The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends.
The subject matter of the article is the cryptographic integrity of digital authentication systems facing quantum computing threats, specifically focusing on post-quantum alternatives and efficient authenticated data structures. The goal is to design and formally analyze VERKLE-FRI—a hybrid architecture synthesizing Verkle tree proof-size reduction with FRI-based quantum-resistant commitments, establishing a scalable, stateless, and quantum-secure framework. The tasks are: analyze limitations of hash-based signatures and Merkle trees; evaluate polynomial commitment schemes (KZG, Bulletproofs, FRI, lattice-based); propose a hybrid Verkle-FRI design; develop a formal security proof against classical and quantum adversaries; execute complexity analysis with concrete implementation parameters. The methods used are: theoretical cryptographic analysis, formal security modeling via reductionist proofs, algebraic methods over finite fields, polynomial interpolation, random oracle model, FRI protocol with DEEP-FRI optimization, Merkle trees, vector commitments, and asymptotic complexity analysis. The following results were achieved: a novel architecture where Verkle node vectors are polynomial-encoded, committed via Merkle trees over FRI codewords, and verified through FRI with out-of-domain sampling. A formal proof establishes λ-bit quantum security using 2λ-bit hash functions. Complexity yields proof size O(λ log² N), prover time O(λ N log N), and verifier time O(λ log N). Concrete 128-bit quantum parameters include SHA3-512, field size ≈2²⁵⁵, branching factor 256, and 128 FRI rounds, achieving soundness error ≤2⁻¹²⁷. For a concrete benchmark authenticating 2²⁶ elements, a traditional Merkle proof requires ≈0.8 KB, whereas our VERKLE-FRI proof requires ≈180 KB. While larger, this provides quantum resistance and eliminates the trusted setup, a critical trade-off for long-term security. Conclusions. Scientific novelty consists in: 1) the first hybrid Verkle-FRI architecture replacing pairing-based assumptions with hash-based proximity testing; 2) a formal security proof reducing security to hash collision resistance and FRI soundness; 3) quantified efficiency-security trade-offs; 4) a viable pathway for quantum-resistant infrastructure in blockchains, software distribution, and government communications.
Open access
Cryptographic Implementations and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Purpose This study aims to conduct a comprehensive scientometric review of social sustainability in supply chains, analyzing 970 articles published between 2002 and 2024 from Web of Science (WoS). The research addresses the critical gap in understanding social sustainability aspects compared to environmental dimensions in supply chain literature. Design/methodology/approach The study uses CiteSpace software to create structure-based visualizations and networks, analyzing prominent authors, documents, keywords, journals, countries and institutions in the field. The methodology involves systematic review and bibliometric analysis of the literature to identify key themes and patterns in social sustainability based supply chain research Findings The analysis reveals that research is predominantly concentrated in tier-1 and high-GDP nations. Key industries focusing on social sustainability include agriculture, food and beverage, transportation and logistics, manufacturing, fashion and retail sectors. These sectors primarily address issues such as labor regulations, fair wages and local community involvement and diversity. The study identifies major motor themes through co-citation and cluster analysis. Research limitations/implications The study is limited to articles indexed in WoS, potentially excluding relevant research from other databases. Future research directions should focus on advancing social supply chain management, integrating emerging technologies such as Blockchain, sensors and digital transformation, improving risk management, implementing fuzzy logic decision-making and enhancing transparency. Practical implications The findings provide organizations with insights into implementing social sustainability practices across supply chains. The study offers guidance for industry practitioners on addressing social challenges and integrating sustainable practices into their operations, particularly in areas of labor rights, community engagement and technological integration. Social implications The research highlights the importance of addressing social sustainability in global supply chains and its impact on local communities, labor conditions and societal well-being. It emphasizes the need for greater attention to social aspects of sustainability, particularly in developing nations and lower-tier supply chain partners. Originality/value To the best of the authors’ knowledge, this study presents the first large-scale scientometric analysis of social sustainability in supply chains, offering a comprehensive overview of the field’s evolution from 2002 to 2024. It provides valuable insights for policymakers, firms, society and academia while establishing a roadmap for future research and practical implementation of social sustainability in supply chains.
Initial Coin Offerings (ICOs) have emerged as an innovative mechanism for raising capital, particularly for blockchain-based projects. However, the lack of regulatory oversight and the prevalence of low-quality information raise important questions about what truly drives ICO success. While existing literature focuses predominantly on technical and signalling variables, the role of investor decision-making remains theoretically underdeveloped and empirically underexplored. This paper addresses this gap by pursuing two objectives. First, we identify the drivers of ICO success using a probit model applied to an original sample of 535 ICOs conducted between January 2016 and May 2021. Second, we investigate investor decision-making patterns using a novel dataset of 200 active crypto-forum participants over the same period. Our results have three main findings, though with modest statistical strength than initially estimated. (I) Marketing channels are the most consistent predictor of ICO success across the sample period, clearing conventional significance thresholds only in the pooled sample (z = 1.90, p<0.10), with each additional channel raising the probability of soft-cap achievement by approximately 1.0 percentage point. (II) Team presentation and video presentation show no meaningful influence on success in any period. (III) Whitepaper availability is not statistically significant even in pooled sample, reinforcing rather than qualifying its irrelevance as a predictor; the number of accepted cryptocurrency price speculation rather than project fundamentals, consistent with mood and sentiment dominating information-based decision making in ICO markets, though this finding should be read alongside the data limitations discussed in 3.B. These findings contribute to the behavioural finance literature by providing an operational definition of ‘investor mood’ and demonstrating its empirical relevance in crypto markets. We conclude that understanding investor mood is not a secondary question but a necessary complement to technical analysis of ICO success.
This paper presents a threshold-cryptographic architecture for reducing the risk of premature leakage of digital examination papers during the interval between question-paper finalization and examination administration. The proposed design separates the data path from the control path. Examination content is encrypted using a fresh AES-256-GCM key, while the key is protected through envelope encryption under a key-release service. The capability to release that key is distributed using (k,n)-Shamir secret sharing across independent custodians, preventing any single custodian from unilaterally authorizing early release. At the scheduled release time, a quorum-based time authority provides an independently attested timestamp. Once the required time quorum and custodian threshold are satisfied, the key-release service reconstructs its private key within an HSM boundary, unwraps the examination key, and derives recipient-specific keys for individual examination centers. These keys are separately wrapped under each center's registered public key, limiting the impact of a compromise at any single examination center. The paper presents an actor and trust model, an explicit adversary model, a step-by-step release protocol, a threat-to-control security analysis, and a qualitative comparison with physical custody, blockchain-anchored distribution, and time-lock-puzzle-based timed-release cryptography. It also explicitly discusses residual risks, including custodian collusion, post-decryption optical or physical exfiltration, hardware and supply-chain trust, and compromise of the time-authority quorum. The architecture is presented as a research design rather than a claim of unconditional leak prevention. Future work includes implementing a prototype, evaluating quantitative performance, replacing reconstruct-and-zeroize key handling with threshold decryption, evaluating post-quantum key-encapsulation mechanisms, and conducting a formal mechanized security proof.
Open access
2 source records
Chaos-based Image/Signal Encryption
Physical Unclonable Functions (PUFs) and Hardware Security
The rapid diffusion of digital technologies has fundamentally reshaped the way organizations generate and report financial and non-financial information, challenging traditional audit approaches that rely on manual and sample-based procedures. Building on this context, this paper aimed to provide a comprehensive synthesis of empirical evidence regarding the impact of digital technologies on auditing and to identify the key factors influencing their adoption across internal, external, and public sector audit functions during the 2015–2026 period. Using a qualitative descriptive design and a systematic literature review guided by the PICOC framework and PRISMA protocol, 33 relevant articles indexed in Scopus were selected from an initial pool of 959 publications. The findings showed that the use of various technologies, including computer-assisted audit techniques (CAATs), audit analytics, big data, artificial intelligence, robotic process automation, blockchain, and process mining, generally enhanced the effectiveness and efficiency of audit procedures, strengthened internal controls, and reduced errors and financial statement restatements, while simultaneously repositioning auditors as more strategic and data-driven partners. At the same time, the success of digital audit transformation was strongly influenced by technological infrastructure, data governance and security, organizational capabilities, leadership support, regulatory environments, and auditors’ individual competencies, indicating that digitalization was neither a neutral nor an automatic process. This study provides practical implications for audit firms, internal audit units, supreme audit institutions, and regulators in developing more targeted and sustainable digital audit strategies, while also proposing future research directions concerning the organizational and institutional dynamics of digital auditing.
Ms. Gunavarthani S, Dr. Princy J, Ms. Samyuktha S K
The textile industry has undergone a dramatic change in recent times because organizations are incorporating digital technology solutions for addressing issues related to sustainability and fast-tracking the journey toward a circular economy. These include Digital Product Passports (DPP), blockchain, Radio Frequency Identification (RFID), the Internet of Things (IoT), Artificial Intelligence (AI), and Industry 4.0 technologies, among others. The current research intends to conduct a systematic review of the literature on the topic of digital transformation and sustainability in the textile industry. A Systematic Literature Review (SLR) was conducted following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. In all, 55 peer-reviewed journals from 2020 to 2026 have been reviewed based on a structured selection process and analyzed using the thematic analysis approach. Six themes have been identified in the literature, which are as follows: Digital Product Passport, Digital Traceability Technologies, Industry 4.0 & Artificial Intelligence, Circular Economy Practices and Circular Supply Chains, Sustainability and Environmental, Social & Governance (ESG), and Barriers, Challenges and Future Research Directions. The results show that digital technology greatly improves the traceability of products, efficiency, and resource recycling, facilitating sustainability along the supply chain. Yet, issues such as costly digital technology implementation, inadequate digital infrastructure, the absence of standardization in digital data structures, and organizational readiness hinder digital technologies' broader application. This research fills a gap in the literature in that it identifies a consolidated thematic framework explaining the role of digital technologies in transforming the industry sustainably. The results provide insights useful for academic studies, industry professionals, and policymakers working on sustainable textile ecosystems powered by digital technology.
Rahmat, Agus Surono, Agung Iriantoro, Maslihati Nur Hidayati
The digital transformation of land administration in Indonesia has accelerated the adoption of electronic land certificates as an instrument for improving administrative efficiency, data security, and legal certainty. This study examines the legal status of electronic land certificates within Indonesia’s national land law system and identifies the principal legal, institutional, governance, and technological challenges affecting their implementation. Employing a qualitative descriptive design with a normative juridical approach, the study analyzes the Basic Agrarian Law, the Electronic Information and Transactions Law, regulations issued by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN), and relevant legal and scholarly literature. The findings demonstrate that electronic land certificates have a valid legal foundation and offer significant advantages, including faster administrative procedures, enhanced document authentication through certified electronic signatures, improved protection of land records, and reduced risks of physical loss and document forgery. Nevertheless, their implementation remains constrained by regulatory inconsistencies, institutional capacity gaps, unequal digital infrastructure, cybersecurity risks, data protection concerns, and potential disputes arising from electronic system failures. The study further identifies permissioned blockchain as a potential complementary mechanism for strengthening data integrity, traceability, and transactional transparency, provided that its adoption is supported by appropriate legal and institutional safeguards. This study contributes an integrated legal–institutional–technological framework for understanding electronic land administration and argues that regulatory harmonization, strengthened digital governance, institutional capacity development, and resilient cybersecurity infrastructure are essential to ensuring legal certainty and sustainable protection of land rights in Indonesia.
Tokenized representations of cash-like instruments, comprising stablecoins, tokenized money market funds, and tokenized real-world assets, are increasingly positioned as core on-chain financial infrastructure, yet empirical evidence on how these instruments behave in practice remains limited. This paper reports a comparative empirical examination of public transaction-level blockchain data, covering adoption patterns, usage dynamics, and operational characteristics across three parallel case studies: USDC (stablecoin, Circle), BENJI (tokenized money market fund, Franklin Templeton), and BUIDL (tokenized U.S. Treasury, BlackRock via Securitize). On-chain metrics covering issuance and redemption activity, transfer behavior, wallet concentration, velocity proxies, and cross-chain deployment are interpreted against a four-layer reference architecture (asset representation, control-plane governance, settlement and finality, and composability). Results reveal systematic behavioral differences aligned with product intent and governance design: stablecoins function as high-velocity settlement instruments with broad address distribution, while tokenized investment products exhibit batch-oriented issuance, low circulation intensity, and concentrated holdings consistent with institutional custody and regulatory constraints. A live-pipeline extraction for BUIDL on Ethereum over the 90-day window ending 31 January 2026 yields a holder-level Gini coefficient of 0.8706 with a bootstrap 95% confidence interval of [0.7672, 0.9208] and a top-ten concentration share of 98.96%. Cross-chain deployment expands access but preserves reliance on dominant settlement layers. These patterns constitute an evidence-based framework for evaluating tokenized finance as production-grade financial market infrastructure.
Internet of Things (IoT) technologies in the healthcare industry, also known as the Internet of Medical Things (IoMT), have proven to greatly improve patient monitoring, diagnostics, and clinical decision-making. The increasing prevalence of resource-challenged medical devices, wireless connectivity, and cloud services, however, has brought new risks around security and privacy concerns that can now directly impact patient safety and data integrity. In this paper, a thorough study of 41 peer-reviewed research papers from January 2018 through May 2025 revealed the current state of security vulnerabilities and resilience strategies in healthcare IoT systems. It provides a comprehensive analysis of security threats at the device, network, and application levels such as unauthorized access, malware and ransomware, data breaches, and denial-of-service attacks delivered in a systematic manner. This contrasts with existing surveys, which consider single security mechanisms and improve upon various multi-layered security means such as AI-enabled anomaly detection, blockchain-based authentication and auditability, low-compute cryptographic techniques, and privacy-preserving methods such as federated learning. The outcomes also show that although emerging technologies add a great deal of security and trust capabilities, issues on scalability, interoperability, deployment, and regulations are not yet fully addressed. This review highlights important knowledge gaps and offers structured knowledge and future directions for research to address the design of secure, resilient, and practically deployable IoMT architectures for real-world healthcare environments.
The Role of Artificial Intelligence in Optimizing Supply and Demand Management in Modern Businesses: A Review Article Faezeh Mokarrami1 1- mokarrami76@gmail.comM.Sc. Student in Entrepreneurship, Small Business Concentration, Islamic Azad University, Electronic Branch Abstract This narrative review examines how artificial intelligence has transformed supply and demand management in contemporary supply chains, particularly within emerging enterprises and environments characterized by volatility and uncertainty. By integrating conceptual, historical, and applied literature, the article demonstrates how artificial intelligence enhances core supply chain functions—such as demand forecasting, inventory control, logistics planning, supplier selection, and risk management—through data-driven decision-making, intelligent automation, and predictive analytics. Furthermore, the strategic role of artificial intelligence in strengthening supply chain resilience, agility, flexibility, transparency, and sustainability is highlighted, especially when combined with machine learning, deep learning, big data, blockchain, and the Internet of Things. A central axis of this review is the transition from reactive, historical data-based forecasting toward demand sensing and proactive, real-time decision-making. Simultaneously, the article emphasizes that the adoption of artificial intelligence depends not only on technical capabilities but also on organizational readiness, institutional context, data governance, and ethical considerations such as fairness, transparency, and environmental impacts. Ultimately, this review concludes that artificial intelligence has evolved from a marginal tool to a structural driver of competitiveness and recovery capacity in supply chains, although significant gaps remain regarding human-AI collaboration, longitudinal evidence, and context-appropriate adoption.
This study develops and validates an ecological economics framework integrating green logistics practices to enhance supply chain resilience in Southeast Asia's automotive sector. Using mixed-methods analysis of 272 firms across Thailand, Indonesia, and Malaysia, structural equation modelling confirms three hypotheses: green logistics adoption significantly predicts resilience (β=0.38, p<0.001); ecological economics tools (full-cost accounting, ecosystem service valuation) double these gains through heightened environmental cost awareness; and the hybrid framework yields superior economic returns compared to standalone practices. Thailand leads (SCR=65.3) due to BCG policies, while Indonesia lags (ROI=9.1%) amid nickel dependency. Simulations project +34% resilience under carbon pricing scenarios. Qualitative interviews reveal disaster-driven adoption and SME capex barriers, with ECA>4.0 thresholds flipping green logistics from cost to profit centre. Findings advance dynamic capabilities theory with biophysical limits, resolve triple bottom line tensions, and deliver managerial roadmaps (rail pilots→FCA training→blockchain Scope 3) plus ASEAN policy blueprints (CBAM harmonisation, $500M capacity fund). The framework positions the ASEAN automotive sector for regenerative leadership, converting natural capital from externality to competitive asset amid global decarbonisation pressures. These findings offer actionable insights for managers, investors, and policymakers seeking to align profitability with ecological resilience in emerging economies.
Open access
Supply Chain Resilience and Risk Management
Sustainable Supply Chain Management
Infrastructure Resilience and Vulnerability Analysis
Khoya (khoa or mawa) is a traditional dairy product, prepared by heating and concentrating milk, which is widely used in preparation of indigenous milk sweets. But, challenges such as process variability, quality deterioration, microbial contamination, adulteration, limited shelf life and inefficient supply chain management hinder its production and distribution. New solutions to these challenges are available across the khoya value chain due to recent advancements in artificial intelligence (AI) and Industry 4.0 technologies. This review highlights the applications of AI in khoya processing, packaging, transportation, distribution and quality management. The role of machine learning, deep learning, computer vision, Internet of Things (IoT), digital twins, smart sensors, and blockchain in process optimization, automated quality inspection, adulteration detection, shelf-life prediction, intelligent packaging, cold-chain monitoring, logistics optimization and demand forecasting is explored. We also review AI-enabled analytical tools for rapid and non-destructive quality assessment, such as hyperspectral imaging, electronic nose, and electronic tongue. The review also discusses the contribution of AI to improving food safety, traceability, sustainability and operational efficiency, as well as to reducing post-harvest losses and environmental impacts. Finally, the paper discusses the existing challenges, future research directions, and prospects of AI-enabled smart dairy manufacturing. The review finds that AI can play a significant role in improving the quality, safety, efficiency, and sustainability of the khoya industry and helping its transition to intelligent and data-driven dairy processing.
Omojola Awogbemi, S. A. Aasa, Oluwaseun O. Martins, Anthony O. Onokwai
Abstract The worrisome economic, environmental, and energy security implications of the continuous use of fossil-based sources as road transport fuel have made Nigeria consider sustainable alternatives. With the country’s abundant natural gas reserves and growing climate commitments, compressed natural gas (CNG) presents a viable pathway for decarbonizing road transport, curbing urban air pollution, and ensuring energy security. The current study examines the adoption, deployment, and integration of CNG into Nigeria’s road transport ecosystem. The study reviews the CNG resources and infrastructure, impact and achievements, and highlights the challenges of CNG deployment as a road transport fuel, case studies from other jurisdictions, suggestions for improvement, and future research perspectives. Though reasonable grounds have been covered, overcoming the technological and infrastructure gaps, economic and financial inadequacy, health, environmental, and safety issues, ensuring social and stakeholder acceptance, and instituting appropriate policy and regulatory frameworks are fundamental to ensure scalability and energy security. Nigeria can leverage case studies from other jurisdictions to leapfrog and accelerate nationwide deployment, mitigate risks, and guarantee a low-carbon road transport future for Nigeria. More sensitization campaigns, investment and fiscal incentive models, price reduction strategies, and rapid upgrade of CNG infrastructure across the country to ensure wide acceptability, affordability, and nationwide deployment. Future research should integrate lifecycle and techno-economic analysis, smart metering, blockchain tracking, spatial modeling, macroeconomic impact, and process optimization to guide stakeholders in designing a resilient, inclusive, and scalable CNG transport framework for Nigeria.
This paper develops a Quantum-Institutional Automated Negotiation (QIAN) algorithm as an intelligent decision support system for carbon credit markets, contributing to quantum game theory applications in automated negotiation and institutional decision-making. We extend the Eisert–Wilkens–Lewenstein (EWL) framework by introducing an Institutional Filter Function Φ_C that maps continuous quantum strategies—phase shifts and superpositions—onto finite, legally viable contract archetypes. This filter models regulatory, political, and organizational constraints that collapse the infinite quantum strategy space into a tractable finite set, enabling computationally efficient decision support. We prove convergence of the automated negotiation algorithm to a Pareto-superior Nash Equilibrium and demonstrate, through Monte Carlo simulation with literature-calibrated parameters, that the collapsed quantum equilibrium yields a mean joint utility uplift of 13.5% over classical cooperation (95% CI: 9.8%–17.3%, p &lt; 0.001), with the upper bound reaching 17.3% and 26.8% of simulations achieving uplifts in the 15–30% range. The framework maps directly to blockchain-based smart contracts, providing a deployable mechanism for sustainable carbon markets that aligns with SDG 13 (Climate Action) and SDG 17 (Partnerships). This work advances quantum game theory from abstract formalism to computational institutional design, offering a novel decision support approach for negotiation analysis under real-world constraints.
The rapid growth of cybercrime has significantly increased the importance of digital evidence in criminal investigations and judicial proceedings. However, ensuring the admissibility and reliability of electronic evidence remains a complex challenge due to technological advancements, evolving legal standards, cross-border investigations, and concerns regarding evidence integrity. This narrative review examines the legal and forensic dimensions of digital evidence by synthesizing contemporary literature on its sources, characteristics, governing legal frameworks, and the factors influencing its acceptance in court. The review discusses key issues related to authentication, chain of custody, expert testimony, procedural fairness, and evidence validation, while also evaluating the impact of emerging technologies, including artificial intelligence, blockchain, the Internet of Things, and deepfake detection on digital forensic practice. The findings indicate that reliable digital evidence requires standardized forensic procedures, scientifically validated investigative methods, and harmonized legal frameworks capable of addressing rapidly evolving cyber threats. Strengthening collaboration among forensic practitioners, legal professionals, researchers, and policymakers will be essential for improving evidence integrity, enhancing judicial confidence, and supporting effective cybercrime investigations. The review provides an integrated perspective that contributes to ongoing discussions on developing secure, transparent, and legally robust digital evidence management practices.
Abstract Internet of Things (IoT) technologies and healthcare present revolutionary chances to improve operational efficiency, patient outcomes, and tailored medication transformation. This paper thoroughly investigates IoT in healthcare using Latent Dirichlet Allocation (LDA) to spot important trends and research gaps in current work. To achieve this, researchers have comprehensively analyzed 11,586 published papers from 2006 to 2024 which are extracted from Scopus database. Researchers have identified 2, 5, and 10 key topics to define significant areas of the research. Over time, it compares research topics to show how important areas, including wearable technology, artificial intelligence-powered analytics, blockchain for safe data management, and edge computing, have evolved. The paper additionally examines important issues, including data privacy issues, lack of interoperability, and restricted inclusiveness for underprivileged communities. Emphasizing inclusivity, ethical compliance, and pragmatic implementation tactics catered to different healthcare environments, a strategy framework is suggested to help solve these difficulties. This paper helps IoT implementation in healthcare advance by giving actionable insights, particular discoveries, and future research directions, thereby opening the path for more fair, efficient, and sustainable healthcare systems.
Under the dual carbon targets, China's energy companies are speeding up their green transformation, but they usually encounter some common obstacles including lack of capital, weak technical assistance and an incomplete risk control system. The combination of digital technology and financial services provides new approaches to solve these problems. According to the specific characteristics of the transformation of energy enterprises, this research examines the mechanisms of digital finance from two aspects – financing enhancement and technological enhancement. It is found that methods such as digital green loans, bonds and equity financing can efficiently relieve the financial pressure of enterprises, while technologies like big data, blockchain and artificial intelligence can greatly improve the accuracy of emission reduction and the efficiency of energy operation. Furthermore, the enhancing effects have regional differences and threshold characteristics. Thus, countermeasures are put forward from four fields: improving service provision, deepening technological integration, setting up a risk management system and improving policy regulation, which offer guidance for the actual transformation of energy enterprises and the development of relevant policies.
Network slicing and resource provisioning in 6G focus on creating multiple customized virtual networks over a shared infrastructure. However, these approaches also introduce challenges, like increased architectural complexity, higher implementation costs, security vulnerabilities between slices in resource optimization across highly dynamic and heterogeneous network environments. In this work, Exponentially Tactical Unit Algorithm (ETUA) is devised for network slicing in 6G. Initially, blockchain-enabled 6G network is simulated, and the set of features, like user device type, delay rate and packet loss rate are collected from various devices. Moreover, network slicing is done by ETUA that integrates Exponentially Weighted Moving Average (EWMA) and Tactical Unit Algorithm (TUA). Finally, resource allocation is performed using Attention High-order Deep Network (AHoNet) by considering the parameters that includes bit error probability, sum rate and trust. The efficacy of ETUA is examined by bit error probability, utility and latency with 0.012, 0.950 and 0.509 Sec.
The rapid development of Large Language Models (LLMs) has led to the wide adoption of autonomous AI agents. These agents increasingly form decentralized Agent-to-Agent (A2A) networks to collaborate on complex tasks. However, a key bottleneck is the cost of GPU inference, which requires a reliable compensation system for untrusted participants. While blockchains provide accountability, they are too slow and costly. Inspired by Payment Channel Networks (PCN), we propose Ledgent (a portmanteau of Ledger and Agent), an accountable A2A PCN architecture. We combine PCN primitives with the underlying LLM serving infrastructure, enabling pay-as-you-go token streaming, compute-aware routing, and response quality verification without relying on central server. Evaluation from both simulation and prototype shows that Ledgent achieves high throughput, while introducing minimal cryptographic latency overhead.