Interacts with to Improve Growth Performance of Aphids under Amino Acid Stress.

Amino acids play a crucial role in the growth and development of insects. Aphids cannot ingest enough amino acids in plant phloem to meet their requirements, and therefore, they are mainly dependent on the obligate symbiont Buchnera aphidicola to synthesize essential amino acids. Besides , aphids may harbor another facultative symbiont, , which alters the requirement of the cotton-melon aphid Aphis gossypii for amino acid.

Characterization and phylogenetic analysis of the complete mitochondrial genome of (Diptera: Syrphidae).

The complete mitochondrial genome of was sequenced and reported here. The circle genome of the syrphid fly is 15,348 bp in length. There are 38 sequence elements including 13 protein coding genes, 22 tRNA genes, 2 rRNA genes, and a control region.

Complete mitochondrial genome of (Lepidoptera: Sesiidae).

is an important trunk borer of poplar and is widely distributed in China. Here, the complete mitochondrial genome of was sequenced. The circle genome of the clearwing moth is 15,454 bp in length.

A Weightedly Uniform Detectability for Sensor Networks.

In this brief, we study the detectability issues in the context of distributed state estimation problems for a class of locally undetectable sensor networks. First, we introduce a novel detectability condition, i.e.

Weighted Average Consensus-Based Unscented Kalman Filtering.

In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section.

[Research on the application of improved M-P neural network to the determination of lead and zinc ore element contents by energy disperse X-ray fluorescence analysis].

Because of different constraints (such as different kinds of measurable elements, characteristic X-ray energy, changes in matrix composition, etc.), usually it's not easy to get accurate information of elements, resulting in mistakes in later data analysis of energy disperse X-ray fluorescence measurement. The method is based on McCulloch-Pitts neural network (M-P neural network), according to matrix effect, to establish a new neural network model for quantitative forecasting of Zn by taking the data of X-ray fluorescence measurements of Cu, Fe, Pb, etc in lead-zinc mine in western Tianshan as the training sample.