Blockchain Papers

Janaka Ishan Senarathna

No abstract is available for this record.

Abhinav Raghav, Aanjey Mani Tripathi, Rajesh Kumar Chaudhary

Blockchain can secure and verify electronic health records (EHRs) for multi-institution healthcare systems, but Layer-1 storage costs and throughput limitations make full on-chain EHR storage impractical. A new model is proposed named FZRP (Federated-ZK-Rollup Pipeline). It is a hybrid methodology combining Federated Learning (FL), off-chain storage (IPFS), zk-rollup batching with adaptive batch sizing, and parallel proof pipelines to minimize per-record transaction cost while preserving auditability and privacy. Using a synthetic dataset of 50,000 EHRs, it quantifies cost reductions under realistic assumptions and demonstrate orders-of-magnitude per-record savings. A formal cost model, latency and security analyses, and sensitivity studies are provided. The experimental evaluation demonstrates that adaptive batching significantly reduces per-record transaction cost under conservative Layer-1 cost assumptions to as low as $0.000024, achieving over 99.999% cost reduction while maintaining scalability and privacy. The limitations, regulatory considerations, and paths for future work are discussed.

Shapour Shiranifar, Sayyed Mohammad Reza Davoodi, Saeid Aghasi

Purpose: This study aimed to identify and validate the factors influencing the transfer of Blockchain-based services in the digital transformation process of banks. The present study seeks to answer the fundamental question of how a comprehensive framework can be designed to facilitate the successful adoption and implementation of Blockchain technology in banking environments.Methodology: This study used a mixed qualitative approach combining content analysis and the Delphi method. In the first phase, semi-structured interviews were conducted with 20 experts from Saderat Bank of Iran, fintech companies, and IT service providers. The resulting data were analyzed using thematic content analysis, yielding 17 key factors across six thematic areas. These factors were evaluated, and a consensus was reached across three Delphi rounds involving 15 experts. The Delphi process ended with an agreement level of 80% as a reliability criterion.Findings: Components such as interoperability (92%), scalability (88%), digital governance (94%), change management capacity (90%), and customer-centric innovation (91%) play a pivotal role in successful technology transfer. It was found that Blockchain adoption alone will not lead to improved performance unless strong dynamic capabilities and organizational readiness support it. Among the most critical challenges identified are resistance to change (85%), inadequate technical infrastructure (82%), regulatory challenges (79%), and data privacy restrictions (87%). Based on the final findings, success in Blockchain technology transfer requires simultaneous attention to three key dimensions: a) the technical dimension by prioritizing system interoperability and solution scalability; b) the organizational dimension by focusing on developing dynamic capabilities and creating a culture of innovation; c) the environmental dimension by reforming regulatory frameworks and developing security standards. It is suggested that banks invest in specialized employee training, develop a data governance strategy, and partner with fintech startups to pave the way for the successful implementation of this technology.Originality/Value: This study bridges the gap between academic literature and management practice by providing a conceptual and validated framework for decision-makers. The principal value of this research is to outline the essential components for effective and sustainable technology transfer in digitally evolving institutions. This framework can be used as a guide for banks and financial institutions in successfully implementing Blockchain-based solutions. The findings of this study can also serve as a basis for future research on innovative applications of Blockchain in the financial services sector.

Suman Bijapur, Shilpa Patil, Parimala, Shantala P H

With unparalleled threats to the integrity of digital information, democratic practices, and public confidence in media, deepfake technology comprises a new class of harm. Deep generative models can be used to generate realistic looking (and sounding) fake human faces and voices, which is great news for bad actors who seek to spread misinformation, commit crimes and ruin journalism. CyberLink Fights Deepfakes with New AI Model That Uses Neural Network Traditional methods used to identify deepfakes have depended on centralised AI systems that can't be trusted at face value and there is little or no way of proving a piece of content's authenticity. In this work, we have presented a solution that involves multi-modal deepfake detection and has utilized learning-based forgery detection framework to be deployed on blockchain for evidence tamper resistance. The proposed framework employs a hybrid CNN-RNN architecture that computes facial, audio and metadata feature in parallel to detect unseen deepfakes with accuracy of 94.2%, compared to the single-modal baselines (CNN only: 81.3%, and audio only: 67.4%). Novelty: Blockchain timestamping with cryptographically secured certificates of authenticity for third-party verification while protected proprietary detection logic is not revealed. At the computational efficiency and bandwidth threshold required for edge deployment, video processing at 30 FPS and only 2.1 MBs makes this applicable on any average mobile device. It holds for 12k synthetic videos (celebrities, politicians, newscasters) and diverse deepfake generation methods (FaceSwap, DeepFaceLab, StyleGAN). Societal impact: framework mitigates $1.2T annual disinformation damage and champions digital rights through decentralized verification. Via mashable.com Framework addresses the convergence of deepfake detection, blockchain authentication and the case for sustainable cybersecurity: As a global community grapples with synthetic media in ways we've never seen before, support online safety experts to respond.

P.Rupasri, Marka Sandhya

The increasing adoption of blockchain technology has transformed digital transaction systems by providing secure, decentralized, and transparent data management. The vehicle procurement process, however, still relies heavily on conventional procedures involving multiple intermediaries, manual documentation, and lengthy verification mechanisms that often increase operational costs and expose transactions to fraudulent activities. This paper presents a blockchain-enabled smart vehicle procurement framework that modernizes the complete purchasing lifecycle while preserving transaction integrity and user trust. The proposed system utilizes blockchain technology as an immutable distributed ledger for securely storing vehicle records, ownership history, buyer credentials, and transaction information. Smart contracts are employed to automate critical activities including buyer verification, ownership transfer, payment authorization, and regulatory validation without requiring manual intervention. The decentralized architecture minimizes dependency on third-party agencies while improving transparency, reducing processing delays, and enhancing security against data manipulation. Since every transaction is permanently recorded on the blockchain, both buyers and sellers can independently verify the authenticity of vehicle records before completing a purchase. The proposed framework maintains the same operational workflow and implementation strategy as the reference system while offering improved documentation quality and technical presentation. Experimental observations demonstrate that blockchain-assisted procurement significantly improves transaction efficiency, strengthens security, simplifies ownership transfer, and establishes a reliable digital marketplace for modern automotive commerce. The framework represents a scalable solution capable of supporting future intelligent transportation systems and smart mobility applications.

Fernando Frachone Neves, André Luiz Oliveira, Flávia Vancim Frachone MASSA, Tainara Adriani Ribeiro de Jesus · 5 authors

A proliferação da tecnologia blockchain e da mineração de criptomoedas tem gerado interesse de especialistas em sustentabilidade, emergindo um novo campo de estudo, desenvolvendo o conceito de criptomoedas verdes e a sustentabilidade digital. Neste sentido, este estudo realizou uma análise bibliométrica com o objetivo de mapear as tendências, estruturas temáticas e avanços na literatura científica sobre sustentabilidade digital no ecossistema blockchain, com foco em criptomoedas verdes. Para isso, foram analisados 133 artigos científicos extraídos do Web of Science (WOS), utilizando-se o software RStudio. Os resultados revelaram um crescimento acelerado de publicações, com um pico em 2024, indicando um campo de pesquisa em rápida expansão. As contribuições em pesquisa demonstram uma polarização, destacando a China e a Índia como principais polos. Temas dominantes incluem "cryptocurrency", "bitcoin", "blockchain technology", "green bonds", "clean energy" e "renewable energy", enquanto o mapeamento temático identificou "energy consumption", "risk" e "green challenges adoption" como temas motores. Esta revisão bibliométrica confirma o crescente interesse em criptomoedas verdes, impulsionado pela necessidade de mitigar impactos ambientais e alinhar a inovação tecnológica aos Objetivos de Desenvolvimento Sustentável (ODS) da ONU. Conclui-se que o estudo oferece percepções importantes aos formuladores de políticas, investidores e desenvolvedores, visando promover um desenvolvimento digital mais equitativo e alinhado à sustentabilidade.

*1Oraekeyi Anthony Uchenna

Counterfeit and substandard medicines remain a major global public health threat, with the World Health Organization estimating that up to 10% of medicines in low- and middle-income countries are falsified or substandard, exceeding 20% for some therapeutic classes in sub-Saharan Africa. Blockchain technology, with its decentralised, immutable, and transparent digital ledger architecture, has been proposed as a promising solution for strengthening pharmaceutical supply chain traceability, yet little is known about the readiness of community pharmacies in Nigeria to adopt it. This study assessed the knowledge, current practices, and implementation readiness of blockchain-based traceability for counterfeit medicine prevention among community pharmacists in Bayelsa State, Nigeria, and examined the barriers and enablers influencing adoption. A descriptive cross-sectional survey design was employed, using a structured, validated questionnaire administered electronically through the Association of Community Pharmacists of Nigeria (ACPN), Bayelsa State Chapter. A total of 119 valid responses were obtained, exceeding the minimum sample size derived from Yamane's and Cochran's formulae, representing a 79.3% response rate. Data were analysed using descriptive and inferential statistics, including Chi-square tests. Findings revealed that although 71.4% of respondents had heard of blockchain technology, mean knowledge scores across core blockchain concepts were uniformly low (1.26-1.72 on a 5-point scale), reflecting a substantial awareness-comprehension gap. Authentication practices were overwhelmingly manual, with 77.3% relying on visual inspection of NAFDAC numbers and only 9.2% using digital scanning; 68.9% of pharmacists had encountered suspected counterfeit medicines in the past year. Implementation readiness was below average (Mean = 2.70/5.0), driven by strong training willingness (Mean = 3.74) but deficient infrastructure (Mean = 1.80). Resistance to change and high cost were the leading barriers, while user-friendly applications and mandatory regulation were the strongest enablers. The study concludes that community pharmacies in Bayelsa State are not yet structurally ready for blockchain-based traceability, although a receptive attitudinal environment and strong professional motivation exist to support a phased transition. It is recommended that NAFDAC and the Pharmacists Council of Nigeria develop a phased regulatory framework and integrate blockchain literacy into continuing professional development, that government prioritise infrastructure investment, and that technology developers design mobile-first, user-friendly, offline-capable systems, to enable a coordinated transition toward blockchain-enabled pharmaceutical traceability in Nigeria.

Prayote Songklin

No abstract is available for this record.

Mario Rusev, Rafael Schmidt, Edward Lambe, Christian Schmieder · 5 authors

International organizations including the Bank for International Settlements (BIS) have adopted SDMx (The standard for Statistical Data and Metadata) as the standard for exchanging official statistics. Trust in published data is essential for evidence-based policymaking. This paper shows how binding each SDMx dataset to its source using blockchain technology can enhance confidence in official statistics. We present a proof of concept implemented on the XRP Ledger (XRPL) and contribute, as an integrated whole, (i) an SDMx-native canonicalization and per-< Series > hashing pipeline, (ii) a domain-separated Merkle aggregation scheme for batched anchoring, (iii) a self-contained, identity-bound verification artefact in which the SDMx message itself carries both the ordered Merkle leaves and a W3C Verifiable Credential signed by a publisher identity key cryptographically bound to the publisher’s XRPL address via an on-chain attestation registry, so any consumer can re-derive the anchored root and verify the publisher’s identity from the file alone plus a single ledger lookup, (iv) an open-source XRPL-based reference implementation, and (v) a cost model that captures the batch-size / latency / fee trade-off and is solved for an economically optimal batch size. The system enables near-real-time data verification, provides cryptographic integrity guarantees, and establishes a foundation for future extensions, including zero-knowledge proofs and automated verification by AI agents. Measurements on the prototype show median publication latency of 3–5 s and verification latency of 1–2 s under the controlled test conditions described in Section 7. The approach is data-format-agnostic and can be extended to other structured statistical or regulatory formats.

Ayaan Siddiqui

Business Models and Value Creation via the x402 Protocol in Web3

QubitChain Research

The advent of fault-tolerant quantum computing represents the most significant and schedulable threat to the cryptographic foundations of blockchain infrastructure. Over $3.2 trillion in digital assets are currently secured by RSA, Elliptic Curve Cryptography (ECC), and ECDSA: algorithms provably broken by Shor's algorithm running on a Cryptographically Relevant Quantum Computer (CRQC). The Harvest Now, Decrypt Later (HNDL) threat means this risk is not future-dated. Adversaries with archival capability are already harvesting public blockchain data for retrospective decryption. In August 2024, NIST published three finalized post-quantum cryptographic standards: FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA). In 2025, NIST standardized HQC, providing code-based cryptographic diversity alongside the lattice-based primary algorithms. These standards are mandated for U.S. national security systems under NSA CNSA 2.0 and for high-risk sector operators in the EU under the EU PQC Roadmap. This paper introduces QubitChain.io: a natively quantum-safe Layer 1 blockchain implementing all four NIST post-quantum standards from genesis block. The protocol employs hardware Quantum Random Number Generator (QRNG) entropy at both key generation and consensus randomness levels, and introduces Proof of Quantum Entropy (PoQE), a novel consensus mechanism whose validator selection cannot be predicted or manipulated by any adversary regardless of computational capability. The paper provides the complete technical, economic, and governance specification for the QubitChain.io protocol, covering cryptographic architecture, QRNG system design, consensus mechanism, network protocol, tokenomics, governance, and regulatory compliance.

Xin Xu, Zhen Yang, Quanwei Cai, Jingqiang Lin · 7 authors

Version control systems (VCS), including central VCS (CVCS) and distributed VCS (DVCS), are widely adopted to manage changes to software code and various types of documents. Unlike CVCS, where entities obtain data from a central server, each entity in DVCS stores the entire repository and shares it independently. In VCS, existing access control schemes require the participation of a central server and cannot be deployed in a completely distributed scenario. Additionally, these schemes often fail to enforce fine-grained access control for write permissions, which is crucial for collaborative work in a distributed environment. In this paper, we propose a distributed version control system access control scheme (named DVAC), which enforces cryptographic access control on distributed user nodes based on attribute-based encryption (ABE) and attribute-based signature (ABS). DVAC is designed to enforce a cryptographic access control protocol for DVCS, which enables file granularity read and write separation access control without the support of a central server. To ensure the integrity of the core version control functions in DVCS while protecting data security, DVAC incorporates a version control adaptation protocol. Additionally, DVAC leverages Ethereum smart contracts to maintain access control policies, ensuring distributed storage and trusted management of access policies. The architecture of DVAC is designed to seamlessly integrate with existing mature DVCS, such as Git, with minimal modifications. We have implemented a prototype of DVAC and integrated it with Git. A comprehensive performance evaluation was conducted to assess the overhead introduced by DVAC, and it was demonstrated that the overhead is modest.

Yayu Gao, Yong Xiao, Hao Hu, Xubo Li · 8 authors

Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.

Hung Tan Nguyen, Anh-Kiet Ly-Ngo

The construction sector is increasingly exploring smart contracts (SCs) to resolve persistent operational challenges, such as payment delays and disputes. This study systematically analyzes SC and Distributed Ledger Technology (DLT) deployments globally and in Vietnam, identifying key legal, technical, and organizational constraints hindering domestic adoption. To bridge these gaps, a five-step implementation framework tailored to Vietnam's institutional and market conditions is proposed. The framework emphasizes incremental adaptation using regulatory sandboxes, a hybrid architecture integrated with Building Information Modeling (BIM), and fiat-based payment mechanisms. The findings highlight that successful adoption depends primarily on institutional readiness and regulatory alignment rather than the technology itself, advocating a phased approach aligned with local industry capabilities.

Rukhsar Zaka, Faiza Irfan, Sidra Rehman, Muhammad Ahsan Hayat

Cryptocurrency markets are highly volatile, nonlinear, and affected by several internal and external market factors, making price forecasting a challenging task. Accurate cryptocurrency price forecasting can support investors, traders, and financial analysts in making informed decisions. This research paper presents a comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data. The dataset consists of 275 records and 10 features, including Date, High, Low, Open, Close, Volume, and Marketcap. The Close price is selected as the target variable, while High, Low, Open, Volume, and Marketcap are used as predictor variables. Five models are implemented and compared: Linear Regression, Support Vector Regression, Random Forest Regressor, XGBoost Regressor, and Long Short-Term Memory. The models are evaluated using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, R-squared score, and directional accuracy. Experimental results show that the LSTM model achieved the best performance with the lowest RMSE of 2.74, MAE of 1.78, MAPE of 3.91%, and R-squared score of 0.965. The results indicate that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.

Arda Buğra Özer

Verifiable Random Functions (VRFs) are cryptographic primitives that generate unpredictable randomness together with a publicly verifiable proof of correct generation following the protocol, a critical requirement for decentralized applications in blockchain infrastructure, decentralized finance, and online gaming. While distributed VRFs (DVRFs) eliminate dependence on a single trusted authority, existing constructions face a fundamental dilemma: linear proof sizes in the threshold parameter (DDH-DVRF) or reliance on computationally expensive bilinear pairings (GLOW-DVRF, FlexiRand). This thesis resolves both facets of this dilemma with three contributions despite the downside of adding another interaction round amongst the parties involved in the generation. We first introduce DVRFwCP, a distributed VRF with constant-size, pairing-free proofs achieved by layering a threshold structure over a Chaum-Pedersen NIZK system, where we use an augmented secure distributed key generation to produce the required nonce. We then introduce Icy-DVRF, which eliminates the quadratic interaction bottleneck of DVRFwCP by integrating the FROST-style preprocessed nonces, reducing total per-evaluation communication from O(n^2 t) to O(t) while preserving constant-size proofs. Finally, we introduce IcyVeil, the first pairing-free output-private DVRF, which extends Icy-DVRF with a Schnorr-based blinding mechanism that conceals the VRF output until revealed by the user. We evaluate all three constructions theoretically and empirically via Solidity smart-contract implementation on the Ethereum Sepolia testnet. The measurements show a 43.02% reduction in on-chain verification gas cost compared to GLOW-DVRF.

Md Mehedi Hasan Emon, Ratul Islam

This chapter explores the transformative role of Financial Technology (FinTech) in advancing green finance, a crucial component in addressing global sustainability challenges. By integrating technologies such as blockchain, artificial intelligence (AI), big data analytics, and smart contracts, FinTech facilitates the efficient allocation of capital towards sustainable projects. The chapter examines how these innovations enhance transparency, optimize risk assessment, and enable decentralized financing models like peer-to-peer energy trading and tokenization. Additionally, it addresses the challenges posed by greenwashing, market volatility, and regulatory uncertainty, while highlighting the future opportunities for growth in green finance. Ultimately, the chapter underscores the potential of FinTech to drive systemic change and promote a low-carbon, sustainable economy.

Md Mehedi Hasan Emon, Ratul Islam

This chapter explores the transformative potential of blockchain and artificial intelligence (AI) in revolutionizing green finance. It begins by examining the role of digital transformation in driving sustainable financial practices, highlighting the integration of blockchain and AI. The chapter delves into blockchain's applications in enhancing transparency, traceability, and security within green finance, particularly through smart contracts and decentralized finance solutions. It further discusses AI's contributions to improving risk assessment, ESG evaluation, and combating greenwashing. The synergies between blockchain and AI are explored, showing how their combined use optimizes sustainability-focused investments. Additionally, the chapter addresses regulatory and ethical considerations surrounding these technologies. Finally, it discusses emerging trends and opportunities in green finance, providing insights into the future of sustainable financial systems driven by technological innovation.

Andre Septianus Nababan, Raymon Simamora, Cristina Situmorang, Lotar Mateus Sinaga

Pertumbuhan jumlah pengguna dan volume lalu lintas data menuntut jaringan komputer yang mampu menyediakan koneksi stabil, cepat, dan terkontrol. Penelitian ini bertujuan menganalisis kualitas layanan jaringan pada server Debian 12 melalui penerapan algoritma Hierarchical Token Bucket sebagai metode manajemen bandwidth. Penelitian menggunakan metode eksperimen dengan membandingkan kondisi jaringan sebelum dan sesudah penerapan HTB pada topologi bintang yang melibatkan satu server Debian, perangkat MikroTik sebagai switch atau bridge, serta dua client pengujian. Konfigurasi HTB membagi bandwidth sebesar 10 Mbps untuk Client 1 dan 5 Mbps untuk Client 2. Pengukuran dilakukan terhadap parameter Quality of Service yang meliputi throughput, delay, jitter, dan packet loss menggunakan iperf3 dan ping. Hasil penelitian menunjukkan bahwa sebelum penerapan HTB, throughput kedua client relatif sama, yaitu 94,7 Mbps pada Client 1 dan 94,8 Mbps pada Client 2. Setelah HTB diterapkan, throughput menjadi 8,81 Mbps dan 4,71 Mbps, sehingga pembatasan bandwidth berjalan sesuai kebijakan. Nilai delay rata-rata tetap rendah, yaitu 3 ms pada Client 1 dan 5 ms pada Client 2, sedangkan packet loss tetap 0% pada kedua client. Nilai jitter sesudah HTB sebesar 0,292 ms dan 5,862 ms, yang masih termasuk kategori sangat baik berdasarkan standar TIPHON. Temuan ini menunjukkan bahwa HTB efektif mengatur distribusi bandwidth secara adil, terstruktur, dan efisien tanpa menurunkan stabilitas layanan jaringan secara signifikan.

Unknown author, Reema Bhaskar, Unknown author, Unknown author · 7 authors

No abstract is available for this record.

Prajkta Waditwar

The rapid adoption of Large Language Models (LLMs) is transforming enterprise technology procurement from traditional software licensing toward consumption-based artificial intelligence services. Unlike perpetual licenses or Software-as-a-Service (SaaS) subscriptions, modern AI platforms increasingly employ token-based pricing models in which organizations procure computational intelligence according to usage. Existing procurement frameworks were developed for fixed commercial models and provide limited guidance for forecasting demand, negotiating consumption-based contracts, governing multi-vendor AI portfolios, and optimizing enterprise-wide token utilization throughout the AI lifecycle. This paper introduces Token-as-a-Service (TaaS) Procurement, a procurement paradigm that treats AI tokens as strategic enterprise resources requiring continuous commercial governance rather than one-time acquisition. To operationalize this paradigm, the paper proposes the Enterprise Intelligence FinOps (EIFO) framework, which integrates business demand management, token forecasting, commercial sourcing, supplier governance, runtime consumption monitoring, financial accountability, and continuous optimization into a unified enterprise operating model. The paper further develops quantitative models for enterprise AI cost estimation, procurement optimization, governance compliance, and business value measurement, demonstrating how procurement organizations can align AI investments with organizational objectives while maintaining commercial flexibility and governance oversight. By positioning procurement as a continuous enterprise capability rather than a transactional purchasing function, this research establishes a governance foundation for managing consumption-based AI services within evolving Large Language Model ecosystems.

Mulyadi Putra

The Time Token Arends learning model can be used as an alternative learning strategy to improve students’ learning outcomes, particularly in the Fiqh subject. This study aimed to determine the effect of implementing the Time Token Arends learning model on the learning outcomes of Grade XI students at MAS 09 Sidomulyo in the Fiqh subject. The study employed a quantitative approach using a quasi-experimental method with a non-equivalent control group design. The results of the analysis showed a significant difference between the learning outcomes of students taught using the Time Token Arends model and those taught using the conventional model. This finding indicates that the Time Token Arends learning model has a positive effect on students’ learning outcomes.

Manuel Hensmans

Responding to calls to overcome historical amnesia in alternative organization studies, this paper foregrounds myth as a neglected mechanism of social transformation. In contrast to the field’s essentialist focus on alternative values and ideologies, I examine how myth—understood as an empty signifier of alternativity—enables the transformation of hegemonic social imaginaries. Advancing a methodology of critical historical explanation, I analyze the Dutch cooperative banking movement’s historical use of myth between 1891 and 2023. I show how alternative organizations can universalize a foundational myth into a broader social imaginary that becomes intelligible and desirable to society at large, thereby institutionalizing their alternative imaginary within the state. While this study identifies an underexplored pathway to social transformation, it also highlights a largely overlooked form of degeneration: domination degeneration. Whereas existing accounts emphasize degeneration resulting from organizational weakness or failure, domination degeneration arises when organizations attempt to preserve overwhelming historical success and hegemonic closure in the face of renewed demands for alternativity.

Subhan Hadi Kusuma, Takeharu Nagai

SUMMARY Autonomous bioluminescence systems enable continuous light emission in engineered organisms by genetically encoding both luciferase enzymes and their substrate biosynthetic pathways, offering a powerful platform for non-invasive and long-term monitoring of biological processes. However, bioluminescence output is highly sensitive to substrate availability and host metabolic state, often leading to signal instability under energy-limited conditions. Here, we report an alternative luciferin biosynthetic strategy for the bacterial Lux bioluminescence system in which the adenosine triphosphate (ATP)-dependent LuxEC complex is replaced by α-dioxygenase (αDOX), an enzyme that directly converts fatty acids into fatty aldehydes without consuming ATP. Using machine learning–guided directed evolution, we engineered αDOX variants that markedly enhanced bioluminescence intensity when coupled with bacterial luciferase. The resulting ATP-free Lux bioluminescence system enabled single-cell–level bioluminescence imaging and maintained stable light emission under diverse antibiotic treatments, demonstrating enhanced robustness against metabolic perturbations. SIGNIFICANCE Autonomous bioluminescence imaging has become an attractive method for long-term observation of biological phenomena without the need for exogenous substrate addition. However, the light output of existing autonomous bioluminescence systems, including bacterial and fungal pathways, remains ATP-dependent and often declines when cellular metabolism is perturbed, limiting their reliability for quantitative analysis. Here, we report the development of an ATP-independent substrate biosynthesis pathway for the bacterial luciferase system using αDOX. To improve system performance, we applied machine learning– guided directed evolution, which significantly enhanced signal intensity and enabled single-cell bioluminescence imaging. Furthermore, the αDOX-based bacterial luciferase system maintained stable luminescence under antibiotic treatments, in contrast to the conventional ATP-dependent bacterial luciferase system. In summary, our findings establish a robust ATP-independent autonomous bioluminescence imaging platform that enables monitoring of cellular events under metabolic perturbations.