The Carbon platform introduces an innovative paradigm in carbon emissions management by integrating advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Blockchain. This decentralized system automates the processes of carbon credit issuance, verification, and trading, leveraging smart contracts and IoT sensors to ensure transparency and accuracy. Furthermore, by utilizing a Decentralized Autonomous Organization (DAO) structure, the platform enhances stakeholder involvement through community-driven governance, thereby elevating the standards for carbon credit management.
Finance, supply chain, and decentralized applications are some of the industries that have undergone a revolution in relation to blockchain technology, and smart contracts are at the center of this revolution. Smart contracts are computer protocols that are programmed on blockchain systems and which allow transparency, immutability, and decentralization. Nonetheless, governance in a blockchain is a problem area, because the conventional centralized systems are inconsistent with its decentralised characteristic. This article discusses self-governance of blockchain whereby decision making is computerized using smart contracts to achieve decentralized regulations. It reviews the prevailing conditions in blockchain governance, issues and the way smart contracts would enhance transparency, efficiency and security. Also provided in the study are the advantages and drawbacks of decentralized governance, which includes issues of scalability and security, and the möbius strip connection between autonomous governance and blockchain platforms. Moreover, it assesses the place of decentralized autonomous organizations (DAOs) in blockchain governance and the issues of their implementation.
This study examines how firms in the Pokémon Trading Card Game (PTCG) grading industry adapt their business models in response to digital disruption. We employ a qualitative multiple-case design, investigating three leading grading companies – PSA (United States), CCIC (China), and SQC (Thailand) – through 30 in-depth interviews and supplemental document analysis. The findings reveal divergent strategies shaped by both dynamic capabilities and institutional contexts. PSA leverages scale and AI technology to enhance efficiency, CCIC focuses on legitimacy and incremental improvements under regulatory constraints, and SQC pursues exploratory digital initiatives (e.g., NFT-linked trials) to co- create value with its community. These patterns highlight the ambidexterity required for business model innovation in a digitizing niche service sector. The study contributes to business model innovation and digital transformation literature by demonstrating how national institutions and customer engagement influence innovation paths. Practical implications include lessons for balancing core business sustainability with transformative innovation in different regulatory environments.
This work presents a novel decentralized protocol for digital estate planning that integrates advances distributed computing, and cryptography. The original proof-of-concept was constructed using purely solidity contracts. Since then, we have enhanced the implementation into a layer-1 protocol that uses modern interchain communication to connect several heterogeneous chain types. A key contribution of this research is the implementation of several modern cryptographic primitives to support various forms of claims for information validation. These primitives introduce an unmatched level of privacy to the process of digital inheritance. We also demonstrate on a set of heterogeneous smart contracts, following the same spec, on each chain to serve as entry points, gateways, or bridge contracts that are invoked via a path from the will module on our protocol, to the contract. This ensures a fair and secure distribution of digital assets in accordance with the wishes of the decedent without the requirement of moving their funds. This research further extends its innovations with a user interaction model, featuring a check-in system and account abstraction process, which enhances flexibility and user-friendliness without compromising on security. By developing a dedicated permissionless blockchain that is secured by a network of validators, and interchain relayers, the proposed protocol signifies a transformation in the digital estate planning industry and illustrates the potential of blockchain technology in revolutionizing traditional legal and personal spheres. Implementing a cryptoeconomic network at the core of inheritance planning allows for unique incentive compatible economic mechanisms to be constructed.
Programmable blockchains have long been a hot research topic given their tremendous use in decentralized applications. Smart contracts, using blockchains as their underlying technology, inherit the desired properties such as verifiability, immutability, and transparency, which make it a great suit in trustless environments. In this thesis, we consider several decentralized protocols to be built on blockchains, specifically using smart contracts on Ethereum. We used algorithmic and cryptographic tools in our implementations to further improve the level of security and efficiency beyond the state-of-the-art works. We proposed a new approach called Blind Vote, which is an untraceable, secure, efficient, secrecy-preserving, and fully on-chain electronic voting protocol based on the well-known concept of Chaum's blind signatures. We illustrate that our approach achieves the same security guarantees as previous methods such as Tornado Vote [1], while consuming significantly less gas. Thus, we provide a cheaper and considerably more gas-efficient alternative for anonymous blockchain-based voting. On the other hand, we propose a new family of algorithms for private, trustless auctions that protect bidder identities and bid values while remaining practical for smart contract execution. We ensure trustlessness by running the auction logic in a smart contract, thereby eliminating reliance on any single trusted party. This approach prevents bid tampering, front-running, and collusion by enforcing immutability and decentralized verification of bids. The resulting protocol uniquely combines efficiency, trustlessness, and enduring bid privacy, offering a scalable and secure solution for blockchain-based marketplaces and other decentralized applications.
Against the background of the digital economy, the conventional matching strategy of supply and demand is unable to meet the requirements of matching supply and demand industrial interconnection with digitalization as the core element. To solve the trust problem of supply and demand matching contract of digital industrial interconnection, a decision-making framework of supply and demand matching of industrial interconnection based on smart contract is proposed. Based on the massive resource data, a resource characteristic attribute matrix is constructed, and the Fuzzy C-Means algorithm is used to classify and reduce the dimension of massive resource data. Based on the attribute expectation of supply and demand, the objective function with maximum satisfaction is created to realise bidirectional matching. Finally, the effectiveness of the proposed method is demonstrated by an example.
The increasing integration of blockchain technology in supply chains has brought about significant challenges due to the volatility of cryptocurrencies, as it has become an essential aspect of customers’ risk considerations. This study addresses the problem of managing supply chain operations amid such volatility, focusing specifically on pricing, advertising, manufacturer subsidy, and cybersecurity strategies within a manufacturer-retailer framework involving two cryptocurrency-based retailers that have higher market capitalisation compared to others: Ethereum and Bitcoin. The proposed solution employs game theory – a simultaneous game and two Stackelberg games with either retailer as the leader – to identify optimal strategies based on the corresponding parameter values. Accordingly, the study uniquely delivers blockchain-related risks by applying game theory to analyze the decision variables, providing insights into competitive pricing adjustments and leadership strategies for the cryptocurrency-based retailers under varying volatility levels. Results demonstrate that retailer pricing strategies must adapt to changes in wholesale prices and to the difference in cryptocurrency volatility. It also identifies crucial subsidy levels for manufacturers and optimal strategies for retailers under different volatility conditions to sustain profitability and demand.
Cryptocurrencies, powered by blockchain technology, have emerged as a transformative force in global finance, offering alternatives to traditional financial systems by enabling decentralized, secure, and efficient transactions. This paper explores the potential of cryptocurrencies to shape the future of global finance, with a focus on their mainstream adoption, integration with traditional financial systems, and the development of Central Bank Digital Currencies (CBDCs). The paper examines how cryptocurrencies could become more widely accepted by governments, businesses, and consumers, and discusses the role of fintech companies and traditional financial institutions in incorporating these digital assets into existing financial frameworks. Additionally, it analyzes the promise of CBDCs as government-backed alternatives to decentralized cryptocurrencies and the technological advancements required to address scalability and environmental concerns. Despite their potential, cryptocurrencies face significant challenges, including regulatory uncertainty, scalability issues, environmental impact, and public perception. These barriers hinder the widespread adoption of cryptocurrencies, but ongoing innovation, coupled with clearer regulations and public education, could pave the way for broader integration into global finance. This study concludes that while the future of cryptocurrencies holds substantial promise, overcoming these challenges is critical to realizing their potential in transforming financial systems and increasing financial inclusion worldwide. Keywords: Cryptocurrencies, Blokchain Technology, Virtual Finance, Global Finance, Financial System
The escalating complexity and frequency of malware attacks pose a significant challenge to conventional cybersecurity frameworks, particularly in scenarios demanding high data privacy and cross-organizational threat intelligence sharing. Traditional centralized machine learning models for malware detection often rely on aggregating data in a central server, thereby increasing the risk of data breaches and limiting the deployment of models in privacy-sensitive environments such as healthcare, finance, and critical infrastructure. To address these limitations, this study explores an integrated approach that combines Federated Learning (FL) with Explainable Artificial Intelligence (XAI) for enhancing malware detection while preserving user privacy and system confidentiality. Federated learning enables the collaborative training of robust malware classifiers across multiple decentralized nodes without sharing raw data, thus maintaining local data sovereignty and complying with data protection regulations. The proposed framework incorporates deep learning architectures such as convolutional neural networks (CNNs) trained in a federated environment using feature vectors extracted from malicious binaries and behavior logs. To ensure transparency and trust in model predictions, explainable AI techniques specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are integrated, providing actionable insights into the model’s decision-making process. This study also presents a comprehensive evaluation using a benchmark malware dataset distributed across simulated client environments, measuring detection accuracy, communication overhead, privacy leakage, and interpretability performance. Results demonstrate that the FL-XAI approach achieves detection rates comparable to centralized models while ensuring data confidentiality and interpretability. The research contributes to the evolving field of privacy-preserving threat intelligence by offering a scalable and explainable framework suitable for real-time cybersecurity applications.
Smart cities present a transformative paradigm for urban development, yet securing sustainable financing remains a critical challenge. While traditional funding mechanisms struggle with scalability limitations, FinTech innovations like Initial Coin Offerings (ICOs) have emerged as a viable alternative. Leveraging blockchain technology, ICOs enable decentralized capital raising through token sales, offering transparency and global investor access. However, their effectiveness is compromised by market volatility, information asymmetry, and the absence of reliable predictive frameworks. This study addresses these limitations by developing an explainable hybrid machine learning model that combines: (1) Light Gradient Boosting Machine (LGBM) for efficient feature selection through histogram-based learning, (2) Optuna-optimized Extremely Randomized Trees regression that mitigates overfitting via enhanced randomization while excelling with noisy financial data, and (3) interpretability tools including SHAP values and feature importance analysis. Optuna's automated hyperparameter optimization further enhances computational efficiency, enabling robust predictions of post-ICO returns. The proposed model demonstrates superior predictive performance (R²=0.814, MSE=0.005, MAE=0.051), significantly outperforming both linear regression and state-of-the-art ML models. Key findings identify token supply (63% predictive power) as negatively correlated with returns - reflecting dilution effects and investor perceptions of scarcity- while fundraising success (15%) and Bitcoin returns (8%) show positive influences. These results provide practical guidance for investors and regulators, while establishing ICOs as a potential sustainable financing mechanism for smart city initiatives. The study contributes both methodologically through its optimized hybrid architecture and practically by enhancing decision-making in blockchain-based urban development financing.
Although the metaverse is still in its early exploratory stage in the field of nursing, it is gradually demonstrating its potential in digital health and telemedicine care, transforming the traditional nurse-patient interaction model through digital tools and the internet. The "metaverse" represents the merging of two worlds into an immersive, online, virtual, connected environment in which participants actively engage with 3D content and interact using digital avatars. The metaverse entails many possibilities and challenges in attempts to introduce new methods of nursing. This technology has many applications, particularly with respect to assisting in surgery, enhancing chronic disease management, reshaping nursing education, promoting telemedicine, and facilitating psychological interventions. While obstacles may be encountered in various areas, such as trust and security, technology, legislation and regulation, the use of non-fungible tokens as a secure asset for patient data is a potential solution to these issues.
Azmat Ullah, Maria Ilaria Lunesu, Lodovica Marchesi, Roberto Tonelli
This paper presents a blockchain-based Internet of Things (IoT) system for monitoring pizza production in restaurants. IoT devices track temperature and humidity in real-time, while blockchain ensures secure and tamper-proof data. A Raspberry Pi processes sensor data, captures images, triggers alerts, and interacts with smart contracts. The system detects abnormal conditions, enabling quick responses. Blockchain adds transparency and traceability, supporting compliance and audits. Experiments show improved ingredient management, reduced waste, and increased kitchen efficiency.
The CAP theorem asserts a trilemma between consistency, availability, and partition tolerance. This paper introduces a rigorous automata-theoretic and economically grounded framework that reframes the CAP trade-off as a constraint optimization problem. We model distributed systems as partition-aware state machines and embed economic incentive layers to stabilize consensus behavior across adversarially partitioned networks. By incorporating game-theoretic mechanisms into the global transition semantics, we define provable bounds on convergence, liveness, and correctness. Our results demonstrate that availability and consistency can be simultaneously preserved within bounded epsilon margins, effectively extending the classical CAP limits through formal economic control.
Immutability is a core design goal of permissionless public blockchain systems. However, rewrites are more common than is normally understood, and the risk of rewrite, cyberattack, exploit, or black swan event is also high. Taking the position that strict immutability is neither possible on these networks nor the observed reality, this paper uses thematic analysis of node operator interviews to examine the limits of immutability in light of rewrite events. The end result is a qualitative definition of the conditional immutability found on these networks, which we call Practical Immutability. This is immutability contingent on the legitimate governance demands of the network, where network stakeholders place their trust in the governance topology of a network to lend it legitimacy, and thus manage ledger state.
The emerging Web3 has great potential to provide worldwide decentralized services powered by global-range data-driven networks in the future. To ensure the security of Web3 services among diverse user entities, a decentralized identity (DID) system is essential. Especially, a user's access request to Web3 services can be treated as a DID transaction within the blockchain, executed through a consensus mechanism. However, a critical implementation issue arises in the current Web3, i.e., how to deploy network nodes to serve users on a global scale. To address this issue, emerging Low Earth Orbit (LEO) satellite communication systems, such as Starlink, offer a promising solution. With their global coverage and high reliability, these communication satellites can complement terrestrial networks as Web3 deployment infrastructures. In this case, this paper develops three hybrid satellite-ground modes to deploy the blockchain-enabled DID system for Web3 users. Three modes integrate ground nodes and satellites to provide flexible and continuous DID services for worldwide users. Meanwhile, to evaluate the effectiveness of the present hybrid deployment modes, we analyze the complete DID consensus performance of blockchain on three hybrid satellite-ground modes. Moreover, we conduct numerical and simulation experiments to verify the effectiveness of three hybrid satellite-ground modes. The impacts of various system parameters are thoroughly analyzed, providing valuable insights for implementing the worldwide Web3 DID system in real-world network environments.
Construct the first provably secure linear homomorphic ring signature scheme. Ring signatures allow a signer to anonymously sign a message on behalf of a user group (ring) and are widely applied in areas such as identity protection, electronic voting, and privacy enhancement in blockchain. Homomorphic signatures, on the other hand, support verifiable computations on signed data. The integration of anonymity and computability in homomorphic ring signatures holds the potential to create new application scenarios for privacy-preserving distributed systems. It is worth noting that Choi and Kim first introduced the concept of linear homomorphic ring signatures in 2017 and proposed a specific scheme. However, their scheme lacks a complete security proof, leaving its security theoretically unconfirmed. To address this research gap, this paper presents the first provably secure lattice-based linear homomorphic ring signature scheme, designed for scenarios where the ring size is O(log n). This scheme not only combines the anonymity of ring signatures with the malleability of homomorphic signatures but also achieves resistance against quantum attacks.
Gesa Bimantara, Tri Astuti Handayani, M. Aqiel Alam
This study examines the legal status and implications of Bitcoin ownership as a medium of exchange within Indonesia's evolving digital financial system. In light of persistent regulatory ambiguity, the research seeks to understand how legal uncertainty shapes the use and recognition of Bitcoin as a transactional asset. Employing a qualitative socio-legal approach, the study integrates doctrinal legal analysis with empirical findings obtained from in-depth interviews with cryptocurrency users, legal scholars, and financial regulators. The findings reveal that Bitcoin ownership in Indonesia lacks formal legal recognition, as there is no existing state-sanctioned registration system or institutional mechanism to validate cryptographic ownership. Instead, the private key remains the only accepted evidence of control and possession, creating a decentralized system of ownership that operates independently from conventional legal doctrines. Despite regulatory restrictions, Bitcoin continues to be used in informal peer-to-peer transactions, primarily driven by user preferences for privacy, decentralization, and efficiency. This disconnect between legal structures and technological realities generates vulnerabilities for users, particularly in cases involving fraud, inheritance, taxation, or contractual disputes, where no formal recourse exists. The research concludes that Indonesia’s legal framework remains ill-equipped to handle the complexities of decentralized financial assets, posing challenges to legal enforceability and consumer protection. The study recommends the establishment of a voluntary, state-recognized digital asset registration system, along with capacity-building initiatives for regulators. These measures aim to enhance legal certainty, bridge institutional gaps, and support the integration of blockchain-based assets into Indonesia’s formal financial and legal ecosystem.
The rapid integration of AI into IoT systems has outpaced the ability to explain and audit automated decisions, resulting in a serious transparency gap. We address this challenge by proposing a blockchain-based framework to create immutable audit trails of AI-driven IoT decisions. In our approach, each AI inference comprising key inputs, model ID, and output is logged to a permissioned blockchain ledger, ensuring that every decision is traceable and auditable. IoT devices and edge gateways submit cryptographically signed decision records via smart contracts, resulting in an immutable, timestamped log that is tamper-resistant. This decentralized approach guarantees non-repudiation and data integrity while balancing transparency with privacy (e.g., hashing personal data on-chain) to meet data protection norms. Our design aligns with emerging regulations, such as the EU AI Act’s logging mandate and GDPR’s transparency requirements. We demonstrate the framework’s applicability in two domains: healthcare IoT (logging diagnostic AI alerts for accountability) and industrial IoT (tracking autonomous control actions), showing its generalizability to high-stakes environments. Our contributions include the following: (1) a novel architecture for AI decision provenance in IoT, (2) a blockchain-based design to securely record AI decision-making processes, and (3) a simulation informed performance assessment based on projected metrics (throughput, latency, and storage) to assess the approach’s feasibility. By providing a reliable immutable audit trail for AI in IoT, our framework enhances transparency and trust in autonomous systems and offers a much-needed mechanism for auditable AI under increasing regulatory scrutiny.
Rayane Farias dos Santos, César Augusto Tibúrcio Silva
Purpose: The study analyzed the relationship between investor sentiment and the return and trading volume of the main cryptocurrencies in Brazil during the COVID-19 pandemic. Methodology: Two metrics were used to capture investor sentiment: the Happiness Index (HFI) and the Fear Index (FEARS), collected through Twitter and Google tools. Data related to cryptocurrencies were collected from the Cryptocompare website. Quantile regressions were used to analyze variations in the impact of investor sentiment on different types of currencies. Results: The results indicated that happiness and fear affect cryptocurrencies heterogeneously, with HFI causing negative and positive impacts on the return of assets such as BTC, USDC, and USDT. FEARS had a predominantly negative impact on the return of cryptocurrencies such as BTC and BRZ but was positive on ETH. Regarding trading volume, IFH had an ambiguous influence on BRZ, while FEARS reduced the volume of BTC, USDT, and USDC. The distinct patterns of impact identified suggest that investor sentiment may be a key indicator for formulating strategies in a highly volatile and emotionally reactive market. Contributions of the Study: It contributes significantly to the literature by focusing on the Brazilian cryptocurrency market, which has been little explored in international research. It uses a quantile approach to examine how investor sentiment impacts multiple cryptocurrencies, offering a more detailed and non-linear analysis, something rare in the literature. Furthermore, investigating the behavior of cryptocurrencies in Brazil during COVID-19 provides critical insights into how collective emotions, such as fear and euphoria, affect market movements, especially in an environment dominated by individual investors, making the study relevant for emerging markets.
Kostiantyn Orobets, V. I. Shkolnikov, Tetiana Batrachenko, Тетяна Василівна Барановська · 5 authors
Introduction: The legal regime of cryptocurrency in different countries of the world is heterogeneous. In some, it is not defined at all, which leads to legal conflicts, including when qualifying crimes committed with cryptocurrency use. The situation is further complicated because such crimes can occur in the territories of several states where cryptocurrency has a different legal regime. Traditional legislation and mechanisms for combating money laundering and terrorist financing are practically ineffective in the landscape of crimes involving the use of cryptocurrency.Objectives: The aim of the study is to systematise the main patterns of crimes related to the use of cryptocurrency, as well as analyse existing vectors of their legal assessment, appropriate design and application of effective methods of combating these crimes.Methods: Based on the methods of analysis and synthesis, qualitative data analysis, using content analysis as the primary research tool, it is shown that the main problem in preventing the use of cryptocurrency in predicate crimes lies in the technical difficulty of identifying a person or group of persons who carry out cryptocurrency transactions for illegal purposes. Such goals may be aimed at legalising funds, i.e., concealing their illegal origin, making payments in a hidden network, organising various fraudulent schemes, financing terrorism, and other crimes.Results: The article argues that given the technical specifics of cryptocurrency transactions and the technical capabilities of "masking" the origin of cryptocurrency funds, it is necessary to develop methods for studying trace formation and develop an algorithm for establishing and consolidating forensically significant information for this type of crime. The results indicate that the future of law enforcement in the fight against cryptocurrency-related crime will require a multifaceted approach. Agencies must adopt a proactive approach by foreseeing emerging criminal strategies. To protect the public from crimes using digital assets, law enforcement must be flexible, progressive, and technologically savvy as cryptocurrencies continue to develop. The development of provisions on cryptocurrency also determines the theoretical significance of the work as an object and means of committing crimes, a surrogate means of payment during the commission of certain crimes.Conclusions: The practical significance of the work lies in the possibility of using its results to solve problems arising in the law-making activities of state authorities and law enforcement activities, as well as in developing recommendations for improving criminal legislation in the field of cryptocurrency-related crimes.
BACKGROUND: There are substantial issues with the quality of care (QoC) received by persons living with chronic conditions, particularly in low- and middle-income countries (LMICs). One possible channel to improve QoC is through financing, specifically purchasing arrangements for health services. This has been actively explored in high-income country settings, generating a growing body of scientific knowledge. OBJECTIVE: To understand the potential and the constraints of using purchasing arrangements as a way to improve QoC for chronic conditions in resource-constrained settings. METHODS: A Delphi survey was conducted with 49 international participants with content expertise in chronic care management, health financing, or both, and context expertise in resource-constrained settings including in Small Island Developing States or Fragile and Conflict-Affected States, to assess the possible contribution of purchasing arrangements to QoC for chronic conditions with respect to specific types of care providers (e.g. patients and relatives, community health workers, public health centres), decentralized coordination bodies and purchasing agencies in such settings. RESULTS: There was a high level of consensus among the Delphi panel in favour of considering purchasing arrangements as one of the levers to improve QoC for people living with chronic conditions. Specific directions for action were identified along with their caveats. CONCLUSIONS: The challenge of improving the quality of chronic care in resource-constrained settings is extensive and requires immediate attention. Leveraging purchasing arrangements is one promising channel to strengthen quality chronic care in such settings.
This study investigates the dynamic interplay between national currencies of the core BRICS economies and the three strongest monetary assets (US dollar, gold, Bitcoin) in the existing global financial outlook. Using data spanning the inflationary Russia-Ukraine conflict (24 February 2022 to 5 June 2025) and the innovative Quantile-VAR methodology in bear, normal and bull market conditions as expressed by quantiles insights are offered about the potential of transformation of the monetary status quo. Findings reveal that extreme market conditions strengthen the leading potential of Bitcoin and gold in early and later war phases, respectively. This abides by the pseudo-wealth and consumption fluctuations theory of Guzman and Stiglitz (2021) as higher risk-taking appears in turbulent periods for preserving and promoting growth. Shielding from inflation could also work this way. The Brazilian, Chinese and South African currencies gain prominence while the Russian currency acts as a net absorber of shocks. So the US dollar could be partly crowded out. Alterations in monetary asset allocation for investors could serve for better adapting to contemporary financial needs.
Shezon Saleem Mohammed Abdul, Anup Shrestha, Jianming Yong
Blockchain’s promise of decentralised, tamper-resistant services is gaining real traction in three arenas: decentralized finance (DeFi), blockchain gaming, and data-driven analytics. These sectors span finance, entertainment, and information services, offering a representative setting in which to study real-world adoption. This survey analyzes how each domain implements blockchain, identifies the incentives that accelerate uptake, and maps the technical and organizational barriers that still limit scale. By examining peer-reviewed literature and recent industry developments, this review distils common design features such as token incentives, verifiable digital ownership, and immutable data governance. It also pinpoints the following domain-specific challenges: capital efficiency in DeFi, asset portability and community engagement in gaming, and high-volume, low-latency querying in analytics. Moreover, cross-sector links are already forming, with DeFi liquidity tools supporting in-game economies and analytics dashboards improving decision-making across platforms. Building on these findings, this paper offers guidance on stronger interoperability and user-centered design and sets research priorities in consensus optimization, privacy-preserving analytics, and inclusive governance. Together, the insights equip developers, policymakers, and researchers to build scalable, interoperable platforms and reuse proven designs while avoiding common pitfalls.