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Artificial Neural Network In this study, one week data measurement of total 168 data are trained into the model as references so as to observe the behavior of Malaysia power system network.
The input data consist of load active and reactive powers (PL, QL) and together with PV generator active power (PPV) are utilized.
The previous data obtained from power utility is trained then becoming target values for the ANN model to match and equate.
As a matter for verification a set of input data are randomly picked through a week for 24 hour of the day.
From the previous supervised learning algorithm in ANN, the tested data are observed thoroughly and identically.

From the monitoring data for recent 5 years, the rainfall PH in the center is 4.5~5.0 and the acid rain frequency is 55%~60%.
Based on formulas (4) and (18), for the agriculture loss calculation by atmospheric contamination, 5 factors must be decided: crop yield reduction rate, crop planting area, crop unit of mu yield and crop price.They shall be explained in the following: (1), from the reference [20], we know that the crops in Leshan City are rice, wheat, soybean and cole.
In 2008 the crop product in the northern urban agglomeration of Leshan City (three districts two counties and one city), the value of is shown in the table.2: (2), check the market price for crops at the end of 2008 (purchasing price) and we get the value of which is shown in the table.2: (3)，according to the monitoring data in recent 5 years for SO2 and acid rain in Leshan City, the city is mainly the acid rain polluting area but with other pollutants.
From the supervising data for acid rain in recent 5 years in Leshan City, it is [SO4]2-∕[NO3]- =6.4 in the rainfall, which shows that Leshan City has mainly the acid rain.
Step 2 ,an objective analysis procedure to introduce observational data into the step 1wind field to produce a final wind field.

Introduction From recently years’ college students’ physical health data, it can be known that their physique is in the drop tendency.
Fig.1 PDCA cycle 2.3 Principle Component Analysis The principle component analysis is a kind of statistic analysis that transforms many variables to a few principle components by applying the data dimensionality reduction technology.
It makes use of the linear combination method, and transforms many indexes into some typical comprehensive indexes based on losing fewer data information.
Table 1: Partial sample data standardization result table Sample No.
Summary This paper carried out research on college female students physical health feedback data to education department, focused analysis on weight and physical health other indicators correlation degree conditions and test value deviation judgments, studied different sources student physical health data differences.

The above results have been compared with published data about the effects of milling on a ceramic powder.
Line broadening is mostly produced by a reduction in size of the coherently diffracting domains (crystallite size) and by distortions (microstrains), chiefly due to the presence of dislocations.
Regarding this point, the results of the work are examined against the data reported in the literature [8] for ground YBa2Cu3O7 powder.
The data were corrected for instrumental broadening [10] using a well-annealed Ni powder as diffraction standard.
The reduction in particle size brought about an increase in specific surface area (Fig. 5), as demonstrated by the BET measurements.

The average particle size obtained from XRD data is ~25 nm.
The percentage of bacteria reduction (R, %) was calculated using the equation 1: R = (C0 – C) × 100 / C0 (1) where C0 (CFU—colony forming units) is the number of bacteria colonies on the control fabric (untreated fabric without nano-sized CdS) and C (CFU) is the number of bacteria colonies on the fabric loaded with nano-sized CdS.
Assessments displayed percentage of bacterial reduction sample was 97% (Fig. 3a).
While, no efficient reduction obtained in blank sample (Fig. 3b).

The mechanical properties of electroslag welding were improved owing to the reduction of excessive welding heat input by researching welding groove type, guide nozzle, process type and so on. 1.
The mechanical properties of electroslag welding were improved owing to the reduction of excessive welding heat input by researching welding groove type, guide nozzle, process type and so on. 2.
(2) The mechanical properties of electroslag welding were improved owing to the reduction of welding heat input by researching welding groove type, guide nozzle, process type and so on
(4) This study obtained valuable data which are welding conditions and mechanical test results for the electroslag welding process.

The software has integrated the CNC equipment work process, CAD/CAM, turning/milling program, system control programming, etc, using 3D simulation technology, and a lot of charts, data, explanations and exercises to presentation and training the novice.
Parameter selection below in this article: Permissible error δ=0.01mm, Initial step S=(50~100)δ, Reduction factor S1=0.8~0.99, Amplification factor S2=1~3.
(10) (11) The introduction of the DXF format If we want to use DXF files as interactive file, it must be clear that the structure of the data in the DXF file.
Fig.4 Input function and related parameters Software testing and data import Now we come to test the VB software.
First, open the VB program, Input function f（x）=（x*x*x）/30,x Interval is set to(0,20);Permissible error E =0.01mm;Initial step S=0.10mm;Reduction factor S1=0.9 Elongation factor S2=1.5,As shown in Figure 4.

This phtenomenon results in the reduction of a pump’s life and efficiency, vibration in framework, and exra noises [1,4,5].
The images showed the reduction of ferrite ratio through depth to surface.
According to the recorded data from table 1, the feed water quality did not have a significant difference from the manufacturer‘s prescribed analysis, except the Electro Conductivity and amount of Calcium and Silicon which were higher than the prescribed analysis.
The data, given in table 2, disclosed the lower corrosion rate for feed water, so resistance to corrosion for this alloy in feed water is in "well" category [8].
Table 1- Water analys COND (µEqg/lit) TH (ppb) PH Na (mg/lit) SiO2 (mg/lit) NH3 (mg/lit) O2 (mg/lit) Feed water 0.52 0.9 9 5< 16.7 808 5 prescribed water 0.3 Max 0.7 Max 9±1 5 Max 15 800 20 Table 2- Electrochemical data Corrosion rate ( mpy) ICorr ( µA) 2.859 6.237 Distilled water 1.466 3.197 Feed water More investigation by SEM in Figures 3 pointsed out the failure mechanisms of pump inner surface parts.

Despite the following limitations, difficulties in identifying entry parameters, lack of robustness and reliability in quantitative results and so on, FEM simulation can still be considered as a promising approach to the study of the cutting process, allowing the reduction of the experimental cost.
It can be seen from Fig 3 that water vapor cooling, causes a considerable temperature reduction of tool rake face in cutting process, demonstrates the effectiveness of water vapor cooling.
The simulation results show the same temperature trends with the experimental data [5].
The errors between simulation results and experimental data were from 3% to 12%.
The present simulation results showed a good agreement with the experimental data from other researchers.

ANOVA treatment of the measured data indicates that all three experimental factors significantly impact the compressive strength.
Results and Discussion Results from the preliminary experiment are summarized in Table 2, with data for OPC included as references.
Statistical analysis of the data using ANOVA method indicates that the main effects of both aluminosilicate and alkali type are significant at 95% confidence level.
Results of the main experiment are summarized in Fig. 1, in which error bars corresponding to 95% confidence intervals of the average compressive strength data are included.
ANOVA treatment of experimental data identifies KOH and fly ash as raw materials producing faster setting and higher compressive strength due to their higher reactivity as a reacting pair.