Search Results (3)

Paralipsa gularis (Zeller) has become an increasingly destructive pest in both storage and field ecosystems, particularly affecting maize crops across China. As chemical control methods face limitations due to resistance development and environmental concerns, biological control presents a promising alternative. In this study, we isolated and identified a novel strain of Metarhizium sp. from naturally infected P. gularis larvae collected in Yunnan Province, China. Morphological characterization, along with ITS-rDNA and EF-1α-rDNA sequencing, confirmed the fungus as Metarhizium rileyi. The optimal growth medium for this strain was SMAY, and the optimal conditions were 25 °C under continuous light (L:D = 24:0). Laboratory bioassays showed that the strain exhibited high virulence against P. gularis larvae, with cumulative mortality reaching 82% following infestation with 5 × 10⁸ conidia/mL. Biochemical analyses revealed that fungal infection significantly inhibited the activity of the key antioxidant enzyme SOD in the host, while activities of POD, CAT, and detoxification enzymes (P450, CarE, AChE, and GSTs) were significantly increased. These results indicate that immune responses were triggered, and systemic colonization of the host was achieved. Overall, this native M. rileyi strain demonstrates strong potential as an effective biological control agent. Its ability to overcome insect defenses and induce high mortality supports its integration into pest management programs targeting P. gularis. This work advances the understanding of fungal–insect interactions and contributes to sustainable, environmentally safe strategies for managing a pest of economic importance in agricultural ecosystems. Full article

Car front facing style (CFFS) recognition is crucial to enhancing a company’s market competitiveness and brand image. However, there is a problem impeding its development: with the sudden increase in style design information, the traditional methods, based on feature calculation, are insufficient to quickly handle style analysis with a large volume of data. Therefore, we introduced a deep feature-based machine learning approach to solve the problem. Datasets are the basis of machine learning, but there is a lack of references for car style data annotations, which can lead to unreliable style data annotation. Therefore, a CFFS recognition method was proposed for machine-learning data annotation. Specifically, this study proposes a hierarchical model for analyzing CFFS style from the morphological perspective of layout, surface, graphics, and line. Based on the quantitative percentage of the three elements of style, this paper categorizes the CFFS into eight basic types of style and distinguishes the styles by expert analysis to summarize the characteristics of each layout, shape surface, and graphics. We use imagery diagrams and typical CFFS examples and characteristic laws of each style as annotation references to guide manual annotation data. This investigation established a CFFS dataset with eight types of style. The method was evaluated from a design perspective; we found that the accuracy obtained when using this method for CFFS data annotation exceeded that obtained when not using this method by 32.03%. Meanwhile, we used Vgg19, ResNet, ViT, MAE, and MLP-Mixer, five classic classifiers, to classify the dataset; the average accuracy rates were 76.75%, 78.47%, 78.07%, 75.80%, and 81.06%. This method effectively transforms human design knowledge into machine-understandable structured knowledge. There is a symmetric transformation of knowledge in the computer-aided design process, providing a reference for machine learning to deal with abstract style problems. Full article

AI-based text classification is a process to classify Arabic contents into their categories. With the increasing number of Arabic texts in our social life, traditional machine learning approaches are facing different challenges due to the complexity of the morphology and the delicate variation of the Arabic language. This work proposes a model to represent and recognize Arabic text at the character level based on the capability of a deep convolutional neural network (CNN). This system was validated using five-fold cross-validation tests for Arabic text document classification. We have used our proposed system to evaluate Arabic text. The ArCAR system shows its capability to classify Arabic text in character-level. For document classification, the ArCAR system achieves the best performance using the AlKhaleej-balance dataset in terms of accuracy equal to 97.76%. The proposed ArCAR seems to provide a practical solution for accurate Arabic text representation, both for understanding and as a classifications system. Full article