A question arising from the COVID-19 crisis is whether the merits of cases for climate policies have been affected. This article focuses on carbon pricing, in the form of either carbon taxes or emissions trading. It discusses the extent to which relative costs and benefits of introducing carbon pricing may have changed in the context of COVID-19, during both the crisis and the recovery period to follow. In several ways, the case for introducing a carbon price is stronger during the (...) COVID-19 crisis than under normal conditions. Oil costs are lower than normal, so we would expect less harm to consumers compared to normal conditions. Governments have immediate need for diversified new revenue streams in light of both decreased tax receipts and greater use of social safety nets. Finally, supply and demand shocks have led to already destabilized supply-side activities, and carbon pricing would allow this destabilization to equilibrate around greener production for the long-term. The strengthening of the case for introducing carbon pricing now is highly relevant to discussions about recovery measures, especially in the context of policy announcements from the European Union and United States House of Representatives. Key Policy Insights: • Persistently low oil prices mean that consumers will face lower pain from carbon pricing than under normal conditions. • Many consumers are more price-sensitive during the COVID-19 context, which suggests that a greater relative burden from carbon prices would fall upon producers as opposed to consumers than under normal conditions. • Carbon prices in the COVID-19 context can introduce new revenue streams, assisting with fiscal holes or with other green priorities. • Carbon pricing would contribute to a more sustainable COVID-19 recovery period, since many of the costs of revamping supply chains are already being felt while idled labor capacity can be incorporated into firms with lower carbon-intensity. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the (...) development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

Biomedical ontologies are emerging as critical tools in genomic and proteomic research where complex data in disparate resources need to be integrated. A number of ontologies exist that describe the properties that can be attributed to proteins; for example, protein functions are described by Gene Ontology, while human diseases are described by Disease Ontology. There is, however, a gap in the current set of ontologies—one that describes the protein entities themselves and their relationships. We have designed a PRotein Ontology (PRO) (...) to facilitate protein annotation and to guide new experiments. The components of PRO extend from the classification of proteins on the basis of evolutionary relationships to the representation of the multiple protein forms of a gene (products generated by genetic variation, alternative splicing, proteolytic cleavage, and other post-translational modification). PRO will allow the specification of relationships between PRO, GO and other OBO Foundry ontologies. Here we describe the initial development of PRO, illustrated using human proteins from the TGF-beta signaling pathway. (shrink)

This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning and fusion (...) algorithms; the paper closes with concluding observations and a summary of the principal work and contributions of this paper. (shrink)

Although patterns of human activity show a large degree of freedom, they exhibit structural patterns subjected by geographic and social constraints. Aiming at various problems of personalized recommendation in mobile networks, a social network recommendation algorithm is proposed with a variety of context-aware information and combined with a series of social network analysis methods.Based on geographical location and temporal information, potential social relations among users are mined deeply to find the most similar set of users for the target user, then (...) recommendations are carried out incorporating with social relations of the mobile users to effectively solve the problem of recommendation precision. The above study can not only help LBSN designers and developers to better understand their users and grasp their want, but also help to refine the design of their system to provide users with more appropriate applications and services.The experimental results on the real-world dataset verify the feasibility and effectiveness of the proposed algorithm, and it has higher prediction accuracy compared with existing recommendation algorithms. (shrink)