The increasing automation of modern systems—across industry, healthcare, mobility, and beyond—has raised the demand for human reasoning and expertise, while alleviating the burden of repetitive tasks. This transformation is driving us toward Automation 5.0, a new paradigm aimed at unleashing human potential. Recently, the development of foundation models (FMs) has reinvigorated its realization, making it both urgent and critical to explore the concept of Automation 5.0 in this new era. In this article, we define Automation 5.0, discuss its significance, and emphasize its new world, thinking, and technology with the goal of achieving knowledge automation. A framework, based on business FMs, human-oriented operating systems, and scenarios engineering, is proposed, where biological, robotic, and digital humans work together in three modes: autonomous, parallel, and expert/emergency modes. Additionally, a diverse range of its scenarios and applications are summarized and discussed, such as Manufacturing 5.0, Healthcare 5.0, and Transportation 5.0. We believe that Automation 5.0 can drive the co-evolution of productivity and production relations across all domains, propelling society toward a “Safety, Security, Sustainability, Sensitivity, Service, Smartness (6S)” future.

Academic journals serve as pivotal bridges for knowledge dissemination and technology innovation, playing a crucial role in promoting scientific research, industrial progress, and societal development. However, traditional models of journal management and operation, hindered by prolonged peer review processes, overarching publication inefficiencies, and surging page expenditures, are increasingly powerless to address challenges posed by ever-faster knowledge updates, broad information dissemination, and interdisciplinary scholarly work. This has led to an urgent need for smart organizations and intelligent operations of journals. In view of this, the paper identifies the primary issues in current journal management and proposes the concept of smart journals based on TRUE Autonomous Organizations and Operations (TRUE DAO or TAO). This paper introduces the foundational architecture of smart journals and proposes incentive mechanisms for a more dynamic publishing ecosystem. Moreover, a simulation experiment is designed to evaluate an adaptive incentive mechanism, demonstrating significant improvements in review quality and accuracy through personalized incentive allocation strategies. Smart journals not only enable digital transformation but also foster profound innovation and long-term sustainability in publishing.

In the context of the Societies 5.0, a series of discussions on the emerging Fukushima treated radioactive water (FTRW) event was carried out, which has an impact on sustainable development in multiple fields including the economy, culture, and society. In order to comprehensively understand emerging topics and their evolution, explore the impact of people's cognition on their participation, and focus on people's attitudes and public participation in the FTRW event, we propose an evolution analysis framework (EAF) to analyze the massive multilingual comments and news collected from social media platforms in several countries. We design a multilingual topic extraction model (XLM-topic) to detect the patterns of topics and analyze their evolution. Potential relations between the FTRW event's elements are explored by relational reasoning based on a knowledge graph, which is established by entities and relations extracted from comments and news. Moreover, we predict the public attitudes and participation toward the FTRW event by utilizing our custom-designed public opinion cellular automata (POCA). The proposed POCA simulates the information dissemination, cognitive changes, and topic evolution among social groups in virtual spaces. It collaborates with XLM-topic to analyze trends in both physical and virtual spaces. Analysis results indicate that participants in different regions and countries have different attitudes and reactions toward the FTRW event, and the public's cognition on this event will interact with itself. Our study is conducive to promoting the integration and interaction of virtual space and physical space in the context of Societies 5.0, providing decision-making support for building a more harmonious and stable social environment.

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) of 2024. This issue showcases an impressive array of 104 regular papers alongside our Special Issue on Big Data and Computational Social Intelligence for Guaranteed Financial Security, highlighting cutting-edge research aimed at harnessing big data and computational techniques to fortify financial security amidst the digital finance evolution. With a focus on addressing the intricate challenges of financial big data, enhancing the efficacy of artificial intelligence, and covering critical topics from data mining to digital currencies, this issue underscores the vital role of cross-disciplinary efforts in mitigating financial security risks.

Inspired by the term “social radar,” which collects and processes information about social behaviors, this article proposed “sociolinguistic radar,” which represents an emerging branch in social investigation and evaluation system aiming to explore the dynamic correlation between sociophonetic variants and macrosociological categories, such as age, gender, ethnicity, and socioeconomic status. The classic quantitative methods in this field include sociolinguistic surveys and interview, quantitative sociophonetics, and social network analysis. These methods have been proved to be effective in tracking the cognitive correlates of phonological variables. Under the emerging framework of sociolinguistic radar, speakers are no longer passive carriers, but active agents in transforming linguistic styles in the process of forming social differentiations, thus contributing to the construction of new social meaning. With the advancement in neuroscience and artificial intelligence (AI), the neurosociolinguistic and AI-based sociolinguistic radar research will thrive and empower the scope and strength of detecting linguistic variation. The working mechanism of this emerging model leverages neural and AI tool packages to radar and analyze linguistic variation, communication patterns, and diverse sociolinguistic phenomena. This interdisciplinary approach combines the principles of sociolinguistics, which will thoroughly examine the relationship between language and society.