Consensus algorithms are central to blockchain technology and an emerging research area. In this paper, we begin with an overview of the different types and architectures of blockchain networks. Then, with a focus on consortium blockchains, we survey, classify, and assess their principal consensus mechanisms. Furthermore, as consensus mechanisms determine network reliability, enhance performance efficiency, and ensure system security, we conduct a critical analysis of the strengths and weaknesses of consensus algorithms using a taxonomy of three different criteria: reliability, performance, and security. We conclude with insights into current and future research challenges and opportunities in this domain.

Background: The American Medical Association recommends that electronic health record (EHR) notes, often dense and written in nuanced language, be made readable for patients and laypeople, a practice we refer to as the simplification of discharge notes. Our approach to achieving the simplification of discharge notes involves a process of incremental simplification steps to achieve the ideal note. In this paper, we present the first step of this process. Large language models (LLMs) have demonstrated considerable success in text summarization. Such LLM summaries represent the content of EHR notes in an easier-to-read language. However, LLM summaries can also introduce inaccuracies. Objective: This study aims to test the hypothesis that summaries generated by LLMs from highlighted discharge notes will achieve increased accuracy compared to those generated from the original notes. For this purpose, we aim to prove a hypothesis that summaries generated by LLMs of discharge notes in which detailed information is highlighted are likely to be more accurate than summaries of the original notes. Methods: To test our hypothesis, we randomly sampled 15 discharge notes from the MIMIC III database and highlighted their detailed information using an interface terminology we previously developed with machine learning. This interface terminology was curated to encompass detailed information from the discharge notes. The highlighted discharge notes distinguished detailed information, specifically the concepts present in the aforementioned interface terminology, by applying a blue background. To calibrate the LLMs’ summaries for our simplification goal, we chose GPT-4o and used prompt engineering to ensure high-quality prompts and address issues of output inconsistency and prompt sensitivity. We provided both highlighted and unhighlighted versions of each EHR note along with their corresponding prompts to GPT-4o. Each generated summary was manually evaluated to assess its quality using the following evaluation metrics: completeness, correctness, and structural integrity. Results: We used the study sample of 15 discharge notes. On average, summaries from highlighted notes (H-summaries) achieved 96% completeness, 8% higher than the summaries from unhighlighted notes (U-summaries). H-summaries had higher completeness in 13 notes, and U-summaries had higher or equal completeness in 2 notes, resulting in P=.01, which implied statistical significance. Moreover, H-summaries demonstrated better correctness than U-summaries, with fewer instances of erroneous information (2 vs 3 errors, respectively). The number of improper headers was smaller for H-summaries for 11 notes and U-summaries for 4 notes (P=.03; implying statistical significance). Moreover, we identified 8 instances of misplaced information in the U-summaries and only 2 in the H-summaries. We showed that our findings supported the hypothesis that summarizing highlighted discharge notes improves the accuracy of the summaries. Conclusions: Feeding LLMs with highlighted discharge notes, combined with prompt engineering, results in higher-quality summaries in terms of correctness, completeness, and structural integrity compared to unhighlighted discharge notes.

Groupware or collaborative systems allow a team to interact in a shared workspace despite geographical separation or diverse schedules. Current collaborative integrated manufacturing systems tend to focus on the design stage of the development process. However, design is only one stage of standard frameworks for problem-solving-based development, which typically include a more comprehensive set of stages from problem formulation, planning and design to testing and deployment. Problem solving methodologies and tools, originally developed for single-user environments, can be extended to multi-user collaborative problem solving models and systems. This paper considers the characteristics of existing collaborative systems in manufacturing and then proposes a methodology-based, object-oriented view of the structure of such systems, which also addresses such factors as the effect of distributed cognition and social protocols. We then show that existing research on both individual and collaborative problem solving models pertinent to groupware systems theory, as well as work on the effect of factors such as group cognition, group psychology, sociology and team dynamics, support this approach to the design of collaborative manufacturing environments.