Measuring the circular economy's performance

Feature of the article "A taxonomy of circular economy indicators" (Journal of Cleaner Production, 207, 542-559) in Science Trends. Science Trends is committed to providing the highest quality scientific information based on scientific consensus and rigorous fact-checking.Science Trends relies on peer-reviewed scientific journals and authoritative science publications for references. In addition, the majority of content on Science Trends is written by the primary author of the peer-reviewed research in which they are writing

Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision

The usage and impact of deep learning for cleaner production and sustainability purposes remain little explored. This work shows how deep learning can be harnessed to increase sustainability in production and product usage. Specifically, we utilize deep learning-based computer vision to determine the wear states of products. The resulting insights serve as a basis for novel product-service systems with improved integration and result orientation. Moreover, these insights are expected to facilitate product usage improvements and R&D innovations. We demonstrate our approach on two products: machining tools and rotating X-ray anodes. From a technical standpoint, we show that it is possible to recognize the wear state of these products using deep-learning-based computer vision. In particular, we detect wear through microscopic images of the two products. We utilize a U-Net for semantic segmentation to detect wear based on pixel granularity. The resulting mean dice coefficients of 0.631 and 0.603 demonstrate the feasibility of the proposed approach. Consequently, experts can now make better decisions, for example, to improve the machining process parameters. To assess the impact of the proposed approach on environmental sustainability, we perform life cycle assessments that show gains for both products. The results indicate that the emissions of CO2 equivalents are reduced by 12% for machining tools and by 44% for rotating anodes. This work can serve as a guideline and inspire researchers and practitioners to utilize computer vision in similar scenarios to develop sustainable smart product-service systems and enable cleaner production

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Organiser et réussir son atelier d’éco-innovation en une demi-journée

National audienceIl est souvent difficile de réunir les différentes parties prenantes et experts impliqués dans la conception, le développement ou sur l’ensemble des phases du cycle de vie de l’un de vos systèmes (produits ou services) dans le but d’en améliorer la performance environnementale. En effet, les rassembler autour d’une table pendant plusieurs heures ou journées peut s’avérer délicat en raison de leurs contraintes respectives en temps et du coût associé.Afin de ne solliciter ces parties prenantes que pour une demi-journée et ainsi assurer leur bonne participation, cette fiche vous guidera dans l’organisation d’un atelier économe en temps mais pouvant mener à des résultats très satisfaisants en termes d’éco-innovation. Elle présente et détaille le déroulement d’un atelier d’éco-innovation efficace en temps dans la mesure où l’intégralité se déroule en trois heures