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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

: Alfalfa is compressed into pellets, which can not only solve the problem of storage and transportation, but also maintain the nutrition of alfalfa. Therefore, alfalfa pellet has broad market application prospects. In the compression process, the compression piston overcomes the material deformation resistance, the friction between the materials and the friction between the materials and the inner wall of the die. Researching the effect of pressure on the material in the die is helpful to reveal the compressing mechanism and select the reasonable compression parameters. The discrete element simulation analysis provides an effective method for the stress research in the process of alfalfa straw densification. In the simulation analysis of alfalfa straw compression process with EDEM software, the accuracy of the input parameters has significant influences on the simulation results. The simulation parameters used in EDEM software for simulation of alfalfa straw compression were calibrated based on the combination of physical experiment and simulation optimization design in this study. The physical experiments of the contact parameters alfalfa straw-alfalfa straw and alfalfa straw-45 steel were carried out with test equipments such as inclinometer and high-speed camera, and the collision recovery coefficient of alfalfa straw-alfalfa straw ranged from 0.1 to 0.3 while alfalfa straw-45 steel was 0.1-0.6, static friction coefficient of alfalfa straw-alfalfa straw was 0.3-0.6 while alfalfa straw-45 steel was 0.2-0.8, and rolling friction coefficient of alfalfa straw-alfalfa straw and alfalfa straw--45 steel were 0-0.3, respectively. Based on the physical experiment results of contact parameters, the Plackett-Burman test was used to screen the initial parameters. The results of variance analysis showed that the static friction coefficient of alfalfa straw-alfalfa straw, rolling friction coefficient of alfalfa straw-alfalfa straw, static friction coefficient of alfalfa straw -45 steel had a significant effect on the simulation repose angle. Taking the relative error value of repose angle as the evaluation index, the steepest climbing test was carried out to optimize the range of significant parameters. Based on the Box-Behnken test, the second-order regression model of repose angle and significance parameters was established, Taking the 38.88° repose angle obtained from the physical experiment as the target value, the significance parameters were optimized and the optimal combination of parameters was obtained: alfalfa straw-alfalfa straw static friction coefficient 0.45, alfalfa straw-alfalfa straw rolling friction coefficient 0.08, and alfalfa straw -45 steel static friction coefficient 0.54. Finally, the parameters were used in the simulation experiments and repeated three times. The simulated repose angles of alfalfa straw were 39.62°, 37.83°, 38.69°, respectively. The results of T-test (P = 0.778> 0.05) with Origin 2018 software indicated that there was no significant difference in repose angle between the simulation and the physical test, which verified the reliability of the optimal parameter combination. The results showed that it was feasible to calibrate the contact parameters of alfalfa straw with the above optimization experiments. The calibration method can provide reference for the discrete element parameter calibration of other materials and the parameters obtained by the calibration method in this study can provide a parameter basis for the further study on the forming mechanism of the material block and the law of stress transfer in the material layer by using the discrete element method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department 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.)

Corn stalk is abundant in China, which is usually burnt in the field, resulting in environmental pollution and resource waste. Therefore, the recycling of corn stalks is urgent and necessary. However, the cost of transportation and storage is very high because of its loose structure and low bulk density. The best solution for this problem is to compress it into briquettes, pellets, or logs. Due to the rheological properties of corn stalk during the compressing process, the present compressing equipment commonly has the problems of high energy consumption, low efficiency, and quick wear of the key components, which are the bottleneck of its application and extension. At present, a lot of researches on the compression rheological properties of agricultural materials have been done by many domestic and foreign scholars, and stress relaxation has been analyzed by ideal mechanical components and mechanical models. But the most researches are based on the way of traditional compression. There is no relevant research about the influence of assisted vibration on stress relaxation during compression. In order to solve the problems of compressing equipment, assisted vibration was introduced into the compression process of corn stalks in this paper. By using a vibration-assisted densification test system, the stress relaxation experiments for without and with assistive vibration at supplied pressure of 0.4 MPa were performed in the same conditions: the moisture content of corn stalks at 18%, the particle sizes between 1 and 4 mm, the compression speed of 4.6 mm/s, the feeding rate of 15 g, and 10 s relaxation time. Through fitting the experimental data in MATLAB, the stress relaxation of corn stalk in the compression process with and without assistive vibration was expressed by a 2-order Maxwell model, and the coefficients of determination for the models were both greater than 0.9, which indicated that the fitting model was agreeable with the practical conditions. Based on the rheological model, the parameters of rheological model, such as the velocity of stress relaxation, the stress relaxation time, and the elastic restoring force were analyzed. And the results were as follows: from the curves of stress relaxation rate, it could be seen that the stress relaxation velocity obviously slowed down after 5 s without assistive vibration and slowed down after 3 s with assistive vibration during the compression. According to the models of stress relaxation, the stress relaxation time of the first and second order Maxwell models was respectively 0.96 and 99.4 s under the compression without vibration, and respectively 0.57 and 86.96 s under the compression with vibration. The proportion of residual elastic restoring force under the compression without and with vibration was respectively 77.2% and 76.5%. The results show that the vibration can increase the stress relaxation rate, lower the stress relaxation duration and internal residual stress of the block, and thus reduce the elastic deformation recovery after the block is extruded out of the die, improve the quality of product, and reduce the additional resistance when compression again; at the same time, the vibration can greatly save the time of stress relaxation when the block is compressed to the same density. The research provides a new way of lowering energy and raising production efficiency for the compression equipment. [ABSTRACT FROM AUTHOR]

Sheep farming husbandry in western region of China has been developing in the manner of intensive and large-scale facility production. Due to the high density of sheep in house, any unusual behavior, such as sheep fighting, will cause a great loss if the sheep farmer is not aware of its happening and takes measures in time. Since the sound from sheep can not only reflect the status of sheep's health status but also can reflect its response to environment, the behaviors of sheep can be determined by monitoring the sound from the sheep house. This will provide a theoretical basis on evaluation of the welfare level of sheep raising and breeding. In this study, by establishing an audio signal acquisition system for sheep house based on wireless network, the sound signals from 20 sheep under 5 kinds of behaviors (fight, hunger, cough, bite, and search companions) were collected, and then these signals were processed and 720 clear and non-overlapping sound samples were selected in software of Audacity. Although Mel Frequency Cepstrum Coefficients (MFCC) has been widely used for feature extraction of animal sound signals due to its capacities of simulating the processing of speech by human ear and its better noise resistance, it neglects the different contribution of each feature component in characterizing the sound signals from the sheep house. Therefore, in this study, a weighted MFCC method was proposed based on the entropy value method to improve the recognition rate of sheep's sounds. The weighted MFCC with its first and second order differential was optimized to obtain a 19-dimension feature parameter via principal component analysis. The recognition model of support vector machine binary tree, in which parameters of four classifiers were worked out through grid optimization test, was completed according to the sonograms rendering of five different sheep behaviors. And then, these behaviors were recognized and classified respectively with improved MFCC, traditional MFCC and Linear Prediction Cepstrum Coefficient (LPCC). The results demonstrated that the average recognition rate with the improved MFCC for five different sheep behaviors was up to 83.6%, which was 14.0% and 26.7% higher respectively than MFCC and LPCC. The recognition rate of sheep fight and sheep cough were only 80.6% and 79.5%, respectively, because fight sound and cough sound had similar short outbreak characteristics. The bite sound recall rate was reached to 92.5%, showing that the bite sound was with distinguished feature and uneasy to be confused with other sounds. So, the improved MFCC showed the better performance in characterizing the sounds from sheep house, and in raising the recognition rate of sheep behaviors. Modern techniques of sound analysis have provided tools for analyzing and classifying animal sounds. Taking advantage of this, future bioacoustical research on welfare assessment should focus on comprehensive studies of a broad spectrum of animal specific distress vocalizations. Increasing precise attributions of such utterances to environments, behavioral contexts and relevant physiological parameters will lead to a deeper understanding of their meaning and significance with respect to well-being of farm animals. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)