The modern international financial order is undergoing significant structural transformation driven by globalization and digital technological innovation. This paper analyzes this transformation from the perspective of âembedded liberalism,â which formed the foundation of the postwar order. This paper examines the history of how the Bretton Woods system achieved both international liberalization and domestic social stabilization through capital mobility restrictions, verifying that financial liberalization since the 1970s led to governance hollowing out. It then clarifies that while the rise of crypto assets demonstrates a âde-embeddingâ logic that circumvents centralization, it simultaneously triggers attempts at âre-embeddingâ through regulation. The risks of decentralized finance (DeFi) and regulatory fragmentation are difficult to address with conventional governance. Therefore, we conclude that transitioning to a âmulti-layered governance modelâ where states, international institutions, and the protocol layer interact is essential for ensuring the stability of the future international financial order. This amounts to building a modern version of âembedded liberalismâ that reconciles the freedom of technological innovation with social stability.
Esther Uzoka, Bisola Akeju, Olumide Kumuyi, David Excel Ozowara
The Framework for Data Governance and Compliance Across Distributed Multicloud Infrastructures provides a comprehensive model for managing data integrity, privacy, and regulatory alignment in increasingly complex hybrid and multicloud environments. As organizations adopt distributed computing to enhance scalability, resilience, and performance, they face significant challenges in maintaining consistent governance across heterogeneous platforms operated by multiple providers. This framework establishes a unified governance architecture that integrates policy-based orchestration, automated compliance auditing, and federated identity management to ensure data sovereignty, accountability, and interoperability across diverse cloud ecosystems.At its core, the framework emphasizes data classification, lifecycle management, and access control standardization. Sensitive data are categorized by regulatory requirement and security level, while dynamic policies enforce encryption, anonymization, and retention protocols in accordance with frameworks such as GDPR, HIPAA, and ISO 27001. By leveraging federated metadata catalogs and distributed ledgers, the system enables traceable data provenance and immutable audit trails across hybrid environments. A zero-trust security paradigm further ensures that all access requests are continuously verified, regardless of origin, thereby mitigating insider threats and cross-cloud vulnerabilities.The framework also integrates AI-driven compliance monitoring to detect policy violations, automate reporting, and support adaptive governance in real time. Through interoperable APIs and compliance-as-code implementations, organizations can harmonize data policies across public, private, and edge cloud resources while maintaining jurisdictional and contractual adherence.In promoting transparency and resilience, this framework underscores the importance of cross-sector collaboration among regulators, cloud providers, and enterprises. By unifying governance, security, and compliance strategies, it advances a scalable model for secure data management in distributed infrastructuresenabling innovation, regulatory trust, and sustainable digital transformation in the multicloud era.
The scarce resource in agriculture needs to be managed efficiently, and we are developing new solutions to meet our need to manage resource scarcity and to improve irrigation methods. This research proposes the blockchain-enabled Decentralized Water Management System (BD-WMS) based on Blockchain, Smart Contract, Internet of Things (IoT), and Artificial Intelligence (AI) for sustainable irrigation. On a real-time basis, and to see that the data collected is accurate, the BD-WMS is loaded with IoT sensors to measure the soil moisture, pH levels, and weather conditions. Firstly, it records the data in a ledger on blockchain to ensure that there is no corruption of data and that the data cannot be changed in any way. Using smart contracts, dynamic water requirements are complied with to autonomously control irrigation valves according to dynamic water requirements. I also put forth a Tokenized Water Conservation Incentive Model (TWCIM) that distributes blockchain-based tokens to the farmers in exchange for their adoption of water-saving practices that are convertible into a subsidy amount or can be spent on agricultural resources. An AI-powered predictive analytics module plays its part in the further development of the system efficiency, and it predicts the water demands based on the historical data and environmental conditions. In the greenhouse tomato, the studies show up to 40% water savings and about 25% increase in crop yield when compared to conventional water management. It offers a unique solution to the problems that occur in the traditional irrigation model owing to the decentralized control, along with the criteria of incentive-driven conservation. It was proposed as a scalable, secure, and efficient solution to support sustainable agriculture that optimizes efficient water governance and resource preservation.
Sr IT Developer, First Horizon Bank, Memphis, TN, USA, Rushikesh Anantrao Deshpande
The article examines methods for optimizing PL/SQL queries in distributed banking databases, emphasizing the transition from static rule-based mechanisms to adaptive, learning-driven architectures. The studyâs relevance is defined by the increasing complexity of financial data environments that require real-time consistency, fault tolerance, and intelligent workload distribution. The research synthesizes results from seven recent works published between 2021 and 2025, covering neural cost modeling, heuristic algorithms, hybrid plan enumeration, and visualization-based diagnostics. Special attention is devoted to learned cost models and metaheuristic strategies that enhance selectivity estimation, reduce latency, and stabilize throughput in distributed ledger systems. The methodological framework integrates comparative analysis, systematization, and critical evaluation of hybrid, heuristic, and learning-based optimizers. The findings reveal a multi-layered optimization model that combines probabilistic inference, robust plan selection, and heuristic refinement. The conclusions underscore the practical applicability of adaptive PL/SQL optimization for high-volume banking infrastructures and data-intensive financial analytics.
The rapid evolution of blockchain technology has revolutionized digital asset ownership through NonFungible Tokens (NFTs). NFTs enable creators to tokenize unique digital assets such as art, music, and collectibles, ensuring authenticity, transparency, and verifiable ownership. This research paper presents the design and development of a decentralized NFT Marketplace using Solidity Smart Contracts and Pinata IPFS (InterPlanetary File System) integration. The proposed system eliminates the need for intermediaries by leveraging blockchain-based automation, allowing creators to mint, list, and sell NFTs securely while maintaining full ownership control. The marketplace integrates MetaMask wallet authentication for secure transactions and employs Solidity smart contracts to handle NFT minting, transfer, and royalty distribution on the Ethereum blockchain. Additionally, Pinata IPFS provides decentralized storage for digital media and metadata, ensuring data permanence and tamper-proof accessibility. The system architecture combines transparency, security, and user-friendliness, empowering creators with fair compensation and buyers with verifiable proof of ownership. Experimental implementation results demonstrate that the proposed NFT Marketplace provides a reliable, transparent, and scalable environment for digital asset exchange. This study highlights the potential of decentralized systems in reshaping the digital economy and sets the foundation for future enhancements such as multi-chain support, AI-based recommendations, and mobile integration.
Blockchain technology has emerged as a transformative solution for decentralized and secure data management. However, the security of blockchain networks heavily relies on robust cryptographic protocols. This article provides a comprehensive analysis of key cryptographic techniques employed in blockchain security, including hash functions, digital signatures, consensus algorithms, and zero-knowledge proofs. We evaluate their roles in ensuring data integrity, authentication, confidentiality, and resistance to common attacks such as double-spending and Sybil attacks. A comparative study highlights the strengths and limitations of these protocols, guiding future enhancements for blockchain security frameworks
The rapid development of financial technology has introduced cryptocurrency as a transformative innovation within the global financial system, raising fundamental questions regarding its compatibility with value-based financial models, particularly Islamic banking. Grounded in SharÄŤĘżah principles that emphasize ethical finance, risk-sharing, asset-backed transactions, and the prohibition of ribÄ, gharar, and maysir, Islamic banking faces both opportunities and challenges in responding to the emergence of decentralized digital assets. This study aims to explore how Islamic banking can engage with cryptocurrency while maintaining its normative and ethical foundations, as well as to identify the key constraints that limit institutional adoption within global finance. Employing a qualitative research approach, the study conducts a systematic and interpretive review of scholarly literature, regulatory frameworks, and classical as well as contemporary SharÄŤĘżah sources related to Islamic finance, blockchain technology, and cryptocurrency governance. The analysis reveals that blockchain technology demonstrates substantial alignment with Islamic banking principles through its transparency, traceability, and decentralized verification mechanisms, offering institutional potential in areas such as payments, trade finance, and smart contracts. However, the findings also indicate that cryptocurrency markets are characterized by high volatility, speculative behavior, weak real-sector linkage, and fragmented regulatory oversight, which raise significant ethical, financial, and governance concerns for Islamic banking institutions. Regulatory divergence and inconsistent SharÄŤĘżah interpretations further complicate cross-border implementation and scalability. The study concludes that Islamic banking should adopt a selective and principle-oriented approach to cryptocurrency, distinguishing between permissible technological infrastructure and ethically problematic market practices.
Phemelo Tamasiga, Valentine Munyaradzi Dzingai, Helen Onyeaka, Rose Daphnee Tchonkouang ¡ 7 authors
⢠The global energy transition impacts food security in developing countries. Renewable energy improves agricultural productivity but creates trade-offs like land and water competition. ⢠Agri-voltaic systems enhance crop yields and energy efficiency. They help balance food and energy production, reducing fossil fuel dependency. ⢠Renewable energy projects can increase food prices in vulnerable regions. Financial incentives, social protection, education, and public-private partnerships to make renewable energy adoption more accessible and affordable for farmers. ⢠Renewable energy can reduce agricultureâs operational costs but high initial investments limit smallholder farmers. The study recommends subsidies, training, and financial support to help farmers adopt renewable energy while ensuring food security is maintained. ⢠There is an eminent need for an interdisciplinary approach to understanding energy transitionsâ impacts on food security and long-term sustainability. Transitioning to net-zero societies affects how energy is produced and consumed, with consequences for food security. Through a systematic review of 43 peer-reviewed studies that follow the PRISMA protocol, results reveal that renewable energy can enhance agricultural productivity by reducing operational costs, increasing efficiency in irrigation and processing, and providing reliable access to energy. However, challenges exist, including competition for land and water resources between renewable energy projects and food production, high upfront costs of clean energy technologies, limited access to credit facilities, and institutional bottlenecks. To overcome these challenges, recommended policies include offering subsidies and financial incentives to make clean energy more affordable for farmers, as well as providing education and training to support the adoption of sustainable practices. Furthermore, promoting collaboration between the public and private sectors is crucial to stimulate investment in renewable energy infrastructure. Moreover, these policies must be designed for specific national circumstances. High-income or upper-middle-income countries can deploy capital-intensive agrivoltaic and biogas technologies via concessional finance. In contrast, low-income settings should prioritize low-cost, decentralized solar pumps and off-grid dryers to build farmer confidence and trust. Countries with stronger regulatory frameworks and secure land tenure systems are better equipped to support large-scale renewable energy projects. At the same time, regions with weaker governance tend to benefit most from community-owned mini-grids. The mapping of policy options onto economic, institutional, and agro-ecological dimensions provides a nuanced, context-sensitive framework to guide equitable and effective energy transitions in diverse agricultural landscapes.
Open access
Photovoltaic Systems and Sustainability
Water-Energy-Food Nexus Studies
Agriculture Sustainability and Environmental Impact
Ningshuang Zeng, Xuling Ye, Shiqi Chen, Yan Liu ¡ 5 authors
Although existing models and theories have explained systemic behaviors such as demand amplification and disruption propagation, practical challenges in Modular Construction Supply Chains (MCSC) remain unresolved due to production heterogeneity, geographic dispersion, and conflicting stakeholder interests. In addition, the lack of digital infrastructure and process-level data integration continues to hinder the development of automation and intelligent decision-making. To address these issues, this study develops an MCSC coordination system informed by industrial input. The system features a novel dual-engine architecture that integrates blockchain-enabled smart contracts and Robotic Process Automation (RPA). It also incorporates a practice-oriented approach to MCSC Supply Batch (MSB)-based management, using industrial insights to define the MSB as the fundamental coordination unit in process execution. The automatic triggering mechanism enabled by MSBs and dual-engine enables task-to-task transitions while maintaining traceability and operational clarity across supply chain nodes. A real-world case study validates the effectiveness of the proposed system in enhancing traceability, automation, and stakeholder collaboration within MCSC environments.
The rise of non-fungible tokens (NFTs) has increased the risk of fraud and market manipulation. This study introduces a method for detecting wash trading in the NFT marketplace using Graph Neural Networks (GNNs) applied to Ethereum blockchain transaction data. We constructed a heterogeneous graph, used Depth-First Search for labelling, and extracted graph features, including PageRank and degree centrality. We evaluate various classification models: Multilayer Perceptron (MLP), Graph Convolutional Neural Network (GCN), and Heterogeneous Graph Convolutional Neural Network (HeteroGCN). The results show that GNN models, particularly the feature-enhanced HeteroGCN, exhibit superior performance compared to featureless models and traditional tabular baselines. The key contribution of this study is that PageRank and Degree Centrality features significantly improve the accuracy of identifying transactions involved in market manipulation.
Muhammad Ilman Abidin, Ahmad M. Ramli, Laina Rafianti
Non-Fungible Tokens (NFTs) have emerged as a transformative component of Indonesiaâs digital economy, enabling new forms of value creation in art, culture and finance. However, the current regulatory framework remains fragmented, with overlapping responsibilities across financial, intellectual property, trade and digital communications authorities. This research adopts a combined normative juridical analysis (de lege lata) and institutional impact assessment (de lege ferenda), supported by comparative study of global regulatory approaches. The findings demonstrate that existing Indonesian regulations do not sufficiently address the unique characteristics of NFTs, particularly their hybrid nature as digital property, investment assets and instruments of electronic commerce. Additional challenges arise from cross-border transactions, decentralized autonomous organizations (DAOs) and the absence of clear enforcement mechanisms. To overcome these gaps, the study proposes a phased regulatory roadmap: in the short term, a joint decree among relevant ministries and agencies; in the medium term, a dedicated coordination desk under a coordinating ministry; and in the long term, the establishment of a specialized NFT authority. This staged approach balances flexibility with institutional strength, providing legal certainty, investor and creator protection, and alignment with international best practices. Ultimately, Indonesia can leverage NFT governance not only as a domestic legal innovation but also as a strategic opportunity to position itself as a regional hub for digital asset regulation in Southeast Asia.
Collin Arnold Kabwama, Osorachukwu Maurice Ayozie, Justin Njimgou Zeyeum, Adeniran Oluwatoyosi Awe ¡ 6 authors
As Critical National Infrastructure (CNI) becomes increasingly digitized, traditional reactive security assessments are failing to keep pace with automated threats. This paper proposes an autonomous framework integrating Generative Adversarial Networks (GANs) and Distributed Ledger Technology (DLT) for proactive vulnerability discovery and immutable infrastructure hardening. We utilize a Physics-Aware Wasserstein GAN to synthesize protocol-specific attack vectors that identify "zero-day" weaknesses in Industrial Control Systems (ICS) by exploring the operational state space of protocols like DNP3 and Modbus. Discovered vulnerabilities are committed to an Immutable Knowledge Base (IKB) on a sharded, permissioned blockchain, providing a "Single Source of Truth" for cross-sector threat intelligence. To automate mitigation, Smart Contracts orchestrate infrastructure hardening by validating patches through digital twin simulations before network-wide deployment. Experimental results using HELICS and NS-3 demonstrate that this architecture reduces the Mean Time to Remediate (MTTR) from days to sub-second intervals. Finally, we address long-term security by incorporating Post-Quantum Cryptography (PQC) to protect the ledger against emerging quantum threats.
Open access
Smart Grid Security and Resilience
Software-Defined Networks and 5G
Infrastructure Resilience and Vulnerability Analysis
Tingginya risiko manipulasi data dan lemahnya sistem autentikasi tradisional menimbulkan tantangan serius dalam menjaga keamanan identitas digital. Dampak dari permasalahan ini adalah meningkatnya potensi pencurian data, penyalahgunaan identitas, serta rendahnya tingkat kepercayaan terhadap sistem keamanan digital yang ada. Untuk menjawab tantangan tersebut, penelitian ini mengembangkan sistem autentikasi wajah berbasis blockchain yang aman, transparan, dan terdesentralisasi. Sistem dirancang dengan memanfaatkan algoritma SHA-256 untuk mengubah data wajah menjadi hash yang tidak dapat dibalik, sehingga menjaga privasi dan integritas data pengguna. Informasi identitas dicatat ke dalam blockchain melalui smart contract berbasis Ethereum yang dijalankan menggunakan Ganache, dengan bahasa pemrograman Python dan pustaka face_recognition sebagai deteksi wajah serta web3.py untuk integrasi blockchain. Hasil penelitian menunjukkan sistem mampu mengenali wajah secara akurat, memverifikasi identitas secara real-time, serta memastikan data tidak dapat dimanipulasi karena tercatat dalam blockchain. Sistem ini dapat dimanfaatkan oleh institusi pendidikan, perusahaan, maupun instansi pemerintah untuk meningkatkan keamanan akses, sistem absensi, serta perlindungan data sensitif. Penelitian selanjutnya dapat diarahkan pada implementasi di jaringan testnet Ethereum publik agar mendekati skenario dunia nyata, termasuk pengujian biaya gas, performa transaksi, serta integrasi dengan aplikasi berbasis mobile.
This study analyzes how blockchain technology can be interpreted through an economic perspective, viewing network nodes as rational agents whose strategic behavior affects the efficiency and sustainability of decentralized systems. Using a multi-player non-cooperative game with complete but imperfect information, we model validatorsâ decisions in voting-based consensus mechanisms and compare alternative incentive configurations through simulation results. The analysis shows how variations in reward schemes influence validatorsâ behavior and consensus reliability. Extending the framework to Decentralized Autonomous Organizations (DAOs), the study explores how blockchain-based incentives can enhance participation, accountability, and decentralized governance. The findings highlight that incentive design plays a decisive role in aligning individual motivations with collective goals, ensuring both network integrity and long-term sustainability. Overall, this study connects economic theory with blockchain governance, extending its relevance to business and organizational contexts beyond cryptocurrencies.
Prof. Madhavi Bhosale, Abhishek Kangude, Vedant Khandare, Sunil Kajave
Abstract In recent years, advancements in blockchain technology have paved the way for creating transparent, secure, and decentralized digital ecosystems. This paper presents a blockchain-based electronic voting (e-voting) system designed to overcome the limitations of traditional and centralized electronic voting methods. The proposed system integrates Solidity-based smart contracts, a Python middleware API using Web3.py, and a Flutter frontend to create a secure, verifiable, and user-friendly voting platform. The architecture ensures voter anonymity, immutability of votes, and real-time result verification through blockchainâs decentralized ledger. The system employs MetaMask for voter authentication, enabling a one-person-one-vote mechanism and eliminating centralized control or tampering risks. Experimental simulations using Ganache demonstrate efficient transaction processing, transparent result computation, and tamper-proof data storage. The proposed solution enhances security, transparency, and trust in digital elections and serves as a foundation for scalable, real-world implementations in organizational, academic, and governmental voting scenarios. This research contributes toward developing next-generation decentralized voting infrastructures that reinforce democratic integrity and public confidence in electoral processes. ¡ Keywords : Blockchain Technology; E-Voting System; Smart Contracts; Decentralized Applications (DApps); Solidity; Ethereum; Python Web3.py; Flutter Frontend; MetaMask Authentication; Digital Elections; Voter Privacy; Transparency; Immutability; Secure Voting; Electronic Governance
This thesis examines three distinct topics on settlement microstructure and market efficiency in both DeFi and TradFi. Chapter one introduces the thesisâ unifying lens, arguing that settlement microstructure drives market efficiency across these markets. It links the three papers by showing how access in Bitcoin private channels, timing in Ethereum intertemporal gas hedging, and composition in equity market retail participation jointly determine fees, latency, liquidity, and price discovery, while previewing the policy framework that renders these mechanisms legible, bounded, and measurable. Chapter two, based on the working paper âPrivate Settlement in Blockchain Systemsâ with Dr. Alfred Lehar, provides evidence that the settlement market in blockchain systems is not purely transactional and diverges from the predictions of a simple competitive auction model. Using data from the Bitcoin blockchain, we find that 5.88% of transactions, labeled as private, bypass the competitive auction and are routed directly to miners. Despite being more active than the average user, these transactions are consistently confirmed by a single miner, a statistically unlikely outcome in a competitive environment. Our findings suggest that high-demand users form long-term agreements with miners, paying, on average, 20% lower fees. This chapter also documents how such settlement contracts are structured and operate within an unregulated market. Chapter three, based on the working paper âGas Tokens: Market for Future Settlement in the Ethereum Blockchainâ with Dr. Alfred Lehar, examines the implications of gas tokens as a potential market for future settlement within the Ethereum network. We show that sophisticated and frequent users are more engaged in gas token markets, pre-purchasing tokens to hedge against fluctuations in gas prices and paying, on average, 15.25% lower settlement fees. Moreover, bots actively pursue arbitrage opportunities in gas token markets and hold substantial volumes. Our findings indicate that traded gas token prices have strong predictive power for future gas prices. This research contributes to the development of modern financial instruments for price discovery and hedging within the Ethereum network as a two-sided market. We also empirically analyze the implementation of the Ethereum Improvement Proposal EIP-1559 as a natural experiment. Chapter four, based on my working paper âSilencing the Noise: Amplified Effects, A Causal Study on Price Efficiencyâ, investigates the causal effects of noise trader removal on market liquidity. In September 2022, an unexpected internet disruption in Iran restricted noise traders while informed traders retained access through brokers. This disruption led to a 6.65-fold increase in the bid-ask spread and a 46.8% decrease in informed trade speed due to market access asymmetry. Social media censorship in affected regions further amplified information asymmetry, resulting in a 7.2% price impact. Using a five-year analysis of political unrest, this study disentangles the effects of unrest and internet disruption on noise trading activity. The findings reveal that political unrest increases regional noise trading activity, whereas internet disruption decreases it. When both unrest and internet disruption occur simultaneously, regional noise trading activity decreases by 23.5%. This paper provides novel insights into market microstructure and the dynamics of liquidity provision through noise trading in emerging markets.
Filippo Scaramuzza, Renato Cordeiro Ferreira, Giovanni Quattrocchi, Damian Andrew Tamburri ¡ 5 authors
Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the traditional mechanisms used to achieve the verifiable accountability now required by regulations like the EU AI Act. These methods are either expensive or heavily manual, and ill-suited for the opaque, "black box" nature of most Artificial Intelligence (AI) models. A conflict arises: high auditability and verifiability are required by law, but such transparency conflicts with the need to protect the assets being audited (e.g., confidential data and proprietary models). This paper introduces ZKMLOps, an \ac{MLOps} verification framework that operationalizes Zero-Knowledge Proofs (ZKPs) within Machine-Learning Operations lifecycles; a ZKP allows a prover to convince a verifier that a statement is true without revealing any information about the statement itself. By integrating ZKP with established software engineering patterns, ZKMLOps provides a modular and repeatable process for generating verifiable cryptographic evidence-proofs of well-defined computational statements about the audited model and its inputs-that auditors can use as input to a regulatory compliance determination. We evaluate the framework along two dimensions. First, framework viability: orchestration overhead is bounded and stable across architecturally heterogeneous ZKP backends and models of increasing size. Second, cost-versus-assurance trade-offs: the audit-on-demand setting is the regime in which full zero-knowledge auditing is the appropriate tool, where it provides confidentiality and integrity guarantees that lighter-weight alternatives cannot match.
Financial forecasting increasingly uses large neural network models, but their opacity raises challenges for trust and regulatory compliance. We present several approaches to explainable and reliable AI in finance. \emph{First}, we describe how Time-LLM, a time series foundation model, uses a prompt to avoid a wrong directional forecast. \emph{Second}, we show that combining foundation models for time series forecasting with a reliability estimator can filter our unreliable predictions. \emph{Third}, we argue for symbolic reasoning encoding domain rules for transparent justification. These approaches shift emphasize executing only forecasts that are both reliable and explainable. Experiments on equity and cryptocurrency data show that the architecture reduces false positives and supports selective execution. By integrating predictive performance with reliability estimation and rule-based reasoning, our framework advances transparent and auditable financial AI systems.
Watermarking schemes for large language models (LLMs) have been proposed to identify the source of the generated text, mitigating the potential threats emerged from model theft. However, current watermarking solutions hardly resolve the trust issue: the non-public watermark detection cannot prove itself faithfully conducting the detection. We observe that it is attributed to the secret key mostly used in the watermark detection -- it cannot be public, or the adversary may launch removal attacks provided the key; nor can it be private, or the watermarking detection is opaque to the public. To resolve the dilemma, we propose PVMark, a plugin based on zero-knowledge proof (ZKP), enabling the watermark detection process to be publicly verifiable by third parties without disclosing any secret key. PVMark hinges upon the proof of `correct execution' of watermark detection on which a set of ZKP constraints are built, including mapping, random number generation, comparison, and summation. We implement multiple variants of PVMark in Python, Rust and Circom, covering combinations of three watermarking schemes, three hash functions, and four ZKP protocols, to show our approach effectively works under a variety of circumstances. By experimental results, PVMark efficiently enables public verifiability on the state-of-the-art LLM watermarking schemes yet without compromising the watermarking performance, promising to be deployed in practice.
Blockchains face inherent limitations when communicating outside their own ecosystem, largely due to the Byzantine Fault Tolerant (BFT) 3f+1 security model. Trusted Execution Environments (TEEs) are a promising mitigation because they allow a single trusted broker to interface securely with external systems. This paper develops a cost-of-collusion principal-agent model for compromising a TEE in a Data Center Execution Assurance design. The model isolates the main drivers of attack profitability: a K-of-n coordination threshold, independent detection risk q, heterogeneous per-member sanctions F_i, and a short-window flow prize (omega) proportional to the value secured (beta times V). We derive closed-form deterrence thresholds and a conservative design bound (V_safe) that make collusion unprofitable under transparent parameter choices. Calibrations based on time-advantaged arbitrage indicate that plausible TEE parameters can protect on the order of one trillion dollars in value.
Prajakta Sudhir Khade, Aarushi Santosh Gode, Rajeshkumar U. Sambhe
The exponential rise of cyber threats has revealed the vulnerabilities of centralized security systems, including susceptibility to insider attacks, single points of failure, and regulatory inefficiencies. This paper investigates blockchain as a transformative backbone for cybersecurity, focusing on its potential to ensure data integrity, decentralize trust, and mitigate advanced cyber risks. Beginning with a comprehensive literature review, the study examines the fundamentals of blockchain technologyâdistributed ledgers, consensus mechanisms, and cryptographic primitivesâthat enable tamper-proof, transparent, and secure digital ecosystems. The challenges of centralized systems are contrasted with blockchainâs resilience, highlighting its role in eliminating bottlenecks and enhancing trust. Applications across identity management, IoT security, supply chains, and e-governance are analyzed alongside a proposed methodology that integrates blockchain with artificial intelligence, IoT, and quantum-resilient models. Real-world case studies demonstrate blockchainâs adoption in healthcare, government, and industrial systems, while challenges such as scalability, interoperability, and compliance are critically assessed. Collectively, this study underscores blockchainâs pivotal role in shaping next-generation cybersecurity architectures.
Distributed Ledger Technologies (DLTs) underpin Digital Circular Economy (DCE) systems that rely on efficient IoT data flows. Shimmer, a DAG-based DLT optimized for IoT, enables feeless transactions with parallel validation through its tip-selection mechanism. On such ledgers, message fragmentation induces a latencyâthroughput tradeoff as per-block cost rises with parallel validation. Such efficiency lowers energy and congestion, supporting DCE objectives. Yet, end-users cannot control payload size or network load, leading to unpredictable latency and high CPU use on submitting devices, increasing energy consumption. Existing approaches mostly modify ledger internals, overlooking adaptivity or end-user policies. We introduce ABS-TD3, an offline-to-online TD3 agent that receives the total message size and outputs the optimal per-block size for balancing latency and energy-efficient CPU utilization. The agent is pre-trained offline on real data with Retrieval Augmentation and adaptive weights for improved decision making, then transitioned online with prioritized replay and a novelty bonus, balancing exploitation-exploration, yielding stable adaptivity compared to standard RL approaches. ABS-TD3 is implemented on Shimmer and can integrate with future Tangle-based forks of pre-IOTA-Rebased frameworks, exposing the same client-side controls. ABS-TD3 is evaluated on Shimmer by submitting 8 message sizes ranging from 5KB to 100KB, under the 32 KB block-size limit, with 250 iterations per size via IOTA-SDK. Against max, min, random, and fixed-weight baselines, it reduces median latency by about 9 % to 12 % and median CPU utilization by about 12 % to 17 % versus max and random policies, enabling efficient IoT data submission for DCE platforms without altering DLT infrastructure.
This paper presents the first rigorous empirical investigation into a fundamental question of cryptocurrency valuation: Are cryptocurrency prices in line with the prices of fundamental assets? To answer this, we analyze the nine largest cryptocurrencies by market capitalizationâBitcoin (BTC), Ethereum (ETH), Solana (SOL), Binance Coin (BNB), Ripple (XRP), Cardano (ADA), Litecoin (LTC), Tron (TRX), and the stablecoin DAIâagainst a suite of traditional benchmarks, including major fiat currencies (EUR, CAD, JPY), gold, and the S&P500 index. Our dataset spans from 1 January 2014 to 30 June 2025, with start dates varying for newer cryptocurrencies to ensure robust time series analysis. Guided by the asset pricing theory, we formulate a martingale test: if a cryptocurrency is priced in line with a fundamental numeraire asset, its price ratio relative to that numeraire must follow a martingale process. Our extensive empirical analysis reveals that the prices of major cryptocurrencies (BTC, ETH, SOL, BNB) consistently reject the martingale hypothesis when traditional assets (currencies, gold, equities) serve as the numeraire, indicating a decoupling from fundamental valuation anchors. Conversely, when Bitcoin or Ethereum itself is used as the numeraire, most smaller cryptocurrencies are priced in line with these crypto benchmarks, suggesting an internal valuation ecosystem that operates independently of traditional finance.
Real world asset tokenization (RWA) introduces programmable finance on chain tools to the market, while bringing cash like returns. This paper focuses on token treasury bond funds to explore whether they are re anchoring the yield benchmark of decentralized finance (DeFi). The key entry point of the study is to build a de facto "interest rate corridor", which is formed by DeFi's stable monetary loan interest rate around the volatility of token treasury bond yield. The research results show that due to the widespread risk exposure in the tokenized currency market, DeFi USD returns have gradually converged towards short-term interest rate benchmarks. What is more noteworthy is that its stay time in the narrow corridor centered on the yield of token treasury bond is significantly prolonged. This re anchoring effect not only narrows the long-standing divergence between cryptocurrency native interest rates and monetary policy benchmarks, but also reshapes the incentive mechanism for liquidity supply, and further tightens the integration channels between on chain markets and traditional fixed income markets on this basis.