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.
M. Mut-Puigserver, M. Magdalena Payeras-Capellà, Rosa Pericàs-Gornals, Jaume Ramis
This paper introduces FAIR (Federated Agreement for Independent Resolution), a cross-chain protocol designed to enhance scalability, transparency, and auditability in decentralized decision-making processes. Unlike traditional blockchainbased voting systems that rely on a single global chain, FAIR leverages a federated model in which local decisions are conducted on independent blockchains and subsequently aggregated into a global result through a secure cross-chain mechanism. The protocol employs Soulbound Tokens (SBTs) and Rejectable SBTs (RejSBTs) to ensure verifiability, immutability, and explicit acceptance of results, thereby strengthening both the integrity and security of cross-chain operations. We formalize the protocol, provide smart contract implementations, and illustrate its applicability in diverse contexts such as multinational corporations, district-based elections, and decentralized autonomous organizations. FAIR demonstrates that a federated and tokenized approach to decision-making enables efficient large-scale participation while maintaining strong guarantees of trust, transparency, and robustness.
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
Distributed computing faces a persistent multi agent trust dilemma. In the computation process, participants may maliciously attack the system for personal gain by providing false data. Blockchain provides a possible solution for this problem with its immutability and multi-party consensus. However, existing blockchain data throughput has long been queried owing to its exorbitant time and energy costs by consensus mechanisms. This paper proposes a light blockchain structure in distributed computing scenarios. A No-Proof consensus (NPC) mechanism is designed for distributed computing problems with no extra proving process such as Proof-of-Work or Proof-of-Stake. This consensus mechanism notices that the distributed computing result has proven to be valid in the computation process automatically, which does not need to be verified again in the consensus mechanism. Further, the single-threaded data processing ability of the blockchain structure certainly leads to low efficiency when applied to distributed computation problems. An NPC-based blockchain is constructed in this paper to solve this problem. In this structure, the distributed computing is done off chain, and an oracle is designed to upload the computing results to the blockchain asynchronously. Upon the contribution in this paper, a distributed energy trading model is provided as a case study to verify the superiority of the designed blockchain in contrast with other similar structures.
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.
In the need for high-security mechanisms, Trust Management Systems (TMSs) are implemented in vehicular networks such as Vehicular Ad Hoc Networks (VANETs) and the Internet of Vehicles (IoV) to ensure reliable interactions between vehicles. These systems nowadays are a key factor in building up security by evaluating and managing trust relationships among network participants. However, TMSs are inherently very vulnerable to Trust Manipulation Attacks (TMA), where we find malicious nodes attempting to deceive trust models by exploiting their evaluation mechanisms. One critical variant of this attack involves malicious nodes creating multiple fake identities, known as the Sybil attack, to falsely reinforce their trustworthiness. This deception will totally mislead legitimate vehicles, manipulate the decision-making processes, and at the end compromise the overall security and reliability of the network. To address this challenge, we propose a Zero-Knowledge Proof (ZKP)-based trust authentication scheme that ensures each vehicle can prove its legitimacy without exposing sensitive information. Our approach can and will prevent attackers from fabricating multiple identities to manipulate trust values. By integrating cryptographic authentication with trust management, our method significantly strengthens security and ensures that only legitimate vehicles can participate in trust-based evaluations. Through simulations, we demonstrate the effectiveness of our proposed solution in reducing the risk of Sybil-based. The results indicate that our approach not only enhances security but also maintains efficient trust computation, making it a viable solution for real-world vehicular networks.
Blockchain technology has revolutionized the way digital transactions and data are secured, verified, and stored. As the basis of blockchain lies the consensus algorithm, it is a critical component that determines how transactions are validated and blocks are added to the chain. However, the environmental implications of these algorithms have become a growing concern, particularly with energy-intensive models like proof of work (PoW). This chapter explores the environmental impact of various consensus mechanisms, including PoW, Proof of Stake (PoS), delegated proof of stake (DPoS), and newer eco-friendly alternatives. It analyzes the energy consumption, carbon footprint, and scalability of each approach, with a focus on how emerging consensus models aim to balance security, decentralization, and sustainability. This work evaluates blockchain’s broader environmental footprint across industries, including supply chain, energy, and carbon credits, highlighting both challenges and opportunities.
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.
Chunxia Liu, X T Zhu, Gaimei Gao, Junji Li · 5 authors
Security vulnerabilities in smart contracts pose serious threats to the trustworthiness and stability of blockchain systems. To overcome the limitations of existing detection methods in structural modeling and semantic feature representation, this paper proposes a smart contract vulnerability detection method (HEVD) that integrates heterogeneous graph attention networks with expert knowledge. HEVD achieves dynamic synergy between structural features and semantic priors through a multi-head self-attention mechanism. The heterogeneous graph attention network captures deep structural dependencies in smart contract code, while a hierarchical expert knowledge system distills high-confidence vulnerability patterns to provide explicit semantic guidance. This design effectively addresses the semantic gap of models that rely solely on data-driven training and compensates for the lack of global structural awareness in traditional expert-based approaches. Experimental results show that HEVD attains F1scores of 91.03%, 90.48%, and 83.46% for reentrancy, timestamp dependency, and infinite loop vulnerabilities, respectively, significantly outperforming multiple existing methods. Ablation studies further confirm the effectiveness of the proposed framework, demonstrating that the deep fusion of structural representation and semantic priors is key to enhancing smart contract vulnerability detection.
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.
Neeraj Purushotham, Mallepula Likhitha, Y Sai Pragathi, C RaviKiran · 5 authors
Secure and verifiable cloud storage auditing is a significant issue of concern with identity-based systems, especially against malicious auditors and forged proof attacks. In this paper, the enhanced identity-based public audit protocol has been enhanced with blockchain-supported federated trust and quantum resilient cryptographic priming. The suggested Quantum-Resistant Federated Identity-Based Auditing (QFIBA) scheme does not have a single-point dependency on the PKG and proposes a lattice-based identity encryption that ensures post-quantum security. The proposed system implements privacy-preserving audits via zero-knowledge verification made using zk-SNARK without exposing user metadata. The results of experiments carried out on the JPBC and CRYSTALS-Dilithium schemes show that the tag generation cost is reduced by 19.6 per cent, the proof generation overhead is reduced by 14.2 per cent and the verification rate is 12.8 times faster than the better IBPA scheme with the same audit soundness and forgery resistance. Security guarantees: It has been shown that QFIBA is secure to the CDH and LWE assumptions. Such findings confirm the practical effectiveness of the scheme and its resilience to the future cloud ecosystems where federated trust and quantum resilience is needed.
Purpose This study aims to examine here various (nonlinear) time-series comovements between daily changes in both prices and trading volumes of five popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Tron) and diverse types of terror attacks, by all known terrorist organizations (though the authors later focus on the most active ones), across different geographic regions and through lead/lag relations, from July 2010 to July 2021. Design/methodology/approach The authors form 130 dedicated subsamples and use wavelet coherence analyses that provide numerous highly detailed and informative heatmaps. Findings The authors find that the six most active terrorist organizations (Al-Qaida, Taliban, Al-Shabaab, Boko Haram, Houthi and the Islamic State) exhibit mostly short-term comovements with a variety of crypto assets. Occasionally, these interactions last for longer periods of time. Assorted terrorist groups often leverage on periodic adaptations in the prices and trading volumes of crypto assets to enhance their ongoing financing and possibly to adjust their global operations. Practical implications The high-resolution findings and inferences can serve worldwide agencies to better comprehend the relationships between different crypto assets and global terrorism and therefore counter terrorism financing more effectively. Originality/value The authors overcome research gaps in existing literature, where the topic was partly engaged over shorter time frames, through truncated samples and strictly with Bitcoin.
Blockchain Technology Applications and Security
Terrorism, Counterterrorism, and Political Violence
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.
Parisa Bouzari, Maria Fekete-Farkas, Zsigmond Gábor Szalay
This research investigates the efficacy of transformer architectures in classifying sustainability claims made by cryptocurrency projects, addressing a critical gap in automated environmental impact assessment of digital assets. Employing design science research (DSR) methodology, we develop and empirically evaluate a novel framework comparing five state-of-the-art transformer models across multiple performance dimensions. Through rigorous analysis of 300 synthetic cryptocurrency sustainability news articles, we demonstrate that RoBERTa-large-MNLI achieves optimal performance (F1: 1.00) with exceptional prediction stability (0.98)—meaning highly consistent predictions across varied inputs—and minimal entropy (0.05)—indicating strong confidence in classification decisions—albeit at higher computational costs. Our findings challenge conventional assumptions about the inverse relationship between model complexity and prediction reliability in specialized financial domains. The results advance theoretical understanding of transfer learning in sustainable finance while establishing quantitative benchmarks for automated environmental claim verification. This research contributes to both academic literature and regulatory frameworks by providing empirically validated methodologies for distinguishing between substantive and symbolic environmental initiatives in cryptocurrency markets. The findings provide valuable guidelines for cryptocurrency projects, financial institutions, and regulatory bodies seeking to implement automated sustainability assessment systems, while establishing a foundation for future research in the intersection of artificial intelligence and sustainable finance.
The global Blockchain Market, valued at USD 24.20 billion in 2024, is projected to reach USD 301.02 billion by 2030, expanding at an impressive CAGR of 60.2% from 2025 to 2030. The increasing digital payment transactions, rising demand for security, and rapid adoption of cryptocurrencies are fueling market growth. However, challenges such as regulatory uncertainty and high implementation costs remain significant barriers. Despite these limitations, the integration of decentralized finance (DeFi), AI, and advanced technological frameworks presents substantial opportunities for innovation. Leading industry players, including IBM, Ethereum, Hyperledger, and Oracle, are actively engaging in partnerships and technological advancements to strengthen their market presence. As blockchain technology continues to evolve, its applications are expanding across sectors including BFSI, healthcare, government, logistics, and retail. This manuscript provides an in-depth analysis of drivers, restraints, opportunities, segmentation, regional insights, and competitive landscape shaping the future of the global blockchain economy.
Pedro F. F. Abreu, Maria R. F. M. Ferreira, Luis H. O. Mendes, Geraldo A. Sarmento Neto · 8 authors
The proliferation of Internet of Things (IoT) devices necessitates secure, scalable, and cost-effective access control mechanisms. While blockchain and Non-Fungible Tokens (NFTs) offer a decentralized paradigm for managing permissions, they remain vulnerable to off-chain resource exhaustion attacks and present practical implementation challenges for low-cost devices. This paper proposes a novel hybrid architecture that enhances NFT-based access control with an off-chain gateway acting as both a Smart Reputation System (SRS) and a delegated signer. This hybrid model combines fast, off-chain pre-validation with authoritative on-chain verification. The SRS serves as a security firewall, mitigating high-frequency invalid requests by dynamically managing the reputation of each device and imposing temporary bans on malicious actors. By delegating cryptographic signing to the gateway, low-cost IoT devices are absolved of managing private keys, significantly reducing their complexity and cost. An experimental evaluation of the implemented system was conducted to assess its resilience against Denial-of-Service attacks. The findings indicate that the system successfully neutralizes threats in under 3 seconds. During this process, a stable end-to-end latency of approximately 626 ms is maintained for legitimate users, with the gateway’s reputation logic introducing a negligible performance overhead of less than 1%. This hybrid approach proves to be a practical and effective solution for deploying secure and resilient access control in real-world IoT environments.
The integration of smart contracts within blockchain technology represents atransformative approach to intellectual property rights (IPR) management, fundamentally altering traditional copyright enforcement mechanisms. This article demonstrates how distributed computer networks combined with automated regulatory devices provide superior alternatives to conventional IPR handling methods. Smart contracts reduce the need for arbitration through automated execution of predetermined terms and coding protocols. The implementation of blockchain-based smart contract systems enhances proprietary rights management, which can be particularly relevant for the BRICS nations currently facing evolving digital governance challenges. Research indicates that automated proprietary system networks are progressively superseding traditional IPR management approaches. The development of automated governance systems, coupled with decentralized IPR frameworks, presents both opportunities and regulatory challenges for the BRICS countries. Embedded payment mechanisms within smart contracts ensure automatic royalty distribution when copyrighted content is accessed, eliminating manual processing burdens and associated costs for creators. The implementation of smart contracts also enhances agreement integrity and reduces plagiarism risks through the use of immutable blockchain records. This study examines how organizations can establish enhanced trustworthiness and optimize digital business processes through blockchain-based copyright management. Advanced analytical tools accelerate the understanding of both the benefits and limitations within current copyright frameworks. Users are able to seamlessly access blockchain systems, creating multiple account types as required. Every blockchain entry provides transparent records of content usage and account activities. The digital system prevents misrepresentation by maintaining visible platform activities that are accessible to all stakeholders, ensuring comprehensive transparency of development and execution history for all agreement participants.
Federated Learning (FL) has become a promising method for training machine learning models while protecting patient privacy. This systematic review examines the use of privacy-preserving techniques in FL within decentralized healthcare systems. It compares existing methods such as Differential Privacy (DP), Trusted Execution Environment (TEE), Zero Knowledge Proofs (ZKP), Homomorphic Encryption (HE), Watermarking, Blockchain, and Secure Multi-Party Computation (SMPC) based on regulatory compliance, scalability, computational cost, complexity, and mathematical foundations. The principle challenges in decentralized healthcare like heterogeneous data, privacy risks, security threats, and compliance issues have been discussed. The review also highlights the importance of adhering to global regulations like HIPAA, GDPR, and country-specific data protection laws. Furthermore, it discusses open challenges and suggests future research directions to overcome current limitations, including computational efficiency, adversarial attacks, and the creation of policy frameworks for standardization. Overall, this review provides a unique perspective on ethical, secure, and scalable privacy-preserving FL models for the next generation of healthcare applications. • Analyzes essential techniques: Differential Privacy, SMPC, HE, TEE, ZKP, and Blockchain. • Reviews key privacy techniques: DP, SMPC, HE, TEE, ZKP, and Blockchain. • Compares methods based on cost, scalability, and resilience in FL. • Identifies issues such as non-IID data, high communication, and compliance. • Suggests hybrid and hardware-aided frameworks for secure FL. • presents future needs in terms of explainability, interoperability, and quantum security.
기존 이더리움 아키텍처는 트랜잭션 처리속도 문제와 구조상 적용될 수 없는 확장성 문제를 가지고 있었고 이는 대량 트래픽이 발생할 수 있는 기업에서 이더리움을 사용하기 어려운 이유였다. 본 논문에서는 옵티미스틱 롤업 기술을 기반으로 한 솔루션을 제안하였다. 기존 이더리움 아키텍처 구성인 이더리움 단일 노드에서는 평균 트랜잭션 처리속도가 28.15 TPS였고 제안된 아키텍처인 옵티미즘 솔루션을 단일 노드로 구성했을 경우, 기존 아키텍처보다 약 165% 트랜잭션 처리량이 증가한 74.6 TPS인 것을 확인하였으며, 확장할 수 없는 기존 이더리움 아키텍처와 달리 제안된 아키텍처에서 옵티미즘 노드를 10개로 확장하였을 경우 옵티미즘 단일 노드를 구성하였을 때보다 약 2,376% 증가한 699.1 TPS로 트랜잭션 처리가 가능한 것으로 확인되었다. 이를 통해 제안된 아키텍처에서 처리 노드를 확장할수록 선형적으로 트랜잭션 처리량이 증가하는 것을 확인하였다.