The subject of the stud y is a digital token in a cross–border payment infrastructure (hereinafter referred to as CBPI) based on distributed ledger technology (hereinafter referred to as DLT). The purpose of the work is to analyze and scientifically evaluate methodological approaches to the formation of CBPI. The relevance of the work is due to the atmosphere of uncertainty and growing risks of external impact on the cross-border payment infrastructure that the Russian Federation has faced in recent years, as well as the need to address the challenge of ensuring accessibility, continuity, sustainability and security of its operation. As a result of the research, using heterodox, systemic, structural-functional, cybernetic, pragmatic and institutional approaches, the economic characteristics of the payment token have been developed and presented, including the most significant ones for the smooth implementation of cross-border payment transactions. It is concluded that the existing approaches make it possible to determine the main economic characteristics of a digital token in a cross-border payment infrastructure based on DLT, including security, cost stability, liquidity, volatility, as well as auxiliary ones — interoperability, scalability, transactional neutrality, economic isolation.
Daniel Qian, Xiyu Hao, Jinkun Geng, Yuncheng Yao · 7 authors
As Byzantine Fault Tolerant (BFT) protocols are increasingly adopted for user-facing applications such as payments and smart contracts, it is crucial that they provide low latency. To reduce latency, some BFT consensus protocols use a leaderless, speculative, fast path where clients broadcast requests directly to replicas, enabling end-to-end commit latency of two message delays ($2Δ$). However, such a fast path is extremely fragile: concurrent requests can cause replicas to diverge when they receive requests in different orders, triggering costly recovery procedures. This paper presents Aspen, a leaderless speculative BFT protocol that handles concurrent requests while achieving near-optimal latency of $2Δ+ ε$. The $ε$ term is a short waiting delay introduced by Aspen's best effort ordering layer, which uses loosely synchronized clocks and network delay estimates to provide a tentative order. To make its fast path even more robust to intermittent divergence, Aspen adds extra replicas ($n = 3f + 2p + 1$) as well as novel recovery mechanisms that allow the system to tolerate divergence while preserving safety and performance. In experiments with geo-distributed replicas, Aspen reduces the median latency of requests by $1.1\times$--$3.8\times$ compared to state-of-the-art BFT protocols, while sustaining up to $0.75\times$ the peak throughput of throughput-optimized designs.
Martin PereÅ¡Ãni, Tomáš Hladký, Jakub KubÃk, Ivan Homoliak
The aim of this work is to enhance blockchain security by deepening the understanding of selfish mining attacks in various consensus protocols, especially the ones that have the potential to mitigate selfish mining. Previous research was mainly focused on a particular protocol with a single selfish miner, while only limited studies have been conducted on two or more attackers. To address this gap, we proposed a stochastic simulation framework that enables analysis of selfish mining with multiple attackers across various consensus protocols. We created the model of Proof-of-Work (PoW) Nakamoto consensus (serving as the baseline) as well as models of two additional consensus protocols designed to mitigate selfish mining: Fruitchain and Strongchain. Using our framework, thresholds reported in the literature were verified, and several novel thresholds were discovered for 2 and more attackers. We made the source code of our framework available, enabling researchers to evaluate any newly added protocol with one or more selfish miners and cross-compare it with already modeled protocols.
Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability.
Xunqiang Lan, Xiao Tang, Ruonan Zhang, Bin Li · 7 authors
Blockchain plays a crucial role in ensuring the security and integrity of decentralized systems, with the proof-of-work (PoW) mechanism being fundamental for achieving distributed consensus. As PoW blockchains see broader adoption, an increasingly diverse set of miners with varying computing capabilities participate in the network. In this paper, we consider the PoW blockchain mining, where the miners are associated with resource uncertainties. To characterize the uncertainty computing resources at different mining participants, we establish an ambiguous set representing uncertainty of resource distributions. Then, the networked mining is formulated as a non-cooperative game, where distributionally robust performance is calculated for each individual miner to tackle the resource uncertainties. We prove the existence of the equilibrium of the distributionally robust mining game. To derive the equilibrium, we propose the conditional value-at-risk (CVaR)-based reinterpretation of the best response of each miner. We then solve the individual strategy with alternating optimization, which facilitates the iteration among miners towards the game equilibrium. Furthermore, we consider the case that the ambiguity of resource distribution reduces to Gaussian distribution and the case that another uncertainties vanish, and then characterize the properties of the equilibrium therein along with a distributed algorithm to achieve the equilibrium. Simulation results show that the proposed approaches effectively converge to the equilibrium, and effectively tackle the uncertainties in blockchain mining to achieve a robust performance guarantee.
Learning and Employment Record (LER) systems are emerging as critical infrastructure for securely compiling and sharing educational and work achievements. Existing blockchain-based platforms leverage verifiable credentials but typically lack automated skill-credential generation and the ability to incorporate unstructured evidence of learning. In this paper,a privacy-preserving, AI-enabled decentralized LER system is proposed to address these gaps. Digitally signed transcripts from educational institutions are accepted, and verifiable self-issued skill credentials are derived inside a trusted execution environment (TEE) by a natural language processing pipeline that analyzes formal records (e.g., transcripts, syllabi) and informal artifacts. All verification and job-skill matching are performed inside the enclave with selective disclosure, so raw credentials and private keys remain enclave-confined. Job matching relies solely on attested skill vectors and is invariant to non-skill resume fields, thereby reducing opportunities for screening bias.The NLP component was evaluated on sample learner data; the mapping follows the validated Syllabus-to-O*NET methodology,and a stability test across repeated runs observed <5% variance in top-ranked skills. Formal security statements and proof sketches are provided showing that derived credentials are unforgeable and that sensitive information remains confidential. The proposed system thus supports secure education and employment credentialing, robust transcript verification,and automated, privacy-preserving skill extraction within a decentralized framework.
Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.
Open access
2 source records
Machine Learning in Healthcare
Explainable Artificial Intelligence (XAI)
Artificial Intelligence in Healthcare and Education
Nandhini S, Hrithik M, Kamalesh S, Aswin C · 6 authors
ABSTRACT: Centralized digital marketplaces dominate today’s online commerce but suffer from inherent limitations such as single points of failure, lack of transparency, data monopolization, and trust dependency on intermediaries. To address these challenges, this paper presents the design and implementation of a decentralized marketplace built on blockchain technology. The proposed system enables peer-to-peer trading without the involvement of centralized authorities, ensuring transparency, security, and fairness among participants. Smart contracts are employed to automate transactions, enforce business rules, and eliminate the need for trusted third parties. Distributed ledger technology ensures immutability of records, while cryptographic mechanisms provide secure identity management and transaction validation. The marketplace supports secure listings, decentralized payments, dispute resistance, and trustless execution, thereby reducing operational costs and increasing user autonomy. Experimental analysis demonstrates improved reliability, resistance to tampering, and enhanced trust compared to traditional centralized platforms. The proposed decentralized marketplace framework highlights the potential of blockchain-based systems in redefining digital commerce by promoting transparency, decentralization, and user empowerment. Keywords: Decentralized Marketplace, Blockchain Technology, Smart Contracts, Peer-to-Peer Trading, Distributed Ledger, Trustless Transactions, Cryptographic Security, Transparency, Digital Commerce, Disintermediation.
Blockchain technology offers decentralized and secure transaction processing but suffers from critical limitations in scalability, energy efficiency, and latency, hindering its adoption in real-time high-throughput applications. This study proposes a novel Adaptive Global Best–Worst Particle Swarm Optimization (AGBWPSO) algorithm integrated with dynamic sharding to address these challenges effectively. Unlike traditional GBWPSO, the proposed AGBWPSO employs a dual-extremum influence mechanism that combines both global best and worst positions, along with adaptive nonlinear parameter adjustment strategies for the inertia weight, cognitive, and social coefficients. This enhances exploration–exploitation balance, prevents premature convergence, and ensures efficient shard reallocation under dynamic transaction loads. The integration with dynamic sharding enables parallel transaction processing across optimally configured shards, significantly improving blockchain performance metrics. Extensive simulations conducted on Ethereum, Bitcoin, Hyperledger Fabric, financial, and IoT transaction datasets demonstrate that the proposed AGBWPSO achieves up to 5.88% improvement in transaction throughput (TPS), 14.3% reduction in latency, and 20% reduction in energy consumption per transaction compared to existing optimization methods. These results establish AGBWPSO as a robust and scalable solution for enhancing the operational efficiency and sustainability of blockchain networks in real-world applications.
The urban administration in Pakistan has transformed as a result of political and economic shifts. The urban government in Pakistan has been influenced by external financing, which is a reflection of institutional reforms, fiscal decentralization, and the priorities of global development. Over the course of the last three decades, Pakistan's urban management has transitioned from a centralized bureaucratic authority to fragmented local governance systems that are shaped by donor-driven projects and conditional cash inflows. An in-depth analysis of how multilateral development banks and bilateral aid influence urban policy, infrastructure, and service delivery is presented in this specific piece of writing. The evidence demonstrates that the use of external financing has hastened the process of urban modernization while simultaneously exacerbating governance problems such as policy incoherence, accountability deficiencies, and socio-spatial inequities. In this study, political economics research and urban planning perspectives are combined in order to investigate how external funding mechanisms influence the capacities of local governments and the transformation of urban infrastructure in Pakistan's fast-growing cities. The findings highlight the necessity of having governance structures that are adaptable and, in a position, to strike a balance between local interests and global urban finance strategy.
Bambang Leo Handoko, Arta Moro Sundjaja, Evelyn Hendriana
The rapid rise in cryptocurrency presents both opportunities and challenges for retail investors due to its volatility and technological complexity. Research on investment decisions has primarily focused on behavioural finance, often overlooking how learning and literacy shape investor actions. This study addresses this gap by examining how herding behaviour, financial literacy, and digital literacy impact cryptocurrency investment decisions. Grounded in Social Learning Theory and supported by UTAUT to operationalise digital literacy, this study examines how herding behaviour, financial literacy, and digital literacy shape cryptocurrency investment decisions. We analyse survey data from 138 Indonesian retail investors through PLS-SEM. Key findings show that financial literacy (β = 0.443, t = 5.041) and digital literacy (β = 0.495, t = 4.246) are primary determinants of investment decisions, while herding behaviour (β = 0.016, t = 0.628) does not directly influence them but does so indirectly by enhancing investor literacy. This demonstrates that social observation and learning can convert herd-driven impulses into rational choices when mediated by literacy. By extending Social Learning Theory into digital investment contexts, this study provides insights for investors and policymakers seeking to enhance financial and digital literacy.
This study systematically examines the transformative role of Artificial Intelligence (AI) in addressing the persistent challenges of blockchain technology across protocols, smart contracts, and distributed ledger management. Although blockchain offers decentralization, immutability, and transparency, its broader adoption remains constrained by scalability limitations, security vulnerabilities, inefficient consensus mechanisms, and the complexity of contract design and auditing. The findings of this review demonstrate that AI provides promising solutions to these barriers. Reinforcement learning (RL) applied to Proof-of-Stake reduced consensus latency by 30-50%, while NLP-based smart contracts lowered vulnerabilities by up to 40%, though both approaches introduced new concerns related to energy overheads and auditability. In addition, intelligent algorithms enhance ledger efficiency and data analytics, supporting more scalable and secure transaction processing. Drawing on 28 peer-reviewed studies published between 2018 and 2024, and guided by the PRISMA 2020 framework, this paper synthesizes state-of-the-art research, maps sector-specific applications in finance, healthcare, and supply chain management, and highlights unresolved gaps in ethics, reproducibility, and regulatory compliance. Notably, only 12% of the reviewed studies validated their approaches on live networks underscoring the gap between simulation-driven research and real-world deployment. The discussion culminates in the AI–Blockchain Interaction Model (AIBIM), a conceptual framework that systematizes synergies across consensus, contract, and application layers. By integrating empirical insights with critical evaluation, this work emphasizes the interdisciplinary nature of AI–blockchain research and provides actionable directions for advancing decentralized, scalable, and ethically aligned systems. This synthesis provides actionable insights for developers, regulators, and researchers in deploying AI-blockchain systems across finance, healthcare, and supply chains.
The construction industry is a major global consumer of energy and a leading source of greenhouse gas emissions, underscoring the need for transparent, data-driven, and energy-efficient supply chain strategies. This study develops an integrated mixed-integer linear programming (MILP) model for a multi-echelon, multi-product construction supply chain that explicitly incorporates differentiated building energy efficiency levels ( A +, A ++, A +++) as exogenous determinants of material requirements, production processes, and logistics flows. By embedding blockchain-enabled smart contracts, the model automates supplier governance and ensures compliance with delivery reliability, quality standards, and CO 2 performance through predefined incentives and penalties, thereby enhancing transparency and accountability. The framework jointly optimizes facility location, material and product flows, supplier selection, and reverse logistics operations under a CO₂ emission cap, while simultaneously capturing the implications of greenfield and brownfield project conditions. A real-scale numerical case study demonstrates the model’s ability to evaluate the economic–environmental trade-offs arising from increasingly stringent sustainability requirements. The results reveal that although higher energy efficiency levels incur greater initial supply chain costs due to advanced materials and more complex logistics, they lead to substantial reductions in long-term operational energy consumption, rendering the A +++ option the most economically favorable from a lifecycle perspective. Furthermore, the integration of blockchain-enabled smart contracts partially offsets cost escalations by penalizing non-compliant suppliers and rewarding high-performing ones. Overall, the proposed model provides a rigorous and transparent decision-support framework that enables contractors to align supply chain design with energy-efficiency targets, CO 2 -reduction policies, and circular-economy objectives while preserving operational feasibility and supply reliability.
Jack McGarrigle, Jessica Smith, J. Gwyn Griffiths, Jamie Torrance · 6 authors
Background and aims: Dark patterns are online platform design features that influence consumer behaviour to the advantage of the interface designer. In online gambling, such designs may exacerbate gambling-related harms, particularly among vulnerable consumers. This study aims to provide the first scoping review of dark patterns in online gambling. Methods: Following established scoping review frameworks, we systematically searched databases and grey literature using terms related to dark patterns and online gambling. The review protocol was preregistered. Results: Included articles (n = 16) addressed a variety of gambling-related dark patterns: hidden gambling management tools, inducements with complex conditions, minimum balances required to withdraw funds, unnecessary frictions involved in closing an account, high defaults in stake, deposit, reality check and deposit limit settings, and urgency-based gambling prompts. To address inconsistent terminology across studies, we synthesised existing literature by mapping identified dark patterns to a transdisciplinary framework, providing greater conceptual clarity and direction for future research. Discussions and conclusions: The potential for harm from dark patterns is evident, yet evidence on behavioural impacts is limited, hindered by restricted access to proprietary gambling operator data. Research in this area is sparse and fragmented, often using inconsistent terminology. Future studies should empirically investigate the influence of dark patterns on consumer behaviour, especially among vulnerable populations, and evaluate safer design alternatives. We recommend mandating gambling operators to collaborate with researchers to assess platform safety, and shifting the burden of proof onto operators to demonstrate that their platforms prioritise consumer safety and foster responsible gambling environments.
<p><span lang="EN-US" style="font-size: 10.0pt; mso-bidi-font-size: 11.0pt; line-height: 115%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 宋体; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Amidst the rise of Web3, a technology transforming user interactions and challenging corporate control, this study uses a hybrid model of appreciative inquiry that matches the remote and decentralized nature of Web3 communities, to investigate the formation of a blockchain startup and its emergent culture and values. Despite limited resources, the company has built a diverse, global community via digital platforms, exceeding stakeholder expectations. This appreciative inquiry uncovers a community manifesting five core values: excellence, sustainable innovation, inclusivity, continuous learning, and creativity, challenging stereotypes often associated with the Web3 industry. This work advances participative research by introducing a hybrid model of appreciative inquiry tailored for remote and decentralized Web3 communities. By adapting appreciative inquiry to the unique dynamics of blockchain-dependent organizations, this study extends the methodology&rsquo;s applicability and demonstrates its effectiveness in uncovering and fostering core communal values within cutting-edge technological contexts.</span></p>
We introduce Auditable Proof-of-Work (APoW), a novel proof-of-work (PoW) construction inspired by Hashcash-style nonce searching, which enables the auditing of other miners' work through accountable re-scanning of the nonce space. The proposed scheme allows a miner to probabilistically attest to having searched specified regions of the nonce space in earlier mining rounds, while concurrently earning rewards for performing productive work for a new block or pool share. This capability enables miners belonging to a mining pools to audit another miner's claimed effort retroactively, thereby allowing the probabilistic detection of block withholding attacks (BWAs) without requiring trusted hardware or trusted third parties. As a consequence, the construction supports the design of decentralized mining pools in which work attribution is verifiable and withholding incentives are substantially reduced. The scheme preserves the fundamental properties of conventional PoW, including public verifiability and difficulty adjustment, while adding an orthogonal auditability layer tailored to pool-based mining. Finally, while a full deployment of APoW in Bitcoin would require a consensus rule change and minor modifications to mining ASICs, the construction remains practically useful even without consensus changes, for instance, as a pool-level auditing mechanism that enables verifiable pay-for-auditing using existing pool reserves.
Francisco Angulo de Lafuente, Vladimir Veselov, Richard Goodman
We propose a theoretical framework--Holographic Reservoir Computing (HRC)--which hypothesizes that the thermodynamic noise and timing dynamics in voltage-stressed Bitcoin mining ASICs (BM1366) could potentially serve as a physical reservoir computing substrate. We present the CHIMERA (Conscious Hybrid Intelligence via Miner-Embedded Resonance Architecture) system architecture, which treats the SHA-256 hashing pipeline not as an entropy source, but as a deterministic diffusion operator whose timing characteristics under controlled voltage and frequency conditions may exhibit computationally useful dynamics. We report preliminary observations of non-Poissonian variability in inter-arrival time statistics during edge-of-stability operation, which we term the "Silicon Heartbeat" hypothesis. Theoretical analysis based on Hierarchical Number System (HNS) representations suggests that such architectures could achieve O(log n) energy scaling compared to traditional von Neumann O(2^n) dependencies. However, we emphasize that these are theoretical projections requiring experimental validation. We present the implemented measurement infrastructure, acknowledge current limitations, and outline the experimental program necessary to confirm or refute these hypotheses. This work contributes to the emerging field of thermodynamic computing by proposing a novel approach to repurposing obsolete cryptographic hardware for neuromorphic applications.
Prediction markets are designed to aggregate dispersed information about future events, yet today's ecosystem is fragmented across heterogeneous operator-run platforms and blockchain-based protocols that independently list economically identical events. In the absence of a shared notion of event identity, liquidity fails to pool across venues, arbitrage becomes capital-intensive or unenforceable, and prices systematically violate the Law of One Price. As a result, market prices reflect platform-local beliefs rather than a single, globally aggregated probability, undermining the core information-aggregation function of prediction markets. We address this gap by introducing a semantic alignment framework that makes cross-platform event identity explicit through joint analysis of natural-language descriptions, resolution semantics, and temporal scope. Applying this framework, we construct the first human-validated, cross-platform dataset of aligned prediction markets, covering over 100 000 events across ten major venues from 2018 to 2025. Using this dataset, we show that roughly 6% of all events are concurrently listed across platforms and that semantically equivalent markets exhibit persistent execution-aware price deviations of 2-4% on average, even in highly liquid and information-rich settings. These mispricings give rise to persistent cross-platform arbitrage opportunities driven by structural frictions rather than informational disagreement. Overall, our results demonstrate that semantic non-fungibility is a fundamental barrier to price convergence, and that resolving event identity is a prerequisite for prediction markets to aggregate information at a global scale.
Oana Panazan, Catalin GHEORGHE, Aamir Aijaz Syed, Ahmed Jeribi
This study examines the dynamic interactions between precious metals, cryptocurrencies, stablecoins, safe-haven currencies, and two key macroeconomic indicators, the 5-year breakeven inflation expectation (T5YIE) and the 10-year minus 3-month Treasury yield spread (T10Y3M), over January 2016–July 2025. To capture nonlinear and multi-scale dependencies, the study applies Quantile-on-Quantile Regression (QQR) in combination with wavelet coherence (WCO) and wavelet transform coherence (WTC). The results indicate that major cryptocurrencies such as Bitcoin and Ethereum do not display robust or systematic links with inflation expectations or recession risk, limiting their role as macro-financial hedges. By contrast, the Japanese yen and Swiss franc show pronounced tail sensitivities, reaffirming their safe-haven status, while gold and its tokenized counterparts (DGX, PAXG) exhibit persistent long-run coherence with inflation expectations. Stablecoins demonstrate unstable short-term linkages shaped by liquidity shocks and market frictions. The research provides new evidence on the heterogeneous roles of digital and traditional assets in shaping macroeconomic expectations. The findings carry implications for investors, who should continue to rely on gold and safe-haven currencies for crisis hedging, and for regulators concerned with the systemic stability of emerging digital instruments.
Lina Bautista López, Edgar Esaul Vite Gómez, Lizet Manzo MartÃnez
This article offers a multidisciplinary approach to the study of cryptocurrencies through the analysis of different academic documents. Analysis is an effort to address the issue of such digital assets from an overview rather than a particular one. The objective is that cryptocurrencies are understood in their concept, origin and operation by those interested in the subject who are not immersed in it. Therefore, two theories that are the monetary theory and the economic theory of the law are considered to support the research in its several aspects such as the economic, legal, social, among others. The analysis makes it possible to identify common trends in the authors without departing from their own opinion of cryptocurrencies considering their discipline.
For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods struggled. As data availability continues to expand, the integration of machine learning techniques is likely to further enhance forecasting capabilities across various fields. Today, hybrid techniques are gaining popularity, as they combine the advantages of different approaches to deliver improved predictive performance and more advanced visualization analytics for decision support. These hybrid approaches can provide better prediction, and at the same time, they can develop a more sophisticated set of visualization analytics for decision support. Recently, the integration of cross-entropy, fuzzy logic, and attention mechanisms in hybrid forecasting models has enhanced their ability to capture complex and uncertain patterns in financial and energy markets. In this study, we propose a hybrid ANN–LSTM deep learning model optimized with cross-entropy, fuzzy logic, and an attention mechanism to enhance the forecasting of financial and energy time series, specifically Ethereum and natural gas prices. Our models combine the feature extraction strength of ANN with the temporal learning of LSTM, while cross-entropy improves convergence, fuzzy logic handles uncertainty, and attention refines feature weighting. Since inaccurate forecasts can lead to greater estimation uncertainty and increased financial and operational risk, improving predictive reliability is essential for effective risk mitigation. These techniques prove effective not only in improving estimation accuracy but also in minimizing financial risks and supporting more informed investment decisions.
The convergence of the Internet of Things and edge computing represents a fundamental transformation in distributed computing architecture. Traditional cloud-centric models introduce latency and connectivity dependencies flawed for time-touchy packages. Side computing addresses such constraints by positioning computational sources at network peripheries. Distributed processing paradigms restructure data pipelines through intermediate layers between endpoint devices and centralized infrastructure. Fog nodes extend cloud capabilities to locations where data originates. Tiered computation models distinguish between device-level processing, gateway computation, and cloud-based analytics. Aspect synthetic intelligence allows deployment of state-of-the-art machine learning models on resource-limited hardware. Neural network compression strategies consisting of quantization and pruning lessen version complexity while keeping accuracy. Fifth-generation wireless networks provide a connectivity fabric essential for distributed deployments. Multi-access edge computing positions processing resources at radio access network edges. Computation offloading transfers tasks from mobile devices to edge servers strategically. Security frameworks address expanded attack surfaces through zero-trust models and blockchain-based identity management. Distributed ledger architectures eliminate centralized credential repositories. Smart contracts automate security policy enforcement across edge networks reliably.
Hisham Mohamed Hassan Al Hammadi, Muhammad Hafiz bin Badarulzaman, Abdulaziz Fahmi Omar Faqera
The regulatory architecture governing cryptocurrencies and virtual assets in the United Arab Emirates has expanded markedly through Federal Decree-Law No. 20 of 2018, Cabinet Decision No. 10 of 2019, Federal Decree-Law No. 46 of 2021, and Dubai Law No. 4 of 2022, reflecting the state’s ambition to position itself as a leading digital finance hub while addressing money laundering risks. Notwithstanding this legislative progress, significant challenges persist, stemming from the decentralized and pseudonymous nature of cryptocurrencies, fragmented institutional oversight across federal and emirate-level authorities, and constrained supervisory capacity for real-time monitoring. Existing scholarship has largely overlooked the interaction between legal design and institutional enforcement dynamics within the UAE’s cryptocurrency regime, creating a critical gap this study addresses. The study critically evaluates the legal and institutional frameworks governing cryptocurrencies, examines enforcement and compliance vulnerabilities within AML mechanisms, and assesses regulatory risks associated with cryptocurrency market adoption. Employing an exploratory qualitative doctrinal methodology, the analysis systematically examines primary legislation alongside secondary sources drawn from high-impact journals, authoritative monographs, and institutional reports, subjected to rigorous thematic analysis. Guided by Institutional Theory, the findings demonstrate that while the UAE’s framework is normatively comprehensive, enforcement effectiveness is undermined by coordination deficits and technological constraints. The study advances targeted recommendations to enhance regulatory coherence, institutional integration, and risk-based supervision, contributing to legal, financial regulation, international governance, and digital risk studies, while identifying directions for future comparative inquiry.
USE VERSION 23/24 Until I'm done updating. The Costello Constant (CC) Formula base (e/phi - 1/pi) and the Recursive Costello sequence it was extracted from that's governed by the Rule n(+1) = n + f(n), where f(n) is the Greatest Proper Divisor of n(-1); f(n1) = 1. Which locks into an OOE or OE cycle, When mapped onto the complex plan Y(ix) = (e/phi -1/pi)^(0±ix) and use x as a function of time to cretes a 3rd dimention frma a duel helix where intersection of the 2 spiraling lines cancel out from complete annihilation and return a value of zero when calculated, this helix is anchored to the origin by raising it to the power of zero, the even exponent of I is one helical arm, the negative value of I is the odd value helical arm. Points where they annihilate the x values are the zeta zeros value with a frequeny ~ 10.33715124… the slope of the sequence points on a semi logarithmic graph when they align perfectly straight… or the inverse of... when joining sequential odds treating the O O E cycles as only 2 values (plot points, both odds as one single unit, multiplied by the value of CC ~ 1.3616... gives the exact value zeta zero 1, in the sequence this is equivalent to the Attractor a10 (16) when looking at ratios between zero 1 and zero 2 as an x/y it matches exactly to (13+16+17/3)/(17/25/26) this number and it's simplest reduced form 268/183 also are the exact ratio of certain toma in chemicals. And te genes which map a certain protein. I assume other ratios between consecutive numbers and the sequence will reveal some wonders in the universe that have remained untold until this moment. I've been ignored for weeks now which has giving me the time to dive into a level of certainty beyond any shadow of a doubt. On the regular graph when treating odds consecutive as one and evens as one connecting all evens and connecting All Odds creates two distinct lines where are the formula of the Costello constant is right in the middle. Basically turning the Zeta zeros into an algebraic problem by connecting the dots odds and evens where intersects on the equation graphed is the location of the Zeta zeros. Mic drop. V6. Added details about the zero timing overlap with formula being dictated by timing of pair sequential numbers in the sequence being used. V7. Added Defining Costello Constant's Value, Definition, And Symbol. V8. Added Data Set Of Sequence Numbers As T Values V9. Eureka! Offset fixed! "^0 + it" is the golden key it's officially solved. The Costello spiral is the structure, The zeta zeros are mapping the features of it. V10. Added Needed Proof V11. Complete revamp fixing errors in construction. I'm a non-academic... I'm trying here... Alone... V12. Updated Formatting Pages 1 - 2 Finalized V13. Update Pages 1 - 3 Finalized, 4 - 7 Drafted V14. Finalized Doc 1 Current Version Is A Fully Closed Loop System Logic, It's Proof By Fundamental Law. Costello Spiral Diagrams Reflects Older .809... Helix Radius Matching Pre 1.0000 Radius Formula Reduction. "This Fundamental Law is scale-invariant; while earlier diagrams (0.809) and the finalized 1.0000 reduction represent different magnitudes, the underlying closed-loop logic and intersection intersections remain constant. The 1.0000 Unit Radius represents the simplest, normalized state of the Costello Spiral." One last note to whom it may concern... I did this completely independent starting from the ground up with no previous research into other publishments, I started with the desire to make a sequence that was novel, and just kept making connections one after another. I've watched a couple YouTubes in the past that had discussed vaguely The mystery of the Zeta zeros and that's about the extent of my outside knowledge. I didn't set out to discover the secret for it, my series ran into it by its nature itself. V15. Updated format to Latex, added much more vigorous math proof, order of logic still needs tweaking. V16. Added data point charts into Latex pdf. V17. Formatting Fixes V18. Added -1 somewhere... Oops V19. Added how the Costello Spiral solves the Collatz Conjecture too. V20. Added hypothesis of the twin Prime conjecture V21. Fixed Rooke Mistakes... Double Statements... Out of order stuffs.... V22. More Formatting Fixes. V23. Lots better, 25+ years sine education environment, first proof... Getting there... V24. Added formula for ratio relationship of factors to the zero spacing, but messes up my formatt big time... Lullz.. im fixing it. I hate all these loops I have to jump through honestly, taking away from time that I could just be diving further in the numbers as usual. I'm almost giving up a couple times I just went back to my paper notebooks. V25. Well maybe have about 10% of the information out now... Main problem is I don't know what's most important to show I don't know what the world knows or not... Like I don't know what to add next the list is too big... Semi-prime Costello sequence numbers that are close together align with Zeta zeros close together.. eg., 7171... So much work... I've tried showing my math and I get laughed at... I'mma just keep on pushing... It may not be conventional to add your thoughts or whatever... But I'm a break the fifth wall right now... From two weeks now I've tried reaching out... All skepticism.. it just hit me tonight... It's because it's all sounds too good to be true... I didn't know that... I'm trying to do too much at once... I mean on top of my work that I'm doing I had to learn the formal language... I've had to learn how to code... I've had to learn Python script so I can run my old numbers... And for 2 weeks now I've been pushing... To show people ONE of my creations. Maybe the world is just not ready.... .. .. . Maybe. It's hard to forget, everything I regret. So why do I neglect, the chances that I get, To make those things correct... When I've tried to reflect... I just lost more respect... How did i ever let my mindset behind set get so inept. While im On the subject if I may be direct. I digress... It is best to get the rest of my chest. Im blessed but made a mess whats more or less my nest. I feel i failed my quest, I have failed my own test. It's a sure bet soon I'll take my last breath. Back to work... V26. Gtting there... Please use V23 complete copy until i stop mesing up my work with copy pasts twice deleed everything. V Edition2 V27. New formatt next few additions should be coming back to back to back as I string the old with the new. Refer to V22/23 for older complete outline, V Edition2 V28. Brought over some data from my research pfd, order and simplification are needed. V Edition2 V29. Stitching in the dimensional transitions from the number line to a real plane to complex plane to the manifold. Still need smooth transitioning. V Ediion2 V30. Added a good chunk to complex/manifold section, I just want to get it uploaded, I still have to prune it and smooth it. And make sure the stuff at the end is stated the way it's supposed to before I can remove it. Editiom2 V31. Added 10.3 frequency of spiral is the slope of sequence on log xy. Deleted doubles. Edition2 V32 Added dada set at end, refining python code number generator to add next. Edition2 V33 Changed Description on Zenodo added some info to I - III, refer to Ver 23 in tandem as f now after reading to complete the info aquired. Lots more to come... Edition2 V33.2 Keep Pushing Unil The World Listens... Changed Sequence Formula Formatt of f(n) Fixed Order still have to move over more sections from research Pdf. Including making sure pdf reflects duel helix is intersecting as counter clockwise 1 string and clockwise the other, reforming old 180° opposition, to actual intersection. At 0° Edition2 V34. Updated High Precision Value Of Slope using 500 sequence Values, Added bar graph for delta 2 equalization, other minor adjustments. Edition2 V35. Fixing all formulas to compensate for the change of what f(a_n) is.. as befor the rule a_n+1 = a_n + f(a_n-1) when f(a_n) meant a_n's GPD.. but for clearity f(a_n) now means a_n-1's GDP... To remove a LAG extra thought... Royal pain but a necessity.... Almost done converting everything. Edition2 V36 Formalized Pages 1-2 of actual proof after index, added rigor and made it more succinct. Eution2 V37. Showed how 10.337... slight miss alignment snap perfectly to 10.333 and perfectly aligned to zz1 now that start up terms 1-9 are removed from calculations. Edition2 V38 Formed formulas using the costello constant for prime density and how many primes exist in any limit, gives exct answer at 1,000,000. Edition2 v39 Finalized pages 1-4 Edition3.1 Finalize Format Starting To Translate. Page 1 done, Page 2 in progress Edition3.2 Actual Professional Formatt Learned And Applied.Pae 1/2 almost good. Should be a quick transition building back a strong base from dra in previous versions. Edition3.3 Added .6 Parity Limit, Growth Factor & Graph. Edition3.4 Added Symmetry/2-adic Sections & Tables Edition3.5 added the singularit Edition3.6 Formatt ambiguities removed, added minor info, Organized Zenodo Ledger, Edition3.7 Unified formatt formatt & variables, added log/non lomgrph real graphs, n more. Edition3.8 Added Changed To Font/Formatt Added Graphs Other Minor Additions Edition3.9 Bulletproofed Logic up to Lambda parity Density 0.6, 2:3. Edition3.10 Defined Lambda and lambda, added parity density equations and table Edition3.11 Added High Precision Lambda Values, 2 Graphs (1 Custom Expanding Y Axis} Edition3.12 Learned Python... Wrote and added script for producing Verifiable Data, Include plain txt file and 2 Appendix to PDF with Program and sample data. Edition3.13 Streamlined f function by introduction of spa divisor set mapped to n. Defined Tau and some other minor stuffs. Edition3.14 Added plain txt documents of raw Latex Code And Python Sequence Engine edition3.15 Added Infinit tetration of B = C,, LogB(C) =
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Advanced Physical and Chemical Molecular Interactions