This article examines human-artificial intelligence (AI) teaming in Ukrainian combat operations from 2022 to 2025, exploring the integration of AI systems with human decision-making in military contexts and how crisis-driven innovation can lead to human-AI teaming. The research addresses three questions: the effectiveness of human-AI teams compared with human-only or fully autonomous systems; the effect of organizational structures on the sustainability of AI integration; and strategic implications for the development of the doctrine and international security governance in the case of the North Atlantic Treaty Organization (NATO). The methodology uses mixed-method comparative case study analysis of three different Ukrainian systems: the Geographic Information System (GIS) Arta geospatial intelligence platform, the reconnaissance-strike unmanned aerial vehicle complex, and volunteer-supported decentralized targeting networks. Data collection was a combination of technical reports, operational battlefield metrics, and the standards of the NATO doctrines. Findings show that Ukrainian human-AI systems show good tactical performance in permissive electromagnetic environments with response times of 30 to 45 seconds and targeting accuracy of two meters but have significant vulnerabilities to electronic warfare—31% mission failure rates. Volunteer networks are highly resilient and have slower decision cycles. The research adds to strategic security, deterrence theory, military innovation theory, and organizational theory through the identification of mechanisms by which human-AI systems affect the stability of deterrence and offers recommendations for the development of the doctrine and international governance of AI for NATO.
Ethereum is a widely adopted blockchain platform that supports a large number of decentralized applications. Despite its rapid growth, Ethereum remains vulnerable to security threats, particularly phishing attacks that exploit transactional behavior. This study investigates the effectiveness of tree-based ensemble learning models for detecting phishing transactions on the Ethereum network using an imbalanced transaction dataset. Seven tree-based ensemble classifiers are empirically evaluated under a cost-sensitive learning framework, with performance assessed using the Matthews Correlation Coefficient (MCC) as the primary metric. The results show that boosting-based ensembles substantially outperform bagging-based approaches and a single decision tree. In particular, Gradient Boosting achieves the strongest detection performance with an MCC of 0.9742, while CatBoost provides a trade-off between detection performance and computational efficiency, achieving competitive detection accuracy with the lowest average inference time (approximately 1.54 µs per transaction). The findings demonstrate that accurate and robust phishing detection can be achieved using a compact feature representation, enabling practical deployment with reduced computational overhead.
Cryptocurrency fraud on blockchain platforms continues to cause substantial financial losses, creating an urgent need for detection systems that are not only accurate but also interpretable for operational and regulatory use. In this paper, we propose an explainable framework for Ethereum fraud detection integrating an XGBoost ensemble with TreeSHAP. This system achieves high predictive performance (96.3% F1-score, 96.6% recall) while providing model-level transparency via an interactive chatbot interface. Evaluation using fidelity and stability metrics confirms the reliability of the SHAP-based insights, while user-role simulations demonstrate that our structured delivery enhances clarity and actionability over standard visualizations. This work offers a practical, transparent foundation for deploying robust AI in high-risk financial environments without sacrificing accuracy.
NFT (Non-Fungible Token), son yıllarda kripto varlık ekosisteminde önemli bir dönüşüm yaratmış dijital varlıklardır. Dijital sanat eserlerinden koleksiyonluk eşyalara, oyun içi varlıklardan sanal gayrimenkullere kadar birçok alanda kullanılmakta olup, dijital içeriklerin özgünlük ve sahiplik niteliklerini kripto varlık biçiminde temsil etmektedir. Bu çalışmada NFT kavramı, tarihsel gelişimi, kullanım alanları ve türleriyle, NFT’lerin güvenilirliğini sağlayan Blokzincir, ikinci nesli Ethereum ile akıllı sözleşmeler gibi teknik yapısı üzerinde durularak hukuki niteliği konusunda değerlendirmeler yer almaktadır.
Non-Fungible Tokens (NFTs) are unique digital assets built on blockchain technology that can represent ownership of data or digital items. Although widely associated with digital art and collectibles, NFTs are increasingly being explored for healthcare applications.[1] This review examines how NFTs could be used in managing health data, improving supply chains, enabling secure identities, and supporting emerging digital health services. While NFTs show promise in enhancing transparency, security, and patient control, their adoption is still limited due to technical, regulatory, and ethical challenges.[2]
Open access
2 source records
Blockchain Technology Applications and Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
Oblivious Transfer (OT) is a fundamental cryptographic primitive enabling privacy-preserving computation and constitutes a core building block for secure multi-party computation while supporting a wide range of security-sensitive applications: private information retrieval, zero-knowledge proofs, and password-authenticated key exchange, to cite a few. While recent advances in OT extension have significantly reduced amortised costs, their reliance on batches of random base OTs and substantial pre-computation phases limits their practicality in scenarios where the number of transfers is modest or where communication latency and client-side computation are critical constraints. In such settings, efficient base OT protocols remain both relevant and necessary. In this work, we introduce $I$-$(OT)^2$, a novel base 1-out-of-2 OT protocol grounded in the quadratic residuosity problem, specifically designed to minimise receiver-side computation and interaction. Our construction is particularly appealing on client--server architectures in which the receiver operates on low-power hardware, such as Internet of Things (IoT) devices. Through a lightweight offline pre-computation phase, $I$-$(OT)^2$ shifts the on-transfer computational burden almost entirely to the Sender, while reducing online communication to only six messages and four digests exchanged. We provide a detailed description of the protocol, accompanied by a formal proof of its security. Moreover, to demonstrate the viability of $I$-$(OT)^2$, we also present an open-source proof-of-concept implementation (in C language) evaluated on real IoT hardware. Results are staggering: for 128-bit security using a 3072-bit RSA modulus, the receiver incurs an average online cost per OT as low as 2.80 μs on desktop platforms and 39.90 μs on IoT devices, more than 10$\times$ faster than the well known SimplestOT.
Shor's algorithm represents the main threat of quantum computers to cryptography. In order to precisely understand its feasibility, many authors have worked towards reducing its costs, either at the logical level (assuming a fault-tolerant architecture), or at the physical level (taking into account the constraints of envisioned hardware). In particular, recent works by Chevignard et al. (CRYPTO 2024) and Gidney (arXiv 2025) used improved arithmetic to significantly reduce the qubit cost of factoring RSA public keys. Even more recently, Babbush et al. (arXiv 2026) improved the cost of computing elliptic curve discrete logarithms, with a reduction of a factor 2 to 3 in gate count and qubit count compared to a previous work by Litinski (arXiv 2023). Their result relies on optimized point addition circuits on elliptic curves over prime fields. However they did not reveal their logical quantum circuits, relying instead on a zero-knowledge proof. In this paper, we detail a quantum logical circuit architecture which gives similar results as Babbush et al., with a slightly higher number of qubits (around 1.5% increase) and a slightly smaller Toffoli gate count (between 6.5% and 10% reduction) for the curve secp256k1. We also give gate counts for a generic variant of the circuit, which is valid for any prime field.
Background and Gap Information: Positive, negative and hybrid external confirmation processes are the pillars of audit evidence as posed in the standard ISA 505, however the response rate is very low 48-72%, fraud is not detected 12-38% and there is an unresolvable conflict between assurance and data privacy. This “auditor’s dilemma” intensifies with cross-border transactions and tight data privacy regulations such as Iraq’s Personal Data Protection Law No. 10 of 2024. Although there are recent proposals based on blockchain or homomorphic encryption, none of them has presented a mathematically zero-knowledge, empirically verified, and regulatorily compliant confirmation protocol that seamlessly performs over heterogeneous ERP systems without leaking its underlying commercial data. Objective: We present the Decentralized Confirmation Matrix (DCM) - a game-changing evidence of audit protocol based on zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) to cryptographically prove a set of external transactions, while leaking only the fact that they are consistent (“valid/invalid”) together with a timestamp. The paper (1) details the DCM design, including its novel dual-nullifier and heterogeneous trust models; (2) presents an empirical comparison of DCM with traditional techniques based on authentic Iraqi state-owned enterprise (SOE) data; (3) scrutinizes DCM against Iraqi higher educational certification standards and Scopus Q1 repeatability requirements; and (4) delivers an open-source route-to-implementation (ZKCaaS). Methodology: We developed a complete DCM prototype using Circom 2.1.6 and SnarkJS over a permissioned blockchain sandbox (Iraqi National Blockchain Sandbox). 4We acquired genuine transaction logs (n=25,000+ confirmations) from three Iraqi SOEs: Northern Refineries Company (Baghdad), Basra Oil Terminal (Basra), and Iraqi Telecommunications Company (Erbil).A controlled field experiment with 45 Iraqi auditors (repeated measures, counterbalanced) was conducted to evaluate DCM vis-a-vis traditional positive and email-hybrid confirmations on response time, error rates, cost, auditor satisfaction (UTAUT2), and attack resilience. Results: DCM reduced average confirmation response time by 99.6% (to 0.05 days), attained a 100% response rate by automation, elevated fraud detection from traditional 62% to 97%, and brought in cost per confirmation (from 6% to 0.45%). Auditor satisfaction rated 4.6/5, and the dual‑nullifier scheme prevented 100% of replay and collusion attacks - a guarantee not found in any prior work. Audit risk (ISA 315) decreased by 93% (from 6% to 0.45%).Cross‑platform rollup between SAP and Oracle succeeded at 98%, solving a long‑standing interoperability ga. Conclusion: DCM is the first practical, privacy-preserving, and empirically superior external consistency checking protocol that satisfies ISA 500/505 while enabling “cryptographically sealed evidence” as a novel evidence type. The article is in line with the quality requirements of the Iraqi accreditation agency as well as Scopus Q1, which consider theoretical novelty, empirical rigour and open‑source replicability. We propose a strategic vision for 2025-2030 and an Autonomous Audit Agent (AAA) for full automation.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Cryptographic wallets play a vital role in securing digital assets within blockchain networks by managing private keys that authorize secure transactions. However, side channel analysis (SCA) attacks have become a serious threat, enabling attackers to extract sensitive information by exploiting algorithmic weaknesses in microcontroller-based wallets, resulting in the loss of millions of dollars in digital assets. In hierarchically deterministic (HD) systems, the compromise of a single primary key can endanger all subsequent child keys, while the use of independent keys for each account introduces complexity and challenges in key management. This work presents HardVault, a field programmable gate array (FPGA)-based cryptocurrency wallet that supports both Bitcoin and Ethereum. HardVault introduces the first hardware wallet architecture that implements both non-deterministic (ND) and HD key generation modes directly in hardware, giving users the flexibility to choose either approach based on their security and usability needs. By leveraging constant-time operations and hardware-enforced private-key isolation, the design significantly improves resilience to SCA attacks. In addition, the architecture prioritizes resource efficiency to minimize area usage without compromising security, making it well-suited for compact, portable hardware wallet applications. Implementation on a ZCU104 FPGA shows that HardVault uses only 27% of available look-up tables (LUTs). Compared to the Trezor One cryptocurrency (crypto) wallet, the proposed implementation achieves$9\times $higher energy efficiency,$8\times $lower latency, and$7\times $higher throughput.
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
This report synthesises findings from 3 peer-reviewed papers addressing the following research question: How does the integration of persistent homology-based topological features in MTGCL compare to other graph contrastive learning methods (e.g., GTCL, GCMC) in terms of anomaly detection accuracy and. Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.7/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the integration of persistent homology-based topological features in MTGCL compare to other graph contrastive learning methods (e.g., GTCL, GCMC) in terms of anomaly detection accuracy and inference latency on large-scale Ethereum transaction datasets? Autonomous literature synthesis. Automated review score: 8.7/10. Full text and citation available at Assignee Research.
We present Aggios, a scalable and privacy preserving proxy voting system designed for frequent and large-scale elections such as Decentralized Autonomous Organizations (DAO), when storing votes on the bulletin board is expensive. To this end, Aggios introduces ‘aggregators’: entities to which voters delegate their votes, and who then post their batched proofs on the public ledger. Aggios achieves strong integrity guarantees: only authorized voters can vote, votes are counted correctly, voters are assured their vote is counted.
In the Internet of Vehicles (IoV), the large-scale deployment of smart vehicles has triggered new road traffic safety challenges. Particularly, existing vehicle accident report transmission schemes still face challenges such as privacy leakage, Single Point of Failure(SPOF), physical cloning attacks, and excessive computational overhead. To address these issues, this paper proposes a secure accident report transmission scheme that uses Non-Interactive Zero-Knowledge Proof (NIZKP) and Physically Unclonable Functions (PUF). This paper designs a decentralized authentication scheme for vehicle registration that prevents SPOF and privacy leakage. We also use the PUF to realize two-factor authentication login, which effectively resists physical cloning attacks. In addition, the authentication process uses NIZKP based on the Pedersen commitment to realize authentication for accident report coordination. At the end of the accident report coordination, it is passed into the blockchain for storage, realizing the secure transmission of accident reports. To reduce the storage as well as computation overhead, this paper uses a key derivation function to update the key. Finally, formal security analysis was conducted using the Real or Random (ROR) model and the ProVerif tool, the results prove that the proposed protocol meets security requirements. Comparing our proposed scheme with related schemes, the computational overhead of our V2V scheme is reduced by 42.4%, with higher security and lower communication overhead.
Open access
Vehicular Ad Hoc Networks (VANETs)
Physical Unclonable Functions (PUFs) and Hardware Security
This paper discusses the technological development from Web 1.0 to Web 3.0, focusing on their corresponding economic models. The study begins by analyzing the Web 1.0 portal economy, followed by an in-depth exploration of the rise of the Web 2.0 platform economy and its associated challenges, including the lemon market, platform monopolies, price discrimination, and algorithmic asymmetries. To address those issues, this study elaborates on the Web 3.0 token economy and emphasizes the crucial role of decentralized technologies like blockchain in bringing new production factors and relationships. This inspires the proposal of the Decentralized Economy (DeEco), a novel user-autonomous economic model that integrates advanced Artificial Intelligence (AI) technologies with blockchain. Furthermore, the key techniques for formulating DeEco are analyzed, including Decentralized Autonomous Organizations and Operations (DAOs), Decentralized Value Systems (DVSs), Decentralized Physical Infrastructure Networks (DePIN) and digital humans. This study not only offers a co-evolutionary perspective of web technologies and economic forms but also introduces an innovative economic paradigm to support open, diverse and intelligent societies.
Ch Sree Kumar, Jatindra Kumar Dash, K. Hemant Kumar Reddy
Blockchain-enabled healthcare infrastructures demand latency-aware, privacy-preserving, and scalable transaction management due to the stochastic and high-volume nature of clinical data processing in decentralized environments. In this study, we propose a blockchain-aware Modified M/M/C (Mo M/M/C) queueing framework specifically designed for NFT-enabled healthcare systems integrated with Zero-Knowledge Proof (ZKP)-based verification. Unlike classical queueing models that assume single-stage service, the proposed model incorporates a multi-stage transaction pipeline consisting of medical processing, NFT tokenization, ZKP verification, and blockchain validation. Accordingly, an effective service rate formulation and blockchain-adjusted arrival rate are analytically derived to capture cryptographic overhead, consensus latency, and smart contract execution delays within a unified stochastic framework. Patient records, prescriptions, and diagnostic data are securely encapsulated as NFTs to ensure immutability, traceability, and decentralized ownership, while ZKP protocols enable privacy-preserving authentication without exposing sensitive medical information. The model further integrates dynamic priority-aware scheduling and validation-aware utilization to optimize resource allocation under heterogeneous healthcare workloads. Extensive discrete-event simulations conducted over scalable transaction volumes (1,000–100,000) evaluate key performance metrics including throughput, average waiting time, system response time, and latency. The results demonstrate that the proposed Mo M/M/C framework significantly improves queue stability, reduces congestion, and enhances throughput compared to FIFO, LIFO, SIRO, and standard M/M/C models. Overall, the revised framework provides a mathematically grounded, cryptography-aware, and blockchain-consistent solution for secure and real-time healthcare transaction management
Olha Kovalchuk, Ruslan Shevchuk, Serhiy Banakh, N. P. Holota · 6 authors
Abstract This study examines the relationships between national cryptocurrency regulation, anti-money laundering (AML) risks, and decentralized finance (DeFi) adoption across global jurisdictions. Using correspondence analysis, correlation techniques, and regression modeling with control variables, we analyze data from the Basel AML Index and Retail DeFi Rankings to identify structural patterns in the interaction between regulatory frameworks, institutional quality, and digital asset ecosystems. The results reveal a counterintuitive global distribution in which advanced economies with strong regulatory regimes and low AML risks tend to exhibit limited retail DeFi activity, whereas jurisdictions characterized by weaker institutions and higher money laundering risks show significantly higher levels of DeFi usage. Further, the correspondence analysis identifies three distinct clusters of countries defined by specific configurations of regulatory approaches, AML effectiveness, and DeFi adoption, indicating that these relationships are configurational rather than purely linear. Robustness checks demonstrate that qualitative features of regulatory regimes are more strongly associated with DeFi adoption than conventional quantitative indicators of economic development or governance quality, thereby distinguishing DeFi diffusion from broader cryptocurrency usage dynamics. Mediation analysis provides partial support for a compensatory pattern: financial inclusion is a significant negative predictor of DeFi adoption, though a statistically confirmed mediation pathway between AML risk and DeFi activity through financial exclusion was not established. The study also highlights substantial global regulatory fragmentation, with 57% of jurisdictions classified as “Undecided” or “Improving,” underscoring the ongoing difficulty of reconciling financial innovation with stability and risk mitigation. These findings provide evidence-based guidance for policymakers designing adaptive regulatory frameworks and establish a foundation for further research on the evolution of digital finance regulation.
Decentralized Finance (DeFi) represents an emerging financial ecosystem that offers services such as lending, investing, and trading without traditional intermediaries like banks or financial institutions. Unlike conventional financial systems, users interact directly with software programs called smart contracts that encode financial logic and automate service delivery. This novel ecosystem promises transparency through public blockchain ledgers that make all transactions visible and inclusion through open access that eliminates traditional barriers to financial participation. Additionally, DeFi enables decentralized governance where users participate in protocol decision-making, and smart contracts facilitate advanced financial engineering through compositional service integration. However, despite these technical innovations, DeFi introduces significant challenges related to transaction complexity, governance concentration, and cybersecurity vulnerabilities that undermine its foundational promises. This thesis develops computational methods to systematically investigate these challenges in Decentralized Finance through empirical analysis of blockchain data. First, to address the complexity of DeFi compositions, we developed an algorithm that extracts fundamental building blocks from individual transactions, revealing recurring patterns and hidden interdependencies between financial services and assets that manual analysis cannot capture at scale. Second, we applied network analysis techniques and introduced novel measurements to examine the governance structures of decentralized applications, focusing on contributors with development and administrative roles. Our analysis revealed common voting patterns and centralized decision-making that contradict claims of decentralized governance. Third, we adapted a difference-in-differences statistical framework to quantify the economic impact of cybercrime on governance tokens, demonstrating that indirect effects on prices and trading volumes significantly exceed the direct losses suffered by immediate victims. These computational methods collectively provide the first systematic, large-scale analytical framework for empirically investigating DeFi ecosystems, revealing fundamental gaps between theoretical promises of transparency and inclusion and practical realities. The findings have significant implications for researchers, policymakers, and practitioners by establishing evidence-based approaches to measuring decentralization claims and systemic risks in blockchain-based financial systems.
Ahmad Ahmad, Muhammad Said, Abdillah Abdillah, Abdulloh Munir
The rapid expansion of Decentralized Finance (DeFi), powered by blockchain technology, has transformed global financial systems by offering peer-to-peer, intermediary-free services. However, its compatibility with Islamic economic law (hukum ekonomi syariah) remains uncertain due to potential violations of Sharia principles such as the prohibition of riba (usury), gharar (excessive uncertainty), and maysir (speculation). This study addresses this gap by employing a qualitative maqāṣid al-sharī‘ah-based analysis to assess the alignment of DeFi mechanisms decentralized exchanges, lending protocols, and smart contracts with Islamic ethical and legal values. Data were collected through literature review and document analysis from classical Islamic sources, fatwas, and current DeFi documentation. The findings show that while many DeFi practices contain non-compliant elements, their underlying technology particularly smart contracts and decentralized governance holds significant potential for adaptation. When structured using Sharia-compliant contracts such as murābaḥah, mushārakah, or wakālah, and guided by maqāṣid objectives like ḥifẓ al-māl (preservation of wealth) and ḥifẓ al-dīn (preservation of faith), DeFi can support financial inclusion, transparency, and justice in accordance with Islamic law. This study proposes a normative framework for building Sharia-compliant DeFi platforms, integrating technical innovations with ethical governance, thereby offering a transformative model for Islamic finance in the digital era.
This paper analyzes transaction fees on blockchains by considering that they form a priority queue and users play a queueing game. Using an M/G^K/1 priority queue model, we provide new insights into the dynamics governing transaction fees and their impact on user behavior. We derive semi-closed form expressions for steady-state quantities and extend the relationship between user delay costs and transaction fees to general block generation times. We apply the model to the Bitcoin network and simulate user responses under various scenarios. Cross-chain analysis across Bitcoin, Dogecoin, and Litecoin reveals similarities in normalized cost structures.
Real temporal interaction streams carry predictive structure in short-horizon motif patterns -- repetition, reciprocity, star diversity, triadic flow -- that vanilla temporal graph neural networks (TGNNs) often fail to expose to their edge scorers. We show this concretely on MOOC interaction prediction, where a small four-feature family of past-window star counts already delivers most of the lift over a strong static GNN. Across a wide set of real and synthetic temporal datasets we find that motif activity organizes consistently along three scale-stable axes (dyadic recency/reciprocity, star diversity, triadic flow), and we use this empirical structure to design a compact 13-coordinate, leakage-safe, candidate-local motif feature map h(u, v, t) that linearly embeds into any static or temporal encoder without architectural changes. A temporal Weisfeiler-Leman (WL) analysis places the augmentation relative to the first level of an anchored temporal-WL hierarchy and exhibits a candidate-anchored pair on which motif features distinguish. We demonstrate empirically that the same augmentation consistently lifts performance across heterogeneous tasks: TGB link-property prediction across all five baselines, edge classification on Bitcoin Alpha/OTC and MOOC, and graph-level classification of synthetic temporal generators.
Real-world asset tokenization is often presented as a mechanism for improving the liquidity of traditionally illiquid assets. However, on-chain representation and secondary-market liquidity are distinct outcomes. This paper examines whether tokenized real-world assets exhibit meaningful observed liquidity and identifies the token characteristics associated with higher market activity. Using token-level data from RWA.xyz and supplemental contract-level observations from Etherscan, the study constructs an Ethereum-based monthly panel of non-stablecoin real-world assets across three prominent categories: U.S. Treasury-backed tokens, gold-backed commodity tokens, and private-credit-related tokens. Liquidity is measured using turnover, active addresses, and an active-month indicator. The empirical design combines descriptive statistics, non-parametric group tests, and exploratory panel regressions suited to short and sparse token histories. The results show substantial heterogeneity across asset categories. Gold-backed tokens exhibit broader holder bases and more persistent on-chain activity than many Treasury and private-credit-related products, while outstanding asset value alone does not reliably predict observed liquidity. The paper contributes to the literature by developing a clearer empirical measurement framework for real-world-asset liquidity and showing that tokenization and liquidity should be analyzed as distinct outcomes.
Andi Aidir Arsy, Dewi Salmita, Muhammad Syafaat, Noval · 5 authors
Purpose - This study examines the association between regional investment, leverage, and regional financial independence within the fiscal decentralization framework. Design/methodology/approach - A quantitative associative approach is employed using pooled panel data from 13 regency and municipal governments in Central Sulawesi Province during 2018–2024. The relationships among variables are analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with WarpPLS. The analysis is grounded in fiscal decentralization theory and agency theory to explain local government financial management behavior. Finding/Results – The results indicate that regional investment and leverage are positively and significantly associated with regional financial independence in the pooled PLS-SEM model. Long-term investment is related to stronger fiscal capacity, while leverage may serve as a supportive financing instrument when managed prudently. Together, both variables explain a moderate proportion of the variation in regional financial independence. Originality/Value - This study contributes empirical evidence on how regional investment and leverage are linked to local fiscal autonomy in Central Sulawesi, an underrepresented provincial context in Indonesian local government finance studies. The findings provide practical insights for local governments to improve productive long-term investment and maintain prudent liability management. This study is limited to one province and two explanatory variables; therefore, future research may expand regional coverage and include governance quality, revenue effectiveness, transfer dependence, and expenditure efficiency.
By chance or by destiny, Bitcoin mining companies have found themselves with a golden opportunity in their hands: they possess the most scarce asset of the 21st century—energy. Something similar happened back in the mid-19th century, railroad companies acquired millions of acres of land and rights-of-way strictly to lay down train tracks with the main idea of a business fundamentally focused on physical transportation. However, when the telegraph was invented, they realized that the optimal location to deploy electrical communication lines was right alongside those very train tracks. They already possessed the cleared terrain, the physical security, and the legal rights-of-way. And as we have seen, the structural mispricing identified in this thesis represents a finite, high-velocity arbitrage window. Where currently, Wall Street's evaluation models remain anchored to old crypto-mining frameworks, valuing these entities on cyclical hash-rate economics rather than the long-duration infrastructure value of their underlying energized grid connections.
The article examines the economic essence of asset tokenization as a new form of microeconomic relations in the context of financial market digitalization. The existing approaches to interpreting the concept of "asset tokenization" in domestic and foreign scientific literature are generalized, and the author's definition of this economic category is proposed as an institutional-technological mechanism for digitalizing property rights that forms a new architecture of microeconomic relations among market participants. The existing approaches to the classification of tokenized assets are analyzed, in particular the regulatory approach of the U.S. Securities and Exchange Commission (SEC) and the approach of the Financial Stability Board (FSB) based on the reference asset category. On the basis of their critical analysis, the author proposes a multidimensional classification of tokens according to six criteria: functional purpose, role in decentralized finance, method of collateralization, nature of issuance, fungibility, and jurisdictional characteristic. The microeconomic effects of asset tokenization are systematized, encompassing five interrelated groups: structural effects (fractionalization of property rights, disintermediation, formation of new market structures), transactional and price effects (reduction of transaction costs, improvement of asset liquidity), behavioral effects (transformation of incentives and decision-making patterns of economic agents), market equilibrium effects (expansion of supply and demand), and network effects (economies of scale, risks of market fragmentation). It is established that these effects are interconnected and collectively form a new microeconomic environment for the functioning of financial markets.