The China Development Bank (CDB) (simplified Chinese: 国家开发银行; traditional Chinese: 國家開發銀行; pinyin: Guójiā Kāifā Yínháng) is a development bank in the People's Republic of China (PRC), led by a cabinet minister at the Governor level, under the direct jurisdiction of the State Council. As one of three policy banks in China, it is responsible for raising funds for large-scale infrastructure projects, including the Three Gorges Dam and the Shanghai Pudong International Airport. Established by the Policy Banks Law of 1994, the bank is described as the engine that powers the national government's economic development policies.Debts issued by the China Development Bank are owned by local banks and treated as risk-free assets under the proposed People's Republic of China capital adequacy rules (i.e. the same treatment as PRC government bonds). The bank is the second-largest bond issuer in China after the Ministry of Finance. In 2009, it accounted for about a quarter of the country's yuan bonds and is the biggest foreign-currency lender.
This paper investigates the influence of science-technology cross-coherence on invention impacts, with a focus on the pharmaceutical sector. By proposing a new indicator derived from a co-occurence probability approach, we capture the joint structural alignment of scientific and technological knowledge within inventions and its nuanced role in shaping inventive outcomes. Utilizing unconditional quantile regressions on a dataset of United States Patent and Trademark Office pharmaceutical patents granted between 2010 and 2019, alongside their citations of scientific publications, we identify an inverted U-shaped relationship between science-technology coherence and invention impact. This effect is particularly pronounced for high-impact and breakthrough inventions. Furthermore, the influential mechanisms of scientific and technological knowledge vary across the impact distribution. Technological diversification exerts contrasting effects on low- versus high-impact inventions, while distant scientific knowledge both in cognitive and geographical terms, contributes most strongly to invention impact at the upper tail of the distribution. Overall, the findings highlight the importance of balanced science-technology coherence in fostering high-impact inventive outcomes and advance a structural perspective on science-technology interactions in innovation.
Traditional Turing machines are semantically poor, they only concern the syntactic manipulation of symbols, discarding the mathematical semantics behind the symbols. This semantic deficiency is considered the root cause of the three major barriers: relativization, natural proofs, and algebrization. This paper proposes the Complex Boolean Turing Machine (CBTM), elevating computational symbols to algebraic elements in GF(4), so that each operation has a clear mathematical interpretation. The core insight of the CBTM is: Non-deterministic computation corresponds to algebraic field extension, when reading a symbol representing a new dimension, the computation must branch into two paths, just as introducing a new element α into the field ℚ yields the extension ℚ(α). We separate old data from new dimensions via the projection operators ℜ𝔢 and ℑ𝔪, and introduce a dual-tape perspective to intuitively decompose abstract algebraic symbols into a real tape (deterministic computation) and an imaginary tape (non-deterministic control). Moreover, the algebraic semantics of the CBTM naturally support arbitrary k-way non-determinism: by introducing multiple new dimensions, we can generate high-dimensional algebraic extensions ℚ(α_1,…,α_d), whose dimension 2^d corresponds exactly to the number of branches. We prove that the CBTM is polynomially equivalent to classical Turing machines and non-deterministic Turing machines, with 𝐏_cb=𝐏 and 𝐍𝐏_cb=𝐍𝐏. Thus, the CBTM does not introduce hyper-computation but provides a new algebraic perspective for understanding the essence of non-determinism. This work serves as the computational model foundation for the series of papers.
Stablecoins, as cryptocurrencies pegged to real-world assets, leverage blockchain technology to achieve price stability and low-cost cross-border payments, posing significant challenges to traditional banks’ foreign exchange operations. This paper examines the definition, price stabilization mechanisms, and applications of stablecoins in cross-border payments and foreign exchange settlement, analyzing their impact on bank deposits, cross-border remittances, and trade finance. Stablecoins’ high liquidity, low transaction costs, and decentralized nature drive their adoption in high-inflation countries and DeFi, but their credit foundation relies on issuers’ reserves, vulnerable to regulatory constraints and trust crises, as evidenced by the 2023 USDC depegging event. Domestic banks could leverage e-CNY infrastructure, digital asset custody, and supply chain finance innovations to respond. Regulators must strengthen stablecoin oversight and promote e-CNY internationalization to ensure financial stability and global competitiveness.
Financial technology(fintech)is a key variable reshaping national financial competitiveness,and promoting the digital upgrade of the monetary system is a core task in building a financially strong nation.As a pivotal component of the monetary system,the electronic Chinese yuan(e-CNY),based on its inherent fintech attributes,serves as a critical practice for driving the monetary system towards a more advanced stage of evolution.Currently,the traditional monetary system faces three major challenges:low efficiency and high risks in cross-border payments,structural attenuation in monetary policy transmission,and a lack of digital safe-haven assets.Starting from the pilot practices of the e-CNY,this paper analyses its three core fintech characteristics—the centralized-distributed hybrid architecture,programmability,and account-Token hybrid design—and elaborates on the solutions it provides to address the aforementioned challenges.This study systematically establishes the strategic positioning and development path of the e-CNY.Finally,it proposes recommendations for the high-quality and sustainable development of the e-CNY.
The Complex Boolean Turing Machine (CBTM) characterizes non-deterministic computation using the abstract generator α, but the abstractness of α makes it difficult to understand intuitively. In this paper, by concretizing α as the algebraic number √(2), we introduce the Real Boolean Turing Machine (RBTM) and propose the dual-tape perspective, decomposing each tape into a real tape (storing rational coefficients a) and an imaginary tape (storing irrational coefficients b). The “1”s on the imaginary tape intuitively mark the locations of “new dimensions,” laying a physical foundation for subsequent dynamic dimension tracking. More importantly, we prove the Generator Independence Theorem: computational power is independent of the specific choice of generator, whether using √(2), √(3), or the imaginary unit i, the corresponding automata are isomorphic. This reveals that the essence of non-determinism lies in the fact of “introducing a new element incommensurable with the base field,” rather than the algebraic identity of the generator. Furthermore, we introduce the generator extraction operator and analyze its limitations within a static framework, highlighting the necessity of introducing a dynamic IVM. The RBTM serves both as a visualized instance of the CBTM and as a bridge to the subsequent dynamic dimension tracking of the Imaginary-part Verification Machine(IVM).
