Adverse Childhood Experiences and Adolescent Mental Disorders: Protective Mechanisms of Family Functioning, Social Capital, and Civic Engagement

This study aimed to investigate the protective effect of family functioning, social capital, and civic engagement on mental health in adolescents with adverse childhood experiences (ACEs). Parents of adolescents aged 12 to 17 years (N = 20,708) who participated in the 2015-2016 National Survey of Children’s Health were surveyed about their children’ ACEs (e.g., parental divorce, being a victim of violence, living with anyone who had substance abuse) and current mental health disorders, including depression, anxiety, and behavioral problems. Parents were further asked about their family functioning, perceived social capital, and adolescents’ civic engagement. Structural equation modeling was conducted to test the hypothesized pathways using Mplus 8.0.Based on parents’ report, about 55% of adolescents have experienced at least one kind of ACE. The final structural model proved an excellent fit to the data (CFI = 0.96, RMSEA = 0.009, WRMR = 1.12). A significantly positive relationship was found between adolescents’ ACEs and current mental disorders (β = 0.13, p \u3c 0.05), and the effect was partially mediated by family functioning (β = 0.10, p \u3c 0.001) and civic engagement (β = 0.01, p \u3c 0.05), but not by social capital (β = 0.01, p = 0.12). Low household income (β = -0.24, p \u3c 0.001) and low parent education (β = -0.05, p \u3c 0.05) significantly increased adolescents’ likelihood of having ACEs. Early assessment and interventions for children with ACEs are necessary to prevent the development of mental disorders in adolescence, especially for minority adolescents and those of low socioeconomic status. Safe, nurturing, and supportive home and social environments can help mitigate the detrimental impact of childhood adversity

Temporal Trends and Disparities in Suicidal Behaviors by Sex and Sexual Identity Among Asian American Adolescents

Importance: Although suicide is the second leading cause of death among Asian American adolescents, there is a dearth of studies examining overall and possible sex and sexual orientation disparities in the trends in suicidal behaviors among Asian American adolescents. Such information is crucial to inform targeted efforts of suicide prevention among Asian American adolescents. Objective: To examine temporal trends and sex and sexual orientation disparities in trends of nonfatal suicidal behaviors in Asian American adolescents from 1991 through 2019. Design, setting, and participants: This cross-sectional study used data from the national Youth Risk Behavior Survey from 1991 through 2019, analyzing a representative sample of US adolescents in grades 9 through 12 using a 3-stage cluster-sampling design. Data were analyzed from October through November 2020. Exposures: Calendar year, sex, sexual identity, sex of sexual contact, and interaction terms of these factors. Main outcomes and measures: Crude prevalence and annual percentage changes (APCs) in self-reported suicidal ideation, suicide plan, suicide attempts, and injury by suicide attempt for the overall sample and by sex, sexual identity, and sex of sexual contacts were calculated. Sexual minorities were defined as individuals whose sexual identity was gay or lesbian, bisexual, or not sure. Results: Among 7619 Asian Americans who participated in the Youth Risk Behavior Survey from 1991 to 2019 (mean [SD] age, 16.09 [1.29] years; 3760 [47.1%] female adolescents), 1576 individuals completed the sexual identity and behaviors questions after 2015 (mean [SD] age, 15.97 [1.28] years; 810 [49.2%] female adolescents). From 2009 through 2019, there was a 1.3-fold (95% CI, -0.8 to 3.3; P = .22) increase in suicide attempts and a 1.7-fold (95% CI, -2.6 to 5.9; P = .45) increase in injury by suicide attempt among Asian American female adolescents, although these increases were not statistically significant. Among 39 Asian American adolescents who identified as gay, lesbian, or bisexual or who were attracted to and had sexual contact with partners of the same sex or both sexes, compared with 1556 Asian American adolescents who were heterosexual, prevalence was greater for suicidal ideation (24 individuals [68.2%] vs 223 individuals [15.0%]; P < .001), suicide plan (15 individuals [57.7%] vs 179 individuals [11.9%]; P < .001), suicide attempts (14 individuals [41.0%] vs 74 individuals [5.5%]; P < .001), and injury by suicide attempt (5 individuals [17.6%] vs 23 individuals [1.7%]; P < .001) between 2015 and 2019. These sexual minorities identified by sexual identity and sexual contact also had an increasing rate over this period in suicide plan (APC, 10.5%; 95% CI, 4.4% to 16.9%; P < .001). Conclusions and relevance: This study found significant increases in rates of suicide plan among Asian American adolescents who were sexual minorities identified by sexual identity and sexual contact together. These findings suggest that suicide screening needs to inquire about sexual minority status in terms of sexual identity and sex of sexual contact when identifying Asian American adolescents who are at risk for suicidal behaviors. Culturally relevant suicide-prevention programs addressing unique risk and protective factors, racial discrimination, and sexual orientation-related stigma may be needed for Asian American adolescents

Joint spectrum shrinking maps on projections

Let $\mathcal H$ be a finite dimensional complex Hilbert space with dimension $n \ge 3$ and $\mathcal P(\mathcal H)$ the set of projections on $\mathcal H$. Let $\varphi: \mathcal P(\mathcal H) \to \mathcal P(\mathcal H)$ be a surjective map. We show that $\varphi$ shrinks the joint spectrum of any two projections if and only if it is joint spectrum preserving for any two projections and thus is induced by a ring automorphism on $\mathbb C$ in a particular way. In addition, for an arbitrary $k \ge 3$, $\varphi$ shrinks the joint spectrum of any $k$ projections if and only if it is induced by a unitary or an anti-unitary. Assume that $\phi$ is a surjective map on the Grassmann space of rank one projections. We show that $\phi$ is joint spectrum preserving for any $n$ rank one projections if and only if it can be extended to a surjective map on $\mathcal P(\mathcal{H})$ which is spectrum preserving for any two projections. Moreover, for any $k >n$, $\phi$ is joint spectrum shrinking for any $k$ rank one projections if and only if it is induced by a unitary or an anti-unitary.Comment: 14 page

Thermodynamic Properties of Nano-Silver and Alloy Particles

Non

Guidelines for Air-Stable Lithium/Sodium Layered Oxide Cathodes

The rational design of intercalation materials plays an indispensable role in continuously improving the performance of rechargeable batteries. The capability of some very promising layered oxide materials for positive electrodes (cathodes), such as sodium layered oxides and Ni-rich lithium layered oxides, are limited by several key challenges. Air stability is one of the issues that should be tackled appropriately. In this Perspective, we present the reaction mechanisms of layered oxides when exposed to moist atmospheres, the critical factors that affect the air stability of layered oxides, and the practical strategies toward air-stable electrodes. Based on the above understandings, we highlighted several pivotal research directions for further investigations of air stability of layered oxides. We expect that continued exploration in understanding the air stability of layered oxides will help to advance the design and lower the expense of cost-effective and high-energy cathodes for Li- and Na-ion battery technologies

A novel square root adaptive unscented Kalman filter combined with variable forgetting factor recursive least square method for accurate state-of-charge estimation of lithium-ion batteries.

Lithium-ion battery state-of-charge (SOC) serves as an important battery state parameter monitored by the battery management system (BMS), real-time and accurate estimation of the SOC is vital for safe, reasonable, and efficient use of the battery as well as the development of BMS technology. Taking the ternary lithium battery as the research object, based on the second-order RC equivalent circuit model, a variable forgetting factor least square method (VFFRLS) is used for parameter identification and a combination of the square root of covariance and noise statistics estimation techniques to estimate the SOC, to solve the problem of dispersion of the unscented Kalman filter and the error covariance tends to infinity with iterative calculation, thus ensuring the accuracy of SOC estimation. The feasibility and robustness of the algorithm and the battery state estimation strategy are verified under HPPC and BBDST conditions with maximum errors of 1.41% and 1.53%, respectively. The experimental results show that the combined algorithm of VFFRLS and SRAUKF has good robustness and stability, and has high accuracy in the SOC estimation of Li-ion batteries, which provides a reference for the research of lithium-ion batteries

Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather