In the fresh tomato market, it is crucial to sort and sell tomatoes based on their quality. This is important to enhance the competitiveness and profitability of the market. However, the manual sorting process is subjective and inefficient. To address this issue, we have developed an automatic tomato sorting system that uses the Raspberry PI 4B as the control platform for the robot arm. This system has been integrated with a human–computer interaction interface sorting system. Our experimental results indicate that this sorting method has an accuracy rate of 99.1% and an efficiency of 1350 tomatoes per hour. This development is in line with modern agricultural mechanization and intelligence needs.

Industrial wireless sensor networks (IWSNs) play an increasingly important role in digital and intelligent industries; however, the communication, computing, and energy performance of their nodes are constrained by the demands and cost constraints of large-scale deployment. In this case, efficient task allocation plays a key role in improving the performance of the IWSNs. Task allocation in IWSNs is a nondeterministic polynomial (NP) hard problem whose complexity increases with an increase in node and task size. In this study, chaotic elite clone particle swarm optimization (CECPSO) was proposed. The algorithm first introduces the chaos theory to optimize the initial population. Subsequently, an elite cloning strategy was designed, which not only accelerated the exploration of the solution space and improved the accuracy of the solution but also avoided the problem of falling into the local optimal solution in the early stage through the dynamic adjustment strategy. In addition, the algorithm employs an exponential nonlinear decreasing inertia weight function that balances local and global search capabilities. By comparing the performance of CECPSO, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in different experimental scenarios, we found that CECPSO is superior to PSO, GA, and SA in terms of the convergence rate and overall performance. Under the conditions of 40 sensors and 240 tasks, CECPSO’s performance improvement of CECPSO relative to PSO, GA, and SA reached 6.6%, 21.23%, and 17.01%, respectively. Experimental results show that the proposed algorithm can effectively improve the overall performance of IWSNs.

Two-dimensional (2D) intrinsic ferromagnets with perpendicular magnetic anisotropy (PMA) have been experimentally verified as promising candidates for nanoscale spintronic devices and magnetic random-access memories. In this work, we systematically investigate the stability, electronic structure, and magnetic properties of monolayer MOCl (M = Ti, V, Cr, Mo) via first-principles calculations. The results demonstrate that allshi ciju monolayers MOCl (M = Ti, V, Cr, Mo) are intrinsic ferromagnetic semiconductors, with magnetic moments of 1.0 μ_B/Ti atom, 2.0 μ_B/V atom, 2.5 μ_B/Cr atom and 3.0 μ_B/Mo atom, respectively. Notably, both monolayers TiOCl and CrOCl exhibit perpendicular magnetic anisotropic energy (MAE), which is mainly contributed by metal atoms Ti and Cr, respectively. Drawing on the second-order perturbation theory, we conduct an analysis of the density of states and the magnetic anisotropy energy (MAE) resolved by d orbitals for Ti and Cr atoms. Our analysis shows that in monolayer TiOCl, the MAE of Ti atoms mainly stems from the disparities in matrix elements between the d_yz and d_x²−y² (d_xz) orbitals. Conversely, in monolayer CrOCl, the MAE of Cr atoms is largely due to the differences in matrix elements between the d_xy (d_yz) and d_x²−y² (d_z²) orbitals. Biaxial strain can efficiently regulate the MAE of monolayer CrOCl. Specifically, when under tensile strain, the MAE of monolayer CrOCl experiences a substantial increase. Our research results indicate that both monolayers TiOCl and CrOCl have significant potential for use in spintronic devices and high-density data storage systems.