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-field data for sample NMG1 for three piezo frequencies at drive voltage of 30 Vp-p.
NMG2 NMG1 The relatively large scatter in the data in Figures 7 - 12 reflects the stochastic nature of twinboundary motion; twin boundaries that move in one actuation event intersect different pinning sites in a subsequent actuation [12]
-strain data for sample NMG2 under an application of piezoelectric actuation with 30 Vp-p and varying frequency [17,18].
The approximate amplitude of the stress wave expected from the piezo stack is calculated from the data in Fig. 16 using Eq. 8 (see Fig. 17).
-σbias data, with and without acoustic assistance, are plotted in Fig. 21.

Aluminum Reduction in SC1 Sasha J.
Trends such as lower temperature/concentration SC1, and higher temperature/concentration SC2 have reached a point of diminishing returns for metal contamination reduction.
Surface metals data collected over 4 months are presented in Figure 3 for an 8 hour bath life.
Kweskin, Diffusion and Defect Data Part B Solid State Phenomena, 187, pp. 275-278

The digital filters by WT and new TV are applied on the sample data of actual measurement system to investigate their performance of noise reduction.
The denoising methods by WT and new TV are applied on the sample data to study their performance of noise reduction.
Figure 10 shows the measured profile data and filtered profile data by WT and new TV.
Figure 11 shows the distributions of GBIR of (a) measured profile data and two profile data filtered with (b) WT and (c) new TV.
(a) Measured data (b) WT (c) new TV Fig. 10 Measured data and filtered data with two methods (a) Measured data (b) WT (c) new TV Fig. 11 Histograms of GBIR for raw and filtered data with two methods Table 3 Comparison of error and standard deviation Measured data Filtered by WT Filtered by new TV Error : d[μm] 0.0415 0.0409 -0.0061 Standard deviation : σGBIR[μm] 0.0330 0.0349 0.0364 References [1] H.

The world market produces a wide range of coatings, applied in limited thickness, transparent and opaque, high initial performance, although data on their durability are still missing.
The evolution of Luminance of samples artificially aged Data show that the coordinate b* increases progressively towards yellow, while the coordinate a* is kept constant.
The procedure of the time rescaling, as codified by ISO 15686 and UNI 11156, was followed, representing the comparison of data derived by artificial aging, with the measured data in outdoor conditions.
The data of photocatalytic activity to one year of natural aging are not yet available.
Additional data may clarify the decay characteristics of the product.

As a result, a maximum radiated sound power reduction of 3.66W and a maximum sound pressure level reduction of 4.7 dB are successfully achieved.
It can find out the best plan for the overall optimization by selecting the reasonably test conditions, arrangement of the experiment, and analysis of the experimental data.
The optimization software iSIGHT-FD presents seven DOE technologies for the design of experiment, including Central Composite, Data File, Full Factorial, Latin Hypercube, Optimal Latin Hypercube, Orthogonal Array, and Parameter Study and so on.
Then, each test results is put into a data file corresponding to the sixteen times test.
After optimization, the radiated sound power has a reduction of 3.66W, and the four sensitive points has a reduction of 2.1dB, 4.7dB, 0.7dB, and 0.9dB respectively.

Proportion of budget to be implemented (P) and accident reduction target (T) are the input policy parameters.
i = Counter of class of accidents fn= Degree of severity (Dimensionless) ρai= Probability of occurrence of accident class i Model Application Using data collected by Adebiyi and Ajayeoba [9] Scenarios experimentation was carried out with the proportion of available Budget (P) and desired accident reduction Target (T) controlling the mechanism of the safety programme using system dynamic software STELLA (version 10.0.3).
Table 2 summarizes the data of the case study organization while Table 2: Summary of the Case Study Data Size of workforce [Persons] 145 Number of shifts 2 Working hours per day 16 Number of working days/year 241 days Production Capacity of the plant Size of manufacturing equipment (Heavy duty/light duty) Light duty Identified types hazards Work at Height, Body traps in moving machine parts, enclosed surfaces, machinery related accident Level of expertise of the workers Intermediate Type of Equipment Majorly semi-automatic Safety Certification Health & Safety (OHSAS 18001), Environment (ISO 14001) Welfare and Compensation Package Effective Source: Adebiyi and Ajayeoba [9] Table 3 gives the estimates of Model Parameters.
Likewise, that it will be cost effective to use the lowest reduction percentages of P =0.9 instead of using the whole budget to achieve 15% and 20% reductions of the target accidents.
A data – Based Evaluation of the relationship between Occupational Safety and Operating Performance.

Simulation experiment data come from KDD Cup 1999 data set, which collects 7 million network connection records, covering a variety of intrusion data types and normal data.
Simulation experiment select data from training data and 10% subdata set of testing data, of which 2000 data strips forming the training set and test set of 3000 data strips, attack types included in training set are less than that of the test set.
Discrete data performs box dividing process according to data characteristics, which divide data into different boxes, taking the median value of the same box data as their value and data in different boxes has no intersection.
Attribute reduction - dimension reduction treatment Data of intrusion detection data set comes from network packet information captured, some feature data of which has great contribution in determining if there is an intrusion behavior, and some has no contribution to determine intrusion behavior.
Attribute reduction is conducted in KDD CUP 99 data set.

Development of Pass Schedule Calculation Procedure Data preparation.
Data preparation is the module to prepare data for model calculation.
These data include initial data of strip, roll data, equipment parameters, limit check data and model parameters.
After the initial data are confirmed, the model system will carry on limit check to judge the rationality of the initial data.
The result of calculation value and actual data are shown in Table 2.

To reduce these damages, stormwater runoff reduction facilities were deemed necessary.
Surface runoff, precipitation runoff and temperature were recorded with 5 second interval of data logger measurement.
Schematic diagram of experiment device Results Water-quantity reduction The averages of water-quantity reduction rate in each permeable pavement are presented in Table 1 when rainfall rate with hydraulic pump set 25 and 50 mm/h for the duration 30 minutes.
Surface Flow rate Valve Artificial rainfall Pump Precipitation runoff Artificial sunset Surface runoff Permeable pavement Data logger measurement M Storage vessel Temperature sensor runoff means the effluent of pavement non-filtered and precipitation runoff means the effluent of pavement filtered in system.
Type B pavement obtained runoff reduction rate of 95.8 and 81.4 % in 25 and 50 mm/h of rainfall condition, respectively.

Papers lacking full-text access or relevant data. 6.
2.5 Data Extraction Data extraction was performed using a structured form to ensure consistency.
Key data points included: · Study identification (authors and publication year)
They focus on systematically collecting and analyzing data.
Evidence from a study that achieved a 65% reduction in elevator maintenance needs highlights the benefits of data-driven approaches [60].