To solve large-scale traveling salesman problem (TSP) with better performance, this paper proposes an entropy-based dynamic heterogeneous ant colony optimization (EDHACO). The allotropic mechanism and the heterogeneous colonies model are proposed to balance the convergence and the solution diversity. First, entropy is used to measure the diversity, and the entropy-based allotropic mechanism with three communication strategies can improve the adaptability of EDHACO. Then, the heterogeneous colonies with complementary advantages are proposed to balance the convergence speed and the diversity of the algorithm. Besides, two operators are proposed to improve the performance of the algorithm. The adaptive 3-opt operator can be used to accelerate the convergence, and the dynamic-pheromone-reset operator can be introduced to avoid trapping in a local optimum. Finally, EDHACO is applied to solve TSPs, and the experimental results suggest that it has better performance with higher stability and precision in TSP instances, especially in the large-scale TSP instances.

Due to the advantages of improving vegetable quality and reducing labor, technology for the orderly harvesting of green leafy vegetables has always been the focus of research. The core of the technology is the clamping and conveying device. At present, technology for the orderly harvesting of green leafy vegetables has several difficult problems, such as the plugging of the clamping and conveying device, great damage caused by mechanical clamping and high transportation loss. A green leafy vegetable is essentially a viscoelastic body and plastic deformation is an important index to measure its mechanical damage. Therefore, based on vegetable linear viscoelastic characteristics, we determined the deformation and plastic damage mechanism caused by orderly clamping and conveying. A rheological constitutive model and mathematical equations of the damage deformation value were constructed for the green leafy vegetable mechanical clamping process. Viscoelastic parameters of green leafy vegetable samples were obtained by creep experiments. The elastic clamping force and clamping spacing were analyzed systematically when the flexible clamping and conveying device was clamping, conveying and collecting. Under different spring stiffness and clamping time combinations, green leafy vegetable plastic damage deformation values were calculated and the vegetable damage regularity analyzed. After comprehensive consideration, we concluded that, when the harvester forward speed was 0.6 km/h, the optimal parameter combination of the flexible clamping and conveying device was a conveying roller rotation speed of 80 r/min, and a spring stiffness combination of 2.0 N/mm and 0.6 N/mm. Finally, a bench test verified that the mechanized harvest effect was best under a combination of parameters. Thus, we proved that the method is appropriate for studying the effects of clamping and conveying devices on green leafy vegetable damage based on viscoelastic rheological characteristics.

This paper aimed to predict the mechanical composition of machine-picked fresh tea leaves (MPFTLs) using near-infrared spectroscopy (NIRS) rapidly and non-destructively. Samples of MPFTL with different mechanical composition ratios were collected and subjected to NIRS analysis. Subsequently, various preprocessing methods were employed to eliminate extraneous noise information. Next, characteristic spectral information was extracted using the backward interval partial least squares (biPLS) method, which was subsequently subjected to principal component analysis (PCA). Finally, a predictive model was constructed by applying the back propagation artificial neural network (BP-ANN) method, which was tested by external samples to assess its predictive efficacy, and the results were expressed as root mean square error and determination coefficient of prediction (R_p²). The optimal spectral pretreatment method was the following: (standard normal variate (SNV) + second derivative (SD)). Four characteristic spectral subintervals of ([2, 3, 7, 10]) were screened out, and the cumulative contribution rate of 95.20%, attributable to the first three principal components, was determined. When the tanh transfer function was applied to construct the BP-ANN-NIRS model, the results demonstrated optimal performance, exhibiting a root mean square error and a determination coefficient of prediction (R_p²) of 0.976 and 0.027, respectively. The absolute values of prediction deviation for all prediction set samples were found to be less than 0.04. The results of the best BP-ANN model for external samples were found to be in close agreement with those of the prediction set model. NIRS technology has successfully achieved the forecasting of the mechanical composition of machine-picked fresh tea leaves rapidly and accurately, providing a fair and convenient new method for purchasing fresh tea raw materials by machines, according to their quality, and promoting the sustainable high-quality and healthy development of the tea industry.

Toxoplasma gondii is an opportunistic protozoan parasite that can infect almost all warm-blooded animals including humans with a worldwide distribution. Micronemes play an important role in invasion process of T. gondii, associated with the attachment, motility, and host cell recognition. In this research, sequence diversity in microneme protein 6 (MIC6) gene among 16 T. gondii isolates from different hosts and geographical regions and 1 reference strain was examined. The results showed that the sequence of all the examined T. gondii strains was 1,050 bp in length, and their A + T content was between 45.7% and 46.1%. Sequence analysis presented 33 nucleotide mutation positions (0-1.1%), resulting in 23 amino acid substitutions (0-2.3%) aligned with T. gondii RH strain. Moreover, T. gondii strains representing the 3 classical genotypes (Type I, II, and III) were separated into different clusters based on the locus of MIC6 using phylogenetic analyses by Bayesian inference (BI), maximum parsimony (MP), and maximum likelihood (ML), but T. gondii strains belonging to ToxoDB #9 were separated into different clusters. Our results suggested that MIC6 gene is not a suitable marker for T. gondii population genetic studies.

Coordinating a shared fleet across multiple owners under tight time windows is a challenging multi-objective problem balancing cost, timeliness, and equity. We study it for multi-cooperative agricultural machinery dispatch, formulating the Multi-Cooperative Agricultural Machinery Scheduling Problem under Continuous Workload Sharing (MAMSP-CWS) as a three-objective model that minimizes inter-area transfer cost, time-window violation, and cross-cooperative workload imbalance. To approximate the Pareto front, we develop a Multi-Objective Hybrid Particle Swarm Optimization with Tabu Search and Sparsity Repair (MO-HPSO-TS-SR), which couples particle-swarm search, tabu-search refinement, and a sparsity-repair operator within an external crowding-distance archive. The method is evaluated on three scales (a real instance from Liyang, China, and two synthetic ones) against NSGA-II-CWS and HTSMOGA-CWS over 20 independent runs each. MO-HPSO-TS-SR attains the best mean value on every metric-by-scale combination, with a decisive convergence advantage (hypervolume and IGD; Holm-adjusted $p<0.001$, Cliff’s $\delta \approx 1$). A mechanism decomposition identifies Sparsity Repair as the dominant contributor to hypervolume, with Tabu Search as a complementary refiner. The advantage over NSGA-II-CWS widens with problem scale, from 19.4% on the Small instance to 74.2% on the Large instance, reflecting the disproportionate degradation of the genetic baseline rather than a growing advantage of the proposed method. Beyond agriculture, the framework extends to other continuous-encoding scheduling problems, providing a transferable decision-support tool.