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The reduction of weight of steel reinforcement by subjecting reinforced concrete specimens to chloride water (6% concentration) was 2.61 % for ordinary reinforced concrete and by using waterproof –polymer concrete with poly fibers, the reduction was less and its value was 0.93% and that was for 6 months' exposure, and for 1-year exposure, the reduction in steel reinforcement weight was 4.72% for ordinary concrete and 1.4% for waterproof concrete.
The ordinary concrete (reference mixes) gives average compressive strength of 33.2 MPa for cylinders and flexural strength of 2.74 MPa and by using admixtures and polyfibers compressive and flexural strength increased gradually with increasing the dosage of integral waterproofing admixture until 2% , the compressive strength then increased to 46.2 MPa and flexural strength increased to 9.45 MPa .the absorption of concrete decreases by using admixtures 5.9 % to 1.17 % and the reduction of steel reinforcement for specimens subjected to chloride water was high for ordinary concrete and very less for modified concrete , and for 2% integral waterproof admixture ,the reduction becomes 0.93 % while for ordinary concrete was 2.6 % and that was for 6 months exposure for chloride attack , and for 12 months attack, the reduction also was decreased from 4.7 to 1.4% and that attributed to the action of polymers and the chains of polycarboxylate inside waterproof admixture which decrease the water/cement
It should be noted that figures 4 and 5 show the increase in compressive strength and flexural strength for various mixes by using the same percentages of polypropylene fibers and integral water-proof admixture, the SBR admixture can be concluded from the same X-direction of the figure as mentioned in data in table 6, so no need to put the values in SBR ratios, and that also correct in figures 6 and 7 which are described the reduction in absorption and weight loss for steel reinforcement.
Details of results data used (note: all modified mixes contain polypropylene fiber range from 0.25% to 2%).
The relationship between percentages of waterproofing admixture, polypropylene fiber, and Reduction in steel reinforcement weight (note: the same X-direction of curve contain SBR % Ranged between 0%-8%) References [1].

This data is collected after key stages are completed.
Data is also collected concerning the environmental conditions during each project stage.
The data collected is often voluminous.
Because of the volume of data collected and the lack of data collection in an electronic format it is almost impossible to use this information as a management tool.
The tool is currently focused on eliminating the collection of data in paper format and contains the logic to perform analyses on the data collected to ensure its compliance with specifications.

Synthesis and Characterization of Li0.97K0.03FePO4/graphene Composites by Carbonthermal Reduction Method Yan Wanga, Zhe-sheng Fengb, Lu-lin Wangc, Jin-ju Chend, and Zhen-yu Hee State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China awangy_cd@qq.com, bfzs@uestc.edu.cn, ccygnet821@gmail.com, djinjuchen@uestc.edu.cn Keywords: Lithium-ion batteries; carbothermal reduction method; Li0.97K0.03FePO4/graphene Abstract.
Li0.97K0.03FePO4 and Li0.97K0.03FePO4/graphene composites were synthesized by carbothermal reduction method using acetylene black as carbon source.
In this paper, we choose acetylene black as reductive agent and carbonaceous coating source to prepare Li0.97K0.03FePO4 composites by carbothermal reduction method.
X-Ray diffraction (XRD) data was collected by Rigaku D/MAX-rA X-ray diffractometer with Cu radiation for 2 ranging from 10 to 70°.
Conclusions In this paper, Li0.97K0.03FePO4 and Li0.97K0.03FePO4/graphene composites were successfully synthesized by carbothermal reduction method using acetylene black as carbon source.

Control the energy consumption from traditional buildings to accomplish the goal in energy-savings and emissions reduction.
Energy- saving and emission reduction in traditional buildings goes to the civil buildings.
And the potential of carbon reduction is huge within implementation of energy-saving measures.
Normalize the standard in low-carbon building industry to achieve energy- saving and carbon reduction in building.
Acknowledgment: Project supported by Key project of Zhejiang Philosophical and Social Science Program 2011(Grant No.11YD08Z)，And also funded by Key soft science project of Zhejiang association for science and technology in 2011(Grant No. 11SKX0012) References: [1] Information on http://www.ipcc.ch/publications_and_data/publications_and_data_reports.shtml [2] Information on http://www.sallan.org/pdf-docs/0484 (2008).pdf [3]Zhang.K.M: Low Carbon Development, (China Environmental Science Publications, china2009)

Ltd. coupled to a PC, and the system was controlled and data recorded using the CorrTest software package.
On the scan towards more negative potentials, a reduction wave is observed at Epc -0.453V.
When and , the equation 1 is approximated to express as (2) There is a linear relationship between the net cathodic current density and overpotential, and the ratio of overpotential and net cathodic current density is charge transfer resistance Rct, that is (3) According to the test data of cathodic polarization curves of graphite felt electrode in electrolyte with soluble lead(II), Fig. 2 showed that the linear relationship between net cathodic current density and overpotential.
The oxidation and reduction peak potentials were basically unchanged with the increase of cycle number, and the oxidation and reduction peak potentials were -0.044 V and -0.454 V, respectively.
The reduction peak potentials of Pb(II)/Pb couple on the graphite felts treated with methanesulfonic acid (99.8%) increased and the oxidation peak potentials decreased, which decreased the difference of the reduction and oxidation and was advantageous to oxidation and reduction reactions of Pb(II)/Pb couple on the graphite felts.

Practical relevance of developed approach to control of ESR metal quality is revealed by data of achieved parameters which characterize properties of pipes and pipeline elements in energy and nuclear power industries which rely on ESR ingots as work-piece blanks.
There is no possibility to control the completeness of oxidation-reduction processes objectively and operatively.
Such distant benefit, however, does not arouse too much enthusiasm from metal consumers, hence the cost reduction for ESR metal items has become a challenging task.
Practical relevance of applications from the technique under development is revealed through the actual data about qualitative indicators for pipes and pipeline elements operated in thermal and nuclear power engineering and manufactured from ESR ingots, such data being accumulated by JSC “RPA “CNIITMASH”.
Assessment of technical and economic prerequisites for ESR use as a means of cost reduction for casing and pipe work pieces (Table 1) illustrate a drop of material and power expenditures, reduction in duration of thermal strain conversion and machining operations in comparison to a solid or hollow forging-grade ingot.

Nanostructured powders of cobalt and nickel, due to their high saturation induction, are used to make a magnetic liquids and materials, which is a part of the recording and data storage systems and they are widely applicable in biology and medicine.
According to the small-angle X-ray scattering data estimated on method [9], size contribution functions for nickel and cobalt particles have bimodal character (Fig. 3).
For the reduction of metal cobalt form cobalt carbonate the ratio υ(N2H4)/υ(Co)CoCO3 exceeds 15.
If the reduction agent’s concentrations decrease, the reaction decelerates considerably and the initial reagents might be reduced only partially.
Bakar, Effect of pH on Formation of Nickel Nanostructures through Chemical Reduction Method, Procedia Eng. 68 (2013) 43-48

Due to early of energy management system (EMS) is focus on energy data acquisition and centralized monitoring, and the deep analysis of the energy system is not enough, so it can't use the useful information from energy data mining effectively, especially the optimization of energy use, and can not meet the requirements of it.
The energy center management system above basically achieved in a variety of energy data acquisition and monitoring,and the control of the important energy medium.
The defect of previous energy flow detection technology are as follows: Quality of energy flow of real-time data,Flow distribution of real-time data,Network running state data collection is not complete, and cannot complete traceability or monitoring of energy medium to flow in the process of production process, can not meet the real-time monitoring and energy management scheduling requirements.
l In the different production processes of different types of energy, such as the department distributed management and real-time data sharing.
Then use industrial sensing technology and wireless sensor network (WSN) technology to solve bad energy flow under the condition of real-time data acquisition and transmission; Secondly, in view of the different production processes, such as department of production monitoring and control network protocol, to carry on the multiple network fusion, realizing the communication and interoperability; Then, establish various types of energy in the different production processes, such as department of distributed management mode and real-time data sharing mechanism; Eventually build a ideal energy center for integrated data platform (logical model), build energy monitoring information perception based on industrial and the Internet of things technology platform..

Quantitative and qualitative comparisons of the results obtained by the new method with the results achieved from the other speckle noise reduction techniques demonstrate its higher performance for speckle reduction in SAR images.
Recently, there has been considerably interest in using the wavelet transforms as a powerful tool for recovering SAR images from noisy data [2].
For a range of shifts, one shifts the data, De-noises the shifted data, and then un-shifts the de-noised data.
In order to change the multiplicative nature of the noise to additive one, we apply a logarithmic transformation to the image data.
Taking logarithm of the both sides of Eq. (5), we will have (5) where and represent logarithms of the noisy data, signal and noise, respectively.

In this model,gives a knowledge acquisition method that based on rough set theory,the Rough-Fuzzy RBF neural network are constructed according to the results of the knowledge acquisition,the PSO are used to optimize the network parameters.This paper take number plate for example to conduct a simulation experiment.The results shows that the model can simplify the network training sample,optimize the network structure and enhance the systems study efficiency and the precision. 1, Introduction Rough set theories[1]（Rough Sets,RS）is raised by the scientist Z.Pawlak in Poland in 1982,it uses decision table to expression knowledge base,based on the thought of the equivalence class and the indistinguishability,describe the importance of different attribute in knowledge representation,through the algorithm of knowledge reduction,to reduce the dimension of the space of knowledge representation,and access data from the rules for logical reasoning.Particle swarm optimization algorithm (PSO)[2]
The knowledge in the knowledge base are not equally important,even some knowledge is redundant.Knowledge reduction is on the condition of to keep the classification ability of knowledge is same,delete the knowledge which not relevant or not important.
（2）Discretization of decision table:When treatment decision table using the rough set theory,required values of attributes in decision table by using the discrete data expression.This paper in order to achieve discretization,using good clustering characteristics of SOFM[4] network in order for each condition attribute clustering sample attribute value.The input nodes of SOFM network is 1,the number of output nodes is various properties of discrete interval number,the trained network weights on behalf of the cluster center for each discrete interval
（3）Reduction of decision table:Attribute reduction requirement on the premise of to keep the dependencies between decision table decision attributes and attribute do not change ,delete irrelevant or unimportant attributes.This paper uses Johnson algorithm [5]to reduction
The first layer: Input layer,its value is the exact value of the feature vector,expressed as ,the number of neuron in this layer is the attributes number be reductioned.