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

Development of Digital Education in the Age of Digital Transformation: Citing China’s Practice in Smart Education as a Case Study

Digital education has been catalyzing educational transition, transforming the educational views and instructional techniques while also providing opportunities for high-quality educational development. This is a subject that all nations throughout the world are concerned about. The worldwide practice of integrating digital technologies in teaching has yielded positive results. The Global Digital Education Conference, which was held in Beijing, China, in 2023, called for global collaboration on digital education development. This paper sought to illustrate the significance of digital education for educational reform and to investigate ways for digital education development in this context, using China’s smart education practice as evidence

Neural Inheritance Relation Guided One-Shot Layer Assignment Search

Layer assignment is seldom picked out as an independent research topic in neural architecture search. In this paper, for the first time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100. Through analyzing this dataset, we discover a neural inheritance relation among the networks with different layer assignments, that is, the optimal layer assignments for deeper networks always inherit from those for shallow networks. Inspired by this neural inheritance relation, we propose an efficient one-shot layer assignment search approach via inherited sampling. Specifically, the optimal layer assignment searched in the shallow network can be provided as a strong sampling priori to train and search the deeper ones in supernet, which extremely reduces the network search space. Comprehensive experiments carried out on CIFAR-100 illustrate the efficiency of our proposed method. Our search results are strongly consistent with the optimal ones directly selected from the architecture dataset. To further confirm the generalization of our proposed method, we also conduct experiments on Tiny-ImageNet and ImageNet. Our searched results are remarkably superior to the handcrafted ones under the unchanged computational budgets. The neural inheritance relation discovered in this paper can provide insights to the universal neural architecture search.Comment: AAAI202

2-(Methoxy­meth­yl)adamantan-2-yl 2-methyl­acrylate

The title compound, C16H24O3, has a cage-type mol­ecular structure and is of inter­est with respect to its photochemical properties. The structure displays non-classical inter­molecular C—H⋯O hydrogen bonding, which links the mol­ecules into a three-dimensional network

Thermal Performance Improvement of Prefab Houses by Covering Retro-reflective Materials

AbstractOwing to the small thermal inertia of prefab houses, indoor thermal environment is poor and occupants are tormented especially under the high solar radiation. Retro-reflective materials can make it a reasonable choice to decrease radiation heat gain due to their high reflectivity for solar radiation. Comparative results show that the indoor air temperature of the prefab house by covering retro-reflectivity materials can reduce by more than 7°C, while the reduced value is close to 10°C for the peak radiant temperature and 7°C for indoor average radiant temperature. It shows retro-reflective materials have a significant effect on thermal performance improvement of prefab houses. Through the comparative study of different walls, it is found that the top, south and east walls are the better choices and that the north wall is the worst one to cover retro-reflectivity materials

Comparative Study of In-situ Test and Laboratory Test on Material Reflectivity

AbstractThis paper gives the theory algorithm of material reflectivity, and works out the in-situ material reflectivity combined with in-situ conditions, researches the influence rules of material's reflectivity under practical solar radiation intensity, and the feasibility of this simple in-situ test method is researched by the comparison of in-situ test result and laboratory test result

Scalable colored sub-ambient radiative coolers based on a polymer-Tamm photonic structure

Daytime radiative coolers cool objects below the air temperature without any electricity input, while most of them are limited by a silvery or whitish appearance. Colored daytime radiative coolers (CDRCs) with diverse colors, scalable manufacture, and sub-ambient cooling have not been achieved. We introduce a polymer-Tamm photonic structure to enable a high infrared emittance and an engineered absorbed solar irradiance, governed by the quality factor (Q-factor). We theoretically determine the theoretical thresholds for sub-ambient cooling through yellow, magenta, and cyan CDRCs. We experimentally fabricate and observe a temperature drop of 2.6-8.8 degrees Celsius on average during daytime and 4.0-4.4degrees Celsius during nighttime. Furthermore, we demonstrate a scalable-manufactured magenta CDRC with a width of 60 cm and a length of 500 cm by a roll-to-roll deposition technique. This work provides guidelines for large-scale CDRCs and offers unprecedented opportunities for potential applications with energy-saving, aesthetic, and visual comfort demands

Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Actuator control takes a pivotal role in achieving stability and accuracy, particularly in the context of multi-axial real-time hybrid simulation (maRTHS). In maRTHS, multiple hydraulic actuators are necessitated to apply precise motions to experimental substructures thus necessitating the application of multiple-input multiple-output (MIMO)control strategies. This study evaluates the data-driven nonlinear autoregressive with external input (NARX) based compensation for the servo-hydraulic dynamics within the maRTHS benchmark model. Different from previous study, nonlinear terms are incorporated into the NARX model. Online least square and ridge regression techniques are utilized to estimate the model coefficients to achieve optimal compensation. The influence of various model order and window length is assessed for the NARX model-based compensation. The findings of this research demonstrate that NARX-based compensation has significant potential not only in facilitating precise actuator control for maRTHS but also in enabling robust control in the presence of unknown uncertainties inherent to the servo-hydraulic system

Bidirectional causality between immunoglobulin G N-glycosylation and metabolic traits: A mendelian randomization study

Although the association between immunoglobulin G (IgG) N-glycosylation and metabolic traits has been previously identified, the causal association between them remains unclear. In this work, we used Mendelian randomization (MR) analysis to integrate genome-wide association studies (GWASs) and quantitative trait loci (QTLs) data in order to investigate the bidirectional causal association of IgG N-glycosylation with metabolic traits. In the forward MR analysis, 59 (including nine putatively causal glycan peaks (GPs) for body mass index (BMI) (GP1, GP6, etc.) and seven for fasting plasma glucose (FPG) (GP1, GP5, etc.)) and 15 (including five putatively causal GPs for BMI (GP2, GP11, etc.) and four for FPG (GP1, GP10, etc.)) genetically determined IgG N-glycans were identified as being associated with metabolic traits in one- and two-sample MR studies, respectively, by integrating IgG N-glycan-QTL variants with GWAS results for metabolic traits (all P \u3c 0.05). Accordingly, in the reverse MR analysis of the integrated metabolic-QTL variants with the GWAS results for IgG N-glycosylation traits, 72 (including one putatively causal metabolic trait for GP1 (high-density lipoprotein cholesterol (HDL-C)) and five for GP2 (FPG, systolic blood pressure (SBP), etc.)) and four (including one putatively causal metabolic trait for GP3 (HDL-C) and one for GP9 (HDL-C)) genetically determined metabolic traits were found to be related to the risk of IgG N-glycosylation in one- and two-sample MR studies, respectively (all P \u3c 0.05). Notably, genetically determined associations of GP11 BMI (fixed-effects model-Beta with standard error (SE): 0.106 (0.034) and 0.010 (0.005)) and HDL-C GP9 (fixed-effects model-Beta with SE: –0.071 (0.022) and –0.306 (0.151)) were identified in both the one- and two-sample MR settings, which were further confirmed by a meta-analysis combining the one- and two-sample MR results (fixed-effects model-Beta with 95% confidence interval (95% CI): 0.0109 (0.0012, 0.0207) and –0.0759 (–0.1186, –0.0332), respectively). In conclusion, the comprehensively bidirectional MR analyses provide suggestive evidence of bidirectional causality between IgG N-glycosylation and metabolic traits, possibly revealing a new richness in the biological mechanism between IgG N-glycosylation and metabolic traits. © 2022 THE AUTHOR

Attention Diversification for Domain Generalization

Convolutional neural networks (CNNs) have demonstrated gratifying results at learning discriminative features. However, when applied to unseen domains, state-of-the-art models are usually prone to errors due to domain shift. After investigating this issue from the perspective of shortcut learning, we find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. Briefly, Intra-Model Attention Diversification Regularization is equipped on the high-level feature maps to achieve in-channel discrimination and cross-channel diversification via forcing different channels to pay their most salient attention to different spatial locations. Besides, Inter-Model Attention Diversification Regularization is proposed to further provide task-related attention diversification and domain-related attention suppression, which is a paradigm of "simulate, divide and assemble": simulate domain shift via exploiting multiple domain-specific models, divide attention maps into task-related and domain-related groups, and assemble them within each group respectively to execute regularization. Extensive experiments and analyses are conducted on various benchmarks to demonstrate that our method achieves state-of-the-art performance over other competing methods. Code is available at https://github.com/hikvision-research/DomainGeneralization.Comment: ECCV 2022. Code available at https://github.com/hikvision-research/DomainGeneralizatio