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The analysis of the results shows a significant reduction of programming duration as well as a reduction of programming errors compared with Teach-In.
This gestural input may be supported through pointing devices or data gloves.
The study results show a significant reduction of the programming duration for spatial programming.
Regarding the mean value we obtained a reduction to 12.9 % (robot programmer: 15.4 %) compared with Teach-In for Labyrinth and a reduction to 10.3 % (robot programmer: 7.5 %) for Pick&Place.
By means of spatial programming and the AR simulation, a reduction to 16 % and 19 % could be realised.

The size reduction of silver particles can increase the effectiveness of its anti-microbial activity, due to the larger surface area [4].
Some chemical methods which used to form AgNPs are chemical reduction, photochemical and sonochemical.
Chemical reduction is very popular way because of its convenience factor, relatively low cost, and likely to be produced on a large scale [7].
From the data, it can be concluded that o,p-dihydroxybenzoic acid has the best sensitivity.
A reducing agent with higher reduction ability will give the smaller nanoparticle size because it can reduce more silver ion.

This is discussed using data from literature and an alternative approach to identify the process stability window is given.
Introduction Weight reduction in the transport is a critical factor for an immediate reduction of climate impact, especially since significant amounts of electricity still is fossil-based. [1] Similarly, a capability increase, such offered by semisolid metal processing, processes may improve process yields and allow more efficient designs allowing for weigh reduction and resource efficiency for more sectors than the transport industry. [2, 3] The control of solid fraction and stable operations are critical for the production of high quality parts independent on processing route [4, 5].
Conventional stability analysis for semisolid processing In the current paper data from Payandeh et al [6] will be re-analysed with respect to the processing window to better understand the chemical segregation and deviations in solid fractions observed.
Methodology The data used in this analysis was published by Payandeh et al. [6] with the chemical compositions shown in Table 1a, for the alloys and in Table 1b for the liquid and solid portions of the slurry Table 1 a.
The alloy composition from Payandeh et al. [6] Base alloy, xAlB Liquidus temperature Alloy Si Fe Mg Al 1 1.69 0.8 0.39 97.12 646 2 2.49 0.8 0.4 96.31 641 3 3.67 0.75 0.41 95.17 636 4 4.56 0.75 0.4 94.29 629 ThermoCalcTM and the TCAL6 database was used to calculate the evolution of solid fraction for both the base alloy and the remaining liquid portion of the melt and to produce data to assess the slopes of the curves.

Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space.
Input data were vibration signals acquired from bearings through standard data acquisition process.
In the training process, the data set was also trained using RBF kernel function as comparison.
(a) Daubechies wavelet with PC data (b) Daubechies wavelet with IC data Fig. 3 Separation boundaries of W-SVM.
Vapnik, Estimation Dependences Based on Empirical Data, Springer Verlag, Berlin, 1982.

Some useful data for both materials are shown in Table 2.
These data, plus the length of the packed powder, allowed the determination of particle volume fraction Vp.
Data for pure tin and Al-Si alloy.
The data for the infiltration height versus P can be satisfactorily fitted by means of Darcy´s law [6]: () 1 2 P V tk2 h p Δ⋅ −⋅ ⋅⋅ = μ (1) where Vp is the particle volume fraction, μ the viscosity of the alloy and k is the permeability of the compact.
Data for threshold pressure and infiltration rate for the graphite preforms infiltrated with pure tin and Al-12Si alloy are shown in Table 3.

The Labview software (National Instruments) and a PCI-MIO-16E-4 I/O module were used for generating and applying the potential program as well as acquiring data such as current intensities.
M/ligand (1:1) stoichiometry was found from analytical data (Table 2).
Two oxidation (A', B') and two reduction (C', D') peaks were observed for Co(PAN)@NaY/CT.
By combining surface analysis and spectroscopic data it was possible to confirm that the CoPAN can be encapsulated in the Y supercages zeolite, without damage to the original matrix or loss of its crystallinity.
Azevedo for collecting the powder diffraction data and Dr.

Our previous data for barrier tapes are also shown for comparison [11].
For comparison, our previous data for the tapes with thicker SrZrO3 + Bi-2212 barriers and wtape = 3.1 mm are also plotted [11].
As can be seen, Qm data show the maximum around operating frequency fop = 260 Hz.
The data for non-twisted tapes with their tape widths (wtape) of 2.7 mm and 4 mm are also plotted.
The data for non-twisted tapes with same tape width (= 2.7 mm) and wider one (= 3.7 mm) are also plotted as references.

Single-section detection algorithm only detected changes in the traffic data based on a single-section detector to determine whether there is a traffic incident; its representative algorithm is McMaster algorithm.
However, based on the fixed traffic detector AID algorithm performance is largely dependent on the acquisition of fixed traffic detector traffic data quality.
Therefore, how to ease conflicts between the detection performance of the traffic data collection costs and AID algorithm has become one of the key issues to be resolved.
Fixed traffic detector optimized layout principle is based on the AID algorithm detects effect and traffic data collection costs to determine the optimal solution for fixed traffic detector layout.
Automatically based on the multi-source data, road traffic incident detection algorithm of [D].

The ANFIS network should be trained to learn about the data and its nature.
In order to determine the error between the simulated and the experimental data, the root mean square error (RMSE) is used.
In Fig. 3, the RMSE of training and checking data sets are shown for each epoch.
The terminated RMSE of training data is 0.0032.
Therefore, Fig. 4 indicates that the results derived from the ANFIS-based model agree with the experimental data well.

The reduction rate of byssal plaques was dependent on enzyme concentration and treatment duration.
The reduction in production of byssal plaques was subsequently calculated by using the equation: reduction percentage =100×[1-(A/A0)],where A is the number of byssal plaques in enzyme solution and A0 is the number of byssal plaques in filtered natural sea water.
Cells were counted in 30 fields of view from each of three replicates to provide cell settlement data.
Subtilisin reached a higher percentage reduction in the prevention (78.8%) than in the removal test (43.3%).
Subtilisin reached a higher percentage reduction in the prevention than that in the removal test.