Damodar Bihani, Bright Chibunna Ubamadu, Andrew Ifesinachi Daraojimba
The evolution of Web3 has ushered in a paradigm shift from centralized control to decentralized, user-centric ecosystems powered by blockchain technology. At the core of these ecosystems lies tokenomics—the strategic design and management of token economies—which plays a crucial role in ensuring long-term sustainability, scalability, and user engagement. This paper presents a strategic framework for understanding and optimizing tokenomics within Web3 ecosystems, integrating insights from game theory, behavioral economics, and blockchain governance. It identifies key components of effective tokenomic models, including token utility, supply mechanisms, distribution strategies, and incentive alignment. The proposed framework emphasizes the importance of balancing inflationary and deflationary forces, designing value accrual mechanisms that benefit both users and network developers, and embedding governance protocols that enhance transparency and resilience. Additionally, this study explores the interplay between token utility and network effects, underscoring how strategic token design can accelerate ecosystem growth while maintaining decentralization. By analyzing successful Web3 projects such as Ethereum, Polkadot, and Cosmos, the paper extracts best practices and highlights potential pitfalls that hinder ecosystem scalability and trust. Furthermore, it evaluates regulatory implications, sustainability challenges, and market volatility, proposing adaptive policy mechanisms to future-proof token economies. The framework provides a roadmap for developers, investors, and policymakers aiming to build or assess blockchain ecosystems that are not only technologically sound but also economically viable. In doing so, this research bridges the gap between technical blockchain design and economic sustainability, offering actionable insights for fostering inclusive, community-driven, and robust Web3 infrastructures. As blockchain adoption accelerates globally, strategic tokenomics will be pivotal in shaping the next generation of digital economies, ensuring equitable value creation and distribution in decentralized environments.
Blockchains and peer-to-peer systems are part of a trend towards computer systems that are "radically decentralised", by which we mean that they 1) run across many participants, 2) without central control, and 3) are such that qualities 1 and 2 are essential to the system's intended use cases. We propose a notion of topological space, which we call a "semitopology", to help us mathematically model such systems. We treat participants as points in a space, which are organised into "actionable coalitions". An actionable coalition is any set of participants who collectively have the resources to collaborate (if they choose) to progress according to the system's rules, without involving any other participants in the system. It turns out that much useful information about the system can be obtained \emph{just} by viewing it as a semitopology and studying its actionable coalitions. For example: we will prove a mathematical sense in which if every actionable coalition of some point p has nonempty intersection with every actionable coalition of another point q -- note that this is the negation of the famous Hausdorff separation property from topology -- then p and q must remain in agreement. This is of practical interest, because remaining in agreement is a key correctness property in many distributed systems. For example in blockchain, participants disagreeing is called "forking", and blockchain designers try hard to avoid it. We provide an accessible introduction to: the technical context of decentralised systems; why we build them and find them useful; how they motivate the theory of semitopological spaces; and we sketch some basic theorems and applications of the resulting mathematics.
Francesco Salzano, Lodovica Marchesi, Cosmo Kevin Antenucci, Simone Scalabrino · 7 authors
Abstract In this paper, we investigate the strategies adopted by Solidity developers to fix security vulnerabilities in smart contracts. Vulnerabilities are categorized using the DASP TOP 10 taxonomy, and fixing strategies are extracted from 364 commits collected from open-source Solidity projects on GitHub. Each commit was selected through a two-phase process: an initial filter using natural language processing techniques, followed by manual validation. We assessed whether these fixes adhere to established academic guidelines. Our analysis shows that 60.55% of the commits aligned with at least one literature-based recommendation, particularly for well-documented vulnerability types such as Reentrancy and Arithmetic. However, adherence dropped significantly for categories like Denial of Service, Time Manipulation, and Bad Randomness, highlighting gaps between academic best practices and real-world developer behavior. From the remaining 143 non-aligned commits, we identified 27 novel fixing strategies not previously discussed in the literature. To evaluate their quality, we conducted a structured questionnaire involving 9 experts from both academia and industry. Their feedback indicated high perceived effectiveness of the new fixes, especially for vulnerabilities like Reentrancy and Unchecked Return Values. Generalizability received more varied responses, suggesting context-specific applicability. Finally, we performed a post-fix evolution analysis on over 6700 subsequent commits to assess the long-term stability of the fixes. Most patches remained unchanged, confirming their persistence in production code. Our findings offer practical insights into how vulnerabilities are fixed in smart contracts today, reveal promising emerging patterns, and help bridge the gap between academic guidelines and developer practices.
Andreas Plesner, Turlan Kuzhagaliyev, Roger Wattenhofer
Over the past years, advances in artificial intelligence (AI) have demonstrated how AI can solve many perception and generation tasks, such as image classification and text writing, yet reasoning remains a challenge. This paper introduces the FLIP dataset, a benchmark for evaluating AI reasoning capabilities based on human verification tasks on the Idena blockchain. FLIP challenges present users with two orderings of 4 images, requiring them to identify the logically coherent one. By emphasizing sequential reasoning, visual storytelling, and common sense, FLIP provides a unique testbed for multimodal AI systems. Our experiments evaluate state-of-the-art models, leveraging both vision-language models (VLMs) and large language models (LLMs). Results reveal that even the best open-sourced and closed-sourced models achieve maximum accuracies of 75.5% and 77.9%, respectively, in zero-shot settings, compared to human performance of 95.3%. Captioning models aid reasoning models by providing text descriptions of images, yielding better results than when using the raw images directly, 69.6% vs. 75.2% for Gemini 1.5 Pro. Combining the predictions from 15 models in an ensemble increases the accuracy to 85.2%. These findings highlight the limitations of existing reasoning models and the need for robust multimodal benchmarks like FLIP. The full codebase and dataset will be available at https://github.com/aplesner/FLIP-Reasoning-Challenge.
Andrew Lewis-Pye, Joachim Neu, Tim Roughgarden, Luca Zanolini
Safety and liveness are the two classical security properties of consensus protocols. Recent works have strengthened safety with accountability: should any safety violation occur, a sizable fraction of adversary nodes can be proven to be protocol violators. This paper studies to what extent analogous accountability guarantees are achievable for liveness. To reveal the full complexity of this question, we introduce an interpolation between the classical synchronous and partially-synchronous models that we call the $x$-partially-synchronous network model in which, intuitively, at most an $x$ fraction of the time steps in any sufficiently long interval are asynchronous (and, as with a partially-synchronous network, all time steps are synchronous following the passage of an unknown "global stablization time"). We prove a precise characterization of the parameter regime in which accountable liveness is achievable: if and only if $x < 1/2$ and $f < n/2$, where $n$ denotes the number of nodes and $f$ the number of nodes controlled by an adversary. We further refine the problem statement and our analysis by parameterizing by the number of violating nodes identified following a liveness violation, and provide evidence that the guarantees achieved by our protocol are near-optimal (as a function of $x$ and $f$). Our results provide rigorous foundations for liveness-accountability heuristics such as the "inactivity leaks" employed in Ethereum.
From viral jokes to a billion-dollar phenomenon, meme coins have become one of the most popular segments in cryptocurrency markets. Unlike utility-focused crypto assets like Bitcoin, meme coins derive value primarily from community sentiment, making them vulnerable to manipulation. This study presents an unprecedented cross-chain analysis of the meme coin ecosystem, examining 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base. We characterize their tokenomics and track their growth in a three-month longitudinal analysis. We discover that among high-return tokens (>100%), an alarming 82.8% show evidence of artificial growth strategies designed to create a misleading appearance of market interest. These include wash trading and a new form of manipulation we define as Liquidity Pool-Based Price Inflation (LPI), where small strategic purchases trigger dramatic price increases. We find that profit extraction schemes, such as pump and dumps and rug pulls, typically follow initial manipulations like wash trading or LPI, indicating how early manipulations create the foundation for later exploitation. We quantify the economic impact of these schemes, identifying over 17,000 victimized addresses with realized losses exceeding $9.3 million. These findings reveal that combined manipulations are widespread among high-performing meme coins, suggesting that their dramatic gains are often driven by coordinated efforts rather than natural market dynamics.
As Ethereum continues to thrive, the Ethereum Virtual Machine (EVM) has become the cornerstone powering tens of millions of active smart contracts. Intuitively, security issues in EVMs could lead to inconsistent behaviors among smart contracts or even denial-of-service of the entire blockchain network. However, to the best of our knowledge, only a limited number of studies focus on the security of EVMs. Moreover, they suffer from 1) insufficient test input diversity and invalid semantics; and 2) the inability to automatically identify bugs and locate root causes. To bridge this gap, we propose O p D iffer , a differential testing framework for EVM, which takes advantage of LLMs and static analysis methods to address the above two limitations. We conducted the largest-scale evaluation, covering nine EVMs and uncovering 26 previously unknown bugs, 22 of which have been confirmed by developers and three have been assigned CNVD IDs. Compared to state-of-the-art baselines, O p D iffer can improve code coverage by at most 71.06%, 148.40% and 655.56%, respectively. Through an analysis of real-world deployed Ethereum contracts, we estimate that 7.21% of the contracts could trigger our identified EVM bugs under certain environmental settings, potentially resulting in severe negative impact on the Ethereum ecosystem.
Yifan Cao, Reza Hadi Mogavi, Meng Xia, Leo Yu‐Ho Lo · 9 authors
Today's world is witnessing an unparalleled rate of technological transformation. The emergence of non-fungible tokens (NFTs) has transformed how we handle digital assets and value. These tokens have captured the interest of scholars and businesspeople alike. However, NFTs have recently seen a sharp decline in popularity. While cryptocurrency volatility and monetary policies greatly influenced NFT market trends, the community aspects of NFT projects--particularly trust-based interactions--also play a crucial role in NFT adoption and sustainability. From a social computing perspective, understanding these trust dynamics offers valuable insights for the development of both the NFT ecosystem and the broader digital economy. China presents a compelling context for examining these dynamics, offering a unique intersection of technological innovation and traditional cultural values. Through an in-depth qualitative study of Chinese NFT communities, we examine how socio-cultural factors influence trust formation and development. We analyzed discussions from eight prominent WeChat groups dedicated to NFTs and conducted 21 semi-structured interviews with three types of NFT community members. We found that trust in Chinese NFT communities is significantly molded by local cultural values. To be precise, Confucian virtues, such as benevolence, propriety , and integrity , play a crucial role in shaping these trust relationships. Our research identifies three critical trust dimensions in China's NFT market: (1) technological , (2) institutional , and (3) social . We examined the challenges in cultivating each dimension. Based on these insights, we developed tailored trust-building guidelines for Chinese NFT stakeholders. These guidelines address trust issues that factor into NFT's declining popularity and could offer valuable strategies for CSCW researchers, developers, and designers aiming to enhance trust in global NFT communities. Our research urges CSCW scholars to take into account the unique socio-cultural contexts when developing trust-enhancing strategies for digital innovations and online interactions.
The recent proliferation of blockchain-based decentralized applications (DApp) has catalyzed transformative advancements in distributed systems, with extensive deployments observed across financial, entertainment, media, and cybersecurity domains. These trustless architectures, characterized by their decentralized nature and elimination of third-party intermediaries, have garnered substantial institutional attention. Consequently, the escalating security challenges confronting DApp demand rigorous scholarly investigation. This study initiates with a systematic analysis of behavioral patterns derived from empirical DApp datasets, establishing foundational insights for subsequent methodological developments. The principal security vulnerabilities in Ethereum-based smart contracts developed via Solidity are then critically examined. Specifically, reentrancy vulnerability attacks are addressed by formally representing contract logic using highly expressive code fragments. This enables precise source code-level detection via bidirectional long short-term memory networks with attention mechanisms (BLSTM-ATT). Regarding privacy preservation challenges, contemporary solutions are evaluated through dual analytical lenses: identity privacy preservation and transaction anonymity enhancement, while proposing future research trajectories in cryptographic obfuscation techniques.
The emergence of cryptocurrencies is a response of technological development to the loss of trust in fiat money and the global banking settlement system. The aim of this study is to review existing ideas about the essence and anthropogenic impact of cryptocurrencies and determine the feasibility of their further use. Methods of contextual selection, system analysis, and general scientific methods were employed. The results show that the politico-economic contradiction of cryptocurrencies is their unreliability to preserve value concurrently with the high value of their production and use. Cryptocurrencies, having not yet become a mass phenomenon, already have a significant impact on nature, and their mass use conflicts with the transition to a green economy. The key conclusion is that increased use of cryptocurrencies will lead to an exponential rise in the use of limited resources. The production and use of cryptocurrencies as goods are associated with both obvious costs and significant negative external effects. Most importantly, governments view cryptocurrency as a threat to public finances rather than an environmental one. A possible solution to these contradictions may be the synthesis of centralized and decentralized currencies.
Data privacy and integrity maintenance becomes more difficult as cloud computing expands. Such complex and advanced cyberattacks need for stronger and better defenses than traditional security measures can offer. A research paper that combines blockchain technology with quantum cryptography to improve data security in the cloud paradigm. In addition, aspects of blockchain itself – its distributed and cryptographically verified ledger – offer the possibility of transparency and immutability of the life of the data. Excited by that? Unlike this, Quantum Cryptography applies the concepts of quantum mechanical physics in the form of quantum key distribution (QKD) which theoretically allows secure communication that is resistant to computational attacks (and even those executed on quantum computers). This paper proposes a cloud hybrid security framework that combines these two pathbreaking technologies to provide an end-to-end data security mechanism in cloud computing. This framework deploys the construction of smart contracts of Blockchain to automate the enforcement of such security policies while using Quantum cryptography's unbreakable encryption to enable the secure transmission of valuable data and information. Moreover, This study also covers practical usage, performance evaluation, and integration challenges of integrating Blockchain/Quantum Cryptography with cloud infrastructures.It has been established through the comparative analysis of existing security models that this hybrid approach offers improved mitigation towards the cyber threats ranging from data breaches, unauthorized access, and man-in-the-middle attacks. The experimental results demonstrate that integrating Blockchain and Quantum Cryptography can significantly enhance cloud data privacy, integrity, and trustworthiness. With this advancement, quantum-secured cloud-computing environments will be built to guard their data with a security measure that offers a strong, future-proofed level of security and prevents quantum-based threats that may arise in the future.
The rising energy demands of large data centers call for energy-efficient AI training methods. Federated Learning (FL), a decentralized paradigm, offers a solution by enabling model training across distributed devices without centralizing sensitive data. This review explores FL's integration with distributed data centers to achieve energy efficiency, analyzing methods like federated averaging and energy-aware protocols to minimize resource use. It highlights techniques such as model compression, quantization, and adaptive FL to reduce on-device computation while maintaining performance. Practical implementation is discussed through tools like TensorFlow Federated and PySyft, with case studies from healthcare, finance, and IoT showcasing cost reductions and sustainability. Future research directions include combining FL with edge computing and low-power AI hardware, emphasizing FL's potential for scalable, sustainable AI.
Hang Yuan, Lei Yu, Zhirong Huang, Jingyuan Zhang · 10 authors
Smart contract vulnerabilities pose significant security risks to blockchain systems, potentially leading to severe financial losses. Existing methods face several limitations: (1) Program analysis-based approaches rely on predefined patterns, lacking flexibility for new vulnerability types; (2) Deep learning-based methods lack explanations; (3) Large language model-based approaches suffer from high false positives. We propose MOS, a smart contract vulnerability detection framework based on mixture-of-experts tuning (MOE-Tuning) of large language models. First, we conduct continual pre-training on a large-scale smart contract dataset to provide domain-enhanced initialization. Second, we construct a high-quality MOE-Tuning dataset through a multi-stage pipeline combining LLM generation and expert verification for reliable explanations. Third, we design a vulnerability-aware routing mechanism that activates the most relevant expert networks by analyzing code features and their matching degree with experts. Finally, we extend the feed-forward layers into multiple parallel expert networks, each specializing in specific vulnerability patterns. We employ a dual-objective loss function: one for optimizing detection and explanation performance, and another for ensuring reasonable distribution of vulnerability types to experts through entropy calculation. Experiments show that MOS significantly outperforms existing methods with average improvements of 6.32% in F1 score and 4.80% in accuracy. The vulnerability explanations achieve positive ratings (scores of 3-4 on a 4-point scale) of 82.96%, 85.21% and 94.58% for correctness, completeness, and conciseness through human and LLM evaluation.
Francesco Zola, Jon Ander Medina, A. Venturi, Raúl Orduna-Urrutia
Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi.
Serving as the first touch point for users to the cryptocurrency world, cryptocurrency wallets allow users to manage, receive, and transmit digital assets on blockchain networks and interact with emerging decentralized finance (DeFi) applications. Unfortunately, cryptocurrency wallets have always been the prime targets for attackers, and incidents of wallet breaches have been reported from time to time. Although some recent studies have characterized the vulnerabilities and scams related to wallets, they have generally been characterized in coarse granularity, overlooking potential risks inherent in detailed designs of cryptocurrency wallets, especially from perspectives including user interaction and advanced features. To fill the void, in this paper, we present a fine-grained security analysis on browser-based cryptocurrency wallets. To pinpoint security issues of components in wallets, we design WalletProbe, a mutation-based testing framework based on visual-level oracles. We have identified 13 attack vectors that can be abused by attackers to exploit cryptocurrency wallets and exposed 21 concrete attack strategies. By applying WalletProbe on 39 widely-adopted browser-based wallet extensions, we astonishingly figure out all of them can be abused to steal crypto assets from innocent users. Identified potential attack vectors were reported to wallet developers timely and 26 issues have been patched already. It is, hence, urgent for our community to take action to mitigate threats related to cryptocurrency wallets. We promise to release all code and data to promote the development of the community.
Decentralised applications (dApps) that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be tracked and analysed, which can reveal user-behaviour information. A user behaviour analysis pipeline was proposed to present how this type of information can be extracted and analysed to identify separate behavioural clusters that can describe how users behave in the game. The pipeline starts with the collection of transaction data, involving smart contracts, that is collected from a blockchain-based game called Planet IX. Both the raw transaction information and the transaction events are considered in the data collection. From this data, separate game actions can be formed and those are leveraged to present how and when the users conducted their in-game activities in the form of user flows. An extended version of these user flows also presents how the Non-Fungible Tokens (NFTs) are being leveraged in the user actions. The latter is given as input for a Graph Neural Network (GNN) model to provide graph embeddings for these flows which then can be leveraged by clustering algorithms to cluster user behaviours into separate behavioural clusters. We benchmark and compare well-known clustering algorithms as a part of the proposed method. The user behaviour clusters were analysed and visualised in a graph format. It was found that behavioural information can be extracted regarding the users that belong to these clusters. Such information can be exploited by malicious users to their advantage. To demonstrate this, a privacy threat model was also presented based on the results that correspond to multiple potentially affected areas.
To address the challenges of inefficiency, vulnerability to attacks, and the lack of transparency and fairness in distributed power trading, a distributed power trading mechanism based on blockchain smart contracts is proposed. Firstly, a strategy for matching distributed power trading orders is designed, and a model for managing user credit is constructed using the entropy weight method and deviation cost. Secondly, a smart contract for distributed power trading is compiled using the Solidity language. Finally, the designed smart contract is deployed, tested, and a distributed power trading network is simulated. The security analysis indicates that the Merkle Tree structure and PoW consensus mechanism effectively ensure the data and system security of distributed power transactions. Simulation test results demonstrate that the transaction mechanism can reliably complete transaction settlements, safeguard the interests of trustworthy users, and enhance the economic benefits of both parties involved in the transaction.
In recent years, the term Metaverse emerged as one of the most compelling concepts, captivating the interest of international companies such as Tencent, ByteDance, Microsoft, and Facebook. These company recognized the Metaverse as a pivotal element for future success and have since made significant investments in this area. The Metaverse is still in its developmental stages, requiring the integration and advancement of various technologies to bring its vision to life. One of the key technologies associated with the Metaverse is blockchain, known for its decentralization, security, trustworthiness, and ability to manage time-series data. These characteristics align perfectly with the ecosystem of the Metaverse, making blockchain foundational for its security and infrastructure. This paper introduces both blockchain and the Metaverse ecosystem while exploring the application of the blockchain within the Metaverse, including decentralization, consensus mechanisms, hash algorithms, timestamping, smart contracts, distributed storage, distributed ledgers, and non-fungible tokens (NFTs) to provide insights for researchers investigating these topics.
In the context of cloud computing, services are held on cloud servers, where the clients send their data to the server and obtain the results returned by server. However, the computation, data and results are prone to tampering due to the vulnerabilities on the server side. Thus, verifying the integrity of computation is important in the client-server setting. The cryptographic method known as Zero-Knowledge Proof (ZKP) is renowned for facilitating private and verifiable computing. ZKP allows the client to validate that the results from the server are computed correctly without violating the privacy of the server’s intellectual property. Zero-Knowledge Succinct NonInteractive Argument of Knowledge (zkSNARKs), in particular, has been widely applied in various applications like blockchain and verifiable machine learning. Despite their popularity, existing zkSNARKs approaches remain highly computationally intensive. For instance, even basic operations like matrix multiplication require an extensive number of constraints, resulting in significant overhead. In addressing this challenge, we introduce $z k V C$, which optimizes the ZKP computation for matrix multiplication, enabling rapid proof generation on the server side and efficient verification on the client side. zkVC integrates optimized ZKP modules, such as Constraint-reduced Polynomial Circuit (CRPC) and Prefix-Sum Query (PSQ), collectively yielding a more than $\mathbf{1 2}$-fold increase in proof speed over prior methods. The code is available at https://github.com/UCF-Lou-Lab-PET/zkformer.
Silvio Meneguzzo, Claudio Schifanella, Valentina Gatteschi, Giuseppe Destefanis
Decentralised Autonomous Organisations (DAOs) automate governance and resource allocation through smart contracts, aiming to shift decision-making to distributed token holders. However, many DAOs face sustainability challenges linked to limited user participation, concentrated voting power, and technical design constraints. This paper addresses these issues by identifying research gaps in DAO evaluation and introducing a framework of Key Performance Indicators (KPIs) that capture governance efficiency, financial robustness, decentralisation, and community engagement. We apply the framework to a custom-built dataset of real-world DAOs constructed from on-chain data and analysed using non-parametric methods. The results reveal recurring governance patterns, including low participation rates and high proposer concentration, which may undermine long-term viability. The proposed KPIs offer a replicable, data-driven method for assessing DAO governance structures and identifying potential areas for improvement. These findings support a multidimensional approach to evaluating decentralised systems and provide practical tools for researchers and practitioners working to improve the resilience and effectiveness of DAO-based governance models.
Recent account allocation studies in sharded blockchains are typically miner-driven, requiring miners to perform global optimizations for all accounts to enhance system-wide performance. This forces each miner to maintain a complete copy of the entire ledger, resulting in significant storage, communication, and computation overhead. In this work, we explore an alternative research direction by proposing Mosaic, the first client-driven framework for distributed, lightweight local optimization. Rather than relying on miners to allocate all accounts, Mosaic enables clients to independently execute a local algorithm to determine their residing shards. Clients can submit migration requests to a beacon chain when relocation is necessary. Mosaic naturally addresses key limitations of miner-driven approaches, including the lack of miner incentives and the significant overhead. While clients are flexible to adopt any algorithm for shard allocation, we design and implement a reference algorithm, Pilot, to guide them. Clients execute Pilot to maximize their own benefits, such as reduced transaction fees and confirmation latency. On a real-world Ethereum dataset, we implement and evaluate Pilot against state-of-the-art miner-driven global optimization solutions. The results demonstrate that Mosaic significantly enhances computational efficiency, achieving a four-order-of-magnitude reduction in computation time, with the reduced input data size from 1.44 GB to an average of 228.66 bytes per account. Despite these efficiency gains, Pilot introduces only about a 5% increase in the cross-shard ratio and maintains approximately 98% of the system throughput, demonstrating a minimal trade-off in overall effectiveness.
Smart contract technology based on artificial intelligence background is gradually becoming a brand-new path to improve the efficiency of economic transactions due to its unique advantages. This paper initially explores the impact of smart contract technology on economic transaction efficiency through empirical analysis of models and data. The credit mechanism is introduced as an intermediate variable to analyze its mediating effect in the process of improving economic transaction efficiency by smart contract technology. The optimization of Fabric transaction mechanism is realized by using the improved credit model, which further exerts the role of smart contract technology in enhancing economic transaction efficiency. The principal component analysis is used to calculate the comprehensive score of economic transaction efficiency before and after the optimization of smart contract trading mechanism to show the effect of the development of smart contract technology on the improvement of economic transaction efficiency. This paper concludes that the development of smart contract technology will significantly and positively promote the improvement of economic transaction efficiency through benchmark regression analysis, mediation effect test and other methods. After the optimization of smart contract transaction mechanism, the comprehensive score of economic transaction efficiency produces significant improvement compared with the pre-optimization period, in which the average value of the comprehensive score of transaction efficiency in Guangdong, Jiangsu, Shanghai, and Beijing is improved by 20.18%, 24.52%, 33.77%, and 35.54%, respectively. It further indicates that smart contract technology is an effective path to improve economic transaction efficiency.
This article examines the transformative advancements in real-time payment processing optimization and their profound impact on digital commerce. It explores how millisecond-level processing improvements significantly enhance conversion rates in high-volume e-commerce environments. Key innovations discussed include intelligent predictive routing algorithms that leverage historical data to make real-time transaction routing decisions, the implementation of standardized interfaces like Payment Request API and ISO 20022, and privacy-preserving optimization techniques compliant with evolving regulatory frameworks. It further analyzes the substantial impact of these optimizations on e-commerce conversion rates, demonstrating how they reduce cart abandonment and improve customer trust and retention. Looking ahead, the article considers emerging technologies such as Central Bank Digital Currencies and distributed ledger systems that promise to further revolutionize payment processing with faster settlement times, lower costs, and expanded financial inclusion. The article findings suggest that payment processing optimization has evolved beyond technical consideration to become a strategic business imperative with measurable revenue impact and broader economic implications.
Blockchain-based digital voting systems have emerged as a promising solution to enhance the security, transparency, and accessibility of voting processes. By leveraging distributed ledger technology, these systems aim to mitigate various challenges associated with traditional voting methods, such as fraud, manipulation, and logistical complexities. However, the adoption of blockchain in voting introduces both security and usability considerations that must be carefully evaluated. In this paper, we conduct a comprehensive analysis of blockchain-based digital voting systems, focusing on their security and usability aspects. We examine the underlying cryptographic mechanisms, consensus protocols, and smart contract implementations to assess their resilience against potential attacks and vulnerabilities. Additionally, we investigate the user experience, accessibility, and scalability of these systems to evaluate their usability in real-world voting scenarios. Through this analysis, we aim to provide insights into the strengths, limitations, and trade-offs associated with blockchain-based digital voting systems, facilitating informed decision-making and further research in the field of secure and user-friendly electronic voting technologies.