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After complete Pt(IV) reduction, the cells were centrifuged.
Catalytic activity of the preparations in the reduction of Cr(VI).
Corresponding data for pre-platinised cells (Fig. 2B) showed that the control was almost twice as active as any of the Bio-PGMs; none showed more than 40% Cr reduction over 3h.
The data are for preplatinised cells.
Data are means ± SEM from 3 experiments.

As FRF is sensitive to the changes of the structural physical parameter, it is often used as the original data for structural damage identification.
Wu[1] uses the former 200 data of FRF from experiment as input to the BP neural network to identify the damage of a 3-layer building.
Using all available vibration transmissibility data, let us form matrix [H(w)]M×N which has M rows of vibration transmissibility, each with N frequency points.
Structural damage detection using artificial neural networks and measured FRF data reduced via principal componet projection.
Imregun combined neural network and reduced FRF techniques for slight damage detection using measured response data.

The preparation method for trimetallic alloy catalysts on NrGO was NaBH4 reduction.
The composition of catalysts could be confirmed by Energy dispersive spectroscopy (EDS) data and the phase of alloy particles were verified by electron diffraction (SAD) patterns.
Because we believe that the NaBH4 reduction method can improve the dispersion of metal particle and decrease the particle size[20].
The SEM images and EDS data were collected by Field Emission Scanning Electron Microscope (JEOL JSM-6335F).
Shen, Nitrogen-doped Graphene-Supported Transition-metals Carbide Electrocatalysts for Oxygen Reduction Reaction, Scientific Reports, 5 (2015) 10389

The conventional OSD method assumes a unique value of activation energy for all the data points.
The stress ruptures data of alloy MS3 containing 9% Cr is plotted in Fig. 1.
The multi region analysis could represent very well all the creep rupture data points via dividing them into several data sets.
Each data set has a unique value of activation energy.
The multi region analysis method examines can describe the creep rupture data points well.

This is confirmed by quantitative grain size data, which are represented in Table I.
All these data points are gathered for one specific IF steel which was submitted to a cold rolling reduction of 95%.
The data suggest that this grain refining has saturated beyond heating rates of 1000°C/s.
The grain size data obtained in this study are in reasonable agreement with data published by Muljono et al [3].
In Fig. 3 the textures derived from the OIM data are shown.

The pseudo-first-order kinetic model for 2,4¢-DDT and 4,4¢-DDT reduction with zero-valent iron was proposed.
The data from the variable-pH experiments (between 3.6 and 8.8) suggested that pH does not play a role in the rate-determination step.
Oxidation-reduction potential was measured with a redox combination electrode (Pt/ Ag/ AgCl) placed into the test reactor.
Results and discussions Reduction of DDT by zero-valent iron After sealed the reaction vial, the oxidation-reduction potential of solution measured with a redox combination electrode first rapidly decrease, and continued slowly decrease to –350mV.
Reduction of 4,4¢-DDT was generally faster than 2,4¢-DDT.

Results (data not shown) indicated that CaCO3 quickly increased the pH values and masked the possible neutralisation effect of added substrates.
Substrate materials used in this study were able to promote bacterial sulphate reduction and metal precipitation.
Sulphate reduction was highest in flasks with ethanol supplement and lowest in flasks with no SRB source i.e. anaerobic sludge.
Sulphate reduction and metal precipitation in different test conditions.
Metal and sulphate reduction vs. redox potential.

For the problem of feature redundancy of physiological signals-based emotion recognition and low efficiency of traditional feature reduction algorithms on great sample data, this paper proposed an improved adaptive genetic algorithm (IAGA) to solve the problem of emotion feature selection, and then presented a weighted kNN classifier (wkNN) to classify features by making full use of emotion sample information.
We demonstrated a case study of emotion recognition application and verified the algorithm's validity by the analysis of experimental simulation data and the comparison of several recognition methods.
For classic pattern recognition issues such as feature selection and dimensionality reduction included in emotion recognition methods, Intelligent computing methods such as genetic algorithm and particle swarm optimization algorithm can be adopted to address such combinatorial optimization issues. k-Nearest Neighbors method is a simple and effective algorithm during the step of feature classification This paper proposed an improved adaptive genetic algorithm (IAGA) based on the analysis above; aiming at the situation that traditional GA would fall into local optimums easily.
Fig. 1 Process of emotion recognition based on physiological signals Data Preprocessing and Emotion Feature Matrix Generation.
Then corresponding data conversion and typical statistical values (e.g. second order difference, signal amplitude, heart rate) of these signals are calculated to obtain 4 feature matrixes with 193 features: ECG 100*84, EMG 100*21, SC 100*21, RSP 100*67.

Research on utilizing Environmental Friendly Materials of Barrier Board for Particle Reduction of High-turbidity Raw Water Jian-Liang Chen1, a, Yi-Kuo Chang2,Yun-Hwei Shen 1, Kun-Liao Chen 1,b and Wun-Jiun Guo 1 1 Department of Resources Engineering, National Cheng Kung University, Tainan City 701, Taiwan 2 Department of Safety Health and Environmental Engineering, Central Taiwan University of Science and Technology, Taichung 406, Taiwan a1969liang@gmail.com, b c44514451@gmail.com Keywords: high-turbidity raw water, environmental friendly materials, barrier board treatment, particle reduction.
The rectangular container made of commercial PE material will be used for turbidly reduction, the size of which is 70cm × 47cm × 45cm, side view as show in Fig. 1.
Guide the water in the turbidly reduction flowing through the Orifices Wall and Baffle Board.
The data listed below explains that when the water quality is It justifies that significant efficiency of grain settling can be achieved along with the increase of accumulation percentage and the reduction of flow rate as well as longer staying time.

The algorithm can be described as follows: Step1: Discretize the sample data of intrusion detection.
Step2: Compute the kernel feature of the sample data.
Simulation Results There are a total of five million samples in the KDD99 data sets.
The sample data is firstly discretized and the decision table is obtained.
So the KPCA method can effectively reduce the dimension of the input data, while the main information is reserved.