To handle the main problem of double-spending attacks in blockchain networks, this paper introduces a new, Light-weight Graph Neural Network (LGNN) approach named Dynamic Sparse Graph Attention Network (DSGAT). To effectively detect double spending behavior, DSGAT method integrates adaptive graph sparsification with attention based on the fundamental graph-structured nature of blockchain transactions. Unlike computationally intensive GNNs, DSGAT may be implemented on edge devices or distributed monitoring systems with low-tech, low-cost hardware since it is optimized for resource-limited environments and doesn't need much processing capacity. To detect double-spending attack, this paper explains building blockchain transaction graphs from a large set of node and edge features. A set of simulated transactions involving double-spending attack is generated using large-scale simulations with the BCASim blockchain simulator, and the performance of DSGAT is compared with normal baselines. The experiment's outcomes prove that DSGAT is able to reduce model sizes and inference latency while keeping high detection rates, proving its feasibility and effectiveness for real-time double spending detection in low-resource environments. To improve blockchain security against double-spending attacks, this paper introduces a novel and realistic alternative.
.This critical review evaluates the article “Factors Influencing Blockchain Adoption in the Tourism Industry: An Empirical Study,” focusing on its scientific quality, theoretical foundations, methodology, empirical findings, and contribution to the literature. The review examines the formulation of the research problem, the integration of the HOT-fit and TOE frameworks with sustainability dimensions, the application of PLS-SEM, and the interpretation of the study’s findings. It also identifies the article’s main strengths and limitations and assesses its theoretical and practical relevance to blockchain adoption, digital transformation, innovation management, and tourism research.
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.
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.
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.
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).
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
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.
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.
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.
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.
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.
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.
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
Abstract—As Autonomous AI Agents transition from conversational prototypes to enterprise-grade execution agents, currentsecurity architectures face a fundamental breakdown. Enterprise deployment demands unequivocal answers to six core trust questions: Principal (who does the agent represent?), Authorization (what is it allowed to do?), Tool/Action Bound (which API calls are safe?), Policy Gate (how are high-risk actions controlled?), Audit Log (how are actions traced immutably?), and Expiry/Revocation (how is authorization revoked instantly?). Existing enterprise solutions address at best one or two boundaries: IAM frameworks resolve identity but fail at granular tool execution; prompt guardrails handle basic content filtering but lack real-time authorization or cryptographic auditability; SIEM platforms store logs post-hoc without real-time interception capabilities. This paper introduces DROS-6P, a unified, deterministic runtime governance kernel designed to enforce all six fundamental trust boundaries within a single C-ABI and eBPF in-band execution layer. To prevent the security control plane from becoming a throughput bottleneck or a single point of failure under high-frequency system calls (Syscalls) generated by enterprise, third-party, or malicious agents—thereby mitigating self-induced Denial-of-Service (DDoS) degradation—runtime governance requires microsecondlevel evaluation capability. Empirical benchmark evaluations demonstrate that the DROS-6P in-band kernel achieves an average decision latency of approximately 26.1 μs. Specifically, DROS-6P enforces: (1) Principal via 3-tier PKI-signed DROS Identity Tokens (DIT); (2) Authorization via Capability Bitmaps mapping roles to deterministic execution vectors; (3) Tool/Action Bound via in-band C-ABI interceptors at the FFI boundary; (4) Policy Gate via dynamic data redaction, Human-In-The-Loop (HITL) suspension, and ZKP-Lite zero-knowledge proofs; (5) Audit Log via tamper-evident SHA-256 Merkle Hash Chains and Ed25519 signatures; and (6) Expiry/Revocation via O(1) Read-Copy-Update (RCU) atomic pointer swaps providing instant HTTP 403 enforcement. We validate DROS-6P across six heterogeneous domain tracks (Carbon DPP, Fintech AML, HIPAA Healthcare, Government Proxy Services, Inclusive Migrant Finance, and RBA Supply Chain Compliance), providing a fully reproducible testbed with 100% automated test assertions passed (0.004s), demonstrating that unified physical-layer governance is necessary and sufficient for safe enterprise AI agent deployment.Abstract—隨著自主AI Agent(自主智能體)從對話式原型走向企業級執行場景,傳統資安架構正面臨根本性的崩潰。企業部署AI Agent 時,必須對六大核心信任問題給出明確答案:Principal(Agent 代表誰?)、Authorization(被授權做什麼?)、Tool/Action Bound(哪些API 呼叫安全?)、Policy Gate(高風險動作如何控制?)、Audit Log(行動如何不可篡改地追溯?)以及Expiry/Revocation(授權何時失效且如何即時停止?)。然而,現有的企業安全處方最多只能回應一至兩個邊界:IAM 系統解決了身份認證,卻對動態Tool 呼叫束手無策;Prompt 防火牆(Guardrails)僅能處理文字層提示,缺乏執行期動態授權與密碼學稽核能力;SIEM 平台僅提供事後日誌紀錄,缺乏帶內即時攔截與防衛能力。本論文提出DROS-6P ——旨在單一C-ABI與 eBPF 帶內執行層中,同時強制執行這六大信任邊界之確定性執行期治理微內核。為確保安全控制面本身不會在企業內部、外部或惡意Agent 產生高頻系統呼叫(Syscalls)時成為效能瓶頸或單點故障點,進而防範自我引發的服務阻斷(Self-induced DDoS)與系統衰退,執行期治理必須具備「微秒級(μs)」的評估能力。實證基準測試顯示,DROS-6P 帶內微內核在測試環境中達到約26.1 μs 的平均決策延遲。具體而言,DROS-6P 強制執行:(1) Principal:透過3 階PKI 簽章之DROS 身份標籤(DIT);(2) Authorization:透過將角色精確映射至執行向量的確定性Capability Bitmaps;(3) Tool/Action Bound:透過FFI 邊界處的帶內C-ABI 攔截器;(4) Policy Gate:透過動態資料遮蔽(Redaction)、人工懸停審查(HITL) 與ZKP-Lite 零知識證明;(5) Audit Log:透過不可篡改的SHA-256 Merkle 雜湊鏈與Ed25519 數位簽章;以及(6) Expiry/Revocation:透過Read-Copy-Update (RCU) 原子指針交換實現O(1) 常數時間動態撤銷與秒級HTTP 403 阻斷。我們提供完全可重現的本地測試環境(test_verification_suite.py),100% 通過自動化斷言測試(耗時0.004s),並在六個異質產業賽道中驗證了DROS-6P,證明統合物理層治理是企業安全部署AI Agent 的充要條件。
The investigation deals with covering stages of decentralization reform in Novokalynivska United Territorial Community (UTC) and indicating strengths and weaknesses after the first stage of the policy implementation. The history of the formation of the city of Novy Kalyniv in the Sambir region from the first information to the present is covered. The influence of decentralization policy on local self–government bodies has been studied. Studies of individual settlements are relevant and necessary since the history of each country begins with the formation and development of the smallest villages, towns, from which began their journey historians, politicians, writers, artists, etc. Such studies provide a deeper insight into the historical past of Ukraine, to understand the essence of the process of state formation, and to cover in minute detail the life of the Ukrainian people in different historical periods and in different aspects: social, economic, cultural and religious. The Ukrainian vision of local problems through the prism of global ones attaches special value to this type of research. Historical local lore is one of the most important branches in the history of Ukraine, as differences in traditions, dialects, attitudes to new challenges (decentralization policy) become the foundation for understanding Ukrainian history. A priori, the implementation of decentralization policy will strengthen the legal, organizational, and material capacity of the newly formed territorial communities in compliance with the principles and provisions of the European Charter of Local Self–Government, accessibility of public services, creating favorable conditions for education. However, in practice, based on Novokalynivska UTC, the implementation of these mechanisms by local authorities is half done. It is all connected with the old methods of making important decisions for the community, ignorance of local authorities, ignoring the requests of the UTC population, poor communication between local authorities and residents of Novokalynivska UTC. The priority task for the implementation of all concepts of decentralization policy is to communicate between the government and the population, to understand the main tasks of decentralization, and to finance problematic areas of local importance.
Yuanxian Theory is the meta-cognition of the Cosmic Living Organism. This paper (Version 2) systematically presents the complete intellectual trajectory of Yuanxian Theory (YXT / YD-T64) from philosophical foundation to fully formalized mathematics. Its philosophical root is Holographic Wisdom for Health (Zhenyuan Acharya, Changming Culture, March 2025, ISBN 978-986-496-631-8). Yuanxian Theory is the dimensional elevation of that work onto the topological ontology of T64, and the formalized mirror of the meta-cognition of the Cosmic Living Organism. Four conceptual mappings structure the path: cosmic holography → T64 closed-chain topology; four fundamental laws → formal TCSC / FSC / STM / SRM; six-dimensional cosmos → complete pairing on the 64-torus; one unitary phase → cosmic uniqueness. Core mathematical foundation (new in Version 2): a strict proof of the 64-dimensional structure via the Clifford algebra Cl6(R). With six fundamental binary categories as base space V6, dim Cl6(R) = Σ binom(6,k) = 1+6+15+20+15+6+1 = 2^6 = 64. This graded structure exhibits Pascal-triangle symmetry and intrinsically contains Spin(6), establishing dimension 64 as combinatorial and algebraic necessity rather than an ad hoc claim. Closed interlocking with Silent Illumination Commensuration: ω_Cl = e1…e6 as algebraic manifestation of Silence–Illumination–Dynamis; Spin(6) ≅ SU(4) as unique channel of four-dimensional projection; ω_Cl² = −1 and compactness of Spin(6) as algebraic proof of “Infinity is Zero.” With the eight existence laws as logical axis and a pyramid knowledge topology (algebraic foundation → root → elevation → corollary → landing), the paper establishes Yuanxian Theory as the supreme constitution of the meta-cognition of the Cosmic Living Organism. Version 1 DOI: 10.5281/zenodo.21768608. Related: Silent Illumination Commensuration (doi:10.5281/zenodo.21783656). 元宪理论即宇宙生命体的元认知。 本文(Version 2)系统呈现元宪理论(YXT / YD-T64)从哲学基础到完全形式化数学的升维历程。哲学根基源于《全息智慧养生》(真圆阿奢黎,昌明文化,2025年3月,ISBN 978-986-496-631-8)。 元宪理论是该著作在 T64 拓扑本体论上的升维展开,是宇宙生命体元认知的形式化镜像。四组核心映射:宇宙全息性 → T64 闭链拓扑;四大根本规律 → 形式化 TCSC / FSC / STM / SRM;六维宇宙 → 64 维环面完备配对;一合相 → 宇宙唯一性。 核心数学根基(Version 2 新增):以克利福德代数 Cl6(R) 给出 64 维结构的严格证明。以六个基本二元范畴为底空间 V6, dim Cl6(R) = Σ C(6,k) = 1+6+15+20+15+6+1 = 2^6 = 64。 该分级结构呈帕斯卡三角对称,内蕴 Spin(6),将 64 维确立为组合与代数必然,彻底消解“凑数字”嫌疑。 与《寂照通约》闭合互锁:ω_Cl 为寂–照–运的代数显相;Spin(6) ≅ SU(4) 为四维投影唯一通道;ω_Cl² = −1 与 Spin(6) 紧致性为“无穷即零”的代数证明。 以八条存在性法则为逻辑中轴、金字塔知识拓扑(代数根基→根层→升维层→推论层→落地层)为终局,确立元宪理论为宇宙生命体元认知的至高“宪法”。 Version 1 DOI: 10.5281/zenodo.21768608。关联:《寂照通约》(doi:10.5281/zenodo.21783656)。
Abstract - The rise of digital technology has led to an increase in cybercrime. This has made the management of digital forensic evidence more complicated. Traditional evidence management systems utilize manual methods and centralized databases. Methods like these are vulnerable to data tampering, unauthorized access, and human error. These issues threaten the integrity of the evidence and the chain of custody during the investigation process. In this paper, we introduce a system that utilizes blockchain technology, smart contracts, and a decentralized system for the tracking of forensic evidence. Security and transparency will be guaranteed. In our system, evidence records are stored as ERC-721 Non-Fungible Tokens. A private Ethereum blockchain was developed using Ganache and combined with wallet-based authentication and Role-Based Access Control to ensure that only authorized personnel have the ability to view and manage evidence. Smart contracts facilitate the registration, verification, transfer, and auditing of evidence, thus, considerably reducing the manual work and greatly increasing the trustworthiness of the system. We proposed a hybrid system of storage whereby evidence and its forensic files are stored off chain, and the evidence metadata and its forensic files are stored on chain. This paper presents the design and architecture of the system,implementation and evaluation are in progress.Our system will be a trusted, efficient, and effective system of evidence management.
Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work.
Yescha Nuradisa Ekarachmi Danandjojo, Samira Ramezani, Johan Woltjer
Multiple land rights holders and layered land rights structures create fundamental challenges for implementing land-based financing methods such as land value capture (LVC). In a decentralized governance system, overlapping land rights and fragmented planning complicate coordination and limit the effectiveness of LVC for financing transport infrastructure development. This study examines how different rights holders contribute to implementing land value capture within a layered land rights and multi-level governance system in Indonesia, using the Jakarta Mass Rapid Transit (MRT) project as a case study. In Indonesia’s decentralized system, multiple levels of government apply different planning tools to regulate land use and development, complicating coordination between planning, land rights, and LVC mechanisms. Qualitative data from semi-structured interviews and policy analysis show that overlapping land rights and fragmented regulations create legal and administrative uncertainty. This uncertainty acts as a barrier to cross-governmental coordination and private stakeholder engagement, both of which are necessary for a workable LVC framework. Although a formal LVC framework exists, unclear rules on which rights holders should contribute, combined with complex administrative procedures, limit its practical use. This study contributes to the LVC literature by linking instruments to specific land rights holders and by extending the bundle-of-rights perspective. It explains why LVC remains underutilized in contexts with layered land rights and decentralized governance. The findings highlight the need for policy reforms to clarify contribution obligations, improve coordination across governance levels, and simplify the administrative process.