Search results

Typical 4-nitrophenol (4-NP) reduction was adopted to probe the catalytic performances.
The Pt-CNTs present a high reaction rate constant of 0.624 min-1 in the catalytic reduction of 4-NP.
The reduction of 4-NP evaluated the catalytic activity to 4-aminophenol (4-AP) with NaBH4.
The XRD data were recorded on D/MAX-RC (Rigaku, Japan) with Cu Kα as the radiation source, l=0.1541nm, scanning rate of 2°/min from 2θ=10° to 90°.
At pH=10, the catalyst exhibits the superior ability for reduction from 4-NP to 4-AP.

Challenges of Logistics Cost Reduction in China under the Background of Low-carbon Economy Logistics Cost Reduction is Facing Tremendous Challenges in China Because of Backward Technology.
Data show that the general fuel consumption of truck is up to 0.05kg / t, 8 times higher than water transport.
Therefore, water transport has a greater advantage in the reduction of transport cost.
Logistics information technology will use Bar Code Technology, Smart Cards, Artificial Intelligence, Automatic Identification System, Automatic Scanning, EDI (Electronic Data Interchange), EOS (Electronic Ordering System), INTERNET, GPS / GIS (Global Positioning System / Geographic Information Systems) and other support of information technologies to collect, process and analysis large quantities of information timely and effectively, and finally to achieve accurate, timely delivery, a steady supply chain and other objectives of low-carbon logistics management[5].
Obviously, the development of low-carbon reverse logistics plays an important role in energy conservation and emission reduction, which provides opportunities for logistics cost reduction.

And the outputs are mechanical properties namely ultimate strength, yield strength, elongation, reduction of area, plane strain fracture toughness and microstructure concerned parameters such as β phase fraction, β phase grain size, substructure length and thickness.
The ANN is trained with experimental data and achieves a very good performance, which has already been applied to the optimization of processing for forging of aero-parts.
The inputs are working temperatures, deformation extent, deformation rate and heat treatment conditions, and the outputs are ultimate strength, yield strength, elongation, reduction of area, plane strain fracture toughness, βphase fraction, βphase grain size, substructure length and thickness.
So the experimental temperatures are 923℃, 938℃, 953℃, 960℃, 968℃, 973℃ chosen according to beta and near beta forging theory [10], the deformation extents are 15%,30%, 40%, 50%, the deformation rates are 1.4mm/s, 1.7mm/s and the corresponding heat treatment conditions (as shown in Table 1) are transformed to numerical data because BP network can only deal with numerical ones.
Through comparing the network outputs with the experimental data (Table 3), it can see that the BP network displays very high adaptability and precision, the predicted outputs with the testing samples have relatively low error and most of the generalized outputs with the validating samples are within the 15% error, only a few have high error values.

The kinetic characteristics of the reduction are determined, since they fix at which rate the plating can proceed.
Prior to the kinetic study, requiring measurements in solutions of varying composition, the thermodynamic data on complex formation between Ag + and −2 32OS ions are analysed.
The kinetic characteristics of the reduction are determined, since they fix at which rate the plating can proceed.
Prior to the kinetic study, requiring measurements in solutions of varying composition, the thermodynamic data on complex formation between Ag + and −2 32OS ions are analysed.
It can be seen that i-1 is a linear function of both concentration and rotation speed parameters, so it is clear that the reduction follows a mechanism involving mass and charge transfer steps.

When the input signal Vin = Vref, the output data is 128; Vin> Vref, the output data is greater than 128; otherwise the data is less than 128.
Data acquisition process is shown in Fig.5.
Fig.5 Schematic of data acquisition The data collection part consists of analog circuits and digital circuits.
A/D 1 continuously collects data without the external input signal.
Collect ADC data from channel 1 continuously and determine whether the value of the data acquisition and the standard signal amplitude value are consistent.

It was observed from data in Table 2 that the addition of SHSRA did not induce any substantial change in the water retention, cohesiveness, or mobility compared with the specimens without SHSRA.
Table 2 Data related to slump and air content of concrete with and without SHSRA Slump（mm） Air content（%） Workability Water retention Cohesiveness Mobility Reference 155 1.80 good good good SHSRA-1 125 2.10 good good good SHSRA-2 130 2.10 good good good SHSRA-3 155 1.15 good good good Influence of SHSRA on drying shrinkage.
Fig. 2 Curves of heat evolution rate and heat release versus time Table 4 listed the data related to hydration reaction for cementitious materials.
Table 4 Data of hydration reaction Maximum heat evolution rate（s-1） tmax * (h) Total heat release after 1 day (J·g-1) Total heat release after 3 day (J·g-1) Reference 6.68 8.90 109.07 167.31 SHSRA-A (1.0%) 5.18 12.8 75.54 160.23 SHSRA-C (3.0%) 4.44 14.5 64.77 151.98 * time for occurrence of maximum heat evolution peak Influence of SHSRA on strength.
The reduction caused by SHSRA decreased with increasing age time.

The data of each material in the roof has been defined based on the thermal conductivity.
The differences between SolidWorks simulation results and experimental data are less than 1%, and the SolidWorks simulation results are agreeable with the experimental data.
Fig. 10: Comparison between experiment and SolidWorks result without insulation for rooftop The comparison data with Glasswool insulation between the experiment and SolidWorks data is shown in Fig. 11.
Fig. 14 presents the result data for without insulation and with insulation applied.
With that, the comparison of the data can be more accurate.

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.

Configuration of 36 Testing Holes , Cp Data Collecting Procedure The data collecting procedure is conducted after determining or measuring the whole instrument that support in this data collecting procedure.
Data Collecting Steps : 1.
When the blower has already stable, conduct data collection. 4.
The data collection of the pressure distribution with the variation of the distance between ring with 10o sloping angle, is conducted by collecting the data from the cylinder surface. 5.
The data collection is taken three times at every variation of distance between ring.

Introduction Environment and disaster reduction small satellites (A and B satellites) were launched by rocket on September 6 2008.
B.Hyperspectral data preprocessing 1) Data format conversion: First converting the .HDF format to ENVI standard format file and the projection transformation was performed, which projection parameters were: Projection - UTM Zone 47, Datum is WGS 84. 2) Radiometric calibration: The Level2 HSI data stored radiated intensity, so the true radiance was calculated as (1) Lλ = L/10 (1) Where: Lλ is radiance on the satellite, its unit is W/ (m2.sr.um), L is radiation intensity.
Atmospheric Correction of Hyper- spectral Imagery: Evaluation of the FLAASH Algorithm with AVRIS Data.
[8] GHULAMAbduWasit, Qin Qiming,Zhu Lijiang. 6S Model Based Atmospheric Correction of Visibleand Near-Infrared Data and Sensitivity Analysis.
[10] Xu Meng, Yu Fan, Li Yachun, et al., The Method of Atmospheric Correction on the EOS/ MODIS Data with 6S Model.