Subtyping treatment-seeking gaming disorder patients

Background and aims: Gaming Disorder (GD) is characterized by a pattern of persistent and uncontrolled gaming behavior that causes a marked impairment in important areas of functioning. The evolution of the worldwide incidence of this disorder warrants further studies focused on examining the existence of different subtypes within clinical samples, in order to tailor treatment. This study explored the existence of different profiles of patients seeking treatment for GD through a data-driven approach. Methods: The sample included n = 107 patients receiving treatment for GD (92% men and 8% women) ranging between 14 and 60 years old (mean age = 24.1, SD = 10). A two-step clustering analysis approach explored the existence of different underlying GD profiles based on a broad set of indicators, including sociodemographic features, clinical course of the condition (e.g., onset or evolution), psychopathological symptoms, and personality traits. Results: Two GD profiles emerged. The first cluster grouped together patients who presented with a lower psychological impact (n = 72, 66.1%), whereas the second cluster comprised patients with a higher psychological impact (n = 35, 32.7%). Cluster comparisons revealed that those patients presenting the higher impact were older, with a later onset of pathological gaming patterns, and more pronounced psychopathological symptoms and dysfunctional personality profiles. Conclusions: GD severity is influenced by specific demographic, clinical, and psychopathological factors. The identification of two separate profiles provides empirical evidence that contributes to the conceptualization of this disorder, as well as to the development of reliable and valid screening tools and effective intervention plans focused on the precise characteristics of the treatment-seeking patients

Deep learning based simulation for automotive software development

The automotive industry is in the midst of a new reality where software is increasingly becoming the primary tool for delivering value to customers. While this has vastly improved their product offerings, vehicle manufacturers are increasingly facing the need to continuously develop, test, and deliver functionality, while maintaining high levels of quality. One important tool for achieving this is simulation-based testing where the external operating environment of a software system is simulated, enabling incremental development with rapid test feedback. However, the traditional practice of manually specifying simulation models for complex external environments involves immense engineering effort, while remaining vulnerable to inevitable assumptions and simplifications. Exploiting the increased availability of data that captures operational environments and scenarios from the field, this work takes a deep learning approach to train models that realistically simulate external environments, significantly increasing the credibility of simulation-driven software development.\ua0First, focusing on simulating the input dependencies of automotive software functions, this work uses techniques of deep generative modeling to develop a framework for realistic test stimulus generation. Such models are trained self-supervised using recorded time-series field data and simulate the input environment much more credibly than manually specified models. With the credibility of stimulus generation being an important concern, an important concept of similarity as plausibility is introduced to evaluate the quality of generation during model training. Second, this work develops new techniques for sampling generative models that enable the controlled generation of test stimulus. Allowing testers to limit the range of scenarios considered for testing, the Metric-based Linear Interpolation (MLERP) sampling algorithm automatically chooses test stimuli that are verifiably similar to a user-supplied reference, and therefore measurably credible. While controllability eases the design of tests, credibility increases trust in the testing process. Third, recognizing that sampling may be an inefficient process for stimulus generation, this work develops a technique that extracts properties from actual code under test in order to automatically search for appropriate test stimuli within the specified range of test scenarios. Fourth, further addressing the question of credible stimulus generation, this work introduces techniques that examine training data for biases in sample representation. Overall, by taking a data-driven deep learning approach, techniques and tools developed in this work vastly expands the credibility of incremental automotive software development under simulated conditions

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

Psychiatric Disorders

A psychiatric disorder is defined as any complex condition that involves the impairment of cognitive, emotional, or behavioral functioning. Aside from knowing the physical organic factors, its causal pathology has remained a mystery. Regarding recent advances in psychiatry and neurosciences, psychiatric disorders have been closely associated with socio-cultural, psychological, biochemical, epigenetic or neural-networking factors. A need for diverse approaches or support strategies is present, which should serve as common knowledge, empathetic views or useful skills for specialists in the filed. This book contains multifarious and powerful papers from all over the world, addressing themes such as the neurosciences, psychosocial interventions, medical factors, possible vulnerability and traumatic events. Doubtlessly, this book will be fruitful for future development and collaboration in "world psychiatry"

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808