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However, the data points from ABS5GF and ABS10GF fits well with the models from Voigt and Reuss, whereby Reuss can be considered as linear in the range between 0-5 vol.%.
Tekinalp et al. observed a small reduction of long fibres from 20 vol.% fibre content [7].
However, a reduction of longer fibres could not be measured within this work.
It could be seen that tensile strength and stiffness of ABS 0°-specimens only scarcely differ from data sheet.
[9] Pascal Pinter, Benjamin Bertram, Kay André Weidenmann, A novel method for the determination of fibre length distributions from µct-data, In 6th Conference on Industrial Computed Tomography, Wels, Austria (iCT 2016) 2016

The Breast Imaging Reporting And Data System (BI-RADS) standard is a classification system providing the following classification: • BI-RADS 1: Almost entirely fat (0-25%)
The local patterns computed in the MLTP represent the continuous fragments of the foregrounds.Histograms can offer a good representation of the empirical distribution of data.
The main factors in Sturge's Rule are the pixels number and the data range.
The reduction of image bins allows to eliminate noisy artifacts.
The neural network architecture adopted in our work is composed by 90 as (x1, ....x90) input data, 1 hidden layer that contains 20 neurons and 1 output neuron for decision.

In the output operation, you can not handle the received data, but should read SPI_RDBR register, while it will be empty, to prevent the follow-up effective data being read; in the input operation, usually only sending an invalid data 0 or 0xFF is ok.
Each time PPI will transfer a row of data to the LCD screen to display, but the front section of the data is invalid, that is, it will be delayed for some time to wait the effective data arriving to write on the screen, and here we set the delay as 99 clock cycles, which is for 2.5-inch LCD screen.
When PPI is used to read data, this register will save the number of bytes to read, and when PPI is used to output data, this register will save the number of output bytes in the form of a negative.
For example, we will read 100 bytes of data, then set the counter to 100, counting up to a total of 100, and when we want to output 100 bytes of data, then set the counter to 99, the counter is essentially from -99 to 0, thus is total 100.
Eran Gal, Sivan Toledo, Algorithms and Data Structures for Flash Memories, USA: datasheet, 2005

But it’s difficult to get such data traces since network traffic data is very privacy-sensitive, especially when it contains payloads or IP addresses.
Identifying the true-positive anomalies requires combing through vast amounts of data that are sometimes of poor quality due to data-reduction techniques such as sampling.
A researcher might wish to (1) verify published results by evaluating the same algorithm on the same data, (2) investigate the robustness of a published algorithm by applying it to different data, or (3) compare a novel algorithm against the published one by using the same data.
Data traces Data traces with manual labeling or attack traffic injection can be directly used for evaluation of anomaly detectors.
Therefore, anomalies in the data were identified using available manual labeling methods: visual inspection of timeseries and top-n queries directly on the flow data.

Clustering analysis to reasonably divide data sets in order to discover novel, useful and understandable patterns or data distribution in data sets.
In the feature space of the data set, data points of certain categories appear in relatively close clusters.
N(m,n) describes the how proper it is that data point xm selects data point xn as its exemplar.
Clustering by passing messages between data points[J].
Finding groups in data: an introduction to clustering analysis[M].

And the relevant model are compared and verified with the experimental datas. .
It is known that experimental values are in agreement with those predicted by Maxwell-Eucken equation at low filler content[10], while the discrepancy is present between the experimental data and the numerical simulation results at much higher filler content (≥15%).
From Fig.3, a reduction in CTE was observed with increasing filler loading.
It can be seen that the CTE obtained from experimental data is lower than that of the theoretical model.

Research method diagram This scientific article's title is based on data analysis by Vosviewer, which identifies scientific works that have not undergone extensive research.
The national goal is to reduce carbon emissions from buildings because they are significant in carbon reduction plans.
Comparison between building and environment Development causes a reduction in land, which damages the environmental ecosystem.
Gaffney et al., "Maximizing value of genetic sequence data requires an enabling environment and urgency," Glob Food Sec, vol. 33, 2022, doi: 10.1016/j.gfs.2022.100619
Han, "Trend analysis on adoption of virtual and augmented reality in the architecture, engineering, and construction industry," Data (Basel), 2020, [Online].

Figure 2 shows how this hierarchy is broken down into mappings of vertical type (PS, SP, PP) and lateral type, the latter involving a reduction of order of the material representation to affect a reduction of the degrees of freedom.
(noise and control factors) � Unparameterizable: random microstructure Model Parameter Uncertainty (parameter uncertainty) � Incomplete knowledge of model parameters due to insufficient or inaccurate data Model Structural Uncertainty (model uncertainty) � Uncertain structure of a model due to insufficient knowledge (approximations and simplifications) about a system.
Type III robust design is typically required to manage (a) y = f(x,z) x= Control Factors z= Noise Factors y= Responses TYPE III: Uncertainty in function relationship between control/noise and response • Natural system randomness due to unparameterizable variability (natural uncertainty) • Simplifying assumptions (model structure uncertainty) • Limited data (model parameter uncertainty) TYPE III: Uncertainty in function relationship between control/noise and response • Natural system randomness due to unparameterizable variability (natural uncertainty) • Simplifying assumptions (model structure uncertainty) • Limited data (model parameter uncertainty) TYPE I: Natural Uncertainty in system variables that designer cannot control • Variability in boundary conditions • Operating conditions TYPE I: Natural Uncertainty in system variables that designer cannot control • Variability in boundary conditions • Operating conditions TYPE II: Natural uncertainty
Three types of robust design, including insensitivity to uncertainty in noise factors (Type I) and control factors (design variables) (Type II), as well as uncerainty in models (PS and SP) and microstructure (Type III) [15]. inherent variability that is difficult or impossible to parameterize, such as stochastic microstructure, (b) limited data, and (c) limited knowledge in new domains such as a new class of microstructures, as shown in Fig. 5.

Although anodisation was done at the times of 10-30 min in each electrolyte, the optimal times for maximal photocatalytic performance (data not shown) observed to be 10 min in 2 M H2SO4 and 15 min in 2 M H3PO4.
There was a slight reduction in the TiO2 peak heights for the plates anodised in H3PO4, suggesting that this film was slightly thicker.
These data show that the sample anodised in H2SO4 is rougher than that of the sample anodised in H3PO4.
It can be seen that the sample anodised in H2SO4 resulted in ~15% degradation while the sample anodised in H3PO4 was considerably more effective with ~42% degradation These data represent another contradiction in that the former sample would have been expected to exhibit superior performance owing to its greater roughness (and hence surface area) and the presence of mixed anatase-rutile, which is considered to enhance the efficiency [23].
These data parallel those of Figure 4, although the extents of degradation are considerably less.

Comparing the results of thermodynamic calculations with specific experimental data, such as for Mg + NaNO3 mixtures widely used in various pyrotechnic products [2], with combustion temperatures reaching 2000…4000 K, differences between them do not exceed 10…15%.
From the calculated data on the influence of paraffin and NaF additives on the maximum combustion temperature of the mixtures, it can be observed that increasing the paraffin content to εp = 0.2 leads to a reduction in Тсmax by 1.3...1.4 times, while increasing the NaF content to εf =0.02 results in an increase in Тсmax by 1.2...1.3 times.
To create such a database, it is necessary to systematize the provided computational data into straightforward statistical models that are convenient for practical assessments.
This will lead to a reduction in the fire hazard posed by pyrotechnic products that prematurely ignite due to external thermal influences.
As a result of the conducted thermodynamic calculations, new data on the dependencies of the main characteristics of the combustion process of pyrotechnic mixtures Mg + NaNO3 + paraffin + NaF and Al + NaNO3 + paraffin + NaF (combustion product temperatures (Tc, K), content of high-temperature condensate in them (gсMg, gсAl ) and unoxidized metal gMgс, gAlс)) on the excess oxidizer coefficient (α), paraffin addition (εp), and NaF addition (εf): Mg + NaNO3 + paraffin + NaF mixture