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Results indicate that while n-MOSFETs maintain threshold voltage shifts below 3% and exhibit robust characteristics up to 400˚C, p-MOSFETs exhibit permanent threshold voltage shifts of up to 10% and mobility reductions of 15% particularly above 300˚C DC stress.
Even though same gate bias was applied to stress the dielectrics, the reduction in threshold voltage at higher temperature causes an increase in the effective electric field.
Few data points are off from the trend which indicates the charged mobile ion movement in gate oxide which can be ignored from the analysis.
These BTI characteristics include an increase in interface trap density (Dit), a reduction in field effect (FE) mobility, and a rise in threshold voltage, which notably do not revert even after the stress is removed.
Bias temperature instability (BTI) tests revealed minimal threshold voltage shifts for n-MOSFETs under extreme thermal stress, highlighting their durability, whereas p-MOSFETs faced significant permanent threshold voltage shifts and mobility reductions above 300°C, pointing to un-passivated deep level traps within the bandgap near the valence band.

Pulsed EPD experiments demonstrate a reduction in deposition yield but also elimination of macro-pore generation in the low voltage case.
The data implies a more complex dependence of deposit yield on process parameters than simply pulse width.
The data suggests that yield is dependent upon duty cycle.
Combined with Besra et al.9, this data supports the dependency of deposition yield on both frequency and duty cycle of the pulses.
The data presented here facilitates continued work with the envisioned refractory metal mesh and boron carbide matrix.

The abdominal CT volume data is used as the algorithm input.
The volume data is a 3D array of pixels.
The advantages include the reduction of required human interaction and computing time.
Step 5.After traversing all pixels, we will get the volume data of labels as segmentation result.
Result The medical data used for algorithm evaluation consists of 3D abdominal CT data from 10 different healthy patients of mixed gender (5 Men, 5 Women) and age(18 to 48 years old).

Data collection During the research, data about crashes at roundabouts in the Czech Republic have been collected.
Data from 2009 could not be used because of dfifferent methodology for obligatory crash registration in the Czech Republic in compare with previous years.
We took out the roundabouts with bypasses or traffic light and mini roundabouts from the base data collection.
The final data collection for detail analysis covers 69 roundabouts - 59 single-lane roundabouts and 10 double-lane roundabouts.
For the double-lane roundabout the optimal size of inscribed circle diameter is around 80 meters, but there is also insufficient data for exact findings.

It is important to note the main benefits of additive manufacturing with 3D printing to the building construction sector, and these benefits are [3,4]: • Shorter building time [5]; • Mass customization and CO2 footprint reduction [6]; • Use of the right amount of material; • Generation of complex shape design at controlled costs; • Reduction of arduous human labour; • Increased creativity for architects; • Optimization of acoustic or thermal properties of the building [7].
In addition, in the last years the national and international economic reality has implied a high number of restrictions in the building sector, due to the financial situation and the reduction of the investment observed in the companies associated to the construction sector.
This will avoid complex formworks as well as a reduction in costs and manufacturing time.
The advantages of this technology are clear, namely, the high efficiency, and the reduction of waste and accidents.
[12] Eurostat, Production in construction - annual data.

According to the pyramid data structure [1], the image could be defined as a sequence , and “” is the depth which is the key point observed by a human.
This approximation becomes exact in the limit of infinite data.
The evaluation is performed on the data set by us, because there is no data set which has several types of object having the same semantic and the different vision features.
The data set has 200 pictures(all the resolution of the images in the experiment are ), and each chair has the same number of pictures.
Jordan, Modeling Annotated Data, Proc. 26th Ann.

On the basis of data availability, we establish the competitiveness evaluation index system of urban Logistics, which included 8 levels and 17 aspects, as follow: 1） Logistics service capability: Logistics in essence is service industry. 2）The level of modern information technology: Information technology is the technical foundation and support for the modernization of urban logistics.3）Logistics standard: Standardization is the foundation of the urban logistics modernization. 4）Logistics cost efficiency: This is another important factor of the competitiveness of urban logistics.5）Infrastructure: Evaluation indicators include the circulating capacity of the logistics network design, infrastructure grade, and the total investment of logistics infrastructure.6）Regional economy: The development of urban logistics is restricted and influenced by the regional economic level.7）Supporting factors of logistics: Policy and the talents are both the important factors.8）Environmental factors: From
the viewpoint of sustainable development, we need to improve the production together with reduction of energy consumption and pollution.
Step one: Standardization the original data of 4 enterprises in Table 2 Table 2 Standardization data C1 0.8 0.8 0.7 0.7 0.7 0.9 0.9 1 1 0.7 0.7 0.8 0.8 0.7 0.6 0.7 0.5 C2 0.9 0.7 0.6 0.7 0.9 1 0.9 0.8 0.6 0.7 0.7 0.6 0.5 0.7 0.7 0.8 0.9 C3 0.7 0.8 0.9 0.9 0.7 0.7 0.5 0.7 0.7 0.8 0.7 0.7 0.6 0.5 0.8 0.7 0.7 C4 1 0.9 0.9 0.7 0.9 0.7 1 0.9 0.7 0.9 0.7 0.9 0.8 0.8 0.8 0.6 0.8 Step two: From the Eq. (1) to (6), obtain the matrix data of the correlation coefficient was in Table 3. 0.56 0.56 0.45 0.45 0.45 0.71 0.71 1.00 1.00 0.45 0.45 0.56 0.56 0.45 0.38 0.45 0.33 0.71 0.45 0.38 0.45 0.71 1.00 1.00 0.56 0.38 0.45 0.45 0.38 0.33 0.45 0.45 0.56 0.71 0.45 0.56 0.71 0.71 0.45 0.45 0.45 0.45 0.45 0.56 0.45 0.45 0.38 0.33 0.56 0.45 0.45 1.00 0.71 0.71 0.71 0.71 0.45 0.45 0.71 0.45 0.71 0.45 0.71 0.56 0.56 0.56 0.38 0.56 Table 3 The correlation coefficient matrix data Step three: Using Delphi method to obtain index weight and Eq. (10) to find the rank of
Conclusions The evaluation of urban logistics competitiveness involves a large number of different kinds of quantitative data, as well as a large number of qualitative indicators related to logistics service and logistics links.
Data processing of small samples based on grey distance information approach[J].

To solve above problems, the manufacturers of the capacity find that lead-acid battery capacity of discharging termination voltage can be indirect reaction after testing the size of the battery capacity through a large number of testing data and expert experience.
The model is designed to make the battery production cycle become shorter and more applicability, to achieve energy conservation and consumption reduction, to improve the product factory qualified rate, intelligent, finally to realize the battery cost reduced, to adapt to the modern industrial development.
Taking a power system of Chongqing valve control type sealed lead acid battery production technology for example[4], this paper builds up the model of battery capacity and streamline of the auxiliary variables in the soft sensing from the enterprise long-term accumulation of the formula, craft and the rich real-time data of battery performance, using data mining methods to realize we only measure some of battery performance parameters to obtain indirectly the purpose of the lead-acid battery termination voltage instead of all, which provides theoretical feasibility of omitting the battery discharge capacity in the process of production inspection process, achieving corporate goals to reduce energy consumption and cost savings. 2 RReliefF Algorithm Using feature selection of regression algorithm RReliefF algorithm[5], calculate respectively weight of the original auxiliary variable values, according to the following steps: 1) Select the sample from sample set, and choose k samples nearest
the dominant variable value of the sample i, and is the maximum and the minimum of m samples. 3) Calculation weight set of the sample under the condition of original auxiliary variable A, press type, the equation is ; stands for the original auxiliary variable A value of the sample , （）is the original auxiliary variable value of the sample i, and is the maximum and the minimum of m samples. 4) Calculate weight set of the sample under the condition of the dominant variable and the original auxiliary variable A, the equation is ; 5) Repeat the four steps (m-1) times, each time you select different samples, obtaining m m m ; 6) Calculate,, in turn, where is the sum of , is the sum of , and is the sum of ; 7) Use the following type to calculate weight value of the original auxiliary variable A, the equation is ; All weights are calculated according to the original auxiliary variables. 3 Lead-acid battery production data
Table 1 The importance of eight different auxiliary variables Battery voltage of charging 32hours Battery voltage of charging 8hours Battery voltage of charging 31hours Battery voltage of discharging 0 hour Battery voltage of charging 26 hours Battery voltage of discharging 6 hours Battery voltage of charging 6 hours Battery conductance Sorting 0.1798 0.1953 0.1978 0.2100 0.2278 0.2426 0.2487 0.2562 83754261 According to this data set, in the learning process of BP neural network, each group randomly selects 130 samples as training data, 42 group of data as the test sample, all trained 10 times, and get the best results, training forecast output, training the relative prediction error, inspection predicted output and the relative prediction error.

Test channel showing locations of thermocouples and test tubes with inserts Table 1 Sizes of micro-fin tube with inserts Data Reduction The quality entering the test section (), can be solved in the following equitation (1).
As expected, the data showed the heat transfer coefficient (h) increases with increasing Reynolds number (Re) for all the test tubes.
Fig. 4 shows the comparison of the experimental heat transfer coefficient data with the calculated values from the correlation of this study.
All the data were within 30% of the predicted values.
Pate, Using solubility data for HFC-134a and ester lubricant mixtures to model an in-tube evaporator or condenser, ASHRAE Trans. 99 (1993) 383-391

Attribute reduction technology in the standard rough set model depends on the lower approximation, so it is sensitive to noise data and results in many valuable rules unextracted.
In VPRS, an error precision is introduced in the basic rough set model, and it allows a certain degree of fault tolerance in computing the approximate dependence value, so it provides a good way to deal with data inconsistency caused by the noise [6,7].
Variable Precision Rough Set In variable precision rough set theory, an error precision is introduced in the basic rough set model, and it allows a certain degree of fault tolerance in computing the approximate dependence value, so it provides a good way to deal with data inconsistency caused by the noise.
[2] Wu Z, Huang N E: Ensemble empirical mode decomposition: a noise assisted data analysis method, Advances in Adaptive Data Analysis, vol. 1(2009): p. 1-41
[9] Xu Huijian, Guo Feipeng: Combining Rough Set and Principal Component Analysis for Preprocessing on Commercial Data Stream, JCIT, Vol. 7 (2012): p. 132 ~ 140.