Automatic sentiment analysis of public opinion on nuclear energy

Opinion mining and sentiment analysis based on social media has been developed these years, especially with the popularity of social media and the development of machine learning. But in the community of nuclear engineering and technology, sentiment analysis is seldom studied, let alone the automatic analysis by using machine learning algorithms. This work concentrates on the public sentiment mining of nuclear energy in German-speaking countries based on the public comments of nuclear news in social media by using the automatic methodology, since compared with the news itself, the comments are closer to the public real opinions. The results showed that majority comments kept in neutral sentiment. 23% of comments were in positive tones, which were approximate 4 times those in negative tones. The concerning issues of the public are the innovative technology development, safety, nuclear waste, accidents and the cost of nuclear power. Decision tree, random forest and long short-term memory networks (LSTM) are adopted for the automatic sentiment analysis. The results show that all of the proposed methods can be applied in practice to some extent. But as a deep learning algorithm, LSTM gets the highest accuracy approximately 85.6% with also the best robustness of all.

Methodological developments and practice in characterisation of unconventional and legacy waste

Radioactive waste management requires planned and systematic actions to provide confidence that the entire system, processes and final products will satisfy given requirements for quality. The characterisation process is dependent on setting clear characterisation objectives and gathering the right information to underpin the decisions that need to be taken to manage the waste safely. This paper reviews experience of characterisation of waste generated from past nuclear activities that were not conducted in compliance consistent with current criteria, or from unexpected situations that were not planned for. This experience shows that the development of a reliable and efficient characterisation and categorisation methodology is a common challenge for such wastes, referred to here as unconventional and legacy (UL) waste. Through the activites of the Nuclear Energy Agency Expert Group on the Characterisation of Unconventional and Legacy Waste (EGCUL), consideration has been given to widely used waste stream characterisation procedures and methods that were originally developed primarily for application in conventional decommissioning work. Although they provide a substantial basis for characterisation, there are various additional factors that commonly need to be taken into account in the case of UL waste. By analysing the challenges and lessons learned from a variety of case studies and other international experience, it has been possible to identify opportunities for adaptations and enhancements to these characterisation methologies, and these are set out and explained. The need for integration of waste characterisation with other aspects of strategic planning for UL waste management is discussed, including characterisation to address any non-radiological hazards.The analysed case studies have also highlighted the importance of developing a robust legislative and regulatory framework in parallel with an appropriate waste infrastructure to treat, store and dispose of UL waste. Finally, the basic features of a UL waste characterisation roadmap are presented, including the interactions within a wider UL waste management programme and key areas for further consideration and possible development. It is anticipated such work can be supported by continued international cooperation.

Advancements in Probabilistic Safety Assessment of Nuclear Energy for Sustainability

Since the publication of the first comprehensive Probabilistic Safety Assessment (PSA) study—known as WASH-1400—in the US, PSA has developed into an effective and systematic method of identifying hazards, and evaluating and prioritizing the risks in nuclear facilities [...]

Russian climate aims at risk from pushback or inaction

Significance They include a commitment to reach net zero by 2060, which President Vladimir Putin announced ahead of the COP26 conference, and a new government strategy to reduce greenhouse gas (GHG) emissions. Impacts Russia will continue to lobby for nuclear energy to be an accepted technology within the global climate agenda. Despite the possible benefits of a warming climate, Russian farmers face immediate challenges from floods and droughts. The strategy's citation of a clean hydrogen industry demonstrates the government's ambition in this area.

Methods of Thermal Treatment of Radioactive Waste

Throughout the world, and especially in the European Union, numerous technologies for the thermal treatment of radioactive waste are available or being developed. These technologies can be applied to a large range of different radioactive waste streams, including non-standard types of waste that present specific waste management challenges. Thermal treatment can result in a significant reduction in volume and hazard, which are beneficial for safe storage and disposal. Thermal treatment also removes organic material that can form complexing agents and increase the mobility of radionuclides in the landfill. In the paper, basic thermal techniques are presented, and some examples of the installations are shown. Common knowledge of these methods may result in an increased public acceptance of nuclear energy in a country just introducing it, as Poland is.