Feasible Paths for Small Hydropower Participation in Electricity Market Trading in Sichuan Province Under Seasonal Inflow Constraints
Sichuan Province boasts abundant small hydropower (SHP) resources, but its operational revenue has continued to decline in recent years, facing dual pressures from seasonal inflow constraints and the acceleration of electricity market reform. This paper takes Sichuan's SHP as the research object and systematically analyzes its feasible paths for participating in electricity market trading under seasonal inflow constraints. First, it sorts out the resource distribution and output characteristics of Sichuan's SHP, as well as the main trading varieties and rule systems for SHP. Second, it dissects the main problems under the current model from three dimensions: deviation settlement mechanism, wet-dry price structure, and policy environment changes. Third, it designs three types of trading paths—medium- and long-term (MLT) contracts as the mainstay, incremental participation in spot/grid agency power purchase during the wet season, and contract energy transfer during the dry season—and provides differentiated selection suggestions from three dimensions: installed capacity, regulation capacity, and inflow forecasting. The research shows that the three paths can be used in combination to form a strategic framework of "basic contracts + marginal increments + risk transfer". Run-of-river (ROR) power plants without regulation capacity should focus on using contract transfer to hedge under-generation risks; daily regulation power plants have intraday price spread arbitrage capabilities; annual regulation power plants can achieve strategic arbitrage. A dynamic switching mechanism based on inflow forecasting (increasing contract proportion in wet years and initiating transfer in advance in dry years) helps improve annual revenue stability. This paper provides operable market trading strategy references for Sichuan SHP owners and theoretical basis for policymakers to improve market rules adapted to SHP characteristics.
The Evolution and Operation Mechanism of Russia's Emergency Management System: A Theoretical Analysis Based on the Framework of Collaborative Governance and Resilience
This paper takes the construction of Russia's emergency management system as the research object, analyzing its evolutionary trajectory, operational mechanisms, and practical characteristics under the dual contexts of federalism and geophysical vulnerability. By constructing a theoretical analytical framework of "Unified Leadership - Decentralized Execution - Military-Civil Synergy," and integrating the theoretical perspectives of Collaborative Governance and Resilience, this study systematically examines the institutional logic, multi-stakeholder coordination mechanisms, and technological empowerment paths of Russia's emergency management system. The research finds that Russia's emergency management system realizes the unification of top-level design and resource allocation through the vertical coordination of the President - Government - Ministry of Emergency Situations (EMERCOM), while leveraging the advantages of military-civil integration to enhance professional rescue capabilities. Furthermore, the "Whole-of-Society" disaster prevention and mitigation system centered on public education and social participation effectively improves social resilience. However, challenges such as information silos, regional imbalances, and the digital transformation of emergency management still exist. Based on this, this paper proposes optimization suggestions for improving the efficiency of cross-departmental collaboration, strengthening international cooperation, and deepening the modernization of emergency management systems, providing theoretical references and practical insights for comparative studies of global emergency governance.
Rethinking Collaborative Governance in Federal Systems: A Theoretical Analysis of the U.S. Emergency Management System and Its Cross-National Implications
This paper conducts a theoretical examination of the U.S. emergency management system, focusing on its framework, core practices, and underlying theoretical constructs. Utilizing a combination of literature review and qualitative content analysis based on the provided conceptual diagram, this study identifies the "Dual-Track Federalism and Collaborative Governance Model" as the central mechanism of the U.S. system. The analysis delves into six core characteristics: Unified Command, Tiered Response, Risk Early Warning, Professionalized Rescue, Social Participation, and Inter-departmental Linkage. Furthermore, the study introduces a comparative perspective, juxtaposing the U.S. model with other global systems to highlight its unique strengths and limitations. The findings suggest that while the U.S. model excels in resource mobilization and inter-agency coordination, it faces significant challenges in vertical bureaucratic efficiency and equity in resource distribution during large-scale disasters. The study concludes by extracting practical insights for enhancing global disaster resilience, emphasizing the importance of legal frameworks, technological infrastructure, and social capital in modern emergency management.
Governing Catastrophe in a Federation: A Comparative Analysis of Emergency Management Systems in Australia and Germany
This paper examines the evolution, institutional architecture, and theoretical underpinnings of emergency management (EM) systems in federal democracies, with a particular focus on Australia as a paradigmatic case of "risk-based collaborative governance." Utilizing a mixed-methods comparative approach, the study analyzes the structural dichotomy between centralized political authority and decentralized operational execution. Drawing upon the Institutional Analysis and Development (IAD) framework and resilience theory, this research interrogates how federated states reconcile the tension between national standards and local autonomy in disaster risk reduction. Through a critical analysis of legislative frameworks, intergovernmental relations, and community engagement mechanisms, the study reveals that effective EM in federations requires sophisticated "meta-governance"—the capacity to coordinate governance without appropriating it. The paper concludes by proposing a refined model of "resilient federalism" that integrates polycentric governance principles to enhance adaptive capacity in an era of climate volatility.
A Critical Analysis of the U.S. Emergency Management System: A Theoretical and Comparative Perspective
In an era characterized by escalating climate volatility, urbanization, and complex transnational threats, the efficacy of national emergency management systems has become a paramount concern for public administration and social stability. The United States, with its long history of grappling with diverse catastrophic events, has developed one of the world's most intricate and debated emergency management architectures. This paper presents a theoretical and empirical analysis of the U.S. emergency management system, focusing explicitly on its hierarchical framework, core practical characteristics, and operational mechanisms as delineated in recent policy frameworks. Utilizing a multi-method analytical approach that combines document analysis with comparative case study insights, this research examines the interplay between federalist principles and collaborative governance. The study identifies the dual nature of the U.S. model: on one hand, it features a sophisticated "whole-process" management cycle encompassing unified command, tiered response, and multi-departmental coordination; on the other hand, it reveals inherent systemic tensions arising from constitutional constraints and socio-political polarization. By contextualizing the U.S. framework within broader theoretical discourses—such as polycentric governance, collaborative federalism, and crisis decision-making theory—this paper argues that the resilience of the U.S. system is predominantly driven by technological empowerment and societal pluralism, yet remains vulnerable to issues of bureaucratic fragmentation and resource allocation inefficiencies. Furthermore, adopting a comparative lens, this study contrasts the U.S. model with other federal systems to extract transferable lessons regarding information sharing and social mobilization. The findings suggest that while the U.S. emphasis on risk anticipation and professionalization offers valuable insights, its limitations underscore the necessity for adaptive institutional reforms. This research contributes to the existing literature by providing a nuanced, up-to-date critique of the U.S. system, highlighting its theoretical underpinnings and practical implications for global disaster risk reduction. The study concludes by outlining the limitations of the current framework, including the politicization of disaster response, and proposes avenues for future research focusing on digital governance and cross-jurisdictional learning.
A Critical Analysis of Japan's Public Emergency Management System: Theoretical Framework, Institutional Design, and Practical Implications
This paper conducts a comprehensive theoretical and empirical analysis of Japan's public emergency management system, focusing on its institutional design, operational mechanisms, and underlying theoretical paradigms. By integrating classic theories such as crisis management theory, resilience theory, and collaborative governance, the study examines the structural characteristics of Japan's emergency management framework, with particular attention to its early warning systems, community-based disaster response, and multi-level coordination mechanisms. The research employs a qualitative case study approach, utilizing official government documents, academic literature, and international organization reports to construct a robust analytical framework. Key findings indicate that Japan's system is characterized by a proactive prevention-oriented approach, strong community resilience, and a hybrid model of centralized command and decentralized execution. However, challenges remain in addressing cross-cutting issues such as inter-agency coordination and the integration of new technologies. The study concludes with practical implications for enhancing global disaster resilience and proposes avenues for future research.
A Comparative Study on the Construction of Public Emergency Management Systems: Focus on the German Model and Its Practical Implications
This paper conducts an in-depth theoretical analysis and literature review of Germany's public emergency management system, exploring its construction logic, operational mechanisms, and practical implications. By integrating classic theoretical frameworks such as federalism, collaborative governance, and multi-center governance, this study clarifies the core concepts of emergency management system construction, including "decentralized responsibility," "inter-departmental collaboration," and "social participation." The research finds that Germany's emergency management system, characterized by federal-state-local decentralization, state and local dominance, federal coordination support, and extensive social participation, provides a valuable reference for other countries. Its experience in risk assessment and early warning, disaster response coordination, and cross-departmental linkage mechanisms offers important insights for optimizing emergency management systems. This study aims to contribute to the theoretical development of public emergency management and provide practical guidance for policy-makers.
Exploring Talent Cultivation Models for Chinese Language and Literature Majors under the Background of the New Liberal Arts
The construction of the new liberal arts in China emphasizes disciplinary integration, industry-education collaboration, and innovation as core directives, prompting a comprehensive transformation of traditional liberal arts talent cultivation systems. As a foundational discipline in Chinese higher education, the Chinese Language and Literature major has long been constrained by rigid training models, pronounced disciplinary barriers, insufficient practical education, and a disconnect between talent output and dynamic societal needs. This paper, rooted in the 2024 Heilongjiang Provincial Higher Education Teaching Reform Project and based on the educational orientation of local private universities, analyzes the practical dilemmas and developmental bottlenecks in talent cultivation for Chinese Language and Literature majors from the perspective of the new liberal arts. It innovatively constructs a “12345” collaborative education model, exploring reform pathways across five dimensions: cultivation objectives, curriculum system, teaching models, practical platforms, and support mechanisms. The research aims to address the shortcomings of traditional humanities education by nurturing Chinese Language and Literature professionals equipped with literary literacy, innovative thinking, interdisciplinary competence, and practical capacity. The findings offer vital references for teaching reform and high-quality talent cultivation in liberal arts at local private institutions.
Theoretical Research on Time Scaling in Multi-layer Electronic Systems-Exploring Huawei's "Ta (τ) Law"
Over the past six decades, the semiconductor industry has advanced through the geometric scaling paradigm of Moore's Law—achieving smaller transistors, higher integration density, and lower unit costs. However, as manufacturing processes approach dual physical and economic ceilings (post-7 nm technology sees rising transistor costs instead of falling reductions, while EUV depreciation dominates wafer costs), the marginal returns of purely "size reduction" strategies have sharply declined. In 2026, Hua Weitingbo's team formally proposed the "Tau Scaling Theory (τ Scaling/Time Scaling Theory for Multi-Layer Electronic Systems)," advocating systematic time-scale compression using the time constant τ as the unified optimization goal across multiple system layers—from picosecond-level transistor switching to second-level system loads spanning approximately 12 orders of magnitude. Building on this theoretical framework and industrial context, this paper rigorously establishes a hierarchical time-scale decomposition model for multi-layer electronic systems, derives τ expressions and scaling relationships at each layer, and validates through MATLAB numerical simulations: when geometric scaling reaches its limit, equivalent system performance can still exhibit nearly exponential growth by leveraging four key strategies—parasitic reduction, logic folding (key path shortening), 3D interconnect topology optimization, and full-stack co-scheduling—to compress τ. This paper simultaneously establishes a conceptual framework linking τ scaling with classical Dennard scaling, the Rent rule, and the RC delay model, while providing a quantitative discussion on the applicable boundaries of this paradigm.
Copyright (c) 2026 Siyi Zeng, Chaoqun Zhang, Wulong Gao (Author)
The Incorporation of Cryptocurrencies into International Balance of Payments Statistics and the Challenges to Monetary Policy Independence in Major Economies: A Comparative Analysis Based on the IMF (BPM7/2025 SNA) Framework – The Paths of the Federal Res
This paper elevates the question of "whether and how encrypt assets enter the Balance of Payments (BOP) and International Investment Position (IIP)" from an accounting technical issue to a practical implication for the monetary policy independence (MPI) of major economies. The central thesis is that when a category of cross-border "quasi-currency/quasi-asset" can be held by both residents and non-residents yet remains largely invisible within traditional correspondent banking systems, it not only distorts capital account signals and affects foreign exchange/reserve management logic, but also partially "externalizes" private credit creation and redemption risks onto central bank balance sheets and liquidity management boundaries through the coupling of stablecoins and short-term government financing markets. The paper first examines the IMF's latest statistical framework (BPM7/2025 SNA framework, with GN F.18 classification gradually converging toward the treatment of "liabilities = financial assets / no corresponding liabilities = non-productive non-financial assets"), then uses published cross-border flow and stablecoin contagion evidence from the BIS and the New York Fed to demonstrate that even if "Bitcoin-like assets" do not become reserve assets, they can still undermine the stability of the MPI transmission mechanism— "interest rate–exchange rate–capital flows" —by altering net errors and omissions (NEOs) in capital accounts, shadow exchange demands, and regulatory arbitrage channels. The approach to adopting "stablecoin-like instruments" (liability-bearing financial instruments) directly determines whether monetary sovereignty is replaced by private dollar or offshore fiat currencies. The analysis then compares the Federal Reserve (with its market infrastructure development, banking supervision mechanisms, and the trend toward using stablecoins as payment instruments) with the European Central Bank (through its defensive unified MiCA legislation, euro anchoring, and measures to curb currency substitution), proposing a three-dimensional response framework encompassing statistics, regulation, and liquidity safety nets. It further provides asymmetric policy implications for emerging markets versus reserve currency issuers.
Copyright (c) 2026 Kevin Augusto (Author)
The AI-Driven Resilience Engine: Research on the Framework Construction and Behavioral Logic of BCM Agents
With the deepening of digital transformation, the business environment and cybersecurity threats faced by enterprises are becoming increasingly complex and volatile. Traditional Business Continuity Management (BCM) models, which rely heavily on manual labor and passive response, can no longer meet organizations' urgent demand for "resilience." The explosion of Generative Artificial Intelligence (GenAI) and Agent technologies provides a novel paradigm for the intelligent and automated reconstruction of BCM. This paper takes the "BCM Agent Framework and Behavioral Logic Diagram" as its research object, systematically deconstructing its core closed-loop logic of "Continuous Perception, Generating Recommendations, Guiding Actions, Validating Capabilities, and Promoting Growth." Firstly, the paper analyzes the pain points of digital transformation currently facing BCM. Secondly, it deeply dissects the seven-layer architecture system of the BCM Agent, focusing on the core engine's seven capability modules, the structured precipitation mechanism of the organizational memory base, and the seven-step closed-loop behavioral logic from perception to capability enhancement. Thirdly, combining the latest practical cases in the financial industry and intelligent operations, it demonstrates the core value of AI-BCM Agents in risk prediction, automated emergency response, and cross-departmental collaboration. Finally, drawing on research results from core journals in the past three years, this paper proposes future trends and governance challenges for BCM Agents, providing theoretical references and practical pathways for enterprises to build a "trustworthy, visible, and controllable" digital resilience system
Research on Data Intellectual Property Protection and Value Assessment in the AI Era: A Tri-Jurisdictional Comparative Study of China, the United States, and the European Union
As artificial intelligence reshapes the global economic infrastructure, data has ascended from a by-product of digital activity to the primary factor of production in the twenty-first century. Yet the very legal regimes tasked with protecting data-derived value remain profoundly fragmented. This paper conducts a comparative institutional analysis of how China, the United States, and the European Union construct data intellectual property (IP) protection frameworks, and how those divergent architectures interact with market mechanisms for valuing data assets. Drawing on three paradigmatic cases—(1) hiQ Labs v. LinkedIn (U.S.), (2) the EU's sui generis database right regime under the Database Directive and subsequent Data Act reform, and (3) China's emerging data-IP registration pilot system exemplified by the Shanghai and Zhejiang platform-data disputes—this study identifies systematic patterns: the U.S. relies on a decentralized, litigation-driven mosaic (trade secrets, CFAA, contract, copyright) that maximizes allocative flexibility at the cost of transactional uncertainty; the EU deploys a rights-centric, regulatory-dense architecture (database right + GDPR + Data Act) that secures individual and producer protections but imposes heavy compliance frictions on data monetization; and China pursues a state-guided, registration-enabled property-rights experiment ("three-rights" bifurcation + data IP pilots) designed to engineer a tradable data-asset class from above. The paper then develops an interaction-mechanism framework showing how each jurisdiction's legal DNA shapes which valuation methodologies (cost / income / market) dominate, what discount rates the market applies, and where liquidity emerges or stalls. Business and policy implications are drawn for firms operating across jurisdictions in the AI supply chain.
Copyright (c) 2026 Ben・Anle Augusto (Author)
Application scenarios of Chinese language teaching and research supported by digital technology
Based on the "Teacher Professional Development" dimension of the "Teacher Digital Literacy" educational industry standard released by the Ministry of Education in 222, and relying on the "AI-empowered" digital technology system, this paper explores three digital application scenarios for primary school Chinese teaching and research: the personal reflection—self-observation promoting teaching improvement; the team teaching and research scenario—data interpretation guiding targeted discussion; and the archive construction scenario—classroom slicing mapping out the growth trajectorydigital technology
A Formal Investigation of Tau (τ) Scaling Theory for Multi-Layer Electronic Systems
The semiconductor industry has relied on geometric scaling—the relentless reduction of transistor feature sizes—as its primary performance engine for over half a century, epitomized by Moore's Law. Yet as physical and economic barriers converge below the ∼3 nm frontier, the classical Dennard scaling framework has collapsed, and the marginal return on pure dimensional shrink has entered a regime of steeply diminishing returns. Against this backdrop, the recently articulated Tao (τ) Scaling Law (He, IEEE ISCAS 2026) proposes a paradigm shift: replace "how small can we make it?" with "how fast can the entire layered stack operate?", using the time constant τ as a universal, cross-hierarchical optimization metric that spans device physics, circuit topology, chip architecture, and system-level interconnect. This paper provides the first independent, formally grounded mathematical treatment of τ-scaling theory for multi-layer electronic systems. We construct a four-tier hierarchical delay decomposition model—device (ℓ = 0) → gate/interconnect (ℓ = 1) → functional block/chip (ℓ = 2) → system (ℓ = 3)—and derive closed-form expressions for how τ propagates upward through each layer under both classical geometric scaling and the proposed τ-multi-lever regime (logic folding, vertical integration, RC-minimizing topologies, full-stack scheduling). We then execute a parametric MATLAB simulation campaign across 10 generational steps with Monte Carlo variation, comparing three strategy families: (A) pure geometric shrink, (B) stagnation-with-τ-levers, and (C) a hybrid optimal control policy. Our results yield three non-obvious findings: 1. τ-scaling is not a replacement for device-level innovation but a reallocation: it shifts the dominant delay term from irreducible intrinsic switching to topological and communicational terms, meaning its efficacy is bounded by Rent's Rule exponents and thermal constraints on 3D stacking density. 2. We derive a critical fold ratio λ\* ≈ 1.22 per generation above which τ-scaling sustains >12% per-step effective performance gain even when κ→1 (geometry frozen); below this, the benefit is swallowed by via-resistance and test/debug overhead. The true disruptive claim of the τ framework is not physics-breaking but metric-reframing: by placing τ\_e2e (end-to-end time constant) rather than transistor count at the center of the design objective, it forces cross-layer co-design that classical siloed scaling never internalized—and our Pareto-front analysis shows this alone can recover 40–60% of the "lost Moore slope" without EUV sub-2nm lithography.
Enterprise Management Information Systems and Their Improvement
The basic functions of the management information system: data collection and input, responsible for data storage, responsible for information transmission in the system, data processing, model establishment, solution, application and modification, output information, provide information to all management levels, support decision-making, and management and maintenance of the management information system itself. The latter is mainly based on the structured system specification, mainly including system analysis, system design and so on.As we all know, management information system is a science formed on the basis of management science, system science, information science and computer science in order to meet the needs of modern management. It studies the whole process of information processing and decision-making in management system, and discusses the implementation method of computer. It is a system composed of human, computer, communication equipment and other hardware and software, which can collect, process, store, transmit, maintain and use management information. Management information system can promote enterprises to develop in the direction of information, make enterprises in a virtuous circle of sensitive information, scientific management and accurate decision-making, and bring higher economic benefits to enterprises. Therefore, management information system is an important symbol of enterprise modernization and an only way for enterprise development.Information system is widely used in the management of various affairs, which promotes the improvement of enterprise management. Management information system serves for management. Its development and establishment is an effective way for enterprises to get rid of backward management methods and realize management modernization. The management information system unifies, standardizes and modernizes the management work, greatly improves the efficiency of management, and makes the modern management form a unified and efficient system. In the past, the traditional management method was manual operation with human as the main body. Although managers invested a lot of time inevitably have limitations or personal subjectivity and one sidedness. The man machine system established by the management information system using the system idea, taking the computer as the information processing means, taking the modern communication equipment as the basic transmission tool, and capable of managing decision-makers to provide information services, is undoubtedly a major measure to integrate management with modernization and improve management quality with science and technology. The management information system gives a large number of complex information processing to the computer, so that people and computers can give full play to their respective strengths, organize a harmonious and effective system, and bring convenience to modern management.In modern management, computer management information system has become an indispensable helper of enterprise management, and its wide application has become an important symbol of management modernization. In the modernization of enterprisemanagement, the modernization of organization, method and control is inseparable from the modernization of management means. With the development of science and technology, especially the development of information technology and communication technology, computers and networks are gradually applied to modern management. Facing more and more information resources and more complex internal and external environment of enterprises, it is necessary for enterprises to establish an efficient and practical management information system to provide guarantee for enterprise management decision making and control, which is the inevitable trend to realize management modernization.
Path Exploration for the Construction of China-Russia Cross-Border Language Service System Against the Belt and Road Initiative: A Digital-Driven Perspective
Amid the high-quality development of the Belt and Road Initiative (BRI) and its in-depth alignment with the Eurasian Economic Union, China-Russia cross-border cooperation in economy, trade, culture and infrastructure has entered a new stage. Cross-border language services, as a core medium of bilateral exchanges, are critical to breaking language barriers and facilitating connectivity. However, traditional China-Russia cross-border language services are plagued by fragmented supply, low efficiency and weak professional adaptability, which can no longer match the digital trend of cross-border cooperation. From the perspective of digital empowerment, this paper clarifies the theoretical connotation of cross-border language service system, analyzes the current development and practical dilemmas of China-Russia cross-border language services, and explores digital-driven construction paths from five dimensions: top-level planning, platform building, talent cultivation, technological innovation and governance improvement. The study finds that digital technologies including artificial intelligence, big data and blockchain can reshape the supply model of cross-border language services, resolve traditional bottlenecks, and build an integrated, intelligent and standardized service ecosystem. This research enriches the theoretical research on cross-border language services under the BRI, and provides practical references for upgrading China-Russia cross-border cooperation and advancing the internationalization of language service industry
Research on the Application of Artificial Intelligence Models in Smart Energy and Carbon Emission Systems
At this critical juncture of advancing the "dual carbon" goals and transforming energy structures, the efficient coordination between smart energy systems and carbon emission monitoring systems serves as the cornerstone for implementing green development concepts and achieving sustainable growth. Artificial intelligence technologies, with their robust data processing capabilities, deep feature extraction, and intelligent decision-making prowess, offer innovative solutions to address persistent challenges in traditional energy systems—including low energy efficiency, insufficient carbon emission monitoring accuracy, and supply-demand imbalance. This study systematically examines the application status of mainstream AI models (including machine learning, deep learning, and reinforcement learning) across the entire smart energy production, transmission, and consumption chain, as well as carbon emission accounting, monitoring, and optimization processes. It thoroughly analyzes core issues such as data heterogeneity, model generalization limitations, and inadequate technological integration, while exploring practical scenarios through case studies to demonstrate the effectiveness of different AI models. Finally, the research outlines future trends in deep integration between AI and smart energy systems/carbon monitoring systems, proposing targeted optimization pathways and implementation strategies. These insights provide theoretical foundations and practical references for accelerating digital transformation in energy systems, enhancing precision carbon management, and facilitating the realization of "dual carbon" objectives
Interpreting Standardization Readiness Levels for Chip R&D: Comparative Analysis and Dynamic Evolution Model Based on the NIST Framework
The Standardization Readiness Level (SRL) in chip development serves as a critical metric for evaluating the transformation of technological achievements into industry standards. This study focuses on the 2026 "Summary Report on Chip Development Standardization Readiness Levels" (hereinafter referred to as the "NIST Report") published by the National Institute of Standards and Technology (NIST), systematically analyzing its SRL framework design logic, hierarchical definitions, and implementation processes. By comparing the supporting standard system of the EU's "European Chips Act" with the standardization framework of Japan's Ministry of Economy, Trade and Industry (METI) "Semiconductor Strategy 2.0," the study reveals similarities and differences in SRL practices across different institutional environments. Furthermore, a three-dimensional dynamic evolution model integrating "technology-industry-policy" dimensions is constructed to simulate SRL progression throughout the entire chip development lifecycle. Findings indicate that the NIST framework centers on "collaborative validation," achieving closed-loop management from concept to standardization through five-tiered hierarchical structures. The EU system emphasizes cross-border resource integration but faces limitations in decision-making efficiency, while the Japanese framework prioritizes corporate leadership but encounters international compatibility challenges. Dynamic modeling demonstrates that policy support and industrial collaboration serve as core drivers for SRL upgrades. This study provides theoretical references and practical guidance for optimizing standardization strategies among chip development stakeholders and enhancing standard systems for policymakers
Copyright (c) 2026 Fanyang Zeng, Cong Bo (Author)
In-depth Analysis of Practical Application of Intelligent Manufacturing Capability Maturity Model: An Empirical Study Based on the IRaD Paradigm
Against the macro background of accelerated digital transformation in global manufacturing and the in-depth implementation of China's "Manufacturing Power" strategy, the Intelligent Manufacturing Capability Maturity Model (IMMCM) has become a core tool for manufacturing enterprises to evaluate transformation progress and optimize resource allocation. However, existing academic research is mostly limited to interpreting standard texts or qualitative descriptions of individual enterprises, lacking quantitative validation based on large-sample data and cross-industry heterogeneity analysis. To address this, this study adopts the IMRaD (Introduction, Methods, Results, and Discussion) standard academic paradigm to construct an IMMCM evaluation index system integrating four dimensions: "personnel, technology, resources, and manufacturing," and introduces an improved AHP-entropy weighting method for combined weighting. The study selects 120 large-scale enterprises in China's electronic components and high-end equipment manufacturing industries as empirical samples, with data sources covering the National Bureau of Statistics Industrial Enterprise Database (2023-2025) and the National Industrial Information Security Development Research Center Intelligent Manufacturing Evaluation Platform. Through multiple regression analysis and structural equation modeling (SEM), the causal relationship between IMMCM and operational performance index (OPI) is empirically examined. The research findings demonstrate: (1) The IMMCM composite score exhibits a significant positive correlation with corporate operational performance (β=0.732, p<0.01), indicating that enhanced maturity levels directly translate into economic benefits; (2) Hierarchical regression analysis reveals that the foundational resource layer (equipment connectivity rate and data collection rate) and system integration layer (MES/ERP integration degree) demonstrate the strongest explanatory power for performance metrics (R²=0.586), constituting critical bottlenecks in current manufacturing enterprise transformation; (3) In-depth case studies further confirm that precision-driven upgrades following the IMMCM diagnostic pathway can reduce manufacturing costs by 18.7% and shorten delivery cycles by 26.4%. Theoretical contributions of this study include expanding the quantitative application scope of dynamic capability theory in smart manufacturing domains, while practical implications involve providing enterprises with tiered digital transformation roadmaps and offering data support for government policy formulation in industrial development
Copyright (c) 2026 Gang Zhao, Tao Lan (Author)
Research on the Integration of Artificial Intelligence and Zero-Emission Technology in the Era of Digital Economy
Against the dual backdrop of digital economy permeating all global socio-economic sectors and carbon neutrality goals becoming a worldwide consensus, the deep integration of artificial intelligence (AI) and zero-emission technologies has emerged as a pivotal pathway to drive industrial green transformation, address energy-environmental challenges, and achieve high-quality sustainable development. Grounded in the technological characteristics and development trends of the digital economy era, this study systematically analyzes the intrinsic logic and core mechanisms of AI-zero emission technology convergence. It examines practical applications and implementation outcomes in key sectors including energy, industry, construction, and transportation, while conducting in-depth analysis of current challenges such as computational energy consumption, technical barriers, data silos, and institutional deficiencies. Targeted optimization strategies are proposed encompassing technological breakthroughs, system architecture development, policy safeguards, and ecosystem cultivation. The research aims to provide theoretical foundations and practical references for deepening AI-zero emission technology integration, advancing zero-carbon society construction, and achieving coordinated progress between economic development and ecological conservation
Copyright (c) 2026 Fanyang Zeng (Author)
Research on the Economic Benefits of Supply Chain Business Process Modeling for the China-Europe Railway Express in the China-Belarus Industrial Park: A Quantitative Assessment Based on Logistics Efficiency Improvement
As the core vehicle of the "Belt and Road" initiative, the China-Europe Railway Express has profoundly transformed the operation model of transnational supply chains by reconstructing the Eurasian continental logistics network. Taking the China-Belarus Industrial Park as a typical case, this study constructs a supply chain business process framework based on the SCOR model and systematically evaluates the economic impact of the China-Europe Railway Express on supply chain optimization in the park during 2015-2022 by employing quantitative methods such as DEA efficiency evaluation, difference-in-differences (DID), cost-benefit analysis, and CGE simulation. The study finds that: 1.Logistics efficiency has been significantly improved: average transportation time reduced by 48.3% (32.5 days to 16.8 days), unit logistics cost decreased by 32.7% (280 USD to 188 USD/ton), and on-time performance rate increased to 95.6% (+22.3 percentage points). 2.Quantitative results of economic benefits: Direct cost savings of $110.4 million (cumulative 2015-2022) Time value of income: $6.192 million Trade growth contributed $280 million Industrial cluster effect drives $1.2 billion in investment 3.Heterogeneity analysis revealed that cost savings accounted for 35% in electronics enterprises, while biopharmaceutical companies reduced quality losses by 20% due to improved reliability. 4.CGE simulation prediction: By 2030, a 50% increase in train frequency would generate an additional economic benefit of $320 million. This study innovatively combines business process modeling with empirical analysis of multi-method integration, providing micro-level decision support for "Belt and Road" logistics cooperation and offering a new perspective for global supply chain resilience research.
Copyright (c) 2026 OMELYANYUK• Alexander •Mikhailovich, FanYang Zeng, Chaoqun Zhang (Author)
Research on Smart Agricultural Technology and Standardization of High-standard Farmland in Belarus
Belarus (hereinafter referred to as "Belarus") stands as a traditional agricultural powerhouse in Central and Eastern Europe, where agriculture serves as the cornerstone of its national economy. Leveraging its unique black soil resources and well-developed agricultural infrastructure, Belarus is progressively transitioning its production model toward scaling, intensification, and intelligentization. The adoption of smart agriculture technologies and standardized construction of high-standard farmland represent core strategies for overcoming agricultural efficiency bottlenecks, ensuring regional food security, and enhancing international competitiveness. This study employs literature review, field research (with case studies from Belarus 'major agricultural regions), and comparative analysis to systematically examine the current development status, application scenarios, and core advantages of smart agriculture technologies in Belarus. It delves into the standardized system framework for high-standard farmland management spanning planning, construction, maintenance, and utilization, explores integration pathways between smart agriculture technologies and standardized farmland development while identifying existing challenges. Practical optimization strategies are proposed based on Belarus' agricultural realities, providing theoretical references and practical insights for China and other countries advancing smart agriculture and high-standard farmland development. Research indicates that Belarus has established a preliminary smart agriculture technology system encompassing precision sowing, intelligent irrigation, green pest control, and smart agricultural machinery. Standardized farmland construction has developed clear norms in critical aspects such as land consolidation, facility integration, and soil improvement. However, significant gaps persist in integration depth, technological implementation effectiveness, and professional talent support. To promote deep integration between the two sectors and facilitate Belarus' high-quality agricultural development, measures such as improving policy support systems, strengthening technological innovation and localization adaptation, establishing standardized systems, and cultivating professional talent teams should be implemented
Copyright (c) 2026 Chaoqun Zhang (Author)
Research on Key Technologies for Analyzing High-efficiency Low-carbon Intelligent Irrigation Decision-making
Against the backdrop of global water scarcity and the advancement of carbon peaking and carbon neutrality goals, agricultural irrigation—being the largest water consumption sector—has become pivotal for resolving water supply-demand imbalances and achieving sustainable green agriculture through efficient, low-carbon, and intelligent transformation. Smart irrigation decision-making technologies integrate sensing, modeling, control, and optimization systems to enable precise irrigation management, enhancing water utilization efficiency while reducing energy consumption and carbon emissions, thus serving as a cornerstone for modern agricultural development. Israel, a global leader in agricultural water conservation and smart irrigation, has developed world-class low-carbon intelligent irrigation decision systems through decades of technological research and practical implementation in extreme arid environments. Its technical expertise and application models provide valuable references for arid and semi-arid regions worldwide. This study focuses on Israeli agricultural research to systematically analyze the core principles and technical frameworks of efficient low-carbon irrigation decision-making. It examines Israel's breakthroughs in key technologies—including irrigation sensing, decision modeling, low-carbon control, and system integration—alongside case studies of their applications. The research identifies current implementation challenges and bottlenecks, while aligning with global carbon neutrality objectives and agricultural modernization needs to explore future technological trends and optimization pathways. Research indicates that the core advantages of Israel's high-efficiency, low-carbon intelligent irrigation decision-making technology lie in "precision sensing, intelligent modeling, low-carbon control, and integrated systems." By integrating multi-source data fusion, dynamic model optimization, renewable energy coupling, and end-to-end system integration, this technology achieves dual improvements in water resource utilization efficiency and carbon reduction benefits. Furthermore, the deep integration of technological R&D with agricultural practices, coupled with a robust industry-academia collaboration mechanism, provides solid foundations for large-scale implementation. This study offers valuable insights for developing, applying, and promoting high-efficiency, low-carbon intelligent irrigation technologies in China and other countries, contributing to global agricultural progress toward water conservation, low-carbon practices, and high-quality development
Research on China-Belarus Trade Services in the Era of New Quality Productivity
New-type productivity, driven by technological innovation as its core engine, is reshaping global industrial chains, supply chains, and service ecosystems. Building upon the theoretical framework of new-type productivity, this study systematically examines the current status of trade service cooperation, policy coordination mechanisms, and dynamic effects between China and Belarus under this framework. By analyzing panel data on service trade, goods trade, digital economy, and policy coordination from 2010 to 2023, we construct a VAR model to analyze dynamic correlations among key variables. Case studies including China-Belarus Industrial Parks, China-Europe Railway Express, and cross-border e-commerce platforms reveal structural characteristics of service trade growth and policy-driven pathways. Findings indicate that three new-type productivity factors—digital technology, green innovation, and policy coordination—exert significant positive impacts on service trade expansion and structural upgrading. For every 1% increase in integration between digital economy and service trade, total service trade volume grows by 0.82%. Additionally, each 1-unit improvement in policy coordination reduces the volatility range of service trade growth rates by 12%. This research provides quantitative evidence and practical insights for deepening service trade cooperation between China and Belarus
Copyright (c) 2026 Fanyang Zeng, Wulong Gao, Chaoqun Zhang (Author)
From Atomic Structure to Multidimensional Isomeric Space: Exploring Unknown Dimensions of Life and Consciousness
The essence of life and consciousness remains a central question in humanity's prolonged exploration of civilization. From classical philosophical speculation to modern scientific empirical research, our understanding of this proposition has deepened continuously yet remains elusive of ultimate answers. As fundamental units of matter, atoms exhibit ordered microscopic structures and dynamic interactions that serve as the material foundation for constructing life systems and nurturing conscious activities. The multidimensional heterogeneous space theory transcends traditional three-dimensional spatial constraints, offering novel perspectives for interpreting life complexity and the immaterial nature of consciousness. This study begins with atomic structural characteristics, systematically tracing hierarchical evolutionary logic from atoms to molecules, cells, and living organisms while analyzing how atomic interactions shape life's material carriers. Building upon this foundation, we introduce core concepts and theoretical frameworks of multidimensional heterogeneous spaces to explore manifestations of spatial heterogeneity and its intrinsic connections with life processes and consciousness emergence. Further analysis addresses current research bottlenecks in life origin studies, consciousness essence investigations, and multidimensional space exploration. Integrating cutting-edge advancements in quantum mechanics, neuroscience, and cosmology, we propose a cross-level exploration pathway spanning atomic scales to multidimensional spaces. Finally, we outline future research directions to provide theoretical references and practical paradigms for deciphering unknown dimensions of life and consciousness. Studies indicate that the orderliness of life originates from precise regulation of atomic structures and energy coupling across multidimensional spaces. The emergence of consciousness may represent a macroscopic manifestation of energy-information interactions within multidimensional heterogeneous spaces. The profound interconnection between these two phenomena holds promise for groundbreaking advancements in cross-scale research spanning atomic and multidimensional spatial dimensions
Copyright (c) 2026 Aristotle, Ohaia, Miocon (Author)
Exploring the Integration Research of Carbon Trading and RWA Technology
At a critical juncture where global carbon neutrality goals continue to advance and carbon trading markets pursue high-quality development, traditional carbon trading systems face growing challenges including information opacity, insufficient asset liquidity, low transaction efficiency, cross-border circulation barriers, and dual billing risks. Real World Assets (RWA), leveraging blockchain's decentralized, traceable, immutable, programmable, and divisible technical characteristics, offers a novel approach to addressing carbon market dilemmas and unlocking carbon asset value. This study examines the current state of carbon trading markets and the core mechanisms of RWA technology, systematically analyzing the intrinsic logic, core value, and operational models of their integration. It explores practical applications of carbon trading-RWA convergence in carbon asset ownership confirmation, transaction circulation, regulatory accounting, and risk management scenarios. The research thoroughly assesses real-world challenges including legal compliance, technological security, market regulation, standardization, and talent shortages during integration. Targeted optimization strategies are proposed, encompassing institutional system improvements, technological breakthroughs, unified industry standards, enhanced regulatory frameworks, and professional talent cultivation. These efforts aim to deepen the synergy between carbon trading and RWA technology, enhance market efficiency and liquidity, support carbon peak and neutrality objectives, while providing theoretical foundations and practical references for green finance and digital technology integration.
Copyright (c) 2026 Xuexue Zhang, Fanyang Zeng (Author)
Analysis of the composition and condition of the Chinese railway transport
Researching the technological and constructional progress of China’s railway transportation is fundamental to understanding its history. While technology and construction provide the basic framework, an economic history perspective is indispensable for scholarly research in this field. Currently, China is optimizing its network layout through advanced equipment and scientific management to improve innovation and market competitiveness, ultimately supporting the nation's comprehensive development. Railway transportation is a product of modern global economic evolution. Its expansion has facilitated regional specialization and accelerated economic globalization. By exploring the historical types and technological milestones of China's railways, this paper examines their future trajectory and their growing impact on international trade patterns.
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.