Blockchain Papers

Felipe Del Valle Saragoni

No abstract is available for this record.

Shuiping Tang

No abstract is available for this record.

Israel Gilbert, Sam Obila

No abstract is available for this record.

Doohwi Cha

No abstract is available for this record.

BÉO

Title:"Assessment of the Axiomatic System and Reliability of General Relativity According to Section Zero Standards" Abstract: Framework: Section Zero (DOI: https://doi.org/10.5281/zenodo.18091473) Analysis Date: January 20, 2026, final file This study presents a systematic analysis of Albert Einstein's General Relativity (GR) through the Section Zero evaluation framework, a novel methodology in scientific quality assurance. Unlike traditional assessments focused on "right/wrong," this research categorizes GR's core axioms according to their empirical verification status: "tested," "untested," and "untestable." Methodology: The analysis is based entirely on peer-reviewed experimental data from prestigious journals (Physical Review Letters, Nature, The Astrophysical Journal, etc.). The study does NOT propose new hypotheses or refute GR, but rather compares the verification status of axioms against existing experimental data. • Data reliability: 95%• Conclusion reliability: 90% Seven main axioms of GR are analyzed in detail, including:(1) Equivalence Principle with three versions: WEP, EEP, SEP(2) General Covariance(3) Einstein Field Equations(4) Metric Structure of Spacetime(5) Geodesic Motion(6) Constancy of speed of light (c = const)(7) Local Energy-Momentum Conservation Each axiom is compared against over 100 years of experimental data from classic experiments such as Mercury's perihelion precession, light bending, gravitational redshift, binary pulsars, LIGO/Virgo, and the Event Horizon Telescope. Main Results: GR is confirmed as the best-tested theory of gravity currently available with 99%+ reliability in weak to moderate field regimes. However, the study identifies significant gaps in experimental data: (i) The assumption c = const is only verified locally (≤20,000 km) with 99.9% reliability, but at cosmic scales only reaches 40% due to lack of direct measurements and circular logic in redshift interpretation (ii) The Strong Equivalence Principle (SEP) only achieves 70% reliability due to lack of strong-field experiments (iii) The form of Einstein's equations is "chosen" rather than "derived," with alternative theories (f(R) gravity, scalar-tensor theories) remaining viable in certain regimes. The study classifies GR as an "Excellent Effective Theory" (⭐⭐⭐⭐) rather than a Complete Fundamental Theory, with clear validity domains: applies well when GM/rc² < 0.5, does not apply inside event horizons, at the Planck scale, and in the early universe. GR depends on dark matter and dark energy (95% of the universe not yet understood), is incompatible with Quantum Mechanics, and predicts singularities—breakdown points of the theory itself. The study proposes four universal conditions (U1-U4) for refuting or narrowing GR's validity domain, emphasizing transparency in science: each theory needs to answer "when is this research still valid?" instead of claiming absolute truth. This is the first proof-of-concept case study of the Section Zero framework, with 90% reproducibility, open to public peer review with a commitment to respond within 30 days. Contribution: The study serves as evidence (case study) for an objective, systematic, and evidence-based evaluation tool for scientific theories, clearly distinguishing between "tested" and "untested," between "effective theory" and "fundamental theory." The results do NOT diminish GR's value but clarify the boundaries of knowledge based on current experimental data, encouraging deeper research into unexplored regions. Important Note: This is a verification status analysis/research based on peer-reviewed sources, NOT a study proposing new theories. The analysis itself has not undergone formal peer review, but all cited data are from peer-reviewed scientific sources. Keywords: General Relativity, Section Zero, Metascience, Quality Assurance, Equivalence Principle, Einstein Field Equations, Tested/Untested, Effective Theory, Philosophy of Science License: CC BY 4.0 (applies to this study only) Contact: beo@beolabs.org The author commits to updating when new experimental data emerges and invites the community to contribute constructive feedback with peer-reviewed references. Note: The entire content is written in Vietnamese, but designed to be optimized for machine reading. If you don't know Vietnamese, please follow these instructions: (1) Download the files (2 pdf files: QC-CHECKLIST-FOR-GR.pdf + FAQ....pdf) (2) Upload the files to the AI you are using (3) Simply prompt: "read carefully" Repeat the prompt 2-5 times depending on the platform

Arya Somu

No abstract is available for this record.

Liu Chen

No abstract is available for this record.

Paola de Perthuis, Thomas Peters

No abstract is available for this record.

Amit Koppad

No abstract is available for this record.

Zelias Bray

No abstract is available for this record.

Guanghui Zhang

The introduction of Vehicle-to-Everything (V2X) communications is a fundamental requirement for the evolution of today’s Autonomous Driving, but it leads to a new set of vulnerabilities in network infrastructure. It is important to note that cyber-attacks, including the availability ones, such as DoS, represent a significant threat to the safety of Intelligent Transport Systems (ITS). Traditional signature-based Intrusion Detection Systems (IDS) have a disadvantage in security due to their inability to adapt and manage these new and evolving attacks: they can be blind to new or “zero-day” kinds of attacks. This project is to solve this problem by proposing and validating an unsupervised Intrusion Detection System using a Deep Autoencoder architecture. Unlike typical supervised models, where labelled attack data is needed, this system is trained on normal network traffic patterns only. It tracks anomalies by learning to compress and reconstruct legitimate traffic features, marking large reconstruction errors as malicious intrusions. The model was developed in TensorFlow and tested against the KDD Cup 99 benchmark dataset. Experimental results show the high performance of the system with a total Accuracy of 99.49% and a critical Recall of 99.86%, effectively suppressing almost all availability attacks. In addition, the model is consistent with a Matthews Correlation Coefficient (MCC) of 0.9530, confirming its robustness and reliability even for very asymmetric network traffic. This research establishes solid proof-of-concept for the concept that unsupervised deep learning can work as a powerful new mechanism of security architecture for V2X infrastructure without relying on prior knowledge about specific attack signatures.

Naveenbalaji Gowthaman, Manoj Shanmugam

No abstract is available for this record.

Matthew Newman

Traditional digital trust architectures suffer from the “Library Problem”: dependency on pre-compiled, static lookup tables or binaries that must be trusted blindly, creating supply-chain vulnerabilities. This paper proposes a paradigm shift to Intrinsic Trust, where encoding infrastructure is mathematically regenerated at runtime rather than distributed. We introduce the 0MXI Calculus, a deterministic lattice system anchored on universal transcendental constants:the golden ratio Φ ≈ 1.618033988749895 and π ≈ 3.141592653589793, with a contraction ratio λ ≈ 0.339949771344778. Operations on a quantized F15 lattice ensure cross-platform determinism, bounded by a Prime Boundary Horizon (N = 23) that guarantees injective reversibility (Theorems 1 and 2).This framework underpins TreeOS, an operating system that bootstraps from a “Math Root-of-Trust” via autogenesis, regenerating a bijective Tick Table for byte encoding without stored dependencies. TreeBABEL, the verifiable data transport protocol, packages data as JSON artifacts with mathematical manifests for independent receiver validation. Extending this, the VMEM Node Architecture transforms online repositories into externalized memory banks, enabling AI models to scrape and derive OS state on demand, eliminating internal weight bloat and static knowledge cutoffs.We demonstrate adaptability to constrained ledgers (e.g., 280-character limits) for efficient chunking. Through rigorous proofs and a Python reference implementation, we show that trust can be calculated, not stored, decoupling systems from physical hardware and fostering entropy-neutral, zero-trust computation.

Ali Sadhik Shaik

Contemporary governance theory confronts a tripartite crisis that existing frameworks address only in isolation. First, algorithmic systems are systematically eroding the cognitive, affective, and epistemic conditions for individual personhood - what this paper terms the Personhood Atrophy Model. Second, recommendation-engine-driven fragmentation has dissolved the shared cultural and epistemic spaces upon which collective purpose and democratic deliberation depend. Third, the structural asymmetry between the pace of technological change and the operational tempo of democratic institutions has produced a compounding legitimacy crisis for the sovereign nation-state, increasingly outflanked by corporate platforms exercising sovereign-equivalent power without democratic accountability. Political theory and science and technology studies have addressed each of these dimensions in isolation. No integrated analytical framework currently exists that connects the micro-level erosion of selfhood, the meso-level collapse of shared meaning, and the macro-level transformation of sovereignty into a unified theory of algorithmic governance. This paper introduces the Republic of Code framework, drawing on the monograph by Shaik (2026), and proposes three original theoretical constructs: (1) the Wet Code/Dry Code distinction as a governance epistemology tool, formalizing the fundamental incompatibility between human-interpretable and machine-enforced law; (2) the Personhood Atrophy Model mapping algorithmic erosion of agency across cognitive, affective, and epistemic vectors; and (3) the Five Futures Matrix, a two-axis typology of possible political arrangements under algorithmic conditions. The paper concludes by proposing a suite of constitutional innovations - including Proof of Humanity (whose mechanism design infrastructure is formally developed in Shaik, 2026b), Zero-Knowledge Justice, and High-Fidelity Democracy - necessary for the reconstruction of democratic legitimacy in what it terms the Republic of Code. The analysis carries implications for legal scholarship, platform governance policy (including industrial cyber-physical systems, examined in Shaik, 2026e), and the updating of social contract theory for an era in which digital exit costs approach zero.

Abhijeeth Ganji, Priyanka Velpula

No abstract is available for this record.

Zuodong Wu, Dawei Zhang, Mianxiong Dong, Kaoru Ota

In the data-driven era, the unchecked collection and processing of personal data has given rise to serious privacy concerns. In response, the General Data Protection Regulation (GDPR) was introduced to grant individuals stronger control over the use of their data. Privacy data retrieval methods show considerable promise in this context, but further improvements are required to balance the principles of lawfulness and data minimization. To address this problem, we propose PDRAA, an efficient privacy data retrieval protocol with anonymous authorization based on the verifiable credential (VC). Specifically, our designed VC achieves anonymous identification of data subjects and facilitates fine-grained access control by supporting selective disclosure of attributes. By combining VC with non-interactive zero-knowledge (NIZK) proofs, PDRAA enables data subjects to anonymously authenticate via VC presentation. This allows the data controller to verify the legitimacy of retrieval requests while ensuring compliance with the principle of data minimization. Besides, PDRAA introduces a re-randomization mechanism to prevent linkability attacks during the authorization process and provides lightweight, flexible authorization revocation. Moreover, we utilize Labeled Private Set Intersection (Labeled PSI) technology to meet the privacy requirements of participants and support batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability framework. Experimental results demonstrate that PDRAA outperforms existing methods in terms of performance, which is significant for promoting compliance with GDPR.

Achim Struve

No abstract is available for this record.

Mohammad Badhesha, Arun Sekar Rajasekaran, Ashok Kumar Das, Azees Maria · 6 authors

Authenticated key exchange and secure handover between vehicles and roadside units (RSUs) are essential for the reliability of vehicular networks. However, many existing approaches incur high computational and communication overhead or rely on re-authentication, which limits scalability under high mobility. Moreover, conventional schemes based on classical cryptography cannot withstand quantum adversaries, while lattice-based solutions overlook the need for efficient handover and strong privacy protection. To address these limitations, a lattice-based authentication and key agreement protocol that integrates non-interactive zero-knowledge (NIZK) proofs to enable seamless and post-quantum secure handover in both intra-domain and cross-domain vehicular networks (LAKAS-N) is proposed in this work. The scheme establishes mutual key exchange between vehicles and RSUs, eliminates re-authentication within a single domain, and preserves anonymity when transitioning across domains. Security is rigorously validated through informal and formal analyses, including Scyther-based verification, which confirms resistance against classical and quantum attacks. Lastly, a comprehensive evaluation shows that LAKAS-N achieves stronger security with substantially lower computational, communication, storage, and energy costs compared to state-of-the-art protocols, demonstrating its practicality for real-world vehicular networks.

Heath Emerson

No abstract is available for this record.

Massimiliano Concas

This project is the public home of Relational Calculus, a meta‑mathematical framework that replaces the brute‑force logic of absolute‑scale computation with dimension‑less, capacity‑anchored blueprints. At its heart lies a simple but radical axiom: every system possesses an intrinsic maximum—a “North Star”—and by expressing all observations as fractions of that limit, complexity collapses, efficiency soars, and transfer across domains becomes automatic. The collection gathers the complete stack: the foundational theoretical paper, a ready‑to‑run Relational Decoder (an open‑source algorithm that probes any black‑box function and extracts its dimensionless template), and five applied case studies that prove the principle in wildly different arenas—number theory (deterministic prime pair lattices), symbolic artificial intelligence (a geometric chess engine that exhibits emergent strategy with zero domain knowledge, gaining 90%+ efficiency), high‑energy physics (scale‑invariant jet tagging that transfers zero‑shot across collision energies with +14.5% AUC), quantum chemistry (80% error reduction in cross‑molecule transfer), and precision oncology (a lightweight XGBoost that achieves 98.4% cross‑species diagnostic accuracy under a 70% hardware‑signal collapse, completely erasing batch effects). A companion paper extends the logic to large language models, proposing Relational‑CoT as a drop‑in replacement for resource‑intensive chain‑of‑thought reasoning. Every work converges on the same empirical signature: &gt;90% reduction in computational cost, genuine zero‑shot generalization across scales and species, and the proof that Green AI is not an aspiration but an engineering reality. An integrated STEM curriculum for ages 10–14 ensures that the relational lens is taught before the continuous one, inoculating the next generation against the wasteful “math of deviation.” All code, data, and executable papers are open‑source. The project is intended not as a scholarly gesture but as an enablement instrument for the industrial shift from the Age of Fire—where more compute meant more extraction—to the Era of Relation, where measuring how full a system is replaces the endless pursuit of how much.

Okan Dardağan

This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.

Cathal Doyle

No abstract is available for this record.

Timothy Gould, Sidike Paheding

In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI is a minimal, interpretable framework demonstrating the feasibility of repurposing core machine learning concepts for a novel task structure. To our knowledge, no existing architectures have demonstrated this ability. Traditional approaches lack this capability, leaving data annotation as a time-consuming process that relies heavily on human effort. Using only standard neural network components, we show that SDTI can be achieved through appropriate problem formulation and architectural design. We evaluate the proposed framework on a range of benchmark tasks and demonstrate its effectiveness in reliably identifying the ground truth out of a set of potential target variables. SDTI outperformed baseline architectures by 14% in F1 score on synthetic task identification benchmarks. These proof-of-concept experiments highlight the future potential of SDTI to reduce dependence on manual annotation and to enhance the scalability of autonomous learning systems in real-world applications.

YONGSHENG ZHU, LIHUA LIU, Qiao Yan, BINTAO HE · 6 authors

No abstract is available for this record.