Particulate matter (PM) exposure, amongst others caused by emissions and industrial processes, is an important source of respiratory and cardiovascular diseases. There are situations in which blue-collar workers in roadwork companies are at risk. This study investigated perceptions of risk and mitigation of employees in roadwork (construction and maintenance) companies concerning PM, as well as their views on methods to empower safety behavior, by means of a mental models approach. We held semi-structured interviews with twenty-two employees (three safety specialists, seven site managers and twelve blue-collar workers) in three different roadwork companies. We found that most workers are aware of the existence of PM and reduction methods, but that their knowledge about PM itself appears to be fragmented and incomplete. Moreover, road workers do not protect themselves consistently against PM. To improve safety instructions, we recommend focusing on health effects, reduction methods and the rationale behind them, and keeping workers’ mental models into account. We also recommend a healthy dialogue about work-related risk within the company hierarchy, to alleviate both information-related and motivation-related safety issues. https://doi.org/10.1016/j.ssci.2019.06.043 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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Organizing entrepreneurial collaboration in small, self-directed teams is gaining popularity. The underlying co-creation processes of developing a shared team vision were analyzed with a core focus on three underlying processes that originate from the shared mental models framework. These processes are: 1) the emergence of individual visions and vision integration, 2) conflict solving, and 3) redesigning the emerging knowledge structure. Key in the analysis is the impact of these three processes on two outcome variables: 1)the perceived strength of the co-creation process, 2) the final team vision. The influence of business expertise and the relationship between personality traits and intellectual synergy was also studied. The impact of the three quality shared mental model (SMM) variables proves to be significant and strong, but indirect. To be effective, individual visions need to be debated during a second conflict phase. Subsequently, redesigning the shared knowledge structure resulting from the conflict solving phase is a key process in a third elaboration phase. This sequence positively influences the experienced strength of the co-creation process, the latter directly enhancing the quality of the final team vision. The indirect effect reveals that in order to be effective, the three SMM processes need to be combined, and that the influence follows a specific path. Furthermore, higher averages as well as a diversity of business expertise enhance the quality of the final team vision. Significant relationships between personality and an intellectual synergy were found. The results offer applicable insights for team learning and group dynamics in developing an entrepreneurial team vision. LinkedIn: https://www.linkedin.com/in/rainer-hensel-phd-8ba44a43/ https://www.linkedin.com/in/ronald-visser-4591034/
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Background Running-related injuries (RRIs) can be considered the primary enemy of runners. Most literature on injury prediction and prevention overlooks the mental aspects of overtraining and under-recovery, despite their potential role in injury prediction and prevention. Consequently, knowledge on the role of mental aspects in RRIs is lacking. Objective To investigate mental aspects of overtraining and under-recovery by means of an online injury prevention programme. Methods and analysis The ‘Take a Mental Break!’ study is a randomised controlled trial with a 12 month follow-up. After completing a web-based baseline survey, half and full marathon runners were randomly assigned to the intervention group or the control group. Participants of the intervention group obtained access to an online injury prevention programme, consisting of a running-related smartphone application. This app provided the participants of the intervention group with information on how to prevent overtraining and RRIs with special attention to mental aspects. The primary outcome measure is any self-reported RRI over the past 12 months. Secondary outcome measures include vigour, fatigue, sleep and perceived running performance. Regression analysis will be conducted to investigate whether the injury prevention programme has led to a lower prevalence of RRIs, better health and improved perceived running performance. Ethics and dissemination The Medical Ethics Committee of the University Medical Center Utrecht, the Netherlands, has exempted the current study from ethical approval (reference number: NL64342.041.17). Results of the study will be communicated through scientific articles in peer-reviewed journals, scientific reports and presentations on scientific conferences.
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The transition towards an economy of wellbeing is complex, systemic, dynamic and uncertain. Individuals and organizations struggle to connect with and embrace their changing context. They need to create a mindset for the emergence of a culture of economic well-being. This requires a paradigm shift in the way reality is constructed. This emergence begins with the mindset of each individual, starting bottom-up. A mindset of economic well-being is built using agency, freedom, and responsibility to understand personal values, the multi-identity self, the mental models, and the individual context. A culture is created by waving individual mindsets together and allowing shared values, and new stories for their joint context to emerge. It is from this place of connection with the self and the other, that individuals' intrinsic motivation to act is found to engage in the transitions towards an economy of well-being. This project explores this theoretical framework further. Businesses play a key role in the transition toward an economy of well-being; they are instrumental in generating multiple types of value and redefining growth. They are key in the creation of the resilient world needed to respond to the complex and uncertain of our era. Varta-Valorisatielab, De-Kleine-Aarde, and Het Groene Brein are frontrunner organizations that understand their impact and influence. They are making bold strategic choices to lead their organizations towards an economy of well-being. Unfortunately, they often experience resistance from stakeholders. To address this resistance, the consortium in the proposal seeks to answer the research question: How can individuals who connect with their multi-identity-self, (via personal values, mental models, and personal context) develop a mindset of well-being that enables them to better connect with their stakeholders (the other) and together address the transitional needs of their collective context for the emergence of a culture of the economy of wellbeing?
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.
PBL is the initiator of the Work Programme Monitoring and Management Circular Economy 2019-2023, a collaboration between CBS, CML, CPB, RIVM, TNO, UU. Holidays and mobility are part of the consumption domains that PBL researches, and this project aims to calculate the environmental gains per person per year of the various circular behavioural options for both holiday behaviour and daily mobility. For both behaviours, a range of typical (default) trips are defined and for each several circular option explored for CO2 emissions, Global warming potential and land use. The holiday part is supplied by the Centre for Sustainability, Tourism and Transport (CSTT) of the BUas Academy of Tourism (AfT). The mobility part is carried out by the Urban Intelligence professorship of the Academy for Built Environment and Logistics (ABEL).The research question is “what is the environmental impact of various circular (behavioural) options around 1) holidays and 2) passenger mobility?” The consumer perspective is demarcated as follows:For holidays, transportation and accommodation are included, but not food, attractions visited and holiday activitiesFor mobility, it concerns only the circular options of passenger transport and private means of transport (i.e. freight transport, business travel and commuting are excluded). Not only some typical trips will be evaluated, but also the possession of a car and its alternatives.For the calculations, we make use of public databases, our own models and the EAP (Environmental Analysis Program) model developed by the University of Groningen. BUAs projectmembers: Centre for Sustainability, Tourism and Transport (AT), Urban Intelligence (ABEL).
