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Education for sustainable development (ESD) is considered vital to the success of the United Nations¿ sustainable development goals. Systems thinking has been identified as a core competency that must be included in ESD. However, systems thinking-orientated ESD learning tools, established methods of the assessment of sustainability skills, and formal trials to demonstrate the effectiveness of such learning tools are all lacking. This research presents a randomised controlled trial (n = 106) to investigate whether an innovative online sustainability learning tool that incorporates two factors, systems thinking and system dynamics simulation, increases the understanding of a specific sustainability problem. A further aim was to investigate whether these factors also support the transfer of knowledge to a second problem with a similar systemic structure. The effects of the two factors were tested separately and in combination using a two-by-two factorial study design. ANOVA and related inferential statistical techniques were used to analyse the effect of the factors on sustainability understanding. Cohen¿s d effect sizes were also calculated. Simulation alone was found to increase ESD learning outcomes significantly, and also to support the transfer of skills, although less significantly. Qualitative feedback was also gathered from participants, most of whom reported finding systems thinking and simulation very helpful. This research was undertaken for the PhD studies of the corresponding author at the National University of Ireland Galway (NUIG) and was supported by funding from ResponSEAble (EU Horizon 2020 project number 652643), Ireland’s Higher Education Authority and Department of Further and Higher Education, Research, Innovation and Science (through the IT Investment Fund and ComputerDISC, and the COVID-19 Costed Extension), and the NUIG PhD Write-Up Bursary. peer-reviewed

Abstract Due to the spread of Coronavirus disease 2019 (COVID-19), the world has encountered an ongoing pandemic to date. It is a highly contagious disease. In addition to the vaccination, social distancing and isolation of patients are proven to be one of the commonly used strategies to reduce the spread of disease. For efficient social distancing, contact tracing is a critical requirement in the incubation period of 14-days of the disease to contain any further spread. However, we identify that there is a lack of reliable and practical social interaction tracking methods and prediction methods for the probability of getting the disease. This paper focuses on user tracking and predicting the infection probability based on these social interactions. We first developed an energy-efficient BLE (Bluetooth Low Energy) based social interaction tracking system to achieve this. Then, based on the collected data, we propose an algorithm to predict the possibility of getting the COVID-19. Finally, to show the practicality of our solution, we implemented a prototype with a mobile app and a web monitoring tool for healthcare authorities. In addition to that, to analyze the proposed algorithm’s behaviour, we performed a simulation of the system using a graph-based model.

One Health (OH) positions health professionals as agents for change and provides a platform to manage determinants of health that are often not comprehensively captured in medicine or public health alone. However, due to the organization of societies and disciplines, and the sectoral allocation of resources, the development of transdisciplinary approaches requires effort and perseverance. Therefore, there is a need to provide evidence on the added value of OH for governments, researchers, funding bodies, and stakeholders. This paper outlines a conceptual framework of what OH approaches can encompass and the added values they can provide. The framework was developed during a workshop conducted by the “Network for Evaluation of One Health,” an Action funded by the European Cooperation in Science and Technology. By systematically describing the various aspects of OH, we provide the basis for measuring and monitoring the integration of disciplines, sectors, and stakeholders in health initiatives. The framework identifies the social, economic, and environmental drivers leading to integrated approaches to health and illustrates how these evoke characteristic OH operations, i.e., thinking, planning, and working, and require supporting infrastructures to allow learning, sharing, and systemic organization. It also describes the OH outcomes (i.e., sustainability, health and welfare, interspecies equity and stewardship, effectiveness, and efficiency), which are not possible to obtain through sectoral approaches alone, and their alignment with aspects of sustainable development based on society, environment, and economy. peer-reviewed

Abstract Many nations responded to the corona virus disease‐2019 (COVID‐19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial‐condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near‐surface temperature or rainfall during 2020–2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID‐19‐related emission reductions on near‐term climate. Key Points Lockdown restrictions during COVID‐19 have reduced emissions of aerosols and greenhouse gases12 CMIP6 Earth system models have performed coordinated experiments to assess the impact of this on climateAerosol amounts are reduced over southern and eastern Asia but there is no detectable change in annually averaged temperature or precipitation

COVID-19 pandemic is on a trajectory to cause catastrophic global upheaval with the potential to alter geopolitical and socio-economic norms. Many countries are frantically responding with staggering financial stimulus recovery initiatives. This opinion-paper reviews challenges, opportunities, and potential solutions for the post-COVID-19 era that focuses on intensive sustaining of agri-food supply chain in tandem with meeting the high demand for new green deal innovation. For example, the development of wet peatland innovation, known as Paludiculture, can intensively sustain and blend agri-food and green innovations that will help support COVID-19 pandemic transitioning. The future looks bright for the creation of new sustainability multi-actor innovation hubs that will support, connect, and enable businesses to recover and pivot beyond the COVID-19 pandemic. The nexus between first ‘Green Deal’ initiative supporting 64 selected European Startups and SMEs (European Innovation Council) and 43 Irish Disruptive Technology projects are addressed in the context of cross-cutting developments and relevance to COVID-19. Candidate areas for future consideration will focus on climate action, digitization, manufacturing, and sustainable food production, security, and waste mitigation. Recommendations are also provided to facilitate community transitioning, training, enterprise, and employment to low carbon economy. yes

The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic) Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance. Science Foundation Ireland Insight Research Centre