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BackgroundTo provide participants with a more real and immersive intervening experience, virtual reality (VR) and/or augmented reality (AR) technologies have been integrated into some bystander intervention training programs and studies measuring bystander behaviors. ObjectiveWe focused on whether VR or AR can be used as a tool to enhance training bystanders. We reviewed the evidence from empirical studies that used VR and/or AR as a tool for examining bystander behaviors in the domain of interpersonal violence research. MethodsTwo librarians searched for articles in databases, including APA PsycInfo (Ovid), Criminal Justice Abstracts (EBSCO), Medline (Ovid), Applied Social Sciences Index & Abstracts (ProQuest), Sociological Abstracts (ProQuest), and Scopus till April 15, 2020. Studies focusing on bystander behaviors in conflict situations were included. All study types (except reviews) written in English in any discipline were included. ResultsThe search resulted in 12,972 articles from six databases, and the articles were imported into Covidence. Eleven studies met the inclusion and exclusion criteria. All 11 articles examined the use of VR as a tool for studying bystander behaviors. Most of the studies were conducted in US young adults. The types of interpersonal violence were school bullying, dating violence, sexual violence/assault, and soccer-associated violence. VR technology was used as an observational measure and bystander intervention program. We evaluated the different uses of VR for bystander behaviors and noted a lack of empirical evidence for AR as a tool. We also discuss the empirical evidence regarding the design, effectiveness, and limitations of implementing VR as a tool in the reviewed studies. ConclusionsThe reviewed results have implications and recommendations for future research in designing and implementing VR/AR technology in the area of interpersonal violence. Future studies in this area may further contribute to the use of VR as an observational measure and explore the potential use of AR to study bystander behaviors.

BackgroundFamily violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time. ObjectiveThis study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter. MethodsWe analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets. ResultsWe extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence–related news (eg, Tara Reade, Melissa DeRosa). ConclusionsThis study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.

BackgroundIt is important to measure the public response to the COVID-19 pandemic. Twitter is an important data source for infodemiology studies involving public response monitoring. ObjectiveThe objective of this study is to examine COVID-19–related discussions, concerns, and sentiments using tweets posted by Twitter users. MethodsWe analyzed 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags (eg, “coronavirus,” “COVID-19,” “quarantine”) from March 7 to April 21, 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigrams and bigrams, salient topics and themes, and sentiments in the collected tweets. ResultsPopular unigrams included “virus,” “lockdown,” and “quarantine.” Popular bigrams included “COVID-19,” “stay home,” “corona virus,” “social distancing,” and “new cases.” We identified 13 discussion topics and categorized them into 5 different themes: (1) public health measures to slow the spread of COVID-19, (2) social stigma associated with COVID-19, (3) COVID-19 news, cases, and deaths, (4) COVID-19 in the United States, and (5) COVID-19 in the rest of the world. Across all identified topics, the dominant sentiments for the spread of COVID-19 were anticipation that measures can be taken, followed by mixed feelings of trust, anger, and fear related to different topics. The public tweets revealed a significant feeling of fear when people discussed new COVID-19 cases and deaths compared to other topics. ConclusionsThis study showed that Twitter data and machine learning approaches can be leveraged for an infodemiology study, enabling research into evolving public discussions and sentiments during the COVID-19 pandemic. As the situation rapidly evolves, several topics are consistently dominant on Twitter, such as confirmed cases and death rates, preventive measures, health authorities and government policies, COVID-19 stigma, and negative psychological reactions (eg, fear). Real-time monitoring and assessment of Twitter discussions and concerns could provide useful data for public health emergency responses and planning. Pandemic-related fear, stigma, and mental health concerns are already evident and may continue to influence public trust when a second wave of COVID-19 occurs or there is a new surge of the current pandemic.

ObjectiveCongenital heart disease （CHD） is a common birth defect in children， with its incidence increasing annually. Because of their special health status， the vaccination situation for children with CHD has become a focal point of attention. This paper systematically reviews the current status of vaccination among children with CHD in China to identify existing issues in vaccination efforts for children with CHD， provide guidance for increasing vaccination rates among children with CHD， reduce the incidence of vaccine-preventable diseases， and provide ideas for subsequent research.MethodsLiterature on the vaccination of children with CHD in China was searched in both Chinese and English databases， including CNKI， Wanfang Data Knowledge Service Platform， VIP， Chinese Biomedical and Web of Science， PubMed， EmBase， and the Cochrane Library. Relevant literature was identified based on inclusion and exclusion criteria， and data extraction was carried out to summarize the research results.ResultsA total of 15 studies were included. Three articles focusing on expert consensus （guideline recommendations） for vaccination of children with CHD in China all agreed that vaccination is both feasible and necessary for children with CHD. Systematic analysis found that the overall timely vaccination rate for children with CHD was between 34% and 50%， with a notable delay in vaccination. However， after professional outpatient assessment， the vast majority of children with CHD were advised to be vaccinated according to the national recommended schedule， with only about 2% of them being advised to delay vaccination. The proportion of children with CHD who reported suspected adverse events following immunization （AEFI） was low and not significantly different from that of healthy/control children.ConclusionThe safety of vaccinations for children with CHD in China is relatively high， but the total timely vaccination rate is currently low. In order to improve the coverage and timeliness of vaccination in children with CHD， it is recommended that relevant departments provide vaccination policy support. There is a lack of research on the effectiveness of vaccination in children with CHD， so further studies are urgently needed to further improve the vaccination strategy for children with CHD in China.

Teacher identities and beliefs influence instructional practices. In order to explore this process, this self-study was conducted by three literacy professors from different ethnic backgrounds including one African-American professor, one Chinese national professor and one White professor. The purpose of this study was to examine how professors' literacy identities are shaped and how sharing these identities, experiences and beliefs in meaningful professional dialogues influences instructional practice. We examined the role of our identities and beliefs on our instructional practices using multiple forms of qualitative data such as journal entries, digital stories, and critical group discussions. Despite the range of differences among participants, we also shared commonalities in our identities and experiences. Findings from the study reveal that our literacy stories shaped our identities, our identities and beliefs shaped our instruction, and our reflective process shaped change in our practices. Through this process we were able to adjust our instruction in order to meet the diverse needs of our students. This reflective process influenced and improved our instructional decisions.

This survey study provides a holistic picture of English teachers' qualification and the status quo of English instruction in secondary school classrooms in four lowest-achieving rural districts in Beijing, China. It covers a wide variety of aspects related to language instruction, including teachers' professional background and credentials, lesson preparation and instructional materials, teachers' instructional languages, factors that limit English instruction, roles teachers play in classrooms, and classroom instruction on pronunciation and fluency, vocabulary, grammar, and comprehension. Results shows how inequality in resources and the lack of teaching standards and qualifications can negatively impact students, further hindering their academic and professional opportunities. The findings provide valuable information to the teacher preparation and professional development in these districts and hopefully bring attention to the Department of Education in China about the educational needs of the low achieving rural districts in Beijing.

The increasing number of anti-trafficking organizations and funding for anti-trafficking services have greatly out-paced evaluative efforts resulting in critical knowledge gaps, which have been underscored by recent recommendations for the development of greater evaluation capacity in the anti-trafficking field. In response to these calls, this paper reports on the development and feasibility testing of an evaluation protocol to generate practice-based evidence for an anti-trafficking transitional housing program. Guided by formative evaluation and evaluability frameworks, our practitioner-researcher team had two aims: (1) develop an evaluation protocol, and (2) test the protocol with a feasibility trial. To the best of our knowledge, this is one of only a few reports concerning anti-trafficking housing program evaluations, particularly one with many foreign-national survivors as evaluation participants. In addition to presenting evaluation findings, the team herein documented decisions and strategies related to conceptualizing, designing, and conducting the evaluation to offer approaches for future evaluations.

With the gradual increase in demand for new energy, new types of energy storage devices have rapidly developed in recent years. Currently, new energy batteries led by lithium-ion batteries have begun to be applied in the automotive field. However, lithium-ion batteries suffer from issues such as low energy density, slow charging rates, and short operational lifespans. To mitigate and address the drawbacks of lithium-ion batteries, researchers have begun developing supercapacitors. This article first categorizes and compares some common energy storage devices, concluding that supercapacitors have significant advantages in charging rates and stability. Then, based on the dimensionality of nanomaterials, it categorizes and compares electrode materials used in supercapacitors, discussing three ways of constructing electrodes using 1D, 2D, and 1D-2D combined materials. Through classification, comparison, and discussion, it ultimately concludes that growing nanomaterials on a millimeter-scale structural substrate effectively increases material specific surface area and stability, thereby greatly enhancing the performance of supercapacitors.

At present, non-coal mine safety supervision mainly includes safety management inspection and on-site safety supervision inspection. They rely on safety information management platform and on-site inspection by safety inspectors respectively. There are problems of low supervision efficiency, delayed information acquisition, and it is difficult to realize dynamic and comprehensive supervision. In view of the above problems, this paper puts forward a non-coal mine safety supervision mode based on risk monitoring and early warning, considering the characteristics of non-coal mines, such as large number, scattered, poor, small, and the differences in risks and informatization management and control of different types of non-coal mines. Based on the risk monitoring and early warning system of non-coal mine, the risk monitoring index data in five aspects of personnel, environment, equipment and facilities, management and monitoring topics are extracted. The single risk early warning analysis and comprehensive risk alarm analysis are carried out in two ways of risk point and risk surface respectively. The graded push of single risk early warning and the graded control of comprehensive risk alarm can be realized. The monitoring data and special topics of various types of non-coal mines are sorted out. The trigger and disposal mechanism of single risk graded early warning is introduced. This study focuses on the construction process of the model for comprehensive risk research and judgment and graded supervision. Based on the risk monitoring data of non-coal mines, the risk monitoring index system is established. The entropy weight method is adopted to give weight to the risk monitoring index, and the scoring standard is formulated to score the risk index of non-coal mines. Based on the index weight and score, the comprehensive risk level of mining enterprises is determined. The non-coal mine safety supervision mode based on risk monitoring and early warning realizes the graded supervision and inspection of non-coal mines by supervision departments under different supervision periods and ranges. The mode optimizes the allocation of supervision and inspection resources, and improves the efficiency of supervision and inspection.