As a discipline with nearly 80 years of development, artificial intelligence (AI) has continuously expanded its boundaries and enriched its research directions. However, there remain controversies in the academic community regarding its disciplinary attributes, research core, and other fundamental issues. This paper conducted a systematic analysis of the disciplinary attributes of AI and examines its disciplinary positioning from multiple dimensions. It analyzed AI's four core goals—thinking like a human, acting like a human, thinking rationally, and acting rationally—and pointed out that AI is a methodology rather than merely a simulation of humans, serving as a new paradigm for solving complex problems. Moreover, AI has transcended the scope of traditional computer science subdisciplines. The paper emphasized that AI is a "meta−level exploration method" for science and technology, and it is currently still in the stage of "pre−paradigm science".
In recent years, light−driven micro/nanomotors have emerged as a novel type of miniature power device, leveraging their adjustable energy input, reversible switching state and remote operability. They have demonstrated broad application prospects in water environment treatment, biomedical fields, and biosensing. This paper aims to systematically summarize the research progress of light−driven micro/nanomotors, elucidate their propulsion mechanisms, and highlight typical applications driven by different types of light (ultraviolet, visible, and near−infrared light). Despite the substantial potential of light−driven micro/nanomotors in various domains, they still face numerous technical challenges, such as low propulsion efficiency, limited motion control precision, and issues with the biocompatibility of materials. Future research directions may include the integration of multiple driving methods, enhancement of light energy conversion efficiency, and development of biocompatible materials. These efforts will promote the performance improvement and application expansion of light−driven micro/nanomotors, bringing revolutionary changes to environmental protection and biomedical fields. By overcoming current technical obstacles, light−driven micro/nanomotors are expected to play a more significant role in future scientific research and practical applications.
Autonomous intelligent unmanned systems operating in real−world open environments—characterized by dynamic complexity, multi−agent coupling, incomplete information, and strong social constraints—face critical challenges such as insufficient compliance modeling, limited social risk perception, complex collaborative conflicts, and delayed abnormal response. To address these issues, this paper proposes an Embodied Social Perception Intelligence Framework, which integrates embodied perception (including proprioceptive, internal, exteroceptive, interactive, and intention perception) with social radar, and introduces Agentic AI as a top−level decision−making and control mechanism to achieve multi−level and autonomous cognitive decision−making. The framework adopts a five−layer architecture—perception, reasoning, execution, feedback, and meta−control—establishing a dynamic closed loop from multimodal perception to compliant behavior generation. By fusing physical and social environmental information, the proposed framework significantly enhances the task adaptability, collective coordination efficiency, and compliance reliability of autonomous intelligent unmanned systems in complex and uncertain scenarios such as urban governance, emergency rescue, and social security. This work provides a new technical pathway toward trustworthy, explainable, and sustainable autonomous intelligent systems.
Embodied intelligence represents a new stage in the evolution of artificial intelligence, marking a transition from "perception−cognition" to an integrated paradigm of "perception−cognition−action." The Vision−Language−Action (VLA) model provides a critical technological pathway for enabling autonomous agent operation in the real world by unifying visual perception, language understanding, and action generation. This paper systematically reviews the development trajectory and representative achievements of VLA technologies, and summarizes their architectural paradigm, which includes multi−modal perception, semantic fusion mechanisms, reinforcement and imitation learning, world models, and hierarchical action output. By considering application scenarios such as autonomous driving, human–computer interaction, and industrial equipment, we further analyze the core challenges faced by VLA development, including the scarcity of data resources, limited generalization and transferability, insufficient interpretability, and increasing computational demands, and we outline the future development trends.
With the convergence and innovation of emerging information technologies, Digital Twin (DT) technology has become a key enabler for digital transformation and the evolution of intelligent systems. It has been widely applied and received significant attention in fields such as industrial manufacturing, smart cities, and intelligent transportation. However, existing research on traditional DT technologies has predominantly focused on the modeling and analysis of physical entities ("objects"), with limited systematic integration of "human" and "environmental" factors. The lack of exploration into human−environment interactions makes it difficult for current digital twin frameworks to meet the advanced demands of complex intelligent systems for multi−level, comprehensive interaction capabilities. In view of this, this paper innovatively introduces the "object−human−environment" interactive vision and comprehensively and systematically analyzes the research frontiers and progress of digital twin technology from the three core dimensions of intelligent physical entity (object), intelligent individual (human), and virtual−real fusion environment (environment). Firstly, the paper analyzes the traditional digital twin technology system with "object" as the core and focuses on its theoretical origin, framework, and application. Secondly, it discusses the definition, development context, national policies, and core technologies of digital people driven by AI. Finally, expand the vision to the dimension of "environment" and explore the application practice of "environment" in multiple scenes of the meta−universe, deeply discuss the deep integration and interaction mechanism of the three elements of "object", "human" and "environment", reveal how the three interact and promote each other, and provide support for the construction of the meta−universe. Furthermore, this study discusses the current research challenges and future development trends of digital twin technology from the perspective of "Object−Human−Environment" interaction and proposes three key research directions: (1) developing an intelligent, multi−layered data fusion framework; (2) exploring AIGC−enabled intelligent virtual−real mapping and native virtual evolution; and (3) constructing novel virtual economy architectures and intelligent governance systems. The research outcomes provide both theoretical foundations and practical insights for building next−generation digital twin systems characterized by multi−agent collaborative perception, multimodal intelligent interaction, and closed−loop integration of virtual and real environments.
The intelligence of unmanned vessel systems is undergoing a profound transformation from remote control to embodied autonomous forms, with the core being the realization of advanced intelligent behavior through multimodal perception, environmental interaction, and closed−loop learning. This paper systematically reviews the key advances of embodied intelligence in unmanned vessels, highlighting that semantic control loops, digital twin validation, and evaluation systems are moving from methodological exploration to engineering integration, and have already begun to provide preliminary application support in port and inland waterway scenarios. However, current technologies still face bottlenecks in perception stability, rule interpretability, and deployment resources. Therefore, this paper recommends focusing on breakthroughs in strengthening autonomous closed−loop intelligence systems, establishing standard and trustworthy validation environments, and promoting lightweight and collaborative deployment to enhance system reliability, compliance, and scalability, providing support for the development of intelligent ship technology and the implementation of our marine strategy.
Humanoid robots, benefiting from their human−like morphology and locomotion capability, are regarded as promising platforms for future service, rescue, and industrial applications; however, achieving stable and reliable walking in unstructured environments remains highly challenging. This paper provides a comprehensive review of recent advances in humanoid locomotion planning and control, with a focus on gait planning, trajectory generation, whole−body control, and learning−driven approaches. We summarize the core concepts and implementation frameworks of representative methods, compare their applicable scenarios, strengths, and limitations, and present a hierarchical categorization of existing research. Moreover, this work discusses key technical bottlenecks that hinder environmental adaptability and dynamic stability. Finally, we outline future research directions, including multimodal perception integration, co−optimization of learning and control, whole−body motion skill learning, and safety assurance, and offer suggestions toward standardization and large−scale deployment.
With the rapid development of high−speed transportation networks and the continuous advancement of sixth−generation (6G) mobile communication technologies, the demand for reliable wireless connectivity in high−mobility scenarios has grown significantly. However, the high Doppler shift induced by user mobility leads to rapidly time−varying channels, significantly degrading communication reliability and transmission quality. To address this challenge, this paper proposes an integrated sensing and communication (ISAC) approach, where the base station simultaneously communicates with high−speed users and receives echo signals to predict the Doppler shift. The predicted Doppler shift is then pre−compensated to reduce signal processing complexity at the receiver and improve communication quality. Specifically, this paper introduces an intelligent online Doppler shift prediction method based on long short−term memory (LSTM) networks for orthogonal frequency division multiplexing (OFDM) systems in high−speed mobility scenarios. In this method, the base station estimates the current Doppler shift based on received echo signals and e
Swallowing disorders have a high incidence among patients with cognitive dysfunction and significantly impact their quality of life. The Swallowing Quality of Life questionnaire (SWAL−QOL), a widely used tool for assessing swallowing function and related quality of life, has previously excluded individuals with Mild to moderate cognitive dysfunction from its applicable population in prior studies. This study is the first to systematically validate the applicability of the Chinese version of SWAL−QOL in patients with cognitive dysfunction. The research included 122 patients with Mild to moderate cognitive dysfunction, and explored the inter−rater reliability, internal consistency reliability, content validity and construct validity of the results among the participants. The results demonstrated that the inter−rater reliability coefficients for each dimension (except for the food choice dimension) were all above 0.8, and the overall internal consistency Cronbach's α value reached 0.971. The construct validity analysis showed that the scale was significantly correlated with swallowing function assessment tools such as the Kubota Water Swallowing Test and the EAT−10 scale. Three common factors were extracted: psychosocial function, nutritional intake patterns, and physiological function regulation (cumulative variance contribution rate of 72.742%), revealing the multidimensional impact of dysphagia on patients' psychological, physiological, and social functions. Research indicates that the Chinese version of the SWAL−QOL scale can serve as a reliable assessment tool for swallowing−related quality of life in patients with Mild to moderate cognitive dysfunction, providing theoretical support for the development of individualized intervention strategies and the enhancement of patients' quality of life.
As a strategic scientific and technological biomedicine field in the 21st century, nanobiomaterials are profoundly reshaping disease diagnosis and treatment paradigms while driving transformative shifts in global scientific competition. The developmental status and strategic challenges of China's nanobiomaterials sector in terms of industry-university-institute are systematically examined. By comparing competitive dynamics across key dimensions, this study highlights China's leading-edge advantages in specific subfields such as biomedical coatings, upconversion imaging probes, and nanozymes, while identifying current structural contradictions such as the disconnection between basic research and application, barriers to interdisciplinary collaboration, and the imbalance between regulation and industry adaptation. Within the framework of the new nationwide system, by drawing on the technological leapfrog experiences of mature industries, we deduce the possible three-phase evolutionary trajectory of China's nanobiomaterials field. From process breakthroughs in the technical development phase to standard dominance in the industrial expansion phase, and ultimately to transformative paradigm innovation in the global leadership phase.This study suggests that China can establish a trinity framework of "technology−industry−governance" solutions based on nanobiomaterials in the bioeconomy era through a clinical demand−driven R&D model, a trillion−level industrial fund strategy, and international standard breakthroughs, thereby providing a practical blueprint for achieving high−level scientific and technological self−reliance.
Li Zhensheng is a renowned wheat geneticist and breeder in China, an expert in agricultural development strategy, and a member of the Chinese Academy of Sciences. This article systematically reviews Li Zhensheng's academic development, traces his scholarly contributions and scientific ethos, and endeavours to chart the path of agricultural science and technology self-reliance he forged as a strategic scientist. Li Zhensheng's scientific career not only encapsulates the Chinese nation's journey from "building the nation based on agriculture" to "empowering agriculture through science and technology," but also stands as a vivid embodiment of the spirit of scientists. Having experienced famine in his youth, he resolved to ensure the Chinese people had enough to eat, and transformed the traditional concept that "agriculture is the bedrock of the nation" into scientific research motivation. By applying chromosome engineering breeding techniques innovatively, he overcame the global challenge of distant hybridization, shortening the breeding cycle from decades to three years, which laid the foundation for China's self-reliance in seed science and technology. He dedicated himself to leading young scientists in "conducting research in the fields," making the "Coastal Grass Belt Initiative" which was undertaken by his team became a case in point for saline-alkali land remediation. Carrying forward and promoting the noble spirit of the older generation of scientists, exemplified by Li Zhensheng, could inspire a new generation of scientific and technological workers to persevere in their endeavours and strive for excellence, thereby injecting powerful spiritual momentum into the development of innovation-oriented country.
