Blockchain has been widely recognized as a trusted computing paradigm underpinning Decentralized Applications (DApps). However, low performance and poor scalability of the existing mainstream blockchain designs and implementations render their prospects unattainable, so-called trilemma . Directed Acyclic Graph (DAG) emerged as an alternative blockchain architecture to address the performance and scalability issue. However, the capacity and feasibility of DAG-based blockchain systems still remain of concern, due to a lack of open source implementations and convincing published experimental results. In this article, we propose a DAG-based blockchain, named 3D-DAG , to greatly improve the scalability and performance without compromising security. The system addresses the trilemma of decentralization, scalability, and security. Technically, our 3D-DAG consists of two layers of chains: DAGchain and mainchain . The DAGchain organizes the transactions in DAG topology, without having to solve the forks. They select a small group of validators from a large number of miners to make up the committee for consensus procedures. The design of DAGchain improves scalability by enabling parallel transaction processing. Based on that, the mainchain plays the role of checkpoint, periodically packaging and recording received transactions from DAGchain for finality. We also provide a prototype implementation with detailed evaluations. Experimental results show our 3D-DAG can meet the performance (a peak value of \(9.82\times 10^{4}\) TPS) that is expected by DApps in the context of Web3.
Chunhong Liu, Zihang Sang, Li Duan, Jingxiong Wang · 6 authors
Security vulnerabilities in smart contracts can have severe economic consequences. Existing smart contract vulnerability detection methods rely primarily on rigid rules defined by experts and have difficulty in detecting unknown vulnerabilities. This article proposes a new Anomalous Smart Contract Detector, named ASCD, to effectively detect known and unknown vulnerabilities in smart contracts. This is achieved by interpreting unknown vulnerabilities as code anomalies and detecting them with an anomaly detection technique named DeepSVDD. This is also attributed to a new design of feature extraction, in which we compile smart contract source codes into opcodes, extract semantic features from opcode sequences, and control flow features from control flow graphs. By joining LSTM and GIN, the semantic and control flow features are fused to offer a comprehensive representation of smart contracts suitable for anomaly detection. Extensive experiments were conducted to verify the ASCD model, and more than 30,000 smart contracts were tested. The new model offers a significantly better F1-score than existing methods in detecting known vulnerabilities and achieves a high accuracy of 77% in detecting unknown vulnerabilities.
It is stated that the Decentralized Finance (DeFi) is transforming the financial industry because it provides its users with such services as lending, borrowing, trading, and insurance on decentralized terms using blockchain technology. DeFi is cheaper, transparent, and secure because it is developed on decentralized platforms, including Ethereum. But it is also encircled by such threats as regulatory risk, security risk and market risk. In this paper, the comparison of the DeFi and traditional financial systems will be presented involving such central capabilities of the DeFi as decentralized exchanges, liquidity pools, and lending platforms. It also mentions the discrepancies in rules, the positives of financial inclusions and the DeFi negatives such as volatility and absence of consumer protections. The paper gives a concluding look at what traditional institutions and regulators can do to react and cooperate with DeFi.
Federated Learning (FL) offers an attractive framework for collaboratively training AI models while preserving data privacy. However, it also introduces challenges in verifying the integrity and authenticity of model updates across diverse clients. Zero-Knowledge Proofs (ZKP) provide a promising means to address these issues by verifying computations without revealing underlying data. Yet, global verification using ZKP remains computationally expensive and does not scale well. To overcome these limitations, we propose a novel approach grounded in two key principles: (a) partial verification, targeting carefully selected subsets of data, can effectively mitigate adversarial attacks; and (b) robust data verification is essential, ensuring not only the consistency of model parameters but also the authenticity of the underlying data. We highlight the potential operation of this partial verification system, discuss novel research directions, and outline strategies for a wider integration into FL architectures.
Pasquale De Rosa, Simon Queyrut, Yérom-David Bromberg, Pascal Felber · 5 authors
The Ethereum Virtual Machine (EVM) is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications (dApps). The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through deceptive means, e.g., fake websites, wallet scams, or malicious smart contracts, aiming to steal sensitive information or funds. A timely detection of phishing activities in the EVM is therefore crucial to preserve the user trust and network integrity. Some state-of-the art approaches to phishing detection in smart contracts rely on the online analysis of transactions and their traces. However, replaying transactions often exposes sensitive user data and interactions, with several security concerns. In this work, we present PhishingHook, a framework that applies machine learning techniques to detect phishing activities in smart contracts by directly analyzing the contract’s bytecode and its constituent opcodes. We evaluate the efficacy of such techniques in identifying malicious patterns, suspicious function calls, or anomalous behaviors within the contract’s code itself before it is deployed or interacted with. We experimentally compare 16 techniques, belonging to four main categories (Histogram Similarity Classifiers, Vision Models, Language Models and Vulnerability Detection Models), using 7,000 real-world malware smart contracts. Our results demonstrate the efficiency of PhishingHook in performing phishing classification systems, with about 90% average accuracy among all the models. We support experimental reproducibility, and we release our code and datasets to the research community.
Initially introduced to Ethereum via Flashbots' MEV-boost, Proposer-Builder Separation allows proposers to auction off blockspace to a market of transaction orderers, known as builders. PBS is currently available to validators through the aforementioned MEV-boost, but its unregulated and relay-dependent nature has much of the Ethereum community calling for its enshrinement. Providing a protocol-integrated PBS marketspace and communication channel for payload outsourcing is termed PBS enshrinement. Although ePBS potentially introduces native MEV mitigation mechanisms and reduces validator operation costs, fears of multiparty collusion and chain stagnation are all too real. In addition to mitigating these potential drawbacks, PBS research pursues many tenets revered by Web3 enthusiasts, including but not limited to, censorship resistance, validator reward equity, and deflationary finance. The subsequent SoK will identify current PBS mechanisms, the need for enshrinement, additions to the ePBS upgrade, and the existing or potential on-chain socioeconomic implications of each.
Emerging crypto economies still hemorrhage digital assets because legacy wallets leak private keys at almost every layer of the software stack, from user-space libraries to kernel memory dumps. This paper solves that twin crisis of security and interoperability by re-imagining key management as a platform-level service anchored in ARM TrustZone through OP-TEE. Our architecture fractures the traditional monolithic Trusted Application into per-chain modules housed in a multi-tenant TA store, finally breaking OP-TEE's single-binary ceiling. A cryptographically sealed firmware-over-the-air pipeline welds each TA set to an Android system image, enabling hot-swap updates while Verified Boot enforces rollback protection. Every package carries a chained signature developer first, registry second so even a compromised supply chain cannot smuggle malicious code past the Secure World's RSA-PSS gatekeeper. Inside the TEE, strict inter-TA isolation, cache partitioning, and GP-compliant crypto APIs ensure secrets never bleed across trust boundaries or timing domains. The Rich Execution Environment can interact only via hardware-mediated Secure Monitor Calls, collapsing the surface exposed to malware in Android space. End-users enjoy a single polished interface yet can install or retire Bitcoin, Ethereum, Solana, or tomorrow's chain with one tap, shrinking both storage footprint and audit scope. For auditors, the composition model slashes duplicated verification effort by quarantining blockchain logic inside narrowly scoped modules that share formally specified interfaces. Our threat analysis spans six adversary layers and shows how the design neutralizes REE malware sniffing, OTA injection, and cross-module side channels without exotic hardware. A reference implementation on AOSP exports a Wallet Manager HAL, custom SELinux domains, and a CI/CD pipeline that vet community modules before release. The result is not merely another hardware wallet but a programmable substrate that can evolve at the velocity of the blockchain ecosystem. By welding radical extensibility to hardware-anchored assurance, the platform closes the security-usability gap that has long stymied mass-market self-custody. We posit that modular TEEs are the missing OS primitive for Web3, much as virtual memory unlocked multi-tasking in classical computing. Together, these contributions sketch a blueprint for multi-chain asset management that is auditable, resilient, and poised for global deployment.
Rug pulls present a critical threat in Decentralized Finance (DeFi), causing substantial financial losses and eroding ecosystem trust. Despite research advances, effective detection remains hampered by fragmented taxonomies, limited datasets, and inadequate tool evaluations. Through systematic analysis of academic and industry sources, we develop a comprehensive taxonomy of 35 distinct rug pull types, including 9 previously undocumented variants. Our analysis reveals significant detection gaps: existing datasets cover only 20% of known types, leading us to create an enhanced dataset of 2,391 instances that increases coverage to 82.9%. Evaluation of 13 detection tools shows substantial capability variation (25.7% to 62.9%), with 9 types completely undetectable. Most critically, tool performance degrades significantly when facing complex attacks, with maximum detection rates dropping from 55.6% for single-vector cases to 31.3% for compound scenarios. These findings provide essential insights for developing more robust security testing approaches for smart contract vulnerabilities in decentralized systems.
We present the availability and reproducibility report of the ACM SIGMOD 2025 paper titled ''InTime: Towards Performance Predictability In Byzantine Fault Tolerant Proof-of-Stake Consensus''. Following the instructions provided the authors, we evaluated the artifacts hosted on Github. The reviewers confirmed that the codebase is functional and evaluatable. Reproducibility was straightforward as the authors provided automated scripts that simplified the execution of experiments. Experiments on single machine were reproduced while experiments requiring a large cluster of machines are constrained by limited resource capacity and instructions. Hence, the paper's core claims were partially reproduced.
Lei Yu, Zhirong Huang, Hang Yuan, Shiqi Cheng · 11 authors
Smart contract vulnerability detection is a critical challenge in the rapidly evolving blockchain landscape. Existing vulnerability detection methods face two main issues: (1) Existing datasets lack comprehensiveness and sufficient quality, with limited vulnerability type coverage and insufficient distinction between high-quality and low-quality explanations for preference learning. (2) Large language models (LLMs) often struggle with accurately interpreting specific concepts in smart contract security. Through our empirical analysis, we found that even after continual pre-training and supervised fine-tuning, LLMs still exhibit limitations in precisely understanding the execution order of state changes in smart contracts, which can lead to incorrect vulnerability explanations despite making correct detection decisions. These limitations result in poor detection performance, leading to potentially severe financial losses. To address these challenges, we propose Smart-LLaMA-DPO, an advanced detection method based on the LLaMA-3.1-8B. First, we construct a comprehensive dataset covering four vulnerability types and machine-unauditable vulnerabilities, containing labels, detailed explanations, and precise vulnerability locations for Supervised Fine-Tuning (SFT), as well as paired high-quality and low-quality outputs for Direct Preference Optimization (DPO). Second, we perform continual pre-training using large-scale smart contract code to enhance the LLM's understanding of specific security practices in smart contracts. Futhermore, we conduct supervised fine-tuning with our comprehensive dataset. Finally, we apply DPO, which leverages human feedback to improve the quality of generated explanations. Smart-LLaMA-DPO utilizes a specially designed loss function that encourages the LLM to increase the probability of preferred outputs while decreasing the probability of non-preferred outputs, thereby enhancing the LLM's ability to generate high-quality explanations. We evaluate Smart-LLaMA-DPO on four major vulnerability types: reentrancy, timestamp dependence, integer overflow/underflow, and delegatecall, as well as machine-unauditable vulnerabilities. Our method significantly outperforms state-of-the-art baselines, with average improvements of 10.43% in F1 score and 7.87% in accuracy. Moreover, both LLM evaluation and human evaluation demonstrate the superior quality of explanations generated by Smart-LLaMA-DPO in terms of correctness, thoroughness, and clarity.
As financial institutions increasingly migrate their core platforms to microservices-based architectures, the challenge of managing distributed transactions has gained critical importance. Banking APIs typically require atomicity and consistency across multiple services—such as account management, fraud detection, notifications, and audit trails all of which operate independently with isolated data stores. In such an ecosystem, ensuring consistency, performance, and fault tolerance becomes a balancing act that traditional and modern transaction patterns attempt to resolve differently. This paper explores and contrasts two dominant approaches to distributed transaction management: the Two-Phase Commit (2PC) protocol and the Saga Pattern, particularly in the context of mission-critical banking applications. 2PC has long been considered the gold standard for ensuring atomicity and strong consistency in distributed systems. However, its blocking nature, reliance on a centralized coordinator, and vulnerability to network partitions make it less suitable for high-throughput, globally distributed systems common in modern fintech platforms. On the other hand, the Saga Pattern, an eventual consistency model that orchestrates a sequence of local transactions with compensating rollback operations—offers better fault tolerance and non-blocking behavior. Yet, its trade-offs include the complexity of compensating logic, lack of strict ACID guarantees, and potential for data anomalies if not carefully implemented. To ground the discussion in real-world reliability needs, I introduce a chaos engineering-based simulation that demonstrates the behavior of both 2PC and Saga under controlled failure scenarios, such as inter-service latency spikes and partial service outages. We benchmark recovery times, resource locking, system availability, and data reconciliation behavior using a representative banking microservice architecture deployed in a containerized environment. My findings reveal that Saga outperforms 2PC in terms of availability and fault recovery, making it suitable for user-facing, latency-sensitive operations. However, 2PC remains superior for operations demanding immediate consistency and compliance with strict audit requirements, such as core ledger updates. Based on this analysis, we propose a hybrid transaction strategy that applies 2PC to core financial operations and Saga to surrounding auxiliary services, striking a balance between performance and correctness. This study offers practical design insights for architects building resilient, scalable, and regulation-compliant financial systems. It also highlights the need for adaptive orchestration platforms capable of dynamically selecting transaction models based on context and SLA requirements.
As governmental health information systems become increasingly digitized, the need for collaborative analytics across decentralized regions has intensified.However, privacy concerns, regulatory constraints, and infrastructure disparities have limited the extent to which sensitive health data can be aggregated and analyzed across jurisdictions.This paper explores the integration of blockchain technology with federated learning (FL) to enable privacy-preserving data analytics across distributed governmental health information systems.By combining FL's decentralized model training capabilities with blockchain's immutable, transparent ledger and consensus mechanisms, the proposed framework ensures secure, auditable, and policy-compliant data collaboration without requiring raw data exchange.The framework leverages smart contracts to automate access control, consensus validation, and compliance enforcement among participating health institutions.Each node (representing a governmental health entity) trains models locally and shares only encrypted model parameters, which are validated and recorded on the blockchain.This eliminates the need for centralized authorities and reduces the risk of data leakage or manipulation.A core contribution of this work lies in addressing public-sector constraints such as legacy infrastructure, heterogeneous data standards, and institutional trust gaps through a modular, interoperable design.The system includes support for dynamic node participation, real-time updates, and compatibility with health data standards such as HL7 and FHIR.Use-case simulations across municipal, regional, and national health departments demonstrate improved efficiency in outbreak prediction, chronic disease surveillance, and population-level risk stratification while maintaining strict compliance with data protection regulations.This paper advances a scalable and trustworthy architecture for cross-border health collaboration, offering a blueprint for digital public health infrastructures in the age of data sovereignty and distributed intelligence.
Digitalization is reshaping global consumption patterns and exposing the limits of value-added tax (VAT) regimes designed for an analogue economy. While Brazilian tax-reform proposals focus on merging six cascading levies into a single VAT-style tax, this structural simplification alone will not align the system with the realities of cross-border electronic commerce. Drawing on OECD guidelines and the BEPS Action 1 report, this article reviews two decades of international debate on destination-based VAT, especially for business-to-consumer supplies of intangibles. It highlights the practical impasse surrounding customer identification, jurisdictional allocation and collection in high-volume, real-time digital marketplaces. The paper argues that recent suggestions to shift liability to dominant e-commerce platforms, although politically feasible, still rely on “verified self-identification” and fragmented registration procedures that are ill-suited to the velocity of automated transactions. As a forward-looking alternative, the study explores how distributed-ledger technology—particularly smart-contract-enabled split-payment mechanisms executed on blockchain networks and settled with central-bank digital currencies—could embed compliance by design, enhance traceability, and lower administrative costs for both taxpayers and revenue authorities. Implementation prerequisites (digital identities, standardized e-invoicing, regulatory sandboxes) and technological hurdles (scalability, interoperability, tokenization) are mapped, providing a research agenda for a VAT 4.0 architecture. The conclusion contends that harnessing blockchain’s immutability and programmability is essential for a resilient, fraud-resistant indirect-tax system capable of addressing the borderless nature of 21st-century commerce.
Shereen Ismail, Raouf Mehannaoui, Eden Teshome Hunde, Hassan Reza
IoT devices are typically resource-constrained, with limited computational power, storage, and energy. Holochain, an emerging distributed ledger technology (DLT), offers the benefits of blockchain while overcoming its limitations, such as the reliance on consensus algorithms and a globally synchronized ledger. As a result, Holochain has garnered attention in the research community as a promising solution for distributed IoT applications. This paper reviews various DLTs in IoT distributed networks, focusing on the motivation for utilizing Holochain in these environments. We explore its key applications, challenges, and research insights. We propose the HoloSec framework, a conceptual security framework for IoT distributed networks that leverages Holochain’s agent-centric architecture, advanced cryptography, and machine learning (ML). The paper also illustrates the setup and implementation of a Holochain-based IoT network for a healthcare scenario and compares the performance of Holochain with traditional blockchain solutions. Initial experimental results show that Holochain achieves a latency of around 50 ms for data publishing and 30 ms for retrieval, with a throughput of approximately 20 transactions per second (TPS) on a single node, significantly outperforming blockchain, which shows higher latency (200 ms publish, 100 ms retrieve) and lower throughput (10 TPS). Finally, we examine key challenges associated with Holochain and outline future research directions aimed at enhancing its interoperability, scalability, security, and regulatory compliance in IoT environments.
Mahsa Bashari, Saleh Ghavidel, Mehdi Fathabadi, Masoud Soufimajidpour
This study examines the environmental impact of cryptocurrency mining, specifically its contribution to CO2 emissions , in nine countries that account for 90% of global mining: the United States, China, Russia, Canada, Germany , Malaysia, Kazakhstan, Ireland, and Iran. Utilizing monthly panel data from 2019 to 2022 across nine countries and applying both pooled and fixed effects econometric techniques, the analysis reveals that ”energy intensity” (the amount of energy used to produce a unit of GDP), as a moderator variable, influences the effect of cryptocurrency mining on CO2 emissions. Specifically, in countries where the annual energy intensity growth rate is greater than − 6 % , cryptocurrency mining tends to result in higher CO 2 emissions. Conversely, in countries with a growth rate of energy intensity below -6%, cryptocurrency mining results in lower CO2 emissions. The findings indicate that all nine countries experience a positive impact on CO2 emissions, albeit to varying degrees. The countries are categorized into three groups based on their performance: underperformers (Russia, the United States, Canada), neutral-effect countries (Iran, Kazakhstan, China), and positive performers (Ireland, Germany, Malaysia). This research underscores the urgent need for sustainable practices in cryptocurrency mining to mitigate its environmental effects.
Purpose This empirical study aims to expand the literature on the application of blockchain by analysing the factors that influence Italian winemakers. Design/methodology/approach An online questionnaire was used to collect data from 114 Italian wine producers. The factors influencing the adoption of blockchain technology were based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT). Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data using the SmartPLS computer program. Findings The PLS-SEM results show that two dimensions of the extended UTAUT, including performance expectancy and trust, positively influence blockchain technology adoption. At the same time, effort expectancy, social influence and facilitating conditions are found to have no significant influence on the intention to adopt blockchain technology. The intention to adopt blockchain is therefore linked to the expectation of improving the winery’s production efficiency, on the one hand, and the greater transparency that this brings, and the benefit that this can bring to consumers, on the other. Originality/value The empirical results of this study fill a gap in the existing literature regarding the factors influencing the adoption of blockchain by Italian winemakers. This study found that, out of five factors, only two have an impact on behaviour, thus providing insight into this issue that has not been clearly examined in previous studies.
Lawrence Martin Mankata, Prince Antwi-Afari, S. Thomas Ng
The construction industry's shift to a circular economy has been hindered by multiple challenges. The emergence of blockchain has however demonstrated promising potential in overcoming these barriers. Nonetheless, there is limited research regarding the system implementation dynamics of blockchain-based circular economy applications within the construction supply chain. This paper proposes a blockchain-based construction supply chain framework for advancing circular economy in the construction industry . Systematic Evidence Synthesis (SES), prototyping, and case study triangulation approaches are adopted to review, experiment, and validate the findings. Relevant exploratory and experimental cases are identifed from Scopus and Web of Science databases for the SES process . The initial findings highlighted the main implementation domains, to include material passports, waste trading, and reverse logistics. Hyperledger and Ethereum are further identifed as the leading implementation platforms for developing prototypes. The key challenges identified from prototype development included, limited simulation samples, limited performance scalability, and uncertainty with return on investment . To address the challenges and gaps identified, a blockchain-based circular construction supply chain (BCCSC) framework is proposed. Modules from the proposed framework are experimented through a proof-of-concept prototype to demonstrate feasibility. Finally, selected cross-industry cases were triangulated to draw conceptual parallels and potential drawbacks in implementation. Through a blockchain-based web marketplace, stakeholder interactions in the construction supply chain can be deepened to support circular business models. Furthermore, the framework's modularization allows for easy scalability and practical implementation. Recommendations are made towards research in cost reduction and enhanced collaboration strategies, as well as developing full-scale modules to demonstrate end-to-end functionality.
Under the wave of digital transformation, traditional accounting processes face challenges in audit risk control. Blockchain technology, with its unique advantages, offers a new approach to addressing this issue. Through case studies, this paper conducts an in-depth exploration of the application value of blockchain in accounting processes from the perspective of audit risk assessment. It provides a detailed analysis of the advantages and disadvantages. At the same time, by understanding the definitions, relationships, and differences between accounting and auditing, and by examining the traditional accounting process, it is found that there are audit risks in terms of information accuracy and completeness. By comparing and analyzing the distributed ledger feature of blockchain technology, it can be seen that the application of blockchain technology enhances the confidentiality of information. Research also shows that the application of blockchain technology in accounting processes can significantly enhance the credibility and authenticity of audit evidence, reduce audit inspection risks, and better assist auditors in identifying problems in accounting. The main research results of this paper are to explore the application value of blockchain technology in accounting processes from the perspective of audit risk assessment, thereby better addressing the drawbacks of traditional accounting processes.
The article investigates the problem of ensuring the veracity and traceability of production data in digital factories, where EU regulatory requirements and a high level of counterfeiting create a critical deficit of trust in source information. The objective of this study is to analyze the architectures of blockchain solutions for data verification in supply chains and to develop a phased implementation plan that considers regulatory obligations and the protection of trade secrets. The novelty lies in a combined approach: classification of DLT networks according to scalability, cost, and privacy criteria; use of Merkle trees and zero-knowledge proofs to preserve confidential data while proving authenticity; and justification of architecture choice through practical case studies (IBM Food Trust, VeChain, Airbus/Circularise, SAP). The study demonstrates that blockchain enables a reduction in batch traceability time from days to seconds, a reduction in manual operations to 67%, and an increase in data matching accuracy to 92%. However, the immutability of the ledger does not eliminate the immutable garbage problem: the veracity of records depends on sensor calibration and procedural control, which requires preliminary semantic normalization and master data management. A phased implementation enables the minimization of risks and the assessment of economic impacts. To comply with DPP, it is recommended to introduce decentralized identifiers (DID) and Verifiable Credentials, as well as the integration of zero-knowledge proofs. Thus, blockchain transitions from a trial technology to a necessary component of the practical infrastructure for sustainable production chains. This article will help managers and experts in digitalization and supply chain management.
Ravi Kumar Munaganuri, Yamarthi Narasimha Rao, Sai Chandana Bolem
This research is anchored on the burning need for irrigation optimization and crop water use efficiency improvement, which remains a challenge in smart agriculture processes. Traditional irrigation methods normally lead to inefficiency, resulting in wasted water and non-maximum crops. These traditional ways normally lack attributes of real-time adaptability and secure data management—things that are very key to modernizing agricultural practices. In this work, artificial intelligence (AI), Internet of Things (IoT), and blockchain techniques will be integrated to design a comprehensive system for monitoring and predicting soil moisture levels. In the proposed model, long short-term memory (LSTM) networks are considered for soil moisture level prediction, taking into consideration past data, weather, and crop type. LSTM networks are chosen here for their high performance in timestamp series prediction tasks with an mean average error (MAE) of 0.02 m 3 /m 3 over a 7-day forecast horizon. For real-time monitoring, IoT sensors based on long range wide area network (LoRaWAN) technology are field-deployed for conducting long-range communications while consuming very limited energy to extend the sensor battery life over 5 years and bring down the data transmission latency below 5 s. It has an inbuilt permissioned blockchain framework—Hyperledger Fabric—which offers a secure and transparent system for data management and maintaining a record of soil moisture data, irrigation events, and metadata from sensors. This ensures the immutability and integrity of sets of data. Smart contracts automate irrigation upon reaching preconfigured soil moisture thresholds, and hence zero data integrity breaches occur with a transaction throughput of 1,000 transactions per second, taken into view with smart contract execution latency of less than 2 s. Moreover, it utilizes reinforcement learning with Deep Q-Learning to derive an optimized irrigation schedule. In this regard, it enables learning optimal irrigation policies and implements them to improve efficiency in the usage of water by 25% and increases crop yield by 15% compared to the traditional methods. Clearly from field trials, results indicate evident efficiency of the integrated system: a 20% water usage reduction and a 12% increase in crop yield within one growing season. This is rather an innovative take on irrigation practices, increasing a great deal of accuracy and sustainability for such and providing a really strong solution toward better agricultural productivity and resource management.
This study examines the dynamic connectedness between Bitcoin and various financial assets, including the stock market, gold, oil, bonds, and exchange rates, as well as explores portfolio strategies involving these assets. The study covers the period from January 2, 2015, to March 1, 2024. The quantile connectedness approach and portfolio strategies are utilized in the analysis. The findings are as follows: Intermarket volatility spillover significantly increases under extreme conditions. Bitcoin emerges as a transmitter during bullish markets and acts as a receiver in bearish and normal market conditions. Gold serves as a receiver in extreme conditions and a transmitter in normal conditions. Unlike gold, oil acts as a transmitter under extreme conditions and functions as a receiver under normal conditions. Among the fundamental markets, the stock market is the most significant shock transmitter. In risk-mitigating portfolios, the proportion of Bitcoin is low, while the proportions of gold and the dollar index are high. Bitcoin has been found to have low hedging properties. <br />Implications for Central European Audience: Since the emergence of Bitcoin in 2008, the cryptocurrency market has developed rapidly. Bitcoin and cryptocurrencies have come to occupy an important place in financial markets in terms of value and volume. Bitcoin can affect portfolio management in the financial system in terms of diversification, hedging, risk management, portfolio strategies, and linkages between financial assets. This study investigates the linkages, hedging and portfolio strategies between Bitcoin and the stock market, gold, oil, bond and exchange rate markets. The results of the study are important for portfolio managers, risk managers, financial analysts and economic managers.
The emergence of the metaverse has opened up new possibilities for businesses, entrepreneurs, and consumers, reshaping the way industries operate and interact with their audiences. As the virtual world expands, new business opportunities have emerged, allowing entrepreneurs to create innovative products, services, and experiences in entirely digital spaces. However, the potential of the metaverse for entrepreneurship remains underexplored. This research aims to examine how the metaverse offers new business opportunities and challenges for entrepreneurs in various sectors. The study utilizes a qualitative research design, conducting in-depth interviews with entrepreneurs and industry experts who are actively engaging with the metaverse. Data was gathered through semi-structured interviews and analyzed thematically to identify key trends, business models, and challenges faced by startups in virtual environments. The findings reveal that the metaverse provides entrepreneurs with access to a global market, greater consumer engagement, and new forms of monetization through virtual goods and services. However, challenges such as technical barriers, user adoption, and regulatory concerns persist. Entrepreneurs leveraging the metaverse tend to adopt business models that include virtual real estate development, NFTs (non-fungible tokens), and immersive experiences. In conclusion, the metaverse presents significant business opportunities, but entrepreneurs must navigate various challenges related to technology, market dynamics, and consumer behavior.
Nominative signatures allow us to indicate who can verify a signature, and they can be employed to construct a non-transferable signature verification system that prevents the signature verification by a third party in unexpected situations. For example, this system can prevent IOU/loan certificate verification in unexpected situations. However, nominative signatures themselves do not allow the verifier to check whether the funds will be transferred in the future or have been transferred.It would be desirable to verify the fact simultaneously when the system involves a certain money transfer such as cryptocurrencies/cryptoassets. In this paper, we propose a smart contract-based non-transferable signature verification system using nominative signatures. We pay attention to the fact that the invisibility, which is a security requirement to be held for nominative signatures, allows us to publish nominative signatures on the blockchain. Our system can verify whether a money transfer actually will take place, in addition to indicating who can verify a signature. We transform the Hanaoka-Schuldt nominative signature scheme (ACNS 2011, IEICE Trans. 2016) which is constructed over a symmetric pairing to a scheme constructed over an asymmetric pairing, and evaluate the gas cost when a smart contract runs the verification algorithm of the modified Hanaoka-Schuldt nominative signature scheme.
Barcode technology is widely used as an automated identification system that enables rapid and efficient data capture, particularly in retail environments. Despite its practicality, barcode-based systems are increasingly vulnerable to security threats—most notably, barcode substitution fraud. To address these challenges, this paper presents an innovative system for the secure generation and verification of Quick Response (QR) codes using a digital watermarking technique. The proposed method embeds tamper-resistant information within QR codes, enhancing their integrity and making unauthorized modification more difficult. Additionally, a neural network-based authentication model was developed to verify the legitimacy of scanned QR codes. The system was evaluated through experimental testing on a dataset of 5000 QR samples. The results demonstrated high accuracy in distinguishing between genuine and fraudulent QR codes, confirming the system’s effectiveness in supporting fraud prevention in real-world applications.