Cardamine hupingshanensis (K. M. Liu, L. B. Chen, H. F. Bai and L. H. Liu) is a perennial herbal species endemic to China with narrow distribution. It is known as an important plant for investigating the metabolism of selenium in plants because of its ability to accumulate selenium. However, the phylogenetic position of this particular species in Cardamine remains unclear. In this study, we reported the chloroplast genome (cp genome) for the species C. hupingshanensis and analyzed its position within Cardamine. The cp genome of C. hupingshanensis is 155,226 bp in length and exhibits a typical quadripartite structure: one large single copy region (LSC, 84,287 bp), one small single copy region (17,943 bp) and a pair of inverted repeat regions (IRs, 26,498 bp). Guanine-Cytosine (GC) content makes up 36.3% of the total content. The cp genome contains 111 unique genes, including 78 protein-coding genes, 29 tRNA genes and 4 rRNA genes. A total of 115 simple sequences repeats (SSRs) and 49 long repeats were identified in the genome. Comparative analyses among 17 Cardamine species identified the five most variable regions (trnH-GUG-psbA, ndhK-ndhC, trnW-CCA-trnP-UGG, rps11-rpl36 and rpl32-trnL-UAG), which could be used as molecular markers for the classification and phylogenetic analyses of various Cardamine species. Phylogenetic analyses based on 79 protein coding genes revealed that the species C. hupingshanensis is more closely related to the species C. circaeoides. This relationship is supported by their shared morphological characteristics.

Relying on the express freight network, the dispatching of empty pallets based on the pallet pool mode is studied to reuse pallets with the minimum transport cost, enhance the pallet utilization rate, reduce the waste of resources, and save the cost of logistics. Considering the influence of transport efficiency for different modes in transportation process, differences of transportation cost, carbon emissions, and transportation timeliness of demand points required, an optimization model is constructed. The objective of the model is to minimize the total cost including transportation cost, inventory cost, lease cost, and loss cost. According to the structural characteristics of the model, genetic algorithm and improved cloud clonal selection operation is used to solve the model. Finally, the validity and rationality of the optimization model are verified by a case study. The result shows that the total dispatching cost of considering time requirement is 1.8 times the cost without considering the time requirement, respectively, both less than the total cost of pallets leasing. Moreover, when there are 3 supply points and 2 demand points and the number of iterations is 100, after the algorithms are run for 30 times, the worst values are 9305 and 8317 for genetic algorithm and the improved cloud clonal selection operation, respectively. Therefore, the efficiency of the improved cloud clonal selection operation is higher than genetic algorithm.

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Transit network optimization can effectively improve transit efficiency, improve traffic conditions, and reduce the pollution of the environment. In order to better meet the travel demands of passengers, the factors influencing passengers’ satisfaction with a customized bus are fully analyzed. Taking the minimum operating cost of the enterprise as the objective and considering the random travel time constraints of passengers, the customized bus routes are optimized. The K-means clustering analysis is used to classify the passengers’ needs based on the analysis of the passenger travel demand of the customized shuttle bus, and the time stochastic uncertainty under the operating environment of the customized shuttle bus line is fully considered. On the basis of meeting the passenger travel time requirements and minimizing the cost of service operation, an optimization model that maximizes the overall satisfaction of passengers and public transit enterprises is structured. The smaller the value of the objective function is, the lower the operating cost. When the value is negative, it means there is profit. The model is processed by the deterministic processing method of random constraints, and then the hybrid intelligent algorithm is used to solve the model. A stochastic simulation technique is used to train stochastic constraints to approximate uncertain functions. Then, the improved immune clonal algorithm is used to solve the vehicle routing problem. Finally, it is proved by a case that the method can reasonably and efficiently realize the optimization of the customized shuttle bus lines in the region.