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While the KIcPL values decreased up to a loading rate 103 MPam1/2s-1 an increase with a high data scatter was observed above them.
Corresponding to the CTOD values the calculated KICCTOD values revealed a slight decrease and moderate data scatter up to the maximal loading rate.
The conduction and the data reduction of these tests are time consuming and require experienced operators and comprehensive instrumentation.
In a closer view, the data of previous measurements using a former test set-up show constant fracture toughness with low scatter, the new measurements reveal very high scatter and increase.
These CTOD values reveal a significantly lower data scatter and a clearer tendency (increasing loading rate and decreasing CTOD values) than the force based KIc values.

IoT systems empower the comprehensive collection of data pertaining to various aspects of road infrastructure.
This invaluable data serves as the foundation for the development of innovative strategies aimed at curbing urban pollution [15].
Alternatively, employing cameras with integrated embedded PCs, such as the LTM sensor, enables on-site data processing.
These systems offer real-time data, forecasting capabilities, and the identification of vehicles parked in unauthorized zones.
Such data informs city planning and infrastructure development, leading to more efficient urban designs and transportation systems.

Data on operation time, material handling time, die fabrication cost are collected, analyzed and discussed.
The combination die explained in the method 4 is chosen to study for the reduction in the manufacturing costs.

For the comparison of FE analysis result with the measured data of static test, a 3.5m down-scaled wind turbine blade was designed and fabricated using glass fiber epoxy composite materials.
For the reliable structural analysis, a reduction factors (R.F) are applied to the material properties values.

To do this, existing data is used.
For modeling the process, different types of regression equations including linear polynomial, Quadratic polynomial and logarithmic function, are used to interpolate experiment data.
First experimental data and selected material is introduced.
Experimental Method Existing experimental data is used [10].
The data is based on a modified orthogonal array in Taguchi method.

Creep loci evaluation based on the experimental data and model predictions are compared.
When stress enhancements due to the grain boundary sliding are taken into account, the predicted creep loci correlated well with that constructed from the experimental data.
X-ray diffraction is mostly used on the 'composite' specimens (tubes slices stacked together) along three orthogonal directions, z (axial), radial (r) and hoop (θ) for obtaining pole figure data.
Direct pole figure data are needed for the determination of crystallite orientation distribution functions (CODF) and slip-dominated predictions.
Dissipative energy rates ( .W ) can be calculated from the stress-strain rate data of creep tests performed under various stress ratios via the following equation . . .

Line loss rate forecast can help supply companies develop reasonable loss reduction and energy efficiency goals.
This article based on less information needs in gray system model, and with high accuracy, as well as how much of the raw data is not the demanding requirements of characteristics [6].
Gray relational analysis by the geometric relationship of the system data sequence to analyze the degree of correlation between the various factors in the system [7], The use of gray relational grade to determine the line loss rate greater impact variable and as the neural network input variables .
Grey Forecasting Model For Line Loss A.The basic model Gray prediction model GM (1,1) is frequently used ,and the model building process as follows: (1)Accumulated generating operation for the original data sequence: (1) (2) (2)The establishment of differential equations (3) (4) And in the formula (5) (3) a, b, back to the original differential equations: (6) Regressive to get the original data: (7) B.Correction Model In order to improve prediction accuracy, combining the initial data with the predicted results optimized.
The application of double BP neural network combined forecasting model in real-time data predicting)[J].

The paper presents the design of a small-scale building integrated photovoltaic micro-grid system, which is a master-slave system with bi-directional battery inverter as the core of the system.A photovoltaic energy system and management system are established, which has the function of real-time data monitoring and logging, switches different micro-grid operation mode.
(2)Getting the original measurement data using real-time monitoring of the PV system can provides useful data for system improvement and optimization and scientific research.
Such data is the most original and accurate, which gained by remote monitoring without human intervention factor.
Therefore, this system is the energy monitoring system based on the network of WEB, which is mainly responsible for the equipment condition monitoring, historical data logging, real-time data display and remote transmission, fault diagnosis and alarm, and the achievement of control algorithm (mode switching function)as well.
Due to the function of PC configuration achieved by WEB-based configuration software in energy monitoring system, the function of remote data transfer and remote control can be implemented through the Internet, which facilitates remote engineers, debugging and maintaining the equipment.

The ANFIS model has been developed on the basis of full factorial experimental data.
Therefore, prediction and reduction of SAW distortion are critical to improve the quality of welded structures.
Twenty seven test experiments of SAW square butt joint were conducted to obtained data for ANFIS modelling.
These adaptations were used as training data for the assessment of ANFIS membership functions.
Fig. 4 Simulation block of ANFIS model The percentage error calculated from experimental data and predicted data of ANFIS are presented in Table 1.

Data analysis showed that there was a converse “U” type relationship between industrial water demand and industrial value added.
Data sources and select sample data Industrial value added data was from the "China Statistical Yearbook".
Industrial water demand data was from the Ministry of Water Resources, "Water Resources Bulletin".
We applied historical data from 2001 to 2013 to model industrial water recycling rate.
The industrial structure and policy factor was not included as variables in the model due to limitations of data.