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Such through cracks were monitored during their propagation along the specimen width, in order to have available for the simulation a realistic initial scenario and experimental propagation data useful for the correlation with the simulated crack path and growth rates.
In order to develop an effective riveted reinforcement methodology it is important to be able to accurately determine the complex stress fields created by the doublers as well as the resulting reduction in the stress intensity factors (SIFs) of a crack on the skin.

On the other hand, the lattice expansion in ionic solids with grain size of below10nm occurs either due to the reduction in cation charge state or repulsion of strong surface dipoles leading to a reduction in surface tension in small crystalline.
For thermal-activated process of sputter-grown nanocrystalline HfO2 films, the Arrhenius plot for the XRD data are shown in Fig.3 (c).
The data indeed fit to a linear relation.
This value is expected for crystalline films with the grain sizes obtained, but higher than that of HfO2 films reported by Ramana et al. [18], when compared to the data of some of the crystalline metal-oxide thin films [19-20].
The packing density calculated from the refractive index data of HfO2 films at l = 633 nm (with ns = 2.1 for bulk material and nv for dry voids) is 0.88, 0.90, 0.94, and 0.95, respectively.

The experimental data showed a good compliance with the pseudo-second-order kinetic model, and the adsorption isotherm data met Langmuir models well.
In the present work, two general-purpose equilibrium models were used to fit the experimental data (see the (3) and (4)).
Fig. 9 Langmuir adsorption isotherm Fig. 10 Frundlic h adsorption isotherm The Fig. 9 and 10 were showed that the Langmuir isotherm was fitted much better to the experimental data than the Freundlich isotherm on the basis of the correlation coefficients (RL2=0.998> RF2=0.9308).
Fe-Al layered double hydroxides in bromate reduction: synthesis and reactivity.

Assessment process The medium-sized city smart city assessment process is shown in Figure 1, mainly includes the city screening, index classification and data collection, normative data and summary of three links.
Index classification and data collection In 74 indicators, 48 (65%) items belong to the regional data, 26 (35%) items belonging to the National data.
According to the formal, free and open database for data collection is chosen as the index attributes, but also some enginee-ring research and use of personal data.
Data specification and ranking summary Z-transformation theory is adopt to standerlize the collected data, converting all the index value to a standard number.
Among them, is the standard number; is the original data; is the average value of index data; S as an index to the standard deviation of X.

The testing methods must be adapted to certify the properties stated in the product data sheet.
Holding force reduction 3.
Incorrect design of the welding tool 1.Correct choice of the hemisphere material 2.Simulations with the finite element method 3.Reduction of the holding force 4.Redesign of the sonotrode (in case of ultrasonic welding) Micro holes on the weld seam 1.Temperature of the molten material in the weld seam 2.Snatches of material when the heating element retracts 3.Hold on time too short 1.Adjust the temperature of the heating element 2.Cleaning the surface of the heating element 3.Increasing the hold on time Table 2 presents the most common defects detected with the Liquid Penetrant method as well as the causes and remedies.
Reduction of welding time 2.Reduction of the temperature of the heating element 3.Reduction of the penetration Uneven discharge of molten material on the inner contour of the weld bead 1.Imperfect contact between the heating element and hemispheres 2.Problems of centering the hemispheres 1.Improve the contact between the hemispheres and the heating element 2.Improve the way of fixing and centering the hemispheres in the holding fixtures Cross sectioning in on the weld seam plan Discontinuities or cracks in the welding seam 1.Welding time too short 2.Too low temperature of the heating element 3.Material with poor welding properties 4.Imperfect contact between the heating element and hemispheres 1.Increasing the welding time 2.Increasing the temperature of the heating element 3.Choosing materials with good welding properties 4.Improving the contact between the heating
References [1] Information on http://data.europa.eu/eli/dir/2019/904/oj; [2] Information on https://www.statista.com/statistics/254668/size-of-the-global-antiperspirant-and-deodorant-market/; [3] Information on https://mhplastics.com/product-range/50ml-roll-on-deodorant-cylindrical/ [4] Information on https://www.wppg.com/products/; [5] Information on https://www.scientificamerican.com/article/new-artificial-eye-mimics-a-retinas-natural-curve/; [6] Information on http://www.socialifechicago.com/snarkitecture-beach-art-installation-coming-to-chicago-navy-pier/; [7] Information on http://www.silverlakereservoirs.org/bird-balls-gallery; [8] Information on https://www.precisionplasticball.com/products/hollow-plastic-precision-balls; [9] W.G.

In order to achieve the aim of the research work, a prototype plant of the process was developed using ChemCAD from which dynamics data were generated upon applications of step changes to the reboiler duty and the reflux ratio, which were the input variables of the system.
Thereafter, the transfer function of the process, later represented in Simulink environment, was formulated using the dynamics data and with the aid of MATLAB.
After obtaining the data, the transfer function model of the process having one output and two input variables was developed with the aid of MATLAB [41].
The analysis carried out on the developed transfer function model equation for the reactive distillation process indicated that the fit (showing how well the model was able to represent the data and consequently the system) of the model towards the reboiler duty and the reflux ratio were 79.31% and 79.25% respectively.
Skogestad, Simple analytic rules for model reduction and PID controller tuning, J.

A three-dimensional (3D) dynamometer loading sensors and autonomous data collection gathering equipment was utilized to calculate cutting force.
These declines are due to a reduction in cutting force.
Surprisingly, the largest reduction happens when the cutting speed is at its highest.
As cutting speed is raised, a reduction in mean surface roughness is observed.
While dry cutting enhances tool life by 27 percent, single jet cryogenic cooling covers tool lifetime by 65 percent according to this graphic data.

Monitoring the forces from each direction, the degree of indirect response to the collision, returning feedback to the docking platform after measurement data is analyzed, and amending the docking trajectory of the BLRU in time can achieve the light collision docking during the full process of BLRU.
It also detects the key parameters of the offline assembly and calibration and then transfers them to online assembly and calibration of the BLRU, which is the space docking process and achieves the online reduction of the key parameters of assembly and calibration.
And then the precise positioning monitoring system on the top of the clean canister is turned on to capture BLRU postures and the related positioning relation of the long rail in the little clean cars and then transfer the data to the docking platform.
After the data is added, the deformation x of the sensor which is also the voltage signal output from the pressure measurement sensor can truly reflect the collision force.
The test data of the force direction from the X-axis direction is shown in Table 1.

Of the available data, half of the cases was used to modify the network weights in the learning process by creating a set of learners.
Spent the rest of the data to evaluate the prediction errors during learning (25% of the data - a set of validation) and to an independent determination of the correctness of the network after it has been created (25% of the data - a set of test).
On the basis of data made regression fit is R2=0.92 and standard error of 28.1.
Due to the serious inaccuracies resulting from regression analysis it was decided to analyze the data using a neural network.
The correctness of the results is highly dependent on the proper preparation of a representative set of experimental data, the use of simplification or even miss some data.

Experimental investigation can provide valuable data for locating the damage and establishing the damage criteria.
Besides this, the correlation of the experimental data with predictive method is difficult since it requires a large number of tests to generate damage curves.
The ability of aluminium foams to mitigate blast wave loads was checked and the test data was extracted to verify the corresponding computer codes.
As reviewed above, carefully designed experiments, no matter scaled or full scale trials, can provide intuitional observations as well as a wealth of useful data for structural response under blast loading condition.
Guyan, Reduction of mass and stiffness matrices, AIAA Journal, 3 (1965) 380.