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In order to apply the volumetric method, elastic-plastic stress field around notches are considered and notch strength reduction factor are determined.
Finally, the predictions of the two methods were compared with experimental fatigue data for notched specimens.
The Volumetric Method The volumetric approach uses an elastic-plastic distribution and relative stress gradient to calculate the effective distance Xeff, the effective stress σeff and the notch strength reduction factor Kf: (4) where σyy(x) is the fatigue crack opening stress along notch tips, φ(x,χ) is the weight function and σn is net stress [5].
Adopting a 4 order polynomial function for σyy(x), with ai the polynomial coefficients: (5) the relative stress gradient is derived and the effective distance Xeff, which corresponds to minimum point of relative gradient, the effective stress σeff and the notch strength reduction factor Kf are determined with a Mathcad 14.0 routine, for an unit weight function .

According to the reports[5], reduction of U(VI) to U(IV) by SRB involves at least three processes: (1) U(VI) binding to the cell surface and to extracellular biopolymers (biosorption); (2) chemical reduction of U(VI) by microbially generated hydrogen sulfide (H2S);and (3) bioreduction of U(VI), which is enzymatic dissimilatory metal reduction with U(VI) acting as a terminal electronacceptor.
As shown in Fig.3, the rate of reducing U (VI) by the reduction of electron transfer was fast, esp. in the first 6 hours.
It could be seen by comparing and analyzing the data that the U (VI) removed through the reduction mechanism of electron transfer accounts for nearly 80% of the total amount of U (VI) removed by original bacteria fluid.
SRB may detoxicate Cu2+ in some other way, which will also affect the rate of removing U (VI) by SRB. 2.4.1 Inhibition effect of Cu2+ on U (VI) reduction and removal by H2S It could be seen from Fig.6 that Cu2+ could inhibit U (VI) reduction and removal by H2S to some extent.
The reduction of U (VI) by H2S is realized by the H2S generated during the process of electron transfer.

The improvement of cryptogram query hit rate and the reduction of redundant data in query results are the challenges in the outsourced database (ODB) model.
First of all, we divide the data which need to be encrypted into two types: one is string-type data and the other is numeric data including pure numeric data or the data that can be converted into numeric data, such as string-type data only contain numbers, time-type data, etc.
For string-type data, a new k-Mapping Character-data Encryption Scheme (k-MCES) is used.
However, ES return much redundant data.
Integrity Auditing of Outsourced Data.

The effects of cooling rate and austenite structure on bainite formation was investigated by means of electron backscatter diffraction analysis and processing of obtained orientation data.
The data on local orientations were treated using MTEX software [8].
To choose the OR, which corresponds better to the data of the present investigation, the relationships obtained in Refs. [7, 10] were compared.
Austenite deformation results in the packet size reduction.
Note that the packet refinement does not imply a reduction of the effective grain size.

The benefit of using ICA is at reducing highdimensional data sets into rich accurate summary data sets.
Therefore a reduction technique such statistical data reduction techniques or independent component analysis (ICA) is required before classifier introduction.
The statistical data reduction techniques [20] used the most sensitive AE statistical measures in a windowed type fashion (every 100 pts along the STFT signal).
This data reduction technique was only applied to the AE extracted signals reference the burn and chatter phenomena trials.
GP suffers from large n-dimensional data problems and therefore requires n-dimensional reduction techniques to ensure the data applied to the classifier is both salient and rich in terms of describing the actual phenomena.

DD is a typical data-centric protocol for wireless sensor networks [1].
For instance, setting the gradient diffusion depth threshold value is used to narrow the range of the dissemination of interest messages, then to reduce the amount of data transmitted over the network in the literature [2]; Passive Clustering strategy is introduced into the Directed Diffusion Protocol in the Literatures [3, 4], plenty of reduction of the directional flooding in the interest and exploration of data, greatly improved the overall performance of the network.
When nodes propagate sensing data, the packet CH information field has five values: the unique marked node node_id, node_status, two CH nodes (ch1_id,ch2_id), energy_surplus(with a total of 17 bytes).
In other words, gateway only forward the packet of the CH which takes this node as CH; the ordinary nodes only receive packets from the CHs, and sent the data matching interest to the CH.
In a word, the proposed algorithm adopts CH declaration mechanism based on energy threshold and gateway selection mechanism based on residual energy, it has many merits: 1) it not only balances the energy consumption, but also reduces the probability of re-cluster's method in the network topology maintenance mechanism. 2) Comparing with PCDD ,EPCDD packet flooding overhead further decrease, and packet flooding overhead reduction is far greater than the overhead increase due to the increased length of the single information packet, so EPCDD has better energyefficiency; 3)the probability of collision greatly reduce while the number of network connections is further reduction, therefore, the packet's delay performance will be better, and packet reception rate is also higher;4) With the increasing of the nodes ,the greater is the network density, the more is the number of network packet, flooding overhead reduction and reduction in the number of the network connections become

The random variables of reliability analysis should be made statistical on the basis of test data.
Test data and statistical value are shown as table 1.
It shown that data can not be made statistical because there were only four of fault f212.
Failure probability is more than 99% if analyzed according to conventional data and index.
Slope engineering is usually designed under condition of without sufficient sample test data.

For the variable specific surface area, using the data of Table 4 and the factorial experimental design, the Table 5 was obtained.
Table 5.24 experimental factorial designs –Estimated effects and coefficients for the data of Table 4.
Figure 3 shows the x-ray diffraction data of sample 5.
The x-ray diffraction data shows that samples 1 and 2 have 100% and 96.3% of a-alumina.
Statistics for experimenters: an introduction to design, data analysis, and model building.

Motor Reduction gear-box PIV Frequency Controller Spline Spline Pile caps with piles Displacement-controlled unit Static loading unit Load-controlled unit Computer with data logger A B Legends: 1.
Reduction gear-box. 4.
The reduction was also found to be increasingly significant with the increment of L/D ratio.
First of all, collection of sufficient data for soil, pile material, loading and loading (cyclic loading in particular), the design values of frequency and amplitude and frequency are to be evaluated.
Initialize soil, pile material and static load data Design Recommendations Conventional design of pile under static loads Collect wave and wind load data and regularize for mean cyclic load on piles Evaluate design amplitude and frequency Evaluate design pile degradation factor Check against ultimate failure and acceptable settlement Yes No Re-Design End Is factor of safety satisfactory ?

The cone data and ESEM analyses results show that incorporation of a small amount of ZHS greatly promotes the char formation of PVC and decreases the amount of hazardous gases released in PVC during combustion.
Fig. 1 and Fig. 2 show that ZHS can effectively reduce the data of HRR, THR, CO and CO2 production.
The corresponding cone data are presented in Table 3.
There is a 12.9% reduction in the THR for the PVC1 sample compared to pure PVC0.
Table 3 The corresponding cone data Samples pk-HRR (kW/m2) THR (MJ/kg) pk-CO (kg/kg) pk-CO2 (kg/kg) PVC0 141.27 29.269 0.01975 0.07963 PVC1 100.43 25.504 0.00781 0.06600 * pk-HRR: The highest value of heat release rate THR: Cumulative heat energy released during flaming per unit mass of the sample.