Adverse childhood experiences and deviant peer affiliation among Chinese delinquent adolescents: the role of relative deprivation and age

BackgroundDeviant peer affiliation is considered a potential risk factor for adolescent delinquency. Due to the serious situation of adolescent delinquency in China, it is necessary to investigate the mechanisms by which adolescents associate with deviant peers.ObjectivesThe purpose of this study was to examine the association between adverse childhood experiences (ACEs) and deviant peer affiliation, the mediating effect of relative deprivation, and the moderating effect of age in a sample of Chinese delinquent adolescents.MethodsFive hundred and forty-two Special School students aged 11–18 years were interviewed and completed questionnaires, including demographics, adverse childhood experiences, deviant peer affiliation, and relative deprivation.Results(1) After controlling for gender, adverse childhood experiences and deviant peer affiliation were significantly and positively associated among delinquent adolescents. (2) The effect of ACEs on deviant peer affiliation was mediated by relative deprivation. (3) Age played a moderating role not only in the relationship between ACEs and relative deprivation, but also in the indirect relationship in which ACEs influence deviant peer affiliation through relative deprivation; specifically, the indirect effect of ACEs influencing deviant peer affiliation through relative deprivation was stronger in early adolescence compared with late adolescence.ConclusionOverall, early ACEs play an important role in deviant peer affiliation among delinquent adolescents and relative deprivation is an important mediating variable. The results of the present study emphasize the importance of cognitive interventions for delinquent adolescents who experience ACEs in early adolescence, which may be instructive for the prevention of adolescent delinquency

Glucose-fueled Micromotors with Highly Efficient Visible Light Photocatalytic Propulsion

Synthetic micro/nanomotors fueled by glucose are highly desired for numerous practical applications because of the biocompatibility of their required fuel. However, currently all of the glucose-fueled micro/nanomotors are based on enzyme-catalytic-driven mechanisms, which usually suffer from strict operation conditions and weak propulsion characteristics that greatly limit their applications. Here, we report a highly efficient glucose-fueled cuprous oxide@N-doped carbon nanotube (Cu_2O@N-CNT) micromotor, which can be activated by environment-friendly visible-light photocatalysis. The speeds of such Cu_2O@N-CNT micromotors can reach up to 18.71 μm/s, which is comparable to conventional Pt-based catalytic Janus micromotors usually fueled by toxic H_2O_2 fuel. In addition, the velocities of such motors can be efficiently regulated by multiple approaches, such as adjusting the N-CNT content within the micromotors, glucose concentrations, or light intensities. Furthermore, the Cu_2O@N-CNT micromotors exhibit a highly controllable negative phototaxis behavior (moving away from light sources). Such motors with outstanding propulsion in biological environments and wireless, repeatable, and light-modulated three-dimensional motion control are extremely attractive for future practical applications

DualVC 2: Dynamic Masked Convolution for Unified Streaming and Non-Streaming Voice Conversion

Voice conversion is becoming increasingly popular, and a growing number of application scenarios require models with streaming inference capabilities. The recently proposed DualVC attempts to achieve this objective through streaming model architecture design and intra-model knowledge distillation along with hybrid predictive coding to compensate for the lack of future information. However, DualVC encounters several problems that limit its performance. First, the autoregressive decoder has error accumulation in its nature and limits the inference speed as well. Second, the causal convolution enables streaming capability but cannot sufficiently use future information within chunks. Third, the model is unable to effectively address the noise in the unvoiced segments, lowering the sound quality. In this paper, we propose DualVC 2 to address these issues. Specifically, the model backbone is migrated to a Conformer-based architecture, empowering parallel inference. Causal convolution is replaced by non-causal convolution with dynamic chunk mask to make better use of within-chunk future information. Also, quiet attention is introduced to enhance the model's noise robustness. Experiments show that DualVC 2 outperforms DualVC and other baseline systems in both subjective and objective metrics, with only 186.4 ms latency. Our audio samples are made publicly available.Comment: Accepted by ICASSP202