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Properties of the coarse aggregate Table 2.
Properties of sand Table 3.
Physical and engineering properties of the GFRP I beam Fig. 1.
Kawai, Flexural Properties of Reinforced Recycled Concrete Beams, in: E.
Ramli, Mechanical strength and drying shrinkage properties of concrete containing treated coarse recycled concrete aggregates, Construction and Building Materials 68(0) (2014) 726-739

This software is widely recognized for its capability to optimize and explore the effects of multiple factors on a response variable through statistical models such as the Central Composite Design (CCD).
The anticorrosion efficacy of AGL extract is largely due to the properties of heterocyclic compounds found in the extract.
These results shows that factors such as temperature, time, and extract concentration exert a considerable influence on the corrosion rate.The second-order polynomial model obtained for corrosion rate is expressed in Eq. (5).
The statistical significance of the models was confirmed, and the interaction between responses and factors was demonstrated through three-dimensional plots.
Elechi, Development of regression models to predict and optimize the composition and the mechanical properties of aluminium bronze alloy, Advances in Materials and Processing Technologies, 8(sup3) (2022) 1227-1244

It is hard to get good adhesion and plastic property between copper layer and steel core.
It is necessary to make a finished product through the heat treatment in continuous drawing processing, it is easy to cause coating falling off or cracking, the mechanical properties and electrical characteristics of CCS are affected.
But because the thickness of copper coating layer are affected by some factors such as the temperature of steel, the temperature of liquid copper and the height of liquid surface[17], it is difficult to control the process.

Fig.1 Picture of the cantilever, Fig.2 Schematic diagram of CRAS, Fig.3 Picture of CRAS platform Table 1 Dimensions and physical properties of the cantilever Dimensions (mm) Density (kg/m3) Young modulus (GPa) Poisson ratio 600(L)×120(W)×20(H) 7800 214 0.33 Classical CRAS experimental modal test for modal frequency of the cantilever Experimental modal analysis of the real structure lies in that exciting force and moving response of each discrete excitation point are measured simultaneously, as the real structure is excited by hammer.
CRAS represents a computer random signal and vibration analytic system, which combined with vibration trial, data processing and computer, for experimental modal analysis of physical mechanical structure.
However, the fifth stage of modal frequency is lower than experimental modal test schemes, because that the mass of sensors would significantly affect the results
(3) For experimental modal test, many influencing factors would affect the results, including excitation position, force, direction, position of the sensors glue-attached on the cantilever, the stiffness and the isotropy of the cantilever, the property like adhesiveness and thickness of the glue, the mass of the sensors, stability of the system.

In light of these factors, it is essential to conduct scientific studies to improve the productivity of drilling operations.
The longest drilling time corresponds to the titanium part and its low machinability property.
In addition, all the tools showed some degree of coating loss, typically affecting edge lengths between 1 and 2 mm.
The severity of this type of wear varied among the different fiber-metal tools studied, with some affecting up to one-third of the total length of the main cutting edge and the width of the zone with a loss of coating of up to 0.2–0.3 mm.
Optimization of thrust, torque, entry, and exist delamination factor during drilling of CFRP composites.

Appropriate choices of material properties are necessary to have an accurate simulation of the reinforcement system in numerical modelling.
As listed in Table 1, the properties of the embankment, soft clay and stone columns were obtained from the published work of Alkhorshid et al. [34].
The dimensions and properties of the locally weak zone (Sabkha soil) were chosen to match values stated by Benmebarek et al. [31] as B = 0.6 m and DEP = 3 m respectively width and depth of the LWZ as shown schematically in Fig. 2.
Mechanical improvement of soils below ground surface.
Engineering properties of sabkha soils in the Benghazi plain and construction problems.

Bias of ring laser gyro (RLG) changes with temperature in the non-linear way, which is an important restraining factor for improving the accuracy of RLG.
Temperature characteristics and test of RLG’s bias Temperature mainly impacts RLG’s bias from the following aspects: in terms of heat source, the gyro fevers in working and needs several hours to reach equilibrium, in addition, the temperature field will change more complex when ambient temperature and other conditions change, so the change of both gyro’s own temperature and ambient temperature will affect the performance of the gyro; in terms of physical properties, refractive index of gas, thermal conductivity of materials, optical properties of optical devices will change; in terms of geometrical characteristic, the device’s expansion and contraction, bending can result in optical changes and the increasing loss of resonant system; finally, the change of temperature field causes gas flow’s change, which results in the imbalance of discharge current of two arms, and exacerbates the change of bias that brought by Langmuir effect.
These changes will all affect the output of RLG.
Measurement data at all temperature spots of a certain type mechanical dithered RLG in the experiment is shown as Table 1.

As a new technology of neural networks applied to a wider variety of fields, because it is compared with the conventional neural network control technology has its unique properties, can in theory be any non-linear mapping approach can be an effective solution nonlinear complex systems modeling, forecasting and other issues.
And fault-tolerant neural networks are relatively strong, even if part of the neural network is not functioning, it will not affect the final calculation case.
Learning BP neural network is composed of two back-propagation process forward propagation and error signal composed in the forward propagation, input samples from the input signal into the network layer, and finally sent to the output layer through the hidden layer processing, if the output does not match the expectations, put the back-propagation errors, according to changes in the above weight adjustment factor to adjust the weights until the output of the output layer of the network to meet the requirement.
Patra: Mechanical Systems and Signal Processing, Vol.21 (2007), p.466-479 [5] O.

At present, partial fishing machines are single controlled and work with staffs, it will affect the capture efficiency.
Jigging machine more used in the scene is complex occasions, such as harsh environment, many disturbing factors maritime etc.
In order to avoid the bus communication affect to the master system, we use the way of the optical coupler isolation.
(3) PC receives the return address from the slave, modifies the properties, Settings =“9600，S，8，1”, prepares to issue orders.
[4] GulaiNi:submitted to Fishery mechanical instrument(1996), In Chinese

The results show that the migration of sand with wind blow can be well simulated using SPH method and the embankment height is an important factor for controlling sand cover disaster of desert highway.
The construction and maintenance of desert highway is influenced by many natural factors seriously, the sand cover is the most important factor that affect the use of desert highway [3].
There are two main factors that cause sand disaster: the first is the sand cover caused by the wind-blown-sand flow; the second is the sand cover causing by the dune migration [5] .
The whole flow field is discretized as a series of `particle`, these particles can act as interposition points with material property and also can move acted by the external and internal force as well, and it is also the internal function.
Besides, the velocity of wind has a great impact on the sand cover phenomenon, and the higher the former, the more serious the latter. 0 500 1000 1500 2000 2500 3000 3500 4000 4500 t =0s t =5. 5s t =11s t =22s time number of sand par t i cl e 0- 2m 2- 3m 3- 7m 7- 8m 8- 9m (a) Beginning velocity of wind is 1.0m/s (b) 0 500 1000 1500 2000 2500 3000 3500 4000 4500 t =0s t =5. 5s t =11s t =22s time number of sand par t i cl e 0- 2m 2- 3m 3- 7m 7- 8m 8- 9m (c) Beginning velocity of wind is 3.0m/s Fig.4 The result of numerical imitates There are also many factors that can cause the highway sand cover disaster, the influence of wind blow has been considered in the research.