1,836 research outputs found
Case study on the impact of cogeneration and thermal storage on the flexibility of the power system
This work investigates the optimal operation of cogeneration plants combined with thermal storage. To do so, a combined heat and power (CHP) plant model is formulated and incorporated into Dispa-SET, a JRC in-house unit commitment and dispatch model. The cogeneration model sets technical feasible operational regions for different heat uses defined by temperature requirements
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
ChatGPT has drawn considerable attention from both the general public and
domain experts with its remarkable text generation capabilities. This has
subsequently led to the emergence of diverse applications in the field of
biomedicine and health. In this work, we examine the diverse applications of
large language models (LLMs), such as ChatGPT, in biomedicine and health.
Specifically we explore the areas of biomedical information retrieval, question
answering, medical text summarization, information extraction, and medical
education, and investigate whether LLMs possess the transformative power to
revolutionize these tasks or whether the distinct complexities of biomedical
domain presents unique challenges. Following an extensive literature survey, we
find that significant advances have been made in the field of text generation
tasks, surpassing the previous state-of-the-art methods. For other
applications, the advances have been modest. Overall, LLMs have not yet
revolutionized the biomedicine, but recent rapid progress indicates that such
methods hold great potential to provide valuable means for accelerating
discovery and improving health. We also find that the use of LLMs, like
ChatGPT, in the fields of biomedicine and health entails various risks and
challenges, including fabricated information in its generated responses, as
well as legal and privacy concerns associated with sensitive patient data. We
believe this first-of-its-kind survey can provide a comprehensive overview to
biomedical researchers and healthcare practitioners on the opportunities and
challenges associated with using ChatGPT and other LLMs for transforming
biomedicine and health
COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres
In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single- and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractive-oriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.This research has been supported by the project “Desarrollo de TĂ©cnicas Inteligentes e Interactivas de MinerĂa de Textos” (PROMETEO/2009/119) and the project reference ACOMP/2011/001 from the Valencian Government, as well as by the Spanish Government (grant no. TIN2009-13391-C04-01)
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