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However, these studies often overlook the associated reduction in compressive strength when crumb rubber replaces fine aggregates, leading to comparisons between concretes with different strengths.
This oversight can lead to misconceptions about the true benefits of crumb rubber, as the reduction in compressive strength caused by rubber inclusion may itself influence the observed improvements in dynamic behaviour.
A Data Acquisition System (DAS) records the impact time history, while a 200 kN load cell attached to the impactor measures the impact force.

Paper presents and discusses data obtained in a testing program on the replacement of limestone powder in asphalt concretes by fly ashes.
Economic consequence would be a reduction in demand for bitumen.
Average data of the Marshall Test are presented in Table 5, and in Fig. 3 and Fig. 4.
Figure 5 presents the basic data of the creep tests.
Experimental data on ultimate tensile stress and strain, Poisson’s ratio and Young’s modulus of six samples (three with Dutch type of fly ash - DF – three normal asphalt concrete specimens with limestone powder as filler – ST) are presented in Table 6.

In contrast to the very rich literature on modeling and the determination of the thermal conductivity of nanofluids [4-6] the forced convection data are limited.
They observed that there is no systematic tendency in heat transfer coefficient for water-Al2O3 nanofluid, except for that the 1%vol. data are lower by about 10% as pure water data with the same boundary conditions.
The data show that higher heat transfer coefficients were achieved mostly in the entrance region of micro-channels.
Photographs of the tested nanofluids Data reduction and uncertainty estimation The Nusselt number is calculated as: (1) where the thermal conductivity of the tested nanofluids is taken from [16].

The data were correlated to develop the relationship Sh α Re x 1 Introduction In the recent years, the water contamination by heavy metals in both surface and ground water raised the environmental alert for many scientist and engineers due to their toxicity and impact on human and aquatic life [1].
Cementation is used as a general term to describe the process whereby a metal is precipitated from a solution of its salts by another electropositive metal by spontaneous electrochemical reduction to its elemental metallic state.
Fig. 4a Plot of Log Re vs Log Sh among various surface roughness with reference to the smooth surface @ conc 0.05 N Geometry 90° Fig. 4b Plot of Log Re vs Log Sh among various surface roughness with reference to the smooth surface @ conc 0.05 N Geometry 45° The data for four blade 90° turbine impeller (radial flow type) fit the equation Sh α Re0.69.
The data for four blade 45° pitch turbine impeller (axial flow type) fit the equation Sh α Re0.74.
Data Corporation (1973)

Regarding the grain boundary self-diffusion in nickel, a large amount of scattered data and discrepancies related to incorrect application of diffusion regimes can be found in the literature.
In the present paper we undertake a systematic diffusion and microstructure study to understand the reason for the diffusion data dispersion.
The obtained data were further analyzed in terms of the CSL model.
In the process of data evaluation, only the profile tail was smoothed.
Kozma: Handbook of Grain and Interphase Boundary Diffusion Data (Ziegler Press, Stuttgart 1989)

The results obtained in the welding environment by performing remote measurements based on PM (particulate matter) analysis, sensor data and received signal strength (RSS) have shown that it is possible to detect the areas affected by fumes and with improper climate conditions, to track hazardous objects and to control operations in real-time.
A mote can provide data containing all these parameters.
In Fig. 5 the WSN is formed from 5 such nodes which communicate data regarding humidity, temperature and light from sensors to the gateway (GW) in real time.
In the context of unpredictable transmission medium [7] such as an industrial environment, unreliable data communications and distance estimation are common issues that are addressed in order to perform precise tracking of moving (hazardous) objects and to develop proper strategies to deal with interference, attenuation and multi-path that affect the network performance.
Conclusions Based on the experimental measurements it was possible to determine: - the areas affected by the fumes from welding processes (PM2.5 and PM10 data); - the areas with improper work conditions (sensor data); - the mapping of the transmission medium (RSSI data); -the impact of in-band interference; - location of hazardous objects (RSSI data); - the measures necessary to reduce dust particles concentration (ventilation) and improve environmental conditions (climate control and lighting).

The formation of a simple pattern of rough sets theory has the following procedures: first of all, classify the data of little difference involved in a decision information system, then establish indistinguishable relationship, and finally carry out attributes and attribute values reduct in the decision table so as to eliminate unnecessary rows and unimportant attribute values. 1.2 Characteristics of rough sets theory in dealing with data Rough sets don’t require priori knowledge but only relying on the information of data themselves.
A rough set is a powerful tool for analyzing data.
It can firstly express and deal with incomplete information, secondly simplify data and obtain the minimal expression of knowledge while maintaining key information, thirdly identify and assess dependency among data and bring to light a pattern of simple concept and lastly obtain rules and knowledge which are easy to prove from empirical data.
We get Table 2 after coding the data in Table 1.
Applying Distinguishable Matrix of Rough Sets in Attribute Reduction of Concept Lattices[J].Computer Engineering, 2004, 30(20)

Through environmental factor monitoring sub platform, can realize the crop growth environment real-time monitoring, get obtain information and provides basis data for intelligent decision-making sub platform.
Environmental factor monitoring sub platform mainly realize of temperature, humidity, light, carbon dioxide and other environmental factors data collection in agricultural production and video surveillance in field production.
Among them, the sensor is responsible for collecting temperature, humidity and other environmental factors data, and sends these data through the bus to the concentrator; The camera transports video data through the video line to the concentrator; The concentrator includes an XML encoder, all data is encoded into XML format before it communicate with the server.
Fig.2 Structure diagram of sensor communication Fig.3 Intelligent decision-making sub platform’s work flow 3.2 Intelligent decision-making sub platform Intelligent decision-making sub platform provides timely decision support and control according to the real-time monitoring of the environmental factors data and data variation.
A variety of monitoring data are inputted from the input module, this sub platform will generate an alarm signal as soon as the data reaching certain threshold, and then alarm user through sound, Email, SMS and other forms; At the same time, according to the abnormal condition, the decision module independent from the knowledge base to find solutions for automatic control, and the control information is delivered to each actuator through the output module.

Our data seems to follow the predicted Bloch behavior; however, best fit is only obtained when a and b both left as free-fitting parameters.
The excellent agreement with experimental data by varying both these parameters is shown in Figs. 3(a-c).
The errors in the Tirr and Tmax are shown in brackets with respective data.
Fig. 4 shows the ZFC data of sample C, which exhibit very broad hump and hence not fitted with the theory.
AC susceptibility data of unirradiated and irradiated samples Fig. 6 represents AC-susceptibility (cac) data of unirradiated and irradiated samples designated by solid line and open circles, respectively.

The data to be extended can be monitored and searched through FIR technique.
Fig. 1 Fuzzy inductive Reasoning Process The fuzzification process of FIR is to convert a quantitative raw data to a qualitative raw data which includes a discrete class value, a fuzzy membership value and a side value.
End Extending Algorithm Assume the underlying signal is a data vector .
Step 6: Let the fuzzified form of vector data be the current input state for front endpoint extension and the fuzzified form of vector data the current input state for back endpoint extension.
Repeat step 4 and step 5 to obtain the second extended data and respectively.