy and resources, higher fuel efficiency and weight reduction have been intensively demanded for energy related machines, automobiles, trains, airplanes, etc.
From the viewpoints of weight reduction and higher fuel efficiency, application of magnesium alloys as structural materials would be one of the most promising ways.
the empirical creep data from the tests under a constant engineering stress.
Therefore, the difference of creep curves between under tensile and compressive loadings would not only result from the reduction of cross sectional area during tensile loading, but also would be induced by the difference of creep deformation mechanism.
The estimation method of creep curve under a constant true stress was proposed by considering the reduction of cross sectional area in tensile loading.

The data is recorded in a database for later analysis and visualization if required.
Fig. 1: Single Actions Mechanical Press From these variables, the speed and tonnage data will be used to develop the proposal of this article.
Hybrid Twin Enrichment Model Available Data.
The data has been measured with a mechanical thickness gauge such the one that can be seen in figure 6.
Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data.

This opens up a precedent for using conventional production data to analyze gas reservoir, but it has followed the empirical method used by Arps and it also assumed that the flow pressure is constant.
With the data of common gas wellhead pressure and production, we adjust the parameters of a geological model of single well and well bore model to fit the production or pressure of gas wells for getting single well control area, gas well controlled reserve, reservoir permeability, skin factor.
Using the conventional oil or gas reservoirs of observational data and the single well geological data, it has reckoned the drainage radius of the single well, controlled reserve, reservoir permeability, skin factors and other parameters.
Finally, we can get cumulative yield reduction ratio of observation wells (table 1).
The reduction rate of yield of the observation well is less than 20% when the yield of the active well is less than 16.8×104m3/d.

Firstly, the data should be preprocessed through accumulation and normalization.
All the samples are collected from the cost index published by Shanghai Construction and Building Materials Market Management Station [8] since 2005. 18 of them are taken as training data while others are taken as testing data.
Table 1 shows the normalized training data.
Table1 Training data base after normalization 　type Input vectors Output vector NO.
Table4 Quantization of the features of the project Input vectors T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 3 59.88 16 1 2 2 2 116.96 2 2 2 Put the quantized data into the testing data base to compute with MATLAB and then calculate the output through IAGO.

The data related to manufacturing were collected and evaluated the performance of the current facility design using E-VSM and found ways to reduce energy consumption and production cost.
All pertinent data, such as cycle time, is captured in data boxes.
Current State Map Fig.1 shows the current state map developed based on the data collected from the shop floor.
In a machining operation, the material is removed as per the data given as input through the program and then the product moves to the next station where the pressing operation is processed.
Due to the reduction of the cycle time production rate of the product gets increased.

Information Entropy Method for Evaluating Regional Earthquake Relative disaster-carrying Capability Juan Liu1, a, Jingyu Su1, b, Wei Wang1, c and Donghui Ma2, d 1Institute of Earthquake Resistance and Disaster Reduction, Beijing University of Technology, Beijing 100124, China 2 College of Architecture & Urban Planning, Beijing University of Technology, Beijing 100124, China alidongzhixue@sina.com, bjysu@bjut.edu.cn, chautww@126.com, dieemdh@163.com Keywords: information entropy method; earthquake relative disaster-carrying capability; evaluation method Abstract.
Only in this way, our country’s urban earthquake disaster reduction can develop evenly.
Disaster-carrying capability Disaster prevention ability Social factors Employment Education Medical treatment Social security Economic factors Disaster prevention investment Monitoring and forecasting facilities Environmental factors Environmental protection efforts Resilience capability Social factors Population density Population situation Economic factors Wealth density of fixed foundation Engineering anti-seismic capability Resilience of building structures Resilience of lifeline subsystems Lifeline system correlation Disaster response capacity Social factors Ability of medical assistance Government emergency response ability Economic factors Lifeline restore ability Internal and external communications developed degrees Drainage conditions Secondary disasters Environmental factors Temporary relief distribution center Disaster recovery capability Social factors Production and construction HR Economic factors Economic diversity Wealth savings Insurance Environmental factors Environmental quality Data
Processing We may get standardization of indicators according to standardize the original data matrices.
We adopt the basic data of the cities and standardize them to establish the characteristic matrix.Using the Eq.1～Eq.3, we can get the entropy and the information utility value , and use Eq.5 to work out each indicator’s weight .