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Due to the absence of literary data on the use of air-incorporating additives (AEA) in earth mortars, the objective of this article is to verify the influence of the incorporation of AEA (0,10, 20, and 40% of the total volume of the mixture) in the mechanical properties (compression strength at 28 days), physical (total water absorption by immersion), thermal, and microstructural (scanning electron microscopy) of the referred mortars.
Under compression loads, the stiffness reduction decreases the risk of eventual cracking, however, the reduction in strength should be controlled to meet normative limits. 1.
From the data obtained, it was also possible to determine the thermal effusivity (Ef) of the mortars.
Thermal effusivity also varied from 0.73 to 0.97, providing reductions of up to 24.34%.
This reduction must be controlled in order to meet the minimum normative standards.

The fatigue vulnerability estimation method including the effects induced by the corrosion rate and the traffic variation with time has been proposed to evaluate the reduction of fatigue strength in steel bridge members.
Monte-Carlo simulation is used for reliability analysis which provides the data used to obtain fatigue vulnerability curves.
Therefore, it is necessary to evaluate the fatigue strength of steel bridge members considering corrosion rate with time in order to reflect the interacting reduction effects between fatigue and corrosion.
To introduce the corrosion effect into the conventional fatigue limit state function, the critical number of stress cycle considering corrosion effect, cfN is defined as, fcccf KNN /= , (1) where cN is the number of stress cycle and fcK is the fatigue strength reduction factor caused by corrosion and can be expressed in terms of the depth of average corrosion as follows [5]: Strain History Data for a Detail Strain History Data for a Detail Stress Range Spectrum Stress Range Spectrum Equivalent Stress Range Equivalent Stress Range Calculate the Cumulative stress cycles (N) Calculate the Cumulative stress cycles (N) Limit State Function Limit State Function Calculate the Fatigue Vulnerability (V) Calculate the Fatigue Vulnerability (V) Stress History Data Stress History Data Rainflow cycle-counting Stress Spectrum
The measured variable stress data obtained from Korea Infrastructure Safety & Technology Corporation, and traffic information is provided by Seoul Metropolitan Government as well as Ministry of Construction & Transportation.

In this work, a computational modeling method to describe the thermal behavior in the TiCl4 reduction reactor was investigated and validated with the measured temperature distribution in a 500kg titanium sponge-capacity pilotscale reactor in terms of various reduction ratios.
Each Mg level with progression of reduction process in Fig. 2 (b) ~ (d) indicates 250mm, 450mm, 650mm.
(a) (b) (c) (d) Fig. 2 Schematic diagram of geometry applied in computational modeling: (a) initial (b) 25 pct reduction (c) 50 pct reduction (d) 75 pct reduction Results and Discussion Temperature distribution in the reduction process is showed in Fig. 3.
During the early stage in the reduction process, the bottom of the reactor is the biggest part for heat elimination.
Acknowledgements The authors thank to RIST for providing experimental data and support form POSCO, South Korea (Grant No. 4.0004073.01) is gratefully acknowledged.

By our analysis from the experimental data, the speckle CR can be reduced about 25% which is close to the theoretical value of 29.3%.
Therefore the speckle reduction is highly desirable.
For speckle reduction, there are several methods proposed.
Theoretical Calculation And Simulations Of Speckle Reduction.
On our analysis from the experimental data, the speckle CR can be reduced 25% which is close to the simulation result of 29.3%.

For uniformity test, the mixing time data consists of 14 observational data per job type and is divided into two subgroups for the uniformity test.
Truck mixer Transfer Time Control Limits The departure administration data consists of 140 observational data is divided into 14 subgroups for the uniformity test.
Data on day 1 has an efficiency of 30% and data on day 2 has an efficiency of 26.2%.
Data on day 3 has an efficiency of 23.6%, data on day 4 has an efficiency of 27.1%, and data on day 5 has an efficiency of 14.3%.
Data on day 6 has an efficiency of 31.1%, data on day 7 has an efficiency of 21.1%, data on day 8 has an efficiency of 23.1 %, the 9th-day data has an efficiency of 17.7%, and the 10th-day data has an efficiency of 23.3%.

,Ltd,Changchun, P.R.China achenaoxue2002@163.com, bliuhailong2002@163.com Keywords: the recovery fresh air heat pump units ; energy saving;emission reduction.
cooler), which the heating (cooler) medium is water in fresh air conditioning unit.In winter,the system used the heat pump units to recovery exhaust heating and to heating the fresh air,it can reduce the heat pump unit loading,and improve the COP in conditioning of the low temperature,prolong the operation time of fresh air unit,improve the efficiency and operation time of heat pump units.The units solve the problems as the heat pump units can not work normally in the low temperature environment in the cold and serious cold regions,fresh air units always were frozen,and the indoor air quality can not be guaranteed in winter.The experimental results showed that the recovery fresh air heat pump units effectively solve the problem which fresh air units can not work well in cold and serious regions, improve indoor air quality, the system had energy conservation and environment protection effects.Comparative analysis of recovery fresh air heat pump unit in engineering application with measured data
Analysis of the emission reduction.At the present, according to the per kg contains about 7000 kcal heating (29302.3kJ ) as a standard of standard coal, and using the Index calculation of saving 1 tons of standard coal can reduces the emission of carbon dioxide about 2620 kg, sulfur dioxide is about 8.5 kg, and nitrogen oxides is about 7.4 kg, the saving quantity of the standard coal and the emission reduction of the carbon dioxide, sulfur dioxide and nitrogen oxides is shown in table 2,and different pollutants governance costs[3] is shown in table 3.
Table2 Coal save and CO2,SO2,NOX emission reduction CO2 SO2 NOX The emission quantity of 1 tons of standard coal （ uint： kg） 2620 8.5 7.4 Saving quantity of standard coal every year(unit: t) 33.74 The quantity of emission reduction(unit: t) 88.51 0.29 0.25 Table 3Different pollutants governance costs CO2 SO2 NOX Quantity （ uint： t） 88.51 0.29 0.25 Unit Governance costs（unit:yuan per kg） 58 1260 2000 Governance costs（unit: million yuan） 5.1336 0.3654 0.50 Due to the new plan, the system reduces pollutant emissions, the quantity of the emission of carbon dioxide is reduced about 88.51 ton in heating period, save the governance costs about 5.1336 million yuan, the quantity of the emission reduction of sulfur dioxide is 0.29 ton, save the governance costs about 0.3654 million yuan, and the quantity of the emission reduction of nitrogen oxides is 0.25 ton, save the governance costs about 0.50 million yuan, the system saves a lot of governance costs, as high as 5.999 million yuan , the
In consideration of environmental protection, if using the new design of engineering, the system reduces pollutant emissions, the quantity of the emission of carbon dioxide is reduced about 88.51 ton in heating period, save the governance costs about 5.1336 million yuan, the quantity of the emission reduction of sulfur dioxide is 0.29 ton, save the governance costs about 0.3654 million yuan, and the quantity of the emission reduction of nitrogen oxides is 0.25 ton, save the governance costs about 0.50 million yuan, the system saves a lot of governance costs, as high as 5.999 million yuan , the system has environmental and economic benefits.

Data mining is to discover knowledge from large amounts of data.
The data may be structured data in the relational database and the data in the data warehouse, and can also be other complex data types, such as object data, spatial data, multimedia data, time series data, text data, and Web data and so on.
There is massive data information in the Web, how to carry on these data and complex applications becomes a hotspot of data mining technology of the database is now found the disciplinarian content from a large number of data, solve the problem of data application quality.
Relative to the Web data, the traditional data in the database structure is very strong, which is the data of fully structured data, but the data on the web are basically originates from the heterogeneous database environment, and most of the data structure is semi-structured, this treatment brought many challenges to the data.
Facing the information often text, graphics, image data such as semi-structured data, and even to be heterogeneous data.

A comparative data depicting the efficiency of various microbes for reduction of Cr(VI) form chromite mine soil is presented in Table-1.
The reduction rate was relatively low during first 24 hours.
The reduction rate increased with increase in time duration.
The best growth and reduction was taken as pH 7, which facilitated ~90% hexavalent chromium reduction.
About 90% Cr(VI) reduction was observed at 35ºC in comparison with only 80% reduction at 25ºC (Fig. 8).

For instance, the data after the background noise reduction treatment was displayed in time waveform diagram and spectrogram, which can reduce the environmental noise interference and make the song information much more clear.
Fig. 2 Color adjustment flowchart Large data processing.
Song Lab realizes the data transmission between sound files and software by a method of combining the multithreading design with stack technology, compared to the way of reading single thread data, reading efficiency of multi-threaded applications data has been greatly improved.
The specific design process as shown in Fig. 3: Thread (1) is data read threads, its main function is to read the *.Wav file data, and then write it in the stack to realize the data cache, it could read large amount of data each time, which can ensure multiple data transferring.
Thread (2) is responsible for reading data from the stack, at the same time inquires the stack data storage condition, and then determines whether needs to read data or not, if necessary, notify device will issue instructions, and then notice data read threads will read data.

Electrocatalytic Activities of Trirutiles Based on MTaO6 (M=Co, �i, Mg) for Oxygen Reduction Reaction Takayuki KONISHI 1, Hideki KAWAI 1, Morihiro SAITO 1, Jun KUWANO 1, and Hidenobu SHIROISHI 2 1.Department of Industrial Chemistry, Faculty of Engineering, Tokyo University of Science, 1 12-1 Ichigayafunagawara, Shinjuku, Tokyo 162-0826, JAPAN 2.Department of Chemical Science and Engineering, Tokyo National College of Technology, 2 1220-2, Kunigida, Hachioji city, Tokyo 193-0997, JAPAN Keywords: electrocatalyst, trirutile, oxygen, MTa2O6, RRDE, ORR ABSTRACT The trirutile oxides MTa2O6 (M=Co,Ni,Mg) [MTs] and the substitution products M1-xNxTa2-yLyO6 (N=Mn, L=Sn,Ti,Zr) [M1-xNxTa2-yLy] were prepared by a conventional solid-state reaction.
The oxygen reduction reaction (ORR) activities were evaluated with the onset potentials (Eon) of the ORR currents, the disk current densities (iD) and the efficiencies (Eff4) of 4-electron reduction, measured by a rotating ring-disk electrode (RRDE) technique.
Platinum has been continuously and commonly used as the cathode catalyst for their practical use because of the excellent activity toward oxygen reduction and the long-term stability.
The lattice parameters were determined through the XRD pattern-processing program JADE 5.0 (Materials Data Inc.).
The efficiency (Eff4) of 4-electron reduction of oxygen can be calculated by the following equation: Eff4 = 100 × (iD − iR / �) / (iD + iR / �), where iD and iR are disk and ring currents, respectively, � (=0.372) is the collection efficiency estimated from the geometrical areas of the ring and disk electrodes.