Vision-Radar Fusion for UAV Perception in Low-Altitude Emergency Rescue: A Survey
Unmanned Aerial Vehicles (UAVs) have emerged as critical tools in emergency rescue operations, particularly in urban low-altitude complex environments characterized by dynamic obstacles, adverse weather, and signal interference. However, traditional single-modality perception systems, reliant on either vision or radar, struggle with limitations such as motion blur, low light conditions, and occlusion, compromising detection accuracy and navigation safety. This survey provides a comprehensive review of vision-radar fusion perception systems tailored for UAVs in such scenarios. We first examine advancements in image quality enhancement techniques, including diffusion models and super-resolution methods like SRGAN and ESRGAN , which address degradation in low-altitude imagery. Next, we explore multi-target detection and recognition via multi-modal fusion frameworks, such as Faster R-CNN-based approaches and cross-view spatial fusion. We then discuss robust multi-target tracking and trajectory prediction, highlighting graph neural networks (GNNs) and Transformer-based models like TrackFormer and MOTR . Finally, we cover low-altitude environment assessment and autonomous path planning, leveraging graph theory, 3D risk mapping, and deep reinforcement learning (e.g., MADDPG). By synthesizing global research trends, challenges like spatio-temporal alignment and real-time processing, and future directions toward end-to-end multi-modal integration, this review underscores the potential of fusion systems to enhance UAV resilience and efficiency in rescue missions. Our analysis reveals a clear trajectory toward AI-driven, adaptive perception, with implications for urban safety and disaster response.
The State of Enterprise-Level AI Commercialization in China: Insights from 2025 Trends and Global Comparisons
The rapid advancement of artificial intelligence (AI) technologies has positioned China as a global frontrunner in AI development, yet the commercialization of enterprise-level AI remains a critical challenge amid evolving policy landscapes, international competition, and market dynamics. This survey synthesizes empirical data from the 2025 China Enterprise-Level AI Commercialization Progress Report, based on a mixed-methods survey of 229 enterprises (81.6% SaaS vendors, 15.4% AI-native firms), with complementary insights from global benchmarks such as the Stanford HAI AI Index 2025 and the World Economic Forum's Blueprint to Action on China's AI-Powered Industry Transformation 2025. Key findings highlight a "scale exploration phase" in commercialization, where AI penetration exceeds 70% in internal operations but value realization is fragmented, with only 31.4% of firms achieving mature expansion. Structural divergences emerge between SaaS firms, which dominate market breadth through incremental "+AI" integrations, and AI-native entities pursuing disruptive "AI+" reconstructions, amid regional concentration in five major hubs (Beijing, Shanghai, Shenzhen, Hangzhou, Guangzhou) capturing 70% of resources. Challenges include demand-side bottlenecks like fragmented scenarios (29.7-30% cited as primary barriers) and low client penetration (54.5% below 10%), while trends point to vertical specialization, value-based pricing, and ecosystem integration as growth engines. Comparative analysis with MERICS' Report on China's AI Stack and RAND's analysis of China's AI industrial policy reveals China's shift toward self-reliant stacks (e.g., Huawei Ascend chips) and policy-driven innovation, contrasting U.S. capital-intensive models where private AI investment reached $109.1 billion in 2024 versus China's $9.3 billion . Contributions include a synthesized framework for assessing commercialization maturity, identification of eight core insights (e.g., organizational readiness as a new threshold), and strategic recommendations for enterprises, investors, and policymakers. This work addresses gaps in prior surveys, such as CEIBS' AI Industry Landscape Report 2025 , by emphasizing granular enterprise data and future trajectories, fostering informed decision-making in AI's transformative era.
Multimodal Large Models-Driven Precise Perception in Complex Low-Altitude UAV Environments: A Survey on Adaptive Edge Intelligence and Swarm Collaboration
Unmanned aerial vehicles (UAVs) equipped with long-endurance remote sensing capabilities have revolutionized applications in economic development, national defense, emergency response, and disaster monitoring. However, traditional centralized processing paradigms suffer from high latency, resource inefficiency, and poor adaptability to dynamic low-altitude environments. This survey reviews advancements in multimodal large models (MLMs) for precise detection and perception in complex low-altitude scenarios, emphasizing three core challenges: enhancing intelligent terminal perception for adaptive learning, bolstering multi-UAV collaborative coverage through federated evolution, and achieving high-fidelity 3D perception via multimodal fusion. We synthesize recent developments in self-evolving online learning frameworks, asynchronous distributed federated optimization, and Transformer-based MLMs tailored for heterogeneous sensor data (e.g., LiDAR and multi-view cameras). Key contributions include a taxonomy of adaptive algorithms mitigating catastrophic forgetting and data heterogeneity, alongside benchmarks for edge deployment in resource-constrained UAV systems. By highlighting gaps in unsupervised multimodal alignment and real-time scalability, this work outlines future directions toward autonomous, resilient UAV swarms, fostering innovations in edge intelligence for spatial information technologies.
Consumption Trends in Science and Technology-Based Anti-Aging Products Among Oriental Women: A Systematic Review
The global anti-aging industry has witnessed exponential growth, driven by demographic shifts, technological advancements, and evolving consumer preferences, particularly in emerging markets like Asia. This survey paper synthesizes extant literature on consumption trends in science and technology-based anti-aging products among Oriental women, with a focal emphasis on East Asian demographics (primarily Chinese, Japanese, and Korean populations). Drawing on the seminal 2025 report by Yicai Business School and Baique Ling, which projects China's anti-aging market to exceed 1,500 billion CNY by 2026 at a 13.3% compound annual growth rate (CAGR), we integrate empirical findings from over 50 peer-reviewed studies spanning 2010–2025 to critically analyze market dynamics, consumer behaviors, and innovation trajectories. Key themes include the declining market concentration favoring domestic brands, heightened awareness of skin aging triggers such as unhealthy sleep patterns among 30–40-year-olds, and preferences for biotech ingredients like boswellic acid (Pro-Xylane) and collagen. We employ a systematic review methodology adhering to PRISMA guidelines, incorporating quantitative syntheses (e.g., meta-analyses of preference data) and qualitative thematic analyses to address gaps in prior Western-centric surveys. Findings reveal that Oriental women prioritize efficacy over brand loyalty, with over 70% favoring international premium products yet showing increasing adoption of tech-infused formulations. Sensitive skin subgroups, comprising 58.3% mild cases, demand milder, targeted innovations. Theoretical contributions extend the Theory of Planned Behavior to cultural contexts, elucidating how lifestyle factors mediate consumption. Practical implications for marketers include strategies for R&D collaborations and digital marketing integrations. Future directions propose longitudinal studies on AI-personalized anti-aging and cross-cultural comparisons. This synthesis underscores the untapped potential in mid-to-high-end segments for domestic brands, amid a market poised for disruption by biotechnology and sustainability trends.
Advances in Cross-Source Intelligent Security Monitoring and Resilience Strategies for Urban Cyber-Physical Power Systems: A Comprehensive Review
The rapid integration of cyber-physical systems (CPS) into power grids has transformed traditional electricity networks into cyber-physical power systems (CPPS), enhancing efficiency but introducing profound cybersecurity vulnerabilities. Coordinated cyber-physical attacks, cascading failures, and information asymmetry exacerbate risks, as evidenced by incidents like the 2019 Venezuela blackout and disruptions during the Russia-Ukraine conflict. This review synthesizes advancements in cross-source intelligent security monitoring and analysis for power systems, focusing on urban CPPS models. Key technologies examined include multi-source log data aggregation for topology modeling, anomaly detection via self-supervised contrastive learning for missing node identification, tri-level optimization defenses against coordinated attacks, and vulnerability assessments using spectral metrics. We further discuss fault recovery strategies incorporating information delay models and graph convolutional networks (GCN) for optimal repair paths. Innovations such as multi-functional module integration (monitoring, powering, control) and asymmetric topology-aware attacks address gaps in high-security urban cores. By evaluating resilience metrics like load loss rates and islanding efficacy [35], this paper highlights pathways to enhance grid robustness, reduce outage costs, and support smart grid evolution. Future directions emphasize scalable AI-driven platforms for real-time threat mitigation, offering a comprehensive framework for researchers and engineers to bolster CPPS resilience.
A Survey on Integrated Training-Inference Architectures for Large Language Models on Multi-GPU Stream Processors
Large language models (LLMs) have revolutionized artificial intelligence, achieving remarkable performance in natural language understanding, generation, and multimodal tasks. However, their unprecedented scale—often comprising billions to trillions of parameters—imposes severe computational demands, particularly in training and inference phases, necessitating advanced parallel processing architectures on multi-GPU arrays. This survey provides a comprehensive overview of integrated training-inference (train-infer) architectures for LLMs on large-scale GPU stream processors, emphasizing multi-GPU stream processing, hypercube tensor parallelism, and hardware-software co-designed compilation frameworks. We trace the evolution of parallelism strategies, including data parallelism, pipeline parallelism, and tensor parallelism, highlighting innovations such as cross-cluster pipeline execution, adaptive NIC selection, and spatiotemporal tensor partitioning to mitigate communication overheads and memory bottlenecks in heterogeneous environments [1, 7, 8, 17]. Key challenges, including scalability in non-homogeneous networks and efficient compilation for diverse hardware, are analyzed alongside state-of-the-art solutions like MLIR-based frameworks and RISC-V accelerators [28, 33]. By synthesizing recent advancements, this survey identifies promising directions for scalable, energy-efficient LLM systems, paving the way for broader deployment in edge computing and high-performance clusters.
Trade and Economic Cooperation Between the People's Republic of China and the EAEU Countries in the Framework of Resolving the Problem of Food Security
The purpose and objectives of the study The purpose of the dissertation is to develop promising areas of trade and economic cooperation between the People's Republic of China and the EAEU countries in the context of solving the problem of food security in modern economic conditions. To achieve the goal of the study, the following tasks were set and solved: –to consider the theoretical aspects of the country's food security, the effects and risks of its decline; –to study the world experience of forming food security of countries; –to study the state policy of China in the field of ensuring food security; –to assess the export opportunities in the food sector of the EAEU countries; –to identify and justify promising areas of food exports to the PRC by the EAEU countries, as well as the implementation of joint investment projects in the field of agriculture. The object of the study is trade and economic cooperation between the People's Republic of China and the EAEU countries in the field of ensuring food security. The subject of the study is the substantiation of the main directions of ensuring food security based on the development of mutually beneficial cooperation between the People's Republic of China and the EAEU countries, the development of the export potential of states, and the improvement of investment activities. Provisions submitted for defense 1. The essence and content of the category "food security" are defined, distinctive characteristics and criteria for assessing the level of food security are identified. 2. An assessment of the position of the People's Republic of China in the field of food security is carried out, the evolution of changes in the nutritional structure of the population of the PRC is considered, 3. Factors complicating the solution of the food problem in the country, the dependence of the domestic food market of China on imports of food products, and strategic directions for ensuring food security in China are identified. 4. The reduction of food security risks in each of the EAEU member states has been revealed, which is primarily facilitated by mutual provision of various types of food products, prospects for the sustainable development of agriculture, ensuring the growth of physical availability of food products in the Union countries, self-sufficiency in basic agricultural products have been established 5. The potential for cooperation between the countries of the Eurasian region in the field of agriculture and its importance for solving the food problem in China have been established, as well as the main areas of development of agricultural exports from the EAEU countries to China: commodity structure, volumes, dynamics and prospects for expanding volumes and improving the quality of products. Personal contribution of the applicant The dissertation is an independently completed scientific study: the provisions submitted for defense have been developed by the author independently, have practical significance, the topic of the dissertation corresponds to the specialty 7-06-0311-01 "Economics". Dissertation approval and information on the use of its results The main provisions, conclusions and results of the dissertation were presented at the 75th scientific and technical conference of pupils, students and master's students, April 22-27, 2024 - Minsk: Belarusian State Technological University. Based on the materials of the dissertation research, an article was published on the topic "The problem of food security and the main directions of its solution in the People's Republic of China". Structure and volume of the dissertation The dissertation consists of an introduction, a general description of the work, three chapters, a conclusion, a list of references. Chapter 1 studies theoretical approaches to the study of the problem of food security, considers the concept of "food security" and its distinctive features, defines the stages of formation of modern approaches to this global problem, and formulates the risks and threats of violating the country's food security. Chapter 2 considers methodological approaches to assessing the food security of states and regions of the world, and provides an overview of the criteria for measuring the level of food security and self-sufficiency of the country in food. The third chapter includes a study of the state policy of China and the countries of the Eurasian region in the field of development of food security, substantiation of the main directions of trade and economic cooperation of the EAEU countries and China in the field of agriculture and ensuring food security of states. The work is presented on 98 pages. The volume occupied by 8 figures, 24 tables and a list of used literature, including 38 sources, is 18 pages.
Copyright (c) 2025 Chaoqun Zhang (Author)
Impact of digital currency on China's traditional financial system
Structure and scope of the master's thesis is 84 pages, 55 references, 4 tables and 3 figures. The master's thesis consists of an introduction, three chapters, a conclusion and a list of references. Key words: supply chain, supply chain diversification, economic security, enterprise economic security, China, The relevance of the topic Post-pandemic supply chain disruptions, geopolitical tensions as well as regional conflicts have highlighted vulnerabilities in centralized supply chains. As a global manufacturing hub, Chinese enterprises face pressure to balance cost efficiency along with resilience. Supply chain diversification is critical for mitigating risks like overreliance upon single markets, raw material shortages, or export restrictions. China’s "dual circulation" strategy emphasizes domestic resilience and global competitiveness, making supply chain diversification a strategic imperative for economic security. Purpose of the research The research purpose is to quantify supply chain diversification's impact upon Chinese enterprises' economic security, identify trade-offs as well as inform strategic decisions. Object of the research The object of the research is the economic security of enterprises in China, defined as their ability to withstand external shocks while maintaining operational stability and profitability. Subject of the research The subject of the research is enterprise supply chain diversification strategies in China. Research objectives 1) To evaluate how diversification levels correlate along with economic security indicators; 2) Analyze industry-specific differences and firm characteristics; 3) foster frameworks for balancing diversification along with cost efficiency. Research methodology The research employs a mixed-methods approach, combining quantitative and qualitative methods. Quantitatively, it uses financial data analysis, regression analysis as well as structural equation modeling (SEM) to measure correlations and test hypotheses related to diversification and economic security. Qualitatively, it includes case studies of firms along with varying levels of diversification to supply in-depth insights. Research results and novelty The research demonstrates a significant positive correlation between supply chain diversification and economic security in Chinese enterprises. Diversified supply chains enhance resilience to external shocks, operational continuity as well as financial stability. The study identifies industry-specific differences together with firm characteristics as key moderators influencing the effectiveness of diversification strategies. Larger firms with advanced technological capabilities together with substantial financial resources are better positioned to benefit from diversification. The research also proposes frameworks to balance diversification with cost efficiency, including the “China Plus N” strategy and strategic inventory management.
A Unified Spatio-Temporal Data Organization Model for Forest and Grassland Resources Based on GeoSOT Encoding
The efficient integration, management, and application of multi-source heterogeneous spatio-temporal data remain a critical challenge in forest and grassland ecological informatics. Traditional GIS-based approaches often suffer from limited scalability, poor adaptability to diverse data modalities, and inadequate support for time-space linkage. To address these limitations, this study proposes a novel spatio-temporal data organization model based on the Geographical Subdivision Grid with One-dimension-integer on Two to n-th power (GeoSOT) encoding framework. We introduce a unified three-domain identifier—composed of spatial code, temporal stamp, and semantic attributes—to support fine-grained partitioning and indexing of both structured (e.g., vector, raster) and unstructured (e.g., video, sensor logs, text) data. The organization model employs multi-level GeoSOT grid cells as spatial anchors, integrating temporal semantics and object-level identifiers to form a one-code-per-element schema, ensuring the uniqueness and traceability of each data entity. A prototype system was implemented using forest resource and fire monitoring datasets from the Asia-Pacific Forestry Center. Comprehensive experiments demonstrate that the proposed model significantly improves data fusion flexibility, retrieval efficiency, and query precision compared to conventional spatial database models. Moreover, the system enables scalable and interactive spatio-temporal queries across multi-modal data sources. This study contributes a generalized, extensible, and semantically rich data organization framework that bridges spatial and temporal dimensions in forest and grassland applications. It holds promise for large-scale ecological monitoring, forest fire early warning, and smart forestry governance. Future work will focus on extending the model to real-time streaming data and integrating intelligent analytics for enhanced decision support.
From Atomic Structure to the Dream World: Exploring the Unknown Dimensions of Life and Consciousness
Modern science establishes that the human body is fundamentally composed of atomic structures. As the basic units of matter, atoms form cells, tissues, and organs through intricate combinations and interactions, sustaining vital life processes. At the microscopic level, atomic types, quantities, and chemical bonding patterns determine the diversity of biological substances and the complexity of physiological functions. For instance, the unique chemical properties of carbon enable the formation of diverse organic molecules, serving as the foundation of life, while oxygen atoms play a crucial role in energy metabolism during respiration.
A Brief Discussion on the Value Representation of Digital Assets in Different Periods
With the rapid advancement of information technology, digital assets are playing an increasingly crucial role in the global economic system. This paper provides an in-depth analysis of how digital assets manifest their value across different periods, examining their characteristics, driving factors, and challenges during the nascent, developmental, mature, and transformative stages. By offering a comprehensive discussion of each stage, this study reveals the evolution of digital asset value, providing valuable insights for enterprises and investors.
A brief discussion on the development of agricultural modernization enabled by digitalization
With the rapid development of information technology today, digitalization has become a key force to promote agricultural modernization. This paper deeply discusses how digitalization provides power for agricultural modernization, analyzes the application of digitalization technology in agricultural production, management, marketing and other key links, and puts forward corresponding strategies and suggestions. This paper first introduces the basic concept of digitalization and the connotation of agricultural modernization, then analyzes the application status of digitalization technology in the agricultural field, then discusses the role of digitalization technology in promoting agricultural modernization, and finally puts forward the strategies and suggestions to promote the development of digitalization enabling agricultural modernization.
Actor-Critic Convolutional Neural Network-Based Centralized Computation Offloading for Vehicular Edge Networks
Efficient computation offloading is essential for addressing the growing demand for low-latency, computation-intensive applications in vehicular edge networks. Existing approaches, including traditional optimization techniques and reinforcement learning-based methods, often struggle to cope with the highly dynamic network environments and the complex task dependencies characterized by Directed Acyclic Graphs (DAGs). To address these challenges, this paper proposes a novel centralized Actor-Critic Convolutional Neural Network-based Offloading Computation (ACCOC) algorithm. By leveraging the Actor-Critic framework enhanced with convolutional layers, ACCOC efficiently handles high-dimensional task offloading decisions, balancing delay and energy consumption under dynamic network conditions. The proposed algorithm is evaluated in a realistic vehicular edge computing environment with varying numbers of users, bandwidth availability, and edge server computational capabilities. Simulation results reveal that ACCOC achieves superior performance compared to baseline methods, including a non-convolutional Actor-Critic algorithm, Deep Q-Network (DQN), and random or fully local execution strategies. Specifically, ACCOC demonstrates faster convergence and higher system utility, achieving an average reward of 0.78 compared to 0.72 for the non-convolutional baseline. Furthermore, the algorithm maintains robust performance across diverse parameter settings, highlighting its scalability and adaptability.
Digital twin simulation for quality and efficiency evaluation on industrial manufacturing: A Theoretical Analysis
This paper presents an integrated intelligent manufacturing quality and efficiency assessment system that leverages real-time data collection, knowledge graphs, and machine learning to optimize assessments and intelligently analyze production processes. A digital twin simulation platform is utilized for precise mapping of physical environments to virtual models, enhancing manufacturing quality and efficiency. The system's objectives include developing a data collection and analysis framework for interoperability of heterogeneous data sources, employing advanced machine learning models for event extraction and causal relationship identification, and constructing a comprehensive causal graph for in-depth quality analysis. A novel method integrating graph convolutional neural networks with knowledge graphs is proposed for precise quality and efficiency assessment, featuring a knowledge graph embedding-based GCN model and a regularized loss function for improved accuracy. The system also incorporates a digital twin approach for accurate simulation, with a focus on online data collection, network resource allocation, and the creation of virtual models through digital modeling techniques. A communication network and data management system are designed to enhance M2M communication, using MILP and cloud computing for network adaptability and data real-timeness. The project aims to automate the design process with STEP standards and build an IMT digital twin system for comprehensive machine tool monitoring, ultimately achieving the intelligent manufacturing goal of "controlling the real with the virtual."
The Relationship between Land Finance and the Tax-Sharing System Reform: An Empirical Study
With the reform of the fiscal and taxation system, the local government adjusts sources of revenue accordingly. In 1994, Chinese government began to the tax-sharing system reform. The local government financial powers and responsibilities did not match. At the same time, the proportion of the land transfer fees in fiscal revenue kept rising after 1994. This article analyzes the relationship between land finance and the tax-sharing reform, and sets up a corresponding model. Then by using the method of regression analysis, it is concluded that they were both positive proportional relationship. Finally, we make suggestions about strengthening the fiscal and taxation system reform and doing a good budget planning.
Advancing Smart Supply Chain Security Through Digital Twin Technology
In the global context of combating COVID-19, the complexity and demands associated with supply chain security have significantly intensified. Moving forward, the innovation and advancement of supply chain security will increasingly reflect the evolving requirements of global economic activities. The integration of digital twin technology promises to usher in a new era of intelligent supply chain security. This technology facilitates the complete digital representation of supply chain security by leveraging advancements in technology, centralized information management, and standardized rule formulation. Unlike the physical world, the virtual realm created by digital twins operates without the constraints of spatial distance, where distance serves merely as a reference rather than a barrier influenced by time zones. The virtual environment, being a folded space, allows for real-time observation and control interventions regardless of the physical separation between points A and B, provided digital twin technology is employed. Consequently, future smart supply chain security should be represented and validated through digital simulation models grounded in digital twin technology.
Copyright (c) 2025 Xuexue Zhang, Dongran Sun (Author)
A Novel Distributed ADMM Method in Large-Scale Applications
Stochastic alternating direction method of multiplier (ADMM) has shown great promise in distributed environments, and improvements in algorithmic flexibility are expected to result in significant advantages. In this paper, we provide Flex-SADMM, a novel stochastic optimization method based on distributed ADMM. To address the subproblems of ADMM, we specifically integrate variance-reduced first-order information and approximate second-order information in order to achieve steady convergence and improve the search direction's precision. Moreover, in contrast to other ADMM-based approaches that require updates from every computational node in each iteration, our framework only requires nodes to update once, at a predetermined iteration interval, greatly increasing flexibility. Experiments validate the effectiveness and improved flexibility of our suggested approach.
Copyright (c) 2025 Heric Tsang, Fable Tsang (Author)
Incremental Cubic Regularized SR1 Quasi-Newton Method and the Applications in Large-Scale Problems
This paper delves into an innovative incremental cubic regularized symmetric rank-1(SR1)method (ICuREG-SR1). By incorporating the cubic regularization technique into SR1,we successfully address the issue of indefinite resulting matrix in SR1. Our core strategy is to adopt an incremental optimization scheme, gradually updating the information of the objective function,which typically involves a sum of multiple independent functions, and is very common in large-scale machine learning tasks. Through numerical experiments on multiple machine learning problems,we find that compared with other traditional algorithms, our proposed algorithm exhibits superior performance in terms of gradient magnitude.
Copyright (c) 2025 Jinmin Hou, Heric Tsang (Author)
On the Method of Architectural Formation Generation Based on the Control of Wind Environment Under the View of Micro Urban Regeneration
This study focuses on micro urban regeneration and explores an architectural formation generation design method centered on wind environment guidance and control, aiming to improve the comfort, livability, and sustainability of urban areas. We comprehensively use Wind Engineering, Architectural design principles, and calculation methods to introduce wind environment guidance and control to optimize the building form and improve ventilation and energy efficiency. Through simulation analysis, the effectiveness of the method was verified, and implementation suggestions were put forward. This method provides a new way to improve urban micro-renewal projects and promote sustainable urban development.