Madina Konyrova, Katipa Chezhimbayeva, Abdul Razaque, Dina S.M. Hassan
The integration of renewable resources and prosumers into smart grids poses challenges related to scalability, transparency, and transmission efficiency. Centralized routing frequently depends on expensive technology and experiences significant losses. This study presents a blockchain-based smart contract system (BSCS) that reduces transmission losses while guaranteeing secure and decentralized energy transfers. The smart grid is represented as a weighted directed graph, with edges denoting actual power losses. Dijkstra’s shortest path algorithm generates optimal paths from the generator to the consumer with minimal loss. These optimal pathways are permanently documented and regulated by permissioned blockchain smart contracts, ensuring tamper-proof and verifiable energy settlement. Validation is performed using an enhanced IEEE 58-bus test system, which is based on the standard IEEE 57-bus network, by incorporating an additional synthetic consumer node (Bus 58) linked to Bus 12 to simulate a flexible prosumer load of 1.5 MW + 0.5 Mvar. Additionally, the synthetic consumer node employs Ganache, Truffle, and Solidity for its implementation. This modification facilitates the assessment of dynamic energy routing and decentralized transaction settlement in extended topology scenarios. The proposed BSCS demonstrates substantial enhancements compared to baseline blockchain systems. Active power losses in transmission lines are diminished, gas consumption declines by approximately 12%, latency is enhanced by as much as 21%, and throughput increases by more than 30%. The rapid deployment and execution of smart contracts within sub-second intervals validate the system's appropriateness for real-time grid operations. The proposed technique combines graph-theoretic optimization with blockchain governance to provide a safe, scalable, and hardware-independent framework for decentralized energy markets.
Вячеслав Осадчий, Катерина Осадча, Світлана Симоненко
Швидкий прогрес у цифрових технологіях сприяє глибинній трансформації системи вищої освіти, відкриваючи нові можливості для інтерактивного та персоналізованого навчання. Одним із найбільш перспективних напрямів є використання Metaverse, який поєднує технології віртуальної, доповненої, мішаної та розширеної реальності, блокчейну, цифрових двійників та non-fungible token. Завдяки цьому Metaverse пропонує захоплюючий, індивідуалізований та гнучкий освітній досвід, що робить його потужним інструментом у сучасній академічній практиці.Метою даного дослідження є здійснення комплексного аналізу наукових джерел, які висвітлюють можливості інтеграції Metaverse із технологіями штучного інтелекту (ШІ) для удосконалення вищої освіти. Для реалізації поставленої мети було проведено відбір наукових праць у трьох міжнародних наукометричних базах (Scopus, Web of Science, ERIC), сформовано пошукову стратегію на основі ключових запитів та визначених критеріїв включення й виключення. Подальший бібліометричний аналіз із використанням програмного забезпечення VOSviewer дозволив здійснити мережеву візуалізацію та проаналізувати спільне використання ключових слів у відібраних публікаціях.Наративний огляд обраних праць дав змогу окреслити основні напрями поєднання Metaverse та ШІ у вищій освіті. Серед ключових шляхів виділено: створення зручних та корисних для користувача освітніх середовищ і їх розширення до віртуальних університетських кампусів; використання інструментів ШІ для забезпечення безпеки та захисту даних у Metaverse; формування персоналізованих навчальних траєкторій на основі поведінкових моделей та індивідуальних уподобань студентів; сприяння комунікації та співпраці в режимі реального часу; генерація навчального контенту для Metaverse; інтеграція фізичного та цифрового світу; реалізація концепції ШІ-тьюторства для підтримки індивідуального навчання.Результати дослідження підтверджують значний потенціал Metaverse у поєднанні з технологіями ШІ для модернізації вищої освіти, підвищення її якості, доступності та інноваційності.
Rob J. Lewis, Jonas Lembrechts, P. D. Walker, Chunli Li · 5 authors
Background and Rationale Despite decades of progress in ecological monitoring, primary biodiversity and environmental data remain unevenly mobilised and poorly interoperable (Hampton et al. 2015, Poisot et al. 2019). Datasets, often gathered with public funds, frequently remain inaccessible or insufficiently described, limiting their reuse in global syntheses (Culina et al. 2018). Ecologists’ concerns about trust, transparency, and control of shared data persist, particularly where data production is resource-intensive or socially embedded. These concerns echo the foundational properties of distributed ledgers, where ownership and governance are distributed across peer networks rather than centralized repositories (Lewis et al. 2023). Forests exemplify both the potential and the challenge of such decentralised infrastructures. As globally significant carbon and biodiversity reservoirs, forests are also deeply fragmented across ownership and jurisdictional boundaries. In Europe alone, over half of forested land is privately owned, yet these actors often lack mechanisms to derive tangible value from stewardship. At the same time, digital twins (macroecological models) that integrate in situ and remotely sensed data, are becoming central to forest policy and monitoring frameworks (e.g., Food and Agriculture Organization of the United Nations (FAO), Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Global Biodiversity Framework (GBF)). ForestWeb3 (FW3) hypothesizes that a decentralised, Findable Accessible, Interoperable, Reusable (FAIR; Wilkinson et al. 2016, Nosek et al. 2022)-aligned data network can unlock the latent value of underused biodiversity data while building trust and incentives for participation.. Objectives Mobilisation and harmonisation of forest biodiversity and environmental data (Objective 1): to spearhead a shift from data curation to data stewardship through a decentralised data infrastructure built on open-source blockchain frameworks. Incentivisation and uptake (Objective 2): to design transnational pathways through which private forest owners and local communities can be economically rewarded for verifiable ecological data via nature-backed digital assets and ReFi mechanisms. Together, these objectives align technical innovation (Objective 1) with behavioural and economic motivation (Objective 2), establishing the groundwork for distributed biodiversity observatories capable of sustaining long-term ecological data flows. Methodological Approach WP 1 develops a blockchain-based data ledger with smart contracts that autonomously manage data registration, access control, and reuse. Metadata and identifiers are immutably recorded on-chain, while primary datasets remain decentralised on contributor-managed nodes. This architecture enables contributors to retain data sovereignty while ensuring transparency and traceability in reuse transactions. WP 2 extends the infrastructure to real-time environmental sensing through the integration of modular Internet ofThings (IoiT)-based microclimate sensors. These devices stream environmental data at high temporal resolution directly into the distributed ledger, forming a Decentralized Physical Infrastructure Network (DePIN) for ecological data. WP 3 links these data streams to the creation of digital twins of forest ecosystems, combining in situ biodiversity observations with satellite and climate datasets to model ecosystem integrity. These models underpin the valuation of nature-backed digital assets, a form of tokenised evidence for ecological performance, providing the data foundation for voluntary biodiversity and carbon markets. Finally, WP4 investigates forest owners’ perceptions, motivations, and barriers to adopting regenerative finance (ReFi)-based conservation mechanisms. Through interviews and a pan-European survey, it explores how varying sociocultural and institutional contexts shape engagement with emerging biodiversity credit schemes, drawing parallels to established Payment for Ecosystem Services frameworks (Kaiser et al. 2021). Significance and Legacy FW3 exemplifies the convergence of data decentralisation, digital sensing, and regenerative economics, a triad capable of transforming how ecological knowledge is produced, verified, and valued. By embedding data provenance and attribution within the infrastructure itself, we addresses long-standing issues of trust and recognition in ecological data sharing. Its incentive mechanisms offer pathways to decouple conservation finance from traditional public funding, potentially scaling stewardship and democratizing data mobilisation across millions of hectares of privately owned forest land. The project’s legacy lies in demonstrating that data infrastructures can be both scientific and economic commons, capable of sustaining biodiversity monitoring through distributed participation. Beyond its immediate technical deliverables, ForestWeb3 contributes to a broader vision of dynamic, self-sustaining ecological data ecosystems that power both global biodiversity frameworks and locally grounded conservation action.
The rapid expansion of cryptocurrencies and decentralized finance (DeFi) has redefined global financial systems, creating new challenges in asset pricing, risk measurement, and systemic stability. This study conducts a comprehensive review of 93 peer-reviewed articles published between 2019 and 2024 to consolidate the fragmented literature on mathematical models applied to cryptocurrencies and DeFi platforms. Using a mixed bibliometric–systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review integrates performance indicators, conceptual mapping, and qualitative synthesis to identify methodological advances and research trends. The findings reveal a progressive convergence between econometric models, such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), stochastic volatility, and Lévy processes, and data-driven approaches based on machine learning (ML), deep learning (DL), and reinforcement learning (RL). These hybrid frameworks enhance predictive accuracy and adaptability in high-frequency and non-linear blockchain markets. The review also highlights optimization-based decision models that integrate Conditional Value-at-Risk (CVaR), network theory, and portfolio analytics for decentralized finance operations. However, interpretability, governance, and environmental sustainability remain underexplored dimensions. The study contributes by classifying mathematical approaches to pricing, volatility, and risk propagation, identifying methodological gaps, and recommending future research on explainable artificial intelligence (AI), environmental and cyber-risk modeling, and real-time validation for transparent and resilient decentralized financial ecosystems. • Review 93 studies analyzing mathematical models in cryptocurrency and digital finance systems. • Identify emerging methods for pricing, risk, and portfolio decisions under high volatility. • Compare deep learning models to traditional methods for forecasting and risk evaluation. • Evaluate decision models that include environmental, risk, and governance factors. • Recommend future research on interpretable tools for real-time decision-making.
Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.
This study aims to analyze the volatility spillovers between Bitcoin and Ethereum, the two main actors in the cryptocurrency market, and altcoins across sectoral and financial groups. Using data from January 1, 2021, to March 6, 2023, the study applied the VAR-based method developed by Diebold and Yılmaz (2012) and measured both directional and total volatility spillovers. The findings show that Bitcoin's volatility largely stems from internal dynamics and spreads to other cryptocurrencies to a limited extent. In contrast, Ethereum is more affected by external shocks and exhibits a stronger volatility spillover across the market. Among altcoin categories, Gaming, Analytics, and DeFi groups were found to be the most influential in volatility transmission, while thematic tokens such as NFT, Web3, and Metaverse were more sensitive to external volatility. In contrast, stablecoins and tokens in the identity and healthcare sectors were found to have relatively low volatility and a more stable structure. These results offer important insights for investors and regulators regarding risk management strategies and portfolio diversification. The study provides a valuable framework for understanding the systematic volatility dynamics within the cryptocurrency ecosystem
The sanctions restrictions imposed on the Russian economy have predetermined the risks of reduced control and blocking cross-border financial channels for international payments. Under these circumstances, an important element of the state’s economic policy is the development of adaptation solutions that ensure the ability to minimize the risks associated with transformation of foreign economic activity. One of the mechanisms that forms the basis for solving this problem is the use of decentralized finance (DeFi) in the practice of international settlements. Meanwhile, it should be noted that in this area of financial and economic relations today there are a number of open issues — ranging from the conceptual framework to the impact of these fintech-innovations on the prospects for the stability of national financial systems and economic development in general. These issues acquire a special level of relevance at the regional level. The subject of the study is the prospects and mechanisms for using DeFi tools in the system of cross-border payments within the region. The object of the study is the foreign trade operations of the Republic of Tatarstan with the People’s Republic of China. The purpose of the study is to develop and test methodological approaches for assessing the potential impact of fintech DeFi tools on the organization foreign economic activity in the region on the stability of gross GRP dynamics. The authors used methods of cointegration analysis, scenario modeling, substantiation of the studied patterns using regression analysis methods, etc. The information and statistical basis for the study were derived from data from the Federal State Statistics Service of the Russian Federation, the National Bureau of Statistics of China, as well as data from the territorial statistical authority of the Republic of Tatarstan. The study resulted in the systematization of the macroeconomic effects of using DeFi technologies for organizing international payments. A series of macroeconomic models have been developed that made it possible to identify and justify the potential for economic growth in the region through the practical application of DeFi tools in the system of organizing transnational settlements under sanction restrictions.
Open access
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Regional Economic Development and Innovation
Economic, Social, and Public Health Issues in Russia and Globally
Digitalization and Economic Development in Agriculture
This essay examines the transformative potential of Non-Fungible Tokens (NFTs) in urban environments, specifically regarding ownership structures, management processes, and civic participation. Employing a mixed-methods research approach, the study demonstrates that NFTs can support fractional ownership, increase transparency within smart cities, and promote urban renewal. Addressing legal challenges, technological limitations, and social equity concerns remains essential. The findings advocate for coordinated efforts among policymakers, urban planners, and technologists to ensure that NFTs contribute to equitable and sustainable urban development.
This article examines the NFT market and art tokenization in the context of money laundering. It explores the evolution of the art market toward digitalization, the definition of NFTs, and their legal and technical aspects. Additionally, it highlights the rapid growth of the market and associated risks, such as fraud, sanction evasion, and money laundering. It discusses mechanisms for concealing illicit funds, as well as the lack of clear regulations and oversight of NFT platforms within the AML/CFT framework. It emphasizes the need for regulatory clarification, the establishment of transaction registries, and addresses other unresolved issues related to NFTs, including intellectual property protection and tax obligations.
This paper introduces Bitcoin-IPC, a protocol that scales Bitcoin through a network of permissionless, interconnected, programmable Proof-of-Stake (PoS) Layer-2 chains, called subnets, whose stake is denominated in L1 BTC. These subnets rely on Bitcoin L1 for the communication of critical information, settlement, and security. Subnets can communicate with each other and with Bitcoin: users deposit BTC from Bitcoin to a subnet and withdraw it back, and transfer wBTC directly between subnets. We provide formal definitions of these bridge protocols, incorporating a firewall property that limits the impact of malicious subnets on the security of the broader network. Our design, inspired by SWIFT messaging and embedded within Bitcoin's SegWit mechanism, enables seamless value transfer across L2 subnets. Uniquely, this mechanism reduces the virtual-byte cost per transaction (vB/tx) by up to 23x, compared to transacting natively on Bitcoin L1, effectively increasing monetary-transaction throughput from 7 tps to over 160 tps, without requiring any modifications to Bitcoin L1.
The paper studies DeFi (decentralized finance) as a decentralized system for the circulation of financial tokens in virtual and cryptocurrency spaces. The subject of the study is the basic concepts, structures, and properties of DeFi. The relevance of the work is determined by the presence of unresolved issues related to the conceptual apparatus and structure of DeFi, factors of reduction and methods for determining the level of decentralization of DeFi, the functioning of the DeFi infrastructure, which highlights the need for further research into the concepts, structures and properties of DeFi. The aim of the study is to form a theoretical and methodological foundation for DeFi by clarifying the conceptual apparatus and identifying the features of DeFi functioning. The methodological framework of the study is based on the following principles: an object-subjective approach to describing entities, a method of structural analysis of objects, a systems approach to model objects, a process approach to analyzing the functioning of systems, and a service approach to analyzing interactions between serving and served systems. The study resulted in the formulation of the concept of DeFi (including the concept of a decentralized system). The following were identified: factors of centralization (reduced decentralization) of DeFi; the structure of DeFi as a set of subsystems for the circulation of virtual financial tokens and crypto tokens; a method for assessing the degree of DeFi decentralization as a system for the circulation of digital financial tokens; a three-tier service model of the DeFi infrastructure; and a model for the interaction of financial token circulation processes. Conclusions: The conceptual framework of DeFi, including the definition of DeFi as a decentralized system for the circulation of financial tokens in virtual and crypto spaces, allows us to identify the functional features of DeFi that ensure conditions for significantly greater transparency of the rules and results of financial transactions compared to traditional centralized financial systems. The use of virtual and crypto tokens, along with other DeFi mechanisms in financial circulation, significantly reduces uncertainty and the associated risks of executing financial agreements between economic entities.
Podcasts are a useful educational resource for improving student success, yet traditional methods of podcasting remain inefficient, vulnerable to censorship and deletion, and access-restricted. One approach to addressing these constraints is utilitarian digital pedagogy, which focuses on the use of digital tools to advance education for the greater good. Framed as such, this article outlines the conceptual and theoretical issues underlying how generative artificial intelligence (genAI), Web3, and open access (OA) improve podcasting’s utility relative to the alternatives: manual creation, Web2, and closed access. The article concludes by looking ahead to the major problems—hallucination, technical complexity, and rights management—to overcome in practice.
Securing blockchain-enabled IoT networks against sophisticated adversarial attacks remains a critical challenge. This paper presents a trust-based delegated consensus framework integrating Fully Homomorphic Encryption (FHE) with Attribute-Based Access Control (ABAC) for privacy-preserving policy evaluation, combined with learning-based defense mechanisms. We systematically compare three reinforcement learning approaches -- tabular Q-learning (RL), Deep RL with Dueling Double DQN (DRL), and Multi-Agent RL (MARL) -- against five distinct attack families: Naive Malicious Attack (NMA), Collusive Rumor Attack (CRA), Adaptive Adversarial Attack (AAA), Byzantine Fault Injection (BFI), and Time-Delayed Poisoning (TDP). Experimental results on a 16-node simulated IoT network reveal significant performance variations: MARL achieves superior detection under collusive attacks (F1=0.85 vs. DRL's 0.68 and RL's 0.50), while DRL and MARL both attain perfect detection (F1=1.00) against adaptive attacks where RL fails (F1=0.50). All agents successfully defend against Byzantine attacks (F1=1.00). Most critically, the Time-Delayed Poisoning attack proves catastrophic for all agents, with F1 scores dropping to 0.11-0.16 after sleeper activation, demonstrating the severe threat posed by trust-building adversaries. Our findings indicate that coordinated multi-agent learning provides measurable advantages for defending against sophisticated trust manipulation attacks in blockchain IoT environments.
An independent, trusted third party or governing body is no longer necessary to conduct secure financial transactions because of blockchain technology. The topic of smart contracts and their ability to facilitate additional computational progress has risen to the forefront of academic and industry conversations in response to the dizzying rate of growth in blockchain technology. The scholarly work takes into account the material that has been assessed by experts and aims to explain the fundamental idea and provide a comprehensive computational analysis of relevant literature. Such an approach contributes to the advancement of decentralized applications (dApps) by providing technical insights into their development frameworks. The initial section presents a brief overview of smart contracts, including their conceptual foundations, system architecture, and application domains. Furthermore, in a detailed review of existing platforms for developing smart contracts, it was found by comparison that the Tron and CoreDAO blockchains offer the most computationally efficient platforms to enhance the quality-of-services (QoS) in decentralized environments. These low-cost transaction models support the creation of resource-efficient smart contracts. In addition, this study includes a simulation work that considers the blockchain transactions as a dataset to train an artificial intelligence model that would support the computational prediction of the success and failure of the transactions. Received: 25 July 2025 | Revised: 23 October 2025 | Accepted: 5 December 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Alock Gupta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration. Kamlesh Lakhwani: Conceptualization, Methodology, Validation, Investigation, Resources, Writing – review & editing, Supervision.
The work is devoted to an overview of modern investment methods, the cryptocurrency market, ways of their development and strategies in this direction. The article analyzes the investment opportunities of cryptocurrencies; presents conclusions about the main advantages and disadvantages of each investment method, the level of risk, determining factors and investment attractiveness. The article considers one of the main methods of investing in cryptocurrency - speculation on the rates of various coins. In particular, two strategies for generating income through speculation are considered: the first is Buy&Hold, it is designed for long-term investment, involves buying cryptocurrency on the exchange and storing it in an account for a certain period of time; the second - the Buy&Sell strategy differs from the previous one in that it is designed for short-term investment. The presented work briefly describes a widely known method of investing in cryptocurrency - mining; in this case, all activities are based on blockchain technology, and the efficiency of the blockchain directly depends on the computing power of the computer. As a result, the profitability of mining is relatively low, special, very expensive equipment is required; In this paper, we have studied and tried to convey to the reader a widespread method of investing in cryptocurrency — initial coin offering (ICO), which means a form of attracting investment funds for the implementation of a project by issuing cryptocurrency. It is argued that the above tactics are also borrowed from the traditional financial market — initial public offering (IPO). In conclusion, the article summarizes the pros and cons of cryptocurrency investment methods; several simple recommendations are presented that will help increase your existing capital and diversify your investment portfolio.
This research investigates the predictive power of news sentiment from Google News on Bitcoin price movements, leveraging a five-year dataset of news headlines (2019 to 2024). By correlating sentiment scores with historical Bitcoin prices, the study employs various machine learning algorithms to forecast price trends. The results indicate that while Decision Tree and Random Forest models offer balanced predictions, Logistic Regression and Support Vector Machines achieve high AUC scores but suffer from class imbalance. In contrast, Naïve Bayes and KNN models prove less effective. The findings suggest that sentiment analysis of news headlines can provide moderate short-term predictions for Bitcoin price fluctuations. This study introduces an innovative tool for investors and market analysts, offering insights into the influence of news sentiment on cryptocurrency prices.
Levina Khulaidah, Syifa Rhamadani, Fadjar Tri Sakti
This study aims to analyze the financial performance of the Bandung City Government during the 2020–2024 period from a fiscal decentralization perspective. The research employs a descriptive quantitative method using secondary data obtained from the Directorate General of Fiscal Balance (DJPK) of the Ministry of Finance. The analysis applies seven regional financial ratios, namely the degree of fiscal decentralization, regional financial independence, fiscal dependency, effectiveness of Local Own-Source Revenue (PAD), expenditure efficiency, expenditure harmony (operational and capital expenditures), and growth ratio. The results indicate that the financial performance of the Bandung City Government has shown improvement, as reflected in the increasing levels of fiscal decentralization and financial independence. However, fiscal dependency remains relatively high, the effectiveness of PAD has not been optimal, and expenditure efficiency is still classified as less efficient. The expenditure structure is dominated by operational spending, while capital expenditure allocation remains relatively low. In addition, regional revenue growth during the study period is considered low and unstable. Therefore, optimizing PAD, improving expenditure efficiency, and restructuring the budget composition are necessary to support sustainable fiscal decentralization. Keywords: Fiscal decentralization, Regional financial performance, Bandung city, Local owns source revenue, Regional financial rations
This paper examines the recurring dynamics of financial crises through a comparative case study of the Dotcom bubble, the 2008 global financial crisis, and the ongoing cryptocurrency era. The objective is to investigate whether cryptocurrencies represent a genuine financial revolution or a repetition of past speculative manias. Using a qualitative methodology, the study applies a behavioral finance framework to analyse biases such as herding, overconfidence, and FOMO, and combines this with the evaluation of market data, including IPO trends, interest rates, and volatility indices. The results reveal strong equivalents across all three cycles. In each case, investor sentiment amplified volatility, and speculative assets obscured true risk. Weak regulation left markets vulnerable to collapse. Today’s ICOs are a reflection of IPOs in the Dotcom bubble, meanwhile the regulatory faults in 2008 find similarities in decentralized finance (DeFi). Moreover, the evidence challenges the Efficient Market Hypothesis, which markets illustrate collective perceptions instead of objective fundamentals. The findings suggest that financial markets repeat inefficiencies in new forms. Cryptocurrencies risk becoming another phase in the history of financial instability without coordinated regulation, investor education, and macroprudential monitoring.
Blockchain, originally devised for Bitcoin, has evolved beyond cryptocurrencies to become a transformative technology in banking and finance. Its decentralized, secure, and transparent characteristics promise improved efficiency, reduced fraud, and cost savings. However, challenges such as scalability, regulatory uncertainty, and cybersecurity risks persist. This paper explores the benefits, risks, and future prospects of blockchain adoption in the financial sector. The study includes a review of existing literature, real-world applications, and an analysis of ongoing challenges and potential future developments.
This paper presents the design, implementation, and evaluation of a decentralized system for issuing and verifying academic certificates based on blockchain technology. The proposed solution addresses common limitations of traditional certification models, such as susceptibility to forgery, reliance on centralized infrastructures, and inefficient verification processes. The system is built on the TRON blockchain and integrates smart contracts written in Solidity, a decentralized web application (dApp) for user interaction, and the InterPlanetary File System (IPFS) for decentralized storage of certificate metadata. The methodology comprised architectural design, smart contract development, and the implementation of a web-based interface, followed by functional, security, performance, and usability evaluations. Experimental results show that the system correctly supports certificate issuance and public verification, enforces access control, and resists common misuse scenarios. Performance analysis indicates low confirmation latency and negligible transaction costs, making the solution suitable for large-scale academic environments. Additionally, usability assessment using the System Usability Scale (SUS) resulted in a score of 76.67, indicating good user acceptance. Overall, the results demonstrate the technical feasibility and practical viability of the proposed approach, highlighting the TRON blockchain as an effective and cost-efficient infrastructure for decentralized academic certification systems.
Sara Antinozzi, Liliana Cecere, Francesco Colace, Angelo Lorusso · 6 authors
The integration of Building Information Modelling (BIM), blockchain technology, and smart contracts presents a significant opportunity to fundamentally reevaluate the administration of information and contracts in construction projects. This article introduces a distributed system that amalgamates BIM models, decentralized storage via IPFS, semantic oracles, and smart contracts to automate essential procedures such as versioning, design verification, and payment issuance contingent upon execution milestones. This proof of concept, built on a Proof-of-Authority blockchain using actual IFC models, demonstrates the technical viability of the method and evaluates its performance, constraints, and operational implications. The applications of SAL automation and design review demonstrate that integrating off-chain verification with on-chain documentation can reduce uncertainty, enhance accountability, and enable hitherto unattainable forms of contract automation. The suggested framework acknowledges the need for improved information standards and Oracle governance, demonstrating that integrating BIM and distributed technologies can significantly transform the digitalisation of the construction sector.
Mazin Nawwaf Assi, Sumeet Kaur, Swati Chaudhary, Pompi Das Sengupta · 7 authors
With the fast adoption of artificial intelligence in the art and cultural industry, the production, curation, distribution, and management of creative works have been radically transformed. Intelligent systems that allow artists, curators, institutions, platforms, and intelligent systems to work together in continuous interaction are now known as AI-driven art ecosystems. The paper explores management innovation as it manifests in AI-based art systems, the changes in managerial practices, forms of governance and decision making, in reaction to advanced computational creativity and data-driven work. The paper conceptualizes AI-based art systems as multi-layered systems that include creative production, curatorial intelligence and digital distribution systems such as online galleries and non-fungible token-based markets. It emphasizes the ways in which management innovation is developed in the form of a workflow redesign that combines automation and human-AI partnership to allow efficiency without sacrificing artistic intent and cultural sensitivity. Additionally, the paper focuses on the governance innovations that respond to the issues of transparency, accountability, ethical compliance, and authorship attribution in creative settings with algorithms mediating them. The resource orchestration is considered a key managerial competency with a focus on the strategic alignment of data resources, innovative talent, and computing resources. The study further examines the AI-enhanced decision-making in the context of art institutions and how the predictive analytics and the intelligent recommendation systems can be used in audience engagement prediction, curatorial planning, and portfolio management. Based on the selected case studies of AI-integrated museums, hybrid creative studios, and global AI-art hubs, the paper finds the best practices and benchmarking perspectives.
Introduction: The decentralization of Subdistrict Health Promoting Hospitals (SHPHs) to Provincial Administrative Organizations (PAOs) in Thailand represents a significant structural reform with direct implications for nurses working in primary healthcare settings. This study aimed to develop a competency model for nurses employed in SHPHs under PAO jurisdiction, ensuring alignment with decentralization policies and local health system needs. Methods: A mixed-methods design was used in 2 phases. Phase 1 employed qualitative methods to explore current nursing roles through in-depth interviews and thematic analysis. Phase 2 involved developing the competency model using quantitative data and the Delphi technique with expert consensus. Results: Findings from phase one indicated that nurses continue to play a vital role in community-based health promotion and care for vulnerable populations. Following decentralization, nurses have adapted to new responsibilities involving local workforce coordination, budgeting, and health information systems, necessitating expanded competencies. The competency model delineates stratified expectations by facility size: small SHPHs require generalist proficiency for autonomous service delivery; medium SHPHs necessitate specialized and collaborative competencies for programmatic functions; and large SHPHs demand advanced skills in systems management, strategic planning, and specialized care to align with institutional complexity. Conclusion: Although nurses’ core responsibilities in primary care remain central, decentralization has introduced new demands requiring advanced clinical, technological, data management, and interprofessional collaboration competencies. These expanded roles have strengthened nurses’ contributions to local health governance under the PAO system.
According to the advent of cryptocurrencies and Bitcoin, many investments and businesses are now conducted online through cryptocurrencies. Among them, Bitcoin uses blockchain technology to make transactions secure, transparent, traceable, and immutable. It also exhibits significant price fluctuations and performance, which has attracted substantial attention, especially in financial sectors. Consequently, a wide range of investors and individuals have turned to investing in the cryptocurrency market. One of the most important challenges in economics is price forecasting for future trades. Cryptocurrencies are no exception, and investors are looking for methods to predict prices; various theories and methods have been proposed in this field. This paper presents a new deep model, called \emph{Parallel Gated Recurrent Units} (PGRU), for cryptocurrency price prediction. In this model, recurrent neural networks forecast prices in a parallel and independent way. The parallel networks utilize different inputs, each representing distinct price-related features. Finally, the outputs of the parallel networks are combined by a neural network to forecast the future price of cryptocurrencies. The experimental results indicate that the proposed model achieves mean absolute percentage errors (MAPE) of 3.243% and 2.641% for window lengths 20 and 15, respectively. Our method therefore attains higher accuracy and efficiency with fewer input data and lower computational cost compared to existing methods.