ABSTRACT This study investigates how perceptions of radicalisation and co-occurring mental health issues differ between mental health care and the security domain, and how these perceptions affect intersectoral collaboration. It is generally thought that intersectoral collaboration is a useful strategy for preventing radicalisation and terrorism, especially when it concerns radicalised persons with mental health issues. It is not clear, however, what perceptions professionals have of radicalisation and collaboration with other disciplines. Data was obtained from focus groups and individual interviews with practitioners and trainers from mental health care and the security domain in the Netherlands. The results show a lack of knowledge about radicalisation in mental health care, whereas in the security domain, there is little understanding of mental health issues. This leads to a mad-bad dichotomy which has a negative effect on collaboration and risk management. Improvement of the intersectoral collaboration by cross-domain familiarization, and strengthening of trust and mutual understanding, should begin with the basic training of professionals in both domains. The Care and Safety Houses in the Netherlands offer a sound base for intersectoral collaboration. Future professionals from different domains ought to be familiarized with each other’s possibilities, limitations, tasks, and roles.
The aim of this project & work package is to develop a European action plan on mental health at work. A major and essential ingredient for this is the involvement of the relevant stakeholders and sharing experiences among them on the national and member state level. The Dutch Ministries of Health and Social Affairs and Employment have decided to participate in this “joint action on the promotion of mental health and well-being” with a specific focus on the work package directed at establishing a framework for action to promote taking action on mental health and well-being at workplaces at national level as well.
Aims. The aim of this study is to gain insight into the level of emotional intelligence of mental health nurses in the Netherlands. Background. The focus in research on emotional intelligence to date has been on a variety of professionals. However, little is known about emotional intelligence in mental health nurses. Method. The emotional intelligence of 98 Dutch nurses caring for psychiatric patients is reported. Data were collected with the Bar-On Emotional Quotient Inventory within a cross-sectional research design. Results. The mean level of emotional intelligence of this sample of professionals is statistically significant higher than the emotional intelligence of the general population. Female nurses score significantly higher than men on the subscales Empathy, Social Responsibility, Interpersonal Relationship, Emotional Self-awareness, Self-Actualisation and Assertiveness. No correlations are found between years of experience and age on the one hand and emotional intelligence on the other hand. Conclusions. The results of this study show that nurses in psychiatric care indeed score above average in the emotional intelligence required to cope with the amount of emotional labour involved in daily mental health practice. Relevance to clinical practice. The ascertained large range in emotional intelligence scores among the mental health nurses challenges us to investigate possible implications which higher or lower emotional intelligence levels may have on the quality of care. For instance, a possible relation between the level of emotional intelligence and the quality of the therapeutic nurse–patient relationship or the relation between the level of emotional intelligence and the manner of coping with situations characterised by a great amount of emotional labour (such as caring for patients who self-harm or are suicidal).
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.
Drones have been verified as the camera of 2024 due to the enormous exponential growth in terms of the relevant technologies and applications such as smart agriculture, transportation, inspection, logistics, surveillance and interaction. Therefore, the commercial solutions to deploy drones in different working places have become a crucial demand for companies. Warehouses are one of the most promising industrial domains to utilize drones to automate different operations such as inventory scanning, goods transportation to the delivery lines, area monitoring on demand and so on. On the other hands, deploying drones (or even mobile robots) in such challenging environment needs to enable accurate state estimation in terms of position and orientation to allow autonomous navigation. This is because GPS signals are not available in warehouses due to the obstruction by the closed-sky areas and the signal deflection by structures. Vision-based positioning systems are the most promising techniques to achieve reliable position estimation in indoor environments. This is because of using low-cost sensors (cameras), the utilization of dense environmental features and the possibilities to operate in indoor/outdoor areas. Therefore, this proposal aims to address a crucial question for industrial applications with our industrial partners to explore limitations and develop solutions towards robust state estimation of drones in challenging environments such as warehouses and greenhouses. The results of this project will be used as the baseline to develop other navigation technologies towards full autonomous deployment of drones such as mapping, localization, docking and maneuvering to safely deploy drones in GPS-denied areas.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
