Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network

Analysis of the Effect of Classroom Reform of English Literature on the Theme of Environmental Protection in Universities Based on Artificial Intelligence Technology

Under the current theme of environmental protection, the effect of English literature classroom teaching reform cannot be analyzed qualitatively. Based on BP neural network, we present an analytical model of the impact of College English Literature Classroom Teaching Reform on environmental protection based on artificial intelligence technology. We analyze the teaching reform of English literature classrooms with the theme of environmental protection under artificial intelligence technology and explore the concept of "intelligent education" and the construction path of university English ecological teaching mode. Based on the in-depth excavation of teaching information, this paper analyzes the impact of college English literature classroom reform on the theme of environmental protection. Combined with BP neural network, a scientific analytic hierarchy process index system model is established, and the weight relationship of various reform influencing factors is given relatively objectively. The scores of the model based on the BP neural network under the six effect analysis indicators of teaching attitude, teaching tools, reform plan, reform teaching content, classroom organization, and reform rationality are 90.97, 86.3, 80.4, 95.7, 84.8, and 87.4, respectively. The results show that this model has a good ability for analysis and evaluation, which also provides some new ideas and entry points for the reform of the current English classroom teaching model. This study has a certain contribution to the breakthrough of college English teaching goals.

Endophytic Fungi as Potential Biocontrol Agents against Rhizoctonia solani J.G. Kühn, the Causal Agent of Rice Sheath Blight Disease

Simple Summary

Rice, together with wheat and corn, is among the most important food crops for mankind. Half of the world’s population consumes rice, mainly in Asia, southern Europe, tropical America, and parts of Africa. Rice sheath blight, caused by Rhizoctonia solani, is one of the main diseases in rice production. The control of this pathogen is difficult due to its ecological behavior, broad host range, and the high survival rate of sclerotia under various environmental conditions. In this research, after morphological and molecular identification of fungal isolates, five superior isolates, including Trichoderma virens, Trichoderma harzianum, Curvularia lunata, Aspergillus fumigatus, and Aspergillus awamori were studied in the in vitro and greenhouse trials, towards their potential to inhibit R. solani. The statistical analysis showed a significant difference between the effectiveness of fungi used in the volatile metabolites assay and in terms of height and fresh weight of plants in the greenhouse. It can be concluded that T. virens and A. fumigatus are the most effective antagonists in rice sheath blight disease control.

Abstract

The rice sheath blight disease, caused by Rhizoctonia solani J.G. Kühn fungus, is a major disease of Oryza sativa L. occurring all over the world. Therefore, efforts need to be undertaken to limit the spread of this pathogen, preferably by using environmentally friendly methods. In the present study, 57 fungal isolates were recovered by surface sterilization technique from 120 rice samples collected from paddy fields in Guilan province, Iran. Biological characterizations of the isolated taxa were performed in vitro, in the dual culture, volatile metabolites, and slide culture methods. Among the studied isolates, Trichoderma virens (J. H. Miller, Giddens and A. A. Foster) Arx was most effective in inhibiting the mycelial growth of R. solani in the dual culture (44.16% inhibition level), while Aspergillus fumigatus Fresen and T. virens had a 62.50–68.75% inhibition efficiency by volatile metabolites. In the slide culture, all of the isolates, except for T. harzianum Rifai, were effective in inhibiting the hyphae growth of R. solani. Under greenhouse conditions, rice plants inoculated with these potential antagonistic fungi showed a reduction in disease severity by even 41.4% as in the case of T. virens. Moreover, phenotypic properties of rice, such as plant height, fresh weight, and dry weight were increased in the plants inoculated with all antagonistic fungi tested, compared to the infected plants, except for the fresh weight of plants inoculated with Curnularia lunata (Wakker) Boedijn. The present in vivo and in vitro studies revealed that T. virens and A. fumigatus are the most effective antagonists in rice sheath blight disease control and could be applied in agricultural practice.

A Coupled Mathematical Model of the Dissemination Route of Short-Term Fund-Raising Fraud

To effectively protect citizens’ property from the infringement of fund-raising fraud, it is necessary to investigate the dissemination, identification, and causation of fund-raising fraud. In this study, the Susceptible Infected Recovered (SIR) model, Back-Propagation (BP) neural network, Fault tree, and Bayesian network were used to analyze the dissemination, identification, and causation of fund-raising fraud. Firstly, relevant data about fund-raising fraud were collected from residents in the same area via a questionnaire survey. Secondly, the SIR model was used to simulate the dissemination of victims, susceptibles, alerts, and fraud amount; the BP neural network was used to identify the data of financial fraud and change the accuracy of the number analysis of neurons and hidden layers; the fault-tree model and the Bayesian network model were employed to analyze the causation and importance of basic events. Finally, the security measures of fund-raising fraud were simulated by changing the dissemination parameters. The results show that (1) for the spread of the scam, the scale of the victims expands sharply with the increase of the fraud cycle, and the victims of the final fraud cycle account for 12.5% of people in the region; (2) for the source of infection of the scam, the initial recognition rate of fraud by the BP neural network varies from 90.9% to 93.9%; (3) for the victims of the scam, reducing fraud publicity, improving risk awareness, and strengthening fraud supervision can effectively reduce the probability of fraud; and (4) reducing the fraud rate can reduce the number of victims and delay the outbreak time. Improving the alert rate can reduce victims on a large scale. Strengthening supervision can restrict the scale of victims and prolong the duration of fraud.