Blockchain (BC) and smart contract (SC), known for their decentralized and secure frameworks, have been successfully applied across industries to enhance security, efficiency, and transparency in technical and engineering aspects such as automated transactions, data management, and supply chain monitoring. In recent years, the construction industry has seen significant advancements in adopting BC and SC. This review paper examines the latest BC and SC prototypes, case studies, and technical applications published over the past four years, focusing on how these academic developments can be transitioned into industry practice. By categorizing the collected studies using Technology Readiness Levels (TRLs) and conducting an in-depth analysis, the paper seeks to identify strategies for accelerating the adoption of these technologies in the construction sector. Reviewing recent advancements, this paper uncovers key trends, benefits, and challenges associated with implementing BC and SC in construction. The findings suggest that these technologies can significantly enhance project efficiency, contract management, and supply chain transparency while fostering trust among stakeholders. While these technologies offer significant potential, critical challenges, including scalability, privacy, and integration complexities, hinder their widespread adoption. Moreover, specific limitations, such as the difficulty in establishing regulatory compliance and technical barriers in data management, remain unresolved. By providing actionable insights and highlighting pathways to bridge the adoption gaps, the paper aims to accelerate the integration of BC and SC into the construction industry, driving innovation, transparency, and operational efficiency.
The integration of blockchain and smart contracts in the construction industry has the potential to revolutionize the tender phase and enhance waste management practices. The prototype is designed to enhance transparency, efficiency, and trustworthiness in Italian public procurement. To analyze the national public procurement database with a view to identifying common issues, which are often related to the lack of trust among stakeholders, Large Language Models (LLMs) are exploited. Blockchain technology has the potential to facilitate this process by eliminating discrepancies and disputes, providing a decentralized, immutable ledger to notarize data related to digital models submitted during the tender phase, thereby ensuring transparency and tamper-proof data. The automation of bid evaluations based on predefined criteria such as those pertaining to waste management, is a key feature of smart contracts, which are of particular importance in the context of construction sustainability. Such assessments are enabled, allowing for unbiased and transparent evaluations based on quantifiable data, with the results recorded automatically on the blockchain. This results in a more efficient tender process and the promotion of sustainable practices, as projects with superior waste management are given priority. A tailor-made blockchain protocol is put forth to delineate requirements and facilitate data exchanges. It establishes standards and procedures for data submission, verification, and evaluation, ensuring secure and transparent interactions and enhancing stakeholder confidence in a fair and transparent evaluation process. In summary, the use of blockchain and smart contracts in the construction tender phase improves data integrity, transparency, and efficiency. The focus on waste management indicators allows for objective project evaluations and the promotion of sustainable practices. This innovative approach has the potential to transform public procurement, establishing a new global standard for the construction industry.
Effective data governance is crucial in modern digital ecosystems, ensuring secure, transparent, and efficient data sharing. Traditional centralized governance models often suffer from trust issues, inefficiencies, and security vulnerabilities. Blockchain technology offers a decentralized and tamper-resistant solution to address these challenges. This paper proposes a blockchain-based data governance architecture that enhances data sharing mechanisms and optimizes smart contract execution. The framework leverages a permissioned blockchain to ensure controlled data access while maintaining data integrity and security. To further improve performance, an optimized smart contract mechanism is introduced using gas-efficient transaction designs and layer-2 scaling solutions. Experimental evaluations demonstrate that the proposed model improves transaction efficiency, reduces computational overhead, and enhances security compared to conventional blockchain-based governance systems. The results highlight the potential of blockchain in establishing a decentralized, efficient, and transparent data governance framework for secure and scalable data exchange.
This paper presents a comprehensive literature review on the application of blockchain technology in ensuring data integrity and security within decentralized applications (dApps). Blockchain, with its inherent features such as immutability, decentralization, and transparency, offers a robust framework for safeguarding data across various sectors, including finance, healthcare, and supply chain management. Through an extensive qualitative analysis of existing studies, this research explores how cryptographic techniques, consensus mechanisms, and blockchain's distributed nature contribute to securing data in decentralized environments. The review examines key findings from the literature, including the integration of advanced cryptographic methods such as zero-knowledge proofs and homomorphic encryption, which enhance data privacy while maintaining integrity. Furthermore, the study discusses the challenges of scalability, energy consumption, and off-chain data security in blockchain-based systems, identifying areas for future research. The review also highlights the importance of hybrid blockchain models and scalable consensus algorithms in addressing the limitations of current blockchain frameworks. Overall, this paper contributes to the growing body of knowledge on blockchain-based data security and integrity in decentralized applications and offers recommendations for further research to enhance the scalability, efficiency, and environmental sustainability of blockchain systems.
Iryna Dashko, Олександр Череп, Любомир Михайліченко
The article comprehensively examines cryptocurrencies as a strategic tool for transforming the investment environment in the context of digitalization of the global economy. The current state of the crypto market is analyzed, key trends in its evolution are identified, and the role of digital assets in the formation of new investment models is characterized. Particular attention is paid to determining the investment potential of cryptocurrencies in the long term, taking into account such advantages as decentralization, market openness, technological innovation and accessibility. The author substantiates the concept of “crypto-horizon” - a new investment paradigm that combines a strategic vision of digital finance development with an understanding of the risks and prospects of cryptocurrencies. The author considers the importance of this concept in the formation of a new type of investor capable of operating in the digital economy, effectively managing risks and using innovative financial instruments. The paper also focuses on the key challenges of the crypto market: high volatility, legal uncertainty, information asymmetry, and limited financial literacy. The SWOT analysis made it possible to identify the strengths and weaknesses of crypto investing, as well as promising areas for the development of digital finance. The importance of state regulation, creation of a regulatory framework, development of digital finance infrastructure and raising public awareness in the field of investment is determined. The author emphasizes the need to form an effective regulatory framework for the integration of cryptocurrencies into the financial system. The role of public policy, educational initiatives, and infrastructure solutions in increasing confidence in digital assets is shown. It is substantiated that successful implementation of the “crypto-horizon” concept is possible only if there is a synergy of technology, regulation and investment culture. As a result, the authors conclude that cryptocurrencies are already playing the role of a digital key to the investment future, and their competent integration into national and international financial systems will be the key to the formation of an innovative, flexible and accessible investment ecosystem for the general population.
Ziwei Li, Jiajing Wu, Zhiying Wu, D. Tan · 9 authors
Smart contracts are self-executing computer programs on blockchains. With the development of blockchain technology, the number of smart contracts has grown rapidly, as has the concern for their security. Regrettably, inconsistencies between the logic implemented in the code and the intentions described in the comments, known as Code–Comment Inconsistencies (CCI), are frequently present in some smart contracts. These inconsistencies can mislead readers in understanding the contract code and, in severe cases, may lead to vulnerabilities and economic losses. Existing learning-based methods are not tailored for smart contract languages, overlook the issue of insufficient context information caused by comment references and nested intentions, and rely on large-scale labeled data; whereas rule-based methods struggle to accommodate the flexibility with which developers express intentions, often resulting in false positives. To tackle the challenges posed by insufficient context information and the scarcity of labeled data, we introduce CCIHunter, a tool designed to detect CCIs in smart contracts. CCIHunter addresses the issue of insufficient context information during data modeling and incorporates a two-stage pre-training process that does not depend on labeled data to enhance its detection capabilities. Specifically, CCIHunter enhances comments based on templates and models code as a heterogeneous graph based on function calls. It utilizes CodeBERT and UniMp to generate embeddings for comments and code, respectively, and then calculates the similarity between these two embeddings. Consistency is judged by combining code embeddings, comment embeddings, and similarity scores. Notably, CCIHunter undergoes a two-stage pre-training that includes contrastive learning and mutation analysis, aiming to improve its ability to bridge the gap between code and comments and to focus on code elements at different granularities. Experimental results demonstrate that CCIHunter achieves a precision of 0.95, a recall of 0.90, and an F1 score of 0.93, outperforming existing tools.
Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While structure learning of DAGs has been an active research area, existing methods do not adequately account for excessive zeros and therefore are not suitable for modeling zero-inflated count data. Moreover, it is often interesting to study differences in the causal networks for data collected from two experimental groups (control vs treatment). To explicitly account for zero-inflation and identify differential causal networks, we propose a novel Bayesian differential zero-inflated negative binomial DAG (DAG0) model. We prove that the causal relationships under the proposed DAG0 are fully identifiable from purely observational, cross-sectional data, using a general proof technique that is applicable beyond the proposed model. Bayesian inference based on parallel-tempered Markov chain Monte Carlo is developed to efficiently explore the multi-modal posterior landscape. We demonstrate the utility of the proposed DAG0 by comparing it with state-of-the-art alternative methods through extensive simulations. An application in a single-cell RNA-sequencing dataset generated under two experimental groups finds some interesting results that appear to be consistent with existing knowledge. A user-friendly R package that implements DAG0 is available at https://github.com/junsoukchoi/BayesDAG0.git.
The sustainable and efficient management of the built environment is a crucial challenge in the increasingly digitalized AEC sector. Innovative technologies such as Building Information Modeling (BIM) and Digital Twin (DT) offer significant opportunities to enhance the operational efficiency and sustainability of physical assets. However, digitalization generates vast amounts of Big Data, and their handling through centralized architectures leads to risks of fragmentation, lack of transparency, and vulnerability to manipulation. In response to these challenges, this study presents an innovative Proof of Concept (PoC) that integrates Blockchain (BT), Digital Twin (DT), and Non-Fungible Token (NFT) technologies to promote decentralized and sustainable data management in the construction industry. The application, called dDT (decentralized Digital Twin), was initially deployed on the Solana blockchain and later integrated with Polygon to leverage EVM compatibility and the ERC-721 standard for NFTs. The platform enables the tokenization of data flows generated by physical assets, ensuring traceability, security, and transparency throughout the entire asset lifecycle. The dDT system represents a sustainable innovation as it creates a secondary data market, fostering collaboration among industry stakeholders and financing new developments through the sale of data-linked NFTs. This decentralized solution addresses fragmentation and transparency issues, promoting more secure, resilient, and sustainable data management practices. The PoC demonstrates how the integration of BT, DT, and NFT can accelerate the transition toward more efficient and innovative practices, with positive impacts on sustainability and technological advancement in the AEC sector.
The traditional cheque clearance systems face several challenges, including processing delays, high operational costs, and a lack of transparency—often resulting in inefficiencies and increased risk of fraud. To address these issues, a Blockchain-Based Cheque Clearance System Using Ethereum is proposed, aiming to revolutionize the banking sector by providing a secure, transparent, and efficient mechanism for cheque transactions between banks and users. This system leverages Ethereum’s decentralized blockchain network and smart contracts to automate the cheque clearance process, ensuring faster and tamper-proof transactions. Historically, cheque clearance relied on manual or centralized procedures that were prone to errors and fraudulent activities. Traditional methods often required inter-bank coordination, causing delays and inconsistencies. Although some digitized systems were introduced over time, they remained centralized and lacked the trust and security required for critical financial operations. Inspired by the capabilities of blockchain technology, this project seeks to overcome these limitations by decentralizing the cheque clearance process. The motivation lies in the growing need for a solution that enhances security, reduces processing time, and ensures data integrity in financial transactions. By adopting blockchain, the system can provide immutable transaction records, minimize human intervention, and significantly reduce operational inefficiencies. The proposed solution uses Ethereum smart contracts to enable real-time verification, validation, and clearance of cheques. Each transaction is permanently recorded on the blockchain, ensuring transparency and auditability. The decentralized nature of Ethereum removes reliance on a central authority, thereby increasing system resilience and reliability. This innovative approach aims to redefine the cheque clearance process, offering banks and users a seamless, secure, and efficient transactional experience.
We explore the feasibility of deploying Bitcoin as the shared monetary standard between Earth and Mars, accounting for physical constraints of interplanetary communication. We introduce a novel primitive, Proof-of-Transit Timestamping (PoTT), to provide cryptographic, tamper-evident audit trails for Bitcoin data across high-latency, intermittently-connected links. Leveraging Delay/Disruption-Tolerant Networking (DTN) and optical low-Earth-orbit (LEO) mesh constellations, we propose an architecture for header-first replication, long-horizon Lightning channels with planetary watchtowers, and secure settlement through federated sidechains or blind-merge-mined (BMM) commit chains. We formalize PoTT, analyze its security model, and show how it measurably improves reliability and accountability without altering Bitcoin consensus or its monetary base. Near-term deployments favor strong federations for local settlement; longer-term, blind-merge-mined commit chains (if adopted) provide an alternative. The Earth L1 monetary base remains unchanged, while Mars can operate a pegged commit chain or strong federation with 1:1 pegged assets for local block production. For transparency, if both time-beacon regimes are simultaneously compromised, PoTT-M2 (and PoTT generally) reduces to administrative assertions rather than cryptographic time-anchoring.
The article explores the evolution of marketing innovations in the retail sector through the lens of technological development and the transformation of consumer expectations. Five key stages of innovation development are identified—traditional, network based, digital, omnichannel, and innovation-technological—each characterized by specific challenges, opportunities, and influencing factors. The traditional stage was marked by a focus on product policy and individual promotions within the physical store. The network-based stage introduced the integration of IT solutions into logistics, CRM systems, and initial customer segmentation. The digital stage was distinguished by the emergence of online stores, mobile marketing, and personalized communication. The omnichannel stage involved the full synchronization of online and offline channels to ensure a holistic customer experience. The innovation-technological stage includes the extensive implementation of artificial intelligence, AR/VR, blockchain, and emotional analytics. The study draws conclusions about the patterns of transition between stages and the role of innovation in transforming business models in retail. Key directions for further development of marketing innovations are identified, including the technologization of customer experience, intelligent marketing automation, a sustainable approach, Web3 integration, the growth of social commerce, and the use of emotional analytics. However, the implementation of these directions is accompanied by a number of challenges related to the rapid pace of technological change, increasing consumer expectations, and the need to adapt business models to new ethical and environmental standards. In Ukraine, these challenges are further intensified by martial law conditions, market instability, limited resources, and the urgent need for rapid transformation of the retail sector to fit the new realities. It is noted that the development vectors of marketing innovations in retail form a complex yet high-potential system of change that requires strategic thinking, flexibility, and readiness to implement new formats of customer interaction. The article has practical significance for marketing professionals, retail business managers, and researchers working on adapting business practices to the evolving digital economy.
Emma Verónica Ramos Farroñán, Gary Christiam Farfán Chilicaus, Luís Edgardo Cruz Salinas, Liliana Correa Rojas · 8 authors
This systematic review synthesizes evidence on economic instruments that mobilize renewable-energy investment in emerging economies, analyzing 50 peer-reviewed studies published between 2015 and 2025 under PRISMA 2020. We advance an Institutional Capacity Integration Framework that ties instrument efficacy to regulatory, market, and coordination capabilities. Green bonds have mobilized roughly USD 500 billion yet work only where robust oversight and liquid markets exist, offering limited gains for decentralized access. Direct subsidies cut renewable electricity costs by 30–50% and connect 45 million people across varied contexts, but pose fiscal–sustainability risks. Carbon pricing schemes remain rare given their administrative complexity, while multilateral climate funds show moderate effectiveness (coefficients 0.3–0.8) dependent on national coordination strength. Bibliometric mapping with Bibliometrix reveals three fragmented paradigms—market efficiency, state intervention, and international cooperation—and highlights geographic gaps: sub-Saharan Africa represents just 16% of studies despite acute financing barriers. Sixty-eight percent of articles employ descriptive designs, constraining causal inference and reflecting tensions between SDG 7 (affordable energy) and SDG 13 (climate action). Our framework rejects one-size-fits-all prescriptions, recommending phased, context-aligned pathways that progressively build capacity. Policymakers should tailor instrument mixes to institutional realities, and researchers must prioritize causal methods and underrepresented regions through focused initiatives for equitable global progress.
The concept of Decentralized Autonomous Organizations (DAOs) has introduced a novel paradigm in organizational governance, characterized by more collaborative decision-making. However, the lack of established organizational frameworks for DAOs presents significant challenges to their constitution, stability, and longevity. Aiming to address this shortcoming, this paper presents a conceptual framework to guide the design of the community governance structure of DAOs. To achieve this aim, we employed two complementary methods: firstly, we conducted a systematic literature review about DAOs and community governance; secondly, we conducted an analysis of the governance methods employed by five DAOs operating in public blockchain ecosystems. The proposed framework provides a valuable tool for DAO founders, developers, and community members to design and implement effective governance structures and contributes to the understanding of DAO governance and further research.
Youwei Huang, Jianwen Li, Bin Hu, Sen Fang · 6 authors
Malicious developer intents in smart contracts constitute significant security threats to decentralized applications, leading to substantial economic losses. Prior work introduced SmartIntentNN, a deep learning model for detecting unsafe developer intents. By combining the Universal Sentence Encoder, a K-means clustering-based intent highlighting mechanism, and a Bidirectional Long Short-Term Memory (BiLSTM) network, the model achieved an F1 score of 0.8633 on an evaluation set of 10,000 real-world smart contracts across ten distinct intent categories. This paper presents SmartIntentV2 (Smart Contract Intent Neural Network Version 2). The primary enhancement is the integration of a BERT-based pre-trained programming language model, which we domain-adaptively pre-train on a dataset of 16,000 real-world smart contracts using a Masked Language Modeling objective. SmartIntentV2 retains the BiLSTM-based multi-label classification network for intent detection. On the same evaluation set of 10,000 smart contracts, it achieves superior performance with an accuracy of 0.9789, precision of 0.9090, recall of 0.9476, and an F1 score of 0.9279, substantially outperforming its predecessor and other baseline models. Notably, SmartIntentV2 also delivers a 65.5% relative improvement in F1 score over GPT-4.1 on this specialized task. These results establish SmartIntentV2 as a new state-of-the-art model for smart contract intent detection.
Agentification serves as a critical enabler of Edge General Intelligence (EGI), transforming massive edge devices into cognitive agents through integrating Large Language Models (LLMs) and perception, reasoning, and acting modules. These agents collaborate across heterogeneous edge infrastructures, forming multi-LLM agentic AI systems that leverage collective intelligence and specialized capabilities to tackle complex, multi-step tasks. However, the collaborative nature of multi-LLM systems introduces critical security vulnerabilities, including insecure inter-LLM communications, expanded attack surfaces, and cross-domain data leakage that traditional perimeter-based security cannot adequately address. To this end, this survey introduces zero-trust security of multi-LLM in EGI, a paradigmatic shift following the ``never trust, always verify'' principle. We begin by systematically analyzing the security risks in multi-LLM systems within EGI contexts. Subsequently, we present the vision of a zero-trust multi-LLM framework in EGI. We then survey key technical progress to facilitate zero-trust multi-LLM systems in EGI. Particularly, we categorize zero-trust security mechanisms into model- and system-level approaches. The former and latter include strong identification, context-aware access control, etc., and proactive maintenance, blockchain-based management, etc., respectively. Finally, we identify critical research directions. This survey serves as the first systematic treatment of zero-trust applied to multi-LLM systems, providing both theoretical foundations and practical strategies.
Rahanatu Suleiman, Akshita Maradapu Vera Venkata Sai, Wei Yu, Chenyu Wang
Digital Twins (DTs) have become essential tools for improving efficiency, security, and decision-making across various industries. DTs enable deeper insight and more informed decision-making through the creation of virtual replicas of physical entities. However, they face privacy and security risks due to their real-time connectivity, making them vulnerable to cyber attacks. These attacks can lead to data breaches, disrupt operations, and cause communication delays, undermining system reliability. To address these risks, integrating advanced security frameworks such as blockchain technology offers a promising solution. Blockchains’ decentralized, tamper-resistant architecture enhances data integrity, transparency, and trust in DT environments. This paper examines security vulnerabilities associated with DTs and explores blockchain-based solutions to mitigate these challenges. A case study is presented involving how blockchain-based DTs can facilitate secure, decentralized data sharing between autonomous connected vehicles and traffic infrastructure. This integration supports real-time vehicle tracking, collision avoidance, and optimized traffic flow through secure data exchange between the DTs of vehicles and traffic lights. The study also reviews performance metrics for evaluating blockchain and DT systems and outlines future research directions. By highlighting the collaboration between blockchain and DTs, the paper proposes a pathway towards building more resilient, secure, and intelligent digital ecosystems for critical applications.
This chapter proposes a formal alternative to blockchain-based ledgers by reconstructing the logic of bilateral exchange relationships using projective geometry and categorical methods. We show that the normative identity of a financial contract can be faithfully embedded into a projective elliptic curve, yielding an algebraic structure isomorphic to double-entry bookkeeping. This geometric realization enables compositional transaction modeling through the elliptic group law and supports structured reasoning about contract compliance, reversibility, and balance. In contrast to distributed ledger technologies, which often fail to preserve bilateral symmetry and internal control logic, our framework enforces normative integrity by construction. We analyze the limitations of blockchain systems in supply chain transparency and auditing and present a category-theoretic model that resolves these deficiencies through local contract verification and structured composition. The resulting framework extends naturally to multi-agent reasoning, tiered supply chains, and digital audit systems, offering a mathematically rigorous foundation for trustworthy and scalable accountability infrastructures.
Over 1.7 billion people lack basic sanitation, and 2 billion rely on contaminated drinking water, predominantly in low- and middle-income countries. Decentralized solutions offer a viable alternative to centralized systems but face barriers in governance, finance, and the Product Development Process (PDP). This paper examines challenges across seven PDP stages—Function, Assembly, Deployment, Maintenance, Upscaling, Disassembly, and Transfer—through a two-step qualitative sequential design approach. Findings reveal critical gaps, including insufficient funding for maintenance, fragmented regulatory frameworks, and neglect of end-of-life management. Humanitarian markets prioritize speed over sustainability, poor markets demand low-cost designs but lack institutional support, and emerging markets face regulatory complexity and uneven scalability. Practical recommendations include simplifying funding, adopting user-centered modular designs, and strengthening local capacity through partnerships. Future research should focus on governance reforms, sustainable maintenance, and scaling pathways to ensure innovations address the urgent needs of underserved populations. Theoretically, this paper advances understanding of how PDP challenges intersect with market typologies in resource-constrained contexts, offering a framework to analyze and address systemic barriers to innovation. This study contributes to the field of innovation for resource-constrained markets by providing actionable insights for technology providers, financers, and local implementers to address systemic governance, financial, and operational gaps in the PDP.
This paper proposes a cryptocurrency portfolio trading system (CPTS) that optimizes trading performance in the cryptocurrency futures market by leveraging reinforcement learning and timeframe analysis. By employing the advantage actor–critic (A2C) algorithm and analysis of variance (ANOVA) portfolios are constructed over multiple timeframes. Data corresponding to the trade of 18 major cryptocurrencies on Binance Futures––between January 2022 and December 2023––are used to show that trading strategies can be effectively categorized into those with high-frequency (10, 30, and 60 min) and low-frequency (daily) timeframes. Empirical results demonstrate statistically significant differences in returns between these timeframe groups, with major cryptocurrencies (e.g., Bitcoin and Ethereum) exhibiting higher returns in high-frequency trading (16–17%) than in daily trading (6–7%) during training. Performance evaluation during the test period revealed that the low-frequency group achieved a 43.06% average return, significantly outperforming the high-frequency group (5.68%). The ANOVA results confirm that both the frequency type and portfolio selection significantly influence trading performance at the 5% significance level. This study offers a novel approach to cryptocurrency trading that considers the distinct characteristics of different timeframes. The effectiveness of combining reinforcement learning with statistical analysis for portfolio optimization in highly volatile cryptocurrency markets is demonstrated.
We examine how Bitcoin and Ethereum volatilities react to macroeconomic data releases from the US, Germany, and Japan before, during, and after their official announcements. Analyzing 5-minute observations from 2016 to 2023, we find that volatility responds significantly to select news categories, particularly in the pre-announcement period. US monetary policy news consistently drives volatility across all phases, with a heightened impact during the pandemic. Ethereum shows greater sensitivity to US announcements than Bitcoin but remains unresponsive to non-US news, especially before the pandemic. Our findings highlight the need to account for both pre- and post-announcement periods when evaluating the intraday price impact of macroeconomic news on cryptocurrencies. • We examine the response of Bitcoin and Ethereum volatilities to macroeconomic figures. • We show that volatility reacts only to a few news categories. • US monetary policy news consistently affects volatility before, during, and after its release. • Ethereum volatility is more sensitive to US announcements compared to Bitcoin. • Ethereum exhibits less pre-announcement volatility and less sensitivity to non-US news.
This paper surveys the academic literature concerning the bubble periods in the cryptocurrency market. This study aims to understand the historical and developmental trajectory of the cryptocurrency market through its various bubble periods. This study also identifies the factors contributing to bubble formation. The study is based on the PRISMA framework for literature review. Based on the review, the cryptocurrency market experienced four major bubbles in 2011, 2013, 2017, and 2021. The enthusiasm for cryptocurrency innovation triggered the 2011 bubble. The 2013 bubble was influenced by the economic crisis that channeled funds to the cryptocurrency market due to their centralized nature. In 2017, the possibilities of Web 3.0 and altcoins increased the enthusiasm of crypto investors. The crypto winter of 2017 subsided with the rise of non-fungible tokens (NFTs), stimulating interest and driving prices in the cryptocurrency market. Specifically, speculation, media coverage, investor sentiment, herding, volatility, and coexplosivity are significant factors that trigger bubble development. Moreover, government policies and regulations can be crucial in sustaining and bursting the bubbles. This review offers a comprehensive view of academic studies on bubble periods in the cryptocurrency market. This study also provides a chronological overview of major bubble periods that have significantly influenced the market development. This study is one of the first reviews conducted to understand the development of the cryptocurrency market through bubble periods and the factors contributing to bubble formation. The study also follows the PRISMA framework for structuring the review, as the literature lacks reviews on bubble periods on the basis of this framework.
The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents-trained using centralized training with decentralized execution (CTDE)-handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems.
The rise of Non-Fungible Tokens (NFTs) and Internet of Things (IoT) devices created new demands for secure data management. To address these needs, we propose LIBLO, a lightweight blockchain-based smart NFT architecture designed for decentralized environments with limited resources. Traditional models mostly depend on heavy computation techniques to ensure the data security. To avoid this, LIBLO introduces a compressed blockchain layer combined with lightweight encryption techniques. This allows secure storage, verification, and controlled access to IoT-generated data without overloading devices. In this architecture, LIBLO acts as a trusted digital framework that securely encapsulates metadata, ownership identity, and access control policies. Each transaction is verified through digital signatures and efficiently recorded on a compressed blockchain ledger. This design ensures privacy, traceability, and integrity and also contributes to energy-efficient implementation in real-time IoT scenarios. Experimental results demonstrate that the suggested LIBLO achieves high encryption strength and strong decryption accuracy of 0.96%, and low error rates of (1.1%). By simplifying cryptographic operations and reducing blockchain complexities, LIBLO presents a practical and adaptable solution for securing digital assets and IoT interactions in smart environments.
The Internet of Things (IoT) is applied in various fields, and the number of physical devices connected to the IoT is increasingly growing. There are significant challenges to the IoT's growth and development, mainly due to the centralized nature and large-scale IoT networks. The emphasis on the decentralization of IoT's architecture can overcome challenges to IoT's capabilities. A promising decentralized platform for IoT is blockchain. Owing to IoT devices' limited resources, traditional consensus algorithms such as PoW and PoS in the blockchain are computationally expensive. Therefore, the PoA consensus algorithm is proposed in the blockchain consensus network for IoT. The PoA selects the validator as Turn-based selection (TBS) that needs optimization and faces system reliability, energy consumption, latency, and low scalability. We propose an efficient, lightweight blockchain for decentralizing IoT architecture by using virtualization and clustering to increase productivity and scalability to address these issues. We also introduce a novel PoA based on the Weight-Based-Selection (WBS) method for validators to validate transactions and add them to the blockchain. By simulation, we evaluated the performance of our proposed WBS method as opposed to TBS. The results show reduced energy consumption, and response time, and increased throughput.