ABSTRACT
Our plans to tackle climate change could be thrown off-track by shocks such as the coronavirus pandemic, the energy supply crisis driven by the Russian invasion of Ukraine, financial crises and other such disruptions. We should therefore identify plans which are as resilient as possible to future risks, by systematically understanding the range of risks to which mitigation plans are vulnerable and how best to reduce such vulnerabilities. Here, we use electricity system decarbonization as a focus area, to highlight the different types of technological solutions, the different risks that may be associated with them, and the approaches, situated in a decision-making under deep uncertainty (DMDU) paradigm, that would allow the identification and enhanced resilience of mitigation pathways. Copyright © 2023 Gambhir and Lempert.
ABSTRACT
Machine Learning is ever advancing field and as more and more research is being done in the field, more applications are being developed for this field and it is now being used in all fields. Also, nowadays people are facing multiples diseases posing danger to human life. This prompted researchers to critically analyse and work to apply Machine learning in the use of prediction of these diseases and using this analysis to assist the medical industry. The idea is to find various datasets of different Diseases like Dengue, Covid-19. Perform analysis on the datasets of these diseases to understand more about them and how much they affect us. There are various models available like KNN, SVM, etc. The task is to work with different models and find out how they perform with data of different diseases and which models are most affective and accurate. © 2022 IEEE.
ABSTRACT
Europe’s capacity to explore the envisaged pathways that achieve its near- and long-term energy and climate objectives needs to be significantly enhanced. In this perspective, we discuss how this capacity is supported by energy and climate-economy models, and how international modelling teams are organised within structured communication channels and consortia as well as coordinate multi-model analyses to provide robust scientific evidence. Noting the lack of such a dedicated channel for the highly active yet currently fragmented European modelling landscape, we highlight the importance of transparency of modelling capabilities and processes, harmonisation of modelling parameters, disclosure of input and output datasets, interlinkages among models of different geographic granularity, and employment of models that transcend the highly harmonised core of tools used in model inter-comparisons. Finally, drawing from the COVID-19 pandemic, we discuss the need to expand the modelling comfort zone, by exploring extreme scenarios, disruptive innovations, and questions that transcend the energy and climate goals across the sustainability spectrum. A comprehensive and comprehensible multi-model framework offers a real example of “collective” science diplomacy, as an instrument to further support the ambitious goals of the EU Green Deal, in compliance with the EU claim to responsible research.