The proliferation of AI-driven Customer Data Platforms (CDPs) processing vast amounts of personal data poses significant risks to minors in cross-border contexts, where existing consent mechanisms fail to ensure verifiable, granular, and revocable consent. This paper proposes a novel Blockchain-Governed Consent Infrastructure (BGCI) specifically designed to address these challenges. Leveraging blockchain’s immutability for auditability, smart contracts for automated policy enforcement, and Privacy-Enhancing Technologies (PETs) like Zero-Knowledge Proofs (ZKPs) for privacy-preserving age verification, the BGCI provides a robust framework for managing minor consent across jurisdictions. We detail a comprehensive architecture, core technical mechanisms, and cross-jurisdictional conflict resolution logic. Integration pathways with AI/CDP data ingestion, model training, and real-time personalization pipelines are defined. Rigorous analysis addresses scalability, security, regulatory compliance, and ethical considerations. The BGCI represents a critical step towards ethical, compliant, and empowering digital experiences for youth in the global data economy.
The integrity and scalability of electoral processes within large-scale academic institutions are often compromised by centralized vulnerabilities and high computational overhead. This paper proposes a novel, hierarchical consortium blockchain framework designed for Indian university ecosystem to facilitate secure, transparent, and high-concurrency e-voting. By utilizing tiered architecture comprising establishment-level private sidechains and global university-wide Ethereum ledger, proposed system optimizes trade-off between voter anonymity and transactional throughput by integrating Linkable Ring Signatures and Zero- Knowledge Proofs to ensure the Secret Ballot principle while maintaining public auditability. Experimental evaluations on with N = 4000 participants demonstrate an average gas consumption of 15,580 units per voter and peak throughput of 181 TPS. Experimental results reveal 11.5% reduction in per-voter processing latency compared to state-of-the-art models, showing proposed framework efficacy for high-density academic environments.
Reliable voice communication is vital throughout the institutions and organizations, but it is prone to the failures of internet connectivity and cellular network in constrained or isolated settings. Our physical system is a SIM-less intranet calling platform that is going to be implemented on a Raspberry Pi based on the open-source Asterisk PBX platform to provide the Voice over Internet Protocol (VoIP) communication between SIP-based clients. Session Initiation Protocol (SIP) takes care of the registration of the user, call setup, and signalling, whereas Real-Time Transport Protocol (RTP) takes care of a data transfer, providing stable peer-to-peer communication. Softphone applications like Zoiper are used to make the connection to the users and authentication is done with the help of Asterisk configuration files like sip.conf and extensions. conf. The viability of using embedded platforms to power safe and decentralized intranet telephony was confirmed by the result of experimental tests that confirmed good connectivity, low developing latency, and audible performance. A solution suggested can be applied to campuses, laboratories, small businesses, and emergency operation where the constant use of the internal communication network is needed and does not rely on the public network. This work will help to develop autonomous VoIP communication structures which make them more robust, private, and affordable by combining low-cost hardware and open-source software.
The Ethereum ecosystem, which secures over $381 billion in assets, fundamentally relies on client APIs as the sole interface between users and the blockchain. However, these critical APIs suffer from widespread implementation inconsistencies, which can lead to financial discrepancies, degraded user experiences, and threats to network reliability. Despite this criticality, existing testing approaches remain manual and incomplete: they require extensive domain expertise, struggle to keep pace with Ethereum's rapid evolution, and fail to distinguish genuine bugs from acceptable implementation variations. We present APIDiffer, the first specification-guided differential testing framework designed to automatically detect API inconsistencies across Ethereum's diverse client ecosystem. APIDiffer transforms API specifications into comprehensive test suites through two key innovations: (1) specification-guided test input generation that creates both syntactically valid and invalid requests enriched with real-time blockchain data, and (2) specification-aware false positive filtering that leverages large language models to distinguish genuine bugs from acceptable variations. Our evaluation across all 11 major Ethereum clients reveals the pervasiveness of API bugs in production systems. APIDiffer uncovered 72 bugs, with 90.28% already confirmed or fixed by developers, including one critical error in the official specifications themselves. Beyond these raw numbers, APIDiffer achieves up to 89.67% higher code coverage than existing tools and reduces false positive rates by 37.38%. The Ethereum community's response validates our impact: developers have integrated our test cases, expressed interest in adopting our methodology, and escalated one bug to the official Ethereum Project Management meeting. By making APIDiffer open-source, we enable continuous validation of Ethereum client API implementations, thereby strengthening the foundational integrity of the entire Ethereum ecosystem.
Abstract Software development plays a central role in digital sustainability, yet developers’ role and engagement remains understudied. Here we analyse nearly a decade of developer discussions available on the code repository Github on Ethereum, a widely used open-source blockchain platform. Using topic modelling, with interpretation supported by large language models and a sustainability framework for software systems, we trace how economic, environmental, social, individual, and technical sustainability themes emerge and evolve over time. We find that sustainability awareness, particularly related to energy efficiency and cost, intensifies during key events such as the transition from proof-of-work to proof-of-stake consensus, which substantially reduced energy use. We identify influential contributors and thematic specialisation, providing a transferable framework for understanding sustainability in emerging developer communities. These findings highlight the role of developer discourse in shaping sustainable software ecosystems and integrating sustainability into open-source development.
O presente artigo formaliza o <i>Economic Centrifugal Dispersion Model</i> (ECDM) como uma estrutura analítica de alta fidelidade para a compreensão da propagação de capital e incentivos em ecossistemas de Web3 e finanças descentralizadas (DeFi). Fundamentado em uma convergência interdisciplinar entre a praxeologia da escola austríaca, a física estatística e a dinâmica de sistemas complexos, o modelo propõe que a injeção monetária em sistemas baseados em <i>blockchain</i> gera forças dispersivas análogas às forças centrífugas. A pesquisa detalha a formulação matemática do modelo, integrando equações diferenciais não lineares para descrever o comportamento de variáveis como o influxo de capital, a velocidade de circulação e a resistência institucional. Adicionalmente, o trabalho explora a aplicação da Lei de Benford como ferramenta de auditoria estatística para detecção de anomalias em transações <i>on-chain</i> e propõe o Índice de Fragilidade Tokenômica (FTF) como métrica de risco sistêmico. Através da análise de expoentes de Lyapunov e diagramas de bifurcação, demonstra-se como pequenas flutuações paramétricas em Organizações Autônomas Descentralizadas (DAOs) podem induzir regimes de caos determinístico. O estudo conclui que a sustentabilidade de protocolos descentralizados depende de um equilíbrio crítico entre a dispersão centrífuga e a coesão institucional, oferecendo um arcabouço para o <i>design</i> de sistemas econômicos resilientes.<br>
This paper analyzes the reconfiguration of business models in the Decentralized Finance (DeFi) ecosystem under the aegis of informational capitalism 4.0.It investigates the paradigmatic transition from restricted innovation to models of open innovation and algorithm-mediated co-creation, based on a new regime of mathematical trust.From a socio-technological perspective, it discusses the tensions between protocol autonomy and state regulation, identifying the challenges that algorithmic governance and social datafication pose to monetary sovereignty and ethics in the technology sector.It is concluded that the success of DeFi depends on the balance between radical decentralization and governance mechanisms that prevent the concentration of power, especially in the context of Latin American development.
Robots are improving their autonomy with minimal human supervision. However, auditable actions, transparent decision processes, and new human-robot interaction models are still missing requirements to achieve extended robot autonomy. To tackle these challenges, we propose RODEO (RObotic DEcentralized Organization), a blockchain-based framework that integrates trust and accountability mechanisms for robots. This paper formalizes Decentralized Autonomous Organizations (DAOs) for service robots. First, it provides a ROS-ETH bridge between the DAO and the robots. Second, it offers templates that enable organizations (e.g., companies, universities) to integrate service robots into their operations. Third, it provides proof-verification mechanisms that allow robot actions to be auditable. In our experimental setup, a mobile robot was deployed as a trash collector in a lab scenario. The robot collects trash and uses a smart bin to sort and dispose of it correctly. Then, the robot submits a proof of the successful operation and is compensated in DAO tokens. Finally, the robot re-invests the acquired funds to purchase battery charging services. Data collected in a three day experiment show that the robot doubled its income and reinvested funds to extend its operating time. The proof validation times of approximately one minute ensured verifiable task execution, while the accumulated robot income successfully funded up to 88 hours of future autonomous operation. The results of this research give insights about how robots and organizations can coordinate tasks and payments with auditable execution proofs and on-chain settlement.
This study investigates the impact of sustainability-related uncertainty (SRU)—captured via the Sustainability-related Uncertainty Index in equal-weighted (ESGUI_EQ) and GDP-weighted (ESGUI_GDP) forms—on the volatility of green financial assets, focusing on decentralized finance (DeFi) protocols and Environmental, Social, and Governance (ESG)-focused Exchange-Traded Funds (ETFs). Employing a fuzzy logic framework, complemented by 3D surface visualization, Rule Viewer analysis, diagnostic validation, and Granger causality tests, the study uncovers non-linear, asymmetric, and time-varying responses of these assets to sustainability ambiguity. Empirical results reveal a structural divergence: DeFi protocols amplify volatility due to fragmented governance, speculative investor behavior, and sensitivity to policy-driven signals, often exhibiting bidirectional predictive feedback with SRU, whereas ESG ETFs maintain stability through diversification, regulatory oversight, and rigorous ESG screening, primarily absorbing sustainability shocks. These findings extend sustainable finance theory by integrating governance, technology, and policy dimensions, and illustrate the value of fuzzy logic combined with Granger causality in modeling complex, ambiguous markets. From a practical standpoint, the study provides actionable guidance for investors, fund managers, and policymakers, emphasizing the importance of technology-informed governance, standardized ESG disclosures, regulatory sandboxes, and continuous monitoring of SRU.
The advancement of blockchain technology has introduced Non-Fungible Tokens (NFTs) as digital assets representing ownership of creative works. However, the burgeoning NFT market precipitates significant legal risks, primarily arising from the dichotomy between the ownership of the digital token and the copyright of the underlying work. This research aims to examine the juridical risks inherent in NFT transactions, given the regulatory lacuna within the Indonesian legal system. Although Law No. 28 of 2014 concerning Copyright provides a normative framework, its application within the NFT ecosystem confronts challenges regarding legal certainty and platform accountability. The findings underscore the exigency of statutory harmonization and a more comprehensive legal protection mechanism, including defined liabilities for Electronic System Providers (ESPs), to mitigate risks and ensure equitable legal protection within Indonesia’s digital economy.
This paper presents a detailed analysis of the environmental impact of Chia Network (Chia for short), a green-claimed blockchain, which uses a Proof of Space and Time (PoST) consensus mechanism. While Chia claims to be a sustainable alternative to Proof-of-Work-based blockchains, our results show that its resource-intensive initialization phase and ongoing operations lead to carbon emissions 18x higher than claimed (0.88 MtCO2/year), exceeding mainstream "green" blockchains by orders of magnitude. We combine experimental measurements from a controlled testbed (Grid'5000) with theoretical modeling of operational and embodied emissions to assess Chia's true sustainability profile.
The smart home is a key application domain within the Society 5.0 vision for a human-centered society. As smart home ecosystems expand with heterogeneous IoT protocols, diverse devices, and evolving threats, autonomous systems must manage comfort, security, energy, and safety for residents. Such autonomous decision-making requires a trust anchor, making blockchain a preferred foundation for transparent and accountable smart home governance. However, realizing this vision requires blockchain-governed smart homes to simultaneously address adaptive consensus, intelligent multi-agent coordination, and resident-controlled governance aligned with the principles of Society 5.0. Existing frameworks rely solely on rigid smart contracts with fixed consensus protocols, employ at most a single AI model without multi-agent coordination, and offer no governance mechanism for residents to control automation behaviour. To address these limitations, this paper presents the Society 5.0-driven human-centered governance-enabled smart home blockchain agent (S5-SHB-Agent). The framework orchestrates ten specialized agents using interchangeable large language models to make decisions across the safety, security, comfort, energy, privacy, and health domains. An adaptive PoW blockchain adjusts the mining difficulty based on transaction volume and emergency conditions, using digital signatures and a Merkle tree to anchor transactions and ensure tamper-evident auditability. A four-tier governance model enables residents to control automation through tiered preferences from routine adjustments to immutable safety thresholds. Evaluation confirms that resident governance correctly separates adjustable comfort priorities from immutable safety thresholds across all tested configurations, while adaptive consensus commits emergency blocks.
The aim of this research is to study XRP cryptoasset price dynamics, with a particular focus on forecasting atypical price movements. Recent studies suggest that topological properties of transaction graphs are highly informative for understanding cryptocurrency price behavior. In this work, we show that specific topological properties of the XRP transaction graphs provide important information about extreme XRP price surges, and can be used for more competitive prediction of anomalous price dynamics.
This study examines access to clean and sustainable energy in the city of Mbandaka, Democratic Republic of Congo. Using a mixed-method approach combining surveys of 150 households and semi-structured interviews, it highlights a strong dependence on traditional energy sources such as wood and charcoal, despite a growing adoption of solar energy. Results show that 30% of households already use solar energy for lighting, while 72% still rely on charcoal for cooking. The main barriers to energy transition are the high initial cost of equipment and the lack of information about clean technologies. The study concludes that the energy transition in Mbandaka is technically feasible and socially desirable but requires institutional support, inclusive financing mechanisms, and participatory governance. It advocates for a territorial approach based on decentralization and environmental education.
Vaishnavi S. Jadhav, Sakshi S. Niphade, S. S. Mahale
Secure data sharing has become a critical challenge in modern digital ecosystems due to increasing data breaches, lack of transparency, and dependence on centralized authorities. Traditional data-sharing mechanisms often suffer from single points of failure, unauthorized access, and limited trust among participating entities. Blockchain technology, with its decentralized, immutable, and cryptographically secure architecture, offers a promising solution to these challenges. This research paper explores the application of blockchain technology for secure data sharing, emphasizing its ability to ensure data integrity, confidentiality, transparency, and access control. The study examines how features such as distributed ledgers, smart contracts, and consensus mechanisms can be leveraged to manage data ownership, enforce access policies, and prevent tampering. Various blockchain-based data-sharing models are reviewed across domains such as healthcare, finance, and supply chain management. The paper also discusses key challenges, including scalability, privacy preservation, interoperability, and regulatory concerns. Finally, future research directions are highlighted to enhance the efficiency and practicality of blockchain-enabled secure datasharing systems
Prior work established that knowledge distillation transfers a detectable provenance trace from teacher to student models, and that API endpoint verification can identify models through logprob order-statistic geometry. Both results were demonstrated on single teacher-student pairs and a six-model API zoo, leaving open whether provenance detection generalizes across model families and whether API verification scales to production-density endpoint populations. We address both questions through a coordinated experimental program spanning four studies. In the first study, we train 24 distilled checkpoints across 7 experimental arms — 3 teacher families (Qwen, Mistral, Llama), 4 student architectures (Qwen-0.5B, Qwen-1.5B, Llama-1B, Gemma-2B), and 2 training protocols (logit-level knowledge distillation and cross-tokenizer supervised fine-tuning) — measuring provenance transfer in both the weight-geometry and API-logprob regimes. Provenance transfer generalizes across the tested matrix: all 14 mature-epoch checkpoints show directional coupling to the teacher (cosine alignment cosθ > 0.8, with 13 of 14 exceeding 0.85). The strongest signal arises in a cross-family arm (Mistral-7B → Llama-1B, scalar convergence 0.858) that is inconsistent with a purely family-restricted transfer hypothesis within the tested matrix. The normalized third logit gap δ_norm remains within 1.4% coefficient of variation across all 31 checkpoints and 4 student architectures — the tightest confirmation of Gumbel-class universality in this experimental program. An extension to mixture-of-experts architecture (Mixtral-8x7B, δ_norm = 0.309) confirms that the universal constant persists under sparse expert routing. In the second contribution, we identify a systematic failure mode of scalar provenance metrics and introduce the geometrically correct directional diagnostic for provenance detection in inner-product spaces. The standard scalar convergence metric Conv_T conflates direction and magnitude into a single value, discarding the directional information that provenance detection requires. In two independent experiments, this produced misleading conclusions: a false spoofing signal (R^2 = 0.995 of apparent cross-family convergence explained by pure knowledge distillation geometry, with the adversarial gradient contributing 4.8%) and a false failure signal (negative Conv_T despite consistent directional coupling at cosθ = 0.91). The alignment diagnostic applies the law of cosines in PPP-residual template space (vectors in R^K with Euclidean distance) to decompose student movement into direction and magnitude, preserving the provenance signal that scalar distance metrics destroy. We establish a measurability threshold: when the baseline-to-teacher distance d(B,T) falls below approximately 1.0, scalar Conv_T becomes unreliable and the directional diagnostic becomes the primary metric. This diagnostic applies to any distillation forensics framework that measures convergence in an inner-product space. In the third contribution, we extend API endpoint verification from 6 models to 14 across 3 commercial providers (OpenAI, Google Vertex AI, xAI), observing zero breaches across 182 pairwise impostor comparisons under per-model adaptive thresholds and three independent enrollment sessions, with a centroid reference protocol (CRP) that replaces the centroid L^2 metric, which produces false breaches at 14-model density. We establish a minimum truncation floor: API endpoints exposing fewer than 7 logprob ranks cannot support reliable verification (signal collapses within one rank of this boundary). Speculative decoding — an increasingly common inference optimization — is shown to be transparent to the verification protocol, with the speculative-decoded fingerprint deviating from the verifier-only fingerprint by 10.6% of the inter-model distance. Finally, we formalize the Trust Paradox in model forensics — a victim cannot prove weight theft without disclosing weights, and a suspect cannot prove innocence without disclosing training data — and propose a three-tier zero-knowledge attestation architecture that addresses it. The first tier (committed distance proof) enables a model owner to prove fingerprint proximity to a public anchor without revealing the fingerprint vector, using standard cryptographic commitments with verifier-controlled thresholds. The second tier (hardware-attested measurement) removes the requirement that the prover be trusted to compute the fingerprint correctly, binding the measurement to a trusted execution environment attestation. The third tier (full zero-knowledge extraction) would eliminate all trust assumptions beyond cryptographic soundness; we present this as an open problem with pre-registered falsification criteria, including a fixed-point precision gate derived from the minimum pairwise separation in the existing 23-model zoo. The architecture defines eight properties that a meaningful zero-knowledge model identity proof must satisfy — extending the formal verification doctrine (311 + 41 = 352 theorems across 17 Coq proof files [1, 2], 0 Admitted) into the cryptographic regime — and six explicit trust assumptions under which the proof statements hold. All three tiers are validated: Tier 1 (committed distance proof) has been implemented and hardened; Tier 2 (hardware-attested measurement) has been validated on production confidential computing hardware (6 models, 1,536 measurements, 0 failures inside an H100 trusted execution environment, with both CPU and GPU attestation tokens bound to a common cryptographic root and structural fingerprints transparent to confidential computing mode); and Tier 3 (full zero-knowledge extraction) has been validated — a complete circuit has been compiled and audited, all four pre-registered falsification criteria have been met, and the proof system operates within practical proving-time and proof-size bounds. The breakthrough discoveries enabled by Tier 3 validation, including an identity-conditioned inference verification architecture, are reported in the companion paper. The experimental results in this paper are grounded in the formal verification stack and measurement infrastructure described in the companion papers [1, 2, 3]. All provenance claims are classified as VALIDATED (empirical); Tier 1 (committed distance proof) has been implemented and hardened, and Tier 2 (hardware-attested measurement) has been validated on production confidential computing hardware — both are classified VALIDATED. Tier 3 (full zero-knowledge extraction) has been validated: a complete circuit was compiled and audited, all four pre-registered falsification criteria were met, and the architecture has been extended into identity-conditioned inference verification [6]. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) 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) 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).
У статті запропоновано модель зберігання та верифікації персональних даних на основі технології розподіленого реєстру (блокчейну), орієнтовану на підвищення довіри до цифрових сервісів. Розглянуто архітектуру системи, що включає модулі збору, шифрування, запису метаданих у блокчейн, контроль доступу за допомогою смарт-контрактів і алгоритми перевірки цілісності даних без їх розкриття. Описано формат блоку для запису, модель управління правами доступу на основі мультипідпису та реалізацію політик доступу у вигляді смарт-контрактів. Проведено експериментальне тестування продуктивності моделі в середовищі Hyperledger Fabric із використанням типових сценаріїв, зокрема перевірки освітніх і медичних записів, електронної ідентифікації тощо. Отримані результати свідчать про високу швидкість верифікації, низьке ресурсне навантаження та масштабованість. Запропоноване рішення демонструє наукову новизну завдяки поєднанню механізмів zero-knowledge proof, гнучких політик доступу й інтеграції з зовнішніми цифровими платформами через API. Розроблена модель може бути основою для створення довірених цифрових інфраструктур у сфері електронного врядування, охорони здоров’я та фінансів.
The housing market is of great significance to the development and advancement of cities, but customary forms of property valuation are frequently biased, time-consuming, and not always effective. This paper focuses on the city of Irbid in Jordan, aiming to collect all the information on apartments and houses, predict the prices of properties, and clarify the key factors influencing the prices. Following the comprehensive cleaning process of the data and exploratory analysis, three ensemble machine learning models were trained and optimized to achieve accurate price predictions. The performance of all three models demonstrated excellent and consistent predictions, highlighting the efficiency of ensemble methods in predicting property prices. SHAP analysis indicated that the size of the house, the number of bedrooms, the number of lounges as well as the location are the most significant factors influencing the prices in Irbid. This reflects the functioning of the local market.
The study applies a qualitative analytical approach utilizing a unique methodological framework: content analysis of global forecasting reports, algorithmic monitoring of social media engagement and comparative benchmarking of material technological specifications. Methods of morphological, colorimetric and semiotic analysis were utilized to identify key aesthetic and technological dominants. The research revealed a fundamental “aesthetic polarization” in 2025: the coexistence of “Phygital” aesthetics (metallics, 3D abstraction, neural network textures) and “Radical naturalness” (biomimicry, tactile surfaces). The concept of “architectural morphology” is introduced and substantiated, where the nail shape is conceptualized as an ergonomic structure with a compensatory function for anatomical correction. It is established that modern nail art requires the implementation of algorithmic design principles, including the Golden Ratio rule and specific colorimetric formulas (60-30-10), to achieve compositional integrity. The further development trajectory of the industry lies in the synergy of artistic modeling, digital services (AR fitting) and biotechnologies (“smart” and regenerative coatings). The work establishes a theoretical basis for elevating professional standards within the nail industry References 1. Belk, R. W. (2013). The extended self in a digital world. Journal of Consumer Research, 40(3), 477-500. 2. Kataila, Natalia. (2021). "Digital Fashion" on Its Way from Niche to the New Norm. 3. Kapferer, Jean-Noël & Michaut, Anne. (2015). Luxury and sustainability: a common future? The match depends on how consumers define luxury. Luxury Research J.. 1. 3. 10.1504/LRJ.2015.069828. 4. Hill, S. E., Rodeheffer, C. D., Griskevicius, V., Durante, K., & White, A. E. (2012). Boosting beauty in an economic decline: mating, spending, and the lipstick effect. Journal of personality and social psychology, 103(2), 275–291. https://doi.org/10.1037/a0028657 5. Lochhead, Robert. (2007). The Role of Polymers in Cosmetics: Recent Trends. ACS Symposium Series. 961. 3-56. 10.1021/bk-2007-0961.ch001. 6. Dorschel, Robert & Hermans, Anne-Mette. (2025). Body, beauty, enrichment: Theorizing the rise of the cosmetic industry through Boltanski and Esquerre's framework of enrichment. Journal of Cultural Economy. 1-17. 10.1080/17530350.2025.2478859. 7. Bhardwaj, Vertica. (2010). Fast fashion: Response to changes in the fashion industry. The International Review of Retail. Distribution and Consumer Research. 165-173. 10.1080/09593960903498300. 8. Goffman, E. (2021). The Presentation of Self in Everyday Life (Revisited ed.). Anchor Books. 9. Kim, E., Fiore, A. M., & Kim, H. (2021). Fashion trends: Analysis and forecasting (3rd ed.). Berg Publishers. 10. Draelos, Z. D. (2024). Cosmetic Dermatology: Products and Procedures (3rd ed.). Wiley-Blackwell. 11. Baran, R., & Maibach, H. I. (2019). Textbook of Nail Diseases: Diagnosis, Therapy, and Surgery (5th ed.). CRC Press. 12. Scher, R. K., & Daniel, C. R. (2005). Nails: Diagnosis, Therapy, Surgery. Elsevier Health Sciences. 13. Rieder, E. A., & Tosti, A. (2016). Cosmetically Induced Disorders of the Nail with Update on Contemporary Nail Manicures. The Journal of clinical and aesthetic dermatology, 9(4), 39–44. 14. de Berker D. (2013). Nail anatomy. Clinics in dermatology, 31(5), 509–515. https://doi.org/10.1016/j.clindermatol.2013.06.006 15. Elliot, A. J., & Maier, M. A. (2014). Color psychology: effects of perceiving color on psychological functioning in humans. Annual review of psychology, 65, 95–120. https://doi.org/10.1146/annurev-psych-010213-115035 16. Labrecque, L. I., & Milne, G. R. (2012). Exciting red and competent blue: The importance of color in marketing. Journal of the Academy of Marketing Science, 40(6), 711-727. 17. Joy, Annamma & Zhu, Ying & Peña-Moreno, Camilo & Brouard, Myriam. (2022). Digital future of luxury brands: Metaverse, digital fashion, and non‐fungible tokens. Strategic Change. 31. 337-343. 10.1002/jsc.2502. 18. Fraser, T., & Banks, A. (2004). Designer’s Color Manual: The Complete Guide to Color Theory and Application. Chronicle Books. 19. Pazda, Adam & Elliot, Andrew & Greitemeyer, Tobias. (2012). Sexy red: Perceived sexual receptivity mediates the red-attraction relation in men viewing woman. Journal of Experimental Social Psychology. 48. 787–790. 10.1016/j.jesp.2011.12.009. 20. Mezger, T. G. (2020). The Rheology Handbook: For those with practical tasks in rheology and viscometry (5th ed.). Vincentz Network. 21. Chevreul, M. E. (2021). The Principles of Harmony and Contrast of Colors and Their Applications to the Arts (Reprint ed.). Schiffer Publishing. (Original work published 1989). 22. Fechner, G. T. (2021). Vorschule der Aesthetik (Propaedeutics of Aesthetics) (Modern Translation). Breitkopf & Härtel. (Original work published 1876).
Samar Alsulaimani, Ming Zhao, Farookh Khadeer Hussain
• Innovative Fractional Ownership Framework: The Fractional Digital Asset Ownership (FDAO) model uses fractional NFTs (FNFTs) to facilitate the co-ownership of digital assets, focusing on software code. • Addressing Ownership Management Challenges: Building on FNFT and blockchain technology, this study proposes an intelligent solution for fractional digital asset ownership that ensures the accurate tracking of ownership rights through the integration of FNFTs with blockchain technology. • Practical Prototype Development: This study demonstrates the FDAO framework’s capability to securely and transparently manage handling digital asset transactions using FNFTs and smart contracts implemented through Remix and OpenZeppelin. • Empirical Evaluation of FNFT Application: This research examines the effectiveness of FNFT frameworks in supporting fractional ownership, highlighting their potential for real-world digital asset applications. • Market Accessibility and Inclusivity: By enabling fractional ownership, the FDAO model increases accessibility to digital assets and supports ownership democratisation. • Identification of Limitations and Future Directions: The study discusses the challenges related to regulatory compliance, scalability, and costs associated with FNFTs and other blockchain platforms and outlines compliance strategies that may support a broad range of applications. A new generation of digital assets is being managed using blockchain technology and non-fungible tokens (NFTs), which introduce novel opportunities for verifying ownership rights and establishing provenance. This paper presents an innovative framework called Fractional Digital Asset Ownership (FDAO), which aims to create NFTs for digital artifacts, such as software code, and extend their functionality through fractionalized NFTs (FNFT). Leveraging the Model-View-Controller (MVC) design pattern, FDAO enables effective co-ownership tracking across the lifecycle of digital assets, providing a structured and efficient mechanism for defining and managing co-ownership. A system prototype has been developed and tested in an integrated development environment (IDE) using decentralised applications (DApps) and smart contracts. Unlike existing NFT-based models, FDAO incorporates an intelligent, automated fractionalization and verification mechanism that combines the ERC-1155 and ERC-20 standards to enhance co-ownership management and scalability. This integration addresses the critical challenges related to transparency, security, and lifecycle management in digital asset co-ownership. The prototype, implemented using Remix and OpenZeppelin, demonstrates how FDAO enables secure, transparent, and efficient transfer and management of digital assets. By integrating FNFT functionality with smart contracts, the framework provides a robust, scalable, and intelligent method for managing digital assets. It also maintains transparency and trust throughout the asset lifecycle.
Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideals of decentralization and censorship resistance are undermined in practice, motivating ongoing efforts to reestablish these properties. Existing proposals for fairness mechanisms depend on the assumption that a sufficient fraction of block proposers adhere to Ethereum's protocols as intended. We refer to such proposers as altruistic, as this behavior may come at the cost of reduced revenue. Prior analyses indicate that a consistent share of 91 percent of proposers delegate block construction to centralized services, effectively signing externally constructed blocks blindly, and are thus not considered altruistic. To assess whether the remaining 9 percent of proposers genuinely exhibit altruistic behavior, we conducted an empirical analysis and found that an additional 6.1 percent also interact with such external services. Further, we found that less than 1.4 percent of proposers consistently acted in accordance with Ethereum's decentralization and censorship resistance objectives. These findings suggest that relying solely on the mere presence of altruistic proposers is insufficient to ensure that proposed fairness mechanisms reestablish Ethereum's ideals, highlighting the need for additional incentive- or penalty-based mechanisms.
Dr. A. Radhika, D. Avinash, D. Sowjanya, K. Karthik · 5 authors
The increasing use of digital communication has made it essential to maintain the confidentiality, integrity, and authenticity of sensitive information. Conventional image steganographic methods offer data hiding in digital images, but they fail to offer effective tamper proofing and secure ownership verification. To overcome these issues, this paper presents a Blockchain-Integrated Secure Image Steganography system using IPFS and Ethereum. In the proposed system, secret data is hidden within digital images using a Least Significant Bit (LSB) image steganographic method developed in Python. The stego images are then stored in the Inter Planetary File System (IPFS) for efficient and decentralized data storage. To ensure data integrity and secure access, the cryptographic hash values of the stego images and their corresponding IPFS Content Identifiers (CIDs) are securely stored on the Ethereum blockchain using smart contracts. The use of blockchain technology provides immutability, transparency, and tamper resistance, and IPFS provides decentralized storage without depending on centralized storage servers. The proposed system is validated to offer high image quality with negligible distortion and robust data security and traceability. This system is applicable for secure data sharing in confidential communication, digital forensics, and secure document transfer.
Open access
Advanced Steganography and Watermarking Techniques
Blockchain technology has evolved incredibly into various domains other than cryptocurrencies such as healthcare, genomics application, agriculture, government schemes, land asset distribution, DeFi, IoT, supply chain management due to its decentralized and secured nature. Consensus mechanism in blockchain networks serves as the backbone to ensure data integrity, provenance, immutability and security. Traditional consensus mechanism faces many challenges like utilization of high energy or carbon, excessive computational resources, staking of cryptocurrency, high reputation of nodes, maximum votes received, scalability and security issues. To tackle this concerns many researchers has proposed solutions and given a comparative analysis of the performance of these algorithms. This paper gives the survey reviews of the consensus mechanism used so far with a comparative analysis on the performance metrics like scalability, latency, and throughput, degree of decentralization, energy and resources efficiency etc. We have divided the consensus algorithms based on two categories i.e Proof based and Acquiescence based. The study highlights critical trade-offs among scalability, energy efficiency, decentralization, fault tolerance, and security resilience. Furthermore, this paper sheds the light on recent innovations addressing mitigation strategies like sharding, off-chain solutions, checkpoint mechanism, and integration of machine learning for anomaly detection, prediction of attack vectors. By systematically comparing consensus protocols and identifying open research challenges, this review aims to provide researchers and practitioners with a clear understanding of current consensus landscapes and provide valuable guidance to the selection and design of suitable mechanisms for next-generation blockchain systems.
Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideal of censorship resistance, together with related fairness properties, is undermined in practice, motivating fairness mechanisms that aim to restore these properties. Several of these mechanisms hand control over block contents to a committee of proposers under a 1-of-n honest assumption: at least one committee member complies with the mechanism even when deviating would increase personal revenue. We refer to such proposers as altruistic. Yet prior work shows that roughly 91 percent of blocks are constructed by centralized block-building services that demonstrably take user-adverse actions for financial gain; the responsible proposers sign these blocks blindly, without any means of intervention. A common reading of this figure is that 9 percent of proposers forgo these gains and act altruistically. Our empirical analysis of the full year 2025 shows that this share is far smaller: at most 1.55 percent of proposers can plausibly be regarded as altruistic, whereas the remaining 98.45 percent of proposers exhibit observable non-altruistic behavior. We interpret 1.55 percent as an upper bound on the prevalence of altruistic proposers. These results imply that committee-based fairness mechanisms that rely on altruistic members would require substantially larger committees than currently proposed. This raises concerns about their practical viability and motivates mechanisms in which fair behavior is the rational choice.