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Summary: The article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising in UAV formation flights. The passive receiving signal UAV positioning model is established by applying the triangular cosine theorem. Additionally, the position relationship model of the UAV is formulated using optimization theory. The article introduces a deep learning‐based deviated UAV adjustment algorithm designed to automatically adjust UAV positions. This is achieved by establishing a triangular relationship between the passive signal receiving UAV and other UAVs. The implementation involves C# programming and MATLAB visualization. The proposed method not only resolves UAV positioning and position adjustment challenges but also optimizes the adjustment strategy. This optimization leads to maximum savings in time and distance costs associated with sending and receiving signals among UAVs, thereby enhancing the overall performance of UAVs during flights. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: A monolithic column‐based mass spectrometry (MS) analysis kit was prepared for whole blood analysis with MS. The kit is disposable and can be used for purification, storage, transportation and direct analysis of whole blood. The kit mainly consists of a capillary for quantitative microsampling, a cation exchange monolithic column for purification and storage, and a syringe for loading sample. This kit is very friendly to various users that one can easily siphon the blood in the kit followed by rapid clean‐up. We established a quantitative method using the kit with a limit detection as low as 0.33 nmol/L, and achieved more than five orders of magnitude enhancement in sensitivity compared to direct nanoelectrospray ionization MS analysis. The column can avoid analyte exposure to environment, which helps the storage of the sample for laboratory analysis. The relative standard deviation of immediate blood analysis and storage blood analysis within 10 d was less than 10%. This method has been successfully applied to the quantitative analysis of procainamide hydrochloride in 2 μL rat blood. These results indicate that this disposable kit does have the potential to achieve highly sensitive quantitative MS analysis in biological samples, which is expected to become a cost‐effective and powerful tool for in vitro diagnostics. [ABSTRACT FROM AUTHOR]

Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model's parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance., Author(s): Yongyi Li (corresponding author) [1]; Ming Zhang [1]; Yixing Ding [2]; Zhenghua Zhou [3]; Lingyu Xu [1] 1. Introduction The ways to predict travel time have undergone a significant [...]