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Weight reduction is a priority across the automotive industry for improved fuel efficiency and CO2 reduction, and crash safety is the first design criterion considered during vehicle development.
Recent research shows that every 10% vehicle weight reduction can yield 5-8% greater fuel economy, while reducing 100 kg of vehicle weight can lead to CO2 reduction up to 12.5 g/km [1].
Material Substitution for Weight Reduction.
For steel, , , gives reasonable agreement with the experimental data by Symonds[5].
Table 2 Weight reduction of front rail in designs with various AHSS Design No.

This paper summarizes and arranges the data of such experiments on composite walls with openings.
Japanese and North American standards both gave the equation of reduction ratio considering openings based on the test data, but the formula is not accurate and needs further study.
This paper summarizes and arranges the data of such experiments of composite walls with openings.
After that, Fei Liu[7] used amended sheathing area ratio as argument, and defined dependent variable parameters, which were peak strength reduction ratio and elastic stiffness reduction ratio , the expression of which were as follows: (6) (7) Calculating equation of reduction ratio for composite wall with openings At First, the paper summarizes the data of such experiments of composite walls with openings, then collects the minimum reduction ratio of the same sheathing area ratio, and gets Table 1.
Conclusions Based on an overview of existing experimental research on shear behaviors of cold-formed thin-walled steel composite walls with openings, This paper summarizes the experimental data on shear capacity of the composite walls with openings, and further analysis with SPSS (Statistical Product and Service Solutions) software was carried out to make a regression analysis and get an equation of reduction ratio considering the effect of openings on the shear capacity.

Fig. 1(b) shows the effect of different chloride concentration on NB reduction by SM-NZVI.
Fig. 1(d) shows the effect nitrate on NB reduction with SM-NZVI.
The data showed a limited effect on NB reduction was acquired at different NO3- dosage, and the effect did not trend to be expanded significantly during the reaction.
Fig. 2(b) shows the effect of Ca2+ on NB reduction.
It means that the enhancement of NB reduction is due to Cl- but not Ca2+.

Manifold learning has made many successful applications in the fields of dimensionality reduction, pattern recognition, and data visualization.
In order to distinguish one particular class from others, we extract various kinds of features as much as possible from the huge amount of data, which will result in large amount of high-dimensional data sets, thus there will be the curse-of-dimensionality.
Broadly speaking, feature selection and dimensionality reduction are two feasible ways to deal with the aforementioned challenges[1].
Based on two-fold cross validation, we compare the performance of HMM with other classical dimension reduction techniques.
[5] S.Roweis and L.Saul,“Nonlinear Dimensionality Reduction by locally linear embedding,” Science, 2000, vol.290

Therefore sulfide hinders the cathodic reduction of O2 on Q235 carbon steel in 3.5% NaCl solution.
The results of EIS were analyzed with fitting the data using Zsimpwin software.
For N2-saturated solution, hydrogen evolution reaction (HER) occurs at potentials more negative than-1.20 V (Fig. 1a), and the reduction peak at −1.15V is attributed to iron oxides reduction[15].
The peak at -0.93V and −1.15V are attributed to ORR and iron oxides reduction, respectively.
The variation of electrode surface restrains the reduction reaction of O2 and iron oxides.

Review of the Application of Water Pinch Technology in Water-saving and Emission Reduction Lihong Meng1, a, Qi Qiao1 and Jingyang Liu1 1 Chinese Research Academy of Environmental Sciences, Beijing 100012, China ayuduanhongri@163.com Keywords: water pinch, water-saving, emission reduction Abstract.
And its application on the water saving emission reduction of other industries is less.
Taking a petrochemical enterprise for example, through applications of software on analysis of water data of the maximum impurity concentration of system exports, optimized system with conventional analysis and system with water networks optimal design software, we could see that use of water pinch technology optimization saved water by 48.7% than original design 14.6% than conventional optimization technology.
Zhu Shenhua[18] compared the system water data of regular analysis optimization and water pinch technology optimization with that of original design, and found that water-saving rate of routine analysis optimization and water-pinch technology optimization were 17.62% and 31.03%, respectively.
Based on experience or data analysis solution as well as the parameter mentioned above, the water network is designed which may have deviation with regeneration concentration in actual operation.

We also attribute this reduction of SF due to the surface etching prior to the drift layer growth.
Basal plane dislocation reduction is observed with a reduction in the interruption temperature (left).
SIMS data indicates etching takes place during the interruption step (Fig. 4).
Interruption at a higher temperature (Experiment-1 in Fig. 2), did not show significant reduction in BPD.
However, when the temperature was lowered further (Experiment-2, Fig.2), a significant reduction in BPD was observed.

Hence, the reduction ratio of gear reducer is required to be higher and higher. 3K type and 2K-2H type planetary gear trains can be designed to have high reduction ratios.
This paper proposes 2K type planetarygear train with high reduction ratio.
(a) Reduction ratio Rr=40 (b) Reduction ratio Rr=50 (c) Reduction ratio Rr=100 Fig.4. 2K type planetary gear reducers with simple planet gears The Gear Data for 2K type Planetary Simple Gear Reducer In follows, the 2K typeplanetary simple gear reducer shown in Fig. 4(a) will be the example to illustrate how to design the two ring gears with different shift coefficient to engage the same planet gear.
Table 1 show the gear data of 2K type planetary simple gear reducer shown in Fig.4(a).
Table 1, Gear data of planetary simple gear reducer shown in Fig. 4(a) Planet gear 3 Ring gear 4 Ring gear 5 Teeth No. 16 38 40 Normal Module (mm) 2.0 2.0 2.0 Shift coefficient (mm) 0.2 0.58 -0.18 Pitch Dia.

JPEG Steganalysis Based on Locality Preserving Projection Dimensionality Reduction Method Zhu Tingting 1,2,3 a, Wang Lina 1,3, Fu Yu2 and Ren Yanzhen 1,3 1 Computer School, Whuhan University, Whuhan, Hubei 430079, China 2 Department of Information Security, Naval University of Engineering, Whuhan, Hubei 430033, China 3 The Key Laboratory of Aerospace Information Security and Trust Computing, Ministry of Education, Wuhan, Hubei 430079, China a zlztt1688@sina.com.cn Keywords: Steganalysis; Co-occurrence matrix; Support Vector Data Description (SVDD); Locality preserving projection (LPP) Abstract.
In this paper, a JPEG steganalysis algorithm based on locality preserving projection (LPP) dimensionality reduction method is proposed for detecting the unseen stego algorithms.
In section 3, the algrithm of feature dimensionality reduction, which use LPP method, is described.
The steganalysis features are as follows: (18) 3 Features Dimensionality Reductions Based on LPP The Locality Preserving Projection (LPP) is a linear approximation of the nonlinear Laplacian Eigenmap [5] .It is an unsupervised technique which is always used to reduce the dimension of feature and aims to preserve the local neighborhood structure of the patterns after the linear dimensionality reduction.
Summary For detecting the unseen stego algorithms and reduce the feature dimension, a JPEG steganalysis algorithm based on locality preserving projection (LPP) dimensionality reduction method is proposed.

Feature selection has become the focus of research areas of applications with high dimensional data.
Introduction Rapid technological developments in Computer Science have resulted in increasing quantities of data, making many of the classical data analysis tools unavailable.
NMF[3] is a good method for dimensionality reduction which has been used in many fields,such as text mining, face recognition, microarray data analysis and so on.It is always considered to be an unsupervised learning algorithms for feature extraction.This paper is to use it for feature selection not for feature extraction.
In this paper,we propose a new feature selection algorithm adopting a two-step strategy for high-dimensional data .The first step is to use improved NMF to compress the great number of data samples to several groups of vectors which can be regarded as the basis of each category in the dataset.
Since relatively few basis vectors are used to represent a lot of data vectors, good approximation can only be achieved if the basis vectors find out latent structure in the data.