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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.

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

Abstract CONTEXT Livestock are the primary source of greenhouse gas (GHG) emissions from agriculture in most African countries, but there is a paucity of baseline data and monitoring of GHG emissions from livestock in Africa, particularly for extreme or shock events. The COVID-19 pandemic represents a novels shock to livestock systems and may result in indirect effects on livestock emissions and other Sustainable Development Goals (SDGs). Due to the pandemic in 2020, extensive pastoralist livestock systems in Northern Kenya were subjected to restrictions on movement, increased costs of transportation, and closure of livestock markets. OBJECTIVE The objective of this study was to assess the indirect effects of the COVID-19 pandemic on GHG emissions from livestock systems in Northern Kenya using proxy data and a three-part framework based on changes in 1) herd size, 2) feed availability, and 3) livestock movement. METHODS We evaluated changes in GHG emissions from livestock systems in Northern Kenya due to the COVID-19 pandemic based on proxy data from crowd-sourced market data, household panel surveys, and remote sensing data on Normalized Difference Vegetation Index (NDVI). Proxy data were obtained before the pandemic in 2019 and after the pandemic in 2020 to compare between years and evaluate the indirect effects of the pandemic and associated restrictions on livestock GHG emissions using the three-part framework. RESULTS AND CONCLUSIONS Overall GHG emissions from livestock in Northern Kenya have decreased due to the pandemic and this was largely driven by reductions in herd size. This reduction in GHG emissions occurred despite an increase in GHG emissions from livestock associated with higher feed availability. Decreased livestock movement due to the pandemic contributed to reductions in GHG emissions from livestock, but such reductions were likely to be small due to limited need for livestock to travel longer distances under the prevailing conditions of high feed availability. SIGNIFICANCE This research shows that assessments of changes in GHG emissions from livestock systems due to shock events can be conducted successfully based on proxy data and the three-part framework developed here. We found that shock events, such as the COVID-19 pandemic, may lead to unexpected results with respect to the direction and magnitude of changes in livestock emissions depending on contextual factors and environmental conditions. Thus, we call for more spatially explicit and continued data collection to assess and monitor the consequences of shock events on GHG emissions from livestock and related SDGs in Africa.