Reviewing the evolution of classical physics and modern economics, all macroeconomic symbols and economic tools invented by humankind in the past (such as fiat currency and GDP) are essentially merely limited fittings to objective reality and "symbolic hallucinations." The previous work, The Entropy Standard: The Biosphere Fluid, Time Ledgers, and the Physical Limits of Civilization, starts from the first principles of thermodynamics and information theory, redefining human society as a "colloidal fluid" dissipative structure on the Earth's surface. It points out that traditional economics, having long been confined to lagging and subjective statistical reports, has driven modern society into terminal crises known as "Mechanization Blockade Disease" and "Complexity Parasitism". To address this, this proposal constructs the "Entropy Standard" economic physics tracking framework. In its underlying logic, this framework resolutely rejects the creation of unchanging, metaphysical absolute physical prototypes or "axioms." This is because social systems differ from the rigid, lifeless matter studied in classical physics; human civilization is a highly non-equilibrium, viscoelastic, and time-evolving complex fluid system. No static axiom or closed formula can lock down the truth once and for all; instead, they would simply devolve into dogma. Therefore, this framework instead embraces high-frequency, large-scale physical-level sampling protocols—sinking the sampling base directly to distributed physical edges (such as smart grid total loads, API call timestamps, and supply chain logistics). By continuously expanding the spatial sampling scope and increasing the temporal sampling frequency, it captures the system's true internal resistance and waste heat loss in real time, thereby endowing the dashboard with self-correcting and adaptive capabilities. Regarding the specific measurement methodology, this framework references the developmental trajectory of classical physics measurement history from "local crudeness" to "fundamental constants and absolute benchmarks," achieving hard-core tracking through two core measurement objects and quantitative mapping equations: Physical Stripping of the Social Total Time Ledger: Utilizing high-frequency timestamps and information-energy interfaces, the waking time of carbon-based individuals is objectively sliced to distinguish between effective working time (which genuinely extracts negative entropy from the physical world) and complexity overhead (which is swallowed by red tape). Circuitry Mapping of Negentropic Energy and System Internal Resistance: Introducing macro Ohm's law and the Joule heat formula, this approach discards illusory monetary prices and directly anchors to physical Joules and the basal metabolic energy level baseline. Through the dynamic tracking of waste heat loss and the establishment of a critical thermal breakdown criterion, it transforms metaphysical economic crises into physical phase-transition processes that can be warned in advance using mathematical integration.
Hassan Cessi Ibrahim, Damilare Timothy Ogunjobi, Philip Mensah
The networks that run operational technology (OT) substations, water treatment plants, oil and gas pipelines, and manufacturing lines are moving from a centralized control to a federated, multi-stakeholder architecture coordinated by permissioned distributed ledgers. Protection and control loops in the electrical grid and other critical infrastructure have protection-relay tripping times, IEC 61850 GOOSE message classes, and SCADA/PMU polling cycles that impose multi-millisecond to sub-second deadlines on protection and control operations, while Byzantine fault-tolerant (BFT) consensus protocols like PBFT, Tendermint, HotStuff, and HoneyBadgerBFT were designed for settlement workloads that can tolerate hundreds of milliseconds to seconds of latency. In this paper, we survey four representative BFT families, discuss their structural latency and scalability constraints for OT deployment, and introduce a hybrid consensus algorithm called IsoBFT (Isochronous Byzantine Fault Tolerance), which combines an optimistic single-round-trip fast path with a PBFT-style fallback mechanism based on a network-stability monitor, and elects a small rotating committee using a verifiable random function (VRF). A formal system model, safety/liveness/termination proof, and security analysis for eight attack classes are provided, with a proposition quantifying the degradation of the practical availability of the safety guarantee when the global Byzantine fraction is approaching one-third. Using realistic Modbus/DNP3/IEC 61850 OT traffic, the discrete-event simulation of the design IsoBFT managed to execute realistic workloads with median consensus latency ranging from 4.90ms at n = 10-50 to 9.17-11.26ms at n = 100 and n = 500, remaining competitive with or better than PBFT and Tendermint across this range. Committee-bounded communication overhead stayed essentially flat with respect to the number of validators from n = 10 to n = 50, but newly completed runs at n = 100 and n = 500 (n = 200 still outstanding) show overhead growing faster than the quadratic scaling of PBFT and Tendermint over that range, together with a heavy P95/P99 latency tail not present at smaller scale; this discrepancy with the theoretical scale-independence result is reported and discussed rather than resolved. IsoBFT could reduce the median latency by approximately 81% and 56% under up to 33% Byzantine faults compared to HotStuff and HoneyBadgerBFT, respectively, at n = 10-50, while maintaining the safety of the system; a Byzantine-resilience sweep at n = 100 shows a narrower advantage over PBFT/Tendermint than at smaller scale.
The democratisation of digital content creation tools has transformed media production, enabling individuals to move from being only consumers to active creators. Yet, content marketplaces and AI ecosystems remain highly centralised, limiting transparency, control, and fair compensation. Generative AI (GenAI) systems, trained on massive web-scraped datasets, exacerbate these issues by reusing creative work without consent, attribution, or reward, raising legal and ethical concerns. This thesis explores how decentralisation can redistribute power in the creative economy by giving creators agency over the use of their media in GenAI. First, we introduce a decentralised registry through which creators can assert opt-in/out preferences for AI training. Content is embedded with provenance metadata and registered with robust fingerprints, enabling provenance tracing even after editing or manipulation. This establishes machine-readable, traceable consent specification as the foundation for downstream attribution and reward. Building on this, we propose methods for training data provenance, attribution, and compensation in GenAI training. The Content ARCs (Authenticity, Rights, Compensation) framework defines a scalable protocol for managing rights and creator compensation. We instantiate this in a decentralised system that traces generative outputs back to the most influential training assets and executes royalty payments to contributors. Several practitioner-facing demonstrators developed in collaboration with GLAM (galleries, libraries, archives, and museums) professionals further illustrate how distributed ledgers could reshape licensing and reward in the creative economy. Further, GenAI models are prone to memorising training data and reproducing it at generation time, a phenomenon that is particularly problematic for copyrighted creative works, where such regurgitation undermines both creator rights and data privacy. To address this challenge, we present a decentralised federated learning protocol for diffusion models that reduces training data memorisation using a novel sample-based metric integrated into the protocol to detect and discourage memorisation. Complementing this, we develop a framework for end-to-end cryptographically verifiable AI pipelines using zero-knowledge proofs to enable trustless, privacy-preserving audits. Finally, we explore privacy-preserving natural language search across decentralised content repositories using encrypted queries for similarity search at scale. In this way, decentralisation supports discovery and access to creative content, completing a holistic body of work for a fairer, more transparent GenAI ecosystem and creative economy.
This article examines the structural tension between territorially organised sanctions regimes and decentralised crypto and tokenised assets. It argues that the principal challenge lies not in the formal applicability of sanctions, but in an enforcement gap: traditional coercive models are designed around identifiable persons and legally ownable property, while distributed ledger systems operate beyond territorial seizure and technical shutdown. The study advances lex digitalis rei sitae as a functional connecting factor linking proprietary and sanctions-related effects to the jurisdiction exercising effective regulatory control over access and liquidity. It further demonstrates that sanctions enforcement increasingly relies on address-based designations, compliance obligations imposed on regulated infrastructure, and programmable compliance embedded in smart contracts and oracle systems. The article concludes that effective digital sanctions require modernised conflict-of-laws rules, explicit recognition of digital identifiers as sanctionable control points, and compliance mechanisms consistent with rule-of-law safeguards.
本文在作者已发表的“消费黑洞”理论基础上,完成从批判到建构的理论跨越。文章指出,传统政治经济学的根本局限在于将“价值”视为一种可被生产、占有与分配的实体性存在。本文提出一个截然不同的起点:分配的本质并非物质财富的权属分割,而是主体贡献的本体论承认。 基于对笛卡尔“我思故我在”、黑格尔—马克思“我劳动故我在”的存在论谱系溯源,本文拓展劳动实践范畴,建构“贡献存在论”(Contribution Ontology)*3(WD-2026-B003),提出“我贡献故我在”的本体论命题。本文确证:人的社会性存在通过劳动、消费、关系、文明四维贡献结构得以显现。这是马克思实践存在论在数字时代的延伸与深化。 依托贡献存在论,本文揭示资本主义的本质矛盾是制度化的“存在论暴力”(Ontological Violence)*4(WD-2026-B004)——资本通过三重褫夺否定消费、关系、文明维度的人类贡献。在此基础上,本文界定“消费无产阶级”(Consumption Proletariat)*5(WD-2026-B005)范畴:同一批劳动者在生产中是劳动无产阶级(被剥夺剩余价值),在消费中是消费无产阶级(被褫夺消费贡献价值)。这是劳动无产阶级的第二重属性,揭示当代资本主义“生产端剥削+消费端褫夺”的双重剥夺结构。 本文论证按消费贡献分配的历史必然性,建构“贡献流动理论”(Contribution Flow Theory)*6(WD-2026-B006)与“消费贡献值六重质变”(Sixfold Qualitative Transformation)*7(WD-2026-B007)核心架构。六重质变遵循“贡献值只升维不归零”的根本原则,实现消费贡献从隐匿到全球流通再到文明守护的全维度价值升维。第四重质变升华生成“公信值”(Public Trust Equity Value,PTV)——包含“社保值”(Personal Social Security Value,SSV)和“社权值”(Personal Governance Rights Value,GRV),标志着贡献从经济领域升华为公共治理领域。第五重质变升华生成“共信币”(Global Trust Coin,GTC)——归国家所有,全球流通,反制资本霸权。第六重质变升华生成“圣火币”(Eternal Fire Coin,EFC)——国家消耗共信币于全人类最高事业时燃烧升华,锚定国际治理话语权。 战略层面,本文提出“利益虹吸效应”(Interest Siphon Effect)*8(WD-2026-B008)理论,论证通过市场化理性选择实现消费者觉醒、资源集聚、主权转移的四阶段和平升维路径。本文确立的按消费贡献分配制度框架,是实现从资本主权到消费者主权(进而指向贡献者主权)文明和平升维的战略方案。 本文的最高文明论断是:共信主义(WD-2026-999)不是与暴力文明、资本文明、劳动文明并列的第四种特殊文明,而是人类文明的完成形态——一个终于承认一切贡献的普遍文明。它是共产主义在数字时代的制度化展开,是人类分配制度演进中从局部到全域的必然升维。共信主义不是资本的敌人,而是资本的归宿——它将资本从压迫和异化的根源,转化为服务人类共同福祉的贡献形态。按贡献分配是对“按资分配”和“按劳分配”进行历史扬弃后的完成形态。 关键词:按贡献分配(WD-2026-000);按消费贡献分配(WD-2026-B001);消费者主权(WD-2026-B002);贡献存在论(WD-2026-B003);存在论暴力(WD-2026-B004);消费无产阶级(WD-2026-B005);贡献流动理论(WD-2026-B006);六重质变(WD-2026-B007);贡献者主权(WD-2026-B009);共信主义(WD-2026-999) This paper,building upon the author‘s previously published theory of the Consumption Black Hole,completes the transition from critique to construction in political economy.It argues that the fundamental limitation of traditional political economy lies in treating“value”as a substantive entity that can be produced,possessed,and distributed.The paper proposes a radically different starting point:the essence of distribution is not the division of material wealth,but the ontological recognition of subjective contribution. Based on a critical examination of the ontological genealogy from Descartes’“I think,therefore I am”to Hegel-Marx‘s“I labor,therefore I am,”this paper extends the category of labor practice to the broader domain of contributive existence.It demonstrates that human social existence manifests through multiple dimensions——labor,consumption,relationality,and civilization——which together constitute the four-dimensional ontological structure of human contribution. Drawing upon this framework,the paper deconstructs the deep operational logic of capitalism:the essential contradiction of capitalism is not superficial distributional inequality,but institutionalized Ontological Violence——the systematic deprivation of contributions in the dimensions of consumption,relationality,and civilization.On this basis,the paper defines the category of the Consumption Proletariat as the second attribute of the proletariat,revealing the complete structural mechanism of dual deprivation in contemporary capitalism. Integrating the materialist premises of digital productive forces——big data,blockchain,and artificial intelligence——this paper demonstrates the historical inevitability of Distribution According to Consumption Contribution.It constructs a Sixfold Qualitative Transformation framework:welfare-based→savings-based→investment-based→public governance(Public Trust Equity Value,embracing Social Security Value and Governance Rights Value)→international(Global Trust Coin)→civilizational(Eternal Fire Coin)——achieving full-dimensional value return from contribution visibility to global circulation to civilizational guardianship.At the strategic level,this paper proposes the Interest Siphon Effect theory,demonstrating that consumer resources constitute the structural core node of capital circulation.It outlines a four-stage peaceful evolutionary path of human civilizational upgrading and capital reclamation from consumer awakening to sovereignty transformation,ultimately pointing toward Contribution Sovereignty. The supreme civilizational thesis of this paper is:Convivialism is not the fourth special civilization alongside the civilizations of violence,capital,and labor,but the completed form of human civilization——a universal civilization that finally recognizes all contributions.It is the institutional unfolding of communism in the digital age and the inevitable upgrading from partial to universal recognition in the evolution of distribution systems.Convivialism is not the enemy of capital,but its ultimate destination——transforming capital from a source of oppression and alienation into a contribution form that serves human common well-being. Keywords:Distribution According to Contribution(DAC,WD-2026-000);Distribution According to Consumption Contribution(DACC,WD-2026-B001);Consumer Sovereignty(WD-2026-B002);Contribution Ontology(WD-2026-B003);Ontological Violence(OV,WD-2026-B004);Consumption Proletariat(CP,WD-2026-B005);Contribution Flow Theory(CFT,WD-2026-B006);Sixfold Qualitative Transformation(SQT,WD-2026-B007);Contribution Sovereignty(WD-2026-B009);Convivialism(WD-2026-999)
Akhter Javed, Huma Gul, Ali Husnain, Rahmat Said · 5 authors
Background: In this study, the increased complexity of today supply chains and explain why conventional forecasting and inventory management techniques are inadequate in today's dynamic and uncertain market conditions. As globalization and data increase, AI has become a gamechanger in delivering better demand forecasting and inventory management, in turn driving a better operation and cost savings. Objectives: This study seeks to assess the performance of AI-based demand forecasting models combined with inventory optimization methods on improving the overall performance of the supply chain. Methods: A quantitative, data-driven methodology was employed, and secondary data were used, including historical demand, inventory levels, and other external data that included seasonality and economic indicators. Demand forecasting models: Advanced machine learning and deep learning models such as Long Short-Term Memory (LSTM), Random Forest and Gradient Boosting were used for demand forecasting. The results of the forecasts were fed into an inventory optimization system using reinforcement learning for dynamic decision-making. Standard deviations like Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to measure the model's performance along with cost-performance analysis. Results: The accuracy of the prediction is significantly higher in AI-based models, especially the LSTM model, than the traditional models, which decreases the errors of the prediction and enhances its responsiveness. AI-powered inventory optimization resulted in significant savings on inventory holding and shortage/cost of order, and improved service levels and inventory stockout rates. The use of external data had yet further improved predictive performance. Conclusion: AI-powered demand forecasting and inventory optimization offer a solid solution to improve the efficiency of the supply chain, make intelligent decisions and minimize operational costs. References Ahn, H. I., Song, Y. C., Olivar, S., Mehta, H., & Tewari, N. (2024). GNN-based probabilistic supply and inventory predictions in supply chain networks. arXiv. Albayrak Ünal, Ö., Erkayman, B., & Usanmaz, B. (2023). Applications of artificial intelligence in inventory management: A systematic review of the literature. Archives of Computational Methods in Engineering. Advance online publication. https://doi.org/10.1007/s11831-023-09977-2 Ayub, M. I., Gharami, A. K., Nitu, F. N., Uddin, M. N., Islam, M. I., Nijhum, A. M., … Yezdani, S. (2025). AI-driven demand forecasting for multi-echelon supply chains: Enhancing forecasting accuracy and operational efficiency through machine learning and deep learning techniques. 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Yurii Koroliuk, Olha Vdovichena, Анатолій Вдовічен
The purpose of the paper is to analyze the relationships between digital skills of participants in the educational process, their readiness for digital transformation, and the barriers to technology integration in economic education in Ukraine, with a particular focus on the implementation of enterprise resource planning (ERP) systems. The study also aims to examine how awareness of digital technologies and perceptions of their benefits influence educational outcomes and professional preparedness. Methodology. The research employs structural equation modeling (SEM) to investigate the relationships between key constructs, including technology awareness, perceived usefulness, educational readiness, digital skills, barriers to implementation, readiness for change, and students’ preparedness for professional activity. The empirical analysis is based on a dataset of 256 respondents (ID 1–256) collected through an online survey conducted between May and August 2025 among individuals involved in economic education in Ukraine. The questionnaire was designed to assess respondents’ awareness, perceptions, and readiness to integrate modern digital technologies (AI, blockchain, ERP, RPA, and digital educational platforms) into the training of economic specialists, as well as the availability of technological resources at the university level. The survey included 33 Likert-scale items covering demographic characteristics, professional experience, digital competencies, attitudes toward emerging technologies, access to infrastructure, alignment of curricula with labour market needs, and barriers to technology integration. The instrument provided both quantitative and qualitative insights into participants’ experiences. Results. The findings demonstrate that digital competencies and perceived usefulness of technologies significantly influence readiness for digital transformation and learning effectiveness. Organizational support and systematic user training are identified as critical success factors for ERP implementation. Furthermore, technology awareness and readiness for change mediate the relationship between digital skills and students’ preparedness. The integration of ERP systems enhances practical competencies in business process management and strengthens analytical thinking. Practical implications. The results provide a foundation for improving economic education through the systematic integration of ERP systems into curricula. The study highlights the importance of investing in digital skills development, academic staff training, and institutional support mechanisms to ensure effective technology adoption and alignment with labour market demands. Value / originality. The paper contributes to the literature by offering an integrated empirical SEM-based model linking digital competencies, ERP adoption, and educational outcomes in the context of Ukraine, providing a scientifically grounded approach to modernising economic education and enhancing graduates’ competitiveness in the global labour market.
John Alexander Taborda, Cesar Enrique Polo Castro, Miguel Martínez
Just energy transitions in the Global South unfold under conditions of institutional fragmentation, fiscal constraints, and high socio-ecological turbulence, making governance capacity a critical bottleneck for effective decarbonization and climate justice. This study proposes the Cybernetic Environmental Hub (CEH) framework, which extends the Viable System Model (VSM) to sustainability governance by integrating AIoT-enabled environmental monitoring, Early Warning Systems, decentralized data governance, and justice-centered institutional design. Methodologically, the article is primarily a conceptual framework paper accompanied by an illustrative single-site qualitative case study designed to probe the plausibility and diagnostic utility of the proposed architecture rather than to generate statistical generalization. The research combines theoretical development with participatory territorial diagnostics in the Caribbean Mining Corridor, where socio-ecological challenges were collected through participatory innovation workshops, thematically coded, and mapped onto the five VSM subsystems to identify systemic “variety gaps.” The analysis indicates that fragmented operational initiatives coexist with weak meta-systemic coordination, limiting adaptive capacity in energy transition processes. The CEH architecture is proposed to address these deficiencies by embedding AIoT sensing, federated learning, blockchain-based coordination, and Early Warning Systems within recursive governance structures and is grounded in a real cyber-physical deployment of around 90 monitoring stations across Albania, La Jagua de Ibirico and Algarrobo. The study also introduces a Territorial Governance Maturity Model (H1–H3) to diagnose systemic learning capacities and transition readiness across technological, institutional, data governance, and justice dimensions. The findings suggest that cybernetic environmental hubs may function as socio-technical infrastructures supporting coordinated, adaptive, and justice-centered energy transitions in the Global South, while comparative empirical evidence remains an agenda for future work.
Abdulaziz Al-Saab, Amal A.M. Elgharbawy Elgharbawy
This study presents a bibliometric and critical review of global research on halal food control systems, with particular attention to countries developing new halal regulatory frameworks, including Saudi Arabia and the wider Arab region. A systematic review of Scopus-indexed publications from 2010 to 2025 was conducted following PRISMA guidelines. The final global corpus comprised 847 peer-reviewed articles analysed using VOSviewer and Biblioshiny through co-authorship, co-citation, bibliographic coupling, keyword co-occurrence, and thematic evolution analyses. An additional subset of 82 Arab-region studies, covering Gulf Cooperation Council and wider Arab League states, was manually coded using the FAO/WHO five-component national food control system framework. The field grew at an annual rate of 18.3%, with Malaysia and Indonesia contributing 43% and 28% of publications, respectively. Five major research clusters were identified: fatwa-based legislation; multi-agency governance; inspection, enforcement, and laboratory systems; information, education, communication, and training; and emerging technologies, including blockchain and artificial intelligence. Despite rapid growth, the literature remains geographically concentrated, theoretically underdeveloped, methodologically homogeneous, and largely silent on the cost-benefit implications of halal control systems. Few studies integrate Maqasid al-Shari’ah with risk-management approaches or examine halal governance as a complete regulatory system. By applying the FAO/WHO framework, this review moves beyond isolated certification and supply-chain perspectives. It demonstrates that Arab-region halal governance exhibits a distinctive “law-rich but evidence-poor” profile and proposes a research agenda addressing institutional performance, empirical evidence, regulatory effectiveness, and economic trade-offs.
Smart technology and innovation have become increasingly important in transforming seaweed aquaculture into a more sustainable, efficient, and data-driven industry. This study conducted a global bibliometric and scientometric review to examine research trends, technological developments, collaboration networks, and emerging scientific directions related to smart seaweed aquaculture. Bibliographic data were retrieved from the Scopus database using a structured search string covering technologies such as Internet of Things (IoT), artificial intelligence (AI), automation, remote sensing, blockchain, machine learning, and digital monitoring systems. The collected datasets were analyzed using RStudio through the Bibliometrix package and visualized using VOSviewer for network mapping and thematic analysis. Findings revealed a continuous increase in scientific publications, particularly after 2015, indicating growing global interest in precision aquaculture and sustainable marine resource management. The results identified China, India, Indonesia, and the United States as leading contributors in terms of research productivity and international collaboration. Keyword co-occurrence and thematic evolution analyses demonstrated the increasing integration of AI, IoT, automation, environmental monitoring, and sustainability-focused technologies within seaweed farming systems. Scientometric clustering further highlighted the interdisciplinary nature of the field, combining marine science, environmental studies, biotechnology, and digital innovation. The study also identified research gaps associated with technological accessibility, collaboration disparities, and sustainable implementation in developing regions. Overall, the findings confirm that smart technologies are playing a transformative role in advancing seaweed aquaculture toward more intelligent, climate-resilient, and sustainable production systems globally.
Lucas Monteiro de Oliveira, Olívia Brandão Melo Campelo
O presente artigo tem por objetivo analisar os limites jurídicos da autoexecução nos smart contracts, especialmente quando tais instrumentos, estruturados em tecnologia blockchain, produzem efeitos obrigacionais de difícil ou impossível reversão técnica. Parte-se do problema segundo o qual a programação contratual pode executar automaticamente prestações, transferências patrimoniais ou efeitos negociais sem intervenção humana posterior, ao mesmo tempo em que o ordenamento jurídico brasileiro preserva institutos como arrependimento, anulação, resolução, restituição, responsabilidade civil e vedação ao enriquecimento sem causa. A pesquisa, de natureza bibliográfica e qualitativa, examina a compatibilidade entre a lógica algorítmica dos contratos inteligentes e os fundamentos clássicos do Direito Civil e do Direito do Consumidor. Sustenta-se que a irreversibilidade técnica não pode ser convertida em irreversibilidade jurídica, pois a eficácia automática do código não afasta a incidência das normas relativas à validade do negócio jurídico, à boa-fé objetiva, à função social do contrato, à proteção do consumidor e à reparação de danos. Conclui-se que os smart contracts podem ser admitidos no ordenamento brasileiro, desde que sua arquitetura tecnológica permaneça subordinada à normatividade jurídica, mediante mecanismos de reversão, compensação, suspensão, auditoria, governança e responsabilização.
Huriye Gonca Di̇ler, Münevvere YILDIZ, N. Serap VURUR, Letife Özdemir
In today's world, sustainability strategies play a critical role in the transformation of global economies and industries. Green Economic Growth (GEG), which prioritizes environmental factors, is gaining increasing importance. Financial and green innovation are identified as the main driving forces behind GEG. However, research on the effects of these factors in OECD countries remains limited, and existing findings often show inconsistencies regarding the direction and magnitude of these effects. This study aims to comprehensively examine the impact of financial and green innovation on GEG in OECD countries. Using annual data from 15 OECD countries for the period 1996–2021, panel data techniques are applied. Cointegration tests are conducted to determine the presence of long-run relationships among the variables. Subsequently, long-run coefficients are estimated using the panel quantile regression method. The robustness of the findings is tested through OLS and fixed effects models. Additionally, causality tests are employed to explore the directional relationships between the variables. The results indicate that green innovation has a positive long-run effect on GEG, whereas financial innovation exerts a negative impact. Causality tests reveal bidirectional relationships among all variables. Policy recommendations include the promotion of green bonds and sustainable finance instruments, support for green investments through regulations that take environmental risks into account, and the expansion of access to green projects via technologies such as blockchain-based carbon markets. This research provides valuable insights for policymakers in designing more effective strategies to foster sustainable economic growth.
The rapid advancement of digital technologies has significantly transformed auditing practices, leading to the emergence of audit analytics as an important research domain that integrates accounting, auditing, and data science. This study aims to examine the evolution, intellectual structure, influential contributions, and emerging research trends in audit analytics through a bibliometric analysis approach. Data were collected from the Scopus database using relevant keywords related to audit analytics and analyzed using VOSviewer to perform citation analysis, keyword co-occurrence analysis, density visualization, and collaboration network analysis. The findings indicate that audit analytics research has experienced substantial development, particularly with the increasing adoption of big data analytics, artificial intelligence, machine learning, predictive analytics, blockchain, and automation technologies. Citation analysis identifies key contributions focusing on the role of big data and artificial intelligence in improving audit quality, audit judgment, fraud detection, and decision-making processes. The keyword analysis reveals that recent research trends have shifted from traditional analytical methods toward intelligent and automated audit systems that support continuous auditing and risk-based decision-making. Furthermore, collaboration analysis demonstrates the global nature of audit analytics research, with the United States emerging as the most influential contributor and strong research connections among countries and institutions. This study contributes to the literature by providing a comprehensive understanding of the development trajectory of audit analytics and identifying future research opportunities related to generative artificial intelligence, explainable AI, cybersecurity, and digital audit transformation.
AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.
As financial fraud becomes more sophisticated and financial services are increasingly digitized, artificial intelligence (AI) and machine learning are emerging as pivotal technologies for risk management and compliance. While research into AI-driven fraud detection is advancing rapidly, the intellectual structure and theoretical underpinnings remain fragmented. This paper provides a systematic review of 118 peer-reviewed articles published between 2015 and 2025, combining bibliometric science mapping with the SPAR-4-SLR protocol to ensure rigour, transparency and replicability. Through co-word network analysis, thematic mapping and conceptual clustering, the study traces the field’s evolution from rule-based systems to adaptive anomaly detection, explainable AI and compliance models, with a focus on digital payment ecosystems and blockchain-enabled applications. The analysis highlights key theoretical anchors, including Fraud Triangle Theory, Agency Theory, Game Theory, Trust and Signalling Theories and regulatory compliance perspectives. It also identifies underexplored areas such as federated learning, algorithmic auditing and cross-jurisdictional intelligence. By mapping theoretical foundations and thematic development, this study offers an evidence-based account of how AI in fraud detection has evolved. It concludes by proposing a future research agenda emphasizing transparency, ethical assurance and global governance alignment, advancing financial risk management through conceptual clarity, methodological guidance and actionable pathways for responsible AI adoption.
Recent advancements in technology, along with the availability of large volumes of healthcare data, offer an opportunity to adopt innovative technologies such as artificial intelligence (AI), machine learning, and big data in healthcare for better health service delivery. The use of innovative technologies such as artificial intelligence, machine learning, and big data enables efficient decision-making, disease detection, and personalized treatment. This paper reviews machine learning and big data in personalized medicine, presenting details about various tools that can be utilized within the context of healthcare, such as predictive modeling, data mining, and healthcare analytics. Furthermore, emerging technologies in personalized medicine have been discussed, including federated learning, blockchain technology, and real-world data. In addition, the paper also discusses existing developments in intelligent healthcare systems, such as patient monitoring, adaptive learning models, and using healthcare analytics for decision-making processes. Additionally, the paper highlights existing key challenges related to applying machine learning and big data for personalized healthcare, including data heterogeneity, lack of high-quality training data, algorithmic biases, difficulty in model interpretation, issues with security and data privacy, and technical barriers. Finally, the paper highlights the research gaps, examines the existing ways of addressing the problem, and provides recommendations regarding the future of personalized medicine using AI technology.
Open access
2 source records
Artificial Intelligence in Healthcare
Machine Learning in Healthcare
Artificial Intelligence in Healthcare and Education
Shahreza Shauqi Ismail, Nura Abubakar Allumi, Yousif Munadhil Ibrahim
Purpose: The aim of this study is to analyze the evolution of research trends in green supply chain management (GSCM) in agriculture, constructing an intellectual framework to improve the efficiency and environmental sustainability of the supply chain with smart agricultural technologies. Design/methodology/approach: The review used the bibliometric approach with two science mapping approaches (i.e., co-citation and co-word analysis) were perfomed to analyze 381 articles published in the Web of Science (WoS) database to investigate past and future research direction in GSCM using the VOSviewer software. Findings: Previous studies mainly focused on CE integration, sustainable agri-food supply chain design, and blockchain in supply chain management, whereas future research is expected to emphasize green logistics, low-carbon supply chains, smart circular systems, and digital innovation in green agri-SCM. Limitations and Research implications: This study is limited to the WoS database and two bibliometric techniques. Future research should validate the identified themes through additional bibliometric and empirical studies. Practical Implications: This study provides practical insights for managers, policymakers, and researchers to support the development of sustainable agricultural supply chains through circular economy, green logistics, and digital technologies. Originality/value: This study provides a comprehensive knowledge map of GSCM in agriculture by identifying past research themes and future research directions through bibliometric analysis
Since announcing the implementation of a single electronic health record for all South Africans, the government has not yet informed healthcare facilities of how this would be accomplished. The siloed South African healthcare system would have to be redesigned to accommodate a single electronic health record. A systematic literature review conducted across three databases returned 9 790 results. By applying ten filters, 22 documents were eventually retrieved for analysis. The analysis showed that existing research focuses on healthcare architectures from a theoretical perspective. Therefore, the literature review revealed a practically based research deficiency and a lack of theoretical studies merged with practical cases. Seeking to enhance the understanding of designing a single electronic health record, the documents were analysed using a qualitative inductive content analysis technique, revealing that a single electronic health record cannot be formulated using a fully centralised architecture as this is not practical. A fully decentralised architecture, such as blockchain, is equally infeasible because this requires significant changes to the existing systems and infrastructure and would require re-skilling system builders. Since the South African healthcare architecture is already decentralised, hybrid architecture incorporating edge computing with clusters of systems and information that connect using middleware should be considered.
Digital accounting has become an emerging concept that integrates traditional accounting with digital technologies and cybersecurity. This article explores, from an academic perspective, the ethical, technical, and information security implications associated with the digital transformation of accounting, both in professional business practice and university teaching. Using a mixed methodology (quantitative and qualitative), the global and regional state of the art is analyzed, incorporating a case study applied in Costa Rica. The findings demonstrate that the rapid digitization of accounting raises ethical dilemmas (such as data confidentiality and algorithmic transparency), technical challenges (adoption of accounting 4.0, artificial intelligence, blockchain, and cloud computing), and significant cybersecurity risks to the protection of financial information. In the Costa Rican and Latin American context, a gap is observed between technological evolution and current regulatory frameworks, as well as a need to strengthen professional training in digital ethics. It concludes with more specific recommendations for academia, the accounting profession, and public policy, aimed at promoting a culture of responsible innovation, improving security controls in financial management, and updating the skills of public accountants to face each of the challenges of the digital age.
The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.
The rapid growth of Real World Asset (RWA) tokenization faces a critical vulnerability: the "Physical Oracle Problem." While blockchain ensures digital immutability, it remains blind to the physical state of the underlying asset (e.g., structural degradation in real estate or hidden damage in naval vessels). This document introduces the Prop Trust Verified Standard (PTVS), a comprehensive forensic methodology designed to bridge this gap. Developed by Aurema Group, PTVS establishes a rigorous protocol for physical asset auditing, combining certified judicial expertise (Perito Judicial) with cryptographic anchoring. The methodology ensures that physical inspections, material verifications, and compliance checks are immutably recorded and linked to smart contracts (e.g., ERC-3643), providing institutional-grade trust for Family Offices, tokenization platforms, and regulatory bodies under frameworks like eIDAS (EU 910/2014). This report outlines the core principles, verification workflows, and case study applications of PTVS in real estate and maritime sectors. Español: El rápido crecimiento de la tokenización de Activos del Mundo Real (RWA) enfrenta una vulnerabilidad crítica: el "Problema del Oráculo Físico". Mientras que la blockchain garantiza la inmutabilidad digital, permanece ciega al estado físico del activo subyacente (ej. degradación estructural en inmuebles o daños ocultos en embarcaciones). Este documento presenta el Estándar Prop Trust Verified (PTVS), una metodología forense integral diseñada para resolver esta brecha. Desarrollado por Aurema Group, PTVS establece un protocolo riguroso de auditoría física de activos, combinando la pericia judicial certificada con el anclaje criptográfico. La metodología garantiza que las inspecciones físicas, verificaciones de materiales y controles de cumplimiento se registren de forma inmutable y se vinculen a contratos inteligentes (ej. ERC-3643), proporcionando confianza de grado institucional para Family Offices, plataformas de tokenización y organismos reguladores bajo marcos como eIDAS (UE 910/2014). Este informe detalla los principios fundamentales, flujos de trabajo de verificación y aplicaciones prácticas de PTVS en los sectores inmobiliario y naval.