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The increase of carbon emission is the main driver of global warming, which has seriously threatened the sustainable development of human society and the balance of the ecosystem. This paper focuses on county-level cities within the Yellow River Basin. Using land use data, energy consumption data and nighttime light remote sensing data, carbon emissions resulting from land use are computed for from 2000 to 2020, and studies its spatiotemporal evolution characteristics and the influencing factors. The results indicate that: (1) From 2000 to 2020, the carbon emissions in the Yellow River Basin increased continuously, with forests and construction areas being the primary carbon sink and source, respectively. Significant variations existed in carbon emissions in different counties, exhibiting an "east-high, west-low" spatial distribution pattern, while the center of carbon emissions showed a tendency to migrate towards the northwest. (2) The intensity of carbon emissions from land use, along with land use structure, economic development level, and population size all influence carbon emissions, with economic development level having the most significant impact. Adjusting industrial structure can effectively restrain the growth of carbon emissions. The area of land occupied per unit of GDP in the secondary industry consistently inhibits carbon emissions. These research findings hold significant implications for scientifically formulating land use policies and promoting basinwide collaborative emissions reduction initiatives in the Yellow River Basin.

Based on the theory of the existing research of the project portfolio management, the paper analyzes the key factors of the project portfolio selection, and evaluates them using the fuzzy comprehensive evaluation method. Finally, the paper builds a project portfolio choice integer programming model based on 0/1 integer programming, which is based on the organizations strategies.

Based on the analysis of traditional state maintenance strategy and big data-based state maintenance strategy, this paper builds a transformer state duration simulation model based on Monte Carlo method and builds an annual cost model based on the running state of the transformer. Finally, a typical case is selected to simulate a time series of 8760 hours of transformer operation under two operational inspection strategies, and the annual cost is calculated separately. The example shows that the annual cost of the transformer is lower under the big data-based inspection strategy.

A two-species model with variable population interactions and harvesting on one of the species is studied. Existence and stability of equilibria and existence of periodic solutions are established, existence of some bifurcation phenomena are analytically and numerically studied, explicit threshold values are computed to determine the kind of interaction ( mutualism, competition, host-parasite) between the two species. A brief discussion on the influence of the harvesting function on the dynamics of the model is also included.