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The evaluation of technological properties of quartzites was done by comparing the curves of relative weight loss with industrial technological data of the ferrosilicon smelting process.
The results in the form of curves of relative loss of mass during heating of samples were compared with technological data from the ferrosilicon furnaces.
The study was conducted in thermobalance Thermogravimat Sartorius 4304 series, in which was modernized and upgraded the recording system and processing of measured data.
The relative weight loss of sample with quartzite Bukowa Góra after 360 min as a function of the flow rate of argon, VAr Summary The usefulness of thermo-gravimetric studies to assess the technological properties of quartzites was determined by comparing the weight loss curves with technological data from the FeSi75 ferrosilicon smelting process.
This is confirmed by data on technical and economic indicators of consumption of raw materials and energy.

This study proposes to use mathematic geometric equations to compose the programming language via which point data are obtained and processed by Solid Works to complete the construction of the model.
Di Francesco and Marin[1] have studied the external gear with asymmetric teeth, and analyzed the reduction of size and weight.
Research Propose This study proposes to use mathematic geometric equations to compose the programming language via which point data are obtained and processed by Solid Works to complete the construction of the model.
The point data are then converted into Solid Works for 3D modeling.
Structural analysis of asymmetrical teeth: reduction of size and weight, Gear Technology,14(5),47-51

ffu = CE ffu*. (3) efu = CE efu*. (4) Table 1 – Environmental reduction factors for various FRP systems and exposure conditions Exposure conditions Fiber type Environmental reduction factor CE Interior exposure (structural interior walls, interior walls, partition walls, etc.)
The design strain efd and design stress ffd used for the design of FRP flexural strengthening of masonry walls can be computed according to Eq. 5 and Eq. 6, respectively. km is a bond reduction coefficient calibrated using available experimental data [4], defined as in Eq. 7.
(5) ffd = Ef  efd. (6) (7) Based on current knowledge and experimental data, Eq. 7 is applicable only when the total force per unit width (per bar for NSM systems), pfm, that the FRP system transfers to the masonry substrate satisfies the limitation given in Eq. 8
The effective strain efe to be used for the design of FRP shear strengthening of masonry walls is computed according to the Eq. 11, where kv is a bond reduction coefficient calibrated using available experimental data [4], defined as in Eq. 12, which is equal for both FRP laminates and NSM FRP systems.
ACI 440.7R recommends the use of ke of 0.55 calibrated using available experimental data [5]. . (17) Detailing (ACI 440.7R Chapter 13) ACI 440.7R provides guidance for detailing of FRP systems including recommendations for development length, lap slices, groove dimensions for NSM systems, FRP spacing limits, FRP anchorage, and load-path continuity.

Data Analysis.
Data was analysed using the SPSS software package (version 13.0).
Data is represented as mean percentage difference with 95% Confidence Bars.
Data is represented as mean percentage difference with 95% Confidence Bars.
Data is represented as mean percentage difference with 95% Confidence Bars.

The artribary waveform data can be downloaded and updated from a communication interface.
When each clock pulse fs comes, the adder adds the frequency control word k and accumulative phase data outputted by accumulator register, and the result is sent to the data input terminal in accumulator register.
It receives and stores the frequency and phase control word data sent by MCU.
The input data in register will control the frequency and phase sent by MCU when the next clock comes.
Arbitrary waveform data register RWREG receives the arbitrary waveform data sent by upper computer, and output selector MUX can realize the swift of arbitrary waveform and phase switching sine wave.

Further reduction of trace level ion from ultra pure water and its effect on electrical property of device.
The metallic impurity data using the ion exchange filter in our study is shown in table 1.
From the data, we confirm that the newly developed filter acts on perfectly removing amines and metallic impurities, even though the UPW contains the ppq level impurities. 0 500 1000 1500 2000 2500 3000 3500 Na Al Zn Count of atom(1E10 atoms/cm2) Without ion exchange filter 10 min With ion exchange filter 10 min Without ion exchange filter 30 min With ion exchange filter 30 min 0 20 40 60 80 100 120 Fe Cu Count of atom(1E10 atom/cm2)Without ion exchange filter 10 min With ion exchange filter 10 min Without ion exchange filter 30 min With ion exchange filter 30 min Figure 5: Amount of metals (Fe,Cu) on wafer which was immersed in ultra pure water with or without being passed through ion exchange filter.
The data clearly shows that high field Qbd value much improves, when employing the filter.
The data reveal that it has the excellent removal efficiency for removing both impurities.

As an important basis of retrieving stored data, the design of rowkey will directly affect the efficiency and success rate of retrieving data in HBase database.
By classifying information and encoding the data type in rowkey design by Huffman coding, data type can be effectively bound with the data content in order to provide the efficient solution for class-based information retrieving.
A large part of these data are unstructured data, so many limitations are exposed when using the traditional relational database to deal with massive unstructured data.
These multiple versions of data sort in descending order, so the visitor can access the latest data first.
The rowkey design, offered by OpenTSDB[7],is asked to bind data to a series of labels and timestamp when generating data.

The data is collected and storage per 10 minutes once, in units of m/s.
That is to say, the predictive time lasts from 30min to 72h, and the collective data period always per 5min or 10min, and they are all statics prediction based on the historical data.
We choose 67 groups data to predict for ultra short term.
To make the comparation easily, under the condition of the same parameters and the same input groups data of the above data, we predict the wind speed of single turbine.
Secondly, enhance the space continuity through referring the meteorology data.

The results showed that there is no much reduction of the fatigue life in corrosive environment, which is 90% of the air fatigue strength.
The mechanical properties at room temperature are ultimate tensile strength of 953 MPa, yield strength of 569 MPa , elongation of 27.8% and section area reduction of 74 %.
Test results and discussion 3.1 Stress-fatigue life data (S-N curve) Fig. 3 presents the S-N results for air and corrosion less than 1×108 cycles.
It is because that the former alloys have less ductility with elongation below 18% and the section area reduction of 55 %, compared to the duplex stainless steel 2507 with high elongation of 27.8% and section area reduction of 74 %.
In contrast, there is no much reduction in the fatigue limit in corrosive environment due to high content of Cr.

A time dependent corrosion model has been developed for failure prediction purposes, based on the historical data of plate thickness reduction.
In addition to TSCF, Wang et al., 2003, also presented corrosion data in a more recent version, this is data collected from 140 single hull tankers with a total number of 110,082 thickness measurements, vessel age range of up to 32 years and this data was collected from the period 1992- 2000 [13].
The last parameter is derived by considering the corrosion wastage data available in each of the hull structures' locations.
The Chi Square method was used to conduct a goodness of fit test in order to gather accurate data in this investigation.
Decision-makers and inspection planners are given the risk rating data in easy forms to assist them in prioritizing inspection activities.