Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.
Background. The modernization of traditional Chinese medicine (TCM) demands systematic data mining using medical records. However, this process is hindered by the fact that many TCM symptoms have the same meaning but different literal expressions (i.e., TCM synonymous symptoms). This problem can be solved by using natural language processing algorithms to construct a high-quality TCM symptom normalization model for normalizing TCM synonymous symptoms to unified literal expressions. Methods. Four types of TCM symptom normalization models, based on natural language processing, were constructed to find a high-quality one: (1) a text sequence generation model based on a bidirectional long short-term memory (Bi-LSTM) neural network with an encoder-decoder structure; (2) a text classification model based on a Bi-LSTM neural network and sigmoid function; (3) a text sequence generation model based on bidirectional encoder representation from transformers (BERT) with sequence-to-sequence training method of unified language model (BERT-UniLM); (4) a text classification model based on BERT and sigmoid function (BERT-Classification). The performance of the models was compared using four metrics: accuracy, recall, precision, and F1-score. Results. The BERT-Classification model outperformed the models based on Bi-LSTM and BERT-UniLM with respect to the four metrics. Conclusions. The BERT-Classification model has superior performance in normalizing expressions of TCM synonymous symptoms.
Background: Numerous clinical studies have shown that atopic dermatitis (AD) is often associated with mental disorders. This could contribute to the overall burden of atopic dermatitis. However, the underlying mechanism of mental health symptoms in AD has not been fully elucidated.Methods: An AD mouse was induced by 2,4-dinitrofluorobenzene (DNFB), which was repeatedly applied to the back skin of the BALB/C mice to establish an atopic dermatitis mental disorder model. The role of neuroinflammation in the pathogenesis of atopic dermatitis mental disorders was then explored.Results: After the stimulation of DNFB for 35 days, the skin lesions, the HE staining of skin lesions, and the behavioral experiments (including elevated plus maze assay and tail suspension test) suggested that the AD mental disorder mouse model was successfully replicated. The expression of neuroinflammatory factors in the hippocampus was then investigated through Western blotting. The results showed a significant increase in the protein expression of NLRP3, caspase-1, and IL-1β.Conclusion: Mental disorders in AD might be related to the neuroinflammatory response in the hippocampus. An alternative yet essential approach to promoting AD recovery could be through reducing neuroinflammation and improving mental disorders.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.