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The histogram results also demonstrate that the Co3O4-NPs possess a relatively larger average particle size compared to the crystallite size estimated from the XRD data.
For further elucidation, the experimental data were fitted by a pseudo first-order kinetic model using the equation, ln(At/A0) = -kt, where At and A0 represent the absorbance of MO dye at time t and at the initial state, respectively, and k is the rate constant of the reduction reaction [57, 58].
Kavas, Synthesis of Co3O4 nanoparticles by oxidation-reduction method and its magnetic characterization.
Khan, Green synthesis of zerovalent copper nanoparticles for efficient reduction of toxic azo dyes congo red and methyl orange.
Khan, Synthesis of potent chitosan beads a suitable alternative for textile dye reduction in sunlight.

These methods were originally introduced in the context of neural network modelling and have been shown tremendous applications in various field including: image processing [2], biomedical data analysis [3], telecommunications [4] and stock analysis [5].
If the noise exists in mixing process, which means the nonlinear lies between the observed data and the source signal, the mixing with noise is approximately seen as a nonlinear mixing process.
If , then the sources are perfectly recovered： (4) Nonlinear blind source separation based on kernel As in SVMs and kernel BSS, we first project the input data to some high-dimensional feature space via a nonlinear mapping[9]： (5) The dot product in feature space can be calculated by kernel: (6) In this case, we just have to evaluate kernels in the input space instead of dot products in the feature space.
The experiment result indicates that the improved algorithm has better ability of noise reduction and feature extraction than fixed rate nonlinear blind source separation.

The factorization can be expressed as following: (1) wheredenotes amatrix and is the number of examples in the dataset, each column of which contains an n-dimensional observed data vector with non-negative values.
The rankof the factorization is usually chosen such that, and hence the compression or dimensionality reduction is achieved.
This results in a compressed version of the original data matrix.
In other words, each data vector is approximated by a linear combination of the columns of, weighted by the components of.

This is because the energy dissipated during impact is mainly absorbed by a combination of matrix damage, fiber fracture and fiber-matrix debonding, thus leading to significant reductions in the load-carrying capability of the material.
Outputs of AE sensors were pre-amplified 40 dB and filtered with the band-pass of 100 kHz to 1.2 MHz, then processed and recorded by using an AE data acquisition system (MISTRAS 2001, PAC).
In Fig. 3, the primary data obtained during the CAI tests are shown as the relationship between compressive load and AE events with time for the uncoated and the ITO 40% coated at two different impact-damage levels.
In Fig. 5, the stress level at which AE activity started were calculated from such a data shown in Fig. 3 and correlated with the maximum compressive strength for each condition of woven-CFRP’s.

After performing the initialization simulation, material characteristics with a reduction were reimported into Mentat again.
The resulting data are processed into graph in Fig. 5.
The resulting data are processed into graphs in Figs. 7 and 8, which are actually the output of our previous efforts.
Comparison of recent fiber orientation models in Autodesk Moldflow Insight simulations with measured fiber orientation data.

The summary of input data for this calculatin is as in Table 1.
Input data used in the calculation of electronic states of CdTe.
Parameters Input data Latice Face centered cubic, FCC Latice constants a = b = c = 6.48 Å Latice angles α,β &γ 90° (α = β = γ) Cd atom position (0,0,0), number of points in the logarithmic radial mesh ( NPT) = 781, maffin-tin radius (RMT) = 2.35, R0 = 0.00001 Te atom Position (0.25,0.25,0.25), NPT = 781, RMT = 2.55, R0 = 0.00001 The k-points that we chose for this calculation was 1000 (10x10x10) and the separation energy of core and valence states was equal to -9.0 Ry.This was done to ensure that the charge will leak the atomic sphere.
Reduction in the value of band gap energy is probably due to the hybridisation of p and d orbitals in the upper most valence band with the d orbitals pushed upwards, making the band gap reduced.

Introduction With the development of energy technology, particulate gas turbine, an important device in industrial waste heat recycling system, is being widely used, and plays the role of energy conservation and emissions reduction.
Combining with the characteristics of turbine machinery, Tabakoff established erosion analysis model and put forward empirical formula of erosion rate through experiment and data processing
Impact point distribution on pressure surface Calculation and Analysis of Blade Erosion The hot-wind tunnel experiment system, as shown in Fig. 5 and Fig. 6, is divided into dust feeding system(consist of electric air heater, particles feeder), heat exchange system and draw-wind system, equipped with the corresponding control system for experiment data and real-time control data collection.

The results obtained at room temperature and some data from literatures are illustrated in Fig. 2 as function to the graphite amount of the composites.
The results of NaNO3-LiNO3 are the measurement data in this paper, and the materials of others are from Pincemin [1] and Zhang[6].
(2) The latent heat of the composite decreased with the increasing EG amount, while the initial temperature of melting and peak temperature do not distinguished vary with slightly reduction during melting and triflingly rise during solidification from the curves of DSC, and the converting latent heat of the PCM in the composites changed not more than 8%.
(3) The thermal conductivity of the composite is increasing with the increasing EG from the data of hot-disk thermal constant analyzer.

There are original data for the design of spring supporting in the high-frequency vibration sieve for slime dewatering.
These data are shown in table 1.
Fig. 1 Structure of high-frequency vibration sieve Fig. 2 Structure of stranded wire helical spring Table 1 The original data for the spring design Name Install load Working load Working Stroke Vibration frequency Parameter 01000F �= 1800TF �= 10mm=δ 25nHz=ν Name Number of spring Material of spring Modulus of elasticity Allowable shear stress Parameter 4 60 2Si Mn 80000G MPa = [ ] 600MPa=τ Fuzzy Optimization Design Model The fuzzy optimization is a method that an optimum solution will be obtained fully considering various kinds of fuzzy quantity and fuzzy relations existed in design variables, objective function and constraint conditions in the conventional optimization design [2].
The relationship of penalty factors for two adjacent iterations is: 1 k k r cr −= , in which, c is the reduction coefficient of penalty factor, and its value is usually at the range of 0.1-0.7.

Introduction The production of sewage sludge from wastewater treatment plants (WWTP) has been continuously increasing for many years in China, and it poses an increasing threat to environmental security[1]. sludge incineration has been improved to be an effective method for sludge treatment, because it is advantaged in reduction of sludge volume, complete destruction of pathogen and organic pollutants, and energy recovery[2-4].
“Database” provides all of basic data for model calculation, “Process Model Worksheet” is a simulator where process model calculation is conducted, and “Graphic Interface Worksheet” provides an easy-to-use graphical interfaces.
Table 1 only shows some key data for process model calculation.
Table 1 Key data for process model calculation Parameter Value Sludge handling capacity 220 t/d Moisture content of wet sludge 80 wt.% Volatile ratio of sludge solid 47.1 wt.% Higher calorific value of sludge (volatile basis) 22990.8 kJ/kg Volatile ratio of coal 60.7 wt.% Higher calorific value of coal (volatile basis) 33608.4 kJ/kg Heat loss of the paddle dryer 10% Heat loss of the fluidized bed 3% Table 2 “Solver” for process optimization Sub optimum() 1 Sheets(“process”).Activate 2 SolverReset 3 SolverOK SetCell:=Range(“objective”), MaxMinVal:=1, ByChange:=Range(“variables”) 4 SolverAdd CellRef:=Range(“constraints”), Relation:=3, FormulaText:=0# 5 SolverSolve UserFinish:=True 6 Beep End Sub Result from process model calculation Fig.3 shows the auxiliary fuel and total energy consumption of sludge drying-incineration system.