In the context of increasing data scale, contemporary optimization algorithms struggle with cost and complexity in addressing the feature selection (FS) problem. This paper introduces a Harris hawks optimization (HHO) variant, enhanced with a multi-strategy augmentation (CXSHHO), for FS. The CXSHHO incorporates a communication and collaboration strategy (CC) into the baseline HHO, facilitating better information exchange among individuals, thereby expediting algorithmic convergence. Additionally, a directional crossover (DX) component refines the algorithm's ability to thoroughly explore the feature space. Furthermore, the soft-rime strategy (SR) broadens population diversity, enabling stochastic exploration of an extensive decision space and reducing the risk of local optima entrapment. The CXSHHO's global optimization efficacy is demonstrated through experiments on 30 functions from CEC2017, where it outperforms 15 established algorithms. Moreover, the paper presents a novel FS method based on CXSHHO, validated across 18 varied datasets from UCI. The results confirm CXSHHO's effectiveness in identifying subsets of features conducive to classification tasks.

In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over $91\%$. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks.

Urban green ecological space is an important measure of sustainable urban development. Among them, landscape connectivity is one of the key factors in maintaining landscape function. Ecological networks can effectively improve regional ecological quality and promote urban landscape connectivity. However, previous studies on ecological networks have mainly focused on biodiversity conservation and lack research on landscape connectivity. This study used morphological spatial pattern analysis methods and utilized connectivity indices to identify ecological sources in the Chaoyang and Nanguan districts of China’s Changchun City and selected environmental and anthropogenic factors to construct an integrated resistance surface. The minimum cumulative resistance model and network structure index were used for urban ecological network construction and node optimization. The results show that the potential ecological network comprises 17 ecological sources and 34 potential corridors, primarily located in forests and water bodies in the east and south regions. However, the northwest has poor habitat quality and uneven distribution of ecological corridors, that warrant prioritization in future planning, construction, and protection efforts. By introducing six supplemental sources and 25 additional corridors, the function and overall connectivity of the regional ecological network can be improved. The study confirmed that the selection of appropriate connectivity thresholds can improve the accuracy of ecological sources identification, and that the influence of anthropogenic factors on ecological resistance cannot be ignored. This study will provide a scientific basis for promoting urban construction and ecological balance.

Greenhouse gas emission is a key issue in the sustainable development of agriculture. To effectively predict the greenhouse gas emissions of beef cattle, a model is proposed based on system dynamics and greenhouse gas emission calculation methods, and a scenario is set as a ‘Straw to Beef’ project in Jilin Province. The model was built on a baseline emission scenario (feed precision: 60%, breeding environment: dry fattening farm, corn straw utilization: burning straw), with single- and comprehensive emission reduction scenarios considered, predicting trends and reduction potentials in greenhouse gas emissions from cattle breeding and straw burning in Jilin Province from 2013 to 2028, measured in CO₂-eq (CO₂ equivalent). The model also explored the impact of 11 controllable variables on greenhouse gas emissions. Results showed that (1) From 2013 to 2022, greenhouse gas emissions from straw burning and cattle breeding in Jilin Province increased significantly and had an annual growth rate of 6.51% in 2020. (2) Single emission reduction scenarios showed an increasing trend in greenhouse gas emissions, while comprehensive emission reduction scenarios showed a decreasing trend. Among them, the S2.2.1 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: burning straw) had the strongest emission reduction ability in the single reduction scenario, the S3.2.2 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: Feed-processing straw) had the strongest emission reduction ability in the comprehensive reduction scenario, reducing emissions by 5.10% and 69.24%, respectively, compared to the baseline scenario. This suggests that the comprehensive emission reduction scenarios which utilized straw resources reasonably can greatly reduce agricultural greenhouse gas emissions. (3) The optimal emission reduction scenario indicated that the higher the proportion of digestible energy in beef cattle’s total energy intake, the more perfect the fecal treatment process, and the higher the utilization rate of straw feed, the lower the greenhouse gas emissions. Therefore, to effectively reduce greenhouse gas emissions from cattle breeding and straw burning in Jilin Province, it is important to implement comprehensive emission reduction scenarios prioritizing the efficient utilization of straw resources and improving beef cattle management practices.

Increasing soil contamination with nickel (Ni) and copper (Cu) is a growing environmental concern, adversely affecting ecosystems and the survival of both plants and animals. This study investigated the morphological and physiological responses of Euphorbia marginata Pursh seedlings to varying concentrations of Ni and Cu over a 45-day period. The findings revealed that low concentrations of Ni and Cu enhanced morphological indexes, root indexes, biomass, and photosynthetic pigment content of E. marginata, while high concentrations inhibited these parameters. Compared to the control, Ni and Cu stresses induced membrane peroxidation, increased cell membrane permeability, and inhibited the synthesis of soluble proteins and proline in the leaves. The seedlings demonstrated an ability to mitigate Ni and Cu toxicity by increasing soluble sugar content and enhancing the activities of peroxidase (POD), superoxide dismutase (SOD), and catalase (CAT). Notably, E. marginata exhibited a higher capacity for Cu²⁺ enrichment and translocation compared to Ni²⁺. Combined Ni and Cu treatments reduced the maximum enrichment and translocation levels of both metals in E. marginata. This study highlights the superior tolerance of E. marginata to Ni and Cu stresses and elucidates the mechanisms underlying its response, providing a theoretical basis for the use of landscape plants in the remediation of heavy-metal-contaminated soils.