Allometric models for aboveground biomass of ten tree species in northeast China

China contains 119 million hectares of natural forest, much of whichis secondary forest. An accurate estimation of the biomass of these forests is imperative because many studies conducted in northeast China have only used primary forest and this may have resulted in biased estimates. This study analyzed secondary forest in the area using information from a forest inventory to develop allometric models of the aboveground biomass (AGB). The parameter values of the diameter at breast height (DBH), tree height (H), and crown length (CL) were derived from a forest inventory of 2,733 trees in a 3.5 ha plot. The wood-specific gravity (WSG) was determined for 109 trees belonging to ten species. A partial sampling method was also used to determine the biomass of branches (including stem, bark and foliage) in 120 trees, which substantially ease the field works. The mean AGB was110,729 kg ha–1. We developed four allometric models from the investigation and evaluated the utility of other 19 published ones for AGB in the ten tree species. Incorporation of full range of variables with WSG-DBH-H-CL, significantly improved the precision of the models. Some of models were chosen that best fitted each tree species with high precision (R2 ≥ 0.939, SEE 0.167). At the latitude level, the estimated AGB of secondary forest was lower than that in mature primary forests, but higher than that in primary broadleaf forest and the average level in other types of forest likewise

Boosting the Cycle Counting Power of Graph Neural Networks with I2^2-GNNs

Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs). The limited representational power of MPNNs inspires the study of provably powerful GNN architectures. However, knowing one model is more powerful than another gives little insight about what functions they can or cannot express. It is still unclear whether these models are able to approximate specific functions such as counting certain graph substructures, which is essential for applications in biology, chemistry and social network analysis. Motivated by this, we propose to study the counting power of Subgraph MPNNs, a recent and popular class of powerful GNN models that extract rooted subgraphs for each node, assign the root node a unique identifier and encode the root node's representation within its rooted subgraph. Specifically, we prove that Subgraph MPNNs fail to count more-than-4-cycles at node level, implying that node representations cannot correctly encode the surrounding substructures like ring systems with more than four atoms. To overcome this limitation, we propose I$^2$-GNNs to extend Subgraph MPNNs by assigning different identifiers for the root node and its neighbors in each subgraph. I$^2$-GNNs' discriminative power is shown to be strictly stronger than Subgraph MPNNs and partially stronger than the 3-WL test. More importantly, I$^2$-GNNs are proven capable of counting all 3, 4, 5 and 6-cycles, covering common substructures like benzene rings in organic chemistry, while still keeping linear complexity. To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees. We validate its counting power in cycle counting tasks and demonstrate its competitive performance in molecular prediction benchmarks

Plasmonic Nanomaterials for Optical Sensor and Energy Storage and Transfer

Nanomaterials including noble metal nanomaterials and some metal oxide nanomaterials exhibit very strong lightmatter interactions under resonant excitation. Very large absorption and scattering at the localized wavelengths can been achieved. Because of their attractive optical properties, optical NPs and nanostructures have been commonly used in various fields from nanophotonics, analytical chemistry, biotechnology, and information storage to energy applications including photovoltaics and photocatalysisphotocatalysi

Auto Adaptive Identification Algorithm Based on Network Traffic Flow

Traffic identification is a key task for any Internet Service Provider (ISP) or network administrator. Machine learning method is an important researchmethod on traffic identification, while impact of the asymmetry router on the  traffic identification is considered, so this paper analyzes the impact of asymmetry routing on traffic identification, and proposes an effective method to decrease the impact, and experimental results show the auto adaptive algorithm can improve the traffic identification