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Besides, design logic is clarified and how the data are analysed by statistical means.
Data Collection To ensure the research quality of the research design, the questionnaires, and the data to be collected, a pre-test is to be conducted at some local universities.
Considering the large sample size, the voluminous data collected, and the complex calculation, statistical software such as SPSS is to be employed for conducting data analysis.
Similarly, five point Likert Scale questionnaires are designed for data collection.
To cope with the complicated determinants, Factor Analysis is to be considered for data reduction before analyzing the data collected.

Thermogravimetry data of the composites presented low weight losses at 1000 °C.
DTA curves showed an endothermic peak at 1350 °C, which was associated to the beginning of carbothermic reduction and/or the formation of silicon oxide and carbide.

Test data are shown in Fig. 4.
Objectives are to obtain the material fatigue limit data and the data on scale-and-surface induced effect of real railway axles.
Test data are given in Table 1.
The data are used to verify the probabilistic S-� curves derived from the material test data by the above mentioned grouped specimen tests and up-and-down tests.
Test data can be seen in Fig. 8b.

However, empirical models are limited by their dependence on the conditions under which the data were collected.
Realistic mock experimental data, informed by rock mechanics literature, is used to validate model predictions.
Semi-empirical models combine theoretical frameworks with experimental data to enhance accuracy in real-world conditions.
Experimental Data.
Zannoun, Physics-Informed and Data-Driven Prediction of Residual Stress in Three-Dimensional Machining, J.

The data collected were then analyzed using SPSS version 23.
The initial test involved data normalization, with a p-value >0.05 indicating normal data distribution.
Furthermore, the data presented in Figure 2 clearly illustrate the concentration-dependent larvicidal effect of the extract.
The data from this study were analyzed using statistical software.
The first step was to perform a normality test to determine whether the data followed a normal distribution.

The data are taken from the log books of past six years of the plant.
Air has highest resistance to the heat; result in reduction of heat transfer from the mould.
Data collection: Data is collected from continuous casting shop of Bokaro steel plant to train the neural network.
Data at the time of breakout: · Input data = 8×90 matrix, representing 90 samples of 8 elements · Target = 1×90 matrix (target is always 1 or alarm) Data at the time of no breakout: · Input data = 8×90 matrix, representing 90 samples of 8 elements · Target = 1×90 matrix (target is always 0 or no alarm) Total data: · Input data = 8×180 matrix, representing 180 samples of 8 elements · Target = 1×180 matrix (target is either 0 or 1 ) There are lots of variations within the input raw temperature data i.e. 8× 180 matrix.
Along with this the confusion matrix shows 100% accurate result of the test data.

Site measurement was conducted to collect meteorological and pavement surface temperature data.
Easy weather station, thermocouple, and data logger were installed on the site (Figure 2).
The data was taken for twelve days from July 1-12, 2011, includes the total of 1728 data observations with ten minutes intervals.
Other measurement was conducted on Civil Engineering Building, National Central University (NCU), Jhongli, Taiwan to obtain actual data of pavement, wall, and roof surface temperature, also meteorological data of NCU.
Concrete pavement real observation data was used as the base for the comparison.

Autonomous Agent �etwork(A��) Based Manufacturing System An Autonomous Agent Network(ANN) agent encapsulates information related to its modeled subsystem product which can be classified as static data and dynamic data.
Static data are remain unchanged when the time and market changes, while dynamic data are changed when the time and outer condition changes.
The advantage for classifying data into static and dynamic is to promote rapid system modeling and information retrieval.
Since static data remain unchanged, system modeling only involves the update and manipulation of dynamic data rather than all data in a modeled system.
In addition, retrieving information becomes much easier due to the systematic classification of data.

When the target is working, the A/D device will convert analog video to digital video data.
The DSP device will store video data into SDRAM in terms of the timing of the blank and synchronization signal.
The code was organized and written for good data locality in order to allow good cache utilization.
Small size data types were chosen so that memory usage is minimized and better parallelism is achieved.
Video input/output raises many performance issues in transferring the video data in real-time.

In addition, it needs to be wired/wireless communication interface to transmit specific data.
For data collection and display devices, there need to collect data and manage equipment.
Sensors are needed to monitor the running status of home appliances .Data collector and equipment are also needed to manage the collected data.
It needs to be connected to the web services, data information displays through the user interface.
The communication protocol used for the exchange of data includes PLC, WiFi, ZigBee, etc.