The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to $66 billion. Vulnerabilities arise when contracts interact with external protocols without verifying the results of the calls, leading to exploit entry points such as flash loan attacks and reentrancy attacks. In this paper, we propose UEChecker, a deep learning-based tool that utilizes a call graph and a Graph Convolutional Network to detect unchecked external call vulnerabilities. We design the following components: An edge prediction module that reconstructs the feature representation of nodes and edges in the call graph; A node aggregation module that captures structural information from both the node itself and its neighbors, thereby enhancing feature representation between nodes and improving the model's understanding of the global graph structure; A Conformer Block module that integrates multi-head attention, convolutional modules, and feedforward neural networks to more effectively capture dependencies of different scales within the call graph, extending beyond immediate neighbors and enhancing the performance of vulnerability detection. Finally, we combine these modules with Graph Convolutional Network to detect unchecked external call vulnerabilities. By auditing the smart contracts of 608 DApps, our results show that our tool achieves an accuracy of 87.59% in detecting unchecked external call vulnerabilities. Furthermore, we compare our tool with GAT, LSTM, and GCN baselines, and in the comparison experiments, UEChecker consistently outperforms these models in terms of accuracy.
The rapid adoption of agentic AI, powered by large language models (LLMs), is transforming enterprise ecosystems with autonomous agents that execute complex workflows. Yet we observe several key security vulnerabilities in LLM-driven multi-agent systems (MASes): fragmented identity frameworks, insecure communication channels, and inadequate defenses against Byzantine agents or adversarial prompts. In this paper, we present the first systematic analysis of these emerging multi-agent risks and explain why the legacy security strategies cannot effectively address these risks. Afterwards, we propose BlockA2A, the first unified multi-agent trust framework that enables secure and verifiable and agent-to-agent interoperability. At a high level, BlockA2A adopts decentralized identifiers (DIDs) to enable fine-grained cross-domain agent authentication, blockchain-anchored ledgers to enable immutable auditability, and smart contracts to dynamically enforce context-aware access control policies. BlockA2A eliminates centralized trust bottlenecks, ensures message authenticity and execution integrity, and guarantees accountability across agent interactions. Furthermore, we propose a Defense Orchestration Engine (DOE) that actively neutralizes attacks through real-time mechanisms, including Byzantine agent flagging, reactive execution halting, and instant permission revocation. Empirical evaluations demonstrate BlockA2A's effectiveness in neutralizing prompt-based, communication-based, behavioral and systemic MAS attacks. We formalize its integration into existing MAS and showcase a practical implementation for Google's A2A protocol. Experiments confirm that BlockA2A and DOE operate with sub-second overhead, enabling scalable deployment in production LLM-based MAS environments.
As an emerging service framework built by combining cryptography, P2P network, consensus mechanism and innovative contract technology, blockchain has been widely used in digital finance, data sharing, message traceability and electronic evidence preservation because of its decentralised, non-tamperable and transaction traceability. However, with the complex and changeable application scenarios of blockchain technology and the continuous enhancement of blockchain attack technology, the security of the blockchain system has been seriously threatened, dramatically affecting the development and application of blockchain technology. This paper aims to analyse the attacks on blockchain from the perspective of cryptography. Firstly, from the cryptography technology in the blockchain, the principle of hash functions, digital signatures, and other technologies, as well as their role in the blockchain, are introduced. Then, based on the six-layer architecture of the blockchain, the attacks on the data layer, the network layer, the consensus layer, the contract layer, the incentive layer and the application layer are analysed, and the methods to mitigate or resist the attacks are proposed. Secondly, the attack principles of 51% attack, Double-Spending attack, Reentrancy attack, Replay attack, Sybil attack and Timestamp Tampering attack were analysed, and the mitigation or defence solutions for these six attacks were designed. Finally, the core problems to be solved in blockchain technology are summarised, and the future development of blockchain security technology is projected.
Evangelos Stavropoulos, Ioanna Karampela, Arjun Singh, Maria K. N. Fountoulakis · 8 authors
—The European research project GHOST challenges the traditional cyber security solutions for the Internet of Things (IoT) sector by exploiting novel technologies, such as blockchain, to provide resilience and integrity of decision making on the communication exchange in a smart home context. When it comes to novel cyber security solutions for extremely heterogeneous environments like IoT and smart homes, the key focus is typically given to the understanding of network activities and elimination of suspicious traffic. The GHOST project adds an extra dimension to this approach by integrating blockchain technology at its core decision mechanism. On a daily basis, each GHOST installation is encountering malicious behaviour and suspicious IoT communications, where easy information sharing with other installations, as well as decentralised decision making, are mandatory features for the efficient protection of the end-user. GHOST's Smart Contracts (SC) are designed to tackle in an easy, yet productive way, the reporting on suspicious IP addresses which the IoT devices in a smart home are trying to communicate with. Two variations of blacklisting smart contracts are presented in this paper, covering a diverse spectrum of possible attack vectors while closely following the Privacy by Design (PbD) principles. A reputation scoring scheme for malicious IPs reporting is integrated in the SC, uncovering the implementation details on the penalisation of existing entries in case of malicious behaviour of reporting devices
The introduction of smart contract functionality marks the advent of the blockchain 2.0 era, enabling blockchain technology to support digital currency transactions and complex distributed applications. However, many smart contracts have been found to contain vulnerabilities and errors, leading to the loss of assets within the blockchain. Despite a range of tools that have been developed to identify vulnerabilities in smart contracts at the source code or bytecode level, most rely on a single modality, reducing performance, accuracy, and limited generalization capabilities. This paper proposes a multimodal deep learning approach, MultiCFV, which is designed specifically to analyze and detect erroneous control flow vulnerability, as well as identify code clones in smart contracts. Bytecode is generated from source code to construct control flow graphs, with graph embedding techniques extracting graph features. Abstract syntax trees are used to obtain syntax features, while code comments capture key commentary words and comment features. These three feature vectors are fused to create a database for code inspection, which is used to detect similar code and identify contract vulnerabilities. Experimental results demonstrate our method effectively combines structural, syntactic, and semantic information, improving the accuracy of smart contract vulnerability detection and clone detection.
Security issues are becoming increasingly significant with the rapid evolution of Non-fungible Tokens (NFTs). As NFTs are traded as digital assets, they have emerged as prime targets for cyber attackers. In the development of NFT smart contracts, there may exist undiscovered defects that could lead to substantial financial losses if exploited. To tackle this issue, this paper presents a framework called NATLM(NFT Assistant LLM), designed to detect potential defects in NFT smart contracts. The framework effectively identifies four common types of vulnerabilities in NFT smart contracts: ERC-721 Reentrancy, Public Burn, Risky Mutable Proxy, and Unlimited Minting. Relying exclusively on large language models (LLMs) for defect detection can lead to a high false-positive rate. To enhance detection performance, NATLM integrates static analysis with LLMs, specifically Gemini Pro 1.5. Initially, NATLM employs static analysis to extract structural, syntactic, and execution flow information from the code, represented through Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). These extracted features are then combined with vectors of known defect examples to create a matrix for input into the knowledge base. Subsequently, the feature vectors and code vectors of the analyzed contract are compared with the contents of the knowledge base. Finally, the LLM performs deep semantic analysis to enhance detection capabilities, providing a more comprehensive and accurate identification of potential security issues. Experimental results indicate that NATLM analyzed 8,672 collected NFT smart contracts, achieving an overall precision of 87.72%, a recall of 89.58%, and an F1 score of 88.94%. The results outperform other baseline experiments, successfully identifying four common types of defects.
Abstract This paper examines current shortcomings of existing legal principles in addressing the immaterial and a-territorial nature of blockchain technology, which challenges foundational legal concepts such as sovereignty and territoriality. It explores how blockchain’s operational framework disrupts traditional legal discourse, particularly in the realm of Private International Law. Specifically, the analysis focuses on the inadequacies of the lex situs principle when applied to crypto assets and blockchain networks. Established legal categories struggle to accommodate inherently digital assets like Non-Fungible Tokens (NFTs), which defy territory-based classifications within Private International Law. To address these challenges, the paper proposes integrating Ricœur’s theories on narration as a means of reconciling legal discourse with blockchain technologies. By viewing legal principles through the lens of narrative structures, this approach suggests two complementary solutions: incorporating token ecologies into the existing legal framework or reimagining a token-centric legal order. Methodologically, the argument draws on Ricœur’s concept of triple mimesis, demonstrating how established legal principles can be adapted to the digital landscape through a narrative-driven perspective.
Health-care is undergoing a considerable digital shift in the present state, which is driven by the rise of new technologies and the changes taking place globally. The movement is rebalancing the provision and availability of health care, at the same time that it highlights the importance of protecting confidential information about patients. Coupled with the cryptographic primitives, blockchain technology provides a formidable answer, as it promises to improve data integrity using decentralized processes. In this paper, a hybrid blockchain-based EHR management and security solution to Electronic Health Records (EHRs) is described. Having considered the drawbacks of the blockchain in its ability to work with large files the system is connected with Ethereum blockchain through Ganache and program construction tools is equipped with the InterPlanetary File System (IPFS). In the hybrid model, one does store each row hash (unique identifier) of the patients records on the blockchain, but one does not store the actual data on the blockchain, instead on IPFS. A Decentralized Application (DApp) built on the programing language of Ethereum, Solidity, and the web3.js interface also allows secure data access via cryptocurrency wallets like MetaMask. The use of smart contracts is deployed to process transactions to achieve transparency and verifiability. To enhance security the Elliptic Curve Digital Signature Algorithm (ECDSA) is adapted to provide unauthorised access. Results of simulation reveal that a suggested method is reliable in providing patient data security, maintain immutability, and secure exchange of data. The approach promotes transparency within the digital health-care systems and strengthens the stakeholder belief by allowing a decentralised structure of these systems.
This paper explores the transformative impact of blockchain technology and smart contracts on the dynamics of trust within the financial sector. Trust is a cornerstone of financial transactions, traditionally established through centralized intermediaries and legal frameworks. However, the advent of blockchain technology introduces a decentralized, transparent, and tamper-resistant trust mechanism. This study aims to analyze how blockchain and smart contracts redefine financial trust by eliminating reliance on third-party intermediaries and automating trust through programmable agreements. Utilizing a mixed-methods approach, including case studies such as JP Morgan’s Quorum blockchain platform, we examine the practical applications of these technologies and their effects on transactional efficiency, data privacy, and trust realization. Key findings reveal that blockchain significantly reduces transaction costs, enhances transparency, and increases security, paving the way for innovative financial products and services. The paper contributes to the understanding of how decentralized technologies are reshaping the future of financial trust and offers insights for regulators and financial institutions navigating this technological shift.
Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu
This study proposes the integration of a hybrid Proof of Work/Proof of Stake (PoW/PoS) consensus mechanism with a Long Short-Term Memory (LSTM) model for anomaly detection in blockchain-based microgrids. The hybrid PoW/PoS model is designed to address common issues in blockchain systems, such as 51% attacks, double-spending, and high energy consumption, by optimizing energy usage and enhancing security. Simulation results show that the system can process transactions with an average throughput of 37.25 transactions per second (TPS), an average latency of 26.84 milliseconds per transaction (ms/tx), and extremely efficient energy consumption per transaction (0.01 kWh/tx). The LSTM model applied for anomaly detection achieves an 89.10% detection rate, a 0.00% false positive rate, and a 0.12 s recovery time, indicating the system's reliability in facing attacks. The hybrid PoW/PoS system demonstrates advantages in both energy efficiency and resilience to attacks compared to individual PoW and PoS systems. This research contributes to the development of safer, more efficient, and scalable blockchain-based microgrids by integrating Artificial Intelligence (AI) to strengthen the system against anomalies and threats.
Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multi-dimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer-DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5\% in terms of the average return excluding the crude oil market due to relatively low fluctuation.
Kapel Dev, Yash Madhwal, Sofia Shevelo, Pavel Osinenko · 5 authors
Unmanned aerial vehicle (UAV) swarms are increasingly used in critical applications such as aerial mapping, environmental monitoring, and autonomous delivery. However, the reliability of these systems is highly dependent on uninterrupted access to the Global Navigation Satellite Systems (GNSS) signals, which can be disrupted in real-world scenarios due to interference, environmental conditions, or adversarial attacks, causing disorientation, collision risks, and mission failure. This paper proposes SwarmRaft, a blockchain-inspired positioning and consensus framework for maintaining coordination and data integrity in UAV swarms operating under GNSS-denied conditions. SwarmRaft leverages the Raft consensus algorithm to enable distributed drones (nodes) to agree on state updates such as location and heading, even in the absence of GNSS signals for one or more nodes. In our prototype, each node uses GNSS and local sensing, and communicates over WiFi in a simulated swarm. Upon signal loss, consensus is used to reconstruct or verify the position of the failed node based on its last known state and trajectory. Our system demonstrates robustness in maintaining swarm coherence and fault tolerance through a lightweight, scalable communication model. This work offers a practical and secure foundation for decentralized drone operation in unpredictable environments.
A smart contract is a computer protocol for creating or improving a contract. A smart contract allows for the creation of valid transactions without the need for an intermediary. With the advent of blockchain technology, the idea of smart contracts has received more attention and has found a wide range of applications. Privacy, digital assets, and data encryption are three important factors in the benefit of blockchain-based smart contracts. This article examines the impact of a blockchain-based approach on smart contracts in the development of e-commerce using data mining. The research method is descriptive with a data mining approach and regression computation, decision trees, and neural networks. The main objective of this research is to determine the impact of blockchain on smart contracts in the development of e-commerce using data mining. The predictive power of smart contracts based on blockchain is 55%, which shows a level higher than 0.5. Therefore it can be said that the proposed model has appropriate predictive power for examining smart contracts.
Ch Aruna, T Varsha, C Shivakumar, M. U. Sreeja · 5 authors
In modern healthcare systems, the centralized storage of electronic health records (EHRs) and associated documents presents significant vulnerabilities, including single points of failure, data tampering, and privacy breaches.This research proposes a decentralized framework that integrates blockchain technology with the InterPlanetary File System (IPFS) to securely manage doctor-patient appointment data, medical reports, prescriptions, and feedback.The system utilizes a permissioned Ethereum-style smart contract, named "Healthcare," to immutably store user profiles, appointment metadata, prescription references, and patient ratings.Sensitive medical files are encrypted using AES-CTR mode, with encryption keys derived via PBKDF2, and pinned to a local IPFS node to ensure both confidentiality and decentralized availability.Patients can register and log in, browse a list of doctors with on-chain average ratings, and book appointments by uploading encrypted medical reports.Later, they can securely download prescriptions in decrypted form.Doctors access the platform to view pending appointments, retrieve patient reports, issue encrypted prescriptions, and publish them on-chain.Patient feedback is also recorded as immutable blockchain transactions.To improve system responsiveness, an in-memory Python cache is synchronized with the blockchain, enabling fast read operations without repeatedly querying the chain.Experimental results demonstrate that the encryption, IPFS pinning, and decryption processes operate within acceptable performance thresholds.The proposed system enhances data integrity, transparency, and fault tolerance: no single authority can alter records without detection, and encrypted files remain retrievable even if specific nodes fail.By combining decentralized metadata storage with secure, off-chain encrypted file handling, this framework offers a robust, patient-centric solution to modern EHR management challenges.
Against the backdrop of integrating the dual carbon strategy with the digital economy, retail enterprises' green supply chains face challenges such as difficult-to-trace carbon emission data, low efficiency in low-carbon collaboration, and imperfect green supplier certification mechanisms. This study leverages blockchain technology to empower retail enterprises, advancing their supply chains toward low-carbon and green development pathways. By utilizing distributed ledgers, it enables real-time sharing and traceability of carbon data across the entire chain, addressing issues of chaotic and distorted data collection. leveraging smart contracts to predefine emission reduction rules and allocate benefits, thereby balancing divergent objectives among supply chain participants to enhance collaborative efficiency; utilizing consensus mechanisms and immutability to establish a transparent, traceable green supplier certification and dynamic oversight system, tackling certification fraud and regulatory loopholes. This provides a feasible solution for the green and low-carbon transformation of retail enterprises, supporting their journey toward sustainable development.
<p>Blockchain technology has revolutionized various industries by enabling decentralized, transparent, and tamper-resistant digital transactions. However, despite its benefits, blockchain-based applications are vulnerable to security threats such as smart contract exploits, 51% attacks, Sybil attacks, and private key compromises, posing significant risks to their integrity and reliability. Traditional security frameworks lack a comprehensive approach to systematically assess and mitigate these risks across different blockchain layers. To address this challenge, this paper proposes the blockchain cybersecurity risk assessment model (BCRAM), a structured framework designed to identify, analyze, evaluate, and mitigate security risks in blockchain systems. The methodology involves categorizing threats, assessing risks using quantitative and qualitative techniques, and validating the model through a case study on Ethereum. Results demonstrate that implementing BCRAM led to a 65% reduction in smart contract exploits, a 70% decrease in phishing incidents, and an 85% improvement in distributed denial of service (DDoS) resilience, proving its effectiveness. This research offers a standardized risk assessment approach, providing valuable insights for developers, security analysts to enhance blockchain security.</p>
With the growing demand for blockchain technology, the deployment of various applications has highlighted the critical chal- lenge of balancing scalability, security, and decentralization—termed the blockchain trilemma. To ensure blockchain’s effectiveness in real-world scenarios, resolving this trilemma with minimal trade-offs is crucial. Despite recent advancements, existing solutions, including Filecoin’s decentralized storage, have not fully addressed these challenges. While Filecoin reduces on-chain data re- dundancy through cryptographic proofs (PoRep/PoSt), its throughput remains insufficient (< 50 TPS) and vulnerable to adversarial attacks like selfish mining. In this study, we propose a novel Filecoin-based architecture that tackles the trilemma by reducing trans- action size to 50.6 bytes via Data Identification Numbers (DINs), achieving 145 TPS (a 20 × improvement over baseline Filecoin) with 1 MB blocks while maintaining 80% chain quality under 45% adversarial influence. Our approach enhances decentralization by minimizing storage requirements (292 bytes per reference block) and lowering hardware demands for mining nodes ($500/n- ode). Cost-efficiency analysis demonstrates a 99.9% reduction in energy consumption (0.001 kWh/Tx) compared to Bitcoin’s PoW (1,200 kWh/Tx) and a 3,448 × improvement in storage efficiency over baseline Filecoin. Security is preserved through PoRep/PoSt optimizations, resisting double-spending and Sybil attacks. Theoretical and empirical evaluations, including adversarial simulations and comparisons with Bitcoin, Ethereum, and Filecoin, validate unprecedented scalability-security-decentralization trade-offs. This work sets a new benchmark for blockchain systems, enabling decentralized applications to rival centralized systems in throughput, cost, and robustness.
Merkle proof-based airdrops are a widely used mechanism in decentralized finance (DeFi) for distributing tokens securely and efficiently to eligible recipients. By leveraging Merkle trees, these airdrops minimize storage requirements and reduce transaction costs while ensuring the integrity of recipient lists. Unfortunately, their importance also attracts sophisticated attacks. Secondary preimage attacks, in particular, pose a major risk to the security of token distribution, potentially enabling adversaries to exploit vulnerabilities and drain tokens from airdrop contracts. This paper introduces FSA: a F ramework for analyzing S econdary preimage A ttacks on Merkle proof-based airdrops, designed to systematically address such vulnerabilities. The framework operates through four core contributions: (1) a validated quantitative damage assessment model using proof-of-concept experiments, (2) an analysis of attack repetition patterns in binary Merkle trees and Merkle Mountain Ranges, (3) identification of key factors influencing attack success, including token balances, hash values, and variable sizes, and (4) evaluation of mitigation strategies such as using non-64-byte leaf values, asymmetric hashing, and proof length validation. Through extensive analysis using the FSA, our experimental results demonstrate the framework's effectiveness in uncovering vulnerability patterns and developing targeted countermeasures.
Margherita Renieri, Letterio Galletta, Alberto Lluch Lafuente, Aleksander Junge · 5 authors
Automated Market Makers ( AMM s) are one of the most used Decentralized Finance services enabling users to exchange crypto-assets directly without intermediaries. However, current protocols impose significant constraints on the liquidity levels required for transactions. In this paper, we propose a liquidity-saving mechanism designed to minimize the liquidity required by AMM services. Our mechanism delays the transactions violating the liquidity constraints in a queue, and, when certain conditions are met, it selects from the queue a feasible transaction sequence that fulfills the constraints and executes them atomically on the blockchain. We provide an operational semantics of such a mechanism that precisely characterizes the interactions between users and AMM s and the conditions when the liquidity-saving mechanism is triggered. Moreover, we show that our mechanism allows for novel liquidity saving behavior for multi-party exchange, multi- AMM arbitrage, and enhances user intent compared to traditional AMM s. Finally, to validate our approach, we develop a simulator and experiment with various application scenarios, yielding insights into the practical implications of our mechanism.
Abstract The construction industry is among the few industries that contribute to the growth and development of the economy; its size gives a representative potential in contributing to economic development. However, the nature of the construction industry in Egypt is plagued by disputes, which often arise from contractual issues, communication breakdowns, and project management challenges during various stages of the project. Furthermore, construction contracts are always viewed as complex and dense paperwork that makes it difficult to extract necessary information, inhibiting smooth operation. This can be solved by implementing smart contracts. A smart contract can include blockchain technology that executes agreed-upon terms automatically and autonomously. This data-driven mechanism automatically issues payments at the end of each clause, reducing the potential for disputes. The aim of this research is to Investigate the potential of smart contracts in reducing disputes in the construction projects. This study will be performed by adopting a qualitative approach through collecting and analysing data from various literature sources, as books, journals, and existing research, to construct a comprehensive understanding from a holistic point of view focusing on relevant keywords as smart contracts and disputes during various stages in construction projects to identify the relationship between them and present it in a relationship matrix. Second, analysis of case studies to investigate the effectiveness of smart contracts and validate the identified relationship and view its potential in construction projects.
William Villegas-Ch, Jaime Govea, Rommel Gutiérrez
This study aims to optimize blockchain consensus mechanisms by integrating artificial intelligence techniques to address critical limitations in latency, scalability, computational efficiency, and security inherent in traditional protocols, such as PoW, PoS, and PBFT. The proposed model combines deep neural networks (DNNs) for feature extraction with deep reinforcement learning (DRL), specifically Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to enable dynamic validator selection and real-time adjustment of consensus difficulty. The training process utilizes a hybrid dataset of historical blockchain records from Ethereum and Hyperledger networks and synthetic data from simulated attack scenarios involving Sybil, 51%, and DoS threats. Experimental evaluations were conducted in private and permitted environments under varying transactional loads. Results show a 60% reduction in confirmation latency compared to PoW, achieving 320 ms, and a 20% improvement over PBFT. Transaction throughput increased to 22,000 transactions per second (TPS), and computational resource consumption was reduced by 30%. The model achieved an attack tolerance of up to 92%, significantly enhancing network resilience. The novelty of this work lies in its autonomous consensus optimization strategy, which enables adaptive and secure protocol behaviour without manual intervention, representing a scalable and efficient solution for future blockchain infrastructures.
This study examines the influence of Social Media Influencers and Fear of Missing Out (FOMO) on cryptocurrency investment interest among Generation Z students at Singaperbangsa University in Karawang. This research uses a quantitative approach with descriptive and verification methods, with data collection through a Likert scale questionnaire. A total of 122 respondents from the university were selected using purposive sampling method. Research variables include Social Media Influencers, FOMO, and Cryptocurrency Investment Interest, with data analyzed using SPSS for multiple regression. The results showed that both Social Media Influencers and FOMO have a significant influence on cryptocurrency investment interest. The influence of Social Media Influencers has a coefficient of 0.362 (p < 0.05), while FOMO shows a coefficient of 0.290 (p < 0.05). The model's R² value of 0.319 indicates that these two variables explain about 31.9% of the variation in investment interest. These results provide important insights into how digital media and social influence affect financial decision-making among the younger generation, especially in the rapidly growing cryptocurrency market.
The article offers an innovative theoretical and methodological framework for analyzing technosocial reality through the synergy of three approaches: transduction (G. Simondon), systems theory (N. Luhmann), and network analysis (M. Castells). In conditions of digital turbulence, when traditional disciplinary models fail to explain the dynamics of hybrid systems (algorithms, digital platforms, cyber-physical spaces), this triad overcomes the limitations of reductionism. Transduction reveals the mechanisms of spontaneous genesis of novelty, network theory maps out the flows of resources and power, while the systemic approach provides the tools for understanding the resilience of structures in a metastable environment. The complementarity of these perspectives allows for capturing the key dynamics of the formation of modernity, which includes significant factors: processuality, nonlinearity, and conflict, inaccessible to each paradigm separately. This theoretical "alchemy" transforms the methodological crisis of the digital age into a powerful tool, where the metastability of technosocial formations acquires intelligible contours. The methodological toolkit of the article is based on the sequential application of four complementary methods within the framework of the complementary triad (Simondon – Luhmann – Castells): comparative analysis, case-oriented modeling, genetic-structural method, and dialectical hermeneutics. This synthetic framework overcomes the static nature of traditional methods, replacing linear causality with the analysis of feedback loops in procedural reality. The key task of the methodology is to unveil the complementary triad for subsequent effective understanding of the contingent and recursive process of the technosocial phase of individuation, producing operationally closed (autoethetic) systems. The study synthesizes three heterogeneous theoretical traditions for the first time, creating a language for analyzing the elusive ontology of the digital age, where human and non-human actors co-evolve through autopoiesis, transductive leaps, and network topologies. The relevance of the work is determined by the crisis of classical sociocultural methods in the face of phenomena such as artificial intelligence in management, which not only executes commands but generates solutions based on data, or blockchain communities that are decentralized around distributed ledger technologies. The practical significance of the approach lies in the development of tools for: forecasting points of bifurcation in technosocial systems (at the intersection of Simondonian metastability and Luhmannian selection); deconstructing the power of algorithms through the lens of network asymmetry (Castells) and operational closure (Luhmann); and the ethical design of digital environments where transduction becomes the creation of a new metastable state of the system.
Blockchain technology has emerged as a promising solution for improving traceability across global supply chains, offering tamper-proof records and increased transparency.However, concerns related to data privacy, confidentiality, and interoperability continue to hinder widespread adoption.This paper proposes a comprehensive framework addressing these key challenges by combining privacy-preserving techniques-such as permissioned ledgers, zero-knowledge proofs, and verifiable credentials-with industry-driven data standards (GS1 EPCIS, W3C Verifiable Credentials).We first review the landscape of blockchain traceability solutions and outline critical requirements from regulatory and operational perspectives.Next, we detail our proposed privacy-preserving and interoperable architecture, incorporating off-chain storage, role-based permissions, and selective disclosure mechanisms to accommodate the diverse needs of modern supply chains.We illustrate these concepts through a high-level system design, accompanied by implementation considerations.Our evaluation highlights that successful adoption depends on carefully balancing transparency and confidentiality, supplemented by robust governance structures and standard APIs.The paper concludes by discussing future directions for blockchain traceability, emphasizing scalability, user-centric design, and cross-chain interoperability as critical enablers of a global, privacypreserving supply chain ecosystem.