ABSTRACT Purpose: We aimed to find the association between screen time (ST)-based sedentary behavior and depression in children and adolescents. Methods: PubMed, Embase, and Web of science database were searched to find eligible studies until April 25, 2021. Data extraction was conducted by two investigators independently, followed by quality assessment for included studies. Odd ration (OR) and 95% confidence intervals (CI) were regarded as effect size index. Heterogeneity test was conducted using Cochran’ s Q test and I2 test. Least squares trend estimation method was used for dose-response meta-analysis. All statistical analyses were conducted using Stata12.0 software. Results: Totally 22 articles containing 197,673 cases were included. The pooled results displayed that there was a significant positive correlation between ST and depression [OR (95%CI) = 1.24 (1.11, 1.38), P < 0.001]. Similar results were observed for watching television (TV), computer use (CU), computer game (CG)/video game (VG) and internet use (IU)/mobile phone (MP) time. Dose-response meta-analysis showed that take 1 hour/day as control, the risk of depression went down and then went up as sedentary time increased for ST (P > 0.05). The risk of depression was significantly increased when TV time beyond 4.5 hours/day (P < 0.05), or CU time beyond 0.5 hours/day (P < 0.05), or CG/VG time beyond 2 hours/day (P < 0.05), or IU/MP time beyond 0.5 hours/day (P < 0.05). Conclusion: ST-based sedentary behavior was associated with the risk of depression in a non-linear dose–response manner for children and adolescents.

In hot working process, the prediction of material constitutive relationship can improve the optimization design process. Recently, the artificial neural network models are considered as a powerful tool to describe the elevated temperature deformation behavior of materials. Based on the experimental data from the isothermal compressions of 42CrMo high strength steel, an artificial neural network (ANN) was trained with standard back-propagation learning algorithm to predict the elevated temperature deformation behavior of 42CrMo steel. The inputs of the ANN model are strain, strain rate and temperature, whereas flow stress is the output. According to the predicted and experimental results, it indicates that the developed ANN model shows a good capacity of modeling complex hot deformation behavior and can accurately tracks the experimental data in a wide temperature range and strain rate range. In addition, the predicted data outside of experimental conditions were obtained, indicating good prediction potentiality of the developed ANN model. The θ -σ curves outside of experimental conditions indicate that the predicted strain-stress curves exhibit a typical dynamic recrystallization softening characteristic of high temperature deformation behavior. Through the coupling of the ANN model and finite element model, the hot compression simulations at the temperature of 1273 K and strain rates of 0.01~10 s-1 were conducted. The results show that the predicted constitutive data outside the experimental conditions successfully improved the prediction accuracy of forming load during the FEM simulation.

In order to investigate the compressive deformation behavior of 3Cr20Ni10W2 alloy, a series of isothermal upsetting experiments were carried out in the temperature range of 1203-1403 K and strain rate range of 0.01-10 s-1 on a Gleeble-1500 thermo-mechanical simulator. The results indicate that the flow stress initially increases to a peak value and then decreases gradually to a steady state. The characteristics of the curves are determined by the interaction of work hardening (WH), dynamic recovery (DRV) and dynamic recrystallization (DRX). The flow stress decreases with increasing temperature and decreasing strain rate. The relationship between microstructure and processing parameters is discussed to give an insight into the hot deformation behavior of 3Cr20Ni10W2 alloy. Then, by regression analysis for constitutive equation, material constants (n, α, β, A and Q) were calculated for the peak stress. Further, the constitutive equation along the flow curve was developed by utilizing an eighth order polynomial of strain for variable coefficients (including n, α, A and Q). The validity of the developed constitutive equation incorporating the influence of strain was verified through comparing the experimental and predicted data by using standard statistical parameters such as correlation coefficient (R) and average absolute relative error (AARE) that are 0.995 and 4.08% respectively.