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International audience; Research and innovation in renewable energy, such as wind and solar, have been supporting the green transformation of energy systems, the backbone of a low-carbon climate-resilient society. The major challenge is to manage the complexity of the grid transformation to allow for higher shares of highly variable renewables while securing the safety of the stability of the grid and a stable energy supply. A great help comes from the ongoing digital transformation where digitisation of infrastructures and assets in research and industry generates multi-dimensional and multi-disciplinary digital data. However, a data user needs help to find the correct data to exploit. This has two significant facets: first, missing data management, i.e., datasets are neither findable because of missing community standard metadata and taxonomies, nor interoperable, i.e., missing standards for data formats; second, data owners having a negative perception of sharing data. To make data ready for data science exploitation, one of the necessary steps to map the existing data and their availability to facilitate their access is to create a taxonomy for the field’s topics. For this, a group of experts in different renewable technologies such as photovoltaics, wind and concentrated solar power and in transversal fields such as life cycle assessment and the EU taxonomy for sustainable activities have been gathered to propose a coherent and detailed taxonomy for renewable energy-related data. The result is a coherent classification of relevant data sources, considering both the general aspects applicable to electricity generation from selected renewable energy technologies and the specific aspects of each of them. It is based on previous relevant work and can be easily extended to other renewable resources not considered in this work and conventional energy technology.

IntroductionShift work is known to reduce productivity and safety at work. Previous studies have suggested that a variety of interrelated factors, such as mood, cognition, and sleep, can affect the performance of shift workers. This study aimed to identify potential pathways from depression, sleep, and cognition to work performance in shift and non-shift workers.Material and methodsOnline survey including the Center for Epidemiologic Studies Depression Scale (CES-D), Cognitive Failure Questionnaire (CFQ), and Pittsburgh Sleep Quality Index (PSQI), as well as two items representing work mistakes were administered to 4,561 shift workers and 2,093 non-shift workers. A multi-group structural equation model (SEM) was used to explore differences in the paths to work mistakes between shift and non-shift workers.ResultsShift workers had higher PSQI, CES-D, and CFQ scores, and made more mistakes at work than non-shift workers. The SEM revealed that PSQI, CES-D, and CFQ scores were significantly related to mistakes at work, with the CFQ being a mediating variable. There were significant differences in the path coefficients of the PSQI and CES-D between shift and non-shift workers. The direct effects of sleep disturbances on mistakes at work were greater in shift workers, while direct effects of depressive symptoms were found only in non-shift workers.DiscussionThe present study found that shift workers made more mistakes at work than non-shift workers, probably because of depressed mood, poor sleep quality, and cognitive inefficiency. Sleep influences work performance in shift workers more directly compared to non-shift workers.

Soil quality indicates the soil’s ability to provide ecosystem services. Reducing the tillage intensity has been suggested as an alternative to conventional tillage for sustaining soil quality. This study aimed to evaluate the effect of soil tillage systems on individual soil quality indicators in comparison to those on grassland with Stagnic Luvisol soil in Estonia. Four soil management systems were compared: no-tillage (NT), minimum tillage (MT), conventional tillage (CT) and grassland (G) as a reference. Soil quality indicators included physical (bulk density, water-stable aggregates, porosity, air-filled pores, moisture content, water-holding capacity, penetration resistance and water permeability), chemical (total N, total soil organic C, permanganate oxidisable C, pH, P, K, Ca and Mg) and biological (earthworm abundance) parameters. CT soils had a significantly lower aggregate stability compared to MT and G soils. The higher penetration resistance of CT under an arable layer suggested the presence of a plough pan. NT improved the soil’s physical quality at 5–10 cm, which was indicated by higher moisture content, water-holding capacity and porosity and a lower bulk density, whereas penetration resistance exceeded 2 MPa in the lower part of the topsoil. NT also had significantly lower total soil organic C and total N compared to MT and G. The absence of tillage in the NT and G systems may have improved the soil’s resistance to moisture loss under dry conditions, which, in turn, improved the soil habitability for earthworms a despite higher density. In general, NT or MT stabilised or increased the soil quality compared to CT.

Damage to devices installed in electric substations, which have shown vulnerable behaviour under strong earthquakes in the last decades, may endanger power delivery in the emergency phases during and after an earthquake. Within seismic risk assessment of power networks, the definition of the fragility functions of electric equipment is paramount. However, in the current literature the availability of such fragility models for some specific electric substation components, including instrument transformers, is relatively limited, this being the reason behind the deployment of the current experimental and numerical research endeavour. Two voltage transformers and two current transformers having different system voltage levels (respectively in the high voltage HV and extra-high voltage EHV ranges) were subjected to shake table tests, and the experimental results were used to calibrate the corresponding 3D numerical models developed in OpenSees. A number of nonlinear dynamic analyses carried out within a multiple-stripe analysis (MSA) framework allowed the derivation of 16 fragility curves for the four transformers in both stand-alone and elevated/supported configurations, considering also two different soil types. Based on the derived curves, one of the voltage transformers is expected to experience light or negligible damage due to earthquake shaking, owing to its high resonance frequencies (and hence stiffness), whilst the remaining three devices may suffer moderate damage under medium to strong shaking intensities; however, their seismic risk is in effect mitigated by the presence of the typically employed supporting column. Comparison against models available in the literature lent valuable reassurance on the adequacy of the employed methodology and the reliability of the derived fragility curves.

Members of the Campylobacter lari group are causative agents of human gastroenteritis and are frequently found in shellfish, marine waters, shorebirds, and marine mammals. Within a One Health context, we used comparative genomics to characterize isolates from a diverse range of sources and geographical locations within Europe and Australia and assess possible transmission of food, animal, and environmental isolates to the human host. A total of 158 C. lari isolates from Australia, Denmark, France, and Germany, which included 82 isolates from human stool and blood, 12 from food, 14 from domestic animal, 19 from waterbirds, and 31 from the environment were analyzed. Genome-wide analysis of the genetic diversity, virulence, and antimicrobial resistance (AMR) traits was carried-out. Most of the isolates belonged to C. lari subsp. lari (Cll; 98, 62.0%), while C. lari subsp. concheus and C. lari urease-positive thermotolerant Campylobacter (UPTC) were represented by 12 (7.6%) and 15 (9.5%) isolates, respectively. Furthermore, 33 (20.9%) isolates were not assigned a subspecies and were thus attributed to distant Campylobacter spp. clades. Whole-genome sequence-derived multilocus sequence typing (MLST) and core-genome MLST (cgMLST) analyses revealed a high genetic diversity with 97 sequence types (STs), including 60 novel STs and 14 cgMLST clusters (≤10 allele differences), respectively. The most prevalent STs were ST-21, ST-70, ST-24, and ST-58 (accounting for 13.3%, 4.4%, 3.8%, and 3.2% of isolates, respectively). A high prevalence of the 125 examined virulence-related loci (from 76.8 to 98.4% per isolate) was observed, especially in Cll isolates, suggesting a probable human pathogenicity of these strains. IMPORTANCE Currently, relatedness between bacterial isolates impacting human health is easily monitored by molecular typing methods. These approaches rely on discrete loci or whole-genome sequence (WGS) analyses. Campylobacter lari is an emergent human pathogen isolated from diverse ecological niches, including fecal material from humans and animals, aquatic environments, and seafood. The presence of C. lari in such diverse sources underlines the importance of adopting an integrated One Health approach in studying C. lari population structure for conducting epidemiological risk assessment. This retrospective study presents a comparative genomics analysis of C. lari isolates retrieved from two different continents (Europe and Australia) and from different sources (human, domestic animals, waterbirds, food, and environment). It was designed to improve knowledge regarding C. lari ecology and pathogenicity, important for developing effective surveillance and disease prevention strategies.

Genetic and exposomal factors contribute to the development of human aging. For example, genetic polymorphisms and exposure to environmental factors (air pollution, tobacco smoke, etc.) influence lung and skin aging traits. For prevention purposes it is highly desirable to know the extent to which each category of the exposome and genetic factors contribute to their development. Estimating such extents, however, is methodologically challenging, mainly because the predictors are often highly correlated. Tackling this challenge, this article proposes to use weighted risk scores to assess combined effects of categories of such predictors, and a measure of relative importance to quantify their relative contribution. The risk score weights are determined via regularized regression and the relative contributions are estimated by the proportion of explained variance in linear regression. This approach is applied to data from a cohort of elderly Caucasian women investigated in 2007–2010 by estimating the relative contribution of genetic and exposomal factors to skin and lung aging. Overall, the models explain 17% (95% CI: [9%, 28%]) of the outcome’s variance for skin aging and 23% ([11%, 34%]) for lung function parameters. For both aging traits, genetic factors make up the largest contribution. The proposed approach enables us to quantify and rank contributions of categories of exposomal and genetic factors to human aging traits and facilitates risk assessment related to common human diseases in general. Obtained rankings can aid political decision making, for example, by prioritizing protective measures such as limit values for certain exposures.

The safe system approach is considered the best practice. Different international organizations highly recommend the implementation of such an approach to significantly improve their road safety performance. The safe system has become a normative approach, a way of thinking. It rests upon different pillars, which represent the framework to be implemented. Among the different dimensions, the management pillar is crucial, which calls for a detailed investigation. First, the aim of this contribution is to understand the implications and requirements of implementing a safe system management model (SSMM). This contribution presents this model and challenges its aims and its limits, especially the promoted management framework. Six main limits of the SSMM are discussed. The different limits of the SSMM emphasize the importance of thinking about singularities and contingencies of the institutional order as well as considering and understanding the context in which a policy is implemented. Second, this contribution proposes an alternative conceptual approach applied to the managerial dimension of the road safety system. It develops the institutional-strategy-environment (ISE) model for elaborating road safety public strategy. This conceptual elaboration is based both on a proven theoretical corpus and on numerous field observations, and expert activities carried out in various countries. The ISE dimensions are essential because they are seldom discussed, even though they are the underpinning of more and more public road safety policies. In this respect, this contribution proposes an alternative approach by considering these three interdependent dimensions. The institutional dimension rests upon a layered approach, while strategy is considered with an adapted SWOT matrix. The environmental dimension is assessed with a Pestel model. The application of such an approach to the road safety field and to test its explanatory power is particularly challenging but opens new avenues for implementing road safety public policy.

This presentation was given during the 2022 AGU Fall Meeting in Chicago, IL. Global policies to mitigate climate change suggest the need of pricing greenhouse gas emissions to attain rapid emissions reductions. The efficacy of these measures is generally assessed at the global or regional scale, while their local impacts are often difficult to anticipate and are consequently overlooked in many analyses. This study addresses this gap by investigating how multisector dynamics across interconnected Water-Energy-Food (WEF) systems at the local scale are impacted by global climate change mitigation efforts. We use a detailed river basin scale operational model of the Zambezi Watercourse in Southern Africa that enables the exploration of synergies, tradeoffs, and vulnerabilities for the local WEF systems. Our analysis includes more than 7,000 scenarios that combine different climate and socio-economic projections with alternative mitigation policies. Future irrigation demands are generated by systematically sampling the Shared Socioeconomic Pathways components along with multiple Shared Policy Assumptions. Future water availability is instead produced by forcing local hydrological models with bias-adjusted climate projections from different combinations of global and regional climate models for different Representative Concentration Pathways. Our results contribute quantitative evidence of the unintended vulnerabilities that may emerge in the Zambezi Watercourse. Policy fragmentation between developed and developing countries in their approach to land-use change emissions can have negative side effects on local water demands, producing favorable conditions for the realization of extensive agricultural projects in Africa where the price associated to the land-use change emission is low. This generates irrigation demands two times higher than under globally coordinated approaches to emissions reductions and constrains the availability of water resources for hydropower production or the provision of ecosystem services. Analogous vulnerabilities could impact many river basins in Southern and Western Africa. It is critical to connect global climate change mitigation policies to local dynamics for a better understanding of the full range of possible future scenarios while supporting policy makers in prioritizing sustainable mitigation and adaptation solutions.