101 research outputs found

    Retrofitting the Brazilian Biodiesel Programme: Implications for Policy Design

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    In the context of oil price volatility and the need to reduce carbon emissions, biofuels are an emerging area of interest for many developing nations as alternative energy sources that, in some instances, can also enhance livelihoods in deprived agricultural areas. There are, however, a number of questions on this front: is it economically and environmentally feasible to incorporate small-scale family farmers into biofuel value chains? Can the production of biofuel feedstocks complement rather than compete with food crops? The experience of Brazil, a pioneer in the adoption of a socially inclusive approach to the production of feedstocks for biodiesel, has elicited much interest. This Policy Research Brief seeks to take stock of recent institutional developments and draw lessons as part of an ongoing learning process in an area where there are still no obvious sustainable business models or easy pathways to foster the inclusion of small-scale farmers. The Brief suggests that incorporation into the biodiesel value chain is both feasible and productive for family farmers. But the extent of the engagement required of intermediaries can be significant in the early stages of the programme in underserviced areas, particularly where farmers are dispersed and have not been extensively involved with market processes. Those embarking on these programmes thus have to consider such a production-support role. Further, the Brief suggests that intercropping (castor and beans, for example) can mitigate the food-fuel tradeoffs. However, the choice of optimal feedstocks from the point of view of equity and sustainability remains an open question.Retrofitting the Brazilian Biodiesel Programme: Implications for Policy Design

    Establishing a Community of Practice for Doctoral Studies Amidst the COVID-19 Pandemic

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    In this discussion paper, we describe our experience completing the first year of the doctorate in nursing program at a large urban academic centre during the COVID-19 pandemic. We highlight the current nursing shortage and the importance of supporting all nursing students, including nurses in doctoral programs, towards successful graduation. We describe the development of a virtual community of practice incorporating five key strategies: Building community, fostering collaboration, strengthening connection, enhancing creativity, and promoting consistency. We believe that utilizing these strategies will contribute to our success and may be relevant to nursing leaders seeking to support the development of more doctorally prepared nurses. Participation in a community of practice early on in doctoral education will not only better prepare students for success in their program, but also continued success as they progress through their careers. It is important for students to not only make connections with peers in their area of academic study, but to also reach out to peers in other disciplines to improve both individual and interdisciplinary growth. Program administrators and educators can encourage the formation of community of practice among novice doctoral students. This encouragement can be achieved using a virtual platform, or in-person networking opportunities. Inviting incoming graduate students to connect with each other and with students from previous cohorts also fosters community of practice formation. Résumé Dans ce texte de discussion, nous décrivons notre expérience relative à la première année du doctorat en sciences infirmières dans un grand centre universitaire urbain pendant la pandémie de COVID-19. Nous soulignons la pénurie actuelle d’infirmières et l’importance de soutenir toutes les étudiantes en sciences infirmières, y compris les infirmières inscrites à un programme de doctorat, vers la réussite de leurs études. Nous décrivons le développement d’une communauté virtuelle de pratique intégrant cinq stratégies clés : créer une communauté, favoriser la collaboration, renforcer les liens, mettre en valeur la créativité et promouvoir la cohérence. Nous pensons que l’utilisation de ces stratégies contribuera à notre succès et pourrait être pertinente pour les infirmières chefs de file qui cherchent à soutenir le développement d’un plus grand nombre d’infirmières préparées au doctorat. La participation à une communauté de pratique dès le début de la formation doctorale permettra non seulement de mieux préparer les étudiantes à réussir dans leur programme, mais favorisera également leur poursuite d’une carrière fructueuse. Il est important pour les étudiantes d’établir des liens non seulement avec des pairs dans leur domaine d’études universitaires, mais qu’ils entrent également en contact avec des pairs d’autres disciplines pour améliorer à la fois leur croissance individuelle et interdisciplinaire. Les administratrices de programme et les enseignantes peuvent encourager la formation d’une communauté de pratique parmi les doctorantes novices. Cet encouragement peut être réalisé à l’aide d’une plateforme virtuelle ou d’occasions de réseautage en personne. Inviter les étudiantes diplômées à entrer en contact les unes avec les autres et avec les étudiantes des cohortes précédentes favorise également la formation d’une communauté de pratique

    Digital Twining of Geophysical Extremes

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    The geophysical research community has developed a relatively large amount of numerical codes and scientific methodologies which are able to numerically simulate through physics the extreme behavior of the Earth systems (for example: volcanoes, tsunamis earthquakes, etc). Furthermore, nowadays, large volumes of data have been acquired and, even near real-time data streams are accessible. Therefore, Earth scientist currently have on their hands the possibility of monitoring these events through sophisticated approaches using the current leading edge computational capabilities provided by pre-exascale computing infrastructures. The implementation and deployments of 12 Digital Twin Components (DTCs), addressing different aspects of geophysical extreme events is being carried out by DT-GEO, a project funded under the Horizon Europe programme (2022-2025). Each DTC is intended as self-contained entity embedding flagship simulation codes, Artificial Intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows which will are executed independently or coupled DTCs in a centralized HPC and/or virtual cloud computing research infrastructure

    A digital twin for geophysical extremes: interim results from the DT-GEO project

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    The DT-GEO project (2022-2025), funded under the Horizon Europe topic call INFRA-2021-TECH-01-01, is implementing an interdisciplinary digital twin for modelling and simulating geophysical extremes at the service of research infrastructures and related communities. The digital twin consists of interrelated Digital Twin Components (DTCs) dealing with geohazards from earthquakes to volcanoes to tsunamis and that harness world-class computational (FENIX, EuroHPC) and data (EPOS) Research Infrastructures, operational monitoring networks, and leading-edge research and academic partnerships in various fields of geophysics. The project is merging and assembling latest developments from other European projects and EuroHPC Centers of Excellence to deploy 12 DTCs, intended as self-contained containerised entities embedding flagship simulation codes, artificial intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralised HPC and virtual cloud computing Research Infrastructures (RIs). Each DTC addresses specific scientific questions and circumvents technical challenges related to hazard assessment, early warning, forecasts, urgent computing, or geo-resource prospection. This presentation summarises the results form the first year of the project including the digital twin architecture and the (meta)data structures enabling (semi-)automatic discovery, contextualisation, and orchestration of software (services) and data assets. This is a preliminary step before verifying the DTCs at 13 Site Demonstrators and starts a long-term community effort towards a twin on Geophysical Extremes integrated in the Destination Earth (DestinE) initiative

    Digital Twinning of Geophysical Extreme Phenomena (DT-GEO)

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    Destination Earth initiative pursues the implementation of a digital model of the Earth. With the aim to help understand and simulate the evolution and behavior of the Earth system components, to aid in better forecasting the impacts on human system processes, ecosystem processes and their interaction. The current state of the art technologies in numerical computations (HPC), data infrastructures (involving data storage, data access, data analysis), enable the possibility of developing numerical clones mimicking Earth¿s geophysical extreme phenomena.A Digital Twin for GEOphysical extremes (DT-GEO),is a new EU project funded under the Horizon Europe programme (2022-2025), with the objective of developing a prototype for a digital twin on geophysical extremes including earthquakes, volcanoes, tsunamis, and anthropogenic-induced extreme events. It will enable analyses, forecasts, and responses to ¿what if¿ scenarios for natural hazards from their genesis phases and across their temporal and spatial scales. The project consortium brings together world-class computational and data Research Infrastructures (RIs), operational monitoring networks, and leading-edge research and academia partnerships in various fields of geophysics. It mergesthe latest outcomes from other European projects and, Centers of Excellence. DT-GEO will deploy and test 12 Digital Twin Components (DTCs). These will be self-contained entities embedding flagship simulation codes, Artificial Intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows for deployment and execution of single or coupled DTCs in centralized HPC and virtual cloud computing Ris. (DT-GEO: A Digital Twin for GEOphysical extremes, project ID 101058129) How to cite: Carbonell, R., Folch, A., Costa, A., Orlecka-Sikora, B., Lanucara, P., Løvholt, F., Macias, J., Brune, S., Gabriel, A.-A., Barsotti, S., Behrens, J., Gomes, J., Schmittbuhl, J., Freda, C., Kocot, J., Giardini, D., Afanasiev, M., Galves, H., and Badia, R.: Digital Twinning of Geophysical Extreme Phenomena (DT-GEO), EGU General Assembly 2023, Vienna, Austria, 24¿28 Apr 2023, EGU23-5674, https://doi.org/10.5194/egusphere-egu23-5674, 2023

    Examining a model of self-conscious emotions : the relationship of physical self-perception and shame and guilt proneness with appraisals in the experience of body-related shame and guilt

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    Self-conscious emotions such as shame and guilt are powerful emotions that can influence an individual’s behaviours and cognitions in many daily activities. These emotions can function as motivators, resulting in increased effort or change of action to reduce or avoid feeling the emotion again. Although considerable research exists regarding self-conscious emotions, little has been done to examine these emotions in relation to the body (Sabiston, Brunet, Kowalski, Wilson, Mack, & Crocker, 2010). Using Tracy and Robins’ (2004) model of self-conscious emotions, the purpose of this study was to examine (a) physical self-concept (PSC) and shame and guilt proneness as predictors of body-related shame and guilt and (b) the mediating role of specific attributions on the relationship in (a). Based on the model, it was hypothesized that shame would be related to stable, global, and uncontrollable attributions whereas guilt would be related to unstable, specific and controllable attributions. These attributions would mediate any effect of physical self-concept, shame proneness, and guilt proneness on body-related shame and guilt. Female participants (N = 284; Mean age = 20.6 ± 1.9 yrs) completed measures of PSC and shame and guilt proneness before reading a hypothetical scenario designed to elicit a negative body-related emotional response, followed by assessment of state shame and guilt and attributions. Shame proneness and PSC were significant predictors of body shame (β = .49; β = -.11) and guilt (β = .41; β = -.14). Control attributions mediated the relationship of PSC with shame and guilt and shame-proneness with body shame. Global attributions mediated the relationship of shame proneness with body shame. Control (β = -.16), stability (β = .16), and global (β = .20) attributions were significant predictors of body guilt, while global (β = .30) and control (β = -.19) attributions were significant predictors of body shame. The study provides partial support for Tracy and Robins’ model for predicting shame, but little support for predicting guilt.Education, Faculty ofKinesiology, School ofGraduat
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