The insertion of nonalternant π-system into the skeletons of [n]Cycloparaphenylenes ([n]CPPs) can significantly alter their optoelectronic properties. We herein presented a nonalternant azulene-embedded carbon nanohoop 1,3-Az[9]CPP. The investigations revealed that...

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The mixed quantum-classical dynamical simulation is essential for studying nonadiabatic phenomena in photophysics and photochemistry. In recent years, many machine learning models have been developed to accelerate the time evolution of the nuclear subsystem. Herein, we implement long short-term memory (LSTM) networks as a propagator to accelerate the time evolution of the electronic subsystem during the fewest-switches surface hopping (FSSH) simulations. A small number of reference trajectories are generated using the original FSSH method, and then the LSTM networks can be built, accompanied by careful examination of typical LSTM–FSSH trajectories that employ the same initial condition and random numbers as the corresponding reference. The constructed network is applied to FSSH to further produce a trajectory ensemble to reveal the mechanism of nonadiabatic processes. Taking Tully’s three models as test systems, we qualitatively reproduced the collective results. This work demonstrates that LSTM can be applied to the most popular surface hopping simulations.

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Objective: To develop a kind self active resistance movement training equipment for cervical spine, so as to restore the dynamic balance of the front and back muscle strength of the cervical spine in order to correct the abnormal physiological radian of the cervical spine, thereby relieving the clinical symptoms. Methods: The air spring with adjustable strength was used to provide the power of resistance active movement, and the neurological training cervical audio-visual synthesis training software was used to automatically guide patients to perform resistance active movement training of the cervical spine. Thirty two patients with cervical spondylosis were enrolled for treatment, and SPSS26 was used to statistically analyze the VAS, Lovett muscle strength, head and neck range of motion and C2-3 vertebral body curvature before and after the treatment. Result: There were significant differences in posterior cervical muscle strength (Lovett), dizziness, shoulder and neck pain (VAS) and C2-3 vertebral curvature before and after training in 32 patients (p ˂ 0.001). Conclusion: The application of neurological training cervical vertebra self rehabilitation training instrument can effectively relieve clinical symptoms, restore head and neck range of motion, improve nape muscle strength, restore the dynamic balance of front and rear cervical muscle strength, and also can correct the abnormal physiological radian of the cervical vertebra.