Centralized governance frameworks in developing economies frequently generate a fundamental tension between national policy coherence and the operational responsiveness required by diverse local labor markets. In Laos, this tension is acutely manifest within the administration of Vocational Education (VE), where provincial and district authorities bear expanding implementation responsibilities without commensurate transfers of fiscal authority, administrative capacity, or technical expertise. This study critically examines the administration models operationalized within local VE governance in Laos, identifies the structural constraints limiting their effectiveness, and proposes an evidence-grounded five-component governance reform framework. Employing a sequential explanatory mixed-methods design, the study integrates quantitative survey data from 781 stakeholders across all provinces, comprising local government officials, VE administrators, educators, and industry representatives, with semi-structured qualitative interviews and systematic policy document analysis. Quantitative findings reveal that administrative model typology (centralized, decentralized, hybrid) accounts for less than 2% of the variance in perceived governance effectiveness, whereas process-level governance qualities, institutional autonomy, stakeholder participation, decentralization depth, and resource adequacy jointly account for 56% of the variance in perceived suitability. Public-Private Partnership (PPP) strength emerges as the strongest predictor of institutional effectiveness. Qualitative findings illuminate the mechanisms underlying these patterns: local authorities function as pragmatic brokers, navigating structural rigidities through informal networks and discretionary project-based initiatives that remain institutionally unsustainable and inequitably distributed. The study identifies a centralization-responsiveness paradox wherein ceremonially adopted decentralization policies coexist with functionally centralized operations, generating accountability ambiguities and inequitable governance costs. Grounded analytically in institutional theory, decentralization theory, and human capital theory, the proposed framework integrates reconstituted multi-level governance architecture, institutionalized demand-side engagement, equitable financing, systematic capacity development, and rigorous performance accountability. Implications are discussed for policymakers, development partners, and VE administrators in comparable transitional economies.
Confirmations of the ZBCN Report, and promises from a relatively new centralized private company trying to justify its existence in decentralized finance.
Autonomous artificial intelligence agents increasingly act across decentralized systems, yet existing authorization models provide limited mechanisms for constraining delegated authority, proving policy compliance, and assigning accountability for machine-initiated actions. This paper presents a policy-enforced identity and accountability framework for cryptographic governance of autonomous AI agents. The proposed architecture binds each agent to a verifiable decentralized identity, machine-readable authorization policies, delegated capability constraints, and tamper-evident action records. Before an action is executed, the framework evaluates identity validity, policy scope, contextual conditions, delegation depth, expiration, and revocation status. Approved actions generate cryptographically verifiable receipts that link the agent, authorizing principal, applicable policy, execution context, and resulting state transition without requiring disclosure of unnecessary sensitive information. The framework also supports attenuated delegation, enabling subordinate agents to receive narrower permissions than their parent agents while preventing privilege amplification. A formal threat model evaluates impersonation, policy substitution, replay attacks, unauthorized delegation, audit-log manipulation, and compromised agent behavior. Security analysis indicates that the architecture strengthens provenance, non-repudiation, least-privilege enforcement, and post-execution auditability across heterogeneous decentralized environments. The proposed approach provides a foundation for governing autonomous agents in blockchain networks, distributed applications, machine-to-machine systems, and multi-agent infrastructures where conventional access control is insufficient. It shifts AI governance from trust-based supervision toward verifiable, policy-bound, and cryptographically accountable execution.
As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.
Blockchain technology, initially developed as a backbone for cryptocurrencies, has rapidly expanded into broader domains of finance and business. Its unique attributes – transparency, decentralization, immutability, and enhanced security – offer solutions to persistent challenges in financial services. This chapter examines blockchain applications beyond cryptocurrency, focusing on its role in transforming financial services such as Know Your Customer (KYC), cross-border payments, and compliance. A case-based exploration of blockchain-enabled KYC demonstrates how distributed ledgers can streamline identity verification while ensuring trust and efficiency. The chapter also outlines benefits, limitations, and potential applications, contributing to a holistic understanding for policymakers, financial institutions, researchers, and practitioners.
The article provides critical analysis of the new Strategy of spatial development of the Russian Federation up to 2030 with the forecast up to 2036 in view of evolution of federal relations and provisions of the institutional theory. Topicality of the research is envisaged by the necessity to comprehend the fact whether the present document in the absence of certain strategies of federative relations can perform the coordinating role in stimulating economic selfdevelopment in regions. The goal of the research is to identify systemic contradictions between declared targets of spatial development and real conditions and mechanisms of governing included in strategic documents. Principle conclusions of the research demonstrate a deep paradox of today’s Russian regional policy: in spite of the declared rejection of the idea of equal development and transition to rhetoric of differentiated approach and resource concentration the effective Strategy does not put forward any withdrawal from the model of controlled asymmetry but reproduces centralized vertical logics of governing. The key institutional gap is successive denial of the important role of institutions of in-regional level, i.e. city agglomerations and strong centers of population, which are recognized as objective reality but are not provided with real power and finance authorities. Tools being introduced, such as common lists of strong centers of population and geo-strategic territories, as well as the digital platform of governing are analyzed as mechanisms of increasing administrative priority fixing from above and tools of vertical supervision but not as the basis of decentralization and providing regions with authorities. The author shows increasing non-coordination in the system of strategic planning. As priority lines in restructuring the article proposes adoption of the federal law about regional development securing the real status of institutions on in-regional level; drastic change in inter-budget relations to raise region independence and restore on the federal level the common center of responsibility for coordination of spatial policy. The research contributed to the discussion about the necessity to elaborate complex strategy of federative relations and showed that without such a document and re-distribution of real power and resources any spatial strategy will not be able to resolve key institutional paradoxes.
Haytham A. Sheerah, Ahmed Arafa, Mansour A. Alfaya, Ashraf B AlDerbas · 10 authors
Problem: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. Approach: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. Local setting: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. Relevant changes: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. Lessons learnt: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making.
Verifiable Random Functions (VRFs) are cryptographic primitives that generate unpredictable randomness together with a publicly verifiable proof of correct generation following the protocol, a critical requirement for decentralized applications in blockchain infrastructure, decentralized finance, and online gaming. While distributed VRFs (DVRFs) eliminate dependence on a single trusted authority, existing constructions face a fundamental dilemma: linear proof sizes in the threshold parameter (DDH-DVRF) or reliance on computationally expensive bilinear pairings (GLOW-DVRF, FlexiRand). This thesis resolves both facets of this dilemma with three contributions despite the downside of adding another interaction round amongst the parties involved in the generation. We first introduce DVRFwCP, a distributed VRF with constant-size, pairing-free proofs achieved by layering a threshold structure over a Chaum-Pedersen NIZK system, where we use an augmented secure distributed key generation to produce the required nonce. We then introduce Icy-DVRF, which eliminates the quadratic interaction bottleneck of DVRFwCP by integrating the FROST-style preprocessed nonces, reducing total per-evaluation communication from O(n^2 t) to O(t) while preserving constant-size proofs. Finally, we introduce IcyVeil, the first pairing-free output-private DVRF, which extends Icy-DVRF with a Schnorr-based blinding mechanism that conceals the VRF output until revealed by the user. We evaluate all three constructions theoretically and empirically via Solidity smart-contract implementation on the Ethereum Sepolia testnet. The measurements show a 43.02% reduction in on-chain verification gas cost compared to GLOW-DVRF.
This chapter explores the transformative role of Financial Technology (FinTech) in advancing green finance, a crucial component in addressing global sustainability challenges. By integrating technologies such as blockchain, artificial intelligence (AI), big data analytics, and smart contracts, FinTech facilitates the efficient allocation of capital towards sustainable projects. The chapter examines how these innovations enhance transparency, optimize risk assessment, and enable decentralized financing models like peer-to-peer energy trading and tokenization. Additionally, it addresses the challenges posed by greenwashing, market volatility, and regulatory uncertainty, while highlighting the future opportunities for growth in green finance. Ultimately, the chapter underscores the potential of FinTech to drive systemic change and promote a low-carbon, sustainable economy.
This chapter explores the transformative potential of blockchain and artificial intelligence (AI) in revolutionizing green finance. It begins by examining the role of digital transformation in driving sustainable financial practices, highlighting the integration of blockchain and AI. The chapter delves into blockchain's applications in enhancing transparency, traceability, and security within green finance, particularly through smart contracts and decentralized finance solutions. It further discusses AI's contributions to improving risk assessment, ESG evaluation, and combating greenwashing. The synergies between blockchain and AI are explored, showing how their combined use optimizes sustainability-focused investments. Additionally, the chapter addresses regulatory and ethical considerations surrounding these technologies. Finally, it discusses emerging trends and opportunities in green finance, providing insights into the future of sustainable financial systems driven by technological innovation.