BackgroundSubstance use disorders (SUDs) are prevalent in the general population, tend to follow a chronic course, are associated with many individual and social problems, and often have their onset in adolescence. However, the knowledge base from prospective population surveys and treatment-outcome studies on the course of SUD in adolescents is limited at best. The present study aims to fill this gap and focuses on a subgroup that is particularly at risk for chronicity: adolescents in addiction treatment. We will investigate the rate of persistent SUD and its predictors longitudinally from adolescence to young adulthood among youth with DSM-5 SUD from the start of their addiction treatment to 2 and 4 years following treatment-entry. In addition to SUD, we will investigate the course of comorbid mental disorders, social functioning, and quality of life and their association with SUD over time.Methods/designIn a naturalistic, multi-center prospective cohort design, we will include youths (n = 420), who consecutively enter addiction treatment at ten participating organizations in the Netherlands. Inclusion is prestratified by treatment organization, to ensure a nationally representative sample. Eligible youths are 16 to 22 years old and seek help for a primary DSM-5 cannabis, alcohol, cocaine or amphetamine use disorder. Assessments focus on lifetime and current substance use and SUD, non-SUD mental disorders, family history, life events, social functioning, treatment history, quality of life, chronic stress indicators (hair cortisol) and neuropsychological tests (computerized executive function tasks) and are conducted at baseline, end of treatment, and 2 and 4 years post-baseline. Baseline data and treatment data (type, intensity, duration) will be used to predict outcome – persistence of or desistance from SUD.DiscussionThere are remarkably few prospective studies worldwide that investigated the course of SUD in adolescents in addiction treatment for longer than 1 year. We are confident that the Youth in Transition study will further our understanding of determinants and consequences of persistent SUD among high-risk adolescents during the critical transition from adolescence to young adulthood.Trial registrationThe Netherlands National Trial Register Trial NL7928. Date of registration January 17, 2019.
This research aims to obtain more insight in the perception of fabric drape and how fabric drape can be cat-egorized With the current 3D virtual technologies to simulate garments the fashion and clothing industry can speed up work processes, improve accuracy and reduce material consumption in fit, design and sales. Although the interest in 3D technology is increasing, the implementation on a large scale emerges only slowly. At the threshold between physical and virtual fitting the fashion industry faces new challenges and demands re-quiring responses out of rule. The measurement of fabric drape started in the first half of the previous cen-tury, after the introduction of 3D garment simulation fabric drape gained interest from more researchers to obtain information for the virtual drape. Intensive research has been undertaken to define ‘fabric hand’, however, research is limited for the definition of fabric drape. Better understanding of how fabrics drape and how they can be selected based on their drape might contribute to the understanding of the virtually as-sessed material and accelerate the selection process of virtually, as well as digitally presented fabrics. For this research the drape coefficient of 13 fabrics, selected based on their drape, was measured with the Cusick drape tester. Images and videos of the fabrics draped on pedestals were presented to an expert tex-tile panel who were asked to define the fabric drape. From these definitions categories, as well as identifying key-words, were derived. During a group session the expert panel evaluated the drape categories and identi-fying key-words. In the next phase an expert user panel, familiar with the assessment of fabrics in a virtual environment, assessed the appropriateness of the categories and identifying key-words which were present-ed along with the fabric drape images and videos. Moreover, both panels judged the stiffness and amount of drape, next to that they indicated similar draping fabrics. The relation between the subjective assessment of drape and the drape coefficient was investigated. The agreement of the user panel with the drape categories defined and evaluated by the textile panel was high. Further, the agreement of the majority of the user panel with the identifying key-words was above 78%. A strong relation was found between the measured drape coefficient and the subjectively assessed stiffness and amount of drape. Additionally, the analysis of the fabrics combined by the panels based on drape simi-larity, as well as the analysis of the drape coefficients, confirms with previous research, that significantly dif-ferent fabrics can have a similar drape. Fabrics can be divided in drape categories based on the way they drape, and the identifying key-words are useful to distinguish between significantly different fabrics with similar fabric drape. Moreover, the cate-gories are related to the drape coefficient.
MULTIFILE
In this project, the AGM R&D team developed and refined the use of a facial scanning rig. The rig is a physical device comprising multiple cameras and lighting that are mounted on scaffolding around a 'scanning volume'. This is an area at which objects are placed before being photographed from multiple angles. The object is typically a person's head, but it can be anything of this approximate size. Software compares the photographs to create a digital 3D recreation - this process is called photogrammetry. The 3D model is then processed by further pieces of software and eventually becomes a face that can be animated inside in Unreal Engine, which is a popular piece of game development software made by the company Epic. This project was funded by Epic's 'Megagrant' system, and the focus of the work is on streamlining and automating the processing pipeline, and on improving the quality of the resulting output. Additional work has been done on skin shaders (simulating the quality of real skin in a digital form) and the use of AI to re/create lifelike hair styles. The R&D work has produced significant savings in regards to the processing time and the quality of facial scans, has produced a system that has benefitted the educational offering of BUas, and has attracted collaborators from the commercial entertainment/simulation industries. This work complements and extends previous work done on the VIBE project, where the focus was on creating lifelike human avatars for the medical industry.
