Experimental Study on the Crushing Properties of Corn Stalks in Square Bales

With the development of straw baling mechanization technology, straw is stored in the form of square baling or round baling. At present, hammer mill or the guilt-cutting and rubbing combined mill is widely used to crush square bales of straw. These two kinds of crushing equipment have disadvantages such as low productivity, large power consumption, and poor crushing effect. This paper aims to study and analyze the crushing characteristics of square baled straw after unbaling, and lay a theoretical foundation for the later research and development of a special square baled straw crusher with high productivity, low power consumption, good crushing effect, and the simulation of the square baled corn straw crushing process. For this purpose, this study carried out a corn bale crushing experiment on the Instron 8801 fatigue test machine, and studied the effects of blade angle, water content and loading speed on corn bale crushing force through the response surface method. Test results showed that the crushing process includes the compression stage and shearing stage; in terms of single factor effect, with the increase in water content and blade angle, the crushing force of the corn bale increased, but the loading speed had no significant effect on the crushing force of the corn bale. In terms of interaction effect, there was interaction effect between moisture content and blade inclination angle, when moisture content was 10%, with the increase in blade inclination angle, the incremental speed of the crushing force also increased gradually. When the blade inclination angle was 10°, with the increase in moisture content, the incremental speed of the crushing force also increased, and the interaction effect of them jointly acted on the crushing force of the corn bales.

Machine learning approaches in Covid-19 severity risk prediction in Morocco

The purpose of this study is to develop and test machine learning-based models for COVID-19 severity prediction. COVID-19 test samples from 337 COVID-19 positive patients at Cheikh Zaid Hospital were grouped according to the severity of their illness. Ours is the first study to estimate illness severity by combining biological and non-biological data from patients with COVID-19. Moreover the use of ML for therapeutic purposes in Morocco is currently restricted, and ours is the first study to investigate the severity of COVID-19. When data analysis approaches were used to uncover patterns and essential characteristics in the data, C-reactive protein, platelets, and D-dimers were determined to be the most associated to COVID-19 severity prediction. In this research, many data reduction algorithms were used, and Machine Learning models were trained to predict the severity of sickness using patient data. A new feature engineering method based on topological data analysis called Uniform Manifold Approximation and Projection (UMAP) shown that it achieves better results. It has 100% accuracy, specificity, sensitivity, and ROC curve in conducting a prognostic prediction using different machine learning classifiers such as X_GBoost, AdaBoost, Random Forest, and ExtraTrees. The proposed approach aims to assist hospitals and medical facilities in determining who should be seen first and who has a higher priority for admission to the hospital.

RESEARCH ON MATERIAL FLOW-STRESS BEHAVIOR AND THE DIE-FORGING PROCESS FOR FORGED-STEEL BRAKE DISCS FOR HIGH-SPEED TRAINS

Due to the large size and complicated features, the brake discs of high-speed trains are difficult to forge, so a reasonable design of the process and the die parameter are prerequisites for successful forming. The flow stress of 23CrNiMoV, a forged-steel brake disc material for high-speed trains, was investigated by a uniaxial compression experiment on a Gleeble 1500 test machine. Based on the obtained flow-stress data, a series of numerical simulation analyses of the die forging of high-speed-train brake discs were carried out by using finite-element software. The effects of forging temperature, flash groove parameters and forming speed on the flow filling, forming load and temperature change of metal during die forging were studied. The simulation results were optimized and better process parameters were obtained. Based on the obtained process parameters, the simulation of the forming process was completed and a better forming quality was obtained.

Pseudo-random number generator based on linear congruence and delayed Fibonacci method

Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators - a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and TestU01.1.2.3 tests and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.

Machine learning applications to predict two-phase flow patterns

Recent advances in artificial intelligence with traditional machine learning algorithms and deep learning architectures solve complex classification problems. This work presents the performance of different artificial intelligence models to classify two-phase flow patterns, showing the best alternatives for this specific classification problem using two-phase flow regimes (liquid and gas) in pipes. Flow patterns are affected by physical variables such as superficial velocity, viscosity, density, and superficial tension. They also depend on the construction characteristics of the pipe, such as the angle of inclination and the diameter. We selected 12 databases (9,029 samples) to train and test machine learning models, considering these variables that influence the flow patterns. The primary dataset is Shoham (1982), containing 5,675 samples with six different flow patterns. An extensive set of metrics validated the results obtained. The most relevant characteristics for training the models using Shoham (1982) dataset are gas and liquid superficial velocities, angle of inclination, and diameter. Regarding the algorithms, the Extra Trees model classifies the flow patterns with the highest degree of fidelity, achieving an accuracy of 98.8%.

Evaluation Flexural Strength of PMMA Resins with the Addition of Nanoparticles

The flexural strength of heat cure acrylic resin was investigated by adding different concentrations of TiO2 and ZrO2 nanoparticles to increase its mechanical properties. ZrO2 and TiO2 nanoparticles were added at 1, 3, and 5% concentrations to the powder portion of heat polymerized acrylic resins. A total of 49 samples were prepared in 65 × 10 × 3 mm size. The structural characterisations of all experimental groups were determined by Fourier transform infrared spectroscopy. Flexural strength of the resin specimens was evaluated with a three-point bending test in a universal test machine and then examined under by scanning electron microscope to assess its topographic characteristics. The highest flexural strength value was obtained for 3% TiO2, while the lowest values were obtained for 1% and 5% TiO2. 1% ZrO2 and 3% TiO2 groups showed statistically higher flexural strength values than the control group. Addition of 3% and 5% ZrO2 and 1% and 5% TiO2 showed statistically lower flexural strength than the control group.

The Effect of Dentin Treated with Calcium Phosphate Desensitizer on Shear Bond Strength with Different Resin Cement: An In Vitro Study

Aim: The aim of this study was to evaluate the effect of calcium phosphate based desensitizing agent on shear bond strength of self-etch/adhesive resin cements to dentin. Materials and Methods: Eighty dentin specimens were prepared from freshly extracted human third molar teeth and were classified, randomly ( n = 20). Half of groups were treated with calcium phosphate based Teethmate Desensitizer and then Panavia V5, RelyX Ultimate (containing self-etch primer), Panavia SA, and RelyX U200 self-adhesive resin cements were luted to all dentin surfaces. All specimens were stored in an incubator at 37°C for 24 h. Shear bond strength was tested by a universal test machine at a 0.5 mm/min crosshead speed. The data were analyzed with a statistical program. Two-way ANOVA was used for statistical differences ( P <.05). Dentin surfaces were examined with scanning electron microscopy (SEM) at x5000 and x10000 magnifications. Results: The higher shear bond values were observed in the groups with Teethmate Desensitizer applied and cemented with self-etch (16.05 ± 6.24 and 14.73 ± 4.75), whereas the lowest bonding values were observed in the groups with self-adhesive resin cement without Teethmate Desensitizer (3.73 ± 0.77 and 5.85 ± 4.19; P <.001). As the main effect of the treatment, the bond strength was 9.39 ± 6.04 in the control group, whereas it was 13.49 ± 5.44 in the Teethmate applied groups ( P <.05) Conclusions: Calcium phosphate desensitizer did not adversely effected shear bond strength of self-etch/adhesive resin cements to dentin. It even significantly increased the shear bond strength of self-adhesive resin cements.

Technique to Investigate Pulverizing and Abrasive Performance of Coals in Mineral Processing Systems

The operating costs of breaking coal particles into fine powder, to achieve optimum combustion for the boilers in a power plant, are made up of power input to carry on an energy intensive comminution mechanism and to overcome friction losses within pulverising machines. The operating costs also include the cost of the replacement of the processing system’s components due to wear. This study presents the development and application of an attrition test machine that enables an investigation of the factors that influence pulverizing efficiency. The attrition tester simulates grinding conditions in real vertical spindle mills. In this kind of mill, as with the tester, the size reduction process results from a shearing action during the redistribution of the coal particles. The redistribution and attrition within the coal bed are forced by movement of the rollers (or by a disc rotation, in the case of the tester). The testing method was oriented toward mechanical properties, i.e., internal friction shear strength, abrasiveness and grindability. This method enables facilitated testing procedures and a more exact simulation of grinding in vertical spindle coal mills. Ball-race mills and Loesche roller mills were used.

Effect of Fullerenes Additions on Physical – Mechanical Properties of Hot-Forged Iron-Based Powder Materials

It is possible to expand the applications ranges of powder material products by enhancing the performance properties of these products in addition to their manufacturability and reliability together, it’s possible by materials structures modification. In this paper, the effect of fullerene (C60) additives to iron-based powder material has been studied. All samples produced by Hot-Forging (HF) powder materials technology. Green and HF density of the obtained samples calculated by volume / weight and Archimede’s principle, respectively. The effect of technological parameters on the microstructure of carbon steels’ samples was done by an ALTAMI MET-1M metallographic microscope. Tensile test executed by using of a universal testing machine UMM –5 and the microhardness (HV10) was measured by REICHERT hardness test machine. The results showed that the HF C60 steels’ samples had higher density and strength of 0.81 and 25%, respectively, with a good plasticity in comparison with graphite steels’ samples.

Testing the magnetoactive elastomer sample for compression with the test machine

A sample of a magnetoactive silicone composite with ferromagnetic fillers is examined on a testing machine. The dependences of the change in the values of the moduli of longitudinal elasticity on deformation are plotted for various modes of compression of the sample. The characteristics of the linear and nonlinear dynamics of changes in the moduli of longitudinal elasticity are given as a function of the magnitudes of the deformations of the material during compression. Within the limits of deformation of the sample, which is 24% of its height, the moduli of longitudinal elasticity are linear. The nonlinear nature of the change in the compression modulus occurs when the sample is deformed over 40%. When the compression ratio of the sample was up to 72%, the compression modulus increased by a factor of 9 without the action of a magnetic field and by a factor of 22 under the action of a magnetic field. The influence of the magnetic field on the growth of the compression moduli with the increase in the compression force ranges is shown. The property of the material to self-healing (“shape memory”) was established after testing in the mode of maximum compression of ultimate loads.