The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.

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SUMMARYTo mitigate airport congestion caused by increasing air traffic demand, the trajectory-based surface operations concept has been proposed to improve surface movement efficiency while maintaining safety. It utilizes decision support tools to provide optimized time-based trajectories for each aircraft and uses automation systems to guide surface movements and monitor their conformance with assigned trajectories. Whether the time-based trajectories can be effectively followed so that the expected benefits can be guaranteed depends firstly on whether these trajectories are realistic. So, this paper first deals with the modeling biases of the network model typically used for taxi trajectory planning via refined taxiway modeling. Then it presents a zone control-based dynamic routing and timing algorithm upon the refined taxiway model to find the shortest time taxi route and timings for an aircraft. Finally, the presented algorithm is integrated with a sequential planning framework to continuously decide taxi routes and timings. Experimental results demonstrate that the solution time for an aircraft can be steadily around a few milliseconds with timely cleaning of expired time windows, showing potential for real-time decision support applications. The results also show the advantages of the proposed methodology over existing approaches.

The low velocity impacts (LVIs) monitoring based on optical fiber Bragg grating (FBG) sensors have attracted more attention in recent years. The center wavelength migrations of FBG sensors were determined by strain and residual strain during and after LVI on composite material structure. We presented a method to discriminate the energy characters of LVI response signals related to LVI locations. By analyzing the wavelet packet energy spectra of LVI response signals monitored by FBG sensors, the sixth node's energy was found to be sensitive to LVI location. Thus, the sixth node's energies as LVI feature values, were used to predict the LVI locations by the method of support vector regression (SVR). By optimization of the SVR models' free parameters, predicting accuracy was 4.62% in the work.