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Copyright of Journal of Agricultural Science & Technology (1008-0864) is the property of Journal of Agricultural Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Developing a flow rate model for the screw feeder and optimizing discharge performance are crucial for achieving automated intelligent precision feeding. This study constructs a mass flow rate model for screw conveyors, considering the coupled structural parameters of the hopper and screw conveyor. The model is developed using single-factor tests and central composite design (CCD) response surface tests and is validated through actual discharge tests. Results indicate that the discharge rate in the hopper–screw conveyor system is primarily influenced by the screw conveyor itself. Among the structural parameters, the hopper inclination angle and the hopper discharge opening length significantly affect the filling coefficient. Validation tests show an average error of 6.8% between the predicted and simulated mass flow rates and 5.0% with the actual mass flow rate, demonstrating the model's high precision and accuracy. [ABSTRACT FROM AUTHOR]

Rice is a widely cultivated food crop worldwide, and threshing is one of the most important operations of combine harvesters in grain production. It is a complex, nonlinear, multi-parameter physical process. The flexible threshing device has unique advantages in reducing the grain damage rate and has already been one of the major concerns in engineering design. Using the measured test database of the flexible threshing test bench, the rotation speed of the threshing cylinder (RS), threshing clearance of the concave sieve (TC), separation clearance of the concave sieve (SC), and feeding quantity (FQ) are used as the input layer. In contrast, the crushing rate (YP), impurity rate of the threshed material (YZ), and loss rate (YS) are used in the output layer. A 4-5-3-3 artificial neural network (ANN) model, with a backpropagation learning algorithm, was developed to predict the threshing performance of the flexible threshing device. Next, we explored the degree to which the inputs affect the outputs. The results showed that the R of the threshing performance model validation set in the hidden layer reached 0.980, and the root mean square error ( R M S E ) and the average absolute error ( M A E ) were less than 0.139 and 0.153, respectively. The built neural network model predicted the performance of the flexible threshing device, and the regression determination coefficient R2 between the prediction data and the experimental data was 0.953. The results showed revealed that the data combined with the ANN method is an effective approach for predicting the threshing performance of the flexible threshing device in rice. Moreover, the sensitivity analysis showed that RS, TC, and SC were crucial factors influencing the performance of the flexible threshing device, with an average relative importance of 15.00%, 14.89%, and 14.32%, respectively. FQ had the least effect on threshing performance, with an average threshing relative importance of 11.65%. Our findings can be leveraged to optimize the threshing performance of future flexible threshing devices. [ABSTRACT FROM AUTHOR]

Objective To investigate the effect of plan-do-check-act (PDCA) cycle in quality management of health education for patients with anorectal disease in the perioperative period. Methods The PDCA cycle method was used in quality improvement of health education for patients with anorectal disease in the perioperative period. Totally 81 patients undergoing surgical treatment for anorectal disease between April and June 2018 were set as the control group, another 77 patients underggoing surgical treatment for anorectal disease between August and October 2018 were set as the observation group. The qualified rate of health education and satisfaction degree of patients were evaluated and compared between two groups. Results The qualified rate of health education in the observation group was higher than that in the control group (P < 0.05). The patient satisfaction score was higher in the observation group than that in the control group (P < 0.05) . Conclusion The PDCA cycle is effective to improve the quality management of health education for patients with anorectal disease in the perioperative period. [ABSTRACT FROM AUTHOR]

Using the theory of two-phase flow dynamics, the law of two-phase flow formed by airflow and sundry grain in the threshing cleaning shunt tube of the 4LZ-0.8 type of rice combine harvester is studied in this paper. The movement differential equation of sundry grain flow is established and the sundry suspension speed is deduced. A method for calculating the suspension velocities of the particles with different sizes and densities is analyzed in the paper. The curve of the ratio of the highest sundry particle velocity to the air velocity varying with air velocity is obtained. The pressure loss in the air and debris flow in shunt tube is analyzed, and the most economical cleaning air velocity under a certain mass flow is calculated. All of these provide a theoretical basis for the parameter design of 4LZ-0.8 type of rice combine harvester's threshing cleaning parts. Material in the flow field is subjected to aerodynamic drag and the role of its own gravity which are related to the size of the pneumatic resistance, the velocity of airflow relative to sundry, the density of material, the shape and so on. When the airflow resistance equals to material gravity, the material in the air keeps in the stable state of suspension. The velocity of the material in a stable floating state is known as the floating velocity (or terminal velocity), and it is only related to the density and shape of the material itself. Taking advantage of the nature that the floating pneumatic resistance is equal to the gravity when material floating, the relationship of the Reynolds number and floating resistance coefficient would be reached. Taking logarithm of both sides of the relationship expression of debris floating and floating resistance coefficient, with logarithm of floating Reynolds number as the horizontal axis, and logarithm of floating resistance coefficient as the vertical axis, the curves for a given dynamic viscosity and density of air are drawn, and a set of straight lines with the same slope can be obtained for the particles with given sizes and densities. Draw the logarithm curve of resistance coefficient and Reynolds number in the same coordinate system, and by the intersections of the curve and each line, the particles with different sizes and densities can get suspended Reynolds number, and floating velocity is obtained after the corresponding computation. In addition, this paper has studied the pressure loss in the process of cleaning, and analyzed various influence factors of pressure loss and their effects on the quantitative relationship of pressure drop. In order to compare the various pressure drops, ascension pressure drop, acceleration pressure drop, suspension pressure drop, friction pressure drop and the total pressure drop curve were drawn in the same coordinates. The velocity corresponding to the lowest pressure in total pressure drop curve can be defined as economic speed, i.e. when cleaning with the speed, pressure loss in the flow separation barrel is minimal. The results can provide the reference for aspirated cleaning of other crops. [ABSTRACT FROM AUTHOR]