An acceleration sensor is an essential component of the vibration measurement, while the passivity and sensitivity are the pivotal features for its application. Here, we report a self-powered and highly sensitive acceleration sensor based on a triboelectric nanogenerator composed of a liquid metal mercury droplet (LMMD) and nanofiber-networked polyvinylidene fluoride (nn-PVDF) film. Due to the ultrahigh surface-to-volume ratio of nn-PVDF film and high surface tension, high mass density, high elastic as well as mechanical robustness of LMMD, the open-circuit voltage and short-circuit current reach up to 15.5 V and 300 nA at the acceleration of 60 m/s, respectively. The acceleration sensor has a wide detection range from 0 to 60 m/s with a high sensitivity of 0.26 V·s/m. Also, the output voltage and current show a negligible decrease over 200,000 cycles, evidently presenting excellent stability. Moreover, a high-speed camera was employed to dynamically capture the motion state of the acceleration sensor for insight into the corresponding work mechanism. Finally, the acceleration sensor was demonstrated to measure the vibration of mechanical equipment and human motion in real time, which has potential applications in equipment vibration monitoring and troubleshooting.

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Data mining is used to mine meaningful and useful information or knowledge from a very large database. Some secure or private information can be discovered by data mining techniques, thus resulting in an inherent risk of threats to privacy. Privacy-preserving data mining (PPDM) has thus arisen in recent years to sanitize the original database for hiding sensitive information, which can be concerned as an NP-hard problem in sanitization process. In this paper, a compact prelarge GA-based (cpGA2DT) algorithm to delete transactions for hiding sensitive itemsets is thus proposed. It solves the limitations of the evolutionary process by adopting both the compact GA-based (cGA) mechanism and the prelarge concept. A flexible fitness function with three adjustable weights is thus designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets with minimal side effects of hiding failure, missing cost, and artificial cost. Experiments are conducted to show the performance of the proposed cpGA2DT algorithm compared to the simple GA-based (sGA2DT) algorithm and the greedy approach in terms of execution time and three side effects.