Unmanned Aerial Vehicle (UAV) formation control represents a form of collaborative crowd behavior. This paper proposes a distributed event-triggered tracking control method for multi-UAV formation operating in complex environments. To address the challenges of an unknown leader motion state, bounded external disturbances, and obstacle avoidance, a composite control architecture is constructed that synchronously integrates state estimation, cooperative control, and communication optimization. Firstly, an Extended State Observer (ESO) is designed to estimate the leader's unknown time-varying velocity and control input, while a Sliding Mode Disturbance Observer (SMDO) achieves precise compensation for unknown external disturbances. Secondly, a distributed cooperative controller without predefined geometric configurations is proposed. This controller integrates tracking error feedback, neighbor velocity coordination, and potential field functions to achieve self-organized formation generation and unified handling of collision/obstacle avoidance. Subsequently, a state-error-threshold-based event-triggered mechanism is established, significantly reducing communication load. Lyapunov theory proves the global asymptotic stability of the system and excludes Zeno behavior. Simulations demonstrate that the proposed control strategy enables the UAV formation to cooperatively track a maneuvering target both with and without obstacles, while effectively reducing inter-agent communication frequency. Future work will extend the method to scenarios under cyber-attacks and further validate it through physical UAV experiments.
The determination of coal's geographical origin is pivotal for assessing coal quality and improving import and export inspections. Conventional methods for determining coal's geographical origin, which primarily involve time-intensive, labor-intensive, and costly chemical experiments, necessitate optimization for greater efficiency. Near-Infrared Spectroscopy (NIRS) offers a viable solution with its accuracy, speed, and non-destructive nature in detecting chemical compositions, which has achieved success in various research fields. This study introduces a non-destructive technique that integrates NIRS with deep learning for identifying the geographical origin of coal, addressing the limitations of conventional approaches. In tackling the issue of abnormal Near-Infrared (NIR) data, the study employs a data cleaning method based on Euclidean distance to identify and eliminate outliers. Standard normal variate transformation is utilized to extract features from the NIR data in the experiment. Furthermore, we introduce an improved U-Net3+ model by integrating residual modules into the encoder and incorporating the concurrent spatial and channel squeeze & excitation attention block to enrich the model's feature capturing ability. Experimental results demonstrate that the optimized U-Net3+ model attains an impressive 97.45% identification accuracy, outperforming other algorithms. The proposed method stands as a promising solution in the realm of coal quality assessment and inspection.
This paper discusses the technological development from Web 1.0 to Web 3.0, focusing on their corresponding economic models. The study begins by analyzing the Web 1.0 portal economy, followed by an in-depth exploration of the rise of the Web 2.0 platform economy and its associated challenges, including the lemon market, platform monopolies, price discrimination, and algorithmic asymmetries. To address those issues, this study elaborates on the Web 3.0 token economy and emphasizes the crucial role of decentralized technologies like blockchain in bringing new production factors and relationships. This inspires the proposal of the Decentralized Economy (DeEco), a novel user-autonomous economic model that integrates advanced Artificial Intelligence (AI) technologies with blockchain. Furthermore, the key techniques for formulating DeEco are analyzed, including Decentralized Autonomous Organizations and Operations (DAOs), Decentralized Value Systems (DVSs), Decentralized Physical Infrastructure Networks (DePIN) and digital humans. This study not only offers a co-evolutionary perspective of web technologies and economic forms but also introduces an innovative economic paradigm to support open, diverse and intelligent societies.
In the context of rapid globalization and the swift advancement of information technology, satellite internet has emerged as a critical infrastructure for global communication services, drawing unprecedented attention to its security challenges. Traditional security measures are increasingly inadequate, struggling to meet the high standards of security, transparency, and reliability required by modern communication networks. Blockchain technology, with its unique attributes such as decentralization, immutability, and smart contracts, presents a promising solution to these security concerns. This paper explores blockchain-based intrinsic security mechanisms for satellite internet, with a particular emphasis on key components such as trusted accounts, data encryption, and access control. The objective is to propose a robust set of strategies and provide technical support for the security management of satellite internet, ultimately aiming to enhance the security and reliability of satellite communication networks on a global scale.
There has been a growing focus on employing intelligent game technology within the realms of game AI and military combat simulation in recent times. This article offers a comprehensive overview of the development of intelligent game theory technology, delving into notable research milestones in game AI and military combat simulation. It examines the technical mechanisms employed. Furthermore, it discusses the challenges associated with integrating intelligent game theory in military contexts and summarizes some future developments to overcome these challenges. This article aims to provide a guidance for the adaptation of intelligent game theory technology from game AI to military combat simulation.
With the increasing complexity of collaboration among various social entities and user demands, the factors affecting the stable development of the data service market are also growing. These factors include the widespread dissemination of information enhancing subjective consciousness, the continuous improvement in intelligence, and the complexification of structural relationships. To achieve effective governance and regulation of the data service market, it is crucial to conduct simulation experiments before making regulatory decisions. However, current research and analysis of the data service market primarily focus on data-level performance, proving inadequate when it comes to measurement and analysis of multiple heterogeneous entities and the integration of various social elements within the data service market. Based on this, this paper innovatively proposes a data service market measurement and network analysis method based on heterogeneous multi-agent modeling. By introducing the service ecosystem theory, we clarify the participants and external factors of the data service market and conduct utility measurements for three-level entities based on value creation. Furthermore, an analytical methodology is devised to precisely assess the influence of heterogeneous networks on utility. Finally, the paper verifies the effectiveness of the proposed method through the analysis of experimental results.
Smart education plays a crucial role in contemporary teaching and learning processes, driving not only the digital transformation of education but also greatly enhancing its personalization, equity, and efficiency. Despite facing numerous challenges, Artificial Intelligence Generated Content (AIGC) presents numerous new opportunities for smart education due to its powerful auto-generation capabilities. This paper comprehensively outlines the challenges of AIGC in smart education, covering several aspects such as auto-generation of teaching content, generation and navigation of learning paths, creation of learner profiles and personalized content, intelligent Question and Answering (Q&A), precise assessment, and smart education platforms. It reviews the research and progress made by scholars both domestically and internationally in response to these challenges. The main contributions of this study include proposing a novel multi-layered research framework, which provides a clear direction for future work on AIGC in smart education, and offering fresh perspectives for innovation and development in smart education.
The emergence of AI Generated Content marks a revolutionary stage in the evolution of collective intelligence. By enabling machines to autonomously generate text, images, code, and knowledge, AI Generated Content redefines how information is produced, distributed, and evaluated within the socio-technical ecosystem. With the interaction of crowd science paradigms, the convergence of large scale generative models and knowledge-augmented reasoning is reshaping the frontiers of creativity, governance, and trust in the digital age[1]
Crowd science is a science that uses the collective intelligence of information, physics, and society as a whole in the context of large-scale online interconnection to improve social and economic efficiency. Artificial Intelligence (AI) has become an increasingly influential force in the field of music, reshaping traditional paradigms of creativity, production, performance, and consumption. This review examines the current applications of AI across key musical domains, including composition, audio production, performance, analysis, recommendation, and interdisciplinary integration. The paper highlights how AI techniques-ranging from symbolic rule-based systems to advanced deep learning architectures-have enabled novel capabilities such as automated melody and lyric generation, live collaboration between humans and AI agents in musical performance, sentiment-aware music analysis, and personalized recommendation systems. Additionally, the review explores the transformative effects of AI on the structure and ecology of the music industry, as well as its expanding role in education and therapy. Despite these advances, unresolved challenges remain in areas such as interpretability, ethical accountability, and the definition of creativity. The paper concludes by outlining future research directions, emphasizing the importance of human-AI collaboration, standardization of methodologies, and the development of adaptive AI systems for live musical contexts. By synthesizing recent progress and identifying open questions, this review provides a foundation for understanding the evolving relationship between AI and music.
This paper discusses the latest progress of generative artificial intelligence in the field of smart finance. With the rapid development of financial technology, generative artificial intelligence has become one of the key technologies to promote the innovation of smart finance. By analyzing the specific applications of generative artificial intelligence in a number of smart financial application scenarios, such as intelligent risk control, credit approval, intelligent investment advice, financial product innovation, and intelligent customer service, this paper reveals its significant advantages in terms of improving the efficiency of financial services, optimizing risk management, enhancing the user experience, and promoting the innovation of financial products. At the same time, this paper also points out the challenges and limitations faced in the application of generative artificial intelligence, such as data quality, model interpretability, technology update speed, and security and privacy, and puts forward corresponding solution strategies. Finally, this paper looks forward to the future development trend of generative artificial intelligence in the field of intelligent finance, and believes that it will continue to promote the innovation and development of the financial industry.
Large Language Models (LLMs) are increasingly employed in knowledge-intensive tasks but often struggle to effectively apply infused knowledge due to textual-structure mismatches between the infusion and reasoning phases. To address this issue, we propose a prompt-based unification strategy that directly learns from factual triples in knowledge graphs while preserving structural consistency across both phases. This unified design enables seamless transfer of factual knowledge to downstream reasoning tasks without requiring architectural modifications. Extensive experiments on two Knowledge Graph Question Answering (KGQA) benchmarks, WebQSP and MetaQA, demonstrate that our approach consistently outperforms strong baselines. Further ablation and robustness analyses verify that structural unification is the key factor driving the improvements, while its compatibility with adapter-tuning and LoRA highlights practical applicability under parameter-efficient fine-tuning settings. Overall, our results suggest that enforcing textual structural consistency provides a simple yet effective principle for reliable knowledge infusion in LLMs, with broad potential across diverse knowledge-intensive domains.
Generative Artificial Intelligence (AI) technology, a significant branch within the AI field, holds immense application potential and exerts profound influence. This paper reviews the origins and evolution of generative AI technology, analyzes the development and transformation of core techniques, examines the implementation of application scenarios, and explores the challenges and opportunities it faces. It provides insights into future development by analyzing the current Transformer-based technical systems and four emerging trends in technological transformation. The development of generative AI technology faces challenges in data resources and privacy protection, among other aspects. The future development of generative artificial intelligence requires close attention to building a green, healthy, and safe artificial intelligence ecosystem, closely linking the development needs of various fields of society, and effectively meeting human well-being.
Chain-of-thought prompting has attracted much attention in Artificial Intelligence (AI). Large Language Models (LLMs) can be instructed to imitate human thought processes step by step, and they have demonstrated surprising reasoning capabilities. However, when faced with complex reasoning tasks, LLMs perform poorly and often produce inaccurate results. This may be due to insufficient knowledge and poor real-time performance, resulting in incorrect inference chains. Inspired by knowledge augmented deep learning and retrieval augmented generation, a more feasible approach is knowledge guided chain-of-thought prompting generation, which introduces a large amount of knowledge, including common, logical, and factual information, into the process of generating a chain of reasoning. Although a large amount of research has been conducted in these areas, there is still a gap in the survey literature on knowledge-guided chain-of-thought prompt generation. In this survey, we introduce the concept of knowledge-driven chain-of-thought generation and discuss how knowledge plays an important role in the process of chain-of-thought generation and enhancement, both in terms of knowledge sources and knowledge use. Then, evaluation guidelines for chain-of-thought reasoning are sorted out. Next, a benchmark task and a public dataset for chain-of-thought prompting are presented. Finally, we conducted a comprehensive examination of the current opportunities and challenges and formulated a series of recommendations for future research directions. This survey may be of assistance to researchers in the understanding of the latest research developments in these areas.
In the digital economy era, the rapid expansion of internet platforms has resulted in highly concentrated market structures in online markets, thereby eliciting intensified scrutiny from regulatory authorities. In this paper, we aim to explore the key factors that shape the boundaries of platform firms by extending the transaction cost theory. We first define the boundaries of platform enterprises and provide specific measurement methods for their boundaries. By analyzing the distinctions between platform enterprises and manufacturing firms, we adapt the classical transaction cost theory to identify the key determinants of platform enterprise boundaries across three dimensions: data assets and digital technology, network effects, and organizational models. Finally, we offer policy recommendations to foster the healthy development of the platform economy based on our theoretical analysis. Our study highlights the critical role of platform boundary decisions within the framework of crowd science, as they fundamentally shape how diverse smart entities are coordinated on the platform to impact resource allocation efficiency and market stability.
With the vigorous development of the digital economy and the deepening of enterprise digital transformation in China, cultivating digital talent has become a focus of academic research. This study examines the literature related to digital talent from 2015 to 2025 in the China National Knowledge Infrastructure (CNKI) journal database and employs a bibliometric analysis approach to investigate the research focus and trends in digital talent development. The analysis covers authors, institutions, keywords, research hotspots, and trend evolution. The findings reveal the following: First, with the advancement of digital transformation policies by governments and enterprises, the number of publications related to digital talent has significantly increased in recent years. Second, a stable core group of authors has yet to emerge domestically, and collaborations between research institutions are mostly confined to specific regions with limited collaboration. Third, digital talent research mainly focuses on the digital economy and digitalization, enterprise digitalization and digital transformation, and the cultivation of digital talent. Finally, on the basis of the analysis results of the knowledge graph and the national situation, implementation strategies for digital talent cultivation are proposed. These strategies inherently align with crowd science principles, where human-machine-object intelligence interactions drive collective evolution, collaborative innovation, and decentralized decision-making to enhance socio-economic efficacy.
The digital economy has become a transformative force, fundamentally reshaping global economic structures, business models, and governance frameworks. At its core lies crowd science and engineering (CSE), which leverages the interconnectedness of diverse smart entities—composed of individuals, enterprises, and governmental agencies—to enhance the stability of the economic system and the efficiency of resource allocation[1]. This synergy fosters smarter, more adaptive systems, enabling innovation and resilience across industries while optimizing processes for sustainable growth.
Competition among platform enterprises is a contest of value creation behaviors by multiple groups and is one of the important manifestations of the crowd science in the field of platform economy. However,the frequent unfair competition among platform enterprises has hindered the healthy and rapid development of the platform economy. Clarifying the source of unfair competition is an important prerequisite to regulate this behavior, and the unfair competition behavior between platform enterprises has a complex generation path. In order to clarify this path, this paper explores the source of unfair competition in the platform economy from the perspective of configuration by qualitative comparative analysis. The results of this paper show that unfair competition among platform enterprises is the result of five factors, such as business difference, enterprise scale difference, innovation ability difference, profitability difference, and regulatory environment. These factors combine with each other to form four configuration paths of unfair competition among platform enterprises. Finally, from the perspective of reducing unfair competition in the industry, this paper puts forward specific ideas to standardize the development of platform economy, including respecting the law of platform development, reducing excessive market intervention, and trying to construct the proactive regulatory model with expected nature.
The application of information technology in tax enforcement represents a paradigmatic case of crowd science within government administration, facilitating the adoption of sound governance principles and the achievement of policy objectives through the enhanced deployment of information technologies. This study empirically examines the impact of advanced information technology in tax enforcement, a critical component of government modernization, on the quality of financial reporting. To establish causality, we leverage the staggered implementation of Stage Three of the Golden Tax Project (GTP-3) as a quasi-natural experiment, which integrates modern information technologies into tax enforcement processes. The difference-in-differences estimation results indicate that GTP-3 significantly curtails corporate earnings manipulation. This effect is particularly pronounced in firms taxed by local taxation bureaus, state-owned enterprises, and those with stronger political connections. Moreover, GTP-3 effectively mitigates the crash risk of stock prices for firms subject to its provisions. Overall, our study contributes to the understanding of how crowd science, such as those deployed in tax enforcement, can improve the information quality of the capital market and intersect with broader societal dynamics.
Human-Machine Collaboration (HMC) is a pivotal manifestation of collective intelligence in the digital age, where the synergistic interaction of humans, machines, and physical systems drives socio-economic evolution. This paper redefines HMC through the lens of co-evolution of human and machine capabilities and distributed decision-making, systematically analyzing its development history, collaboration models, and transformative impact on productivity, innovation, and labor markets. This paper believes that human-machine collaboration is the collaborative participation of people and machines in solving problems. It introduces various models of human-machine collaboration from the perspective of automation and autonomy, and discusses the criteria for selecting appropriate models. In terms of economic and social impact, this article first summarizes the existing quantitative measurement methods at the national, regional, and enterprise levels, and discusses the economic impact and impact path of human-machine collaboration from the micro, market, and macro levels of individuals and enterprises. Finally, this paper proposes future research directions, including the improvement of quantitative data of human-machine collaboration, the clarification of the issue of legal responsibility, the formulation of management-level strategies, and indepth research in the fields of medical care, aviation, banking, etc. This paper aims to deepen the understanding of human-machine collaboration and provide reference for future research.
In the context of dual-circulation development paradigm and high-quality economic growth in full swing, it is crucial to adjust the structure of fiscal revenue and expenditure to drive the crowd intelligence-driven digital economy's development. From the perspectives of fiscal revenue and expenditure structure and market, this study examines the impact of fiscal and taxation policies on the digital economy in China based on the data from 2007 to 2020 (excluding 2021 and 2022 due to COVID-19). The results show that the digital economy's development is positively correlated with several factors, including the proportion of science and technology, financial supervision, energy conservation, environmental protection expenditure, and income tax revenue. Conversely, general public service expenditure, turnover tax, resource tax, and administrative fees have an unfavorable impact on the digital economy. Furthermore, mainly via their impact on the digital economy, general public services, financial regulatory expenditure, and turnover tax revenues indirectly affect the dual-circulation development paradigm. Among the different markets, the consumer market has the most significant impact. Our research provides policy implications for the government in China. In summary, the Chinese government should reduce the scale of general public service expenditure and turnover tax, increase financial supervision, environmental protection and energy conservation, as well as science and technology expenditure. Additionally, regional differences in fiscal revenue and expenditure structure should be considered, and the inter-regional policy intensity should be adjusted based on general macro measures.
