This book describes the principles and methodology of the CARe Model. This eclectic approach offers professionals working with people with a mental health or addiction problem, or persons with other social disadvantages, effective ways of support. The CARe model is meant to support people in their personal development. It is based on principles of psychosocial rehabilitation, recovery and empowerment. The book contains a lot of practical examples. It can be used by professionals in the field, and for the education of present and future professionals. The CARe model is an evidence based approach used by thousands of professionals world-wide
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During tournaments, team sport athletes are exposed to high physical loads due to a large number of games played within a few days. To perform well and prevent injuries, recovery in between these games is crucial. To monitor the recovery kinetics the Total Quality of Recovery (TQR) is suggested as a practical and useful tool (Kentta et al, 1998). The purpose of this study was to explore the feasibility and sensitivity of the TQR as a recovery monitoring tool during a 3-day floorball tournament. Methods Eleven elite Dutch female floorball athletes (age:24.3±4.8, length:171.5±9.1, weight:67.6±8.1) participated in a 3-day tournament. Their recovery was monitored with the TQR scale (6-20) (Kentta et al, 1998). All athletes were asked to rate their recovery each morning and every two hours including;1 hour prior to the game (pre-game), immediately after the game (post-game) and 2 hours post-game. Comparisons were made for the TQR at the beginning and end of the tournament as well as pre- vs. post-game.
The Integrated Recovery Scale IRS was developed by Dutch National Expertise board for Routine Outcome Monitoring. Recovery is multi dimensional: 1. Symptomatic recovery 2. Physical health, 3. Societal recovery 4. Existential: personal recovery. The validation process and first outcomes of the instrument are described.
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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.
