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In order to solve a few problems of silage cutting device and baling device, the position-variable cutting device and rubber "herringbone" steel roll baling device are designed. Using the finite element devices to simulate the main components, and comparing the forces of the two tool materials, it is concluded that 60 Mn steel has better cutting effect compared with 45 steel; The radial deviation between the tool center axis and the theoretical axis is small to 1 mm, which satisfies the requirements, and the test analysis of 5 tools at the constant tool speed shows that the best uniformity appears when the amount of tools is 32, which reaches 91%. By analyzing the linear relationship between fragmentation uniformity and baling density, we conclude that fragmentation uniformity is linearly related to bale density. The motion analysis of the grain accumulation in the baling shows that the angle between the lower plane and the horizontal plane of the synchronous zone directly affects the degree of straw compaction. The improved position-variable cutting device can meet the crushing needs of a large range of different crops, and the floating steel roller design can increase the friction between the axial surface and the straw. Compared with traditional machines and tools, the whole improved device has certain advantages of cutting and baling. [ABSTRACT FROM AUTHOR]

Single-particle tracking is a powerful approach to study the motion of individual molecules and particles. It can uncover heterogeneities that are invisible to ensemble techniques, which places it uniquely among techniques to study mass transport. Analysis of the trajectories obtained with single-particle tracking in inorganic porous hosts is often challenging, because trajectories are short and/or motion is heterogeneous. We present the DiffusionLab software package for motion analysis of such challenging data sets. Trajectories are first classified into populations with similar characteristics to which the motion analysis is tailored in a second step. DiffusionLab provides tools to classify trajectories based on the motion type either with machine learning or manually. It also offers quantitative mean squared displacement analysis of the trajectories. The software can compute the diffusion constant for an individual trajectory if it is sufficiently long, or the average diffusion constant for multiple shorter trajectories. We demonstrate the DiffusionLab approach via the analysis of a simulated data set with motion types frequently observed in inorganic porous hosts, such as zeolites. The software package with graphical user interface and its documentation are freely available. [ABSTRACT FROM AUTHOR]