Real-world systems ranging from airline routes to cryptocurrency transfers are naturally modelled as dynamic graphs whose topology changes over time. Conventional benchmarks judge dynamic-graph learners by a handful of task-specific scores, yet seldom ask whether the embeddings themselves remain a truthful, interpretable reflection of the evolving network. We formalize this requirement as representation integrity and derive a family of indexes that measure how closely embedding changes follow graph changes. Three synthetic scenarios, Gradual Merge, Abrupt Move, and Periodic Re-wiring, are used to screen forty-two candidate indexes. Based on which we recommend one index that passes all of our theoretical and empirical tests. In particular, this validated metric consistently ranks the provably stable UASE and IPP models highest. We then use this index to do a comparative study on representation integrity of common dynamic graph learning models. This study exposes the scenario-specific strengths of neural methods, and shows a strong positive rank correlation with one-step link-prediction AUC. The proposed integrity framework, therefore, offers a task-agnostic and interpretable evaluation tool for dynamic-graph representation quality, providing more explicit guidance for model selection and future architecture design.
Anton Ivashkevich, Matija Piškorec, Claudio J. Tessone
We describe a prototype of a fully capable Ethereum Proof-of-Work (PoW) blockchain network running on multiple Raspberry Pi (RPi) computers. The prototype is easy to set up and is intended to function as a completely standalone system, using a local WiFi router for connectivity. It features LCD screens for visualization of the local state of blockchain ledgers on each RPi, making it ideal for educational purposes and to demonstrate fundamental blockchain concepts to a wide audience. For example, a functioning PoW consensus is easily visible from the LCD screens, as well as consensus degradation which might arise from various factors, including peer-to-peer topology and communication latency - all parameters which can be configured from the central web-based interface.
Vision Transformers achieve strong performance across computer vision tasks but suffer from quadratic computational complexity with respect to token count, limiting deployment in resource-constrained environments. Existing token pruning methods rely on attention scores to identify important tokens, but attention mechanisms capture query-specific relevance rather than intrinsic information content, potentially discarding tokens that carry information for subsequent layers or different downstream tasks. We propose fractal-guided token pruning, a method that leverages the correlation dimension Dcorr of token embeddings as a task-agnostic measure of geometric complexity. Our key insight is that tokens with high Dcorr span higher-dimensional manifolds in representation space, indicating complex patterns, while tokens with low Dcorr collapse to simpler structures representing redundant information. By computing a local Dcorr for each token and pruning those with the lowest values, our method retains geometrically complex tokens independent of attention-based relevance. The correlation dimension quantifies how token embeddings fill the representation space: embeddings from uniform background regions cluster tightly in low-dimensional subspaces (low Dcorr), while embeddings from complex textures or object boundaries spread across higher-dimensional manifolds (high Dcorr), reflecting their richer information content. Experiments on CIFAR-10 and CIFAR-100 with fine-tuned ViT-B/16 models show that fractal-guided pruning consistently outperforms random and norm-based pruning across all tested ratios. At forty percent pruning, fractal pruning maintains 92.26% accuracy on CIFAR-10 with only a 0.99 percentage point drop from the 93.25% baseline while achieving 1.17× speedup. Our approach provides a geometry-based criterion for token importance that complements attention-based methods and shows promising generalization between CIFAR-10 and CIFAR-100 datasets.
Regional banks emerged around the 1960s with the mission of contributing to the development and integration of Latin America, primarily through the financing of infrastructure projects, essential to the region's industrialization and trade flows.In 2000, the South American Regional Integration Initiative (IIRSA) was created, under whose Secretariat the Inter-American Development Bank (IDB), the Development Bank of Latin America (CAF), and FONPLATA -Development Bank -began working together to promote territorial planning and find financing solutions.Even with the dissolution of IIRSA, resulting from the paralysis of the Union of South American Nations (UNASUR) starting in 2017, the coordinated action of the three banks continued, through initiatives such as the Alliance for the Integration and Development of Latin America and the Caribbean (ILAT), the Sucre Declaration, and the "South American Integration Routes," demonstrating the resilience of infrastructure integration in the face of political change.Therefore, the overall objective of this thesis was to analyze the contribution of the development banks IDB, CAF, and FONPLATA to building regional infrastructure integration in South America.The methodology involved identifying integration models and operational concepts under which these banks operate; identifying the specific problems of Latin American regional infrastructure and its financing; and analyzing the performance of IDB, CAF, and FONPLATA both individually, focusing on documents, projects, and institutional structures focused on integration, and collectively, from the emergence of IIRSA to the Integration Routes.It was found that, despite the current general crisis in Latin American regionalism, both intellectual and institutional, the three banks are at the center of building a governance system for financing regional infrastructure in South America.However, they have shifted from a model centralized in IIRSA to one, after the end of this Initiative, focused on decentralized cooperation.
The Nepali government has declared all cryptocurrency-related activities illegal due to its exclusive currency issuance authority and stringent foreign exchange regulations. This prohibition is based on robust anti-money laundering laws and the potential applicability of evolving digital legislation. The article also assesses the costs and risks associated with illicit cryptocurrency activities and encompassing severe legal consequences, financial exposure, and cyber security threats. Finally, it explores the paradox of Nepal Rastra Bank's exploration into a Central Bank Digital Currency (CBDC), suggesting a recognition of digital currency's future while prioritizing national control and stability. The study concludes by emphasizing the imperative for public adherence to existing prohibitions while acknowledging the long-term trajectory towards digital financial innovation.
The global decentralized identity market is undergoing exponential transformation as organizations worldwide shift toward secure, user-centric identity management frameworks. Valued at USD 1.52 billion in 2024, the market is projected to reach USD 56.83 billion by 2030, expanding at an extraordinary CAGR of 82.9% from 2025 to 2030. This manuscript provides an in-depth analysis of decentralized identity systems that utilize blockchain and distributed ledger technologies to eliminate central points of failure, enhance user privacy, and safeguard digital interactions. It further explores the major drivers influencing market growth, including rising identity fraud, rapid digitalization, proliferation of cloud platforms, and implementation of strict regulatory data protection standards. Despite high initial investments posing a challenge for small and medium enterprises, increasing integration of artificial intelligence and machine learning in decentralized identity frameworks offers promising future opportunities. The study concludes with key segmental insights, regional dynamics, competitive landscape, and future implications for the decentralized identity ecosystem.
The competitive hospitality sector faces a growing credibility crisis, where rising consumer skepticism regarding "greenwashing" severely limits the ability of hotels to capture the Sustainable Revenue Premium. This research addresses a critical gap in Sustainable Supply Chain Management (SSCM) literature by empirically modeling the "Credibility Mechanism"—the process by which digital technology resolves information asymmetry to monetize sustainability claims. Focusing on the complex Food and Beverage (F&B) supply chains of emerging archipelagic economies, the study employs a rigorous sequential mixed-methods design. First, Design Science Research was utilized to architect a permissioned cross-chain blockchain framework integrating Zero-Knowledge Proofs (ZKPs) for verifiable, private provenance. Subsequently, Partial Least Squares-Structural Equation Modeling (PLS-SEM) confirmed that blockchain-enabled transparency significantly mitigates perceived greenwashing risk, which in turn fosters Customer Trust. Critically, the study validates financial outcomes using a Stochastic Frontier Bayesian Model (SFBM) applied to longitudinal hotel data. Results demonstrate that adopting this traceable framework yields an 8.4% increase in F&B revenue efficiency and sustains a 5.1% price premium for ethically sourced items. These findings provide profound theoretical advancements by redefining SCM risk mitigation through Information Governance rather than material redundancy. Managerially, the research offers a data-driven justification for high-tech investment, proving that verifiable transparency is a direct revenue driver essential for competitive advantage in opaque markets.
The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids.
Smart contracts enable programmatic agreements but face two persistent problems: high on-chain cost (throughput/latency) and weak privacy (public ledger exposes transaction semantics). We propose a hybrid on-chain/off-chain commitment scheme (HOC-C) that combines lightweight on-chain commitments, verifiable off-chain computation, and succinct zero-knowledge proofs to deliver privacy-preserving contract execution at scale. In HOC-C, sensitive inputs and heavy computations are executed off-chain by a consortium of replicated verifiers; the verifiers publish a succinct zk-SNARK proof of correct execution plus a small state commitment on-chain. The on-chain contract verifies the proof and updates state atomically. To prevent malicious collusion among verifiers, HOC-C integrates an economic incentive layer and challenge windows where anyone can publish refutation proofs; the refutation burden is designed to be less than the honest-verifier cost. We implement HOC-C using a prototype that plugs into an EVM-compatible chain (Ethereum testnet) and evaluate performance for representative workloads (private auctions, confidential supply-chain workflows, private token-transfer batching). The system reduces gas cost by an order of magnitude compared to naive on-chain execution while preserving end-to-end confidentiality for user inputs. We analyze security properties (soundness, liveness, and economic incentive compatibility) and discuss trade-offs: proof generation latency vs. throughput, verifier decentralization vs. amortized cost. HOC-C offers a practical roadmap for adopting private, inexpensive smart contracts on mainstream blockchains.
Transitioning to renewable energy is thus a very important component of global efforts toward combating climate change, especially in emerging economies where energy demand is fast outpacing supply. Carbon markets have emerged as a vital financial mechanism for supporting renewable energy projects by enabling the trade of carbon credits. The following abstract discusses how carbon markets affect multi-dimensionally the financial flows of renewable energy in developing nations: attracting investment, reducing capital costs, driving technology innovation, and delivering decentralized energy. Through case studies from Kenya, India, and Brazil, the article illustrates how carbon markets have indeed served to mobilize such large-scale renewable projects as wind farms and solar installations that improve the lot of rural and underserved communities. Despite the promise of carbon markets, it still faces regulatory gaps, market volatility, high transaction costs, and limited participation from local stakeholders. This may spell out actionable solutions, such as the development of regional carbon trading systems, enhancement of voluntary carbon markets, blended finance models, and the integration of emerging economies into global carbon market initiatives within frameworks like those under the Paris Agreement. Carbon markets could have a real catalyzing role in the transition toward renewable energy, with accelerated rates of greenhouse gas emission reduction and sustainable development in emerging economies, if they are able to successfully address these tacked barriers.
To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing.
Vera Mita Nia, Hermanto Siregar, Roy Sembel, Nimmi Zulbainarni
This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques.
تناولت هذه الدراسة الفقهية مسألة المتاجرة بالرموز غير القابلة للاستبدال (NFTs)، وهي رموز رقمية فريدة تُسجَّل على تقنية البلوك تشين وتُستخدم لإثبات ملكية الأصول الرقمية. وهدفت الدراسة إلى بيان الحكم الفقهي لهذه المعاملات في ضوء القواعد العامة للمعاملات المالية في الشريعة الإسلامية، من خلال تحليل خصائص هذه الرموز ومجالات استخدامها، وبيان مدى انطباق الضوابط الشرعية على تلك المعاملات. وقد خلصت الدراسة إلى أن الحكم يتوقف على طبيعة كل حالة، حيث إن بعض صور المتاجرة بهذه الرموز قد تندرج تحت البيوع الجائزة، إذا توفرت فيها شروط الصحة وانتفت عنها المحاذير الشرعية؛ بينما بعض الصور الأخرى قد تُعد من المعاملات المحرمة، بسبب الغرر أو الجهالة أو المقامرة. وأوصت الدراسة بضرورة وضع أطر شرعية واضحة لتنظيم هذه المعاملات في ظل التطورات الرقمية المتسارعة. This jurisprudential study explores the issue of trading in Non-Fungible Tokens (NFTs), which are unique digital assets registered on blockchain technology and used to prove ownership of digital content. The study aims to determine the Islamic legal ruling on such transactions in light of the general principles of financial dealings in Islamic law, by analyzing the features of NFTs, their uses, and the extent to which they comply with Shariah standards. The study concludes that the ruling depends on the nature of each case. Some forms of NFT trading may be considered permissible sales if the necessary conditions are met and no Shariah violations are involved. However, other forms may be deemed prohibited due to uncertainty, ambiguity, or elements of gambling.
Globally, the construction industry is faced with several challenges like inefficiencies, disputes, and a lack of transparency. This paper uses the case of Lusaka, Zambia to investigate the impact of adopting emerging digital technologies in the construction industry, with a particular focus on smart contracts and blockchain technology. Drawing on existing literature and theoretical frameworks, Technology Acceptance Model (TAM), this paper argues that through the adoption of smart contracts and Blockchain technologies, the construction industry in Zambia and the world over could result in many benefits. Lusaka was an ideal case study for validating these hypothesized benefits. The findings of this research identified both benefits and challenges to the adoption of smart contracts and blockchain technologies. The identified benefits include the efficiency in construction processes, an improvement in the supply chain management, mitigation of risks, and a fostering of greater trust among stakeholders within the construction industry. Emerging from the research data were challenges relating to technological illiteracy, absence of regulatory frameworks, and high costs of initial investment. The paper concludes by emphasizing that the benefits surpass the challenges hence the need for Zambia and other similar developing economies to consider transforming the construction industry processes through adopting blockchain technologies and smart contracts to revolutionizing construction practices.
In the modern context of information technology development, the management of labor processes in complex IT projects acquires the features of self-organization and dynamic adaptation. The article examines the principles of configuring agent interactions within the labor environment of IT projects as a tool for enhancing the efficiency of team management. The agent-based interaction model makes it possible to consider each team member as an autonomous agent capable of making decisions, adapting behavior to the task context, and interacting with other elements of the system within a distributed environment. Conceptual foundations have been developed for constructing the architecture of agent interactions, based on the principles of cognitive exchange, communicative coherence, flexible role distribution, and multilevel task management. It is determined that the key factor in the effectiveness of such interactions is the balance between agent autonomy and centralized process coordination. A systematic classification of agent configuration types is proposed: hierarchical, decentralized, hybrid, and cognitively adaptive, which differ in the level of information connectivity and the system’s response speed. The study also investigates the impact of cognitive factors on the dynamics of interactions between agents, such as trust, intellectual compatibility, role specialization, and the ability for collective learning. A model for assessing the effectiveness of agent interaction is proposed, using indicators of performance, informational transparency, decision synchronization level, and team adaptability index. It is established that the configuration of agent connections directly determines the speed of decision-making, the coherence of actions, and the level of project innovation activity. The results of the study have practical significance for building multi-agent IT team management systems, developing algorithms for adaptive resource allocation, and creating cognitive project management dashboards. The proposed principles can be used to optimize communication processes, reduce the risk of conflicts, and enhance the resilience of organizational structures under conditions of high labor environment complexity.
Open access
Information Systems and Technology Applications
Mathematical Control Systems and Analysis
Technology and Human Factors in Education and Health
In the modern context of information technology development, the management of labor processes in complex IT projects acquires the features of self-organization and dynamic adaptation. The article examines the principles of configuring agent interactions within the labor environment of IT projects as a tool for enhancing the efficiency of team management. The agent-based interaction model makes it possible to consider each team member as an autonomous agent capable of making decisions, adapting behavior to the task context, and interacting with other elements of the system within a distributed environment. Conceptual foundations have been developed for constructing the architecture of agent interactions, based on the principles of cognitive exchange, communicative coherence, flexible role distribution, and multilevel task management. It is determined that the key factor in the effectiveness of such interactions is the balance between agent autonomy and centralized process coordination. A systematic classification of agent configuration types is proposed: hierarchical, decentralized, hybrid, and cognitively adaptive, which differ in the level of information connectivity and the system’s response speed. The study also investigates the impact of cognitive factors on the dynamics of interactions between agents, such as trust, intellectual compatibility, role specialization, and the ability for collective learning. A model for assessing the effectiveness of agent interaction is proposed, using indicators of performance, informational transparency, decision synchronization level, and team adaptability index. It is established that the configuration of agent connections directly determines the speed of decision-making, the coherence of actions, and the level of project innovation activity. The results of the study have practical significance for building multi-agent IT team management systems, developing algorithms for adaptive resource allocation, and creating cognitive project management dashboards. The proposed principles can be used to optimize communication processes, reduce the risk of conflicts, and enhance the resilience of organizational structures under conditions of high labor environment complexity.
This study interrogates climate governance in the Southern Africa’s socio-ecological peripheries, concentrating on how decentralized adaptation policies shape rural livelihoods confronted with deepening climate hazards. The region’s ecosystems are worsening under climate stress, with smallholder farmers and forest-dependent communities already positioned at the social periphery bearing the brunt of more erratic precipitation and rising temperatures. The study utilized secondary materials, including peer-reviewed articles, official policy documents, and theoretical discussions on governance and adaptive responses. Data analysis was conducted through an interpretive and integrative approach, critically juxtaposing insights from distinct disciplinary repositories and constructing thematically coherent groupings. The study found that while decentralisation can enhance adaptive governance, its overall effectiveness hinges on bolstering cross-scale finance, capacity, and integration. The study further established that marginalized populations particularly women and youth continue to be underrepresented in decisional arenas, which undermines the equity of adaptation initiatives. The study concludes that decentralized climate governance can achieve transformation only when it is inclusive, sufficiently financed, and intricately linked to overarching rural development plans.
Open access
Climate change impacts on agriculture
Sustainability and Climate Change Governance
Conservation, Biodiversity, and Resource Management
Recent advances in artificial intelligence (AI), particularly deep learning, have led to widespread adoption across various applications. Yet, a fundamental challenge persists: how can we verify the correctness of AI model inference when model owners cannot (or will not) reveal their parameters? These parameters represent enormous training costs and valuable intellectual property, making transparent verification difficult. In this paper, we introduce a zero-knowledge framework capable of verifying deep learning inference without exposing model internal parameters. Built on recursively composed zero-knowledge proofs and requiring no trusted setup, our framework supports both linear and nonlinear neural network layers, including matrix multiplication, normalization, softmax, and SiLU. Leveraging the Fiat-Shamir heuristic, we obtain a succinct non-interactive argument of knowledge (zkSNARK) with constant-size proofs. To demonstrate the practicality of our approach, we translate the DeepSeek model into a fully SNARK-verifiable version named ZK-DeepSeek and show experimentally that our framework delivers both efficiency and flexibility in real-world AI verification workloads.
This is an accepted article with a DOI pre-assigned that is not yet published.Web3 ecosystems represent an emergent field of digital religion where decentralized infrastructures—spanning smart contracts, token economies, and symbolic interfaces—actively generate novel forms of ritual life. These rituals, deeply embedded in algorithmic processes and economic incentives, cultivate collective identities, symbolic hierarchies, and affective economies marked by hope and betrayal. To interpret these dynamics, this article proposes Distributed Rituals Analysis (DRA), a comprehensive framework synthesizing Lived Religion, Ritualization Theory, and Actor-Network Theory. Drawing on 18 months of ethnographic engagement across diverse Web3 communities —including NFTs, DAOs, and staking protocols—I illustrate how decentralized practices become ritualized through temporal orchestration, symbolic differentiation, and distributed agency. Reflexive participation further reveals the researcher’s complex positionality as both observer and participant. DRA offers conceptual and methodological clarity for exploring emergent rituals in decentralized environments, illuminating how digital infrastructures reconfigure sacredness and collective meaning-making. This framework also lays the groundwork for future comparative inquiries into ritual forms across decentralized and hybrid spaces.
Hsi‐Peng Lu, Ya-Yuan Ku, Kuo‐Lun Hsiao, Wadee Alhalabi
With the rise of blockchain and decentralized technologies, doubts about traditional financial institutions' efficiency have increased. Meanwhile, Web3 offers transparency, security, and autonomy. However, the existing literature overlooks role the role of doubt as a push factor while focusing on the positive effects of trust. Moreover, the role of crypto wallets as a mooring factor remains underexplored. This study applies push-pull-mooring theory to examine Web3 literacy, trust in machines, doubt in institutions, and switching costs. Data were collected from 165 survey respondents. The results indicate that Web3 literacy increases doubt in traditional institutions but does not significantly affect trust in Web3. Additionally, switching costs moderate the relationship between Web3 literacy and doubt. When switching costs are low, doubt rises significantly. This study provides a new perspective on Web3 adoption, showing doubt's push effect and the role of push-pull mooring in migration, thus addressing gaps in the literature. Furthermore, the findings highlight how decentralized finance's trust mechanism is evolving, offering insights for Web3 adoption.
The objective of this article is to identify the impact of corporate investments in Bitcoin on the stability of the cryptocurrency market, with particular emphasis on the investment strategy of MicroStrategy (currently Strategy). The first section of the paper outlines the operational mechanisms of Bitcoin, including its consensus system and the blockchain technology that underpins its security and decentralisation. The second section examines the structure of the cryptocurrency market, identifying its key participants and the mechanisms driving its volatility and dynamics. The third section is dedicated to an analysis of MicroStrategy’s quarterly reports for 2023 to 2024. The final section presents the conclusions, which indicate that the company’s aggressive acquisition strategy is associated with significant financial risk. The analysed data suggest that continued exposure to the highly volatile cryptocurrency market may lead to serious challenges for the firm, potentially undermining its long‑term financial stability. Consequently, this may pose systemic risks to the broader cryptocurrency market, particularly by exerting substantial downward pressure on the supply side.
The article explores the theoretical and methodological principles of integrating the organizational and economic potential of Blockchain technologies into the agribusiness system in the context of increasing demands for transparency and sustainability of value chains. The essence of Blockchain is revealed as a tool for forming a trusted information infrastructure that ensures data immutability, transaction automation and business process optimization based on smart contracts. It is substantiated that the use of distributed ledger technologies contributes to the decentralization of management, reduction of transaction costs, digitalization of product quality control and increase of the export potential of agricultural enterprises. Scientific approaches to the development of Blockchain solutions for agri-food supply chains are analyzed. A mechanism for integrating Blockchain into centralized and decentralized agribusiness platforms is proposed, which involves the development of digital competencies, the creation of support infrastructures and indicators for assessing the readiness of enterprises to implement distributed ledger technologies.
Large Language Models (LLMs) are increasingly deployed in high-stakes financial domains, yet they suffer from specific, reproducible hallucinations when performing arithmetic operations. Current mitigation strategies often treat the model as a black box. In this work, we propose a mechanistic approach to intrinsic hallucination detection. By applying Causal Tracing to the GPT-2 XL architecture on the ConvFinQA benchmark, we identify a dual-stage mechanism for arithmetic reasoning: a distributed computational scratchpad in middle layers (L12-L30) and a decisive aggregation circuit in late layers (specifically Layer 46). We verify this mechanism via an ablation study, demonstrating that suppressing Layer 46 reduces the model's confidence in hallucinatory outputs by 81.8%. Furthermore, we demonstrate that a linear probe trained on this layer generalizes to unseen financial topics with 98% accuracy, suggesting a universal geometry of arithmetic deception.
Abstract This chapter discusses the Albanian model for the equalization of financial disparities between urban and rural municipalities, especially after the 2014 Territorial and Administrative Reform (TAR). This reform reduced the number of local governments, merging 373 rural and urban entities into 61 larger municipalities. It aimed to streamline and harmonize service provision across regions and municipalities, but challenges persist due to the limited financial resources of local governments. The chapter explores the country’s intergovernmental financial framework, including recent reforms, which enhanced municipal responsibilities and financing. Despite reforms Albanian municipalities are heavily reliant on intergovernmental transfers, with unconditional grants playing a crucial role in equalizing financial resources. While the stability and allocation of the unconditional grants has improved since 2017, rural municipalities still struggle due to higher service costs and lower fiscal capacity compared to urban centers. There are also major differences between larger urban areas and the capital, Tirana. The chapter concludes that while Albania has made strides in decentralization, further reforms are necessary to address the ongoing fiscal inequalities between urban and rural local governments, underscoring the need for more robust equalization and financing mechanisms to bridge the gap between urban and rural municipalities.