This study evaluates psychometric properties of the Individual Recovery Outcomes Counter (I.ROC) in a Dutch population of participants with a schizophrenia spectrum disorder (SSD). B. Esther Sportel1*† , Hettie Aardema1†, Nynke Boonstra2 , Johannes Arends1 , Bridey Rudd3 , Margot J. Metz4 , Stynke Castelein5 and Gerdina H.M. Pijnenborg6
MULTIFILE
Introduction The Integrated Recovery Scales (IRS) was developed by the Dutch National Expertise board for routine outcome monitoring with severe mental illnesses. This board aimed to develop a multidimensional recovery measure directed at 1. clinical recovery, 2. physical health, 3. social recovery (work, social contacts, independent living) and 4. existential, personal recovery. The measure had to be short, suited for routine outcome monitoring and present the perspective of both mental health professionals and service users with severe mental illnesses. All aspects are assessed over a period of the pas 6 months. Objectives The objective of this research is validation of the Integral Recovery Scales and to test the revelance for clinical practice and police evaluation. Methods The instrument was tested with 500 individuals with severe mental illnesses (80% individuals with a psychotic disorder), of whom 200 were followed up for 1 year. For the questions concerning clinical recovery, physical health and social recovery mental health care workers conducted semi structured interviews with people living with serious illnesses. The questions concerning personal health were self-rated. We analyzed interrater reliability, convergent and divergent validity and sensitivity to change. Results The instrument has a good validity and is easy to complete for service users and mental health care workers and appropriate for clinical and policy evaluation goals. Conclusions The Integrated Recovery Scales can be a useful instrument for a simple and meaningful routine outcome monitoring. Page: 121
DOCUMENT
Introduction: Although some adults with autism spectrum disorder (ASD) require intensive and specialized ASD treatment, there is little research on how these adults experience the recovery process. Recovery is defined as the significant improvement in general functioning compared to the situation prior to treatment. Methods: This qualitative study describes the recovery process from the perspective of adults on the autism spectrum during intensive inpatient treatment. Semi-structured interviews (n = 15) were carried out and analyzed according to the principles of grounded theory. Results: Our results indicate that, given the specific characteristics of autism, therapeutic interventions and goal-oriented work cannot be carried out successfully, and the recovery process cannot begin, if no good working relationship has been established, and if care is not organized in ways that a person on the autism spectrum finds clear and predictable.
DOCUMENT
The increasing amount of electronic waste (e-waste) urgently requires the use of innovative solutions within the circular economy models in this industry. Sorting of e-waste in a proper manner are essential for the recovery of valuable materials and minimizing environmental problems. The conventional e-waste sorting models are time-consuming processes, which involve laborious manual classification of complex and diverse electronic components. Moreover, the sector is lacking in skilled labor, thus making automation in sorting procedures is an urgent necessity. The project “AdapSort: Adaptive AI for Sorting E-Waste” aims to develop an adaptable AI-based system for optimal and efficient e-waste sorting. The project combines deep learning object detection algorithms with open-world vision-language models to enable adaptive AI models that incorporate operator feedback as part of a continuous learning process. The project initiates with problem analysis, including use case definition, requirement specification, and collection of labeled image data. AI models will be trained and deployed on edge devices for real-time sorting and scalability. Then, the feasibility of developing adaptive AI models that capture the state-of-the-art open-world vision-language models will be investigated. The human-in-the-loop learning is an important feature of this phase, wherein the user is enabled to provide ongoing feedback about how to refine the model further. An interface will be constructed to enable human intervention to facilitate real-time improvement of classification accuracy and sorting of different items. Finally, the project will deliver a proof of concept for the AI-based sorter, validated through selected use cases in collaboration with industrial partners. By integrating AI with human feedback, this project aims to facilitate e-waste management and serve as a foundation for larger projects.
Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is currently used as an important tool for biomedical applications (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this technology and its applications was recently published in a special edition of the Journal of Aerosol Sciences [1]. Even though EHDA is already applied in many different industrial processes, there are not many controlling tools commercially available which can be used to remotely operate the system as well as identify some spray characteristics, e.g. droplet size, operational mode, droplet production ratio. The AECTion project proposes the development of an innovative controlling system based on the electrospray current, signal processing & control and artificial intelligence to build a non-visual tool to control and characterize EHDA processes.
