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In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN). The CNN has superior features for autonomous learning and expression, and feature extraction from original input data can be realized by means of training CNN models that match practical applications. Due to the rapid progress in deep learning technology, the structure of CNN is becoming more and more complex and diverse. Consequently, it gradually replaces the traditional machine learning methods. This paper presents an elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers. On this basis, this paper gives a comprehensive overview of the past and current research status of the applications of CNN models in computer vision fields, e.g., image classification, object detection, and video prediction. In addition, we summarize the challenges and solutions of the deep CNN, and future research directions are also discussed.

Despite the increasing popularity of doctoral education, many students do not complete their studies, and very little information is available about them. Understanding why some students consider that they do not want to, or cannot, continue with their studies is essential to reduce dropout rates and to improve the overall quality of doctoral programmes. This study focuses on the motives students give for considering dropping out of their doctoral degree. Participants were 724 social sciences doctoral students from 56 Spanish universities, who responded to a questionnaire containing doctoral degree conditions questions and an open-ended question on motives for dropping out. Results showed that a third of the sample, mainly the youngest, female and part time students, stated that they had intended to drop out. The most frequent motives for considering dropping out were difficulties in achieving a balance between work, personal life and doctoral studies and problems with socialization. Overall, results offer a complex picture that has implications for the design of doctoral programmes, such as the conditions and demands of part-time doctoral studies or the implementation of educational proposals that facilitate students' academic and personal integration into the scientific community in order to prevent the development of a culture of institutional neglect.

Mobile health (mHealth) apps are revolutionizing the way clinicians and researchers monitor and manage the health of their participants. However, many studies using mHealth apps are hampered by substantial participant dropout or attrition, which may impact the representativeness of the sample and the effectiveness of the study. Therefore, it is imperative for researchers to understand what makes participants stay with mHealth apps or studies using mHealth apps. This study aimed to review the current peer-reviewed research literature to identify the notable factors and strategies used in adult participant engagement and retention. We conducted a systematic search of PubMed, MEDLINE, and PsycINFO databases for mHealth studies that evaluated and assessed issues or strategies to improve the engagement and retention of adults from 2015 to 2020. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Notable themes were identified and narratively compared among different studies. A binomial regression model was generated to examine the factors affecting retention. Of the 389 identified studies, 62 (15.9%) were included in this review. Overall, most studies were partially successful in maintaining participant engagement. Factors related to particular elements of the app (eg, feedback, appropriate reminders, and in-app support from peers or coaches) and research strategies (eg, compensation and niche samples) that promote retention were identified. Factors that obstructed retention were also identified (eg, lack of support features, technical difficulties, and usefulness of the app). The regression model results showed that a participant is more likely to drop out than to be retained. Retaining participants is an omnipresent challenge in mHealth studies. The insights from this review can help inform future studies about the factors and strategies to improve participant retention.

Mobile health (mHealth) apps show vast potential in supporting patients and health care systems with the increasing prevalence and economic costs of noncommunicable diseases (NCDs) worldwide. However, despite the availability of evidence-based mHealth apps, a substantial proportion of users do not adhere to them as intended and may consequently not receive treatment. Therefore, understanding the factors that act as barriers to or facilitators of adherence is a fundamental concern in preventing intervention dropouts and increasing the effectiveness of digital health interventions. This review aimed to help stakeholders develop more effective digital health interventions by identifying factors influencing the continued use of mHealth apps targeting NCDs. We further derived quantified adherence scores for various health domains to validate the qualitative findings and explore adherence benchmarks. A comprehensive systematic literature search (January 2007 to December 2020) was conducted on MEDLINE, Embase, Web of Science, Scopus, and ACM Digital Library. Data on intended use, actual use, and factors influencing adherence were extracted. Intervention-related and patient-related factors with a positive or negative influence on adherence are presented separately for the health domains of NCD self-management, mental health, substance use, nutrition, physical activity, weight loss, multicomponent lifestyle interventions, mindfulness, and other NCDs. Quantified adherence measures, calculated as the ratio between the estimated intended use and actual use, were derived for each study and compared with the qualitative findings. The literature search yielded 2862 potentially relevant articles, of which 99 (3.46%) were included as part of the inclusion criteria. A total of 4 intervention-related factors indicated positive effects on adherence across all health domains: personalization or tailoring of the content of mHealth apps to the individual needs of the user, reminders in the form of individualized push notifications, user-friendly and technically stable app design, and personal support complementary to the digital intervention. Social and gamification features were also identified as drivers of app adherence across several health domains. A wide variety of patient-related factors such as user characteristics or recruitment channels further affects adherence. The derived adherence scores of the included mHealth apps averaged 56.0% (SD 24.4%). This study contributes to the scarce scientific evidence on factors that positively or negatively influence adherence to mHealth apps and is the first to quantitatively compare adherence relative to the intended use of various health domains. As underlying studies mostly have a pilot character with short study durations, research on factors influencing adherence to mHealth apps is still limited. To facilitate future research on mHealth app adherence, researchers should clearly outline and justify the app's intended use; report objective data on actual use relative to the intended use; and, ideally, provide long-term use and retention data.

The efficacy of academic-mind-set interventions has been demonstrated by small-scale, proof-of-concept interventions, generally delivered in person in one school at a time. Whether this approach could be a practical way to raise school achievement on a large scale remains unknown. We therefore delivered brief growth-mind-set and sense-of-purpose interventions through online modules to 1,594 students in 13 geographically diverse high schools. Both interventions were intended to help students persist when they experienced academic difficulty; thus, both were predicted to be most beneficial for poorly performing students. This was the case. Among students at risk of dropping out of high school (one third of the sample), each intervention raised students' semester grade point averages in core academic courses and increased the rate at which students performed satisfactorily in core courses by 6.4 percentage points. We discuss implications for the pipeline from theory to practice and for education reform.

Treatment-resistant schizophrenia (TRS) is associated with high levels of functional impairment, healthcare usage and societal costs. Cross-sectional studies may overestimate TRS rates because of selection bias. We aimed to quantify TRS rates by using first-episode cohorts to improve resource allocation and clozapine access. We undertook a systematic review of TRS rates among people with first-episode psychosis and schizophrenia, with a minimum follow-up of 8 weeks. We searched PubMed, PsycINFO, EMBASE, CINAHL and the Cochrane Database of Systematic Reviews, and meta-analysed TRS rates from included studies. Twelve studies were included, totalling 11 958 participants; six studies were of high quality. The rate of TRS was 22.8% (95% CI 19.1-27.0%, P < 0.001) among all first-episode cohorts and 24.4% (95% CI 19.5-30.0%, P < 0.001) among first-episode schizophrenia cohorts. Subgroup sensitivity analyses by location of recruitment, TRS definition, study quality, time of data collection and retrospective versus prospective data collection did not lead to statistically significant differences in heterogeneity. In a meta-regression, duration of follow-up and percentage drop-out did not significantly affect the overall TRS rate. Men were 1.57 times more likely to develop TRS than women (95% CI 1.11-2.21, P = 0.010). Almost a quarter of people with first-episode psychosis or schizophrenia will develop TRS in the early stages of treatment. When including people with schizophrenia who relapse despite initial response and continuous treatment, rates of TRS may be as high as a third. These high rates of TRS highlight the need for improved access to clozapine and psychosocial supports.

The most critical concern in machine learning is how to make an algorithm that performs well both on training data and new data. No free lunch theorem implies that each specific task needs its own tailored machine learning algorithm to be designed. A set of strategies and preferences are built into learning machines to tune them for the problem at hand. These strategies and preferences, with the core concern of generalization improvement, are collectively known as regularization. In deep learning, because of a considerable number of parameters, a great many forms of regularization methods are available to the deep learning community. Developing more effective regularization strategies has been the subject of significant research efforts in recent years. However, it is difficult for developers to choose the most suitable strategy for their problem at hand, because there is no comparative study regarding the performance of different strategies. In this paper, at the first step, the most effective regularization methods and their variants are presented and analyzed in a systematic approach. At the second step, comparative research on regularization techniques is presented in which the testing errors and computational costs are evaluated in a convolutional neural network, using CIFAR-10 (https://www.cs.toronto.edu/~kriz/cifar.html) dataset. In the end, different regularization methods are compared in terms of accuracy of the network, the number of epochs for the network to be trained and the number of operations per input sample. Also, the results are discussed and interpreted based on the employed strategy. The experiment results showed that weight decay and data augmentation regularizations have little computational side effects so can be used in most applications. In the case of enough computational resources, Dropout family methods are rational to be used. Moreover, in the case of abundant computational resources, batch normalization family and ensemble methods are reasonable strategies to be employed.

The digitization of health care led to a steady increase in the adoption and use of mobile health (mHealth) apps. Germany is the first country in the world to cover the costs of mHealth apps through statutory health insurance. Although the benefits of mHealth apps are discussed in detail, aspects of problems and barriers are rarely studied. This scoping review aimed to map and categorize the evidence on problems and barriers related to the use of mHealth apps. Systematic searches were conducted in the MEDLINE, Embase, and PsycINFO databases. Additional searches were conducted on JMIR Publications and on websites of relevant international organizations. The inclusion criteria were publications dealing with apps similar to those approved in the German health care system, publications addressing problems and barriers related to the use of mHealth apps, and articles published between January 1, 2015, and June 8, 2021. Study selection was performed by 2 reviewers. The manuscript was drafted according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. The analysis of the included publications and categorization of problems and hurdles were performed using MAXQDA (VERBI Software GmbH). The database search identified 1479 publications. Of the 1479 publications, 21 (1.42%) met the inclusion criteria. A further 8 publications were included from citation searching and searching in JMIR Publications. The identified publications were analyzed for problems and barriers. Problems and barriers were classified into 10 categories (\"validity,\" \"usability,\" \"technology,\" \"use and adherence,\" \"data privacy and security,\" \"patient-physician relationship,\" \"knowledge and skills,\" \"individuality,\" \"implementation,\" and \"costs\"). The most frequently mentioned categories were use and adherence (eg, incorporating the app into daily life or dropouts from use; n=22) and usability (eg, ease of use and design; n=19). The search identified various problems and barriers in the context of mHealth apps. Although problems at the app level (such as usability) are studied frequently, problems at the system level are addressed rather vaguely. To ensure optimal use of and care with mHealth apps, it is essential to consider all types of problems and barriers. Therefore, researchers and policy makers should have a special focus on this issue to identify the needs for quality assurance. RR2-10.2196/32702.

This paper provides new evidence on the effects of moving out of disadvantaged neighborhoods on the long-run outcomes of children. I study public housing demolitions in Chicago, which forced low-income households to relocate to less disadvantaged neighborhoods using housing vouchers. Specifically, I compare young adult outcomes of displaced children to their peers who lived in nearby public housing that was not demolished. Displaced children are more likely to be employed and earn more in young adulthood. I also find that displaced children have fewer violent crime arrests. Children displaced at young ages have lower high school dropout rates.

We aimed to establish the treatment effect of physical activity for depression in young people through meta-analysis. Four databases were searched to September 2016 for randomised controlled trials of physical activity interventions for adolescents and young adults, 12–25 years, experiencing a diagnosis or threshold symptoms of depression. Random-effects meta-analysis was used to estimate the standardised mean difference (SMD) between physical activity and control conditions. Subgroup analysis and meta-regression investigated potential treatment effect modifiers. Acceptability was estimated using dropout. Trials were assessed against risk of bias domains and overall quality of evidence was assessed using GRADE criteria. Seventeen trials were eligible and 16 provided data from 771 participants showing a large effect of physical activity on depression symptoms compared to controls (SMD = −0.82, 95% CI = −1.02 to −0.61, p < 0.05, I2 = 38%). The effect remained robust in trials with clinical samples (k = 5, SMD = −0.72, 95% CI = −1.15 to −0.30), and in trials using attention/activity placebo controls (k = 7, SMD = −0.82, 95% CI = −1.05 to −0.59). Dropout was 11% across physical activity arms and equivalent in controls (k = 12, RD = −0.01, 95% CI = −0.04 to 0.03, p = 0.70). However, the quality of RCT-level evidence contributing to the primary analysis was downgraded two levels to LOW (trial-level risk of bias, suspected publication bias), suggesting uncertainty in the size of effect and caution in its interpretation. While physical activity appears to be a promising and acceptable intervention for adolescents and young adults experiencing depression, robust clinical effectiveness trials that minimise risk of bias are required to increase confidence in the current finding. The specific intervention characteristics required to improve depression remain unclear, however best candidates given current evidence may include, but are not limited to, supervised, aerobic-based activity of moderate-to-vigorous intensity, engaged in multiple times per week over eight or more weeks. Further research is needed. (Registration: PROSPERO-CRD 42015024388).