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.
Abstract Green entrepreneurship has emerged as a key driver of sustainable market transformation, linking innovation, environmental stewardship, and social equity. Green entrepreneurship plays a vital role in enabling low-carbon growth by introducing innovative solutions that mitigate environmental impact while generating socio-economic value. The emergence of carbon markets provides a new economic mechanism to reward emission reduction activities, thereby creating sustainable market opportunities. This research explores how carbon finance mechanisms, including carbon credits, offset projects, and nature-based solutions, can support the growth of green enterprises. The study examines the potential of decentralized community-led green entrepreneurship models like coir and bamboo to participate in carbon markets and contribute to sustainable market ecosystems using digital tools and online platforms. It aims to explore how rural fibre-based industries can leverage carbon finance mechanisms to achieve environmental sustainability while enhancing rural livelihoods. By investigating the research intersection of low-carbon innovation, community enterprise, and carbon monetization, this research positions Online Green Entrepreneurship as a transformative pathway for building in Sustainable Markets Ecosystem. Keywords: Green Entrepreneurship, Sustainable Markets, Digital Tools, Online Platforms, Low-Carbon Innovation, Carbon Markets, Carbon Finance, Rural Livelihoods, Community Enterprise, Socio-Economic Value, Ecosystem.
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.
Purpose The absence of cryptocurrency (CC) tax regulations in many countries raises concerns about compliance and potential revenue losses. Understanding the factors that drive CC holders’ tax propensity is crucial for developing effective tax policies. Therefore, this research aims to explore the influence of contextual factors, e.g. CC legitimacy, CC investment risks, and social responsibility, and individual factors, e.g. attitude towards CC tax payment, technological competence and CC financial literacy, on CC tax payment propensity. Additionally, the study delves into the moderating role of financial literacy in the proposed model. Design/methodology/approach An integrated model of TPB-STC (theory of planned behaviour and social cognitive theory) was grounded in this study. Data were collected using a cross-sectional research approach through an online survey responded by CC investors. Findings The study found that attitude towards CC tax payment, social responsibility, legitimacy and CC financial literacy exerted a positive effect on the propensity to pay tax on CC. However, CC investment risks demonstrated a negative effect on propensity. Interestingly, the CC financial literacy-moderated interactions of crypto assets' legitimacy, technological competence and investment risks on CC tax payment propensity were significant. Practical implications The discoveries that emerged from this study contain practical and actionable insights for stakeholders, including regulators, tax authorities and investors. Educational programs focused on enhancing CC financial literacy should be integrated into public finance initiatives to improve taxpayers’ understanding of crypto taxation. Additionally, regulatory bodies can collaborate with crypto exchanges to implement transparent reporting mechanisms, making tax compliance more accessible and straightforward for investors. These actionable steps can help foster a proactive tax-paying culture, even in the absence of formal tax regulations. Originality/value This study provides a theoretical foundation of tax practices behaviour related to crypto in decentralized financial markets, paving the way for future research on self-regulating mechanisms within the present-day fast-moving crypto market.
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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While deep learning models have demonstrated superior performance in cryptocurrency forecasting, their deployment is often hindered by a lack of interpretability and trustworthiness. To bridge this gap, this paper proposes the Cryptocurrency Counterfactual Explanation (CryptoForecastCF) model. Recognizing the inherent volatility and complex non-linear dynamics of cryptocurrency markets, we argue that understanding the sensitivity of model outputs to slight variations in historical conditions is fundamental to robust risk management. CryptoForecastCF employs a gradient-based optimization strategy to generate meaningful counterfactual explanations. Specifically, it identifies minimal modifications, defined as the optimal perturbations to historical market features such as price constrained by â„“1 or â„“2 norms, that are sufficient to steer the model's future predictions into user-specified target intervals. This approach not only elucidates the key driving factors and decision boundaries of opaque models but also equips traders and risk managers with actionable insights, enabling them to identify the specific market shifts required to navigate high-stakes scenarios and mitigate unfavorable predictive outcomes.
The increasing need for sustainable practices has encouraged listed companies to participate in carbon trading markets. Traditional centralized systems for managing carbon trading data often face challenges such as limited transparency, poor traceability, and security risks, leading to inefficiencies and compliance issues. This research proposes a blockchain-based framework with smart contracts to provide a secure, decentralized mechanism for recording and verifying carbon trading data. The system ensures tamper-proof logs of emission allowances, trading transactions, and verification events, enabling real-time access for regulatory authorities. Data preprocessing uses Z-score normalization to standardize inputs, while Kernel Principal Component Analysis (KPCA) reduces dimensionality and extracts relevant features. To improve decision-making and cost-efficiency, a Dynamic Cuckoo Search-mutated Locust Swarm Optimization (DCSLSO) algorithm is embedded within the smart contracts to optimize carbon credit allocation and trading strategies. The framework is evaluated through simulations under varying energy demands, carbon prices, and multi-fuel scenarios, using synthetic datasets from energy-intensive industries. The DCSLSO model is implemented using Python and TensorFlow. This research demonstrates that blockchain technology, combined with intelligent smart contracts, can modernize carbon trading for listed companies, fostering transparency, accountability, and long-term economic sustainability in emissions management. This research highlights the potential of combining blockchain technology with intelligent optimization to modernize carbon markets, promoting transparency, accountability, and sustainable economic growth in emissions management.
The Physics of Truth: Extending WorldSeed from Robotics to Semantics Why is it impossible for a robot to lift a 10-ton rock, yet trivial for an AI (or human) to claim "I can lift a 10-ton rock"? This paper identifies the root cause of both AI Hallucination and Human Deception: the lack of "Energy Cost" in the textual domain (W_Text). In a frictionless semantic environment, generating a lie is thermodynamically equivalent to generating the truth. This work extends the WorldSeed SABO Protocol (State, Action, Boundary, Observer) from the domain of physical robotics (Sim2Real) to the domain of semantic truth (Sim2Fact). We propose that Truth is not a statistical property of language, but a computable property of Grounded State (S) and Costly Action (A). Key Contributions: The Theory of Semantic Gravity: Introducing axiomatic constraints (On-chain State, Action Staking) to make deception computationally or economically prohibitive. SABO Audit of Lies: Analyzing the structural flaws of decoupled observation (O ≠S) in LLMs and social contracts. Three Case Studies:• Literary Hallucination: Using logic boundaries to reject impossible narratives (e.g., Lin Daiyu uprooting a willow tree).• Financial Fraud: Replacing CEO claims with Zero-Knowledge Proofs of Solvency.• Social Default: Using smart contract staking to enforce promises. The Theorem of Semantic Convergence: A formal proof demonstrating that under a strict WorldSeed Runtime, divergent linguistic descriptions must collapse into a unique ontological fixed point. "Meaning is Execution." This paper completes the WorldSeed trinity by providing the philosophical and sociological framework that complements the Axiomatic Specification and the Civilization Operating System.
The rise of Big Tech has created unprecedented concentrations of power. The scaling potential of the modern IT industry is leading to widespread monopolies. For technologies that serve society, a monopoly brings structural dependence, and gives their owners an almost unchallengeable power. To counteract this societal dependence, academia, industry, and society at large proposed various countermeasures to limit the power of technology providers. In this thesis, Paul van Vulpen compares these approaches. The goal is to maintain the benefits of technology while reducing societal dependence on a few powerful actors. This book investigates three approaches. First, software ecosystems outline the collaboration between various interrelated software actors. Second, blockchain and decentralized autonomous organizations offer radical approaches to rethink and decentralize IT governance structures. Finally, digital platform regulations address urgent societal issues that arise from concentrated platform power. The final section concludes that a delicate and organic approach is needed to IT governance. Excessive centralization creates structural risks, but full decentralization is neither practical nor beneficial. The thesis proposes a middle road: Federated Technology Governance (FTG). In FTG, central authority defines architecture, interoperability standards, and maintains the long-term vision. A wide variety of actors handle user interaction, implementation, and collaboration. This framework helps technology providers to create software ecosystems and safeguard the provision of societal benefit for public digital infrastructure. FTG supports the creation of sovereign cloud services, secure operating systems, and public large language models. Could it also be a road to enable technology to serve society and the common good?
Federated learning enables multiple data owners to collaboratively train a global model, but verifying the correctness of submitted updates remains a critical challenge. Malicious clients may poison model weights, and colluding validators can undermine aggregation. We propose DI-FLAME, a decentralized validation framework that introduces a stake-and-proof mechanism for model verification. Each claim is submitted with a justification and stake, evaluated via a black-box credibility function. Validators engage in peer review, and contradictors may challenge weak claims by submitting stronger proofs and higher stakes. Rewards and penalties are dynamically distributed based on justification strength and challenge outcomes. DIFLAME provides robust defense against poisoned updates, even under adversarial majorities. While blockchain infrastructure is not required, DI-FLAME is compatible with with decentralized ledgers, enabling transparent recording of validation outcomes when deployed over a blockchain.
Vehicle-to-Everything (V2X) communication is at the center of autonomous mobility, as it enables vehicles to exchange real-time information with infrastructure, other vehicles on the road, including pedestrians, and the network. V2X has the potential to improve navigation, traffic flow, and safety but also brings with it the associated governance risks of data integrity, privacy, and cybersecurity. These risks are amplified by the interconnected nature of V2X networks, which handle sensitive data like vehicle locations and driver identities, necessitating robust security solutions. This systematic review considers blockchain as a remedy, with its decentralized architecture, cryptographic security protocols, and automation through smart contracts. A review of peer-reviewed literature from 2018 to 2025 highlights blockchain's role in tamper-proof communication, privacy preservation through Zero-Knowledge Proofs and Ring Signatures, and secure transaction automation. For instance, blockchain ensures data immutability by distributing trust across nodes, reducing vulnerabilities like data spoofing, while smart contracts streamline processes like toll payments. Challenges such as scalability, latency, and regulation are addressed with proposals such as 5G, edge computing, and governance frameworks. Hybrid blockchain systems, either public or private are examined in this study to obtain a trade-off between scalability and security in V2X networks. In addition, interoperability is proposed to facilitate the seamless exchange of data between V2X systems. Emerging consensus mechanisms like proof of stake and directed acyclic graphs are proposed to enhance scalability, while federated learning integrations bolster privacy. Pilot projects, such as those in Dubai and Singapore, demonstrate blockchain’s practical efficacy in securing V2X ecosystems. The review positions blockchain as a leading enabler of secure, efficient, and privacy-aware intelligent transport systems. Future research should focus on standardizing governance and addressing latency to ensure global adoption.