Mohammad Karrabi, Farkhondeh Jabari, Asghar Akbari Foroud
The rapid expansion of blockchain technology has led to a surge in patent filings, reflecting intense innovation and competition in this emerging frontier. This chapter examines the “patent boom” in blockchain, analyzing trends, drivers, and implications for technology development, commercialization, and intellectual property management. Key areas of blockchain innovation, such as consensus mechanisms, smart contracts, cryptographic methods, decentralized finance (DeFi), supply chain solutions, and digital identity, are explored in the context of patent activity. The chapter also discusses the geographic and institutional distribution of blockchain patents, highlighting leading countries, companies, and research organizations. Additionally, legal, strategic, and technological challenges associated with patenting in the blockchain space, including overlaps, standardization issues, and open-source tensions, are considered. By systematically reviewing the patent landscape, this chapter provides insights into the dynamics of blockchain innovation, potential barriers to adoption, and opportunities for researchers, developers, and policymakers.
This article proposes a contemporary and innovative approach to portfolio efficiency, aiming to approximate a state of antifragility during periods of heightened geopolitical uncertainty and accelerated technological transformation. The multidisciplinary analysis draws on academic literature, European regulatory frameworks (such as MiCA), reports from international institutions including the World Economic Forum and the International Monetary Fund, as well as conceptual and technical documentation developed by leading platforms in the Web3 ecosystem. In preparing for the transition into a new technological era, the authors present a framework for real estate tokenization through converting property ownership into NFTs and using these tokens as collateral for lending in digital currencies. This approach addresses the problem of low real-estate liquidity and creates conditions for democratizing investment by enabling a low entry threshold and fractional ownership. The model’s antifragility is demonstrated through quantitative analysis, including an evaluation of portfolio volatility and efficiency based on Markowitz theory and the Sharpe ratio, with the results confirming the logic of Taleb’s barbell strategy. The study supports the potential for Bulgaria to position itself as an innovative regional hub for the development of Web3 and the tokenization of real-world assets.
Almost 20 years have now passed since the Nakamoto White Paper first appeared. This is a significant milestone since it allows us to take a serious look at the impact that distributed ledger technology and cryptocurrencies have had on society in general. As a product that is entering its second decade, this is a technology that has had time to scale and refine itself and move from a proof-of-concept/cottage industry phase to a more mainstream product. As such, we can reflect and examine the types of value propositions that are on offer, with a more fully realised picture of the costs of distributed ledgers, both direct and indirect.
A comparison of average Bitcoin prices in US dollars and average Wolf numbers for the solar cycle average for 2009–2025 allowed us to construct a model that explains 63.22% of the data variance. The author predicts a decline in the average annual Bitcoin price in 2026 and 2027.
Existing theories of decentralized systems—typified by blockchain consensusprotocols and distributed autonomous organizations—universally harbor a foundational presupposition: governance rules and protocol structures are fully specifiedprior to system operation, and evolution occurs only within the parameter spaceof those rules. This paper systematically demonstrates the theoretical limits ofthis “fixed protocol” preset, pointing out that when the rules themselves becomethe focal point of conflict, traditional analytical frameworks lack the conceptualresources to address the situation. By integrating the bounded rationality tradition from decision theory with the self-organization ideas from complexity science,this paper proposes “cognitive ecosystem” as an alternative theoretical framework,reconceptualizing participants in decentralized systems as autonomous agents holding evolvable cognitive architectures, and redescribing the system as a whole as afield of structural coupling among multiple cognitive architectures. Under thisframework, forks are not system failures but legitimate expansions of conceptualspace, and consensus is not unanimous agreement but functional differentiationacross cognitive niches. The paper demonstrates the explanatory power of thisframework through the cases of the Bitcoin block size war of 2015–2017 and the2016 The DAO incident, and discusses its further application prospects in the governance of digital infrastructure.
The article examines the problem of formalizing investment cash flow in a distributed ledger environment. Within the framework of the digital transformation of financial relations, the cash flow of an investment project can be represented as a digital twin, recorded in the distributed ledger infrastructure and implemented through smart contracts. The aim of the study is to develop a mathematical model of the digital twin of investment cash flow and an algorithm for its forecasting using neural networks. Theoretical approaches to the interpretation of digital twins are systematized, and the limitations of the classical discounted cash flow model in relation to the digital environment are analyzed. A formalized model of digital cash flow is proposed, taking into account transaction fees of the distributed ledger, algorithmically accrued income, and an extended discount rate structure including technological and regulatory risk premiums. An algorithm for neural network forecasting of the digital twin is developed based on a feature vector integrating financial and infrastructure parameters. A comparative analysis of the digital and classical models is performed, which allowed establishing the structural modification of the investment process in the digital environment. The obtained results can be used in the valuation of digital financial assets and the construction of adaptive systems for forecasting their cash flows.
Ethereum and other prominent cryptocurrencies have revolutionized decentralized finance (DeFi) and blockchain technology. Introduced in 2015 by Vitalik Buterin, Ethereum expanded blockchain&s;s capabilities beyond Bitcoin by enabling smart contracts and decentralized applications (dApps). Operating on a public, permissionless network, Ethereum uses its native cryptocurrency, Ether (ETH), to facilitate transactions and computational services. Smart contracts—self-executing agreements encoded directly into the blockchain are central to Ethereum, enabling automated, trustless interactions without intermediaries. This innovation has fostered a thriving ecosystem of dApps, DeFi protocols, and non-fungible tokens (NFTs), driving adoption across industries. Cryptocurrencies like Binance Coin (BNB), Cardano (ADA), and Solana (SOL) each contribute unique innovations to the blockchain ecosystem. Binance Coin, initially launched as an ERC-20 token on Ethereum, now operates on the Binance Smart Chain (BSC). It offers faster and cheaper transactions, supports decentralized applications, and enables reduced trading fees within the Binance ecosystem. Additionally, BNB facilitates staking, governance participation, and cross-chain compatibility for broader usability. Cardano, founded by Charles Hoskinson, emphasizes a Ethereum and other prominent cryptocurrencies have revolutionized decentralized finance (DeFi) and blockchain technology. Introduced in 2015 by Vitalik Buterin, Ethereum expanded blockchain&s;s capabilities beyond Bitcoin by enabling smart contracts and decentralized applications (dApps). Operating on a public, permissionless network, Ethereum uses its native cryptocurrency, Ether (ETH), to facilitate transactions and computational services. Smart contracts—self-executing agreements encoded directly into the blockchain are central to Ethereum, enabling automated, trustless interactions without intermediaries. This innovation has fostered a thriving ecosystem of dApps, DeFi protocols, and non-fungible tokens (NFTs), driving adoption across industries. Cryptocurrencies like Binance Coin (BNB), Cardano (ADA), and Solana (SOL) each contribute unique innovations to the blockchain ecosystem. Binance Coin, initially launched as an ERC-20 token on Ethereum, now operates on the Binance Smart Chain (BSC). It offers faster and cheaper transactions, supports decentralized applications, and enables reduced trading fees within the Binance ecosystem. Additionally, BNB facilitates staking, governance participation, and cross-chain compatibility for broader usability. Cardano, founded by Charles Hoskinson, emphasizes a research-driven approach with peer-reviewed development. It employs a proof-of-stake consensus mechanism called Ouroboros to enhance scalability and security while maintaining energy efficiency. Cardano’s layered architecture enables seamless upgrades and supports smart contract functionality for decentralized applications.
Blockchain technology rests upon distributed-ledger principles, offering a decentralised, tamper-evident method of recording transactions across networks. The discussion begins with an analysis of network topologies –centralised, decentralised, distributed, and hybrid – each with distinct implications for resilience, control, and scalability. Distributed-Ledger Technology (DLT) is defined as a class of decentralised systems enabling peer-to-peer consensus without central authority, with blockchain as a linear, cryptographically chained variant . Key architectural elements are examined, including node types, data structures, and consensus mechanisms such as Proof-of-Work, Proof-of-Stake, and Byzantine Fault Tolerance, each with distinct trade-offs between security, performance, and energy efficiency. The emergence of smart contracts and tokenisation is presented as transformative: enabling programmable, trust-minimised interactions and asset digitisation, while simultaneously introducing regulatory, legal, and technical challenges . The chapter critically distinguishes blockchain from broader DLTs, highlighting alternative models such as DAG-based systems (e.g., IOTA, Hashgraph) and permissioned frameworks (e.g., Corda). Foundational applications across financial services, supply chains, and digital identity are introduced, setting the stage for industry-specific use cases. Finally, implications for the built environment – including the “Golden Thread” of building lifecycle information – are briefly explored, identifying the potential of blockchain to enhance data integrity, accountability, and compliance in complex asset ecosystems .
This thesis examines the implications of market frictions in international finance and macroeconomics in three contexts. The first chapter documents the effect of trading relationships on client trading outcomes in the over-the-counter (OTC) foreign exchange (FX) derivatives market. The second chapter documents the effect of nominal wage setting frictions on employment. The third chapter examines the behavior of non-U.S. central banks when firms engage in currency mismatch, borrowing more in dollars than given by their dollar operating exposures, emphasizing how imperfect regulation may affect U.S. dollar interest rates. In the first chapter, joint with Gerardo Ferrara, I study whether clients that rely more heavily on a dealer in the OTC FX derivatives market have worse trading outcomes after the dealer is adversely shocked. Using granular transaction-level data, we document that trading relationships are persistent—in an active trading week, clients are more likely to trade with a dealer that they had a relationship with and relied on more heavily. Then, we exploit the March 2023 collapse of Credit Suisse as an exogenous shock to exposed clients’ set of trading alternatives when relationships are persistent. Using difference-in differences analyses, we find that, although Credit Suisse’s EURUSD notional traded and trade count declined, clients that relied less heavily on Credit Suisse did not differentially reduce their Credit Suisse-specific trading activity relative to more reliant clients. Instead, more reliant clients continued trading at the client level and increased activity with other existing dealer relationships without incurring additional costs, relative to less reliant clients. These findings suggest that search and bargaining frictions were not particularly costly for heavily reliant clients after the shock—relationship persistence did not differentially prevent them from reallocating activity to existing alternative dealers, or lead to relatively greater costs, when their relationship dealer came under stress. In the second chapter, joint with Gert Bijnens, Hugo Monnery, and Laura Nicolae, I empirically document the effect of wage changes, driven by wage indexation to inflation, on firm-level employment growth. In Belgium, nearly all employees’ wages are indexed to inflation and firms are grouped into labor agreements that determine the exact timing and frequency at which wages are indexed, e.g. every year or every month. Using firm-level administrative data, we estimate two-stage least squares regressions of firm-level employment growth on wage growth, instrumented by the wage growth implied by the firm’s indexation policy. We find that employment contracts by 0.4% over four quarters for each 1% increase in wages. This result is robust to including NACE sector-date fixed effects and to using only variation in firms’ indexation timing, controlling for their chosen indexation frequency. About one-third of the response comes via anticipation of future wage increases. The elasticity is more than twice as large in magnitude in the post-pandemic period than before it, suggesting strong nonlinearities. Overall, these results show that, by preventing inflation from reducing real wages, inflation indexation reduces employment. In the third chapter, joint with Mitali Das, Gita Gopinath, Taehoon Kim, and Jeremy Stein, I document an externality of central banks’ imperfect regulation of firms that engage in currency mismatch, which results from central banks’ dollar reserve accumulation decisions. We explore how foreign central banks behave when firms engage in currency mismatch. Using a panel of 56 countries, we document that central bank holdings of dollar reserves are correlated with the dollar-denominated bank borrowing of their non-financial corporate sectors. Then, we build a model in which the central bank can deal with private-sector mismatch, and the associated risk of a domestic financial crisis, by: (i) imposing ex ante financial regulations; or (ii) accumulating dollar reserves to serve as an ex post dollar lender of last resort. The model highlights a novel externality: individual central banks may over-accumulate dollar reserves, relative to what a global planner would choose. Under imperfect regulation of currency mismatch, individual central banks do not internalize that their hoarding of reserves exacerbates a global scarcity of dollar-denominated safe assets, which lowers dollar interest rates and encourages firms to further increase the currency mismatch of their liabilities. Relative to the decentralized outcome, a global planner may therefore prefer higher capital requirements and reduced holdings of dollar reserves.
The growth of the crypto markets has changed the investment environment in a profound manner by elevating cryptocurrencies from purely speculative assets to institutional-grade investments. The current paper evaluates the investment characteristics of Bitcoin and Ethereum, the most popular cryptocurrencies, based on the modern portfolio theory framework. According to the analysis carried out for 2020-2025, the Bitcoin asset demonstrates an impressive Sharpe ratio of 1.7, substantially exceeding that of the S&P 500 (0.54) and gold (0.48-0.54). In favorable market conditions, Ethereum outperforms Bitcoin in terms of risk-adjusted returns, exhibiting even better characteristics. The study highlights a change in the mechanism of price fluctuations in the market from the \\\"four-year cycle\\\" to the flow of institutional capital. At the same time, correlation analysis shows that despite the absence of high correlation of these assets with other asset classes over the long term, the correlation between the two increases under market pressure. As a result, 1-4% of portfolio weight can be safely allocated to each asset, depending on the investment strategy.
Historical Context and Problem Statement The digital revolution has created two parallel challenges that have resisted comprehensive solutions: Internet Data Transfer Limitations: Despite decades of progress, internet download speeds remain constrained by inefficient protocols that don't adapt to network topology dynamics. Traditional download managers like IDM operate with static segmentation strategies that ignore the quantum-inspired probabilistic nature of network paths. Web3 Liquidity Fragmentation: Decentralized finance (DeFi) suffers from fragmented liquidity across multiple venues, resulting in significant MEV exploitation. As documented by Qin et al. (2021), MEV extraction has cost users over $680 million in 2021 alone, with no comprehensive solution addressing the root cause. These seemingly disconnected problems share a common underlying structure: both involve the transfer of "value" (data or financial assets) across complex networks where efficiency is hampered by non-resonant transmission strategies.
Theoretical background: In general, authors claim that the business model for any human-beings organisation defines who and how creates values in a socio-economic context. Taking into account the organisational theories presented in literature, authors notice a variety of definitions and components of business models. In addition, values in the business models have different interpretations. By definition, decentralised autonomous organisation (DAO) is using the Blockchain 2.0 technology, which strongly supports its internal operational management, change of attitude towards organisation members’ identification, and controlling internal activities. Purpose of the article: Construction of the Decentralised Autonomous Organisation (DAO) business model for determining DAO strategic development is the main purpose of this study. The authors aim to provide their own proposal of business model, as well as the identification of DAO business model components. The authors expand the DAO business model canvas, and beyond variables included in Osterwalder’s model, and consider some other important DAO features by example of TalentDAO case study. Research methods: The authors have focused on surveys of the management science literature in some popular repositories. Beyond that, they have added a DAO case study. They have done descriptive analysis of publications on business models and DAO business models. The authors applied the case study approach, because they argue that each DAO is different and taking into account suggestions provided by practitioners, the exploratory case study method is the best method to reveal idiosyncrasy of business organisation as well as applicability of theoretical business models for practice of DAO management. Main findings: Through the literature surveys, authors concluded that selected theories in science of management are fundamental for DAO construction and applicable for development of business models. Although the reviewed models are various, they have many common features and allow constructing the authors’ model of DAO business, which is an extension of Osterwalder Business Model Canvas. The authors characterised DAO partners, customers, values, resources, and activities. The authors discussed constraints and risks of DAO activities as well as the applied methods of coordination and control. The authors claim that DAO supports decentralized decision-making and intra-organizational trust intensification. They argue that the case study on DAO business model is an exemplification, which can be useful for development of other similar DAOs.
Blockchain is a revolutionary technology now a days. It has opened various platforms for research too. Blockchain is decentralized distributed network system with no central authorization. Because of its inherent features it has been used in various applications. Features are immutability, anonymity, transparency, decentralized etc. This study gives the basic idea about blockchain technology. The study also investigates consensus mechanisms to add transactions in the network. This study gives an idea about what the various frameworks are and how consensus protocols work under various frameworks. Bitcoin, Ethereum are the common frameworks used in blockchain technology. Proof of Work (PoF) and Proof of Stake (PoS) are the commonly used consensus types in blockchain. The analysis includes considerations about blockchain process, types of blockchain, layered architecture of blockchain, consensus protocols, blockchain framework, applications of blockchain. This study emphasizes the need for better scalability, security and integrity options. In future, Blockchain technology has the potential to integrate with many technologies like Artificial Intelligence (AI), Cloud Computing, Internet of Things (IoT) etc.
Ejiro U, Osiobe, Waleed A., Hammood, Safia, Malallah, Nyore E., Osiobe · 6 authors
Quantum mechanics principles underpin quantum computing, signaling a major shift in how we process information. While it offers immense processing power and potential advantages, it also presents significant challenges for the cryptocurrency industry. This sector has grown rapidly, supporting decentralized finance and empowering users worldwide, but it also attracts malicious actors looking to exploit its vulnerabilities. Traditional cryptography remains strong, yet increasingly sophisticated computational attacks threaten security. As the cryptocurrency market expands, quantum computing offers both opportunities, such as improved transaction security, and risks, like easier decryption for hackers. Understanding quantum technology’s benefits and challenges is crucial as it develops. Currently, data is protected by traditional cryptography, but future, more powerful quantum computers could weaken this security. This article explores potential uses of quantum computing in daily life and business, explains its functions simply, and discusses societal impacts. Its goal is to help students and general readers understand how quantum technology might transform our world through clear language and real-life examples. Topics include the basics of quantum computing, its present and future applications across industries, and its societal effects. We provide a thorough analysis of how quantum computing could reshape society through mathematical insights, practical examples, and future perspectives.
Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Israa Badr Al-Mashhadani, Sajida Memon · 7 authors
The exponential growth of healthcare Internet of Things (IoT) data necessitates secure, low-latency analytics that extend beyond centralized architectures. This paper presents BDAFL DNN, a blockchain-integrated data analytics framework that combines Federated Learning (FL) and Deep Neural Networks (DNNs) for real-time, privacy-preserving healthcare analytics across edge and cloud resources. Local devices such as smartwatches and phones collect noninvasive time series sensor streams (heart rate, temperature, and abdomen sensors), perform on device DNN training, and send only model updates to healthcare edge nodes, where a blockchain ledger validates updates for integrity and traceability; validated updates are then aggregated in the cloud via FL to produce a global model without sharing raw data. In a simulation study against representative baselines, BDAFL DNN reduced execution time, energy use, and resource consumption, lowered the deadline miss ratio, and improved blockchain validation correctness. These results show that integrating blockchain with FL-driven edge and cloud DNN analytics can deliver scalable, secure, and timely insights for future healthcare IoT systems. Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “Blockchain-Powered Dynamic Segmentation in Personal Health Record”. The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility. This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.See expression of concern available at:https://doi.org/10.58496/2026/017 https://mesopotamian.press/journals/index.php/CyberSecurity/article/view/1041
The US market recently moved to a T+1 settlement cycle, which is quite a big step forward, but the real goal here is achieving real time settlement - basically T + 0 or instant settlement - with the ultimate goal of achieving real-time (T+0) settlement, which could significantly enhance the efficiency and stability of U.S. financial markets. This paper proposes a complete solution for instant trade settlement in the US market. Our approach involves using technologies like Distributed Ledger Technology (DLT) and Blockchain and combining them with intelligent automation using Agentic Artificial Intelligence (AI) and Large Language Models (LLMs) for their analytical power. The system being detailed here is built around a private, permissioned blockchain setup. This approach gives us immutable record keeping and atomic settlement capabilities through smart contracts. What this means is that tokenized securities and cash can be transferred instantaneously. When it comes to Agentic AI, this technology is being looked at to handle various tasks autonomously. Tasks like pre trade validation, managing real time liquidity, optimizing collateral, and dealing with post trade exceptions. These capabilities collectively aim to reduce both operational and counterparty risks. As for LLMs, these will work as intelligent interfaces for monitoring and reporting. They will handle regulatory compliance automation and improve market insights using natural language processing. This whole integrated framework being proposed should help drastically cut down counterparty risk, boost liquidity, reduce operational costs, and make things more transparent and auditable. This paper also tackles some key challenges - regulatory alignment, interoperability issues, scalability concerns, and cybersecurity aspects. This provides a realistic roadmap for where U.S. financial market infrastructure might head in the future. This work introduces a comprehensive framework that brings together permissioned distributed ledger technology, autonomous AI agents, and large language models to streamline regulatory processes. The result is a real-time (T+0) settlement system specifically designed to meet the demands of institutional trading in the U.S. financial markets.