Effectiveness of online, school-based Positive Psychology Interventions to improve mental health and wellbeing: A systematic review

Online positive psychology interventions provide a more equitable method for young people to access wellbeing education at school than more traditional face to face programs.  This systematic review aimed to examine the effectiveness of universal, online, school-based, positive psychology interventions using recommendations by the Preferred Reporting Items for Systematic reviews and Meta-analyses - protocols (PRISMA-P). Nine articles were identified for the review and were deductively, thematically analyzed using an enhanced RE-AIM framework which adopts a wider systems perspective including evaluation of socio-ecological readiness system wide buy-in and consideration of micro (individual) to macro (governing bodies) levels of influence, on both reach and adoption. Effectiveness assessment identified common factors for success related primarily to implementation (e.g., readiness, reach, outcomes, adoption, implementation, and maintenance). For example, buy-in from stakeholders was found to be highest when PPIs are age appropriate, engaging and helpful. Also brief, more frequent sessions, may be more effective than less frequent longer sessions and multi-level stakeholder buy-in may result in higher completion rates leading to better overall program effectiveness

The role of physiological and subjective measures of emotion regulation in predicting adolescent wellbeing

Emotion regulation (ER) is a key contributor to psychosocial adjustment in adolescence, while ER deficits contribute to psychological distress and dysfunction. To date, research with adolescents has examined a limited subset of ER processes, often in relation to mental ill-health. This study examined associations between multiple ER measures and wellbeing in a normative sample of 119 adolescents (Mage = 15.73). ER was measured using self-report and physiological (RSA) indices. Multiple measures of positive and negative functioning were examined. After controlling for covariates, hierarchical regression analyses revealed that self-reported ER predicted resilience, perseverance, connectedness, and happiness; and fewer depression and anxiety symptoms. Higher tonic RSA predicted resilience and perseverance. Effect sizes were small to moderate. Theoretical and practical implications are discussed

To muse beyond formal music training: the development and use of a measure of music engagement in everyday life

Music is a highly engaging activity, which serves various functions for individuals. Of particular interest here is the use of music to enhance mental health and well-being. To date, research on the benefits of music has been primarily focused on individuals with music training. The first aim of this research was to extend the concept of musicianship, to operationalize and develop a measure of both music production and music reception. The second aim was to explore well-being outcomes of music use, and identify music engagement factors predicting better health and well-being. The aims were achieved in a series of studies. Having identified a need for a comprehensive measure of musicianship in Chapter 2, Chapter 3 described the development of the multi-dimensional construct of music engagement. The Music USE (MUSE) questionnaire was then used to investigate music uses and their associated well-being (Chapter 4) and psychopathology (Chapter 5) outcomes, with emotion regulation identified as a key mediator of these relationships. Music engagement for the purposes of cognitive and emotional regulation was found to be consistently associated with, and predictive of, subjective indices of adaptive emotion regulation and improved well-being. Chapter 6 described the final study, which utilized neurobiological indices of well-being and emotion regulation capacity to validate links found between subjective self-reports of well-being and music use for cognitive and emotional regulation. Collectively, these studies demonstrated that benefits associated with music engagement are mediated by emotion regulation styles, and highlight the need to consider emotion regulation when evaluating the impact of music engagement on mental health and well-being. Furthermore, this research emphasized the importance of considering the multidimensionality of musicianship, beyond a numerical index of music training or instrument playing, when assessing the impact of music on health and well-being. This thesis provides a foundation for future work on the use of music to promote adaptive emotion regulation for the purposes of improving mental health and well-being

To muse beyond formal music training: the development and use of a measure of music engagement in everyday life

Music is a highly engaging activity, which serves various functions for individuals. Of particular interest here is the use of music to enhance mental health and well-being. To date, research on the benefits of music has been primarily focused on individuals with music training. The first aim of this research was to extend the concept of musicianship, to operationalize and develop a measure of both music production and music reception. The second aim was to explore well-being outcomes of music use, and identify music engagement factors predicting better health and well-being. The aims were achieved in a series of studies. Having identified a need for a comprehensive measure of musicianship in Chapter 2, Chapter 3 described the development of the multi-dimensional construct of music engagement. The Music USE (MUSE) questionnaire was then used to investigate music uses and their associated well-being (Chapter 4) and psychopathology (Chapter 5) outcomes, with emotion regulation identified as a key mediator of these relationships. Music engagement for the purposes of cognitive and emotional regulation was found to be consistently associated with, and predictive of, subjective indices of adaptive emotion regulation and improved well-being. Chapter 6 described the final study, which utilized neurobiological indices of well-being and emotion regulation capacity to validate links found between subjective self-reports of well-being and music use for cognitive and emotional regulation. Collectively, these studies demonstrated that benefits associated with music engagement are mediated by emotion regulation styles, and highlight the need to consider emotion regulation when evaluating the impact of music engagement on mental health and well-being. Furthermore, this research emphasized the importance of considering the multidimensionality of musicianship, beyond a numerical index of music training or instrument playing, when assessing the impact of music on health and well-being. This thesis provides a foundation for future work on the use of music to promote adaptive emotion regulation for the purposes of improving mental health and well-being

Enhanced AlexNet with Super-Resolution for Low-Resolution Face Recognition

With the advancement in deep learning, high-resolution face recognition has achieved outstanding performance that makes it widely adopted in many real-world applications. Face recognition plays a vital role in visual surveillance systems. However, the images captured by the security cameras are at low resolution causing the performance of the low-resolution face recognition relatively inferior. In view of this, we propose an enhanced AlexNet with Super-Resolution and Data Augmentation (SRDA-AlexNet) for low-resolution face recognition. Firstly, image super-resolution improves the quality of the low-resolution images to high-resolution images. Subsequently, data augmentation is applied to generate variations of the images for larger data size. An enhanced AlexNet with batch normalization and dropout regularization is then used for feature extraction. The batch normalization aims to reduce the internal covariate shift by normalizing the input distributions of the mini-batches. Apart from that, the dropout regularization improves the generalization capability and alleviates the overfitting of the model. The extracted features are then classified using k-Nearest Neighbors method for low-resolution face recognition. Empirical results demonstrate that the proposed SRDA-AlexNet outshines the methods in comparison

Application of Fault-Tolerant Mechanism to Reduce Pollution Attacks in Peer-to-Peer Networks

File pollution is a recent security threat to peer-to-peer (P2P) file sharing systems. By disseminating numerous polluted files with mismatched or partially tampered contents in the P2P system, the attacker causes users to download unexpected files. This attack is aimed at frustrating users and making them abandon the system. Present researches on combating file pollution have mostly focused on pollution modeling or evaluating the extent of pollution. Only a few researches have proposed effective methods to eliminate pollution attacks, and they are primarily based on reputation systems and blacklisting mechanisms. However, these methods require exchange of significant feedback among the peers in order to identify the malicious peers or polluted files in the system. In this paper, we describe the application of fault-tolerant mechanism used in the redundant arrays of independent disks system to suppress file pollution attacks based on the concept that P2P file sharing systems currently have global file storage systems. We have extended the previously developed Fluid Model to analyze and evaluate the proposed antipollution mechanism. The model accuracy has been demonstrated by performing several simulation experiments; the proposed mechanism could effectively suppress the pollution and successfully decrease the polluted-time exposure of a P2P file sharing system by approximately 40∼60%

The Hybrid Taguchi-Genetic Algorithm for Mobile Location

To estimate the mobile location is an important topic in wireless communication. It is well known that non-line-of-sight (NLOS) problem is the most pivotal part that causes the estimated error. When we transmit the signal from mobile station (MS) to base stations (BSs), the direct path between MS and BS is sealed off by some obstacles, and the signal measurements will measure the error due to the signal reflection or diffraction. The hybrid Taguchi-genetic algorithm (HTGA) combines the Taguchi method with the genetic algorithm (GA). In this paper, we bring up a novel HTGA algorithm that utilizes time of arrival (TOA) measurements from three BSs to locate MS. The proposed algorithm utilizes the intersections of three TOA circles based on HTGA to estimate the MS location. Finally, we compare HTGA with GA and find that the Taguchi algorithm can enhance genetic algorithm. We also can find that the average convergence of generation number will not be affected no matter which propagation models we use. Obviously HTGA is more robust, statistically sound, and quickly convergent than the other algorithms. The simulation results show that the HTGA can converge more quickly than GA and furthermore the HTGA can enhance the accuracy of the mobile location