Organizations are currently facing substantial challenges regarding becoming circular by 2050 – also referred to as Circular Economy (CE). Subsequently, increasing complexity on all organizational levels creates uncertainty about respective organizational and technological capabilities and adequate strategies to develop these capabilities. Organizations are struggling in picking up the CE ambitions and answering the what’s in it for me question. Scholars are developing models and frameworks to enable organizations to measure CE performance. Over 125 assessment methods are available for micro level assessment – measuring up to 365 different metrics. Moreover, extant literature is available presenting barriers and opportunities for CE transformation focusing on industry, sector, region, etc. And, although a more holistic perspective is required to become circular mature, this is currently lacking. In this paper we present a multi-methodology view on approaches and how they are used (or not). Our main goal is to extend the existing body of knowledge with an eye on applicability and research directions to untie the Gordian knot of measuring circularity.
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Background: A feasible and reliable instrument to measure strength in persons with severe intellectual and visual disabilities (SIVD) is lacking. The aim of our study was to determine feasibility, learning period and reliability of three strength tests.Methods: Twenty-nine participants with SIVD performed the Minimum Sit-to-Stand Height test (MSST), the Leg Extension test (LE) and the 30 seconds Chair-Stand test (30sCS), once per week for 5 weeks. Feasibility was determined by the percentage of successful measurements; learning effect by using paired t test between two consecutive measurements; test–retest reliability by intraclass correlation coefficient and Limits of Agreement and, correlations by Pearson correlations.Results: A sufficient feasibility and learning period of the tests was shown. The methods had sufficient test–retest reliability and moderate-to-sufficient correlations. Conclusion: The MSST, the LE, and the 30sCS are feasible tests for measuring muscle strength in persons with SIVD, having sufficient test re-test reliability.
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Saliva diagnostics have become increasingly popular due to their non-invasive nature and patient-friendly collection process. Various collection methods are available, yet these are not always well standardized for either quantitative or qualitative analysis. In line, the objective of this study was to evaluate if measured levels of various biomarkers in the saliva of healthy individuals were affected by three distinct saliva collection methods: 1) unstimulated saliva, 2) chew stimulated saliva, and 3) oral rinse. Saliva samples from 30 healthy individuals were obtained by the three collection methods. Then, the levels of various salivary biomarkers such as proteins and ions were determined. It was found that levels of various biomarkers obtained from unstimulated saliva were comparable to those in chew stimulated saliva. The levels of potassium, sodium, and amylase activity differed significantly among the three collection methods. Levels of all biomarkers measured using the oral rinse method significantly differed from those obtained from unstimulated and chew-stimulated saliva. In conclusion, both unstimulated and chew-stimulated saliva provided comparable levels for a diverse group of biomarkers. However, the results obtained from the oral rinse method significantly differed from those of unstimulated and chew-stimulated saliva, due to the diluted nature of the saliva extract.
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Due to their diverse funding sources, theatres are under increasing pressure to demonstrate impact on society. The Raad voor Cultuur (2023) for example advised the secretary of state to include societal impact as an additional evaluation measure next to artistic value. Many theaters, such as the Chassé Theater and Parkstad Limburg Theaters, have reformulated their missions to focus on impact of performances on visitors. This is a profound transformation from merely selling tickets and filling seats, and requires new measurement instruments to monitor, manage, and improve impact. Currently available instruments are insufficient, and effective monitoring is crucial to larger future projects that theaters are currently planning to systematically broaden impacts of performances on their communities. The specific goal of this project is to empower theaters to monitor and improve impact by developing a brief experience impact questionnaire, taking existing data from student projects conducted at the Chassé Theater about performing arts experiences on one hand, and experience impact theory innovations on the other, as starting points. We will develop potential items to measure and benchmark against established measures of valued societal outcomes, such as subjective well-being and quality of life. These will be measured in questionnaires developed with project partners Chassé Theater and Parkstad Limburg Theaters and administered before and after performances across a wide range of genres. The resulting data will enable comparison of new questionnaire items with benchmarked measures of valued societal outcomes. The final product of the project will be a brief impact questionnaire, which within several brief self-report instruments and just a few minutes can effectively be used to quantify the impact of a performing arts experience. A workshop and practice-oriented article will make this questionnaire implementable, thereby mobilizing the key enabling methodology of monitoring and impact measurement in the performing arts sector.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
