Abstract: The present invention provides a multi-load wireless power transfer system based on high-order Anti-PT symmetry. By introducing a “W-shaped” anti-resonance mode into a basic WPT platform, an effective Anti-PT non-Hermitian system is conveniently constructed. The “level attraction” of the anti-resonance mode is combined with the “level splitting” of the anti-resonance mode and the resonance mode, to study the “level pinning” effect of high-order Anti-PT symmetry. Compared with the traditional resonant WPT, the anti-resonant WPT has higher security, stability, transfer efficiency, and flexibility. Considering the miniaturization and integration of devices, a “meta coil” is designed by using a “synthetic dimension”, and used to construct a high-order Anti-PT symmetric system, thereby achieving multi-load efficient WPT. The new WPT technology based on the “level pinning” effect of high-order Anti-PT symmetry provides a good application research platform for enriching non-Hermitian physics.

Abstract: A method for determining tobacco-specific nitrosamines (TSNAs) in cigarette smoke using one-step clean-up coupled with LC-MS/MS is provided, including the following steps: collecting a particulate matter of mainstream cigarette smoke with a Cambridge filter pad, mixing the particulate matter of mainstream cigarette smoke, an internal standards solution and water in a 50 mL plastic centrifuge tube, and vortexing the resulting mixture at room temperature to allow extraction; transferring an extraction solution after filtration to a new centrifuge tube, adding dichloromethane, and vortexing the resulting mixture; centrifuging to collect a dichloromethane extraction solution in a lower layer to another centrifuge tube, and placing the centrifuge tube in a water bath to remove dichloromethane; dissolving the resulting extraction solution in water, and transferring the resulting solution to an autosampler vial for LC-MS/MS analysis.

Abstract: A method for determining tobacco-specific nitrosamines (TSNAs) in tobacco using one-step clean-up coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) is provided, including the following steps: destemming flue-cured tobacco or sun-cured tobacco, drying, milling, and passing through a sieve; mixing the resulting tobacco, an internal standards solution and water in a plastic centrifuge tube, and vortexing the resulting mixture at room temperature to allow extraction; transferring an extraction solution after filtration to a new centrifuge tube, adding dichloromethane, and vortexing the resulting mixture; centrifuging to collect a dichloromethane extraction solution in a lower layer to another centrifuge tube, and placing the centrifuge tube in a water bath to remove dichloromethane; dissolving the resulting extraction solution in water, and transferring the resulting solution to an autosampler vial for LC-MS/MS analysis.

Abstract: A set of endpoint security events that reflect known security issues is defined and collected. A corresponding set of endpoint security actions to protect the endpoints is defined and implemented. Machine learning is used to build a data model to reflect the relation between endpoint security events and endpoint security actions. The data model is able to predict the security actions directly from the security events, without the intermediate step of determining a threat level. An endpoint application is developed to use the data model directly and apply the security actions whenever security events occur.