Objective. Trans-corneal electrical stimulation (TcES) produces therapeutic effects on many ophthalmic diseases non-invasively. Existing clinical TcES devices use largely variable design of electrode distribution and stimulation parameters. Better understanding of how electrode configuration paradigms and stimulation parameters influence the electric field distribution on the retina, will be beneficial to the design of next-generation TcES devices. Approach. In this study, we constructed a realistic finite element human head model with fine eyeball structure. Commonly used DTL-Plus and ERG-Jet electrodes were simulated. We then conducted in silico investigations of retina observation surface (ROS) electric field distributions induced by different return electrode configuration paradigms and different stimulus intensities. Main results. Our results suggested that the ROS electric field distribution could be modulated by re-designing TcES electrode settings and stimulus parameters. Under far return location paradigms, either DTL-Plus or ERG-Jet approach could induce almost identical ROS electric field distribution regardless where the far return was located. However, compared with the ERG-Jet mode, DTL-Plus stimulation induced stronger nasal lateralization. In contrast, ERG-Jet stimulation induced relatively stronger temporal lateralization. The ROS lateralization can be further tweaked by changing the DTL-Plus electrode length. Significance. These results may contribute to the understanding of the characteristics of DTL-Plus and ERG-Jet electrodes based electric field distribution on the retina, providing practical implications for the therapeutic application of TcES.

Plant diseases and pests caused by harmful insects has always been a significant threat to agricultural and forestry production. In addition, the threat of invasive insects causes damage to local ecosystems with a decrease in biodiversity and even the extinction of some species, seriously harming the local economy. Governments around the world have invested a significant number of efforts in insect detection and control. With the development of AI, automated identification is an irreversible trend to improve efficiency and reduce government input. Recent researches attempt to apply deep learning tools into the detection and identification of insects, but meeting a series of difficulties. Insect identification abstracted to a fine-grained vision classification task provides unique challenges including the small difference between classes and the large difference within a class. In this study, we propose a pest identification model guided by visual attention, designed to address the above challenges. We establish an attention mechanism from these two perspectives, enhancing attention to foreground features by amplifying fine-grained features and eliminating attention to background biases through counterfactual inference. Our approach ultimately achieves a classification of 102 insect categories with an accuracy of 74.5% and a classification of 40 insect categories with an accuracy of 99.8%. The approach proposed in this study will contribute to the automatic insect detection and identification system in the future as the core technique.