There are alternating impact loads for the diesel engine cylinder block. The topology optimization of the extreme single-working condition cannot guarantee its overall mechanical performance, and the traditional multiworking condition optimization has the problem that the weight coefficients are difficult to determine. Thus, a multiobjective topology optimization method based on analytic hierarchy process is proposed. Firstly, the static, dynamic characteristics and structure efficiency are calculated by the finite element analysis which indicates the direction of topology optimization for the cylinder block. The hierarchical structure model of topology optimization, including 12 weighting coefficients, is constructed considering static multiworking condition stiffness and dynamic multiorder natural frequency. The comprehensive evaluation function for the cylinder block is established by the compromise programming method and the weight coefficients are determined based on analytic hierarchy process. The optimization mathematical model is established and the multiobjective topology optimization of the cylinder block is carried out. The optimization results show that the proposed method can take into account structural multiworking condition performance, which has obvious advantages over the single objective topology optimization. The simulation results show that the static and dynamic characteristics are improved to some extent and the overall mechanical performance of the new model is more uniform with a 5.22% reduction in weight. It shows that the topology structure of the cylinder block is more reasonable.

Meteorological factors and human activities are important factors affecting vegetation change. The change in the Upper Yellow River Basin’s (UYRB’s) ecological environment greatly impacts the ecological environment in the middle and lower reaches of the Yellow River. The purpose of this study was to evaluate remotely sensed imageries and vegetation indices as tools for accurately quantifying the driving forces of vegetation distribution. To accomplish this, we utilized the normalized difference vegetation index (NDVI) to examine the temporal and spatial variability of the vegetation distribution in the UYRB between 2000 and 2020. Based on the geographic detector method, the spatial differentiation, driving force, interaction, and suitability of the NDVI were detected. From 2000 to 2020, the estimated annual NDVI value of the UYRB was 0.515, with notable geographic variation in the distribution. The NDVI showed an obvious upward trend with a rate of 0.038 per 10 years. The vegetation coverage significantly improved. However, the vegetation coverage at the source area of the Yellow River marginally deteriorated. The primary driving factors affecting the spatial distribution of the NDVI were yearly precipitation, elevation, soil type, vegetation type, and annual average temperature, with a predictive power of 47%, 46%, 44%, 41%, and 40%, respectively. The interplay of the components had a stronger impact on the NDVI, and the interaction between the yearly precipitation and the soil type had the highest predictive power, reaching 61%. Natural factors and human activities influence NDVI change, with natural factors playing a significant role. Therefore, we should continue to implement the project of returning farmland to forest (grass), increase the efficiency of vegetation precipitation use, and promote the growth of vegetation so that ecological restoration continues to be effectively improved.

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