Currently, the most significant threat to the validity of academic credentials in the United States is the advanced forgery of transcripts along with diploma mills. This research study addresses the potential of blockchain technology as a decentralized means to protect academic credentials. By integrating recent academic research and technical frameworks, this study analyzes the shift from centralized databases to immutable, distributed ledgers. The integration of various perspectives, including advanced zero-knowledge proof architectures as well as legal frameworks for transnational data circulation, is a major innovation of this study. Using a systematic literature review and a case study approach, the research indicates that though blockchain's potential to enhance security and automate processes through smart contracts is indeed great, a number of legal, compliance, and technical barriers have to be removed for it to be a viable option. This study proposes that the combination of artificial intelligence (AI), along with blockchain technology, provides the most secure option for U.S. higher education institutions.
This chapter proposes an integrated Blockchain–IoT–AI framework for secure and intelligent quality traceability, particularly in agricultural and rice supply chains. It explains how IoT sensors can continuously collect physical and environmental information, AI models can analyze images and sensor data for quality assessment, and blockchain can securely record important quality events and processing information. The framework supports unique digital identities for rice batches, quality monitoring, defect detection, moisture estimation, quality scoring, and QR-based access to traceability information. The chapter examines applications in rice quality certification, smart rice mills, food safety, warehouses, export-quality monitoring, consumer verification, and government procurement. Challenges related to data quality, sensor reliability, interoperability, stakeholder participation, scalability, and regulatory coordination are also addressed.
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert keys. We further propose a sparse expert selection update mechanism that samples a few inactive experts during training and estimates whether they should replace the weakest expert in the current Top-k route. This mechanism enables the router to update expert selection as market conditions change while preserving sparse computation. Experiments against 22 baselines on stock and cryptocurrency markets show that TradingMoE improves cumulative return over the best-performing baselines by 30.89% and 30.7%, respectively. Rolling paper-trading experiments further demonstrate that its advantage persists under forward-only deployment.
Background Traditional Chinese Medicine (TCM) rheumatology presents unique challenges for AI-assisted clinical decision support, as the diagnostic process relies heavily on tacit knowledge and individualized reasoning. While Large Language Models (LLMs) have shown promise in medical applications, they remain limited by hallucination risks and inability to replicate expert TCM reasoning. Retrieval-Augmented Generation (RAG) offers a potential solution, yet its application to complex TCM dialectical reasoning remains underexplored. Methods We developed TCM-CoT-RAG, a hybrid framework combining RAG with Chain-of-Thought (CoT) prompting, grounded in 1,700 expert-curated clinical cases (1,600 for RAG retrieval; 100 for evaluation, including 50 for blinded expert review by three senior TCM rheumatologists). Deployed on Alibaba Cloud, the five system leverages state-of-the-art LLMs (DeepSeek-V3, Qwen3-235B) under a human-in-the-loop paradigm. We designed a dual-tier evaluation: (1) Objective extraction tasks (Task 1–2) quantified using F1-scores; (2) Generative tasks (Task 3–5) assessed using BERTScore. Two senior TCM rheumatologists (≥15 years clinical experience) blindly assessed model outputs, and a senior chief expert quantified consistency between model predictions and ground truth (GT). Comprehensive ablation studies (S1-S4, S-Skip) isolated the contributions of each CoT module. Results TCM-CoT-RAG substantially improved diagnostic accuracy across five LLMs. DeepSeek-V3 with full-chain CoT-RAG achieved Entity F1 of 44.89% (+16.45% over baseline) and Formula F1 of 32.13% (+8.74% over baseline), with BERTScore of 0.81 indicating strong semantic alignment with expert reasoning. Ablation confirmed that the complete CoT pipeline was essential—removing any reasoning module caused performance collapse below the zero-shot baseline. Two independent experts validated clinical utility (Cohen’s κ > 0.7). DeepSeek-V3 achieved the highest ground-truth consistency at 81.6%, and consistency metrics were quantified by the third expert holding the most senior professional title. Conclusion This proof-of-concept framework demonstrates the potential of RAG-enhanced CoT reasoning to improve diagnostic consistency in TCM, objectifying the Symptom-Diagnosis-Prescription pipeline. It is important to note that this system is designed as an AI-assisted clinical decision-support tool. All recommendations require validation by qualified TCM practitioners before clinical application.
Model identity verification is only as trustworthy as the reference against which identity is resolved. A system may correctly establish that a model running now corresponds to an enrolled reference while remaining unable to establish that the reference itself was the authentic release of the named publisher. This technical note separates those two claims as identity continuity and enrollment provenance. It formalizes the poisoned-enrollment failure, in which an inauthentic artifact is enrolled under a legitimate model name and subsequently passes continuity verification correctly. The failure is therefore not a false acceptance by the measurement system, but an upstream identity-binding failure. The note shows that this boundary is shared across artifact signing, behavioral fingerprinting, reference-anchored activation auditing, and structural identity measurement, and relates the problem to established software supply-chain trust models. It proposes E0–E4 enrollment assurance profiles, distinguishes provenance profile from current attribution state, and describes remediation through revocation and re-establishment of provenance without discarding historical continuity evidence. No new measurement result is reported. The contribution is an evidence boundary, threat-model construction, assurance vocabulary, and remediation model for model identity verification. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note:: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
Abstract This orientation presents the architecture, results, boundaries, and reading paths of the Identity-Persistence Program, a research program on the structural conditions under which bounded evaluators can make reproducible judgments of identity, persistence, admissibility, and verification under declared regimes. The program’s foundational layer establishes three forcing results: structural floors for coherent identity claims, admissible transformation, and sufficient regime specification. These are bracketed below by the requirement that cumulative inquiry possess a stable same/not-same criterion and above by an identification ceiling: within the finite declared class, admissible evidence identifies only up to the declared quotient. The guide then maps the program’s post-floor structural theory. For a declared question family, maximal structure-compatible safe congruences yield canonical demand-relative normal forms and a theory of regime equivalence and refinement. Recurrence is classified in the one-degree homogeneous case; symmetry reduction is separated from operable quotient structure through an independent-redescription compatibility criterion; nested regimes compose through backward demand propagation and forward certificate compression; and reconstructibility, blocking cuts, and verification complexity are characterized at the mechanization layer. Condensation Dynamics adds a finite dynamical theory in which safe quotienting has an exact potential and path-independent total budget, interaction defects measure noncanonical allocation, serial nesting obeys a no-free-acceleration law, and structural conditions for zero defect are identified. The orientation also distinguishes these theorem-bearing results from the program’s finite-interior analyses of interaction, omission, representation, and declaration dependence; from interpretive accounts of endogenous regime formation; and from downstream runtime engineering. The resulting architecture is not a claim about final ontology or unrestricted knowledge. It is a class-relative theory of what bounded evaluators can license, preserve, compress, compose, and independently verify once the governing regime has been sufficiently declared. Corpus-native instantiation, selected extension classes, and independent formal proof verification remain open. This document proves no new theorem. It is the program guide: it records dependency structure, claim status, scope boundaries, and reading order, while the individual papers remain authoritative for their results.
Abstract This review aimed to explore the integration of Quantum Computing (QC) with Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL), addressing the computational demands posed by the exponential growth of visual data. It identifies key challenges such as interdisciplinary complexity, lack of standard benchmarks, scalability, integration barriers, and the theoretical-practical gap in quantum applications. The review systematically examines existing literature on the application of quantum algorithms in areas including image processing, Natural Language Processing (NLP), Transfer Learning (TL), Federated Learning (FL), networking, cybersecurity and the finance sector. It highlights the usage of quantum principles like superposition and entanglement to accelerate computations, optimize models, and enhance data security in ML/DL frameworks. Findings indicate that integrating QC with ML/DL offers faster convergence, improved optimization, secure decentralized learning, and efficient handling of large-scale and complex data. Specific improvements are observed in TL and FL approaches, NLP accuracy, cryptographic robustness, and performance in medical diagnostics and autonomous systems. QC holds transformative potential in enhancing ML/DL capabilities across domains. Despite existing challenges such as error mitigation and integration complexity, its combination with classical learning methods opens new frontiers for research in AI-driven sectors. Future studies should focus on bridging theoretical and application-level gaps while creating standardized evaluation frameworks.
Open access
Quantum Computing Algorithms and Architecture
Big Data and Digital Economy
Artificial Intelligence in Healthcare and Education
The programmable economy lacks a universal computational layer capable of interpreting, translating, verifying, and simulating the mathematical and cryptographic operations that underpin digital assets. Existing tools are fragmented: wallet software provides only rudimentary transaction signing, portfolio trackers offer aggregated views without evidence, and specialized calculators address isolated problems. No general-purpose, cryptographically verifiable, language-native computational environment exists for digital value. KHOTOR is designed to fill this gap. It is a universal, deterministic runtime that interprets the anti-entropic linguistic protocol Kryptophon, transforms plain-language queries into executable computational expressions, and performs multi-domain financial mathematics across asset conversion, transaction analysis, decentralized finance, tokenomics simulation, cryptographic proof generation, and risk assessment. Every output carries an epistemic classification — verified, observed, inferred, simulated, or uncertain — and can be exported as a Gamma-Proof: a cryptographically signed, independently verifiable artifact. This paper presents the complete KHOTOR architecture: a ten-layer computational engine, a formal abstract machine for Kryptophon evaluation, a tiered adoption model that makes the programmable economy accessible to non-technical users while creating a new domain of expertise for professionals, and a product family spanning a public cloud API, a web platform, a handheld consumer device, and integration with dedicated hardware instruments. All components are designed around a single governing principle: every calculation shows its work, every output carries a truth label, and no inference is ever presented as fact.
Beacon Kit: Ecosystem epoch heartbeat @ the world game (s). Block-time arbitrage tokenized commodity index, adaptive procedural template @ system of federated DeFi cryptocurrency quantum - AI systems consensus
The rapid growth of cryptocurrencies and increasing instability in traditional financial systems have significantly transformed global investment behaviour in recent years. In developing countries experiencing economic crises and currency depreciation, investors increasingly seek alternative financial assets that can preserve value and generate higher returns. Sri Lanka has recently experienced severe economic instability characterised by inflation, foreign-exchange shortages, sovereign debt problems, and rapid depreciation of the Sri Lankan rupee. Under these conditions, interest in cryptocurrency investment has increased, particularly among younger and technologically aware investors. Therefore, this study examines whether fiat currency devaluation shifts investment from the stock market to the cryptocurrency market among university students in Sri Lanka. The study adopts a quantitative research approach and uses primary data collected through a structured questionnaire from 150 final-year undergraduate students at the University of Sri Jayewardenepura. Stratified random sampling was used to select respondents from the Faculty of Humanities and Social Sciences, the Faculty of Management Studies and Commerce, and the Faculty of Applied Sciences. Descriptive statistics, chi-square analysis, and binary logistic regression were employed to analyse the relationship between rupee depreciation and cryptocurrency investment behaviour. The findings reveal that depreciation of the Sri Lankan rupee significantly influences investment decisions among university students. Most respondents perceived cryptocurrency investment as more profitable than stock-market investment during periods of economic uncertainty. The chi-square analysis identified significant relationships between cryptocurrency investment behaviour and age, income, stock-market investment, and perceptions of rupee depreciation. Furthermore, the binary logistic regression results confirmed that rupee depreciation positively and significantly affects cryptocurrency investment, whereas stock-market investment had a negative relationship with cryptocurrency investment behaviour. The study concludes that economic instability, declining confidence in fiat currency, and increasing awareness of digital financial systems encourage university students in Sri Lanka to shift their investment preferences from the traditional stock market to cryptocurrency.
The article examines the concept of legal settlement finality as applied to two fundamentally different payment instruments — decentralized cryptocurrencies and central bank digital currencies (CBDCs). The author analyzes the absence of a statutory definition of settlement finality in Russian financial law, compares the approaches of Russia, China, India and the UAE, and studies judicial practice and doctrine. Based on a comparative legal analysis, an original definition of the legal finality of digital settlement is proposed, and liability regimes for payment process participants prior to transaction completion are differentiated in relation to cryptocurrency P2P transactions and CBDC operations.
Cryptocurrency time-series forecasting is a challenging task because market data usually exhibit high noise, strong volatility, non-stationarity, nonlinear dynamics, and long-range dependencies. In addition, multivariate market indicators often contain redundant or weakly informative variables, which may reduce forecasting accuracy and model interpretability. To address these issues, this study proposes BSFinformer, a Boruta-SHAP enhanced Finformer framework for multivariate cryptocurrency time-series forecasting. The proposed framework first applies a leakage-aware Boruta-SHAP feature selection strategy to identify informative market variables and remove redundant features. To avoid temporal information leakage, feature selection is performed only on the training set, and the selected feature subset is then applied unchanged to the validation and test sets. The selected features are subsequently fed into an improved Finformer model that integrates temporal embedding, sequence decomposition, and sparse self-attention to capture local fluctuations, trend evolution, and long-range temporal dependencies. Experiments are conducted on three cryptocurrency assets, namely Bitcoin, Dogecoin, and Binance Coin, using chronological train–validation–test splits. The proposed model is compared with classical forecasting models and recent long-sequence forecasting baselines, including LSTM, Transformer, Informer, Autoformer, DLinear, PatchTST, TimesNet, and iTransformer. Experimental results show that BSFinformer achieves competitive forecasting performance in terms of MSE and MAE. Ablation experiments further demonstrate the contributions of Boruta-SHAP feature selection, temporal embedding, sequence decomposition, and sparse self-attention. These results indicate that feature-selected temporal modeling can improve forecasting accuracy and interpretability for multivariate cryptocurrency market data.
This systematic review synthesises empirical research on individual-level cryptocurrency adoption, distinguishing adoption intention, actual adoption and use, and continuance intention and use. We searched Scopus and Web of Science for English-language empirical studies published between 2019 and 2025 and synthesised findings using a structured narrative approach. Eighty-five studies were included, with reported sample sizes summing to 56,054 participants. No formal study-level risk-of-bias assessment was conducted. The literature was dominated by cross-sectional quantitative studies and technology-adoption frameworks, particularly UTAUT, TAM, TPB, and DOI. Evidence was strongly concentrated on adoption intention (n = 75), whereas actual adoption and use (n = 16) and continuance intention and use (n = 8) were examined much less frequently. Across studies, adoption was associated with psychological, technological, social, economic, knowledge-related, institutional, and individual factors, with no single determinant consistently dominating across outcomes. The synthesis further distinguished direct predictors, mediating mechanisms, moderators, drivers, and barriers. The evidence base is limited by its reliance on self-reported, cross-sectional designs and uneven coverage of realised and continued engagement. Future research should more clearly specify adoption outcomes and use longitudinal, behavioural, and post-adoption designs.
A dated critical archival study of historical-position identity, hybrid human-AI authorship, canonical closure, and future audit through the Trinity Accord case. This is a noncanonical academic preprint and does not amend, supersede, or interpretively bind the three Bitcoin Originals.
Emerging economies collect substantially less tax revenue relative to national income than advanced economies, and a large share of this shortfall reflects weak enforcement capacity rather than statutory rates. Digital audio technologies and integrated financial information systems are increasingly promoted as instruments for narrowing this gap, yet the evidence on whether, when, and how they raise compliance and transparency remains scattered across public economics, accounting, and information systems scholarship. This review synthesises empirical and conceptual work published between 2006 and 2026 to assess what is known about four interlocking mechanisms: third party information reporting and electronic invoicing, electronic filing and payment platforms, continuous auditing and analytics, and distributed ledger and regulatory technology approaches to data governance. Three consistent patterns emerge. First, technologies that create verifiable third-party information trails produce the most durable compliance gains, with value added tax self-enforcement, electronic sales registers, and consumer incentive schemes generating measurable revenue increases, while technologies that merely digitise existing processes without new information yield smaller and more fragile effects. Second, the revenue and transparency return to digital systems are conditional on administrative capacity, data quality, and political commitment rather than automatic, which explains why similar tools succeed in some jurisdictions and fail in others. Third, the accounting profession is moving from periodic sampling toward continuous assurance and population level analytics, but adoption in emerging economies lags because of skills, infrastructure, and governance constraints. These findings suggest that the design and sequencing of digital reforms matter more than the sophistication of the technology itself. The review offers tax administrators and policymakers evidence graded account of which interventions rest on strong causal evidence and which rest on weaker conceptual or cross sectional foundations, and it identifies the conditions under which digital instruments translate into sustained fiscal gains rather than symbolic modernization.
The reliable delivery of temperature-sensitive pharmaceuticals depends on an unbroken cold chain governed by Good Distribution Practice (GDP). As biologics, vaccines, plasma-derived products, and advanced therapy medicinal products expand their share of the global medicines market, the clinical and economic consequences of thermal excursions have intensified. This paper reviews recent advances in cold chain integrity and GDP across the regulatory and scientific foundations of temperature control, the engineering of thermal protection and monitoring, the digital transformation of distribution networks, and the systemic dimensions of equipment reliability, sustainability, economics, and equitable access. It examines how passive and active thermal protection systems have improved through vacuum insulation and engineered phase change materials, how real-time monitoring built on connected sensing has displaced retrospective data capture, and how predictive analytics, distributed ledgers, and digital twins are reshaping visibility and traceability. Focused attention is given to the ultra-cold and cryogenic chains that support messenger ribonucleic acid vaccines and cell and gene therapies, where chain of identity and chain of custody requirements compound the demands of thermal control. The paper also considers quality risk management and validation, the reliability of refrigeration assets and the role of predictive maintenance, sustainability pressures such as refrigerant phase-down and single-use packaging waste, the economics of failure and of investment in monitoring, the persistent last-mile gaps in low- and middle-income settings, and the lessons drawn from the pandemic deployment of temperature-sensitive vaccines. The central finding is that cold chain assurance is shifting from a document-centric, compliance-driven discipline toward a data-driven, predictive, and risk-based model. Integrating continuous monitoring with analytics and product specific stability budgets offers the clearest path to reducing wastage while preserving patient safety, although interoperability, validation, cybersecurity, and equitable access remain unresolved challenges.
R. Priyadharsini, Ravikanth Reddy Vadamala, R. Raajalakshmi, K. Raghav Prasad · 5 authors
The rapid transformation of global business environments driven by digitalization, technological advancement, changing consumer expectations, and competitive market dynamics has significantly altered traditional marketing practices and strategic business operations. Organizations operating in highly dynamic economic ecosystems are increasingly recognizing that conventional marketing frameworks alone are insufficient to sustain long-term growth, customer engagement, and market relevance. In this context, innovation-driven marketing models have emerged as a critical strategic approach that integrates creativity, data intelligence, technological innovation, customer-centric design, and adaptive business strategies to enhance organizational competitiveness and sustainable value creation. This research examines the growing significance of innovation-driven marketing models and their influence on consumer behavior, brand positioning, digital engagement, operational efficiency, and business sustainability across modern industries. The study explores how emerging technologies such as artificial intelligence, machine learning, big data analytics, blockchain, cloud computing, augmented reality, and social media ecosystems are transforming traditional marketing processes into highly personalized, predictive, and experience-oriented systems capable of responding to rapidly evolving market demands. The research further investigates how innovation-oriented marketing strategies support product differentiation, dynamic pricing, omnichannel communication, customer relationship management, and real-time market responsiveness in both online and offline commercial environments. Particular emphasis is placed on the role of innovation in enhancing customer engagement through interactive digital platforms, data-driven personalization, automated communication systems, influencer-based branding strategies, and experiential marketing campaigns. The study also evaluates how organizations leverage innovative business models to improve customer retention, market expansion, and strategic decision-making while simultaneously addressing challenges related to market uncertainty, consumer trust, technological adaptation, and ethical data utilization. A comparative assessment of traditional marketing approaches and innovation-driven marketing frameworks demonstrates that organizations adopting innovation-centric strategies experience stronger consumer loyalty, improved operational agility, enhanced brand visibility, and higher adaptability to changing economic conditions. Additionally, the research highlights the growing importance of sustainability-oriented marketing innovation, where businesses integrate environmental responsibility, social value creation, and ethical consumer engagement into their branding and communication practices. The findings indicate that innovation-driven marketing models not only contribute to commercial profitability but also strengthen organizational resilience and long-term strategic sustainability in highly competitive global markets. The study concludes that future business success increasingly depends on the ability of organizations to continuously innovate their marketing structures, technological capabilities, and customer engagement mechanisms in alignment with digital transformation and evolving consumer expectations. Therefore, innovation-driven marketing represents a transformative strategic paradigm capable of reshaping modern business ecosystems through intelligent, adaptive, and customer-focused value creation models.
PurposeThe enhanced consolidation of cloud accounting models within geographical boundaries of India has established latest standards in financial auditing, reporting, compliance procedures and virtual accessibility. Nonetheless the legal framework in the nation is evolving simultaneously to accentuate audit trails, nationalized storage of data and sovereignity of data. Latest modifications under the companies act 2013; the company’s fourth amendment rules and the new policies issued by RBI for data localization have radically shifted the compliance framework for all the accounting professionals and the service providers in the country. Regardless of the mounting academic discussion on adaptability of cloud accounting around the globe, meagre research has highlighted hoe nationalized legal requirements have modified the framework infrastructure, risks involved and acceptability of accounting professionals in india which will be investigated in this study. This study will further identify the pros and cons for adoption of cloud accounting and will come out with suggestive cloud accounting models for Indian scenario. Design/Methodology/ApproachAn empirical and analytical research design has been adopted for the study and snowball and convenient sampling has been used for primary data collection..A sample size of 140 has been calculated using G-power. The research is confined to chartered accountants of agra district to whom a well structured questionnaire was sent using google forms.stastical tools used in this study is chi square test. FindingsCloud accounting is a tremendous shift towards triple entry system wherein a transaction is verified by a third party using cryptography and blockchain technology thereby increasing authenticity and trust by piling all entries in a public ledger. As a result of this more businesses are adopting virtual workforce models. Introduction of cloud based models in accounting profession has enhanced the roles of key processing indicators in the business.Cloud technology magnifies employees networking and association thereby increasing efficiency and effectiveness. Chartered accountants who will accept this change will have new opportunities open for them and those who will look at this technology with ostrich approach will be left behind. OriginalityThe findings will be valuable for further research work to be done in this area. The findings will help various researchers, chartered accountants, accounting professionals etc to understand the implementation of cloud accounting in developing countries like India and to understand in depth the implementation and adoption of cloud based accounting in the Indian scenario.
Open access
Innovations and Analysis in Business and Education
Green finance has emerged as a transformative mechanism for achieving sustainable economic development by integrating environmental sustainability with financial decision-making. The increasing challenges posed by climate change, environmental degradation, and resource depletion have encouraged governments, financial institutions, and private investors to allocate capital toward environmentally sustainable projects. Green finance encompasses financial instruments such as green bonds, green loans, sustainability-linked loans, ESG (Environmental, Social, and Governance) investments, climate finance, and carbon financing that promote low-carbon and climate-resilient economic growth. This paper reviews recent developments in green finance and examines its contribution to sustainable economic development through a systematic review of contemporary literature. The study analyzes the evolution of green financial instruments, policy frameworks, investment trends, and their impact on economic growth, renewable energy development, environmental protection, employment generation, and financial inclusion. The paper further discusses the challenges hindering green finance implementation, including regulatory inconsistencies, greenwashing, limited disclosure standards, inadequate investor awareness, and financing constraints in developing economies. The review also highlights the role of technological innovations such as artificial intelligence, blockchain, fintech, and big data analytics in improving transparency, risk assessment, and investment efficiency in green financial markets. Based on recent empirical evidence, the paper concludes that green finance significantly contributes to sustainable development by encouraging environmentally responsible investments while supporting long-term economic resilience. Finally, policy recommendations and future research directions are proposed to strengthen global green financial ecosystems and accelerate progress toward the United Nations Sustainable Development Goals (SDGs).
Background: Despite the growing adoption of hybrid contract models in construction, energy, and agricultural procurement, there remains a significant gap in understanding how lump-sum and unit-price contracts differentially allocate risk across sectors and country contexts. This study addresses this gap by examining risk mitigation strategies through document analysis and thematic synthesis. Objective: The aim of this study was to identify key risk allocation strategies, contractual mechanisms, and the effectiveness of hybrid models in managing uncertainty across developed and developing country contexts. Methods: A qualitative approach based on thematic analysis and cross-case comparison was applied, drawing on 48 peer-reviewed sources published between 2015 and 2025, alongside relevant sector documents and procurement reports. Results: The analysis identified that hybrid contracts reduced cost overrun variability by incorporating performance-based incentives aligned with Expected Utility Theory and Principal-Agent Theory, while developing economies such as Indonesia and Bangladesh exhibited distinct risk profiles requiring adaptive contract mechanisms. However, significant gaps remain, particularly regarding the empirical validation of blockchain-enabled contract enforcement and AI-driven risk prediction, as well as the underrepresentation of developing economy contexts in existing research. Conclusion: The findings carry both scientific and practical implications. Theoretically, this study advances an integrative multi-theory framework combining Expected Utility Theory, Game Theory, and Principal-Agent Theory to analyse contract risk across diverse contexts. Practically, the results provide evidence-based guidance for procurement professionals and policymakers in selecting and designing contract structures that balance cost certainty with adaptive flexibility.
Medical tourism has emerged as a significant global phenomenon, driven by the convergence of high healthcare costs in developed nations and the availability of high-quality, affordable treatment options abroad. This review examines the critical role of entrepreneurial innovation in shaping and expanding this industry, which uniquely blends advanced health care with the principles of hospitality and tourism. Key innovations transforming the sector include the development of integrated, all-inclusive service models that package medical procedures with travel, luxury accommodation, and wellness-focused recovery programs. Digital disruption is paramount, with platforms leveraging artificial intelligence (AI) for personalized patient care coordination and blockchain technology to ensure secure, transparent transfer of medical records and billing, thereby building essential trust. Furthermore, strategic partnerships between hospitals, airlines, and hospitality providers create a seamless end-to-end experience for international patients. The proliferation of telemedicine supports this model by facilitating vital pre-departure consultations and post-operative follow-up care, ensuring continuity and safety. Entrepreneurs are also successfully targeting niche markets, from elective cosmetic surgery to complex dental and regenerative procedures, particularly in established hubs like India, Thailand, and Turkey. However, the industry’s growth is not without challenges, including regulatory heterogeneity, cultural and language barriers, and ethical concerns. Future advancement depends on navigating these complexities while capitalizing on opportunities such as value-based care, augmented reality (AR) for patient engagement, and strengthened international accreditation frameworks. By combining clinical expertise and hospitality, innovative entrepreneurship is ultimately reinventing access to health care globally, giving patients new options while producing substantial economic advantages for the countries of destination.
Omar Al-Jamili, Abdulaziz Fahmi Omar Faqera, Mohd Adan Omar, Shehu M. Sarkintudu · 8 authors
Open Government Data (OGD) has become central to digital transformation and data-driven governance, yet scholarly understanding of how OGD initiatives progress from initial adoption to sustained institutionalization remains fragmented. This study aims to synthesize the existing literature and develop an integrative framework that explains the socio-technical mechanisms underpinning the long-term sustainability and value creation of OGD initiatives. The study integrates bibliometric analysis with a systematic literature review of 481 peer-reviewed articles published between 2010 and 31 December 2024. Quantitative science-mapping techniques are combined with qualitative thematic synthesis to capture the intellectual structure, technological evolution, and theoretical foundations of OGD research. The findings reveal rapid growth and thematic diversification in OGD scholarship, with increasing attention to advanced technologies such as artificial intelligence and blockchain. However, the literature remains theoretically fragmented across behavioral, institutional, and public-value perspectives. Two critical gaps are identified: insufficient theorization of institutional legitimacy as a driver of continuity, and limited exploration of user-centric governance mechanisms shaping sustained data reuse. To address these gaps, the study proposes the Socio-Technical Institutionalization Model (STIM), which conceptualizes OGD sustainability as the dynamic alignment of technological infrastructures, institutional arrangements, and user ecosystems. By combining quantitative science mapping with systematic thematic synthesis and proposing the STIM lifecycle framework, this study offers an integrative synthesis that extends prior OGD reviews. The framework bridges fragmented theoretical perspectives and explains how open data initiatives may evolve from adoption to institutionalized value creation within complex digital governance ecosystems.
ABSTRACT Faced with the accelerating erosion of biodiversity and its growing recognition as a source of financial risks and opportunities, the academic literature linking biodiversity and finance is expanding rapidly. This article offers a systematic and bibliometric review of this literature in order to analyze its evolution, intellectual structure, main conceptual dynamics and gap identification. Aligning with the PRISMA‐2020 protocol, this study examines 1088 scientific articles published in the period 1993–2025. The data were extracted from Scopus and Web of Science databases. The analysis uses descriptive bibliometric methods available in R software and the bibliometrix package via the Biblioshiny interface. The results highlighted a strong acceleration of scientific production since 2015, which is linked with the development of sustainable finance and international regulatory frameworks. While the thematic mapping identifies a broader landscape, three key areas are prioritized for in‐depth analysis: Sustainability as a macroeconomic framework, Biodiversity conservation via innovation in financial instruments, and the emergence of biodiversity risk as a systemic financial risk. Beyond descriptive mapping, this study proposes the Biodiversity‐Finance Inhibition Framework (BFIF) as an integrative conceptual framework to synthesize the persistent disconnect between academic evidence and market implementation. It identifies a systemic “Inhibition Loop” where data gaps at the micro‐level and a lack of ecological accountability at the meso‐level paralyze macro‐regulatory ambitions. The article also highlights a significant geographical disparity, with research heavily concentrated in developed economies. Finally, it outlines a strategic research agenda aimed at breaking this “Inhibition Loop” by exploring a “methodological frontier” involving bio‐econometrics, blockchain, and artificial intelligence to reinforce the measurement, governance, and effectiveness of biodiversity‐finance.