As global industries confront mounting complexity, regulatory mandates, and urgent sustainability targets, end‑to‑end transparency has become nonnegotiable. Design for Traceability (DfT) delivers a transformative blueprint—encoding traceability into the very DNA of products and materials. By harnessing Smart Identification Technologies (SIT)— including Radio-Frequency Identification (RFID), Near Field Communication (NFC), QR codes, IoT sensors, and blockchain—DfT establishes immutable “digital DNA,” realized through interoperable Digital Product Passports (DPPs) and Material Passports (MPs). These passports grant real‑time visibility, secure authentication, and frictionless data exchange, catalyzing circular resource loops while ensuring compliance with evolving regulations.The DfT framework is anchored by five interdependent pillars: Lifecycle‑Centric Design: Embeds traceability at inception via modular architecture, durable materials, and design-for-disassembly, extending product life and simplifying end-of-life recovery. Digital Traceability Infrastructure: Constructs a secure, interoperable data ecosystem by integrating SIT and distributed ledger technology, enabling continuous monitoring, analytics, and decision support through DPP and MP integration. Circular Business Models: Transitions from one‑time sales to service‑based offerings, remanufacturing, and R‑strategies (Reduce, Reuse, Recycle), unlocking new revenue streams and preserving asset value. Stakeholder Collaboration: Builds shared platforms and decentralized governance to unite manufacturers, regulators, consumers, and recyclers in transparent data‑sharing networks, strengthening trust and supply‑chain resilience. Regulatory Alignment: Integrates traceability into corporate strategy to anticipate stringent sustainability mandates, leveraging digital audits and transparent reporting for streamlined compliance. By interweaving these pillars, DfT empowers organizations to mitigate supply‑chain risks, optimize resource utilization, and accelerate the shift toward a resilient, transparent circular economy. This holistic framework equips policymakers, industry leaders, and designers with actionable strategies to embed sustainability, accountability, and innovation at every stage of the product lifecycle.
Nurul Qosimah Siregar, Nikma Sari Harahap, Anisa Tul Fitri, Hulwati Hulwati · 5 authors
The use of cryptocurrencies as a payment instrument is an interesting social phenomenon to discuss. The use of cryptocurrencies as an official payment instrument is still prohibited by Bank Indonesia, but the use of cryptocurrencies continues to grow in Indonesia. BI limits the use of cryptocurrencies only as assets to be invested, not as legal tender. This research aims to analyze the law of cryptocurrency as a payment instrument from the perspective of economic fiqh. The data analysis technique in this study uses a qualitative analysis method that is descriptive-analytical. The researcher analyzed the suitability of the characteristics of cryptocurrency as a payment instrument with the principles of economic fiqh. A conceptual approach is used to explain the concept of cryptocurrency and a normative-theological approach is used to relate the concept to the provisions of Islamic law. The results of the study show that cryptocurrencies can, in principle, be recognized as objects of legal ownership ( maal mutaqawwam ) in Islam because they have value, can be owned and transferred. Cryptocurrencies can be used as a medium of exchange in Islam on a limited basis, if their use complies with Sharia principles: it is done transparently, free from speculation and usury, and supported by legitimate authorities and systems that guarantee clarity of value and security of transactions. This research emphasizes the importance of developing regulations that are adaptive to digital financial technology, as well as the need to emphasize transparency and avoidance of practices that are contrary to Islamic economic principles. Keywords: Cryptocurrency; Economic Fiqh; Payment Instrument.
The rapid evolution of the digital finance market, driven by the proliferation of cryptocurrencies, decentralized finance (DeFi), and financial technologies (fintech), has opened new investment opportunities while simultaneously presenting serious risks. These include high market volatility, cybersecurity vulnerabilities, and significant regulatory uncertainty. This paper explores the dual challenge of managing investment risks and building reliability within digital finance ecosystems. Using a mixed-methods approach combining a systematic literature review and qualitative expert interviews, the study examines key risk categories—technological, operational, behavioral, and institutional—and how they affect investor confidence. Findings show that the lack of standardized regulation, frequent security breaches, and insufficient investor education contribute to instability and distrust. In response, the paper proposes a multi-layered framework for mitigating these challenges through digital infrastructure enhancement, risk governance reforms, and financial literacy promotion. It also draws on International case studies to identify best practices applicable to emerging markets. The study contributes to academic and practical discourse by offering policy recommendations aimed at developing a more secure, transparent, and inclusive digital financial environment that aligns with sustainable investment goals.
Marc Bosonkie, Benito Kazenza, Rawlance Ndejjo, Marie‐Claire Muyer · 10 authors
Background: The rapid spread of COVID-19 forced governments to urgently implement non-pharmaceutical measures to stop the surge. These restrictions disrupted the provision of nutrition services. This study identified challenges faced by nutrition services using the six components of the health system and preventive strategies that can strengthen nutrition interventions during future outbreaks. Methods: A multiple-case qualitative study was carried out. Purposive sampling was used for recruitment of participants. 57 key informants were selected based on their role in the Nutrition sector at different levels of the health pyramid. The interview guide incorporated nutrition leadership, financing, workforce, infrastructure and commodities, service delivery and information system. Each topic had subtopics on challenges and adaptations. All transcripts were exported to Atlas Ti v22, and thematic analysis was conducted. Results: Initially excluded from the national COVID-19 response, nutrition services were later integrated through advocacy by the National Nutrition Program. Despite limited funding, the government maintained support, and health workers adapted with flexible staffing approaches. Commodity shortages, including Ready-to-Use Therapeutic Food, led to the use of locally produced substitutes. Movement restrictions and fear of infection disrupted essential services such as growth monitoring and immunization. To sustain access, mitigation strategies were implemented, including tailored education, modified weighing methods, and decentralized care. Key innovations included rapid registration with anthropometric protocols, additional service points for child health activities, double-weighing scales to reduce contact, crowd control during Growth Monitoring Promotion, community-based service delivery, and improved digital integration. Conclusions: COVID-19 disrupted all pillars of nutrition services in the DRC but also spurred innovation. Institutionalizing adaptive strategies, securing sustainable funding, and supporting local Ready-to-Use Therapeutic Food production are essential to strengthen resilience and ensure continuity of nutrition services in future health emergencies.
Susanna Levantesi, Gabriella Piscopo, Alba Roviello
Accurate estimation of cryptocurrency market volatility is crucial for investors. The Crypto Volatility Index (CVI) was developed to measure the market’s expectations for the 30-day implied volatility of Bitcoin and Ethereum to address the growing demand for reliable predictions. This study explores the relationship between the CVI and the volatility of traditional financial markets, including the Gold Volatility Index (GVZ), the Crude Oil Volatility Index (OVX), and the S&P500 Volatility Index (VIX). Three other variables are also analyzed: the USD to EUR exchange rate (USDEUR), the Federal Reserve interest rate (FED), and the NASDAQ index. The aim of the research is explanatory: the input variables and the CVI are observed contemporaneously to catch the complex relation between them. Using Pearson correlation, distance correlation, and mutual information, we demonstrate the presence of non-linear relationships between some variables in the dataset. Explanatory analysis is conducted using machine learning techniques, specifically the Random Forest (RF) algorithm and Gradient Boosting Machines (GBM) to account for these potential non-linear interactions. These methods are better suited than standard linear models for identifying complex relationships. In particular, the RF algorithm reaches a better level of accuracy than GBM and avoids overfitting.
The article studies the theoretical and methodological foundations of the functioning of cryptocurrency as an innovative financial instrument in the system of economic potential of an enterprise. The essence of cryptocurrency is revealed from the point of view of its role in the formation of financial resources, ensuring solvency and increasing the competitiveness of business entities. The author's definition of the term "cryptocurrency" is provided. The criteria for classifying cryptocurrencies have been expanded. The expediency of using cryptocurrencies in entrepreneurial activities in the context of digitalization of the economy has been substantiated. The analysis of the advantages and risks of integrating cryptocurrencies into the financial strategy of the enterprise is carried out, as well as methodological approaches to assessing their impact on the overall economic potential are proposed. The authors offer practical recommendations for effective management of cryptocurrency assets, taking into account the current regulatory framework and technological changes.
Isaac David, Liyi Zhou, Dawn Song, Arthur Gervais · 5 authors
The widespread lack of broad source code verification on blockchain explorers such as Etherscan, where despite 78,047,845 smart contracts deployed on Ethereum (as of May 26, 2025), a mere 767,520 (< 1%) are open source, presents a severe impediment to blockchain security. This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode, a fundamental research challenge with direct implications for identifying vulnerabilities and understanding malicious behavior. Prevailing decompilers struggle to reverse bytecode in a readable manner, often yielding convoluted code that critically hampers vulnerability analysis and thwarts efforts to dissect contract functionalities for security auditing. This paper addresses this challenge by introducing a pioneering decompilation pipeline that, for the first time, successfully leverages Large Language Models (LLMs) to transform Ethereum Virtual Machine (EVM) bytecode into human-readable and semantically faithful Solidity code. Our novel methodology first employs rigorous static program analysis to convert bytecode into a structured three-address code (TAC) representation. This intermediate representation then guides a Llama-3.2-3B model, specifically fine-tuned on a comprehensive dataset of 238,446 TAC-to-Solidity function pairs, to generate high-quality Solidity. This approach uniquely recovers meaningful variable names, intricate control flow, and precise function signatures. Our extensive empirical evaluation demonstrates a significant leap beyond traditional decompilers, achieving an average semantic similarity of 0.82 with original source and markedly superior readability. The practical viability and effectiveness of our research are demonstrated through its implementation in a publicly accessible system, available at https://evmdecompiler.com.
Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edge devices. Ensuring that edge devices correctly execute such unlearning operations is critical to maintaining integrity. In this work, we introduce a verification framework leveraging zero-knowledge proofs, specifically zk-SNARKs, to confirm data unlearning on personalized edge-device models without compromising privacy. We have developed algorithms explicitly designed to facilitate unlearning operations that are compatible with efficient zk-SNARK proof generation, ensuring minimal computational and memory overhead suitable for constrained edge environments. Furthermore, our approach carefully preserves personalized enhancements on edge devices, maintaining model performance post-unlearning. Our results affirm the practicality and effectiveness of this verification framework, demonstrating verifiable unlearning with minimal degradation in personalization-induced performance improvements. Our methodology ensures verifiable, privacy-preserving, and effective machine unlearning across edge devices.
Open access
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cs.LG
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Intelligent Tutoring Systems and Adaptive Learning
Lelio Campanile, Mauro Iacono, Michele Mastroianni, Christian Riccio
This paper presents a simulation-based methodology to evaluate the performance of a privacy-compliant edge–blockchain architecture for smart city environments. The proposed model combines edge computing with a private, permissioned blockchain to ensure low-latency processing, secure data management, and verifiable transactions. Using a discrete-event simulation framework, we analyze the behavior of the system under realistic workloads and time-varying traffic conditions. The model captures edge operations, including preprocessing and cryptographic tasks, as well as blockchain validation using Proof of Stake consensus. Several experiments explore saturation thresholds, resource utilization, and latency dynamics, under both synthetic and realistic traffic profiles. Results reveal how architectural bottlenecks shift depending on resource allocation and input rate, and demonstrate the importance of balanced dimensioning between edge and blockchain layers.
Cinthia Paola Pascual Cáceres, José Vicente Berná-Martínez, María Esther Almaral Martínez, Lucía Arnau Muñoz
This study introduces Fort2BCK, an advanced security framework designed to mitigate critical vulnerabilities in healthcare blockchain implementation, specifically data manipulation, unauthorised access and weaknesses in consensus protocols. Fort2BCK employs a dual verification mechanism, combining native consensus algorithm validation with the application of advanced cryptographic signatures (RSA, ECDSA and zero knowledge proofs, ZKPs), thus providing an additional layer of authentication, auditing and resistance to malicious attacks. In contrast to traditional approaches, Fort2BCK significantly reduces the risks of fraud and forgery by independently cryptographically verifying each block before it is integrated into the blockchain, strengthening security in scenarios where conventional consensus models may be vulnerable. In addition, its interoperability with multiple blockchain architectures, including proof of work (PoW), proof of stake (PoS) and delegated proof of stake (DPoS), allows it to effectively mitigate attacks such as the 51% attack in PoW and the nothing-at-stake problem in PoS, through an integrated external validation layer. To evaluate the effectiveness of Fort2BCK, experiments were conducted on a simulated hybrid blockchain network with 100 nodes and 50,000 transactions. The results revealed that Fort2BCK increases security by 35% against block rewrite attacks and decreases the rate of fraudulent transactions by 42%, compared to conventional blockchain systems, while maintaining a computational overhead of less than 8%. Additionally, Fort2BCK ensures compliance with regulations such as HIPAA and GDPR, ensuring that blockchain systems for the healthcare sector meet legal and privacy requirements. These findings demonstrate that Fort2BCK optimises the security, scalability and privacy of medical blockchains, facilitating the secure digitisation of healthcare systems and strengthening trust in clinical data management.
Human genetic data, crucial for advancing personalized medicine, requires secure and privacy-preserving management solutions. Traditional approaches face challenges in scalability, security, and decentralized access control. This study proposes a blockchain-based framework leveraging Thirdweb and Ethereum smart contracts to address these issues. The framework integrates decentralized storage via IPFS for cost-efficient off-chain genetic data storage, while on-chain smart contracts manage access control, encryption, and audit trails. Utilizing Solidity for smart contract development, the system ensures role-based permissions, wallet-based authentication, and immutable transaction logging. Genetic data in FASTA format, sourced from NCBI, is encrypted and linked to IPFS hashes stored on the blockchain. The architecture supports dual interfaces—command-line for developers and a Thirdweb dashboard for end-users—enabling secure data upload, access, and monitoring. Testing demonstrated functional efficacy in data integrity, access verification, and audit capabilities. Results highlight the system’s ability to enhance privacy, eliminate intermediaries, and provide transparent data governance. The integration of Thirdweb further decentralizes operations, aligning with Web 3.0 principles. Key contributions include a scalable model for genetic data sharing, a customizable smart contract template, and a user-centric design. Future work should explore advanced encryption, real-world healthcare integration, and performance optimization under high-throughput conditions. This research bridges biotechnology and blockchain, offering a robust foundation for secure genomic data ecosystems.
Guy Goren, Andrew Hariri, Timothy D. R. Hartley, Ravi Kappiyoor · 6 authors
Existing decentralized storage protocols fall short of the service required by real-world applications. Their throughput, latency, cost-effectiveness, and availability are insufficient for demanding workloads such as video streaming, large-scale data analytics, or AI training. As a result, Web3 data-intensive applications are predominantly dependent on centralized infrastructure. Shelby is a high-performance decentralized storage protocol designed to meet demanding needs. It achieves fast, reliable access to large volumes of data while preserving decentralization guarantees. The architecture reflects lessons from Web2 systems: it separates control and data planes, uses erasure coding with low replication overhead and minimal repair bandwidth, and operates over a dedicated backbone connecting RPC and storage nodes. Reads are paid, which incentivizes good performance. Shelby also introduces a novel auditing protocol that provides strong cryptoeconomic guarantees without compromising performance, a common limitation of other decentralized solutions. The result is a decentralized system that brings Web2-grade performance to production-scale, read-intensive Web3 applications.
This report examines methods for selectively utilizing specific technologies and principles of Decentralized Autonomous Organizations (DAOs) to enhance the efficiency and effectiveness of International Development Cooperation (IDC) projects. Based on the assessment that the full-scale adoption of a DAO is currently unrealistic and entails significant risks in the IDC environment, this study focuses on the partial application of core elements such as the transparency of blockchain, the automation of smart contracts, and immutable record-keeping. This approach aligns with the recent trend where DAOs are gaining attention for their potential to strengthen social impact and improve the inefficiencies and transparency of existing systems in the development cooperation sector (Aaltonen et al., 2024; Staszczak et al., 2024). The report introduces the potential of DAO elements to contribute to improving transparency and efficiency in key management areas across the project cycle. Among these, it deeply explores methods for securing data reliability and enhancing traceability in the field of Monitoring, Evaluation, and Reporting (MER). It analyzes how the immutable record-keeping characteristic of blockchain can compensate for the vulnerabilities of existing centralized systems and enhance the integrity of specific types of MER data (inputs, activities, verifiable outputs). The potential for pre-validating data and enabling controlled data dissemination using smart contracts is also presented. While MER can serve as a core verification foundation for Outcome-Based Pricing (OBP) models, the application of DAO elements in this area still faces significant challenges, including the oracle problem, data quality limitations, and difficulties in measuring outcomes and impacts. Although blockchain can contribute to recording M&E data in a tamper-proof manner and enhancing transparency and traceability (Everconnect, 2024), ensuring the reliability of oracles that bring real-world performance data onto the blockchain remains a key challenge (GoLab, BSG, University of Oxford, 2023). Furthermore, this report explores the use of tokenomics for the specific purpose of project resource mobilization. While this holds the potential to increase the transparency of fundraising and financial management, the report discusses in depth that in the existing IDC project environment, it faces much higher and more complex challenges. These include legal and regulatory uncertainties, market volatility and speculative risks, the difficulty of designing complex tokenomics, the risk of governance conflicts, and the potential to deepen the digital divide. In particular, if an outcome-linked token model is conceived by combining Outcome-Based Pricing (OBP) with tokenomics, it is highly likely to have the nature of profit distribution, making the issue of its classification as a security severe. Moreover, as seen in the case of the Central African Republic's meme coin (CAR Meme), even tokens issued under the pretext of development goals can exhibit extreme market volatility and be exposed to speculative risks despite disclaimers of intrinsic value, supporting a cautious approach to using tokenomics. While tokenomics for development finance can be linked to achieving SDGs or financing regenerative projects (Staszczak et al., 2024), poorly designed tokenomics can lead to project failure (Kivilo et al., 2025), requiring careful design to prevent speculation and ensure long-term utility. Therefore, the use of tokenomics for resource mobilization currently warrants an extremely cautious and limited approach, suitable for research and small-scale experimental stages. The successful introduction of DAO elements depends less on the technology itself and more on a deep analysis of each project's specific context and a problem-solving-oriented approach. A strategy is needed that starts with relatively low-risk areas, such as enhancing data reliability in the MER field, to learn incrementally. For high-risk areas like tokenomics, a strategy of responsible exploration with the utmost priority on legal and ethical considerations is necessary. This report presents strategic considerations and recommendations for development cooperation agencies like KOICA to utilize the potential of these DAO elements responsibly and effectively.
The YulCode dataset presents a comprehensive collection of 348,840 Yul-based smart contract instances, comprising approximately 135,013 unique contracts. These contracts were generated through the compilation of Solidity source files that have been deployed on the Ethereum mainnet, making the dataset directly representative of real-world decentralized applications. YulCode provides a rich foundation for a variety of research and development tasks, including but not limited to machine learning applications, formal verification, optimization analysis, and software engineering tool evaluation in the context of low-level smart contract code. To the best of our knowledge at the time of writing, YulCode is the first and only publicly available dataset that focuses specifically on Yul, an intermediate language designed for the Ethereum Virtual Machine (EVM). As such, it fills a critical gap in the current ecosystem of smart contract datasets and opens new avenues for research and tooling aimed at low-level contract analysis and generation.
High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are (1) labor-intensive and error-prone annotation processes limiting the scale, quality, and evolution of the dataset, and (2) absence of standardized classification rules results in inconsistent vulnerability categories and labeling results across different datasets. To address these limitations, we present FORGE, the first automated approach for constructing smart contract vulnerability datasets. FORGE leverages an LLM-driven pipeline to extract high-quality vulnerabilities from real-world audit reports and classify them according to the CWE, the most widely recognized classification in software security. FORGE employs a divide-and-conquer strategy to extract structured and self-contained vulnerability information from these reports. Additionally, it uses a tree-of-thoughts technique to classify the vulnerability information into the hierarchical CWE classification. To evaluate FORGE's effectiveness, we run FORGE on 6,454 real-world audit reports and generate a dataset comprising 81,390 solidity files and 27,497 vulnerability findings across 296 CWE categories. Manual assessment of the dataset demonstrates high extraction precision and classification consistency with human experts (precision of 95.6% and inter-rater agreement k-$α$ of 0.87). We further validate the practicality of our dataset by benchmarking 13 existing security tools on our dataset. The results reveal the significant limitations in current detection capabilities. Furthermore, by analyzing the severity-frequency distribution patterns through a unified CWE perspective in our dataset, we highlight inconsistency between current smart contract research focus and priorities identified from real-world vulnerabilities...
Rug pull scams have emerged as a persistent threat to cryptocurrency, causing significant financial losses. A typical scenario involves scammers deploying honeypot contracts to attract investments, restricting token sales, and draining the funds, which leaves investors with worthless tokens. Current methods either rely on predefined patterns to detect code risks or utilize statistical transaction data to train detection models. However, real-world Rug Pull schemes often involve a complex interplay between malicious code and suspicious transaction behaviors. These methods, which solely focus on one aspect, fall short in detecting such schemes effectively. In this paper, we propose RPHunter, a novel technique that integrates code and transaction for Rug Pull detection. First, RPHunter establishes declarative rules and performs flow analysis to extract code risk information, further constructing a semantic risk code graph (SRCG). Meanwhile, to leverage transaction information, RPHunter formulates dynamic token transaction activities as a token flow behavior graph (TFBG) in which nodes and edges are characterized from network structure and market manipulation perspectives. Finally, RPHunter employs graph neural networks to extract complementary features from SRCG and TFBG, integrating them through an attention fusion model to enhance the detection of Rug Pull. We manually analyzed 645 Rug Pull incidents from code and transaction aspects and constructed a ground-truth dataset. We evaluated RPHunter on our dataset, achieving a precision of 95.3%, a recall of 93.8% and an F1 score of 94.5%, which highlights superior performance compared to existing methods. Furthermore, when applied to the real-world scenarios, RPHunter has identified 4801 Rug Pull tokens, achieving a precision of 90.7%.
The fact that blockchain technology is decentralized, transparent, and immutable is transforming the face of such industries as finance, healthcare, and logistics. It is nonetheless, difficult in regulatory compliance, data privacy, and law enforcement, specifically blockchain forensics. Blockchain forensics is an activity of tracking transactions and members of illegal organizations like money laundering and cybercrime. Whereas the traceability of the blockchain technology with the transparency it possesses raises no more concerns on the legal issues, the pseudonymity of its participants, on the other hand, makes it quite difficult to identify them, respectively, creating issues within the scope of the data protection, as well as financial regulations. The paper will touch on the practice today of forensics, the regulation and morality of the balance that is there between privacy and criminal investigation. It ends with suggestions of a joint effort in creation of efficient legal frameworks to govern the same, and promotion of innovation.
The increasing demand for clean and reliable energy has driven the adoption of decentralized renewable power generation systems. Integrating Internet of Things (IoT) and blockchain technology can enhance efficiency, transparency, and security in distributed energy networks. IoT enables real-time monitoring and control of renewable energy sources (such as solar, wind, and microgrids), while blockchain ensures tamper-proof energy transactions, peer-to-peer (P2P) energy trading, and automated smart contracts. This study explores a decentralized energy framework where IoT devices collect data on energy production, consumption, and grid stability, while blockchain facilitates trustless energy exchanges among prosumers (producer-consumers). The proposed system eliminates intermediaries, reduces costs, and improves grid resilience by leveraging smart meters, distributed ledgers, and consensus algorithms. The future scope of power generation is based on Decentralized power generation depends on Renewable energy sources (solar, wind). By using decentralized power generation, we can be able to achieve bidirectional power flow, one can able to transmission as well as receiving electrical power. This new concept introduces blockchain technology in Distributed Generation for monitoring and recording energy transactions between two peers. These Peer-to-Peer energy transactions are done in the DC Microgrid using blockchain Technology with smart contracts for energy trading.
Rodrigo Gonçalves Bueno, André Luiz De Souza Carneiro, João Paulo Aragão Pereira
The increasing adoption of tokenized assets, Decentralized Finance (DeFi) applications, and the exploration of Central Bank Digital Currencies (CBDCs) necessitate sophisticated security architectures for Regulated Tokenized Multi-asset Networks (RTMNs). This paper addresses the complex interplay between privacy, and composability within these emerging decentralized financial ecosystems. It is argued that conventional security paradigms, predominantly reliant on perimeter defenses, are insufficient for the distributed and interconnected nature of DeFi infrastructures. While Zero Trust models offer relevant principles, their direct application within regulated, high-performance financial networks, particularly those involving CBDCs or complex DeFi protocols, presents significant challenges regarding compliance and efficiency. This paper introduces a novel framework meticulously designed to support diverse RTMN use cases, including retail/wholesale CBDC, tokenized deposit, stablecoin and multi-asset platforms operating within a DeFi context. A foundational element of this framework is the implementation of cryptographically enforced information compartmentalization. This ensures that each architectural component operates with the minimum necessary information required for its specific function, inherently embedding privacy-by-design and preventing unauthorized access to comprehensive network or transactional data. The proposed framework is architected to guarantee critical properties essential for robust distributed and decentralized systems: (1) Atomicity of transactions; (2) Composability and Programmability; (3) Settlement Finality, providing transaction immutability; (4) Enhanced Privacy and Security, leveraging cryptographic techniques; (5) Support for Distribution and Decentralization; (6) Performance, addressing throughput and latency demands; and (7) Continuous monitoring and auditing, enabling regulatory oversight without compromising user data. It is provided a detailed analysis of the framework's application across distinct RTMN implementations, identifying specific technical challenges and opportunities within the context of tokenized systems. Furthermore, the paper presents a qualitative and functional evaluation of the framework's characteristics applied to the use case of tokenizing Federal Government Securities in an RTMN, such as Drex. Fundamentally, the inherent trade-offs between cryptographic privacy guarantees, compartmentalization, and programmability are examined, exploring optimization strategies relevant to demanding DeFi and institutional applications, such as decentralized trade finance and tokenized debt instruments.
In today's digital era, technological advances have brought major changes in various fields, such as the creative economy. The emergence of crowdfunding platforms and Non-Fungible Tokens (NFTs) as creativeoptions for creative funding is one of the latest developments. Artists, musicians, and other creators have seen how they advertise their work by using NFTs which are unique asset holdings on the blockchain. Incontrast, crowdfunding platforms like Patreon and Kickstarter allow creators to get funding directly from their fans without using conventional intermediaries. The purpose of this research is to find the problems and prospects faced by investors and creators when using NFTs and crowdfunding. Qualitative and quantitative methods were used, with case studies and secondary data analysis. The results show that the main challenges to be faced include legal and regulatory uncertainty, marketvolatility, copyright infringement, digital divide, high transaction costs, and environmental impact. Uncertainty regarding ownership rights and consumer protection is caused by regulatory uncertainty.Both creators and investors face significant risks due to the volatility of the NFT market. The case of plagiarism in NFTs shows that copyright must be strengthened. Some creators cannot use this technology due to the limitations of digital technology. A more environmentally friendly solution is also needed due to the high transaction fees and the impact of the Ethereum blockchain on the environment. In addition, many creators still have difficulty maintaining crowdfunding funding.
Rabbia Basharat, Asma Javaid, Ishrak Alim, Ameer Hamza Khan · 5 authors
Blockchain technology has become an innovative driving force in the digital world and can provide decentralized, open, and safe solutions to various industries. It was firstly created as a foundation of cryptocurrencies but now has a wide range of uses in the realm of finance, health care, supply chains, governance, and cybersecurity. This paper discusses how blockchain could be used to facilitate efficiency, minimize fraud, and promote trust due to innovations such as smart contracts, decentralized finance (DeFi) and tamper-proof record keeping. The widespread usage, however, is associated with numerous barriers, such as the uncertainty about the regulations, high costs of implementation, limitations in scalability, and a lack of technical skills. Professionals in the industry provide an indecisive answer on the survey with half of those surveyed suggesting that it may become mainstream by 2030, 20 percent lacks confidence and 30 percent are unsure. Industry inspection indicates finance to be the closest opportunity with subsequent respective opportunities to healthcare and supply chain, whereas issues such as interoperability and energy consumption are also recognized as current challenges. The study encourages stakeholders (governments, businesses, and developers) to collaborate to overcome adoptions barriers using regulatory perfectness, affordable solutions, and skilled labour force. With these obstacles overcome, blockchain will be able to achieve its potential to transform industry and make the digital world more secured, efficient, and equitable.
In this note, we make a comparison between a novel machine learning method, Long Short-Term Memory (LSTM), and two trading strategies using technical analysis: Exponential Moving Average (EMA) crossing and Moving Average Convergence/Divergence with Average Directional Index (MACD+ADX). The purpose is to use trading signals to maximize profits in the Bitcoin digital commodity. The comparison was motivated by the approval of the first spot Bitcoin exchange-traded funds (ETFs) by the U.S. Securities and Exchange Commission (SEC) on January 9, 2024. The results show that the LSTM algorithm delivers a cumulative return of approximately 65.23% over a testing period of less than nine months, significantly outperforming both the EMA and MACD+ADX strategies, as well as the baseline buy-and-hold approach typically followed by fundamental investors. Our work highlights the potential for further integration between machine learning and technical analysis in the evolving landscape of cryptocurrency markets.
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
Smart contracts have transformed decentralized finance by enabling programmable, trustless transactions. However, their widespread adoption and growing financial significance have attracted persistent and sophisticated threats, such as phishing campaigns and contract-level exploits. Traditional transaction-based threat detection methods often expose sensitive user data and interactions, raising privacy and security concerns. In response, static bytecode analysis has emerged as a proactive mitigation strategy, identifying malicious contracts before they execute harmful actions. Building on this approach, we introduced PhishingHook, the first machine-learning-based framework for detecting phishing activities in smart contracts via static bytecode and opcode analysis, achieving approximately 90% detection accuracy. Nevertheless, two pressing challenges remain: (1) the increasing use of sophisticated bytecode obfuscation techniques designed to evade static analysis, and (2) the heterogeneity of blockchain environments requiring platform-agnostic solutions. This paper presents a vision for ScamDetect (Smart Contract Agnostic Malware Detector), a robust, modular, and platform-agnostic framework for smart contract malware detection. Over the next 2.5 years, ScamDetect will evolve in two stages: first, by tackling obfuscated Ethereum Virtual Machine (EVM) bytecode through graph neural network (GNN) analysis of control flow graphs (CFGs), leveraging GNNs' ability to capture complex structural patterns beyond opcode sequences; and second, by generalizing detection capabilities to emerging runtimes such as WASM. ScamDetect aims to enable proactive, scalable security for the future of decentralized ecosystems.
This study investigates labor relations within MakerDAO, a Decentralized Autonomous Organization (DAO), to understand how decentralized governance impacts organizational structure and worker interactions.The research employs a Grounded Theory methodology, combining voting analysis, document analysis, and content analysis.The quantitative analysis examines MakerDAO's voting data, assessing participation and power concentration.Document analysis explores governance structures through Maker Improvement Proposals (MIPs), while content analysis evaluates interactions in MakerDAO's forum.The findings reveal that, despite decentralization, challenges persist in power distribution and collective decision-making.This study contributes to understanding work in DAOs and its implications for corporate governance.