ABSTRACT
BACKGROUND: Non-suicidal self-injury (NSSI) is prevalent behaviour among adolescents. Although there are different etiological models of NSSI, there is a general lack of evidence-based, comprehensive and transdiagnostic models of NSSI in adolescents. AIMS: The aim of this study was to investigate a model of transdiagnostic factors of NSSI in adolescents, testing a serial mediation model of the relationship between early maladaptive schemas (EMS), distress tolerance and NSSI through experiential avoidance and rumination. METHOD: A community sample was identified of 1014 adolescents aged 13-17, of whom 425 had a history of NSSI. A serial mediation path analytic method was utilised to examine the relationships between NSSI and its associated functions as criterion variables, EMS and distress tolerance as predictors, experiential avoidance as the first mediator and rumination as the second mediator. RESULTS: The path analytic model fit indices were good (X2/d.f. = 2.25, goodness of fit index = 0.98, normed fit index = 0.97, comparative fit index = 0.98, root mean square error of approximation = 0.054, standardised root mean squared residual = 0.028). Rumination significantly mediated the relationship between schemas of 'vulnerability to harm', 'emotional deprivation', 'social isolation', 'insufficient self-control', and NSSI frequency and intrapersonal functions. In serial fashion, experiential avoidance mediated the role of rumination in the relationship between social isolation, and insufficient self-control and NSSI frequency and intrapersonal functions. All indirect effects were significant. CONCLUSIONS: Key indirect effects were found linking maladaptive schemas and distress tolerance to NSSI frequency, and NSSI intrapersonal functions via experiential avoidance and rumination. Thus, it is important to address these transdiagnostic factors with particular emphasis on the sequential mediating role of experiential avoidance and rumination in conceptualisation and therapeutic interventions for NSSI.
ABSTRACT
The widespread use of the internet and the exponential growth in small hardware diversity enable the development of Internet of things (IoT)-based localization systems. We review machine-learning-based approaches for IoT localization systems in this paper. Because of their high prediction accuracy, machine learning methods are now being used to solve localization problems. The paper's main goal is to provide a review of how learning algorithms are used to solve IoT localization problems, as well as to address current challenges. We examine the existing literature for published papers released between 2020 and 2022. These studies are classified according to several criteria, including their learning algorithm, chosen environment, specific covered IoT protocol, and measurement technique. We also discuss the potential applications of learning algorithms in IoT localization, as well as future trends.
ABSTRACT
The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users' inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities.
