Abstract: Described are a system, method, and computer program product for dynamic node classification in temporal-based machine learning classification models. The method includes receiving graph data of a discrete time dynamic graph including graph snapshots, and node classifications associated with all nodes in the discrete time dynamic graph. The method includes converting the discrete time dynamic graph to a time-augmented spatio-temporal graph and generating an adjacency matrix based on a temporal walk of the time-augmented spatio-temporal graph. The method includes generating an adaptive information transition matrix based on the adjacency matrix and determining feature vectors based on the nodes and the node attribute matrix of each graph snapshot.

Abstract: Provided is a system for analyzing a multivariate time series that includes at least one processor programmed or configured to receive a time series of historical data points, determine a historical time period, determine a contemporary time period, determine a first time series of data points associated with a historical transaction metric from the historical time period, determine a second time series of data points associated with a historical target transaction metric from the historical time period, determine a third time series of data points associated with a contemporary transaction metric from the contemporary time period, and generate a machine learning model, wherein the machine learning model is configured to provide an output that comprises a predicted time series of data points associated with a contemporary target transaction metric. Methods and computer program products are also provided.

Abstract: The present disclosure relates to the technical field of plant pests control, in particular to use of Penicillium ehrlichii Z19 in controlling plant pests, and a control method. The Penicillium ehrlichii Z19 has a deposit number of CCTCC NO: M 2021635. The Penicillium ehrlichii Z19 has a desirable insecticidal activity against lepidopteran pests, especially has a 7-day insecticidal effect of not less than 80% on Helicoverpa armigera, Plutella xylostella, Ectropis obliqua, and Spodoptera exigua. In addition, compared with traditional chemical control methods, the method for controlling pests using the Penicillium ehrlichii Z19 in the present disclosure can control the plant pests in a safe, efficient and pollution-free manner. Therefore, the method reduces pollutions to ecological environment, improves quality and safety of agricultural products, and protects human health.

Abstract: The present disclosure belongs to the technical field of plant pest control, and particularly relates to a Camellia sinensis endophyte Penicillium ehrlichii and an application thereof. The present disclosure relates to a novel endophyte separated from a Camellia sinensis body and identified as Penicillium ehrlichii by means of morphology and molecular biology. Biological activity evaluation indicates that Penicillium ehrlichii shows good antagonistic antifungal activity to common pathogenic fungi on several plants. It means that the strain used as a biocontrol fungus for phytopathogens can be applied to environmental-friendly and safe prevention and control of plant diseases.