ObjectivesThe COVID-19 pandemic has taken a significant toll on people worldwide for more than 2 years. Previous studies have highlighted the negative effects of COVID-19 on the mental health of healthcare workers (HCWs) more than the positive changes, such as post-traumatic growth (PTG). Furthermore, most previous studies were cross-sectional surveys without follow-ups. This study draws on PTG follow-up during the COVID-19 outbreak at 12-month intervals for 2 years since 2020. The trajectories and baseline predictors were described.MethodsA convenience sampling method was used to recruit frontline nurses or doctors at the COVID-19-designated hospital who were eligible for this study. A total of 565 HCWs completed the 2 years follow-up and were used for final data analysis. The latent growth mixture models (GMM) was used to identify subgroups of participants with different PTG trajectories. Multinomial logistic regression model was used to find predictors among sociodemographic characteristics and resilience at baseline.ResultsFour trajectory PTG types among HCWs were identified: ‘Persistent, “Steady increase”, “High with drop”, and “Fluctuated rise.” Comparing the “Persistent low” type, the other three categories were all associated with older age, higher education. Furthermore, “Persistent low” was also negatively associated with resilience at baseline.ConclusionThe PTG of HCWs with different characteristics showed different trends over time. It is necessary to increase the measure frequency to understand the PTG status in different times. Improving HCW’s resilience could help improve staff PTG.

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Based on the law of electromagnetic induction, a novel contactless absolute angular position micro-sensor has been designed in this paper. It is mainly composed of three parts: a magnet having two portions shaped as circle segments with different center points, a Hall effect sensor and a signal processing circuit. The linearity of the sensing system is improved by particularly designing spatial magnetic field distribution of asymmetric magnet, and Hall output signal is converted by the second-order low-pass filter circuit into linear analog voltage. Experiment has been performed on a prototype of the angular position sensor and the results demonstrate the proposed scheme is feasible. Theoretic precision and testing error analysis of the sensor has been pursued based on BP neural network at the end of the paper. The error does not exceed ± 0.015°. The prediction result can fully reproduce the output performance of hall sensing system with high accuracy of 0.02%, achieving high-precision angle measurement.

The multi-joints motion planning method by online autolearning mode based on neural network which can realize new trajectories' planning and control of multi-joints manipulator is extensively used in the field of trajectory tracking, planning and control of intelligent robot, especially for rigid manipulator to achieve the discovery of new trajectory, real-time self-learning and control itself. Such method can achieve the real-time self-learning and control of nonlinear complex trajectories, by taking advantage of the global optimal approximation performance of the neural network. The functional relationship can be established between the current trajectory information and trajectory information at the previous N moments by neural network, and because of this, information related to trajectory can be predicted, thereby realizing on-line self-learning of multi-joints. This method is used for the real-time control of intelligent mechanical arm which can reduce the difficulty of numerical solution greatly, improve the efficiency of calculation and boost the ability of real-time self-learning.

The control and drive system of humanoid dexterous hand is mainly a control and drive system with high integration, multi degree of freedom and multi joint cooperation. However, the existing control and drive systems are mainly single motor driven, with low integration and poor coordination ability. Multi motor control and drive system has the characteristics of complexity, difference and mutual disturbance. At present, the control and drive system based on MCU and DSP can not meet the requirements of multi motor system control. Therefore, in order to meet the demand of multi degree of freedom integrated control and drive for dexterous hand, this paper designs an integrated control and drive system for dexterous hand based on FPGA. The system includes encoder reading module, current reading module, vector control module, PWM generator module and communication module, which can realize the position closed-loop control of N PMSM at the same time. This system has the characteristics of good stability, high cooperation ability, low equipment cost, small volume and good heat dissipation. Through experiments, it is verified that the design of the system is feasible, the collaborative ability is strong, and the control performance is stable.