International competitive landscape of administrative intelligent decision-making theory and application: knowledge graph analysis based on literature and patent

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

The continuous goal of discovering extraterrestrial life has driven scientific interest in exoplanets—celestial bodies orbiting stars outside our solar system. Traditional methods of exoplanet detection, reliant on manual analysis and prone to human error, presented unavoidable challenges given the vastness of the universe. This paper aims to discuss the benefits and limitations of AI within the exoplanet detection field, and determine whether the highly-regarded artificial intelligence is as beneficial to astronomical fields as we think. With the introduction of the first ever fully-robotic exoplanet detector which takes high-precision radial velocity measurements to measure the gravitational reflex motion, and advancing computer algorithms that avoid human errors in data analysis, modern advancements in artificial intelligence (AI) technology have not only transformed the efficiency and accuracy of exoplanet detection, but also extended our understanding of these distant worlds. While the use of AI does have its benefits, there are several drawbacks that could potentially hinder further advancement in the field of exoplanet detection.

The development of artificial intelligence (AI) technology has become a major catalyst for digital transformation in Indonesia. However, the accelerated adoption of AI has not been matched by regulatory readiness, especially in the legal and cybersecurity aspects. The national legal framework is still sectoral and has not been able to address the complexity of risks from AI systems implemented in various public and private sectors. This research aims to: (1) identify regulatory weaknesses in monitoring the use of AI in Indonesia; (2) formulate an integrative legal framework between AI regulation and cyber law that is adaptive to technological developments; and (3) provide policy recommendations based on international practices. This research method uses a normative-empirical legal approach with a combination of documentation studies, comparative analysis of international regulations, and semi-structured interviews with experts. The results show that Indonesia experiences significant regulatory gaps, particularly in the application of the principles of transparency, accountability, and AI risk management. Compared to the European Union and the United States, AI regulations in Indonesia are still at the declarative stage without adequate enforcement mechanisms. This study recommends the establishment of a risk-based national legal framework accompanied by the strengthening of independent oversight institutions, AI technical standards, and multi-stakeholder involvement in the regulatory process. These findings are expected to serve as the basis for the development of legal policies that are more adaptive, responsive, and secure to advances in AI technology and the dynamics of cyber threats in Indonesia.

The rapid development and adoption of artificial intelligence (AI) technology has sparked debates about its implications for labor markets, yet the micro-level relationship between AI and labor share remains underexplored. Based on the theory of skill-biased technological change, this study aims to examine whether AI technology increases labor share by labor structure upgrading at the enterprise level. Using panel data for China’s listed companies from 2012 to 2022, this study tests this relationship using a two-way fixed effects model. The empirical results reveal that AI technology significantly increases labor share, with labor structure upgrading playing a mediating role in this relationship. Heterogeneity analysis reveals that the influence of AI technology on labor share is stronger for enterprises characterized by low labor market rigidity, high labor market supply, and talent policy support in external environments, as well as among labor-intensive, high-tech, and non-state-owned enterprises. Notably, this study finds that advancements in AI technology have achieved mutually beneficial outcomes of improving labor share and enhancing total factor productivity. Our research findings provide detailed empirical evidence for enterprises to formulate and implement AI strategies.

Fruits and vegetables preservation based on AI technology: Research progress and application prospects

Using novel comprehensive administrative micro data on business applications in the United States over the period 2004–2023, we examine the characteristics of business applications and resulting startups that aim to develop Artificial Intelligence (AI) technologies or use AI in providing goods and services. AI-related applications begin rising around 2012, accelerate after 2016, and surge in 2023, consistent with major advances in AI technologies around these years. This growth is highly uneven across states and sectors. AI applications are more likely to come from corporations, transition to startups at higher rates, and anticipate greater initial employment than other applications. Startups originating from these applications achieve higher employment and revenue, but have a lower survival rate, reflecting an ‘up-or-out' dynamic. Despite higher revenue, they exhibit a higher labor share and do not have higher labor productivity. The patterns suggest an accelerating AI diffusion through new business formation and a growing contribution from AI-related startups to business dynamism.

Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities

Purpose/Rationale Sport officials operate within settings that dynamically change and shift. While they gather, synthesise, and store experiences related to task, performer, and environmental constraints, their internal mental models of judgement and decision-making individually evolve as they perform in different contexts. However, while a large body of work in psychology and behavioural economics has attempted to capture the way humans make decisions, there is a growing realisation among researchers, evaluators, and educational designers that quality improvement interventions cannot be understood outside of the context in which they occur [Ramaswamy, R., Reed, J., Livesley, N., Boguslavsky, V., Garcia-Elorrio, E., Sax, S., Houleymata D., Kimble L., Parry, G. (2018). Unpacking the black box of improvement. International Journal for Quality in Health Care, 30(suppl_1), 15–19. https://doi.org/10.1093/intqhc/mzy009]. Approach In this futuristic proposal, we put forward our vision of how artificial intelligence technologies can unpack and support the internal collections of cognitive knowledge, context, task goals, and on-field experiences that influence sport officiating development. Findings In what follows, we define what we mean by artificial intelligence and machine learning technologies, briefly highlighting their histories in sport analytics contexts. We outline how education is a promising field for the adoption of artificial intelligence/machine learning technologies and conclude by providing a theoretical case study scenario that describes a potential platform through which perspectives of environment, task and performer expertise might be developed for amateur and elite sport officials. Practical implications Using advanced AI technologies as the basis through which to examine on-field data provides tremendous potential to theoretically tackle the idiosyncrasies of officiating development in a range of sports as it can close the gap between a descriptive analysis (i.e. understanding the interactions undertaken by officials in the presence of others), and a more prescriptive one (i.e. suggesting the actions such officials should have executed). Research contribution We put forward that artificial intelligence technologies can offer sport organisations sophisticated, constructively based help with opening the “black box” of learning related to sport officiating development. By using ecological dynamics as the fundamental framework through which to filter the data collected, statistical information and qualitative ecological outcomes can be linked and managed into understandable and stable development content [Liu, A., Mahapatra, R. P., & Mayuri, A. V. R. (2021). Hybrid design for sports data visualization using AI and big data analytics. Complex & Intelligent Systems, https://doi.org/10.1007/s40747-021-00557-w], that strongly benefits the online development of amateur sport officials.

Artificial intelligence (AI) is increasingly shaping enterprise operations, yet evidence remains limited on how firms in Poland acquire AI capabilities, which AI technologies they adopt, and where they apply them functionally. This study examines (i) AI acquisition modes, (ii) types of AI technologies used, and (iii) application areas and assesses how these patterns differ by firm characteristics (size, operating scope, ownership structure, sector, and market experience). Data were collected through a questionnaire survey of 118 enterprise representatives in Poland (February–July 2025), of whom 49.15% reported using AI and were included in the detailed usage analysis. Using Ward’s minimum variance method, we identify systematic associations between firm characteristics and AI acquisition choices, technology types, and application domains. The findings suggest that larger, internationally active, and foreign-owned firms tend to combine multiple acquisition sources and report more advanced AI technologies and broader application portfolios, while smaller firms more often rely on ready-made solutions and narrower use cases. This study concludes with practical recommendations for managers regarding staged AI adoption, capability building, and governance.

With the accelerated advancement of educational digitalization, the intelligent transformation of academic management has gradually evolved into an inexorable trend in the progression of the education field. This paper delves into the significance of applying the integration of Big Data and Artificial Intelligence (AI) technologies in student academic monitoring and early warning systems. It elaborates on the entire technical architecture, from data collection and processing to model construction and application. Through multi-source data acquisition, efficient data processing, precise model building, and intuitive warning applications, the academic monitoring and early warning system integrating Big Data and AI technologies enables dynamic monitoring of students' academic status and timely alerts, providing robust support for the intelligent transformation of educational management. The innovation of this system lies in its comprehensive utilization of multi-source data and advanced AI technologies, which not only accurately predicts academic risks but also offers personalized intervention suggestions. It provides innovative ideas and practical guidance for the intelligent transformation of educational management, propelling educational management to new heights of intelligence.

Artificial Intelligence Advanced Technology IoT, 5G Robots, and Drones to Combat COVID-19

Funding for artificial intelligence (AI) technology research and development has ascended to a strategic priority in major global countries' scientific and technological agendas. This study explores and compares the multi-dimensional characteristics of AI-related funding projects in China and the United States (US), the global leaders in AI technology development. Specifically, it examines the characteristics of funding entities, the variety of project types, and the organization of topics across various stages of AI technology development, all contextualized within the framework of the technology lifecycle. Our results reveal that the US began funding AI technology projects earlier, and China followed a "catch-up and surpass" path. In terms of the funding agencies, while NSF, NIH, and DoD played leading roles in the US, China's main funding agencies evolved from an NSFC-centered pattern to a multi-agency balanced layout. Regarding the funding types, the US has long emphasized funding research at the applied level, which may be related to its solid technological foundation for AI development, whereas China has primarily funded research at the basic level, gradually increasing support for applied-level research as technologies mature. As for funding topics, the US funding prioritized parallel exploration of multiple topics, emphasizing interdisciplinary technological exploration and swiftly responding to technological breakthroughs to develop diverse application pathways. In contrast, China placed more emphasis on topics related to the fundamental theories and principles of machine learning and its core algorithms, reflecting a distinct evolutionary trajectory guided by national strategic priorities. These findings contribute to a deeper understanding of the differentiated developmental stages and strategic orientations of AI technologies between China and the US, serving as a reference for guiding the planning of future funding allocations.

Advancements in artificial intelligence (AI) technologies have begun to influence almost all aspects of life, including romantic relationships. Despite many companies and other entities now utilizing AI technology to produce products and media that may influence romantic relationship engagement and behavior, few studies to date have attempted to document or describe the ways that individuals engage with multiple AI technologies for romantic or sexual purposes in large national samples. Using a large quota national sample taken from the United States of 2,969 adults, in this exploratory study we sought to examine engagement with AI technologies, specifically the use of image generation of men and women on social media, chat technologies meant to simulate romantic or sexual partners, and AI generated pornography. Results suggested that engagement with AI technologies is relatively high, especially among young adults in their 20’s, suggesting that these emerging technologies represent an element of modern sexual behavior and romantic relationships that warrant further attention. A sizable minority of young adults reported using AI technologies romantically or sexually, including almost one in four young adults engaging with AI chat technologies to replicate romantic interactions. Regression results suggested that various demographic factors predict engagement with AI technologies and that AI technology use was associated with negative individual well-being. These initial descriptive results suggest the need for further research on this emerging behavioral phenomenon as an important component of modern romantic and sexual interactions.

This article considers an urgent problem of personalization of learning in the context of the development of smart cities, where educational processes should be not only effective, but also adapted to the individual needs of students. The authors present an innovative adaptive educational platform developed using advanced artificial intelligence (AI) and machine learning technologies. The platform analyzes students real-time data, including their academic achievements, preferences and learning styles, to offer personalized learning trajectories. The main goal of the research is to demonstrate how AI technologies can be integrated into smart city education systems to improve learning and inclusion. The platform uses machine learning algorithms to automatically customize learning materials, allowing for individualized learning experiences for each student. This not only improves the learning process, but also reduces the workload of teachers, freeing up their time for more creative and strategic tasks. An important aspect of the study is the integration of the platform into the infrastructure of smart cities. The authors emphasize that such technologies not only improve educational processes, but also contribute to the sustainable development of the urban environment. The platform helps optimize the use of educational resources, reduce the cost of education and provide equal access to quality education for all individuals, regardless of their social status or physical capabilities. In conclusion, the article proposes new approaches to the organization of educational processes in smart cities based on AI technologies. The presented platform shows potential for scaling and implementation in various educational institutions, which makes it an important tool for shaping the future of education. The authors also discuss possible challenges associated with the implementation of such technologies, including data privacy and ethical issues in the use of AI, and suggest ways to address them.

Ensuring a comfortable and safe environment for the elderly is becoming an increasingly relevant task in the context of global population aging. Architectural design for elderly care homes in China is a critical aspect that affects the quality of their lives, health, safety, and happiness. Due to the specific needs of the elderly, designing such homes requires a higher level of professionalism and consideration of numerous factors, distinguishing it from traditional architectural design. Existing methods often fail to fully meet these specific needs, necessitating the search for new approaches and technological solutions. The key challenge lies in finding effective solutions to create an architectural environment that meets the complex and diverse needs of the elderly, ensuring their safety, comfort, and high quality of life. The integration of artificial intelligence (AI) technologies has the potential to significantly enhance the functionality and adaptability of such environments, requiring thorough research and practical implementation. This study aims to explore and analyze the auxiliary application of artificial intelligence technology in the architectural design of elderly care homes in China. The work focuses on identifying opportunities to improve the functionality and adaptability of living spaces for the elderly through the integration of AI technologies. Specifically, the study examines the impact of AI on enhancing safety, personalizing the living experience, managing health, and creating a comfortable psychological environment for the residents of elderly care homes. The implementation of artificial intelligence technologies in the architectural design of environments for elderly care homes can significantly increase their intelligence and better meet the needs of elderly individuals. Such environments, developed with the help of AI, can greatly enhance the efficiency and effectiveness of services provided in elderly care homes, making the care process more humane and refined. In the future, with the further advancement of AI technologies, the design of architectural environments for elderly care homes will become even more intelligent and sophisticated, providing safer, more comfortable, and more convenient living conditions for the elderly.