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According to the data, the masteralloy-including mix showed higher shrinkage at 1100 and1300°C which suggested better sintering activity especially at high temperature.
The results of oxygen measurement shows that Mix 2 is better in oxide reduction for both temperatures of 900 and 1100°C.
More rounded and bigger pores at 1300°C also shows higher level of sintering for the masteralloy-containing mix which is in a good agreement with the data in table 3.
This affects the subsequent reduction process by diminishing formation of more stable complex oxides with resulting better carbon diffusion and oxide reduction at the temperature range of 800°C to 1000°C.
It can be concluded that reduction of the stable oxides started earlier in the specimen containing masteralloy.

Data showed that the total acidic number (TAN) value for crude B met the PETRONAS requirement for the TAN value below one using 1500 mg/L of DEA-PEG assisted by Cu/Ca (10:90)/Al2O3 catalyst.
Different concentrations of DEA-PEG (100-1500 mg/L) were considered to determine its effectiveness towards the reduction of NA content in crude B.
Data showed that the TAN values of crude B of 0.39, 1.1 and 0.84 mgKOH/g were obtained when as loaded from 10 to 30% (w/w) on the Ca catalyst surface.
The Ca/Cu(10:90)/Al2O3 catalyst calcined at temperatures of 900, 1000 and 1100°C was used to aid the removal process and, the data is portrayed in Fig. 3.
Elevation of calcination temperatures from 900-1100°C gave a significant effect on the reduction of TAN value for crude B from 0.65 to 0.39 mgKOH/g.

After complete Pt(IV) reduction, the cells were centrifuged.
Catalytic activity of the preparations in the reduction of Cr(VI).
Corresponding data for pre-platinised cells (Fig. 2B) showed that the control was almost twice as active as any of the Bio-PGMs; none showed more than 40% Cr reduction over 3h.
The data are for preplatinised cells.
Data are means ± SEM from 3 experiments.

However, in practice, it is very difficult to obtain large amounts of data sample.
In generic terms, SHM consists of a sensor system, a data gathering system, data processing, damage analysis and a modeling system, etc.
A small sample of modulus of elasticity data then can be obtained based on this group of strength data.
In addition, support reaction data can also be applied to the damage detection module in the SHM of bridges.
New Damage Localization Method for Bridges Using Vertical Support Reaction Data under Truck Load [J], J.

For the problem of feature redundancy of physiological signals-based emotion recognition and low efficiency of traditional feature reduction algorithms on great sample data, this paper proposed an improved adaptive genetic algorithm (IAGA) to solve the problem of emotion feature selection, and then presented a weighted kNN classifier (wkNN) to classify features by making full use of emotion sample information.
We demonstrated a case study of emotion recognition application and verified the algorithm's validity by the analysis of experimental simulation data and the comparison of several recognition methods.
For classic pattern recognition issues such as feature selection and dimensionality reduction included in emotion recognition methods, Intelligent computing methods such as genetic algorithm and particle swarm optimization algorithm can be adopted to address such combinatorial optimization issues. k-Nearest Neighbors method is a simple and effective algorithm during the step of feature classification This paper proposed an improved adaptive genetic algorithm (IAGA) based on the analysis above; aiming at the situation that traditional GA would fall into local optimums easily.
Fig. 1 Process of emotion recognition based on physiological signals Data Preprocessing and Emotion Feature Matrix Generation.
Then corresponding data conversion and typical statistical values (e.g. second order difference, signal amplitude, heart rate) of these signals are calculated to obtain 4 feature matrixes with 193 features: ECG 100*84, EMG 100*21, SC 100*21, RSP 100*67.

We have tried to develop a silver nitrate reduction method, with which a certain reducing agent has played an important role in the reduction of silver ions in an aqueous solution.
The chemical reaction includes commonly reduction process of silver ions.
The reflection peaks are indexed as the fcc (111), (200), (220), and (311) planes which are in accordance with the standard data, indicating that silver nanoparticles are well crystallized.
It is found that the dropping rate of reduction agent solution is very important.
When the reduction agent solution was added quickly, the size of particles grew larger.

Application of Rough Set Feature Selection in the Hazard Assessment of Debris Flow LI Ju1, a, CHANG Jinyi1,b , WANG Xing2, WU Xiaobiao1 1 School of Computer Science and Engineering, Chang Shu Institute of Technology, Changshu, Jiangsu, China 2College of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, China aliju@284532@163.com, bchangjy@cslg.edu.cn Key words: rough set; debris flow,attribute reduction,value reduction Abstract.
The importance of property In decision-making system , is defined as In decision-making system , the attribute importance of is defined as Application of Rough Set Theory in Debris Flow Hazard Assessment The selection of data and discrete data This attributes were selected from a debris flow, a: The maximum amount out of; b: Drainage area;c: Main channel length;d:Watershed relative height; f: Cut density;g: Maximum rainfall; h: Loose solid material reserves; D: Risk Level Table 1 No. a b c d f g h D 1 8.99 50.0 9.00 2.82 15.61 100.41 7000 1 … … … … … … … … … 4 0.17 21 1.92 1.06 5.11 40.21 11.11 2 Table 2 No. a b c d f g h D 1 1 1 1 2 1 2 1 1 … … … … … … … … … 4 0 0 0 0 0 0 0 2 Calculate posC(D): U/{a}={{1,3},{2,4}},U/{D}={{1,3},{2,4}},U/{b}={{1},{2,3,4}},U/{c}={{1},{2,3,4}},U/{d}={{1},{2,3,4}},U/{f}={{1},{2,3,4}},U/{g}={{1,2},{3},{4}}U/{h}={{1,3},{2,4 }},U/{C-a}={{1},{2},{3,{4}},U/{C-b}={{1},{2},{3},{4}},U/{C-c}={{1},{2},{3},{4}},U/{C-d}={{1},{2},{3},{4}},U/{C-g}={{1},{3
Attribute reduction can be achieved through the following two steps: (1) in accordance with this sort of binary groups, delete the property, because its classification does not work

We estimated the effect of the proposed lighting energy management system by calculating the lighting energy reduction based on the real occupancy sensing data and the state of the lights in our office 606.
We used the occupancy sensing data for one day in Feb. 2012.
Each sub zone has its own occupancy sensing data.
The structure of the office at which the real occupancy sensing data is measured.
To show the effect of the proposed system, we estimated the lighting energy reduction based on the real measured occupancy sensing data.

However, there is not still deterministic data and method about the energy saving effect of natural ventilation in residential building.
The wind speed and direction data in ESTPNV in TMY [5] were obtained for the ventilation environment simulation of rooms with the PHOENICS software.
The cooling loads reduction of the living room was the highest among the four rooms.
The cooling loads reduction of bedrooms was small.
The meteorological data for thermal environment analysis in China.

Confidence in published analyses is enhanced by existence of many data for the same or similar alloys.
Some data were determined by the authors' but more came from published reports; in some cases they have been re-calculated in a common manner.
Before 1992, the data base for Mg alloys [25,26] was extremely limited compared to that for Al alloys [10].
Correllation utilizing the Mukherjee-Dorn relationship (Eq. 1) of data from: a) torsion: AZ31.Mc and AZ31Mn (Fig. 2,3) [13,14], tension [43-45]; compression [37,39,46] and b) compression, torsion and creep for AZ91 [9,21].
Data from 4 extrusion trials [66], confirms the right line indicating the limit for surface cracking that is tied to incipient melting.