BackgroundGraduate students frequently face life events， many of which may adversely affect their mental well-being. However， the interaction between life events and the development of depression， anxiety， and somatic symptoms remains unclear. ObjectiveTo explore the relationship between life events and the development of depressive， anxiety and somatic symptoms in graduate students， thereby informing prevention strategies for these conditions. MethodsA sample of 6 722 newly enrolled graduate students at a comprehensive university in Southwest China from September to November 2018 was selected. The assessment was conducted using the Adolescent Self-rating Life Events Checklist （ASLEC）， the 7-item Generalized Anxiety Disorder scale-7 item （GAD-7）， the Patient Health Questionnaire Depression Scale-9 item （PHQ-9）， and the Patient Health Questionnaire-15 （PHQ-15）. Network analysis was implemented by using the bootnet and qgraph packages in the R software （version 4.2.3）， with centrality indices calculated to identify core and bridge symptoms within the network. ResultsThe study encompassed a total of 6 171 graduate students， representing 91.80% of the target population. The prevalence rates of anxiety， depressive， and somatic symptoms among graduate students were 12.59% （777/6 171）， 16.63% （1 026/6 171）， and 27.66% （1 707/6 171）， respectively. Network analysis revealed that 'academic stress' was the core symptom with the highest strength and expected influence （both values=1.207）， while 'feeling down， depressed， or hopeless' was the bridge symptom with the highest bridge strength and bridge expected influence （both values=0.454）. There was no significant difference in global network strength and edge weight between women and men （P>0.05）. ConclusionAcademic stress， emerging as the core symptom， assumes a dominant position within the symptom network and exhibits strong interactions with other negative affective states. There was no gender difference in the network structure.

Objective To explore the bi-directional dynamic associations of bullying perpetration and victimisation with psychopathological symptoms in children and adolescents.Methods The Adolescent Brain Cognitive Development(ABCD)public dataset was used.Three dimensions of bullying perpetration and victimisation and child-adolescent psychopathological symptoms:attention,externalisation and internalisation were collected from 8413 child-adolescents using the peer experiences questionnaire(EPQ)and the brief problem monitoring(BPM)questionnaire,respectively.Bidirectional dynamic associations between bullying and child-adolescent psychopathological symptoms were analysed using cross-lagged networks.Results Bullying implementation to externalising problems was a significant cross-lagged edge(OR=2.36);impulsivity was both the strongest node of out-expected influence(OEI=5.21)and bridge-expected influence(BEI=4.35),and the strongest node of in-expected influence(IEI)was threat of violence(IEI=5.61).Conclusion There is an interaction between bullying and child-adolescent pathology symptoms.Bullying perpetration exacerbates subsequent externalising problems,and impulsivity is the most likely psychopathological symptom to influence both bullying perpetration and externalising internalising problems and it is also the bridging symptom connecting the two.

Due to the constraints of the surgical environment and operational space,laparoscopic surgical instruments employ wire-driven mechanisms.However,factors such as wire rigidity,hysteresis,and motor drive limitations result in the end-effector accuracy of surgical instruments failing to meet ideal requirements.To address the shortcomings of existing multi-degree-of-freedom laparoscopic surgical instruments in achieving end-effector precision,a motion compensation algorithm based on the Autogluon algorithm for a 4-degree-freedom laparoscopic surgical instrument is proposed.A single-degree-of-freedom surgical instrument driven by wire ropes was constructed,and machine learning was utilized to estimate the end-effector position.This estimated position served as a feedback compensation condition to control the end-effector of the surgical instrument.To validate the correctness of this method,it was compared with approaches such as neural networks,linear regression,decision trees,Gaussian processes,and support vector machines.The results demonstrated that the proposed method achieved the smallest mean squared error,maximum error,and mean absolute error,thereby verifying its effectiveness.

Objective To explore the bi-directional dynamic associations of bullying perpetration and victimisation with psychopathological symptoms in children and adolescents.Methods The Adolescent Brain Cognitive Development(ABCD)public dataset was used.Three dimensions of bullying perpetration and victimisation and child-adolescent psychopathological symptoms:attention,externalisation and internalisation were collected from 8413 child-adolescents using the peer experiences questionnaire(EPQ)and the brief problem monitoring(BPM)questionnaire,respectively.Bidirectional dynamic associations between bullying and child-adolescent psychopathological symptoms were analysed using cross-lagged networks.Results Bullying implementation to externalising problems was a significant cross-lagged edge(OR=2.36);impulsivity was both the strongest node of out-expected influence(OEI=5.21)and bridge-expected influence(BEI=4.35),and the strongest node of in-expected influence(IEI)was threat of violence(IEI=5.61).Conclusion There is an interaction between bullying and child-adolescent pathology symptoms.Bullying perpetration exacerbates subsequent externalising problems,and impulsivity is the most likely psychopathological symptom to influence both bullying perpetration and externalising internalising problems and it is also the bridging symptom connecting the two.

Due to the constraints of the surgical environment and operational space,laparoscopic surgical instruments employ wire-driven mechanisms.However,factors such as wire rigidity,hysteresis,and motor drive limitations result in the end-effector accuracy of surgical instruments failing to meet ideal requirements.To address the shortcomings of existing multi-degree-of-freedom laparoscopic surgical instruments in achieving end-effector precision,a motion compensation algorithm based on the Autogluon algorithm for a 4-degree-freedom laparoscopic surgical instrument is proposed.A single-degree-of-freedom surgical instrument driven by wire ropes was constructed,and machine learning was utilized to estimate the end-effector position.This estimated position served as a feedback compensation condition to control the end-effector of the surgical instrument.To validate the correctness of this method,it was compared with approaches such as neural networks,linear regression,decision trees,Gaussian processes,and support vector machines.The results demonstrated that the proposed method achieved the smallest mean squared error,maximum error,and mean absolute error,thereby verifying its effectiveness.