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These overall data will be essential information for the development of antimicrobial films and coatings in food packaging with the extending shelf life.
However, higher temperature under heating process during plasticization is sometimes required but is not a good choice since it may cause serious detrimental effects on a shelf life of chitosan as well as possible reduction in antimicrobial activity [1].

Data on the current sharing capability of the PiN rectifiers is also presented.
A negative differential co-efficient of on-state voltage drop is also observed from Figure 2(b), which is due to the reduction of knee voltage at higher temperatures.

To test whether the moral hazard effect of compulsory insurance exists in China, we empirically reviewed the data obtained from Guangdong, China by using logistic regression.
Since researchers are difficult to obtain random data and trace samples for long period relating to driving behaviors and insurance status, natural experimental data is thereafter the most widely used type of data when doing empirical studies [4,5,13].
Empirical method and data Due to this and the model we’ve discussed above, we draw hypotheses: Hypothesis 1: under compulsory vehicle insurance 1.
These data, obtained from the Guangdong Provincial Security Department, are extracted from the Traffic Management Sector-Specific Incident Case Data Report for the period 2006-2011 and are the only comprehensive and official source of traffic accident data in China recorded by on-scene traffic police.
Together with non-traffic violation accidents, the data contain 11,055 and 10,035 samples relevant to speeding and drunk driving incidents, respectively.

When only moment constraints are considered, Phillips[10] introduced the use of the maximum entropy(Maxent) method for modeling species geographic distributions with presence-only data.
Firstly, the data from engineering are arranged as the observed sample , thus the observed sample is a small sample.
Xu[15] used a method for data processing and optimization in geotechnical engineering to optimize above data, and good data are obtained in table 1 and table 2.
Fig.2 and Fig.3 shown the comparisons of distribution curves of experimental data for driven piles and bored piles.
Fig.2 Comparison of distribution curves of experimental data (driven piles) Fig.3 Comparison of distribution curves of experimental data (bored piles) From Fig.(2) and Fig.(3), the proposed method has an advantage that its fitting is more accuracy than classical probability distribution.

Corresponding module is done by the corresponding sub - VI,sub- VI including communications sub – VI,date block generating sub - VI, data sending sub-VI,data receive sub-VI, data block checking sub-VI , data processing sub-VI,and data Dynamics displaying sub-VI.
Data receiving and processing.
Relative to the passive type, active type receives data a lot easier, when starting to upload data from PC to the slave was given command to upload data, as long as you allow enough time before receiving the data and set to the length of the received data will be able to smoothly transfer the data.
The data uploaded by Slave is composed of two parts : data and the checksum.
Checksum is sum of the sending data ,after the host computer receives data,perform the data block checking sub-VI to verify the checksum of the received data and sends is equal.

Data compression techniques have been investiagted by Zhang et al. [22].
The System Equivalent Reduction Expansion Process (SEREP) is applied to enhance the information available from the sensor data.
The analyst selects one or multiple models for subsequent analysis based on his/her experience and additional observations not captured in the experimental data or objective function [11][36][37].
Their proposed methodology consists in a moving sensor that captures acceleration data as it moves, resulting in a record of acceleration as a function of time and location.
It implements modular design and uses distributed data collecting and processing strategy.

Its availability and reliability are well shown through the data processing examples.
Suppose the original data sequence as, the observed value of variables at Time; m data number, sequence as non-equidistance, i.e. distance is not a constant.
The least square of the parametric vector to be identified is estimated as (6) According to the new information priority principle of the gray system theory, with the full use of new information, we can take the initial value as, and the time response of the grey differential equation is (7) As for formula (7), when,, we can get the fitting value of the original data after reduction (8) The absolute error of the fitting data is: (9) the relative error of the fitting data(%)： (10) fit the mean value of relative errors of the data row .
Considering from the perspective of the minimum error of the whole data, the least square method can minimize the sum of square error of the model to determine the initial conditions, and the new data point error of the fitting here is not zero
Table 1 shows the experimental data of fatigue strength changes of titanium ally as the temperature changes, which is a non-equidistant sequence.

Second, preprocessing--we preprocess obtained data, check data integrity and data consistency, deal with noisy data, complete missing data by statistical methods and form database.
Forth, data mining--adopting different KDD algorithm to mine data according to the aim of what kind of data to find.
Fifth, interpretation/evaluation--evaluating data and deciding whether to deposit data into the database or not.
However, there no standardization but for data formats and for data interchange.
" [4] "Data Mining Curriculum".

Common preparation methods of magnetic nano-adsorbents include co-precipitation method, hydrothermal method, oxidation (reduction) precipitation method, high temperature oxidation method [26-28].
And the corresponding data of the SSA and pore structures are listed in Table 1.
Fig. 8 (a) Nitrogen adsorption-desorption isotherms and (b) the pore size distributions of Fe3O4, Fe3O4@C and Fe3O4@C-G2 Table 1 Special surface area and pore structures of samples Sample SSA [m2/g] V [cm3/g] Vmi [cm3/g] PSD [nm] Fe3O4 131 0.283 0.0488 8.7 Fe3O4@C 16 0.069 0.0045 17.7 Fe3O4@C-G2 62 0.343 0.0240 21.9 Fig. 9 is the XPS data of Fe3O4@C-G2.
At a very low pH value, the hydrogen ions compete with those metal ions for the limited binding sites, resulting in the adsorption reduction of Cu2+.
Chen, Fe3O4@graphene oxide composite: A magnetically separable and efficient catalyst for the reduction of nitroarenes, Mater.

The core-shell model proposes that the outer layer of n-ZVI comprises iron oxides and hydroxides, which act as sites for complex formation, while pure Fe0 nanoparticles form the core responsible for the reduction of contaminants [7,17-20]. n-ZVI has a standard reduction potential of -0.44 V.
This value is lower than the reduction potentials of several metals including Pb, Ni, and Cr.
Whereas Pb(II) immobilization occurs through adsorption and reduction processes, immobilization of Cd is mainly attributed to adsorption [61,62].
Tan, Nano zero valent iron in the 21st century: a data-driven visualization and analysis of research topics and trends, Journal of Cleaner Production, 415 (2023) 137812
Bakker, Manual for calculating critical loads of heavy metals for terrestrial ecosystem: Guidelines for critical limits, calculation methods and input data TNO Institute of Environmental Sciences, Energy Research and Process Innovation.