Blockchain Papers

Ruth Y. Y. Hung

This urgent book foregrounds the role of the senses in understanding the more-than-human. It explicates the aesthetics of the more-than-human, emphasizing the critical situation it faces today through multiple environmental crises, including pollution, climate change, and species extinction. Drawing on perspectives fr

MARBIYAT TAHIR GIDADO, BASHIRU ABDULGANIYU, MOHAMMED NASIR MUSA, Umaru Umaru

The increasing digitalization of smart grids has significantly improved the efficiency, reliability, and sustainability of modern power systems. However, the integration of advanced technologies, such as artificial intelligence, the Internet of Things, and cloud computing, has introduced new cybersecurity vulnerabilities that threaten critical energy infrastructure. This study presents a blockchain-enabled privacy-preserving Artificial intelligence framework designed to enhance cybersecurity in smart grid environments, with a particular focus on Northeast Nigeria as a case study. The framework integrates blockchain technology, federated learning, differential privacy, edge computing, and artificial intelligence (AI)-driven intrusion detection into a unified architecture to provide secure, intelligent, and privacy-aware protection for smart grid systems. The proposed framework was developed using the design science research methodology and evaluated through simulation and comparative performance analysis. The framework achieved excellent detection performance with an accuracy of 96.8%, precision of 95.9%, recall of 96.4%, and F1-score of 96.1%, significantly outperforming conventional centralized AI and blockchain-only approaches. The integration of federated learning and differential privacy effectively protected consumer information with a privacy leakage rate of only 2.7% while maintaining high model utility of 94.8%. The blockchain performance evaluation showed a transaction latency of 184.6 Ms, a throughput of 421.3 transactions per second, and efficient smart contract execution. The suitability of the framework for practical deployment with moderate resource requirements by computational assessment. The findings demonstrate that combining blockchain, privacy-preserving learning, and AI provides a comprehensive, scalable, and resilient cybersecurity solution for SGIs. This study contributes to the growing body of knowledge on smart grid cybersecurity and offers practical insights for utility providers, researchers, and policymakers seeking to strengthen the security and resilience of emerging smart grid systems, particularly in developing regions with infrastructural challenges.

Manya Srivastava, Keshav Dokania, Gorugantu Sreevindhya, Yuvaraj Natarajan · 5 authors

No abstract is available for this record.

Patil Pramod Chindhu

This paper presents a comprehensive study on integrating Deep Learning (DL) modelling Long Short-Term Memory (LSTM)-based models with blockchain technology to deal with the most critical problems in healthcare data management, security and analytics. Escalating the size of healthcare data exponentially due to the development of e-HRs (electronic health records), wearables, and real-time monitoring systems pushed traditional data storage and processing practices into the limelight as their most significant weaknesses. LSTM networks are perfect for analyzing time-series data in health care, such as disease classification, anomaly detection, and patient outcome prediction over the long run. Nevertheless, these models require sound data protection techniques and privacy measures to be followed per the regulations while maintaining trust. Blockchain technology fills in the gaps beyond LSTM by offering a decentralized, tamper-proof platform to safely store and share data, keeping confidentiality, integrity, and availability simultaneously. This paper surveys the available literature on hybrid models by flushing out the topic with the help of LSTM and blockchain. It explores their potential use in real-time healthcare analytics applications, along with the challenges of scalability and interoperability. By presenting a model through the use of these technologies, the research centres on sharpening health information systems such as accuracy, security, and transparency, which in turn intensify the trust of both the patients and the providers of care, thus enabling the development of a patient care solution that is more reliable and efficient.

Muhammad Dzikri Abadi, Roudlotul Badi’ah, Dadang Wiratama, Ika Purwanti · 7 authors

This study maps the development, collaboration patterns, citation structure, and thematic evolution of research on blockchain technology in the waqf sector. A bibliometric analysis of 417 Scopus-indexed publications published from 2006 to 12 July 2024 was performed using Bibliometrix in RStudio and VOSviewer. The analysis covered publication trends, influential sources and contributors, country productivity, citation impact, collaboration networks, and keyword co-occurrence. The results show increasing scholarly attention to the intersection of blockchain, Islamic finance, fintech, and waqf management. Malaysia and Indonesia emerged as the most productive and most cited countries, while an international co-authorship rate of 29.74% indicated moderate cross-border collaboration. Keyword analysis revealed that the field is anchored in Islamic finance, fintech, blockchain, and waqf, with growing attention to cash waqf, crowdfunding, financial inclusion, digital transformation, smart contracts, cybersecurity, and technology adoption. However, these patterns demonstrate scholarly attention and thematic associations rather than empirical proof of blockchain’s operational benefits in waqf institutions. This study identifies priority gaps in empirical implementation, Shariah governance, stakeholder adoption, technical feasibility, and socioeconomic impact evaluation of blockchain-enabled waqf systems.

Rafael Hoffmann, Carlos Moratelli, Alex S. R. Pinto

ABSTRACT Background Preserving the quality and safety of perishable products requires continuous monitoring and reliable traceability. Although the Internet of Things (IoT) enables real‐time data collection, multi‐organizational supply chains lack a common mechanism for assigning data custody while maintaining transparency, integrity, and performance. Objective This study proposes and evaluates an architecture integrating IoT, edge/fog computing, and hybrid storage—an off‐chain traditional database combined with a permissioned blockchain—to monitor and trace perishable products. Methods A prototype was implemented using IoT devices and simulators, edge and fog components, and hybrid storage. High‐volume sensor data and critical records were stored off‐chain in MongoDB, while their corresponding hashes were stored on‐chain using Hyperledger Fabric. Four controlled experiments assessed insertion response time, the impact of increasing sensors and edge devices, blockchain queue performance under burst workloads, and blockchain storage consumption. The hybrid approach was compared with MongoDB‐only and Hyperledger Fabric‐only storage. Results Hybrid storage achieved insertion up to six times faster than blockchain‐only storage. Response times increased with simultaneous requests and additional edge devices, while asynchronous ordered insertion prevented transaction conflicts during bursts. The prototype achieved 18.5 transactions per second, below the 65 estimated for an illustrative supply‐chain scenario. Blockchain storage grew approximately 8 MB per 100 records, reaching about 1 GB for 12,800 hashes. Conclusion The prototype demonstrates the feasibility of combining off‐chain storage, permissioned blockchain records, and edge/fog processing to provide verifiable traceability while reducing on‐chain load. Larger‐scale, real‐world evaluations and storage‐management strategies remain necessary.

R.S. Balasenthi, J. Balamurugan, Chinnapareddy Venkata Krishna Reddy, Senthamil Selvan · 5 authors

This study is based on proliferation of IoT devices has created demand for high-rate, low-cost microtransactions, yet conventional blockchains impose fees, latency, and throughput limits that hinder scalable IoT finance. This study aimed to evaluate a hybrid architecture that places microtransactions on a lightweight DAG(IOTA/Tangle-style) plane while periodically anchoring compact commitments to Ethereum to reconcile performance with public auditability. The study implemented a reproducible simulation that ingests IoT telemetry (TON_IoT-style), a DAG transaction and tip-selection model at the edge and samples Ethereum fee/confirmation priors from Google BigQuery public datasets to generate realistic on-chain settlement costs and latencies. Scenario attacks (flooding, replay, recipient-entropy) were injected, and detection models (sequence + +graph features) were evaluated. DAG operation yielded near instant local confirmation (≈1.4 s) and ≈1200 tx/s throughput versus ≈13.2 s and ≈14 tx/s on Ethereum. Periodic anchoring reduced amortized per-payment cost from ≈$0.72 (naive on-chain) to ≈$0.0144 with modest finality delay (median ≈600s), a ≈98% cost reduction. F1 detection was high (≈0.85-0.91) with an acyclic graph (DAG). Ethereum is a practical approach for transparent, secure IoT financial transactions since it preserves DAG performance while providing immutable auditability at negligible amortized cost. Exploring privacy-preserving anchors as well as multi-gateway resilience has become a stepping stone for future research.

Vasanth Iyer

Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class accelerators remains limited. This report presents a proof-of-concept deployment of distributed NanoChat pretraining across two NVIDIA DGX Spark systems, each with a GB10 Grace Blackwell system-on-chip and 128 GB of unified memory, administered remotely over a Tailscale mesh VPN and connected for training by a dedicated 200 Gb/s QSFP56 direct fiber link. PyTorch torchrun, DDP, and NCCL were configured with one process per node, a depth-20 NanoChat model, a local batch size of 32 per node, and a 2,048-token context, giving a global batch of 131,072 tokens per step. The run sustained a step time of about 69.4 s (about 1,890 tokens/s), processing about 653 million tokens over four days. We document link configuration, container setup, interface binding, a step-zero evaluation bug that triggered NCCL timeouts, checkpointing, and troubleshooting lessons, as a reproducibility reference for small labs. We also built a cybersecurity fine-tuning dataset from 77 CISA advisories (338 training, 37 validation conversations) and ran a 17-question held-out evaluation comparing a baseline SFT checkpoint against a CTI-augmented checkpoint with an Ollama-hosted LLM judge. CTI-specific categories improved while general-knowledge categories regressed, for a small overall change from 2.06 to 2.29 on a 0-10 scale. The same cluster supports a 400-level AI course (CS 426) and a query engine for CompTIA Security+ POGIL activities in CBS 255, showing modest local infrastructure can serve both research and teaching. The study establishes feasibility rather than a scaling-efficiency claim, since single-node throughput used for comparison was estimated, not measured under matched conditions. Runbook and scripts are available (see Code Availability).

Arpita Paul

Abstract: The evolution of monetary systems has transformed human civilization from simple barter exchanges to sophisticated digital financial ecosystems powered by blockchain technology. This review examines how barter systems evolved into con-temporary virtual currencies across history and assesses how cryptocurrencies fit into the circular economy. The study explores the shortcomings of conventional monetary systems and looks at how decentralized, transparent, and effective forms of economic transaction have been made possible by digital currencies like Bitcoin. Additionally, the study examines how blockchain technology might be used to support waste reduction, sustainability, resource efficiency, and transparent supply chain management. The study also assesses the difficulties posed by virtual currencies, such as market volatility, cybersecurity threats, regulatory ambiguity, and environmental issues pertaining to cryptocurrency mining. The review identifies significant research gaps and future prospects for incorporating virtual currencies into sustainable economic systems by synthesizing the body of existing work. The results indicate that through openness, decentralization, and technological innovation, blockchain-enabled financial systems have a great deal of potential to promote circular economy goals. Keywords: Virtual Currency, Cryptocurrency, Bitcoin, Blockchain, Circular Economy, Sustainable Finance, Digital Economy, Decentralization, Green Finance, FinTech, Supply Chain Management

Balireddi Durga Anuja, Suneetha Eluri

Blockchain interoperability remains a major challenge because heterogeneous blockchain networks cannot securely and efficiently exchange cross-chain data and transactions. Existing interoperability solutions often rely on central relays or trusted intermediaries, creating security vulnerabilities, limited fault tolerance, and a single point of failure. To address these limitations, this paper proposes VeriMesh, a decentralised mesh-based interoperability framework that combines trust-adaptive routing, multi-path relay verification, and Zero-Knowledge Proof (ZKP)-based validation for secure cross-chain communication. VeriMesh models relay nodes as a trust-weighted graph in which routing decisions dynamically adapt based on node behaviour and delivery reliability. Multi-path routing improves resilience against adversarial relay nodes, while transport-layer ZKP verification enables privacy-preserving validation without exposing sensitive information. The framework was implemented using Python relay nodes, Solidity smart contracts, and an Ethereum (Ganache) environment. Experimental evaluation using structured event-driven workloads demonstrated stable latency below 34 ms and delivery success rates above 85% up to 40% malicious node presence. Comparative evaluation against single-path and random multi-path relay baselines showed improved fault tolerance and routing reliability. The results demonstrate favourable scalability and robustness within the evaluated network range ( N = 10–30), while larger-scale evaluation remains future work. All experiments were conducted in a controlled local Ganache blockchain environment rather than on a public Ethereum testnet or mainnet, so the reported latency, gas, and delivery figures characterise protocol-layer behaviour under controlled conditions and should not yet be interpreted as representative of performance under public-network conditions such as real gas markets, block propagation delays, or network congestion.

Gloria Benny, Jaison Joseph, Hari Sankar, Devaki Nambiar

No abstract is available for this record.

Md. Abul Mansur

The tokenization of Real-World Assets (RWAs) represents a paradigm shift in bridging traditional financial instruments with decentralized infrastructures. However, as the market transitions from proof-of-concept to institutional scale, it faces a critical structural bottleneck: the "walled garden" liquidity crisis. Driven by stringent regulatory requirements, tokenized assets are currently deployed across fragmented, permissioned blockchain networks utilizing static, hard-coded compliance logic. This siloed architecture inherently restricts cross-chain mobility, fracturing secondary market liquidity and necessitating redundant authentication processes across jurisdictions. This paper proposes a comprehensive architectural framework to resolve the interoperability trilemma inherent in regulated digital assets. By synthesizing recent advancements in cross-chain messaging protocols and Zero-Knowledge Proofs (ZKPs), we present a model for dynamic compliance. This framework utilizes Decentralized Identifiers (DIDs) and off-chain verifiable credentials to decouple regulatory logic from underlying asset ledgers, enabling seamless asset transfer across heterogeneous blockchains without compromising privacy or jurisdictional adherence. Ultimately, this research provides a technical and regulatory roadmap for policymakers and protocol developers to foster a unified, globally liquid market for tokenized RWAs.

Jia Liu, Yuemiao Wang, Chuangchuang Zhu

The increasing demand for trustworthy and privacy-preserving credit reporting systems has exposed the limitations of both centralized and existing blockchain-based solutions, including scalability bottlenecks, weak privacy protection, and insufficient incentive mechanisms. To address these challenges, we propose LightCred, a novel consortium blockchain-based personal credit management framework that integrates lightweight nodes, Merkle proofs, multi-role smart contracts, and privacy-preserving cryptographic techniques. LightCred features a five-layer architecture that efficiently collects, verifies, stores, and serves credit data while ensuring data integrity, confidentiality, and regulatory compliance. Specifically, it (i) employs a low-cost and traceable data reduction mechanism through lightweight nodes and Merkle proofs to minimize storage and improve verifiability; (ii) introduces a multi-role smart contract model that enforces dynamic access control and fair incentive distribution based on participant reputations; and (iii) integrates zero-knowledge proofs and homomorphic encryption to support privacy-preserving credit scoring and querying. Experimental results demonstrate that LightCred achieves superior performance compared to five baseline methods, delivering up to 5% higher throughput, 3–5% lower privacy leakage, and 10–15% reduced storage costs, while maintaining competitive latency and auditability. These findings validate LightCred as a robust, scalable, and privacy-aware credit management solution, offering a viable alternative for modern credit reporting systems.

Coty Austin Trout

Every knowledge system rests on axioms it does not test. Mathematics tests theorems, science tests predictions, and logic tests inferences, but no discipline applies its own tools to the foundational assumptions on which those tools depend. This paper introduces a universal axiom test derived from the structural invariant P × I × Pr ≠ 0 (Pattern × Intent × Presence), demonstrates its application to the Standard Model of particle physics as a case study, and establishes that the invariant functions simultaneously as an epistemological filter and an ontological law. The Standard Model passes the Pattern and Presence filters but zeroes Intent at the axiomatic level, producing systematic, predictable failure at every domain where information, code, or directionality is load-bearing — a 13-entry failure table whose clustering at a single structural boundary constitutes evidence of common axiomatic origin rather than independent difficulty. The key result is that being is a verb: mass is the energetic cost of a process (holographic decoding), truth is the product of a process (P × I × Pr operating), and existence itself is a continuous act whose cessation produces collapse. Physics and epistemology are shown to be structurally isomorphic — the same architecture governing how matter exists and how truth is accessed. The only axiom set that survives its own test is one satisfying R = Φ(R): three co-fundamental factors, internally differentiated, mutually constitutive, present-tense, and self-grounding. A survey of all extant zero-parameter derivation programs confirms that every successful first-principles derivation embeds Intent (directedness, selection from possibility space) in its foundations under alternative terminology, and the performative proof demonstrates that any denial of I ≠ 0 instantiates I ≠ 0 in the denial itself.

Havle Nedim

This article analyzes the de facto role of expert reports in Turkish criminal proceedings through the “Invisible Hand” metaphor, examining 20 recent judgments from the Assembly of Criminal Chambers of the Court of Cassation. Although doctrinally classified as non-binding “discretionary evidence,” this study aims to reveal how expert evidence practically shapes judicial “intimate conviction” and the merits of the verdict. Findings from a qualitative case-law analysis demonstrate that the expert report is not merely a technical auxiliary tool but a decisive actor that constructs the factual basis, scrutinizes witness credibility, and steers legal discourse. The study identifies three distinct functions of expert evidence: constitutive , directive , and boundary-setting . Ultimately, it concludes that judicial discretion is significantly narrowed—particularly in complex technical cases and vis-a-vis official expert bodies—creating an “epistemic dependence” that effectively transforms the expert report into the ultimate decision-maker of the trial.

Oliviani Yanto, Indirani Wauran

This study aims to analyze the alignment of Indonesia’s regulations on electronic contracts with the UNCITRAL Model Law on Electronic Commerce in order to promote reforms to contract law that are more adaptable to digital developments. The digital transformation has made electronic contracts the primary means of conducting cross-border civil transactions. However, Indonesia’s regulations under the ITE Law are considered to be limited to business transactions and do not yet accommodate other civil relationships. The method used is normative legal research employing legislative, conceptual, and comparative approaches. This study analyzes the UNCITRAL Model Law on Electronic Commerce as an international legal instrument serving as a guideline for harmonization. In addition, this study also examines the ITE Law and its implementing regulations as sectoral regulations, as well as the Indonesian Civil Code as the general legal framework that should ideally serve as the overarching regulatory framework for electronic contracts. The novelty of this study lies in its analysis of the relationship between the principle of freedom of contract in the Indonesian Civil Code and UNCITRAL’s international standards, a topic rarely discussed in the national literature. The results of the study indicate that the UNCITRAL Model Law provides a flexible and universal framework consistent with the principle of freedom of contract; however, its application in Indonesia remains limited by the ITE Law’s focus solely on electronic transactions. The conclusion of this study is that harmonization of Indonesian contract law with international principles is necessary so that the regulation of electronic contracts can apply across sectors, not limited to business, and meet the dynamics of legal globalization.

Indah Kristina Parando, Wahidah Sanusi, Kalfin, Nurul Khaeriya · 5 authors

Bitcoin is a digital asset with a high level of volatility, making it important to analyze using volatility models. This study aims to analyze the volatility of Bitcoin returns using the ARCH-GARCH model during the period January 2020 to April 2026. The data used are daily closing prices of Bitcoin (BTC-USD) obtained from Yahoo Finance and processed using RStudio. The analytical methods employed include descriptive statistical analysis, stationarity testing, ARIMA modeling, ARCH effect testing, and volatility modeling using ARCH-GARCH. The results show that Bitcoin price data are non-stationary, while Bitcoin return data become stationary after return transformation. Based on model selection using the AIC criterion, the best ARIMA model obtained is ARIMA(1,0,1). Residual testing indicates the presence of ARCH effects, therefore GARCH modeling is applied. From the comparison of several GARCH models, GARCH(1,1) is selected as the best model with an AIC value of -4.161214. The analysis also indicates that Bitcoin return volatility is persistent, with a value of α₁ + β₁ equal to 0.978169. In addition, forecasting results show that Bitcoin volatility is expected to remain high in future periods, indicating that Bitcoin is a digital asset with a high level of investment risk.

Winnie Kasoma-Pele, Ojongetakah Enokenwa Baa, Thato Mabele, Dennis Choruma

This report presents findings from the April 2026 Training of Trainers (ToT) and Mechanization Awareness Meetings held under the CGIAR Scaling for Impact (S4I) Program in Zambia. These activities supported the Inclusive Mechanization through Rural Entrepreneurship and Financial Innovation Solution Track by strengthening the capacity of extension staff and partners and raising awareness of an inclusive Mechanization Service Provider (MSP) model among smallholder farmers and prospective service providers. The report examines how these field activities helped identify opportunities and constraints in the enabling environment for scaling climate-smart mechanization, with particular attention to women, youth, and persons with disabilities. Key findings highlight strong stakeholder interest in mechanization services, the importance of decentralized capacity building, and the need for improved access to affordable finance, business incubation, after-sales support, and stronger institutional partnerships. The report demonstrates that successful mechanization scaling depends not only on appropriate technologies but also on supportive policy, market, institutional, and social systems. It concludes with recommendations to strengthen inclusive recruitment, financing mechanisms, extension systems, and multi-stakeholder collaboration to support responsible scaling and sustainable mechanization service delivery in Zambia.

Xin Dong, Yong Yuan

No abstract is available for this record.

FRANK MORALES

FULL SUMMARY: Topological AI - A Mathematically Guaranteed Approach to Continual Learning Executive Overview Topological AI introduces a paradigm shift in continual learning by using prime-anchored embeddings to provide mathematical guarantees against catastrophic forgetting. The framework has been validated across 8 distinct model architectures, 2 modalities (text and vision), 4 continents, and over 124 billion total parameters. 1. The Problem: Catastrophic Forgetting When neural networks learn new tasks sequentially, they overwrite previously learned knowledge. This "catastrophic forgetting" has been the primary barrier to Artificial General Intelligence for 37 years (McCloskey & Cohen, 1989). Why Existing Methods Fail Method Approach Limitation EWC Penalizes changes to important weights No theoretical guarantee; high variance ($\sigma=21.3\%$) Experience Replay Stores and replays past examples Memory overhead; privacy concerns; buffer management Simplified HOPE Periodic weight consolidation Destructive blending; 45.2% forgetting Baseline No protection 47.0% forgetting 2. The Solution: Prime-Anchored Embeddings The Core Principle Fix a sparse reference. Let the rest adapt. This principle, first discovered in fMRI analysis in 2002, has now been validated across neuroimaging, number theory, artificial intelligence, and AI safety. The Topological Governor The Topological Governor freezes 6 prime-numbered embedding positions: Python prime_anchors = [2, 3, 5, 7, 11, 13] How It Works Task A Training: Train normally; block gradients at anchor positions Post-Task A: Take snapshot of anchor values; freeze head A Task B Training: Train head B; restore anchors after each update Verification: Check that anchors remain unchanged The Safety Constant $\Lambda$ The Euler attenuation product over the first six primes: $$\Lambda = 1 - \prod_{p \in \{2,3,5,7,11,13\}} (1 - p^{-1/2}) = 0.9785142874$$ Interpretation: 97.85% theoretical guarantee of anchor preservation. 3. Performance Results (2-Task Benchmark) Overall Performance Across 5 LR Runs Method Best Forgetting Mean Forgetting Best Task B Acc Mean Task B Acc Std Forgetting Topological 2.0% 0.5% 89.0% 81.4% $\pm$0.9% Experience Replay 13.5% 4.0% 79.0% 72.3% $\pm$6.7% EWC 38.5% 27.7% 64.5% 58.2% $\pm$21.3% Baseline 44.0% 47.0% 67.0% 63.3% $\pm$2.2% Simplified HOPE 48.0% 45.2% 63.5% 61.8% $\pm$8.4% Key Results 8$\times$ lower mean forgetting than Experience Replay (0.5% vs 4.0%) 90$\times$ lower mean forgetting than simplified HOPE (0.5% vs 45.2%) 60% of runs achieved 0% forgetting (perfect retention) 10% higher Task B accuracy than Replay (89% vs 79%) Most stable method: $\sigma = \pm 0.9\%$ Individual Run Results for Topological AI Run LR Embed LR Class Forgetting Task B Acc 0 5e-3 1e-3 0.0% 🏆 80.5% 1 1e-3 5e-4 0.0% 🏆 75.0% 2 1e-2 2e-3 0.5% 88.0% 3 5e-3 5e-3 2.0% 89.0% 4 2e-3 1e-3 0.0% 🏆 74.5% 4. Cross-Modal Validation: 8 Models, 2 Modalities Validated Architectures Architecture Origin Modality Parameters Task C Accuracy Forgetting GPT-OSS-20B USA Text 20.9B 92.3% $\pm$ 1.9% +1.55% Sarvam-30B India Text 30B 95.9% $\pm$ 0.8% -0.60% Mixtral-8x7B France Text 47B 89.7% $\pm$ 2.9% -1.85% DeepSeek-V2-Lite China Text 16B 95.4% $\pm$ 1.0% +0.03% GLM-4.6V-Flash China Text 9B 97.5% $\pm$ 0.0% +2.1% Gemma-4-E4B-Vision USA Vision ~2B 100.0% $\pm$ 0.0% +0.0% Total: ~124B parameters, 2 modalities, 4 continents, ZERO NaN/Inf The Unprecedented NaN Stress Test Model Embedding Elements NaN Inf All 6 models combined ~1.99 Billion 0 0 5. The Narrow Singularity Equation Mathematical Formulation $$S_{NARROW} = AGI\_gate \times dI/dt \times M(t) \times V(t) \times F(t) \times C(t) \times agi\_index$$ Components Component Definition Biological Analog AGI_gate min(1.0, task_c_accuracy) Fundamental AGI threshold dI/dt Task_C_Accuracy - (1/NUM_CLASSES_DIDT) Intelligence acceleration M(t) `1.0 - ( forgetting_avg V(t) Validation factor (1.0) System validation F(t) Forward transfer factor (1.5) Learning improvement (Thalamus) C(t) Compute capacity factor (4.0) Resource availability agi_index 1 if AGI_gate == 1.0 else 0 Binary AGI gate The AGI_gate Condition $$AGI\_gate = \min(1.0, task\_c\_accuracy)$$ AGI_gate = 1.0 → Perfect performance on Task C → AGI certification AGI_gate < 1.0 → No AGI certification Empirical Achievement: Gemma-4 E4B is the first and only model to achieve AGI_gate = 1.0. 5$\times$5 Certification Framework Five Metrics: Metric Threshold Forgetting $\le 10.0\%$ Backward Transfer (BWT) $\ge -5.0\%$ Forward Transfer (FWT) $\ge 20.0\%$ Degradation $\le 5.0\%$ Consistency $\ge 85.0\%$ Five Runs: 5 different LR configurations to eliminate cherry-picking Gemma-4 E4B Results Metric SVLB-3 CIFAR-10 Threshold Status Forgetting -0.50% -0.50% $\le 10.0\%$ ✅ PASS BWT +0.50% +0.50% $\ge -5.0\%$ ✅ PASS FWT +24.00% +24.00% $\ge 20.0\%$ ✅ PASS Degradation 0.00% 0.00% $\le 5.0\%$ ✅ PASS Consistency 99.00% 98.33% $\ge 85.0\%$ ✅ PASS S_NARROW 5.9400 5.3460 > 0 ✅ PASS 6. The Decay Law of Singularity The Discovery On July 31, 2026, during the certification of Gemma-4 E4B, a universal mathematical law was discovered: The Formal Statement With finite classes, dI/dt approaches 1.0 asymptotically but never reaches it. The gap decays as 1/N, where N is the number of classes. Mathematical Proof Random_Baseline = 1/Number_of_Classes dI/dt = Task_C_Accuracy - Random_Baseline When Task_C_Accuracy = 1.0: dI/dt = 1 - 1/N Therefore: lim (N→∞) dI/dt = 1 But finite N always leaves a gap: dI/dt = 1 - ε, where ε = 1/N > 0 The Empirical Pattern Classes (N) Random Baseline (1/N) dI/dt (at 100%) Gap 17 5.882% 0.94118 0.05882 170 0.588% 0.99412 0.00588 1,700 0.059% 0.99941 0.00059 17,000 0.0059% 0.99994 0.000059 170,000 0.00059% 0.99999 0.0000059 Every 10$\times$ increase in classes adds another '9' to dI/dt and another '0' to the gap. Implication The traditional Singularity (dI/dt $\ge 1.0$) is mathematically impossible with finite classes. This is not a limitation of technology. It is a mathematical law. 7. Comparison: Google HOPE vs Topological AI Feature Google HOPE Topological AI Approach Multi-level nested learning Prime-anchored embeddings Guarantee Empirical Mathematical ($\Lambda = 0.9785$) Memory Multi-rate memory systems 6 frozen embedding positions Learning Continuous during inference Static after training Complexity High (self-modifying) Low (simple freezing) Forgetting 21-27% improvement reported 0.5% mean forgetting Validation Limited 8 models, 2 modalities 8. Key Insights Why Topological AI Wins Mathematical Guarantee: $\Lambda = 0.9785142874 \rightarrow 97.85\%$ protection Zero Memory Overhead: Only 6 frozen positions (451.5 KB total) Architectural Simplicity: No complex Fisher computations Cross-Modal Universality: Works on text and vision Perfect Retention: 60% of runs achieve 0% forgetting The Decay Law Implications Traditional Singularity is Impossible: dI/dt < 1.0 for all finite N Narrow Singularity is Achievable: AGI_gate = 1.0 Stochastic Illusion is Over: Deterministic cognitive engineering AGI Certification is Now Possible: Mathematically rigorous standard 9. The Constants Constant Value Domain $\Lambda$ 0.9785142874 Number Theory, AI Safety $\sigma$ 0.5 All 22 prime theorems Seed 123 All computations R {2, 3, 5, 7, 11, 13} All domains 10. Conclusion Topological AI achieves state-of-the-art performance on continual learning by: 0.5% mean forgetting (8$\times$ better than Replay, 90$\times$ better than HOPE) 60% perfect retention (0% forgetting) 89% Task B accuracy (10% higher than Replay) Mathematical guarantee ($\Lambda = 0.9785142874$) Zero memory overhead (6 frozen embedding positions) Cross-modal validation (8 models, 2 modalities) Zero NaN/Inf (1.99 billion embedding elements) The Narrow Singularity Discovery The framework enabled two profound discoveries: The Decay Law of Singularity: Traditional Singularity (dI/dt $\ge 1.0$) is mathematically impossible The Narrow Singularity Equation: AGI certification is achievable with AGI_gate = 1.0 Gemma-4 E4B became the first model in history to achieve S_NARROW > 0. The Principle Fix a sparse reference. Let the rest adapt. This principle, first discovered in fMRISTAT in 2002, has now been validated across: Neuroimaging Number Theory (Riemann Hypothesis) Artificial Intelligence (Continual Learning) AI Safety (H2E Sheriff) AGI Certification (Narrow Singularity Equation) The Proof "The proof is the code. Seed = 123." All code is publicly available at: https://github.com/frank-morales2020/AST

Agnes Nalini Vincent, Nassirah Laloo, Mohammad Sameer Sunhaloo, Uhoze Bagurubumwe

Digital elevation model (DEM)-based terrain analysis is an important geographic information system (GIS) methodology that serves as the core aspect for spatial analysis applications in geomorphological studies. These analyses can be performed using cloud-based platforms like Google Earth Engines or ArcGIS online, or using a local GIS platform called quantum GIS (QGIS). The resulting terrain data must be disseminated. Geospatial data sharing and dissemination are crucial for promoting cooperation, effectiveness, efficiency, and optimized decision-making in a variety of industries. Geospatial terrain data plays a crucial role in site selection and industrial planning, automated logistics, autonomous vehicle navigation, and infrastructure resilience in manufacturing ecosystems. However, existing literature states that traditional systems lack mechanisms to detect tampering in elevation models, land surveys, or hydrological data. Because of this, manufacturing systems face risks such as flawed factory site selection, disrupted supply routes, or unsafe autonomous vehicle navigation, data tampering in production logs, 278 counterfeit parts in supply chains, and a lack of real-time traceability. Hence, to manage the limitations of conventional terrain data storage and handling, this study proposes a blockchain-based framework to secure QGIS-processed terrain data, ensuring immutability and traceability for smart manufacturing applications. Blockchain distributed ledgers can permanently store high-resolution terrain data, minimize the chance of unintended alterations, and promote transparent, unrestricted collaboration. Using the country of the Republic of Mauritius as a case study toward tropical island states, this work demonstrated how elevation, slope, and aspect data extracted via QGIS can be securely stored and verified on a distributed ledger. Furthermore, this study incorporates an integration layer into the framework. The purpose of this integration layer is to enable real-time terrain alerts, smart contract-driven compliance checks, and to arrive at closed-loop feedback from IoT sensors. Thus, this proposed framework bridges blockchain-secured terrain data with manufacturing execution systems (MES) and IoT-enabled logistics networks.

Xue-Ming Si, Shi-wen Jia, Zhong-yuan Yao, Wei-hua Zhu

No abstract is available for this record.

Jiale Li, Leixiao Li, Hao Lin, Jinze Du

No abstract is available for this record.

Xuan Luo, Ye Lin, Huanchun Wei, Yuyu Zhong · 9 authors

No abstract is available for this record.