The potential of liposome electrokinetic chromatography (LEKC), which is applied to estimate compound penetration through the skin, was evaluated in this report. Quantitative retention-activity relationships (QRARs) were successfully constructed between the compound skin permeability coefficient (log K(p)) and the retention values (log k), as well as some calculated molecular descriptors by the stepwise regression method (R(2) = 0.902). Furthermore, the developed vector method was applied to compare the similarity between the reported lipophilicity measuring scales and compounds through the skin. Both results indicated that the transport of a compound into the liposomal membrane was more similar to its penetration through the skin than that into other systems including the octanol-water system. In a word, LEKC is a promising simple method to predict drug penetration through the skin.

Pedestrian navigation systems could serve as a good supplement for other navigation methods or for extending navigation into areas where other navigation systems are invalid. Due to the accumulation of inertial sensing errors, foot-mounted inertial-sensor-based pedestrian navigation systems (PNSs) suffer from drift, especially heading drift. To mitigate heading drift, considering the complexity of human motion and the environment, we introduce a novel hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update (ZARU) algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM) that outputs an adaptive heading error measurement by fusing heuristic and magnetic algorithms rather than simply switching them, and an error-state Kalman filter (ESKF) that estimates the optimal systematic error. The validation datasets include a Vicon loop dataset that spans 324.3 m in a single room for approximately 300 s and challenging walking datasets that cover large indoor and outdoor environments with a total distance of 12.98 km. A total of five different frameworks with different heading drift correction methods, including the proposed framework, were validated on these datasets, which demonstrated that our proposed ZUPT–ZARU–AFM–ESKF-aided PNS outperforms other frameworks and clearly mitigates heading drift.

Due to the errors arising from low-cost inertial measurement units, pedestrian dead-reckoning (PDR) systems suffer from accumulative velocity and position errors. Previous works proved that the zero-velocity update (ZUPT) method can effectively reduce the velocity errors of the PDR system so that positional errors can be mitigated. The ZUPT algorithm is triggered by a stance phase-detection module that generally adopts a fixed threshold. However, the fixed-threshold-based detector is not robust enough and may not recognize stance phases accurately with dynamic gait speeds. Incorrect detection degrades the positioning accuracy of the overall system. In this paper, to improve the performance of the stance-phase detector, we explore the relationship between threshold and gait speed, and then threshold regression problems are constructed to design machine-learning-based stance-phase detectors that are robust to gait speed. Real-world highly dynamic experiments have illustrated the effectiveness of the proposed methods with dynamic gait speeds. Compared to the best fixed-threshold-based traditional method, the experimental results show that the machine-learning-based methods reduce the minimum root mean squared error (RMSE) of the distance measurement by 22.5%–37.2%, the minimal RMSE of the start–end error by 5.8%–13.2% and the average RMSE of the positional error by 12.4%–17.5%.

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