ABSTRACT
Both larvae and adults of the Henosepilachna vigintioctopunctata feed on leaves of potatoes, tomatoes, and eggplants. Given the variation in planting times of host plants in the Jianghan Plain, host switching between larvae and adults of H. vigintioctopunctata is inevitable to ensure continuous food availability. We evaluated the effect of consistent versus diverse larval and adult host plant feeding experience on growth performance, fecundity, longevity, and feeding preferences of H. vigintioctopunctata through match-mismatch experiments. Host plant quality significantly influences larval development and adult reproduction. Potatoes are identified as the optimal host plant for H. vigintioctopunctata, whereas eggplants significantly negatively affect the adult fecundity. Adult stage host feeding experience determines the fecundity of H. vigintioctopunctata, irrespective of the larval feeding experience. The fecundity of H. vigintioctopunctata adults on eggplant leaves remains significantly lower than that observed on potato leaves. Similarly, adult H. vigintioctopunctata demonstrate a preference for consuming potato leaves, irrespective of the larval feeding experience. Although host switching between larval and adult stages offers lesser benefits for the performance of herbivorous insects compared to a consistent diet with potato leaves, it maintains H. vigintioctopunctata population continuity amidst shortages of high-quality potato hosts.
ABSTRACT
Patients with amyotrophic lateral sclerosis ( ALS ) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.
