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Therefore, the Time Window method may have overlooked some valuable data.
Forgetting model [3] uses the neglected old observation data properly.
It is considered that the user’s interest reduction and the natural law of forgetting is similar, which is usually expressed as a forgetting function.
(3) FWj means the final interest of category Cj. λ is the attenuation factor, which means the more recent historical data, the greater the impact it is, that is recent data should be given greater weight.
Data in table 5 shows that: interest of "movie" from a higher interest rate of 51.2%, after 3 consecutive decline, reached a low interest rate of 13.4%, indicating that the user interest in this category continued to decrease, suggesting interest of this category will continue to fall, therefore, it should be recommended in a lower order, not a first priority in the interest modeling; interest rate of "society" ,through a hidden Time Window, changed from 15.7% to 14.0%, showing relatively constant interest, and "popular music" showed a more significant declining trend, and had dropped to a low interest rate, therefore, "society" should be the priority ranking of the "popular music".

The most complete data about it published in [5].
The quantitative data for steel martensitic class with similar chemical composition deformed stretching [6], are shown in Figure 1 b.
Obtained data are given in Figure 4.
This dependence together with curve in Figure 1 b can also be used to determine the degree of plastic deformation in the data of material local volumes.
Thus, using data of Figure 1 we get dependence plastic deformation e values away from roller surface, which is represented in Fig.5.

Experimental procedure Two extrusion series were performed in order to collect the data applied for verification of simulation results presented in the paper.
Data collected in the experiments and used to fit the corresponding parameters in the extrusion process simulation are given in Table 3.
Particle of type Volume percentage (%) Primary silicon 5.214 Primary Alpha (iron bearing particles) 1.611 Mg2Si 0.577 Secondary Alpha 0.008 Secondary silicon 0.002 Table 3: Experimental data used as input data in the simulation based optimization.
Tbillet [oC] Tmax [oC] V [mm/s] Fmax [MN] Comment 1 490 523 2 3225 NHC* 488 536 3 3306 NHC 487 543 4 3408 NHC 497 550 5 3360 NHC 496 553 6 3412 HC* 2 413 540 8 4131 NHC 415 550 10 4490 HC *NHC = No hot cracking; HC = Hot cracking The collected data allowed construction of the simple extrusion limit diagram for the model alloy (Figure 2) with clear distinction between acceptable and unacceptable surface quality of the extruded rod.
Effect of such a die geometry choice is a reduction of the peak temperature for approximately 20oC compared with the die design with small angle and small radius.

Introduction In guidance of the energy conservation and emissions reduction policy of automobile industry.
By way of combining theory and test data, this paper analysis that reasonable catalyst heating function application can effectively reduce the emission pollutants, which has a certain guiding significance to reduce the emission pollutants.
Related data matching of emission control Emission behavior relates to the match of multiple function modules, such as starting, catalyst heating, the transition condition, the former oxygen closed loop control, after oxygen closed loop control, oil cut-off, clear oxygen and so on.
Of course, matching data is only one of the aspects influencing the performance of vehicle emissions.
Conclusion By way of combining theory and test data, this paper verifies that making use of the catalyst heating function module reasonably can more effectively reduce the discharge of pollutants to meet the National Standard V discharge engineering development’s need.

Models of the inspection and evaluation.The result of the model with some data according to this model, whether the performance and the actual performance of the same system to identify, to determine the accuracy and reliability of the calculation results.
Water resources system analysis model of the structure.Analysis of water resources system simulation model of the structure as shown in Figure 1, is mainly composed of 4 parts, as follows: Fig 1 Structure of simulation model PART1-PART10 is a 10 input data file, to provide all the nodes of the input information.
SEQUEN uses the PART2 input data file, the nodes according to the upstream and downstream order form generalized node network diagram.
RELOUT is the total system or subsystem statistical analysis module. f there areindex，each sample hasvariable，，thus constitutes an order matrix: In general, the original data with different dimensions or volume level, in order to ensure the reliability of the analysis results, the need for variable dimensionless normalization.
The standardization and dimension reduction,variables can be integrated into a new index, the variable can be expressed by the linear: ，the variablecan be expressed by the linear: ype of: Optimal sample: can be by the formula structure: On the absolute difference matrix transform data as follows: The sampleand the optimal sample correlation degree: First of all, to determine the evaluation factor set: ; Then determine the evaluation set:.

There are two ways of obtaining this dependence: (a) using a micromechanical (particle scale) material model and (b) directly fitting the experimental data.
It is possible to use only the densification data (density as a function of time), which is part of the constitutive response, in a finite element analysis to predict the sintering deformation to a good accuracy.
Fig. 5 compares finite element predictions of sintering deformation obtained using the densification data (labelled as DFEM) and the full constitutive law of Eqn. (1) respectively.
Experimental case studies can be found in [12] which showed that finite element analysis using the densification data alone can give good predictions for both solid state and liquid phase sintering.
Finite element predictions of sintering deformation for a cracked film on a rigid substrate obtained using the full constitutive law (Eqn. 1) and densification data respectively.

In order to reduce the influence of pump noise on interior noise, it would be necessary to make an acoustic-vibration test first, and then analyze the test data to find out the main noise source, and finally suggest available measure to control the noise. ⅡⅡⅡⅡ Acoustic-Vibration Test There are two idle conditions on a hybrid electric vehicle: one is engine on, which is the same to conventional car, and the other is engine off with pump working alone, which is unique to HEV.
LMS SCADAS SC316W signal amplifier and intelligent acquisition system was for acquiring data and LMS Test Lab Signature Testing was for testing and recording. ⅢⅢⅢⅢ Signal processing Effective signal processing plays important role in identifying the transfer characteristic of pump noise.
From the statistic data it is easy to get the conclusion that the vibrational energy transferred from pump to car body is reduced to below 10%.
(2) According to the test data, we can get the transfer characteristic of this system.
This paper bases on specific issue and explores the transfer way of noise source through acoustic-vibration test and corresponding data analysis technologies.

TEM pictures (hundred thousandths magnification) of copper metal particles according to different concentrations of potassium sodium tartrate where the concentration is (a) 0mM (b) 0.5 mM (c) 1 mM (d) 2 mM (e) 5 mM (f) 10 mM As shown in the XRD results (Fig. 3) the data shows copper metal when the concentration of potassium sodium tartrate is 0.2M and the concentration of copper ions is 0.1M.
XRD data representations of copper Effect of the concentration of copper ions on controlling the shape of copper oxide Fig. 4.
The XRD data (Fig. 6.), shows a CuO peak when the concentration of copper ion is 0.1M.
Therefore, through the Eh-pH diagram and XRD data, the net shaped particles are identified as copper oxide.
XRD data representations of copper oxide However, when the concentration of copper ions is 1M, the region of copper ions in the diagram becomes the smallest compared to the other concentrations.

Detect corrosion rate based with linear polarization method in order to obtain the experimental data samples such as corrosion current density, corrosion potential and polarization resistance etc. at the very moment of reinforcement corrosion.
Fig.5 below respectively shows the comparison & analysis diagram of theoretical model and statistical model into which experimental data is generated.
The revised model of Xiao Congzhen, which is revised with two elements (pH and etching time), will get fitted well with experimental data.
Inspection result of China University of Mining & Technology model after revise Fig. 6 Comparison & analysis of theoretical model and statistical model which is fitted with experimental result after revise From Fig. 6 we can obviously see that, experimental data has got higher fitting degree with model, which shows corrosion rate model based on the revise is basically right.
(1) Xiao Congzhen theoretical model and statistical model of China University of Mining & Technology model is revised according to experimental data, and two elements - pore fluid pH value of concrete and etching time of reinforcement accelerating corrosion experiment- is added.

Our studies have indicated that the use of α-glycolide results in producing a more homogeneous polyglycolide lactide with molding properties; however, at present there are no data on studying polymerization with α-glycolide in thermodynamics and kinetics.
The decrease in the heating rate of the original mixtures leads to an expected shift of the peak towards lower temperatures as well as reduction of peak area.
One of the calculation methods, according to dynamic DSC data, is Flynn-Wall-Ozawa method, which is based on the following equation [8] lgW=C-Ea'RTmax, where W - the heating rate, C - a constant, Ea' - apparent activation energy, Tmax - temperature at which the maximum velocity of the chemical process is observed (the peak of the thermoanalytical curve).
Inconsistency of the analysis data is explained by the fact that the process is multi-stage, and methods of non-a priori kinetic analysis can’t provide an accurate value for every stage, but only one value (intermediate value) for every degree of conversion.
The corresponding values presented in Table 2 are close to literature data [10, 11] for different polymer systems.