Forests regulate air quality and respond to climate change by storing carbon. Assessing the driving factors of forest aboveground carbon (AGC) storage is of great importance for forest management. We assumed that different forest types would affect the relationship between species richness, stand density, individual tree size variation, and AGC. In order to test and verify it, we analyzed the inventory data of 206 fixed plots (20 m × 20 m) of Jingouling Forest Farm, taking advantage of the piecewise structural equation model (pSEM) to explore the effects of species diversity, stand structure attributes, and topography on the AGC storage in the Wangqing Forest in Jilin Province. In addition, in this study, we aimed to investigate whether the fixed factors (species diversity, stand structure attributes, and topography) influenced AGC storage more significantly than the random factor (forest type). According to the results of pSEM, the selected factors jointly explain the impact on 33% of AGC storage. The relationship between stand density and AGC is positive, and the impact of individual tree size variation on AGC storage is negative. Species richness has direct and indirect impacts on AGC storage, and the indirect impact is more significant through individual tree size variation. Both elevation and slope are significantly negatively associated with AGC storage. Forest type explains the impact on 12% of AGC storage, which means the relationship between AGC and predictors varies across forest types. The results provide a scientific basis for the protection and management decision of natural forests in northeastern China.

Based on a multiple linear regression model, random forest algorithm and generalized additive model, a stand volume model was constructed to provide a theoretical basis for sustainable management. A total of 224 fixed plots in the Jingouling forest farm, Wangqing County, Jilin Province, were used as data sources. Specifically, 157 plots were used as training data, and 77 plots were used as test data. The effects of stand structure variables, topography variables, cutting variables, diversity variables and climate variables on stand volume were analyzed. The random forest algorithm explained 95.51% of the stand volume, and the generalized additive model explained 95.45% of the stand volume. Stand structure variables and topography variables had more influence on the stand volume of spruce-fir than other variables. Among the diversity variables, the evenness index, Shannon index and Simpson index had a relatively greater impact on the stand volume. The cutting times and the intensity of the first cutting had a direct relationship with stand volume. The influence of climate variables on the stand volume was relatively small in the study area.

Estimating forest above‐ground biomass (AGB) productivity constitutes one of the most fundamental topics in forest ecological research. Based on a 30‐ha permanent field plot in Northeastern China, we modeled AGB productivity as output, and topography, species diversity, stand structure, and a stand density variable as input across a series of area scales using the Random Forest (RF) algorithm. As the grain size increased from 10 to 200 m, we found that the relative importance of explanatory variables that drove the variation of biomass productivity varied a lot, and the model accuracy was gradually improved. The minimum sampling area for biomass productivity modeling in this region was 140 × 140 m. Our study shows that the relationship of topography, species diversity, stand structure, and stand density variables with biomass productivity modeled using the RF algorithm changes when moving from scales typical of forest surveys (10 m) to larger scales (200 m) within a controlled methodology. These results should be of considerable interest to scientists concerned with forest assessment.

The current paper addresses the leaderless guaranteed cost synchronization problem with limited budgets given previously for the second-order wireless sensor network. Firstly, a guaranteed cost synchronization protocol with limited budgets is proposed to realize the tradeoff design between the battery power consumption and the synchronization regulation performance. Secondly, sufficient conditions for the leaderless guaranteed cost synchronization for two cases associated with limited budgets are given, respectively, and an upper bound of the cost function is shown. Especially, these criteria present the numerical solutions of control gains, which can ensure the existence of feasible solutions of control gains compared with the criteria obtained by a feasp solver. The numerical solutions are only associated with the minimum nonzero eigenvalue and the maximum eigenvalue. Finally, numerical simulations are given to illustrate theoretical results.

Most mainstream web information extraction models adopt multidimensional analysis and then fusion evaluation, which has issues such as the high cost of vectorization and unreasonable fusion. For this reason, a text mining method of webpages based on the mixed attribute measure and DOM tree segmentation is proposed, which uses the K-Nearest Neighbor algorithm to measure the similarity of the mixed attributes of nodes in the DOM tree to alleviate the shortcomings of previous algorithms that focus solely on categorical or numeric attributes, and improves extraction quality, in addition, the web page segmentation algorithm is used to find the set of target DOM tree nodes, and ultimately information extraction is completed and effectively reduces extraction costs. Experimental results show that, compared to multiple baseline models, the method has over 6.5% and 11.6% improvement in the accuracy and recall rate evaluation indicators across numerous scenarios and clear speed benefits.