Ossama Billy Ibrahim, Muhammad Khoirul Abror, Nur Aisyah, Putri Noer Rahmawati · 5 authors
The phenomenon of Non-Fungible Token (NFT) trading has grown rapidly and has emerged as a promising new digital economic instrument. However, this development is accompanied by an increasing prevalence of scams within the NFT ecosystem, such as rug pulls, phishing, replay attacks, and falsified copyright claims over digital works. Indonesia currently lacks a regulatory framework that specifically governs NFT transactions or provides legal protection for consumers participating in them. This study aims to classify the various forms of NFT scams based on their juridical and technological characteristics, and to assess the adequacy of existing legal instruments in Indonesia, including criminal fraud provisions, consumer protection laws, electronic transaction regulations, and intellectual property rights. The research employs normative legal methods, focusing on statutory and conceptual approaches, as well as an examination of global practices in digital asset regulation. The analysis reveals a regulatory gap, particularly concerning marketplace liability, smart contract due diligence mechanisms, and cross-jurisdictional law enforcement. Therefore, the establishment of a specific regulatory framework that is risk-based, technologically adaptive, and oriented toward digital consumer protection is necessary.
Decision-OS V5 Revised (SiriusA2) addresses a practical AI safety problem: how human oversight can prevent irreversible decisions from being executed under pressure, confusion, coercion, or panic. It is designed for safety-critical, non-medical decision support settings where a user may be authenticated, yet the execution path may still be unsafe. The framework proposes a human-in-the-loop confirmation layer for irreversible risk. Instead of allowing a valid user action to move directly into execution, SiriusA2 routes protected actions through auditable confirmation states such as Request, Observe, Hold, Approve, Reject, Stop Candidate, Execute, and Revoke. The core mechanism is a trajectory-aware duress_score. This score is not an intent classifier, diagnosis, truthfulness score, or autonomous veto. It is an operational control-routing signal used to detect when a valid execution path deviates from an ordinary decision trajectory and approaches irreversible harm. SiriusA2 preserves human final consent through two-step confirmation, an explicit revoke path, optional family multisig, and a Zero-Knowledge approval layer (310/320) that verifies authorization without exposing personally identifiable information. The duress_score does not replace ZK approval or multisig; it routes actions into the confirmation path, while ZK qualification and multisig provide independent authorization conditions before irreversible execution. The revised manuscript integrates the SiriusA Adoption Gate into the main paper. The gate provides a Hold-first confirmation path for irreversible, externally pressured, unusually urgent, or high-stake actions: Request → Observe/Hold → Approve/Reject → Execute/Revoke. A score-based Stop Candidate does not automatically become Stop or Freeze. Stop or Freeze requires an independent non-score condition, such as verified revoke input, ZK-qualified m-of-k approval, policy-defined guardian confirmation, or an emergency protocol condition. If no such condition is available, SiriusA intentionally prefers continued Hold and evidence preservation over an AI-only execution veto. This release also clarifies cold-start behavior, causal bridge support, baseline maturity, corrected event terms, disclosure boundaries for calibration-sensitive parameters, and non-PII audit requirements. Safety is operationalized through auditable state transitions, non-PII KPIs, five-line gate outputs, evidence packaging (ZIP + SHA256), and explicit prohibitions on automatic transmission, payment, or reporting. A minimal proof-of-concept gate exists as a public runtime artifact, demonstrating PASS / DELAY / BLOCK routing, conservative severity merging, evidence union, pre-execution checking, and auditable JSON outputs. Deployment-level validation, calibration, and domain-specific robustness remain future work. Gateway / series index:https://github.com/shin4141/decision-os-paper Recommended read order:V5 Revised (SiriusA2) → V6 (PIC) → V8 (v2)Optional: V7 (AGI definition) Related repositories:- V5 Policy Pack / specification and adoption materials: https://github.com/shin4141/paper-public- Gate engine / MMAR-L0: https://github.com/shin4141/mmar-l0-core- SiriusA core runtime: https://github.com/shin4141/siriusA-core SSOT:GitHub repository “decision-os-paper”.This PDF corresponds to the revised SiriusA2 release candidate committed to the SSOT repository. Release note:This revised release integrates the trajectory-aware duress_score definition, SiriusA Adoption Gate, ZK qualification layer, family multisig, independent non-score Stop conditions, non-PII audit structure, V4-compatible escalation interface, and proof-of-concept gate positioning into the main paper. Transparency / Author’s Note:https://github.com/shin4141/decision-os-paper/blob/main/AUTHORS_NOTE.md
Salman Jan, Hassan Ali Razzaqi, Ali Akarma, Mohammad Riyaz Belgaum
The application of agentic AI systems in autonomous decision-making is growing in the areas of healthcare, smart cities, digital forensics, and supply chain management. Even though these systems are flexible and offer real-time reasoning, they also raise concerns of trust and oversight, and integrity of the information and activities upon which they are founded. The paper suggests a single architecture model comprising of LangChain-based multi-agent system with a permissioned blockchain to guarantee constant monitoring, policy enforcement, and immutable auditability of agentic action. The framework relates the perception conceptualization-action cycle to a blockchain layer of governance that verifies the inputs, evaluates recommended actions, and documents the outcomes of the execution. A Hyperledger Fabric-based system, action executors MCP-integrated, and LangChain agent are introduced and experiments of smart inventory management, traffic-signal control, and healthcare monitoring are done. The results suggest that blockchain-security verification is efficient in preventing unauthorized practices, offers traceability throughout the whole decision-making process, and maintains operational latency within reasonable ranges. The suggested framework provides a universal system of implementing high-impact agentic AI applications that are autonomous yet responsible.
Blockchains offer a decentralized and secure execution environment strong enough to host cryptocurrencies, but the state-replication model makes on-chain computation expensive. To avoid heavy on-chain workloads, systems like Truebit and optimistic rollups use challenge-based protocols, performing computations off-chain and invoking the chain only when challenged. This keeps normal-case costs low and, if at least one honest challenger exists, can catch fraud. What has been less clear is whether honest challengers are actually incentivized and a dishonest proposer is properly damaged under the worst case environment. We build a model with a colluding minority, heterogeneous costs, and three ordering modes. We then ask whether two goals can be met together: honest non-loss and fraud deterrence. Our results are clear: in single-winner designs, the incentive design is impossible or limited in scale. By contrast, in multi-winner designs, we obtain simple, explicit conditions under which both goals hold.
Virtual currency has become one of the most sought-after alternative assets in the past decade with bitcoin being a leading example. value leapt from its starting price of $0.0025 to increase by more than 40 million times that amount, creating one of the greatest rises in value in the entire history of finance. In the past few years, many academic studies show that even though Bitcoin runs independently from traditional finance, but still there is a high correlation between Bitcoin and stock market. In particular, following the introduction of Bitcoin options back in 2017, Bitcoin now appears more predictive of stock return movements than before. Research by Afees A. Salisu and his coworkers display that a solitary Bitcoin price prediction model using an optimized predictive regression framework notably surpasses older ones. but don’t say how long this goes on Therefore this research will go to try and determine the time frame when Bitcoin is better at predicting the future of the stock market as opposed to stock options. Also, we’ll use machine learning techniques to train machine learning models to predict the movements of the stock market and see if they work.
Liaqat Ali, Ahmad Alshamayleh, Amer Ibrahim, Syed Muqtar Ahmed
Cloud technologies have dramatically altered the data storage, access, and scalability landscape, but the resultant dependence on centralized systems creates immense challenges for security, privacy, and unauthorized access. This paper introduces a new framework for enabling information management in cloud environments using blockchain-enabled secure access control. By integrating distributed ledger technology and highly cryptographic access control methods, this framework will facilitate immutable record-keeping, real-time verification of access rights, and the ability to audit user activity. This framework will substantially reduce the risk of data breaches, insider threats, and unauthorized access through its transparency and overall operational reliance. The empirical evaluations confirm that blockchain-enabled access control provides an impressive level of security, while smoothing information management processes and establishing trust and resilience in cloud environments. The results will demonstrate that blockchain can disrupt cloud information management and provide a scalable, privacy-preserving solution for any enterprise or multi-tenant cloud application.
Technologia rozproszonych rejestrów (DLT), która stanowi fundament Internetu Wartości (IoV), przekształca współczesne rynki pożyczkowe. Protokoły pożyczkowe, będące kluczowym komponentem zdecentralizowanych finansów (DeFi), oferują bardziej elastyczne narzędzia alokacji kapitału niż tradycyjne struktury finansowe. Protokoły DeFi, takie jak Compound, Maker i Aave, odpowiadają na wyzwania napotykane przez klasyczne rynki pożyczkowe, w szczególności banki i scentralizowane platformy pożyczkowe. Przedstawimy także szczegółowy opis mechanizmów działania tych protokołów, z uwzględnieniem ich innowacyjnych podejść do zarządzania ryzykiem oraz zabezpieczeniami. Pomimo swojej innowacyjności protokoły DeFi wciąż wykazują zależność od tradycyjnych systemów finansowych, co wskazuje na złożone relacje między tymi dwoma sektorami. Distributed Ledger Technology (DLT), which forms the foundation of the Internet of Value (IoV), is transforming modern lending markets. Lending protocols, a key component of decentralized finance (DeFi), offer more flexible capital allocation tools compared to traditional financial structures. DeFi protocols, such as Compound, Maker, and Aave, address challenges faced by conventional lending markets, particularly banks and centralized lending platforms. This paper also provides a detailed description of how these protocols operate, highlighting their innovative approaches to risk management and collateralization. Despite their innovation, DeFi protocols still exhibit dependence on traditional financial systems, illustrating the complex relationships between these two sectors.
This paper presents an ontology-driven administrative monitoring system that integrates blockchain smart contracts to ensure transparency, accountability, and integrity in organizational processes.This tracks resources and manages administrative workflows in an open and decentralised manner, addressing long-standing governance challenges such as opacity and unaccountability.Many important administrative processes involve the movement of resources from one point to another; these resources and processes can be modelled similarly to the movement of goods in a supply chain.Motivated by the need to improve institutional governance, particularly in contexts where individual actions often undermine fairness, this study combines ontology and blockchain to formalize administrative processes and enhance traceability.A pre-created ontology from the author's previous work, developed for postgraduate administration at the University of Ibadan, was adopted.The ontology defines classes and activities which were translated into blockchain entities and smart contracts, implemented in Solidity, and deployed on the Ethereum test network.Test cases derived from ontology competency questions validate the functional correctness of the smart contracts.The results confirm that administrative activities can be monitored transparently and immutably, providing a foundation for broader applications in public administration, education, and corporate governance.
This study introduces a unified and methodologically symmetric comparative framework for multivariate cryptocurrency forecasting, addressing long-standing inconsistencies in prior research where model families, feature sets, and preprocessing pipelines differ across studies. Under an identical and rigorously controlled experimental setup, we benchmark six deep learning architectures—LSTM, GPT-2, Informer, Autoformer, Temporal Fusion Transformer (TFT), and a Vanilla Transformer—together with four widely used econometric models (ARIMA, VAR, GARCH, and a Random Walk baseline). All models are evaluated using a shared multivariate feature space composed of more than forty technical indicators, identical normalization procedures, harmonized sliding-window formations, and aligned temporal splits across five high-liquidity assets (BTC, ETH, XRP, XLM, and SOL). The experimental results show that transformer-based architectures consistently outperform both the recurrent baseline and classical econometric models across all assets. This superiority arises from the ability of attention mechanisms to capture long-range temporal dependencies and adaptively weight informative time steps, whereas recurrent models suffer from vanishing-gradient limitations and restricted effective memory. The best-performing deep learning models achieve MAPE values of 0.0289 (BTC, GPT-2), 0.0198 (ETH, Autoformer), 0.0418 (XRP, Informer), 0.0469 (XLM, Informer), and 0.0578 (SOL, TFT), substantially improving upon the performance of both LSTM and all econometric baselines. These findings highlight the effectiveness of attention-based architectures in modeling volatility-driven nonlinear dynamics and establish a reproducible, symmetry-preserving benchmark for future research in deep-learning-based financial forecasting.
The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.
Mahad Abdiwali Mohamed, Ahmed Nur Dirie, Abdiaziz Bashir Mohamud, Mohamed Abdisamad Farah · 5 authors
This paper conducts a bibliometric review of the scholarly sources of the intersection of cryptocurrencies, crowdfunding, and Islamic finance, in order to see the trends, contributions, and new directions to make Shariah-compliant FinTech and sustainability. The past decade witnessed the revolution of digital technologies such as blockchain, IoT, and AI in the banking and industries. Cryptocurrencies make the peer-to-peer transactions possible and crowdfunding helps businesses to raise funds. The concept of blockchain and central bank digital currency (CBDCs) will support sustainable finance by improving green bonds and reducing emissions. Crowdfunding in Islamic finance complies with the Shariah, as offered under risk-sharing schemes like the Mudarabah and Qard Hasan; however, the integration of the cryptocurrency as an additional risk management tool faces regulatory and compliance difficulties. Despite recent growing academic attention since 2017, most prominently dropped by Malaysia and Indonesia, there exist gaps in understanding their synergistic role towards financial inclusion and strong sustainability (SS). Blockchain relieves gharar and automates contracts that are Shariah compliant; however, there are still regulatory disagreements. Planned performing and ethics theories, the Theory of Planned Behaviors and Maqasid al-Shariah educate about the open and fair influences in adoption and assessment procedures. Through the VOSviewer and Scopus data (20142025), 158 articles reveal a maximum of publications of 2024, a high of 2020 citations, and the two countries, Malaysia (60 documents) and Indonesia as the most significant ones, with such publications as the Journal of Islamic Accounting and Business Research. Recommendations on transparent, sustainable financial ecosystems involve better blockchain-based crowdfunding, Shariah-ajority digital currencies, and better cryptocurrency determinations.
This paper examines the tensions between existing infrastructure and the need for transitional change in Dutch municipal wastewater collection and treatment. In the Netherlands, sanitation is primarily managed by public actors, with local government playing a major role. The paper demonstrates how local governments navigate these tensions and are both restricted and enabled by the current infrastructure and governance arrangements. Based on interviews, literature reviews, and analyses of statistical trends, it describes five attempts at reform in Dutch sanitation from 1980 to 2020: phosphorus removal; separating stormwater from combined sewers; water cycle companies; energy factories; and decentralized sanitation. The multi-level governance system, with decentralized infrastructure and financing, allows local governments to experiment with alternative practices, develop knowledge, and employ various interactions to mainstream innovations. However, the division of tasks in Dutch sanitation governance tends to optimize sub-systems rather than the entire system. For nationwide implementation, legislation and strong central coordination are essential. Additionally, New Public Management reinforces existing infrastructure lock-in. The paper enhances our understanding of the local government’s role in transitional change and offers insights into how the challenges of existing infrastructure can be mitigated in pursuit of sustainable wastewater solutions.
This paper investigates the Granger causality relationship in Bitcoin mining from environmental, sustainable, and miner’s financial perspectives for the period of February 2017 to January 2025. Using a time-varying Granger causality approach of Shi et al. (2018,2020), we explore how the hashrate, a measure of computational power in the Bitcoin mining process, affects energy consumption, electronic waste, and miners’ revenues. Our findings reveal that an increase in hashrate leads to a significant rise in energy use and e-waste and affects miners’ revenues. In addition, we show that mining revenue Granger causes the hashrate, suggesting economic incentives drive the network security through the hashrate. These results offer new insights for investors, policymakers, and environmental economists. • A time-varying Granger causality approach is adopted. • Higher computational power directly increases electricity demand and electronic waste. • The intensity of competition, as measured by hashrate, has a significant impact on mining profitability. • Higher mining revenues incentivise the use of greater hash power.
Tobias Kranz, Vincent Schaaf, Tobias Guggenberger, Jens Strüker
Decentralized Finance (DeFi) promises to lay ground for a more open financial system enabled by blockchain technology. Therein, stablecoins have recently gained momentum as regulated and trusted payment instruments, increasingly adopted for cross-border transactions and supported by initiatives such as the GENIUS Act in the U.S. and the European MiCAR framework. While stablecoins create the foundation of trust for linking DeFi with traditional finance, the ecosystem still depends heavily on cryptocurrency markets due to limited real-world asset integration. Existing research largely focuses on traditional securities and tradable assets, but scant attention has been paid to one of the world’s largest asset classes, real estate. To address this gap, we propose a framework for the tokenization of real estate for integration into the DeFi ecosystem. Using the Design Science Research (DSR) approach, we construct and evaluate our framework through expert interviews and smart contract simulations. The simulations validate technical feasibility and demonstrate efficiency gains, with batch transfers reducing transaction costs for portfolio purchases. Building on these evaluations, we derive design principles for the nascent field of real-world asset tokenization. These principles highlight the importance of covering the entire product range, pursuing end-to-end compliance, leveraging token standards for interoperability, and extending their functionality for efficiency and scalability. By combining regulatory, organizational, and technical perspectives, our work advances design knowledge for compliant integration of real-world assets into DeFi.
Although Large Language Models (LLM) have shown impressive performance across various domains, there is a shortage of benchmarks for systematically evaluating their in-depth understanding of specialized fields such as blockchain. This study extends the Self-Instruct methodology to introduce BLADE (Blockchain Large Language model Assessment Dataset for Evaluation), a comprehensive benchmark dataset for assessing LLM comprehension in the blockchain domain. BLADE consists of a total of 1,382 questions organized according to a systematic classification of blockchain knowledge, featuring a detailed structure with 15 main categories and 5 sub-categories for each. The benchmark covers the entire spectrum of blockchain knowledge, from its fundamentals to consensus mechanisms, architecture, smart contracts, token economy, Decentralized Finance (DeFi), NFT(Non-Fungible Token)s and digital assets, security, governance, and real-world application cases. In this research, we present a benchmark generation methodology utilizing the domain knowledge of GPT-4.5, which allowed us to create high-quality evaluation items of varying difficulty and types from expert-verified seed questions. The evaluation results of various open-source LLMs, including Qwen, DeepSeek, and Kanana, on BLADE showed that current models exhibit significant differences in their understanding of blockchain, with Qwen2.5-7B-Instruct-1M achieving the highest performance. The BLADE benchmark provides a tool for precisely evaluating and improving the blockchain comprehension of LLMs, thereby promoting the effective fusion of AI and blockchain technology and contributing to the development of more reliable decentralized systems.
Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL) mitigates this by training where data reside and exchanging only model updates, but practical deployments still face two core risks: (1) privacy leakage via gradients or updates (membership inference, gradient inversion) and (2) trust in the aggregator, a single point of failure that can drop, alter, or inject contributions undetected. We present zkFL-Health, an architecture that combines FL with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs) to deliver privacy-preserving, verifiably correct collaborative training for medical AI. Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof (via Halo2/Nova) that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host. Verifier nodes validate the proof and record cryptographic commitments on-chain, providing an immutable audit trail and removing the need to trust any single party. We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost. This framework enables multi-institutional medical AI with strong confidentiality, integrity, and auditability, key properties for clinical adoption and regulatory compliance.
Blockchain technology is a game-changing invention that guarantees digital transactions on decentralized networks. The vital role that cryptography plays in guaranteeing the authenticity, confidentiality, and integrity of blockchains is examined in this paper. To secure the data on the blockchain and validate transactions, we are examining fundamental cryptographic techniques like hashing, symmetric and asymmetric encryption, and digital signatures. Furthermore, advanced cryptographic solutions that have the potential to improve privacy and scalability—such as homomorphic encryption, zero-knowledge proofs, and zk-SNARKs—are being discussed. Along with reviewing consensus techniques like proof of work and proof of stake, the paper contrasts the main blockchains, including those that are still in development, like Ethereum, Solana, and Hyperledger Fabric. Through an analysis of the advantages and disadvantages of existing cryptographic implementations, the study emphasizes the necessity for additional innovation.
Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution measurement and equitable incentive distribution. While blockchain is frequently proposed as a decentralized coordination platform, it inherently introduces high on-chain computation costs and risks exposing sensitive execution information of the agents. Consequently, the core challenge lies in enabling auditable task execution and fair incentive distribution for autonomous LLM agents in trustless environments, while simultaneously preserving their strategic privacy and minimizing on-chain costs. To address this challenge, we propose DAO-Agent, a novel framework that integrates three key technical innovations: (1) an on-chain decentralized autonomous organization (DAO) governance mechanism for transparent coordination and immutable logging; (2) a ZKP mechanism approach that enables Shapley-based contribution measurement off-chain, and (3) a hybrid on-chain/off-chain architecture that verifies ZKP-validated contribution measurements on-chain with minimal computational overhead. We implement DAO-Agent and conduct end-to-end experiments using a crypto trading task as a case study. Experimental results demonstrate that DAO-Agent achieves up to 99.9% reduction in verification gas costs compared to naive on-chain alternatives, with constant-time verification complexity that remains stable as coalition size increases, thereby establishing a scalable foundation for agent coordination in decentralized environments.
This paper proposes an information-theoretic framework for analyzing organizational structures, with particular focus on the distinction between centralized hierarchical systems and loosely coupled, decentralized organizations. While existing theories emphasize efficiency, adaptability, or normative values such as democracy, we argue that the core structural difference lies in whether an organization is capable of generating information rather than merely transmitting or compressing it. We introduce a semi-formal model in which organizations are treated as information-processing systems composed of semi-autonomous cognitive agents. Within this framework, we show that vertically centralized management structures necessarily function as lossy information bottlenecks, whereas decentralized, loosely coupled structures enable information gain through multi-source integration and negotiation. We further propose a novel information-theoretic definition of organizational "organicness" and derive necessary structural conditions for information-generating communication. This model offers a unifying theoretical account connecting organizational theory, information theory, and social epistemology.
Reliable data availability and transparent governance are fundamental requirements for distributed edge-to-cloud systems that must operate across multiple administrative domains. Conventional cloud-centric architectures centralize control and storage, creating bottlenecks and limiting autonomous collaboration at the network edge. This paper introduces a decentralized governance and service-management framework that leverages Decentralized Autonomous Organizations (DAOs) and Decentralized Applications (DApps) to to govern and orchestrate verifiable, tamper-resistant, and continuously accessible data exchange between heterogeneous edge and cloud components. By embedding blockchain-based smart contracts within swarm-enabled edge infrastructures, the approach enables automated decision-making, auditable coordination, and fault-tolerant data sharing without relying on trusted intermediaries. The proposed OASEES framework demonstrates how DAO-driven orchestration can enhance data availability and accountability in real-world scenarios, including energy grid balancing, structural safety monitoring, and predictive maintenance of wind turbines. Results highlight that decentralized governance mechanisms enhance transparency, resilience, and trust, offering a scalable foundation for next-generation edge-to-cloud data ecosystems.
The article systematizes modern methods of zero-knowledge proof (ZKP). Classification features are considered: protocol interactivity, algebraic or stochastic basis, need for trusted setup, type of zero-knowledge, and proof model. Classical schemes (Fiat–Shamir, Schnorr, Blum), modern zk-SNARK and zk-STARK, as well as novel approaches – PLONK, Halo 2, Bulletproofs, lattice-based ZKPs, and machine learning proofs are described. A comparative analysis is conducted according to efficiency, proof size, generation and verification complexity. It is shown that SNARKs provide compactness but require a trusted setup, while STARKs are transparent and post-quantum secure but large. Open problems are highlighted: recursive proofs, standardization, metadata protection, and applications in machine learning. It is concluded that further research in this field is aimed at creating scalable, secure, and quantum-resistant protocols for digital technologies.
Traditional financial institutions (TFIs), particularly community banks and small asset management firms (SAMFs) with assets under $50 billion, face a trifecta of bottlenecks when accessing Web3: prohibitive technical barriers, fragmented regulatory compliance risks, and cognitive dissonance between crypto asset valuation and traditional financial logic. In the U.S. market, constrained by multi-agency oversight (SEC, OFAC, FinCEN), the adoption rate of Web3 access among these small TFIs remains merely 5.2% (SIFMA, 2025), far below the 37.8% penetration among large institutions with assets exceeding$500 billion. Leveraging my dual expertise in quantitative finance (CFA Level III) and Web3 multi-chain development (Uniswap V3/V4 protocol experience, daos.world multi-chain DAO incubation), this study constructs a three-dimensional synergistic theoretical framework integrating regulatory adaptation, technical simplification, and valuation migration. A low-barrier access pathway is proposed, centered on the “TradFi-Web3 Connector” system—featuring compliant wallet custody based on EIP-4337 account abstraction and a traditional finance-derived Web3 asset valuation model. Empirical validation across 8 U.S. small TFIs (4 community banks, 4 SAMFs) over an 8-month period (March–October 2025) demonstrates that this pathway reduces the average onboarding cycle from 2.8 months to 9.7 days (82.5% improvement), cuts compliance costs by 61.3% (from $95,400 to$37,300 per annum), achieves a 92.4% investment decision accuracy rate, and maintains a 100% pass rate in SEC compliance reviews with zero regulatory incidents. This research fills a critical gap in low-barrier Web3 access for resource-constrained TFIs, provides a replicable paradigm for the digital transformation of U.S. traditional finance, and empirically validates the synergy between regulatory compliance and technical innovation in cross-ecosystem integration.
During disaster response, making rapid and well-informed decisions about which areas require immediate attention can save lives. However, current coordination models often struggle with unreliable data, intentional misinformation, and the breakdown of critical communication infrastructure. A decentralized, vote-based blockchain model offers a compelling substrate for achieving this real-time, trusted coordination. This article explores a blockchain-driven approach to rapidly update a dynamic 3D crisis map based on inputs from users and local sensors. Each node submits a timestamped and geotagged vote to a public ledger, enabling agencies to visualize needs as they emerge. However, ensuring the physical authenticity of these claims demands more than cryptography alone. We propose a dual-layer architecture where mobile UAV verifiers perform physical-layer attestation and issue independent location flags to the blockchain. This dual-signature mechanism fuses immutable digital records with sensory-grounded trust. We analyze core technical and human centric challenges, ranging from spoofing and vote ambiguity to verifier compromise and connectivity loss, and outline layered mitigation strategies and future research directions. As a concrete instantiation, we present a UAV mapping scheme leveraging modulated retro-reflector (MRR) sensors and 3D-aware LoS placement to maximize verifiability under urban occlusion, offering a path toward resilient, trust-anchored crisis coordination.
In this paper, we introduce Sark, a reference architecture for transferring unforgeable, stateful, oblivious (USO) assets. We describe the motivation, design, and implementation of the core subsystems of Sark, Porters, which accumulate and roll-up commitments from Clients, and Sloop, a permissioned, crash fault-tolerant (CFT) blockchain system. We analyse the operation of the system using the `CIA Triad': Confidentiality, Availability, and Integrity. We then introduce the concept of \textit{local centrality} and use it to address design trade-offs related to decentralization. Finally, we point to future work on Byzantine fault-tolerance (BFT), and mitigating the local centrality of Porters.