The security and integrity of medical record data is a crucial issue in the era of healthcare service digitalization. Traditional systems still face risks of manipulation, information leaks, and issues with interoperability between healthcare institutions. Blockchain technology has emerged as a promising solution to address this issue thanks to its features of decentralization, openness, and difficulty in modification. One consensus method that can be applied is Proof of Work (PoW), which has proven to maintain the authenticity of transactions on a distributed network. This research aims to design and evaluate a blockchain-based medical record application using the PoW consensus algorithm to ensure the security, transparency, and reliability of medical data storage. The approach used is experimental, involving the development of a blockchain-based application prototype. The PoW algorithm is applied to ensure the validity of medical record data transactions. The evaluation was conducted by measuring the security aspect (resistance to data changes), performance (time to verify transactions), and scalability (number of transactions that can be handled). The results of the experiment show that implementing PoW in a medical record system can maintain data integrity with a high level of resistance to unauthorized changes. The average time for transaction verification is 2.4 seconds per block, with the ability to handle up to 150 transactions per minute. Although the performance of PoW requires significant computational resources, the level of security it offers suggests potential for implementation in larger healthcare systems. The application of blockchain with the PoW algorithm to medical records has proven to improve the security and transparency of medical information. This research successfully met the established objectives, although computational efficiency issues still need to be addressed. Further research is suggested to explore other consensus algorithms such as Proof of Stake (PoS) or Practical Byzantine Fault Tolerance (PBFT) to improve performance without sacrificing security aspects. Keywords: Blockchain, Electronic Health Records (EHR), Proof of Work (PoW), Smart Contract, Healthcare Information System
Cloud computing has transformed data storage, accessibility, and enterprise operations; however, it has also increased exposure to sophisticated cyber threats. Traditional centralized Identity Management Systems (IDMs) often suffer from critical vulnerabilities such as a single point of failure, where the compromise of a central authority can expose sensitive user credentials. This research proposes ZKP-Shield, a security framework that integrates Non-Interactive Zero-Knowledge Proofs (NIZKPs) with a Software-Defined Perimeter (SDP) to create a secure and invisible cloud authentication environment. The proposed architecture eliminates the need to transmit passwords or sensitive identity data by allowing users to mathematically prove their identity without revealing secret information. Simultaneously, the SDP layer conceals cloud resources from unauthorized users by enforcing a âdark cloudâ model, where services remain hidden until authentication is successfully verified. The framework employs cryptographic techniques such as the Discrete Logarithm Problem and the FiatâShamir heuristic to transform interactive proofs into efficient non-interactive authentication processes. Experimental simulations conducted in a distributed cloud environment demonstrate that the ZKP-SDP integration significantly reduces attack surfaces, prevents credential-based attacks, and maintains acceptable latency for enterprise applications. The results indicate that combining cryptographic identity verification with network invisibility provides a scalable and resilient security model for modern cloud infrastructures.
The global logistics sector is confronted with crucial data reliability challenges wherein traditional centralized systems have a 15-20% manual error rate and are highly susceptible to counterfeiting. In this regard, the current research proposes Sentinel, a decentralized supply chain tracking framework utilizing the Polygon Proof-of-Stake blockchain coupled with smart contracts in Solidity for granting immutability to data governance. It follows a hybrid architecture wherein on-chain cryptographic verification is coupled with MongoDB for high-speed off-chain data retrieval. Extensive performance testing was performed on a simulated supply chain network with 10,000 transaction cycles of creation, transfer, and delivery. It shows that Sentinel has been able to achieve 100% in data integrity, thus rejecting all 500 unauthorized ledger modifications attempted during security stress testing. In terms of efficiency, the proposed framework minimized data retrieval latency to less than 180 ms, which was an improvement of 92% compared to traditional decentralized architectures. Additionally, it minimized the transaction cost to roughly âš0.45/unit, thus offering a cost reduction of about 99.9% compared to traditional Ethereum Layer-1 implementations.
Modern insurance organizations have adopted artificial intelligence in narrow, task-specific roles, resulting in fragmented systems that optimize isolated functions without fundamentally reshaping the underwriting and claims lifecycle. This âincrementalismâ yields a human-default, sequential process plagued by structural bottlenecks, inconsistent risk evaluation, and limited transparency. This paper introduces NEXUS (Next-Generation Executive Underwriting and Settlement Intelligence), a framework to re-architect insurance as an AI-native system. NEXUS transitions AI from a peripheral tool to the primary orchestrator of end-to-end processes, conceptualizing the insurance lifecycle as a conversational, agent-orchestrated workflow. It is realized through a unified conversational interface that coordinates a decentralized ecosystem of specialized, collaborative AI agents each responsible for domain-specific reasoning such as geospatial risk assessment, financial verification, or medical outcome analysis. The central innovation is the Truth Score Engine (TSE), a governance-first aggregation mechanism that non-linearly synthesizes agent outputs by weighting evidentiary provenance, confidence estimates, and cross-agent consistency. The TSE governs decisions via a Three-Tiered Confidence Protocol: ⢠High Confidence (>90%) validates outcomes for immediate human sign-off without re-verification; ⢠Medium Confidence (60-90%) routes decision summaries for targeted human review of specific flags; ⢠Low Confidence (<60%) escalates cases as ââRisky,ââ reverting to traditional manual investigation. This protocol yields a single, auditable decision artifact while preserving full traceability of the reasoning pathway. By embedding multi-agent coordination, contextual awareness, and tiered governance at the architectural level, NEXUS demonstrates a scalable pathway toward adaptive, transparent insurance systems. It ensures precision, combats fraud, and dramatically reduces settlement time, positioning AI-native governance as a foundational requirement for deploying trusted, autonomous decision-making in high-stakes financial domains.
Abstract The rapid evolution of computer technology is changing digital ecosystems, business processes, governmental operations, and how humans use computers to perform tasks. This paper is a comprehensive analysis of modern computer technology trends, including advancements in artificial intelligence; cloud computing; edge computing; the internet of things (IoT); 5G networks; blockchain; cybersecurity; quantum computing; emerging technologies such as immersive technologies and robots; big data; and sustainable computing. In this extensive review of how these advances work together to drive digital transformation, this paper synthesizes current research from academic literature with real-world applications of computer technologies from industry. The paper includes discussions regarding the emergence of generative AI and multimodal ML methods, explainable AI, and intelligent automation as new methods to generate better decision-making results and innovations within the business sector. It includes descriptions of multi-cloud/hybrid architectures, serverless computing, edge AI, and fog computing as ways to achieve low-latency scalable infrastructure; and ultimately describes use cases for using IoT with AI-enabled analytic platforms for smart cities; IIoT; and real-time data ecosystems. Cybersecurity subjects discussed in this paper include innovations such as Zero Trust Architecture, AI-based threat detection, and quantum-resistant cryptography. Emerging technology paradigms like blockchain-powered decentralized apps (DApps), Web3 environments, quantum algorithms, AR/VR/MR technologies, and smart robots are examined for potential to change organisations and challenges encountered during their adoption. 'Green computing' strategies are discussed in terms of developing low carbon power systems, creating carbon aware IT systems, and developing sustainable IT practices that reduce environmental impact. This study also explores advances in the fields of human computer interaction, accessibility technology, and ethical governance frameworks, with a focus on society's responsibility to develop inclusive and responsible technological products. The research has revealed multiple challenges that prevent sustainable technology development from progressing, including: scalability; interoperability; regulatory compliance; security threats; digital equity; and adapting to the workforce's new skill sets caused by this shift to sustainable technology. Therefore, developing sustainable technology will require multi-disciplinary co-operation; ethical guidance/path; strategic governance; and continuous innovation in technology development. By combining a technical assessment of IT technology along with a social perspective; an umbrella of knowledge will form to forecast how IT technologies will advance during the period referred to as the era of Intelligent Connected Systems.
Abstract Globalization has fundamentally transformed English literature publishing, creating a dynamic interplay between unprecedented market expansion and significant challenges to creative diversity. This seminar paper investigates how economic mergers, technological disruptions, and cultural flows have reshaped the industry since the 1980s, when independent presses gave way to the dominant "Big Five" conglomerates Penguin Random House, HarperCollins, and others that now control over 80% of English-language fiction sales. These giants prioritize profitable, transmedia blockbusters like the Harry Potter series, fostering a "winner-takes-all" economy where top titles capture 90% of profits, often at the expense of experimental or midlist works.Counterbalancing this consolidation, digital platforms such as Amazon Kindle Direct Publishing and e-books have democratized access, empowering self-published successes like The Martian and enabling diaspora authors from regions like Nigeria and the Philippines to reach global audiences. Translation booms, fueled by prizes like the Man Booker International, introduce hybrid narratives Salman Rushdie's multilingual *Midnight's Children*, Mohsin Hamid's migratory Exit West that embody HomiBhabha's "third space" of cultural negotiation.Yet, risks loom: algorithmic recommendations promote formulaic "McLiterature," English hegemony marginalizes non-translated voices , and profit motives encourage tokenistic diversity. Through case studies of Penguin Random House's global strategies, Amazon's market dominance, and indie rebels like Tilted Axis Press, alongside emerging AI tools and Web3 royalties, the paper evaluates impacts on content, authors, and pluralism. It concludes that while globalization amplifies voices, safeguarding literary risk-taking require balancing commercial imperatives with equitable, culturally rich publishing in our interconnected era.
The purpose of the study is to comprehensively assess the legal and economic prerequisites for integrating prediction markets into the US financial system, taking into account the regulation of derivative financial instruments, gambling legislation, and the characteristics of decentralized management models, as well as to determine the impact of legalization on the informational efficiency of markets and the stability of the financial system. The study uses formal legal analysis of regulatory acts and law enforcement practices of federal authorities, a comparative legal approach to the distinction between financial and gambling regulation, economic and mathematical modeling using autoregressive models with external variables and rational expectations theory, as well as elements of agent-based modeling to assess the risks of price manipulation. The source base consists of relevant scientific publications from 2022 to 2026, analytical materials, and regulatory documents. It has been established that the main barrier to integrating decentralized prediction markets into the US legal framework is the dual legal nature of event contracts, which creates competition between the regulatory regimes governing derivatives markets and the gambling sector. It has been proven that the absence of a centralized issuer in decentralized autonomous organizations complicates state supervision and the identification of the responsible entity. Economic analysis confirmed the ability of binary contracts to aggregate scattered information and form market expectations more efficiently than individual traditional indicators, while also revealing the risks of short-term price distortions. The need to introduce a mixed legal support model, combining distributed registries with a licensed organizational form of activity, is justified. Legalization of prediction markets, provided that there is a clear distinction between financial and gambling regulation, can increase the transparency of market expectations and expand the range of risk management tools without creating excessive systemic threats.
The rapid penetration of decentralized financial mechanisms into the structure of Ukraine's cryptocurrency market, where the volume of DeFi transactions consistently exceeds that of centralized platforms, highlights the need for effective tools to maintain the instant solvency of lending protocols during periods of sharp price fluctuations in digital assets. The purpose of this article is to systematize liquidity risks in decentralized financial systems, conduct a comparative assessment of algorithmic strategies for their minimization, and identify ways to enhance the stress resilience of protocols. The methodological basis of the study consists of a taxonomic analysis for classifying types of risks, a comparative analysis of the effectiveness of key liquidity management strategies, economic-mathematical modeling of cascading liquidation processes, and a correlation analysis of the relationship between the magnitude of cryptoasset price declines and the frequency of protocol failures. The empirical basis consists of on-chain data on the transaction activity of leading DeFi protocols for the period 2024â2026. The results show that hybrid configurationsâwhich combine dynamic interest rate regulation with overcollateralization and decentralized oracle networksâdemonstrate the highest resilience to extreme volatility. It was found that increasing collateral requirements proportionally reduces the probability of cascading liquidations, but simultaneously limits the protocol's capital efficiency, highlighting the need to optimize these parameters. It has been established that compositional links between protocols create a domino effect: a local liquidity shortage in one pool can trigger a chain of forced liquidations in adjacent systems within a critically short time frame. The scientific novelty lies in the development of a typologized scheme for neutralizing liquidity threats, which, unlike existing ones, integrates sentinel oracle, execution liquidation, and reserve insurance instruments into a unified protocol risk management system. The method for estimating margin call thresholds has been improved to account for the historical volatility of specific cryptoassets. The practical significance of the obtained results lies in their potential use by DeFi protocol architects and smart contract developers when designing risk management systems, configuring liquidation auction parameters, and selecting the optimal configuration of oracle networks for the Ukrainian crypto market.
Federated learning (FL) enables collaborative model training over distributed private data. However, sustaining open participation requires incentive mechanisms that compensate contributors for their resources and risks. Enabled by Web3 primitives, especially blockchains, recent FL proposals incorporate incentive mechanisms for open participation, yet most focus primarily on algorithmic design and overlook system-level challenges, including coordination efficiency, secure handling of model updates, and practical usability. We present FWeb3, a practical Web3-enabled FL framework for incentive-aware training in open environments. FWeb3 adopts a modular architecture that separates FL functions from Web3 support services, decoupling the off-chain training and data plane from on-chain settlement while preserving verifiable incentive execution. The framework supports pluggable aggregation and contribution evaluation methods and provides a browser-native DApp interface to lower the participation barrier. We evaluate FWeb3 in real-world settings and show that it supports end-to-end incentive-aware FL with transaction and data-transfer overheads of only 21.3% and 3.4% in WAN; FWeb3 also deploys from zero configuration in under 3 minutes and enables user onboarding in under 1 minute.
Rosa Pericàs Gornals, M. Magdalena Payeras Capellà , Victor Garcia Font, M. Puigserver ¡ 5 authors
The exchange of non-fungible tokens (NFTs) traditionally occurs on centralized NFT marketplaces, which often wield significant control over transactions, as the imposition of restrictions and fees. Additionally, the current NFT standard (ERC-721) lacks certain functionalities that hinder the seamless direct exchange of NFTs between owners. This article introduces a novel protocol that eliminates the need for an intermediary marketplace, as well as their intermediary smart contract or escrow contract, facilitating a fair exchange of NFTs as collectibles. The protocol is represented by an extension of the ERC-721 called Exchangeable NFTs (ExNFTs), which introduces the necessary functionalities to enable direct NFT exchanges. We analyze the limitations of the ERC-721 standard and build upon our previously proposed protocol of Rejectable NFTs (RejNFTs), which introduces a rejectable functionality empowering the receiver to reject the transfer of an NFT. Additionally, we discuss the reasons why achieving a fair direct exchange of NFTs is currently unattainable with the prevailing standards. We present the key functionalities of the ExNFT approach, along with a Solidity implementation of the ExNFTs. Furthermore, we evaluate the performance of the protocol, considering costs and security.
The article is devoted to the study of mechanisms for managing reputational risks in decentralized autonomous organizations (Decentralized Autonomous Organizations â DAO) operating on the basis of blockchain technologies. The relevance of the research is determined by the rapid development of decentralized digital ecosystems, the spread of algorithmic governance models, and the need to ensure trust among participants in an environment where traditional institutional mechanisms of centralized control are absent. Under such conditions, the issue of reputational risk management becomes particularly important, as the level of trust directly affects the stability and sustainability of decentralized organizations. The aim of the study is to identify and substantiate mechanisms for managing reputational risks in decentralized autonomous organizations based on blockchain technologies, taking into account the specific features of their functioning and the principles of decentralized governance. The methodological basis of the research includes methods of systemic analysis, institutional approach, comparative analysis, and modeling. To identify the key factors shaping reputational risks, the study employs the analysis of contemporary scientific publications, generalization of DAO project practices, and examination of digital governance tools within blockchain ecosystems. As a result of the study, the main sources of reputational risks in decentralized autonomous organizations were systematized, including information asymmetry among participants, insufficient transparency of decision-making procedures, technical vulnerabilities of smart contracts, and potential manipulation of voting mechanisms. The key mechanisms for managing reputational risks in the DAO environment were generalized, including participant reputation evaluation systems, transparent decentralized voting mechanisms, smart contract auditing, and moderation tools for digital communities. Based on the conducted analysis, a conceptual model for managing reputational risks in DAO was developed, which provides for the integration of blockchain transparency tools, collective control mechanisms, and procedures for evaluating the reputational behavior of participants in digital ecosystems. The scientific novelty of the study lies in substantiating a conceptual approach to reputational risk management in decentralized autonomous organizations, which combines the capabilities of blockchain infrastructure with self-regulation mechanisms of decentralized digital communities. The practical significance of the obtained results lies in the possibility of their application by developers of DAO projects, blockchain platforms, and digital ecosystems for the development of reputational risk management systems, increasing the level of trust among participants, and ensuring the stable functioning of decentralized organizations.
The paperâs subject is examining decentralized autonomous organizations (DAOs) governance models during the digital economic shift. This study aims to deeply analyze DAOs to find governance aspects, and to develop a model to help integrate them into global finance and public administration. To achieve this goal, the study addressed the following tasks: analyzing existing methods to DAO governance; identifying key risks and opportunities; modeling a hybrid organizational structure; and assessing the role of stablecoins as a stabilizing element. The studyâs novelty comes from the creation of a unique adaptive DAO governance model that combines elements of centralization and decentralization to enhance efficiency and legitimacy. The author used methods of comparative and systemic analysis, modeling, and real-world solutions and regulations. The results prove DAOs can complement traditional governance by making economic interactions more transparent and efficient. The studyâs main takeaway is that DAOs need a balanced regulatory framework and better governance. Government agencies, blockchain developers, and specialists in public and corporate governance can use the work results.
Open access
Blockchain Technology Applications and Security
Digitalization and Economic Development in Agriculture
The article examines digital asset inheritance in Web3 ecosystems, where the economic value of cryptocurrencies, NFTs, tokenised assets, cloud-stored intellectual property and high-value platform accounts is not supported by sufficiently reliable legal and technical mechanisms for intergenerational transfer. The relevance of the topic is determined by the fact that traditional inheritance law is oriented mainly toward tangible objects or documented property rights, whereas blockchain-native assets depend on private keys, platform accounts are restricted by terms of service, and the cross-border nature of digital portfolios complicates the determination of applicable law. The purpose of the study is to develop an integrated conceptual model of the Self-Sovereign Digital Heritage System (SSDHS), combining self-sovereign identity, decentralised identifiers, verifiable credentials, digital safes, smart-contract execution of inheritance conditions and regulatory compliance. The methodological basis includes comparative legal analysis, system analysis, functional modelling, conceptual design and regulatory impact assessment. The article substantiates a six-layer SSDHS architecture consisting of the identity layer, digital asset inventory layer, secure storage layer, blockchain layer, inheritance execution layer and legal compliance layer. It is shown that SSI addresses the problem of cryptographic heir authentication, whereas the digital safe ensures secure preservation of private keys, inheritance instructions, DID material and the digital testament. A comparative analysis of the regulatory frameworks of the United States, the European Union and Ukraine is conducted, including fiduciary access to digital assets, electronic wills, digital identity, crypto-asset markets, personal data protection, virtual assets and electronic identification. The study substantiates that SSDHS can serve as a legal-technological reference model for reducing the risk of digital asset loss caused by inaccessible private keys, improving heir identification reliability, reducing dependence on centralised intermediaries and preparing future legislative solutions for digital heritage.
Decentralized finance (DeFi) has emerged as a transformative paradigm, leveraging programmable blockchains to innovate upon traditional financial services without centralized intermediaries. However, DeFi introduces a unique and highly adversarial security landscape characterized by immutable transactions, complex protocol composability, and transparent execution environments. This survey provides a comprehensive systematization of DeFi security, categorizing vulnerabilities across three distinct layers: technical and code layer, economic and protocol layer, and infrastructure and cross-chain layer. Furthermore, we structure the defense mechanisms according to the protocol lifecycle, including pre-deployment prevention strategies, runtime mitigation techniques, and post-incident response and recovery mechanisms. We also delve into specific phenomena such as maximal extractable value, analyzing its dual role as both a market efficiency tool and a security vector. By synthesizing existing literature and incident reports, this survey establishes a holistic framework for understanding the interplay between code and finance. Finally, we identify critical open challenges and propose future research directions aimed at maturing the discipline of DeFi security and mitigating systemic risks.
Open access
Infrastructure Resilience and Vulnerability Analysis
Job-based smishing scams, where victims are recruited under the guise of remote job opportunities, represent a rapidly growing and understudied threat within the broader landscape of online fraud. In this paper, we present Anansi, the first scalable, end-to-end measurement pipeline designed to systematically engage with, analyze, and characterize job scams in the wild. Anansi combines large language models (LLMs), automated browser agents, and infrastructure fingerprinting tools to collect over 29,000 scam messages, interact with more than 1900 scammers, and extract behavioral, financial, and infrastructural signals at scale. We detail the operational workflows of scammers, uncover extensive reuse of message templates, domains, and cryptocurrency wallets, and identify the social engineering tactics used to defraud victims. Our analysis reveals millions of dollars in cryptocurrency losses, highlighting the use of deceptive techniques such as domain fronting and impersonation of well-known brands. Anansi demonstrates the feasibility and value of automating the engagement with scammers and the analysis of infrastructure, offering a new methodological foundation for studying large-scale fraud ecosystems.
We document the first systematic evidence of negative spillover effects in crypto asset returns across blockchains. Using on-chain data from Ethereum, Solana, Binance Smart Chain, Arbitrum, and Avalanche (2022-2025), we show that surges on one chain often coincide with declines on others, in contrast to the positive co-movements typical of equity markets. These spillovers intensify during attention shocks, proxied by chain activity and extreme return events, and persist after controlling for global equity returns, interest rates, and Bitcoin. Nonlinear factor models reveal that attention-driven capital reallocation, rather than common information, underlies these dynamics. Our findings introduce a new form of cross-market linkage, attention-induced substitution, that shapes risk transmission in crypto markets. The results carry implications for portfolio diversification, systemic risk measurement, and regulation of token launches that may trigger cross-chain capital flight.
Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59\% and improves the TSR by 29\% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.
Multi-horizon price forecasting is central to portfolio allocation, risk management, and algorithmic trading, yet deep learning architectures have proliferated faster than rigorous financial benchmarks can evaluate them. This study provides a controlled comparison of nine architectures (Autoformer, DLinear, iTransformer, LSTM, ModernTCN, N-HiTS, PatchTST, TimesNet, and TimeXer) spanning Transformer, MLP, CNN, and RNN families across cryptocurrency, forex, and equity index markets at 4-hour and 24-hour horizons. A total of 918 experiments were conducted under a strict five-stage protocol including fixed-seed Bayesian hyperparameter optimization, configuration freezing per asset class, multi-seed retraining, uncertainty aggregation, and statistical validation. ModernTCN achieves the best mean rank (1.333) with a 75 percent first-place rate, followed by PatchTST (2.000). Results reveal a clear three-tier ranking structure and show that architecture explains nearly all performance variance, while seed randomness is negligible. Rankings remain stable across horizons despite 2 to 2.5 times error amplification. Directional accuracy remains near 50 percent across all configurations, indicating that MSE-trained models lack directional skill at hourly resolution. The findings highlight the importance of architectural inductive bias over raw parameter count and provide reproducible guidance for multi-step financial forecasting.
Dang Sy Duy, Nguyen Duy Chien, Kapil Dev, Jeff Nijsse
Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training practices such as specifically weight initialisation and normalisation that remain underexplored. We present a systematic ablation of initialisation and normalisation strategies across three GNN architectures (GCN, GAT, and GraphSAGE) on the Elliptic Bitcoin dataset. Our experiments reveal that initialisation and normalisation are architecture-dependent: GraphSAGE achieves the strongest performance with Xavier initialisation alone, GAT benefits most from combining GraphNorm with Xavier initialisation, while GCN shows limited sensitivity to these modifications. These findings offer practical, architecture-specific guidance for deploying GNNs in AML pipelines for datasets with severe class imbalance. We release a reproducible experimental framework with temporal data splits, seeded runs, and full ablation results.
Decentralised finance (DeFi) has profoundly reshaped global capital markets, enabling automatic transactions, eliminating the need for intermediaries, and accelerating transaction settlement times. Despite these significant advancements, institutional involvement in DeFi remains very low. The lack of institutional participation can be attributed to the lack of an enforceable compliance mechanism at the protocol level; that is, once a transaction is confirmed as having been completed on the blockchain, it cannot be undone or disputed in any meaningful way. The existing compliance mechanisms are primarily retrospective, meaning that they generate alerts after a transaction has occurred instead of preventing illicit transfers in advance. Regulated financial institutions that transact in cryptocurrency bear the ultimate financial risk and regulatory burden. The UK FCA has made it very clear through CP25/41 that there are now specific regulatory expectations regarding the existence of adequate pre-settlement controls [2]. We introduce AMTTP Version 4.0, which has been designed to have a four-layer architecture explicitly intended to support deterministic compliance enforcement in DeFi institutions. Layer I provides SDKs, REST APIs, and web applications intended for programmatic and human interaction with AMTTP; Layer II provides a compliance orchestration layer that combines (i) machine learning risk scoring (ii) graph analysis (iii) sanctions screening, and (iv) policy adjudication into a single deterministic decision-making matrix; Layer III consists of an offline training pipeline with a Composite Teacher that uses an AutoencoderEnhanced XGBoost (w = 0.4), seven FATF AML Mode Patterns (w = 0.3), and graph structural properties (w = 0.3) in order to produce pseudo-labels (SLPs) for the Student pipeline across 2,640,000 transactions; and finally, Layer IV supports the physical infrastructure for AMTTP deployment, which consists of 18 smart contracts on Ethereum Sepolia, 17 containerised microservices, and a Database Persistence Tier (MongoDB, Redis, Memgraph, IPFS). The Infrastructure Security features multioracle threshold signatures, replay protection & zkNAF a zeroknowledge proof framework that allows for privacy preserving verification of KYC credentials, risk ranges & non-membership from sanctions. In addition, TLS Encryption, Rate Limiting, Cloudflare Tunnel integration & the UI Integrity Service provide an additional layer of protection at the infrastructure level. This paper aims to demonstrate that deterministic compliance can be integrated into decentralised finance at an architectural level. In order to support this assertion, the client SDKs (TypeScript and Python) are released as open source.1
In March 2025, the United States established a Strategic Bitcoin Reserve by executive order. Bhutan had been quietly mining Bitcoin with hydroelectric power, accumulating over $1 billion without public announcement. By February 2026, more than 145 publicly traded companies held Bitcoin on their balance sheets, collectively exceeding one million BTC. No established academic framework predicted these developments. In his 2023 MIT thesis Softwar, Major Jason Lowery proposed that Bitcoin is best understood not as money or a hedge but as a power projection technology rooted in thermodynamic proof of work. This paper presents the first empirical evaluation of that framework. Nine falsifiable predictions are tested against observed data; five have been confirmed and one partially realized within three years of publication. The developments that monetary, financial, environmental, and security models fail to explain (strategic reserves, geopolitical competition for hash rate, sovereign mining operations) are precisely those the power projection framework predicts.
Traditional paper-based voting system for student organization leaders election has issues related to security, transparency, and trust. This research addressed these issues by implementing a blockchain on e-voting system utilizing smart contracts to ensure the security and transparency of the voting process. The system was developed using the agile software development life cycle (SDLC) methodology and was tested using black-box and system usability scale (SUS) method to evaluate its functionality and usability. Security testing was conducted through unit testing on the smart contract and block verification within the Sepolia network. The results showed that the decentralized e-voting system could prevent vote manipulation and detecting duplicate voters, as evidenced by the unit testing of the smart contract, which confirmed that recorded votes could not be manipulated and attempts to submit multiple votes were detected and rejected. Meanwhile, system transparency was demonstrated through direct verification using a block explorer, showing that the entire voting process and the smart contract code were publicly accessible and transparent. The system was successfully simulated on a small scale within a student organization, and usability testing using the SUS method was conducted with 30 respondents. The test resulted in a score of 72 points, indicating that the system was in the good category and was well accepted by users. Therefore, the decentralized approach in this e-voting system has been proven to enhance transparency and overcome the problems of security issues in the voting process.