У статті досліджується Інтернет 3.0 як децентралізована, енергетично незалежна, штучно-інтелектуальна інфраструктура, що формується на перетині Web3, блокчейн-економіки, ядерної енергетики нового покоління та пост-сингулярних технологій. Аналізуються економічні, організаційні та правові аспекти переходу України до Інтернету 3.0 в умовах післявоєнної реконструкції та наближення до часу інтегрованої сингулярності. Особливу увагу приділено ризику «інтелектуального захоплення» – ситуації, коли технологічна сингулярність може привласнювати права людини на ідеї та інтелектуальний внесок. Автор обґрунтовує необхідність створення системи юридичної та фізичної фіксації і капіталізації прав людини на ідеї, висновки, розробки замість їх захоплення технологіями. Запропоновано концепцію «Інтернету свідомості» як децентралізованої мережі персоніфікованих цифрових двійників, де внесок кожної людини токенізується та захищається через блокчейн. Обґрунтовано, що поєднання енергетично та комунікаційно незалежних дата-центрів на базі малих модульних реакторів, національного блокчейну та Інтернету 3.0 надає Україні можливість стати частиною глобальної інфраструктури накопичення та примноження персоніфікованого інтелектуального капіталу.
Mohammad Adel El Sehayl, Ahmad Almaaz, Khaleel Mershad, Nadine Abbas
The Interplanetary File System (IPFS) is a decentralized peer-to-peer (P2P) protocol for distributed file storage and sharing. It is one of the main pillars towards reaching the Web3 technology, which depends heavily on decentralization. IPFS ensures more control over stored data even across untrusted nodes. However, IPFS lacks various security measures, such as encryption, to ensure the confidentiality of the stored data. This paper suggests a parallel encryption engine incorporated within IPFS to enhance data security while maintaining high performance and speed. The paper specifically proposes a novel parallelized symmetric encryption framework that encrypts data chunks before distributing them across the IPFS network. Also, the engine uses hardware-accelerated instructions to ensure speedup and robustness. Various factors were considered to evaluate the research contributions, such as encryption speed, storage overhead, and retrieval efficiency. The obtained results signify the importance of incorporating encryption into IPFS to ensure data privacy without compromising performance. Furthermore, unauthorized access and data leakage can be prevented through encryption, enabling IPFS to become more suitable for sensitive data storage in decentralized environments. In general, the contributions of this research support the advancement towards Web3 by protecting users’ data without aggravating the IPFS system efficiency and performance.
Zhuolun Xiang, Andrei Tonkikh, Alexander Spiegelman
Despite broad adoption of BFT consensus in blockchains, censorship resistance remains weak: existing designs offer limited inclusion guarantees and allow leaders to exclude transactions. We address this with a new abstraction and protocol stack. We define \emph{Prefix Consensus}, where parties input vectors and output two consistent vectors $(v^{\sf low},v^{\sf high})$ that extend the maximum common prefix of honest inputs and satisfy $v_i^{\sf low}\preceq v_j^{\sf high}$ for all honest parties $i,j$. We show that Prefix Consensus is solvable asynchronously and establish tight round-complexity bounds. We then define \emph{Strong Prefix Consensus}, which additionally requires agreement on the high output, and give a leaderless partially synchronous protocol. Using its accountable variant, we build a leaderless, multi-proposer, censorship-resistant BFT SMR protocol with amortized four-round commit latency under synchronized starts, while guaranteeing that after GST at most $f$ slots can be censored. Finally, we connect Prefix Consensus to graded consensus, obtaining a matching lower bound and a 3-round protocol, and derive leaderless Binary Consensus with improved worst-case complexity.
Visual social media platforms have become primary venues for political discourse, yet we know little about how moralization operates differently across platforms and topics. Analyzing 2,027,595 TikToks and 1,126,972 Instagram posts during the 2024 US presidential election, we demonstrate that issues are not necessarily inherently moralized, but a product of audience demographics, platform architecture, and partisan framing. Using temporal supply-demand analysis and moral foundations scoring (eMFD), we examine the dynamics of key electoral issues. Three key findings emerge. First, moralization patterns diverge dramatically by platform: TikTok's algorithm enabled viral spread of moralized abortion and immigration content despite lower supply, while Instagram amplified economic discourse that aligned supply and demand. Second, traditionally "pragmatic" economic issues became moralized-cryptocurrency discourse invoked loyalty and authority foundations more strongly than any other topic, framing regulation as government overreach. Third, platforms responded to different events: TikTok surged after Harris's nomination across all topics (96% reduction in supply volatility), while Instagram spiked around cryptocurrency policy developments. Semantic network analysis reveals TikTok's circular topology enables cross-cutting exposure while Instagram's fragmented structure isolates Harris from economic discourse. These findings demonstrate that understanding political moralization requires examining platform-specific ecosystems where architecture, demographics, and content strategy interact to determine which issues get moralized and how moral content spreads.
The rapid advancement of artificial intelligence has made the generation of synthetic images widely accessible, increasing concerns related to misinformation, digital forgery, and content authenticity on large-scale online platforms. This paper proposes a blockchain-backed framework for verifying AI-generated images through a registry-based provenance mechanism. Each AI-generated image is assigned a digital fingerprint that preserves similarity using perceptual hashing and is registered at creation time by participating generation platforms. The hashes are stored on a hybrid on-chain/off-chain public blockchain using a Merkle Patricia Trie for tamper-resistant storage (on-chain) and a Burkhard-Keller tree (off-chain) to enable efficient similarity search over large image registries. Verification is performed when images are re-uploaded to digital platforms such as social media services, enabling identification of previously registered AI-generated images even after benign transformations or partial modifications. The proposed system does not aim to universally detect all synthetic images, but instead focuses on verifying the provenance of AI-generated content that has been registered at creation time. By design, this approach complements existing watermarking and learning-based detection methods, providing a platform-agnostic, tamper-proof mechanism for scalable content provenance and authenticity verification at the point of large-scale online distribution.
The digitization of healthcare has generated massive volumes of Electronic Health Records (EHRs), offering unprecedented opportunities for training Artificial Intelligence (AI) models. However, stringent privacy regulations such as GDPR and HIPAA have created data silos that prevent centralized training. Federated Learning (FL) has emerged as a promising solution that enables collaborative model training without sharing raw patient data. Despite its potential, FL remains vulnerable to poisoning and Sybil attacks, in which malicious participants corrupt the global model or infiltrate the network using fake identities. While recent approaches integrate Blockchain technology for auditability, they predominantly rely on probabilistic reputation systems rather than robust cryptographic identity verification. This paper proposes a Trustworthy Blockchain-based Federated Learning (TBFL) framework integrating Self-Sovereign Identity (SSI) standards. By leveraging Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), our architecture ensures only authenticated healthcare entities contribute to the global model. Through comprehensive evaluation using the MIMIC-IV dataset, we demonstrate that anchoring trust in cryptographic identity verification rather than behavioral patterns significantly mitigates security risks while maintaining clinical utility. Our results show the framework successfully neutralizes 100% of Sybil attacks, achieves robust predictive performance (AUC = 0.954, Recall = 0.890), and introduces negligible computational overhead (<0.12%). The approach provides a secure, scalable, and economically viable ecosystem for inter-institutional health data collaboration, with total operational costs of approximately $18 for 100 training rounds across multiple institutions.
Consent is an ethical cornerstone of clinical research and healthcare in general. Although the ethical principles of consent - providing information, ensuring comprehension, and ensuring voluntariness - are well-defined, the technological infrastructure remains outdated. Clinicians are responsible for obtaining informed consent from research subjects or patients, and for managing it before, during, and after clinical trials or care, which is a burden for them. The voluntary nature of participating in clinical research or undergoing medical treatment implies the need for a participant-centric consent management system. However, this is not reflected in most established systems. Not only do most healthcare information systems not follow a user-centric model, but they also create data silos, which significantly reduce the mobility of patient data between different healthcare institutions and impact personalized medicine. Furthermore, consent management tools are outdated. We propose ClinConNet (Clinical Consent Network), a platform that connects researchers and participants based on clinical research projects. ClinConNet is powered by a dynamic consent model based on blockchain and take advantage of dynamic consent interfaces, as well as blockchain and Self-Sovereign Identity systems. ClinConNet is user-centric and provides important privacy features for patients, such as unlinkability, confidentiality, and ownership of identity data. It is also compatible with the right to be forgotten, as defined in many personal data protection regulations, such as the GDPR. We provide a detailed privacy and security analysis in an adversarial model, as well as a Proof of Concept implementation with detailed performance measures that demonstrate the feasibility of our blockchain-based consent management system with a median end-to-end consent establishment time of under 200ms and a throughput of 250TPS.
Andrey Chursin, Lefteris Kokoris-Kogias, Alex Orlov, Alberto Sonnino · 5 authors
Log-Structured Merge-Trees (LSM-trees) dominate persistent key-value storage but suffer from high write amplification from 10x to 30x under random workloads due to repeated compaction. This overhead becomes prohibitive for large values with uniformly distributed keys, a workload common in content-addressable storage, deduplication systems, and blockchain validators. We present Tidehunter, a storage engine that eliminates value compaction by treating the Write-Ahead Log (WAL) as permanent storage rather than a temporary recovery buffer. Values are never overwritten; and small, lazily-flushed index tables map keys to WAL positions. Tidehunter introduces (a) lock-free writes that saturate NVMe drives through atomic allocation and parallel copying, (b) an optimistic index structure that exploits uniform key distributions for single-roundtrip lookups, and (c) epoch-based pruning that reclaims space without blocking writes. On a 1 TB dataset with 1 KB values, Tidehunter achieves 830K writes per second, that is 8.4x higher than RocksDB and 2.9x higher than BlobDB, while improving point queries by 1.7x and existence checks by 15.6x. We validate real-world impact by integrating Tidehunter into Sui, a high-throughput blockchain, where it maintains stable throughput and latency under loads that cause RocksDB-backed validators to collapse. Tidehunter is production-ready and is being deployed in production within Sui.
Todd White, Ryan D. Leece, Thomas G. Calderon, Chris Solano
Cryptocurrency represents the emergence of a new asset class in accounting. This case helps students to think through the challenges associated with accounting for this unique asset by having them play the role of an assistant controller who is tasked with assigning the correct valuation for a recently purchased cryptocurrency asset. By applying the guidelines of the new cryptocurrency standard, Accounting Standards Update No. 2023-08, to the case scenario, students deepen their understanding of fair value accounting, which is often a challenging topic for students to master. Through case analysis, students enhance their knowledge of the accounting regulations pertaining to cryptocurrency, gain the ability to articulate what makes this asset class unique, and come to understand some of the potential ethical issues that could arise when approaching cryptocurrency valuation. Data gathered from pre- and post-tests demonstrate that the case was useful in improving students’ understanding of fair value accounting and how to apply accounting standards in the valuation of cryptocurrency. Furthermore, survey data show students found the case to be a valuable learning tool that increased their awareness of the challenges associated with valuing this distinct asset class.
Accounting Education and Careers
Agricultural and Financial Auditing
Innovations and Analysis in Business and Education
Abstract Product life cycle management (PLM) in large supply chains still suffers from limited transparency, manual record-keeping, and weak traceability of provenance and expiry, which often results in counterfeit products, delayed recalls, and unsafe items reaching consumers. After the advancements in blockchain technologies, immutable, decentralised and auto-generated smart contracts provide a secure, safe, and organised solution for consent to the agreement between two or more parties and help digital assets and transactions to occur efficiently This work proposes a Blockchain–Internet of Things (B-IoT) based smart-contract framework that automates three key phases of the product life cycle: purchase order creation, invoice generation at delivery, and expiry-driven discard management. The proposed system will auto-trigger the smart contract through a program. It will generate the smart contract for a product when the purchase order is placed, generate an invoice, and handle the expired and discarded products. IoT devices will record important parameters such as product ID scanning, recording storage temperature, GPS trackers during transportation, etc. These parameters will help to auto-trigger the smart contract. To tune threshold parameters (e.g., temperature bounds, delay limits) and minimize cost–latency trade-offs, we build a lightweight regression model whose hyper-parameters are optimized using nature-inspired Mayfly (MFA) and Honey Badger (HBA) algorithms. The model predicts gas usage and latency per phase, achieving an RMSE of 0.15 and R 2 ≈ 0.9 on simulated transaction logs, while the final configuration yields an average execution efficiency of 92.3% and accuracy of 94.9% in correctly auto-triggering contract phases. The prototype is implemented using Ganache Truffle Suite, Remix, and MyEtherWallet, and evaluated in terms of gas consumption, functional correctness, and automation benefits over traditional manual contracts. Results demonstrate that the proposed B-IoT smart-contract framework provides transparent, tamper-resistant, and fine-grained product life management suitable for industrial deployments.
INTRODUCTION: Each node keeps the identical block data in the decentralized, tamper-proof distributed ledger known as the blockchain. OBJECTIVES: A blockchain network's working time lengthens, the amount of data that nodes must preserve and synchronize increases noticeably. METHODS: This brings up significant storage performance difficulties. We have started a study from a blockchain data storage standpoint to address this storage performance issue. We propose a distributed hash table (DHT)-based blockchain data archiving approach by analyzing the redundancy state caused by every node in the current blockchain network containing the same data. The block data is introduced in three ways: archived data building, lookup, and interaction with the underlying chain. RESULTS: This is done to ensure that blockchain data is not lost and can be accessed. This reduces storage redundancy and satisfies the practical requirements of storage and access in the blockchain's initial application. CONCLUSION: Experiments on energy transaction data show that the technique suggested in this article has a considerably lower storage occupancy increase rate than fabric storage.
Smart contracts are autonomous programs executed on blockchain platforms such as Ethereum. They facilitate the development of decentralized applications but also introduce significant risks. Since the code of a deployed smart contract is immutable and often controls valuable digital assets, any security vulnerability can lead to severe and irreversible consequences. This paper presents a comprehensive survey of smart-contract vulnerability detection systems, synthesizing findings from over 108 tools across static analysis, formal verification, dynamic fuzzing, machine learning, symbolic execution, and runtime monitoring. A vulnerability taxonomy is organized spanning coding, design, and environmental weaknesses (e.g., reentrancy, arithmetic errors, access-control faults, timestamp dependence, and misuse of pseudo-randomness). Rather than conducting new empirical evaluations, this survey synthesizes performance metrics reported in the literature and proposes a KPI-based framework for comparing tools. However, direct quantitative comparisons are limited by heterogeneous evaluation contexts, datasets, and tool versions across studies. Techniques are contextualized with real attack exemplars, tool capabilities are summarized, and trade-offs among accuracy, scalability, and coverage are highlighted. Open challenges are identified—including compositional reasoning for multi-contract interactions, uncovering businesslogic errors, and balancing precision with throughput—and design guidance is distilled for practitioners. Overall, while detection capabilities have improved substantially, securing smart contracts remains an active frontier that calls for hybrid, multilayer defenses and continuous innovation.
Imoleayo Abraham Awodele, Molusiwa S. Ramabodu, Nathaniel Ayinde Olatunde, Iruka C. Anugwo
Africa is richly endowed with renewable energy resources, including solar, wind, and hydropower, yet the continent faces a significant energy access deficit, with over 600 million people lacking reliable electricity. Traditional fossil fuel-based energy models have proven inadequate for meeting the region's growing energy demands while posing environmental and economic challenges. This study explores the need to transcend these conventional energy paradigms by accelerating the adoption of sustainable, inclusive renewable energy systems tailored to Africa's unique context. Adopting a qualitative research approach, the study employed document analysis of policy reports, scholarly literature, and energy market trends to examine the continent's renewable energy transition. Thematic analysis identified key barriers such as limited access to financing, fragmented regulatory frameworks, and insufficient technical capacity. However, the findings also highlight transformative opportunities, including decentralized energy systems for off-grid rural communities, digital innovations, and international climate finance. The study recommends empowering community-driven energy models, adopting innovative financing mechanisms such as microcredit and crowdfunding and fostering cross-sectoral collaboration. These measures will not only expand energy access but also position Africa as a leader in global climate action, environmental sustainability, and inclusive energy innovation. Keywords: Renewable Energy Transition; Decentralized Energy Systems; Sustainable Development; Africa Energy Policy.
R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree
Agriculture faces multifaceted challenges including climate variability, soil degradation, and supply chain inefficiencies, particularly for smallholder farmers practicing multicropping. This study systematically integrates blockchain technology for secure, transparent transactions with reinforcement learning (RL)-optimized Neutrosophic multi-regression for precise crop loss prediction in multicropping systems. Using real-world data from six crops (rice, banana, turmeric, elephant foot yam, coconut, cocoa), Neutrosophic multi-regression estimated losses with RL hyperparameter tuning, achieving superior prediction accuracy. A blockchain framework was developed for farmer validation, transaction security, and smart contract execution using Ethereum/Ganache. Results demonstrate 25%–35% reduction in predicted crop losses and enhanced supply chain traceability. This Smart Agriculture 5.0 framework advances Agriculture 4.0 through human-AI symbiosis and uncertainty modeling, addressing single-point failures, data privacy, and trust deficits for scalable sustainable farming Through this multidimensional approach, the study endeavors to not only enhance the productivity and sustainability of agricultural practices but also to foster resilience in the face of evolving challenges.
The approval of spot Bitcoin and Ether ETFs in 2024 has ignited widespread interest in tokenization, with market potential estimated at $10 trillion. This shift is accelerating the adoption of decentralized finance (DeFi), crypto staking, artificial intelligence (AI)-driven payments, and private stablecoins. Innovations such as agentic AI and blockchain integration are transforming financial services, though they introduce systemic risks and cybersecurity challenges. U.S. federal and state policies are increasingly supportive of digital assets, including state-level Bitcoin reserves. Meanwhile, the global rise of CBDCs reflects governments’ intent to harness tokenization’s benefits while maintaining monetary control. Ensuring security, transparency, and governance remains critical.
This chapter will focus on the basic concepts of blockchain: the decentralized nature of the network, cryptographic security, and an immutable ledger. This section shall discuss, from the literature review, how blockchain can disruptively promote transparency, efficiency, and security in business processes. Particular attention will be given to main areas of blockchain applications: financial, supply chain, healthcare, and verification of digital identity. In finance, blockchain is pointed out as allowing secure, low-cost transactions and decentralized finance (DeFi) platforms, while in supply chain management, it increases traceability and accountability. Later, this chapter looks at combining blockchain and other new technologies, like the internet of things (IoT) and artificial intelligence (AI), to make business processes even better. It also closely examines problems in adopting blockchain, including unclear regulations, scalability issues, and high energy use in some blockchain networks. Ethical concerns with blockchain use – like its potential to allow anonymous and untraceable actions – are also closely reviewed.
Yaroslava Yakovenko, Zavodovska D., Reichling Peter
Over the past decade, cryptocurrencies have evolved from a niche technological innovation into a global financial phenomenon. Bitcoin, Ethereum, and other digital assets have attracted massive attention from investors, policymakers, and the general public. The central debate surrounding cryptocurrencies centres on whether they represent a financial bubble destined to burst or the foundation of a new, decentralized financial future.
This dissertation investigates the economic and behavioral foundations of decentralized finance (DeFi) where lending, borrowing, and rate discovery are executed by smart contracts rather than traditional financial institutions. Through three complementary essays, it analyzes the design of decentralized credit protocols, the formation of interest rates in decentralized markets, and the fundamental and behavioral drivers of DeFi token valuation.The first essay examines the Atlendis protocol, which enables non- or partially-collateralized lending by combining off-chain underwriting with on-chain execution. The second develops a theoretical model of decentralized rate discovery based on a multi-unit game framework, identifying the conditions for efficiency and the frictions specific to these markets. The third provides an empirical analysis of DeFi token returns, showing that investor sentiment, liquidity dynamics, and behavioral factors play a significant role in price formation alongside economic fundamentals.By bridging financial engineering, theoretical modeling, and empirical research, this thesis sheds light on how DeFi reshapes intermediation, price formation, and governance in a transparent, programmable financial environment.
Дисертацията предлага иновативни решения на някои често срещани предизвикателства в областите на (1) мотивиране на научни приноси, (2) управление на жизнения цикъл на корпоративен софтуер и (3) генериране на доказуемо случайни числа. Предложените решения са разпределени приложения, базирани на блокчейн технологията EOSIO/Antelope. Документът предоставя подробен анализ на съществуващите решения в изброените области. Въз основа на анализа са формулирани насоки и функционалности, които са от голямо значение за проектирането на предложените нови разпределени приложения решаващи трите вида специфични проблеми изброени по-горе. Изграден е работещ прототип за блокчейна Vaulta, базиран на предложената архитектура, позволяващ генериране на публично доказуеми случайни числа, използвайки различни алгоритми, базирани на блокчхеша на бъдещи блокове, генерирани от технологията EOSIO/Antelope.
Oscar Revelo Sánchez, Alexander Barón Salazar, Manuel Bolaños González
This systematic review examines recent advances in blockchain-based electronic voting systems, motivated by the need for more transparent, secure, and verifiable electoral processes. The rapid growth of research between 2022 and 2025 highlights blockchain as a promising foundation for addressing long-standing challenges of integrity, anonymity, and trust in digital elections, particularly in academic contexts where pilot deployments are more feasible. The review followed PRISMA 2020 guidelines and applied the evidence-based methodology proposed by Kitchenham & Charters. Searches were conducted in six major databases, yielding 861 records; after removing duplicates and applying eligibility criteria, 338 studies were retained. Data were extracted using a structured template and synthesised qualitatively due to the conceptual and methodological heterogeneity of the evidence. The included studies reveal significant progress in blockchain architectures, smart contracts, and advanced cryptographic mechanisms—such as blind signatures, zero-knowledge proofs, and homomorphic encryption. Multiple authentication and verification strategies were identified; however, real-world validations remain limited and largely confined to small-scale academic pilots. Overall, blockchain-based voting systems demonstrate conceptual advantages over traditional and conventional electronic models, especially regarding transparency and auditability. Nevertheless, the field requires stronger empirical evaluation, greater scalability, and clearer regulatory alignment to support broader institutional adoption.
This study explores the diversification potential of Bitcoin in a French investment portfolio comprising oil, currency, and gold across three distinct market regimes: a pre-crisis stable period, the COVID-19 pandemic, and the Russia–Ukraine conflict. The purpose is to assess whether Bitcoin can enhance portfolio efficiency and provide hedging opportunities under varying market conditions. The analysis is conducted using daily data for Bitcoin, gold, oil, currency, and the CAC40 index from January 1, 2019, to April 22, 2022. Portfolio performance is evaluated through the Mean–Variance (MV) framework and Stochastic Dominance (SD) analysis, allowing for a robust comparison of risk–return trade-offs and investor preferences. The MV results show that including Bitcoin consistently improves the portfolio’s risk–return profile, evidenced by an upward shift in the efficient frontier across all sub-periods. However, the SD analysis yields more nuanced insights. Before and during the COVID-19 crisis, the portfolio excluding Bitcoin dominates the Bitcoin-inclusive portfolio under second- and third-order stochastic dominance criteria, suggesting that risk-averse investors would prefer the traditional asset mix. In contrast, during the Russia–Ukraine war, no clear stochastic dominance is detected between Bitcoin-inclusive and Bitcoin-exclusive portfolios. These findings emphasize that Bitcoin’s diversification role is highly context- and framework-dependent.
Muhammad Usama, Arshad Aziz, Nada Alasbali, Nazik Alturki · 6 authors
The growing deployment of the Internet of Things (IoT), especially in critical infrastructure, has increased the need for identity systems that are scalable and robust against attacks. However, existing centralized systems have fundamental weaknesses, especially where adversaries use artificial intelligence (AI)-based techniques, such as generative spoofing, model poisoning, and deepfakes to create fake identities. In this paper, we present a novel blockchain-based IoT security system that combines decentralized identity verification, zero-knowledge proofs, Byzantine-resistant federated learning, and formal verification of smart contracts. The proposed architecture eliminates single points of trust, allows device registration while preserving privacy, and provides defense against AI-driven attacks through formally modeled state transitions. Experimental results show that this method shows significant improvements over previous frameworks, including a 48% reduction in false acceptance rate during GAN-based spoofing and speedup the ZKP verification. This work provides a blockchain-enabled identity management system for IoT to encounter AI-based threats and maintain a balance between performance and security with the help of adversarial simulation, symbolic execution, and threshold cryptography.