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Given that the challenge for evolutionary algorithms when solving many-objective optimization problems lies in balancing the convergence and diversity, a many-objective evolutionary algorithm based on vector angle selection and indicator deletion (MOEA/AS-ID), is proposed. In this algorithm, a coordinated mechanism that includes two strategies is designed in the environmental selection process to delete the solutions with poor convergence and diversity one by one, retaining the elitist to participate in the evolution process for the next generation. To be specific, the former strategy based on vector angle selection is used to select a pair of solutions with a similar search direction in the objective space, and the latter indicator-based deletion strategy which uses the [ISDE+] indicator (indicator shift-based density estimation) that takes into account the convergence and diversity of a single solution, is employed to compare the selected pair of solutions and delete the solution with a smaller indicator value, then encourage the population to converge to the Pareto optimal front toward all directions. Finally, the balance between convergence and diversity of the solution set is achieved. On DTLZ (Deb-Thiele-Laumanns-Zitzler), SDTLZ (scaled DTLZ), and MaF (many-objective function) three benchmark test suites with various characteristics，MOEA/AS-ID and six recently proposed many-objective evolutionary algorithms covering all current types perform extensive comparative simulation experiments and numerical results analysis. Simulation results and numerical analysis show that MOEA/AS-ID has strong competitiveness in balancing the convergence and diversity when solving many-objective optimization problems with various characteristics.

The environment of open-pit mines is distinctive, and the conditions of the roads in them are complex and constantly changing. Insufficient lighting in the area being mined can make it challenging to identify and position multiple targets on the roads. This, in turn, affects the results of detection and poses serious risks to the safe operation of uncrewed mining trucks.Currently available models to identify obstacles on roads cannot accommodate the impact of poor lighting, and thus, yield inaccurate results. They also have significant shortcomings in identifying small obstacles in the mining area. In this study, we develop a multi-target model of detection for the dark/light environment of an open-pit mine using hyperbolic embedding to address the above-mentioned issues. We introduce the Retinex-Net convolutional neural network to the image preprocessing stage of the detection model to enhance dark images and improve their clarity. To address the issue of an excessively large number of features in the dataset without a clear preference for focus, we incorporate the global attention mechanism into the improved process of feature extraction. This enabled the collection of critical feature-related information in three dimensions. Finally, we incorporate a fully connected hyperbolic layer into the prediction stage of the model to minimize feature loss and prevent overfitting. The results of experiments to verify the proposed model showed that ① it could reliably classify and accurately identify large-scale targets in the low-light environment of the open-pit mining area, and was able to highly accurately identify mining trucks and small vehicles over long distances. It could also accurately identify and locate scaled targets, including pedestrians, such that this satisfies meeting the safety-related requirements of uncrewed mining trucks operating in diverse environments.② The model achieved an accuracy of detection of 98.6% while maintaining a speed of 51.52 frames/s, where this was 20.31%, 18.51%, 10.53%, 8.39%, and 13.24% higher than the accuracies of the SSD, YOLOv4, YOLOv5, YOLOx, and YOLOv7, respectively. Its accuracy of detection of pedestrians, mining trucks, and excavators on mining roads exceeded 97%.

Vehicle location deviation could easily occur in complex road network of open-pit mines, which seriously affect production vehicle route planning and intelligent scheduling.In this light, this study proposed a method for high-precision positioning and rectification of open pit minecars based on an improved HMM(Hiddden Markov Model).Specifically, this study clipped the road section by the complex road network map of the constructed open-pit mine, and cleaned the positioning trajectory data of the minecart, whose density was sparsed and segmented; a buffer zone was established to search for candidate road points of the trajectory, so as to improve the efficiency of the minecart positioning and correction under the complex road network; the HMM optimization model of positioning deviation correction was established by calculating the positioning observation probability and transition probability of the minecart.The optimal deviation correctionwas conducted in combination with the Viterbi algorithm to achieve high-precision positioning and deviation correction of the open-pit minecart.Results indicate that the method produces a better correction effect than the original HMM positioning correction method, the correction accuracy can reach 89.2 %, and the average correction time is only 0.055 s.This could effectively correct the positioning coordinates of open-pit mine vehicles under complex backgrounds.

There are two main aspects of research in multi-objective optimization algorithm, namely, convergence and diversity. While, it is difficult for original algorithms to maintain the diversity of solutions in the high-dimensional objective space. In order to enhance the diversity of algorithms in many-objective optimization problems, a new distance dominance relation is proposed in this paper. Firstly, in order to ensure the convergence of the algorithm, in the same niche, the distance dominance relation calculates the distance from the candidate solution to the ideal point as the fitness value, and selects the candidate solution with good fitness value as the non-dominant solution.Then, in order to enhance the diversity of the algorithm, the distance dominance relation sets each candidate solution to have the same niche and ensures that only one optimal solution is retained in the same niche. Finally, the VaEA algorithm is improved based on the proposed distance dominance relation, and the algorithm is named VaEA-DDR. On the DTLZ and IDTLZ test of 5, 8, 10, 15 dimensional objectives, the improved algorithm is compared with six commonly used algorithms. Experimental results show that the improved algorithm is highly competitive and can significantly enhance the diversity of the algorithm.

Objective: To establish ulcerative colitis (UC) rat model of spleen deficiency and dampness, and to explore effect of Jianpi-Huazhu pill and its different deconditions on TLR-4/MyD88/NF-κB pathway. Methods: A total of 64 Wistar rats were randomly divided into normal control group (n=8) and model group (n=56). UC rat model was established by traditional Chinese medicine syndrome combined with ethanol-2, 4, 6-trinitrobenzene sulfonic acid enema method. After successfully modeling, rats were randomly divided into model group (M), Mesalazine group (A), Jianpi-Huazhi pill group (B), Jianpi-Qinghua decoction group (C), Jianpi-Qinghua-Huoxue decoction group (D), Jianpi-Qinghua-Daozhi decoction group (E) and Qinghua-Daozhi-Huoxue decoction group (F), with 8 rats in each group, given corresponding drugs by gavage for 4 weeks. At end of course of treatment, all rats were sacrificed and samples were taken. Changes of body weight, disease activity and pathological changes of colon were observed. TNF-α expression in colon tissue was detected by ELISA and immunohistochemistry. RT-PCR and Western blot were used to detect mRNA and protein expressions of TLR-4, MyD88 and NF-κB in rat colon tissue. Results: Rats weight in Jianpi-Huazhi pill group, Jianpi- Qinghua decoction group, Jianpi-Qinghua-Huoxue decoction group and Jianpi-Qinghua-Daozhi decoction group were obviously higher than that of model group (P<0.01), all treatment groups could obviously improve UC model rat pathological damage and disease activity index in colon tissue, and Jianpi-Huazhi pill group and Jianpi-Qinghua-Huoxue group showed better efficacy. All treatment groups could inhibit mRNA and protein expressions of TLR-4, MyD88 and NF-κB in colon tissue (P<0.001). Mesalazine group, Jianpi-Huazhi pill group, Jianpi-Qinghua-Daozhi group and Qinghua-Daozhi-Huoxue group were significantly better than Jianpi-Qinghua group and Jianpi-Qinghua-Huoxue group in reducing NF-κB mRNA and protein expressions (P<0.05). All groups could reduce expression of TNF-α in colon tissue (P<0.001), and there was no significant difference between groups (P>0.05). Conclusion: Jianpi- Huazhi pill and its various deconditions may play roles through TLR-4/MyD88/NF-κB pathway. Among them, Coptid-Rhizoma coptifolia, stews woody incense, Fengwei herb and gung ginger may be core drugs of this recipe, which has certain guiding significance for clinical practice. [ABSTRACT FROM AUTHOR]