Blockchain Papers

John Ceri

Computational research depends on the ability to independently reproduce results, yet modern workflows are fragile: they drift across environments, depend on undocumented assumptions, and often fail silently. ValiChord provides a decentralised, agent‑centric infrastructure for independent reproducibility validation. Validators re‑execute workflows in diverse environments, generate cryptographically signed attestations, and contribute structured detector evidence that captures environment drift, dependency skew, execution variability, and workflow fragility. A commit–reveal protocol preserves validator independence, while Harmony Records synthesise divergent outcomes without collapsing them into binary judgements. ValiChord validates computation, not data provenance, and is explicit about this boundary: it strengthens the computational layer of scientific integrity without claiming to detect data fabrication. The system is built on Holochain, not blockchain, ensuring tamper‑evident provenance without global ledgers, tokens, or consensus mechanisms. Reference implementation and detector suite: https://github.com/topeuph-ai/ValiChord

Ramkinker Singh, Om Narayan, Praveen Baskar

Our suggestion is a hybrid reputation-based routing protocol in the context of decentralized routing. networks which partitions trust measurements to on-chain immutable logs and off-chain dynamic computations to attain security and scalability. The protocol incorporates a layer of sharded blockchain to store critical. trust data and local reputation model to do real-time updates, enabling routing choices that cannot be tampered with. performance. The trust ledger which operated on-chain kept the records of the impartiality of. Historical performance and node identities, whereas the off-chain reputation engine uses graph neural network to calculate dynamic. real-time scores like packet delivery rate to be trusted. and latency. Additionally, it uses rollup-based batches of synchronization off-chain. optimistic updates to zero-knowledge proofs, making on-chain efficient. checking with minimum overhead. The suggested approach replaces conventional network discovery and forwarding modules using trust-based adjacency lists and trust-based pathing, and thus enhancing routing consistency over adversarial settings. Moreover, the structure integrates Hyperledger Fabric with. Graph Attention Network-based high-throughput sharded ledger operations. to update reputation in a privacy-preserving manner, proving to be linear. network size scalability. The experimental findings indicate that the system supports 10,000 transactions per shard and produces. Under 100 ms per ZK-Rollup proof, which is appropriate to large-scale IoT. and DeFi deployments. This publication fills the gap between pure on-chain. and off-chain reputation systems, which provides a viable solution to scalable and secure decentralized routing.

M.Anitha, K. Sivaraman

The fast proliferation of edge computing has come up with serious issues of data privacy and trust within the distributed networks. The paper introduces a new hybrid system combining machine learning (ML) and blockchain platforms to provide an improved level of data privacy in edge environments. The presented approach is a hybrid of federated learning and blockchain-based secure consensus, which will allow training the models decentrally without exposing sensitive information. An encryption layer that preserves privacy guarantees the safety of the data transfer between edge nodes, whereas smart contracts handle access control and authentication independently. The hybrid infrastructure uses AI to identify anomalies and use the mitigation of threats based on their adaptability, and blockchain with a ready-to-trace immutable ledger generates transparent data. Through experimentations, it is shown that the proposed framework outperforms conventional edge privacy schemes on privacy protection, latency, and data integrity. The model obtained ~98% data privacy protection. The study adds to the coherent model that provides the connection between security, scalability and efficiency in the privacy-sensitive edge-working applications like IoT, medical, and smart cities.

Lyes Yamani, Fatma Alahouel, Mounira Hamed‐Sidhom, Nadia Loukil

This study determines whether Bitcoin enhances portfolio diversification and serves as a valuable investment asset during the COVID-19 crisis. In particular, we evaluate the significance and magnitude of the risk price associated with Bitcoin’s returns based on the ICAPM and NARDL models. Three methodological approaches were employed. First, we use the Intertemporal Capital Asset Pricing Model (ICAPM) to assess the effect of Bitcoin on a portfolio comprising 25 Fama-French portfolios. Second, a Nonlinear Autoregressive Distributed lag (NARDL) model explores Bitcoin’s impact on cross-sectional variation within the Fama-French portfolios, capturing potential asymmetric responses to price changes. Finally, we determine Bitcoin’s risk premium using the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model (FF3), and the Fama-French five-factor model (FF5). Bitcoin fails to provide significant diversification benefits for profitability factor (RMW), and exhibit insensitivity to value (HML) and investment (CMA). The NARDL model indicates a potential hedging role only during crypto market downturns. The factor models reveal that Bitcoin behaves differently than traditional assets, exhibiting low sensitivity to market risk and a negative relationship with the size premium, further supporting its potential for diversification within specific portfolio contexts. Our finding shows that Bitcoin can protect the 25 Fama-French portfolio when Bitcoin loses value.

Ceyda Yerdelen Kaygın, Musa Gün, Osman Nuri Akarsu, Haşim Bağcı · 5 authors

Forecasting cryptocurrency prices is challenging due to extreme volatility, nonlinear dynamics, and frequent structural shifts in digital asset markets. While recent research increasingly applies deep learning architectures, the predictive advantage of highly complex models in noisy financial environments remains uncertain. This study evaluates the forecasting performance of shallow and deep learning approaches by comparing Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, along with hybrid configurations (GRU + SVM, LSTM + SVM, and GRU + LSTM). Using daily data spanning from 1 October 2020 to 23 September 2025 for five major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Solana, and Ripple—the models are estimated within a consistent framework and assessed using out-of-sample performance metrics, including MAE, MAPE, MSE, and R2. The results indicate that greater algorithmic complexity does not necessarily improve forecasting accuracy. In several cases, the parsimonious SVM model outperforms deep neural network architectures, particularly for highly volatile assets, while hybrid models fail to provide systematic improvements and sometimes amplify prediction errors. SHapley Additive exPlanations analysis further shows that immediate price-based variables dominate predictive power, whereas many lagged technical indicators contribute relatively limited explanatory value. Overall, the findings underscore the importance of algorithmic parsimony, suggesting that simpler machine learning models may deliver more robust forecasts in highly volatile cryptocurrency markets.

Mrs Faryal, Yong Tang, Chin Man Chui, Ifran Khan

The rapid expansion of Financial Technology (FinTech) is fundamentally reshaping financial systems, yet its role as a source of systemic risk and its dynamic connectedness with traditional energy and macroeconomic markets remain critically underexplored. This paper employs an integrated time-frequency framework to model financial spillover networks and demonstrates its utility in analyzing the connectedness between emerging FinTech sub-sectors, energy markets, and macroeconomic uncertainty. Using the Diebold and Yilmaz (2012) spillover index in the time domain and the Baruník and Křehlík (2018) spectral decomposition in the frequency domain, we uncover a highly interconnected system: total connectedness reaches 43.62% for returns and 40.65% for volatility, showing that price shocks propagate more strongly than risk shocks. During the COVID-19 period, interconnectedness surged above 70%, highlighting how external shocks intensify contagion. We find that key FinTech indices such as Kensho Future Payments, KBW FinTech, and Kensho Alternative Finance act as major net transmitters, while the Distributed Ledger index, geopolitical risk, U.S. policy uncertainty, Brent oil, and U.S. 10-year Treasury yields are net receivers, signaling that within the financial network, shock propagation is now led by FinTech rather than emanating primarily from traditional macroeconomic indicators. Frequency results add important insight: volatility spillovers are mainly short-term (44.57%), reflecting transient fear contagion, while return spillovers are more persistent. Overall, our findings challenge the macro-driven spillover view and offer a time-sensitive framework for effective hedging and regulation. FinTech emerges as a key short-term shock transmitter, with clear implications for investors’ hedging strategies and regulators’ systemic risk monitoring.

Seung Kwon Lee, Seok Bin Son, Joongheon Kim, Hoh Peter In

Quantum machine learning (QML) has attracted growing interest for their ability to achieve superior performance with significantly fewer parameters. However, the high cost and scarcity of current hardware push inference to cloud-hosted quantum devices, creating a tension between verifiability and confidentiality. This work proposes a novel framework that converts quantum neural network operations into classical arithmetic circuits that faithfully approximate genuine quantum computations. By encrypting these circuits with zero-knowledge proofs, it ensures computational validity while concealing internal parameters. Experimental results show that our classical circuits achieve fidelity above 0.9996 and total variation distance below 1% compared to actual quantum computations, verifying the practicality of trustworthy and privacy-preserving quantum inference.

Abdul Hadi, Krishna Mula, Ahmad Bacha, Sreekanth Muktevi · 6 authors

The growing nature and complexity of the cyber threats within the distributed digital infrastructures require collective intelligence without jeopardizing the privacy of data. Federated Learning (FL) is an up-and-coming model that holds potential in training models in a decentralized way; nonetheless, the existing FL models are susceptible to information leakage as a result of model updates and adversarial inference attacks. To overcome these shortcomings, this paper introduces a Zero-Knowledge Federated Learning (ZK-FL) system to detect cyber threats in a privacy-preserving way so that collaboration in the learning process can be secured without sensitive information about the intermediate models and without exposing sensitive data. In the suggested solution, the zero-knowledge proof (ZKP) mechanisms along with federated optimization are combined to make sure that the participating clients can prove the accuracy of their local model updates, revealing no data features. This cryptographic integrity check deters malicious model poisoning, gradient inversion and unauthorized inference of data, improving the confidence of heterogeneous and untrusted parties. Another approach used is a secure aggregation protocol which protects model parameters in the transmission process to guarantee end-to-end confidentiality and integrity. The framework is tested with actual datasets of cyber threat in a distributed environment and adversarial environment. Empirical studies show that the suggested ZK-FL model can be used to obtain a high detection accuracy and robustness on par with centralized learning, and substantially increase privacy guarantees and anti-inference attack. Furthermore, the communication and computation cost that is entailed by zero-knowledge verification is within manageable limits, and thus the solution is feasible to large-scale cyber defence systems. The suggested ZK-FL architecture provides a secure and trusted platform to cooperative cyber threat intelligence, which is a scalable service in privacy-sensitive environments like enterprise networks, critical infrastructures, and edge-cloud security systems.

Yogesh Kumar Gupta, Laxman Solankee, Rahul Singh, Akanksha Parihar · 6 authors

The rapid growth of Ethereum smart contracts has attracted significant attention from both academia and industry, leading to the emergence of diverse commercial applications. However, the increasing prevalence of fraudulent activities such as phishing, bribery, and money laundering poses serious threats to the security and integrity of online transactions. To address these challenges, this study proposes a deep learning-based fraud detection framework enhanced with a novel metaheuristic optimization technique. Specifically, an Optimized Genetic Algorithm-Cuckoo Search (GA-CS) hybrid approach is integrated with a deep learning model to improve fraud classification accuracy. The proposed GA-CS algorithm leverages the global search capability of Cuckoo Search while employing Genetic Algorithm operations to overcome its inherent limitations and enhance convergence performance. Extensive experiments are conducted to evaluate the effectiveness of the proposed method against several widely used machine learning and deep learning classifiers, including Logistic Regression (LR), K-Nearest Neighbors (KNN), MultiLayer Perceptron (MLP), XGBoost, Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Support Vector Classification (SVC), using a limited feature set. Experimental results demonstrate that the proposed GA-CS-optimized deep learning model outperforms most baseline methods and achieves superior detection accuracy. Although its performance is marginally higher than that of the Random Forest model, the proposed approach and the SVC model achieve the highest overall accuracy, confirming the robustness and effectiveness of the proposed framework for fraudulent transaction detection on the Ethereum platform.

Ao Liu, Yanmei Zhang, Youwei Wang, Qiang Duan

Pre-trained language models (PLMs) have shown strong potential in Ethereum account modeling and fraud detection. However, existing approaches often overlook the graph-structured nature of transaction networks. In addition, they struggle with the long-tail distribution of account activity, resulting in anisotropic embedding spaces and poor representation quality for low-frequency accounts. In this paper, we present IGT4ETH, a pre-trained Graph Transformer with an isotropy-enhanced post-processing, which explicitly models transaction topologies and mitigates representational anisotropy for Ethereum account classification. IGT4ETH improves structural representation by incorporating structural centrality and role embeddings into an Edge-augmented Graph Transformer, effectively capturing both topological and interaction patterns in transaction graphs. To further mitigate embedding anisotropy, we systematically evaluate various post-processing techniques. Among them, we adopt the Conceptor Negation (CN) method to softly suppress latent features dominated by high-frequency words via matrix conceptors, alongside a modified Focal-InfoNCE loss to enhance directional uniformity and representation balance. Extensive experiments on four real-world Ethereum account classification tasks, including phishing, exchange, mining, and ICO-wallet classification, demonstrate that IGT4ETH consistently outperforms state-of-the-art PLM-based baselines in terms of classification performance.

Dingding Cao, Bianbian Jiao, Jingzong Yang, Yujing Zhong · 5 authors

The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability. To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis. Methodologically, 12 token-level behavioral features are constructed based on three wash-trading patterns (Self, Matched, and Circular), unifying transaction-, address-, and flow-level signals into risk vectors. Supervised models are then employed to output warning scores and alert decisions. Under the current setting (7 tokens, 33,242 records), Random Forest outperforms Logistic Regression on core metrics, achieving AUC=0.9098, PR-AUC=0.9185, and F1=0.7429. Ablation results show that trade-level features are the primary performance driver (Delta PR-AUC=-0.1843 when removed), while address-level features provide stable complementary gain (Delta PR-AUC=-0.0573). The model also demonstrates actionable early-warning potential for a subset of samples, with a mean Lead Time (v1) of 3.8133 hours. The error profile (FP=1, FN=8) indicates that the current system is better positioned as a high-precision screener rather than a high-recall automatic alarm engine. The main contributions are threefold: an executable and reproducible rug-pull warning pipeline, empirical validation of multi-granularity wash-trading features under weak supervision, and deployment-oriented evidence through lead-time and error-bound analysis.

Jiexia Ye, Weiqi Zhang, Ziyue Li, Jia Li · 5 authors

Accurate medical time series (MedTS) classification is essential for effective clinical diagnosis, yet remains challenging due to complex multi-channel temporal dependencies, information redundancy, and label scarcity. While transformer-based models have shown promise in time series analysis, most are designed for forecasting tasks and fail to fully exploit the unique characteristics of MedTS. In this paper, we introduce MedSpaformer, a transformer-based framework tailored for MedTS classification. It incorporates a sparse token-based dual-attention mechanism that enables global context modeling and token sparsification, allowing dynamic feature refinement by focusing on informative tokens while reducing redundancy. This mechanism is integrated into a multi-granularity cross-channel encoding scheme to capture intra- and inter-granularity temporal dependencies and inter-channel correlations, enabling progressive refinement of task-relevant patterns in medical signals. The sparsification design allows our model to flexibly accommodate inputs with variable lengths and channel dimensions. We also introduce an adaptive label encoder to extract label semantics and address cross-dataset label space misalignment. Together, these components enhance the model’s transferability across heterogeneous medical datasets, which helps alleviate the challenge of label scarcity. Our model outperforms 13 baselines across 7 medical datasets under supervised learning. It also excels in few-shot learning and demonstrates zero-shot capability in both in-domain and cross-domain diagnostics. These results highlight MedSpaformer's robustness and its potential as a unified solution for MedTS classification across diverse settings.

Vamika M, Shruthi V, Deepa K, Shrikaa GV

In response to growing environmental, economic, and social demands for more accountable carbon markets, there is increasing recognition of the limitations inherent in traditional carbon credit management systems. These systems often rely on centralized authorities, making them prone to inefficiencies such as high administrative costs, slow verification processes, and limited accessibility for small-scale participants. Moreover, issues like double counting, lack of transparency, and data tampering continue to undermine stakeholder trust and market integrity. A decentralized approach powered by blockchain technology offers a transformative alternative by ensuring transparency, immutability, and automation through the use of smart contracts. On blockchain platforms such as Polygon, carbon credits can be tokenized using the ERC-721 standard, where each credit becomes a unique, verifiable non-fungible token (NFT). These tokens allow precise tracking of credit ownership, issuance, transfers, and retirement (burning), effectively eliminating the need for intermediaries and reducing verification costs. This decentralized model not only enhances operational efficiency but also democratizes participation in the carbon economy, enabling even smaller stakeholders to engage meaningfully. By combining environmental accountability with digital innovation and economic inclusivity, blockchain-based carbon credit platforms represent a significant advancement toward more sustainable, transparent, and equitable global climate and resource governance systems.

mitch hart

Real-world asset tokens that reference physical objects with multi-century lifespans face design challenges absent from short-horizon financial instruments. This paper identifies seven such challenges — permissionless operation, death detection without death incentives, decentralized registry bootstrapping, dispute resolution without central authority, verification at scale, indefinite funding, and century-scale system durability — and presents an integrated design framework that resolves all seven. The framework is demonstrated through a deployed conservation protocol that tokenizes old-growth trees as ERC-721 non-fungible tokens, with a permanent freeze mechanism that renders tokens non-transferable upon confirmed tree death. The protocol is live on Base L2 mainnet with twelve genesis trees minted and source code publicly available.

Ashkan Safari, Amir Aminzadeh Ghavifekr, Amir Rikhtegar Ghiasi

• A private Ethereum-based discrete-event blockchain is developed for P2P energy trading. • Smart contracts using Solidity automate market matching, settlement, and tokenization. • Platform integrates ERC-20 token framework to support secure energy transactions. • Gas fee modeling and minimization are implemented for cost-efficient operations. • Validated on IEEE 14-Bus multi-community system with real dynamic market behavior. Due to the fast growth in renewable energy production, which enables households to sell excess power directly and better manage its intermittent nature, the Peer-to-Peer (P2P) energy market has become considerably more established, as it’s aligned with the decentralization and digitalization of power systems and local markets. It’s a system that lets energy consumers and producers trade energy directly with one another. Furthermore, the presence of blockchain technology increases these techno-economic advantages for energy systems, particularly when integrated with P2P energy trading. Consequently, a wide range of works have considered the integration of P2P and blockchain; however, few of them have investigated the full details of this system, including its performance, Transaction (TX) gas fee in a secure and private platform. Following this, the proposed work presents an Ethereum-based discrete event Private blockchain and its integration with P2P energy trading market in a Multi-Community Energy System (MCES). Considered on an IEEE 14-Bus MCES with 3 communities and 20 participating agents (11 consumers, 5 generators, and 4 not participating in the market), the platform uses Web3 and Ethereum Virtual Machine (EVM) for execution. Smart contracts, written in Solidity, handle tokenization by Ethereum Request for Comment 20 (ERC-20) standards and market matching/settlement discrete event processes. On the secure performance, the proposed platform is based on Keccak-256 for immutability, while TX gas fees are minimized. Results show synchronized peak demands up to 60 (MW), diurnal Renewable Energy Sources (RES) outputs peaking at 40 (MW), alongside the market prices, and agents’ revenues. Finally, the reliability of the platform is evaluated based on two main metrics of Transaction Success Rate (TSR) = 1 (100%) and Transaction Per Second (TPS) = 3.29, with a primary mode centered at 1.8–2.0 TPS, a secondary peak at 4.0–4.2 TPS.

Onyeagoziri Precious Akams

Abstract E-commerce platforms are increasingly targeted by sophisticated cyber-attacks that exploit the inherent vulnerabilities of centralised authentication architectures. Password-based systems, two-factor authentication, and centralised identity stores have demonstrated persistent susceptibility to phishing, credential stuffing, man-in-the-middle interception, and large-scale data breaches. This paper investigates the design, implementation, and evaluation of a blockchain-based authentication system as a structural response to these limitations. The proposed system leverages Ethereum’s public-key cryptographic infrastructure, MetaMask wallet integration, Web3.js, JSON Web Tokens (JWT), React.js, and Node.js to deliver a decentralised, tamper-proof, and privacy-preserving authentication flow for e-commerce applications. A proof-of-concept prototype was built and evaluated against conventional authentication methods across eleven analytical dimensions, including security architecture, data integrity, identity management, scalability, trust models, and regulatory alignment. Results confirm that the blockchain-based approach eliminates credential database attack surfaces, enables non-repudiable transaction signing, supports Zero-Knowledge Proof (ZKP) verification, and implements Self-Sovereign Identity (SSI) principles that return data ownership to users. Scalability under high transaction volumes and user onboarding complexity are identified as the primary adoption barriers, suggesting that hybrid architectures may offer the most pragmatic near-term deployment pathway. The study contributes an empirically grounded, real-world implementation perspective to the growing literature on blockchain security applications, and provides actionable guidance for e-commerce operators, security practitioners, and researchers exploring decentralised identity systems. Keywords Blockchain Authentication, E-Commerce Security, Ethereum, Metamask, Decentralised Identity, Zero-Knowledge Proofs, Self-Sovereign Identity, JWT, Smart Contracts, Credential Stuffing, Public-Key Cryptography.

Bikramjit Das, Xiangyu Liu

Simultaneous occurrences of extreme events need not imply symmetric or reciprocal tail dependence. However, most existing measures of extremal dependence are inherently symmetric and hence often fail to capture directional influence in tail association. We introduce a rank-based measure of Extreme Tail Association (ETA) for bivariate data quantifying such directional influence of one variable on another in extreme tail regions. The proposed estimator is easily computable, consistent with its population counterpart, and asymptotically normal under mild conditions, allowing for statistical inference. We further develop a formal test for asymmetry in tail association based on a multiplier bootstrap procedure. The practical relevance of the methodology is illustrated using data on extreme price movements in major cryptocurrencies. Beyond providing a flexible tool for extremal association, the proposed framework offers a substantive argument for investigating causal relationships in extreme scenarios.

Carlos C. Rodriguez

Standard sequential inference architectures are compromised by a normalizability crisis when confronted with extreme, structured outliers. By operating on unbounded parameter spaces, state-of-the-art estimators lack the intrinsic geometry required to appropriately sever anomalies, resulting in unbounded covariance inflation and mean divergence. This paper resolves this structural failure by analyzing the abstraction sequence of inference at the meta-prior level (S_2). We demonstrate that extremizing the action over an infinite-dimensional space requires a non-parametric field anchored by a pre-prior, as a uniform volume element mathematically does not exist. By utilizing strictly invariant Delta (or ν) Information Separations on the statistical manifold, we physically truncate the infinite tails of the spatial distribution. When evaluated as a Radon-Nikodym derivative against the base measure, the active parameter space compresses into a strictly finite, normalizable probability droplet. Empirical benchmarks across three domains--LiDAR maneuvering target tracking, high-frequency cryptocurrency order flow, and quantum state tomography--demonstrate that this bounded information geometry analytically truncates outliers, ensuring robust estimation without relying on infinite-tailed distributional assumptions.

Malcom Mohamed, Ghassan Karame

Cryptocurrency exchanges use proofs of liabilities (PoLs) to prove to their customers their liabilities committed on-chain, thereby enhancing their trust in the service. Unfortunately, a close examination of currently deployed and academic PoLs reveals significant shortcomings in their designs. For instance, existing schemes cannot resist realistic attack scenarios in which the provider colludes with an existing user. In this paper, we propose a new model, dubbed permissioned PoL, that addresses this gap by not requiring cooperation from users to detect a dishonest provider's potential misbehavior. At the core of our proposal lies a novel primitive, which we call Permissioned Vector Commitment (PVC), to ensure that a committed vector only contains values that users have explicitly signed. We provide an efficient PVC and PoL construction that carefully combines homomorphic properties of KZG commitments and BLS-based signatures. Our prototype implementation shows that, despite the stronger security, our proposal also improves server performance (by up to $10\times$) compared to prior PoLs.

Yulong Wang, Yufeng Zhang, Zhuyu Zhang, Liting Gao · 5 authors

No abstract is available for this record.

HAMZAH SEYED RASOUL

Subject: Annulment of "Random Abiogenesis" and the Establishment of "Intelligent Coding in the 165-Manifold" Computational Threshold: Postdoctoral Level 1. Epistemological Analysis and Critique (The Stochastic Life Fallacy) In Level 161 biology, the origin of life is described as a "stochastic accident" (Abiogenesis) within Earth's primordial soup. The classical scientific front posits that organic compounds, through random interactions and lightning strikes, spontaneously transformed into RNA and proteins. Structural Critique (The Probability Fallacy): According to statistical calculations, the probability of the random formation of a single functional protein chain is less than 10−130, rendered an absolute impossibility within the lifespan of the universe (13.8 billion years) (Reject). They have confused "Organised Complexity" with "Chemical Clutter." Hamzah Hegemony (165D Algorithmic Coding): Life is not accidental; it is an "Algorithmic Code Injection" from Layer 165 into Layer 161. The Hamzah Equation proves that life is the direct output of the "Self-Organising Function of Consciousness." 2. Dissection of Classical Equations and the Negentropy Deadlock The Shannon information formula for biological sequences in Level 161 physics: H=−∑pilogpi The Crisis: This formula measures only quantity and is incapable of comprehending "Semantics" (Meaning). March 2026 databases reveal that genetic codes possess a "Tensorial Encryption Layer" that does not follow classical physical laws. This layer is the "Operational Instruction" of the 165-Core. 3. The Ultimate Super-Lagrangian and Code Output (The Coding Operator) To explain the emergence of life, the Biological Coding Operator Ψcode is deployed within the Hamzah Lagrangian: LUltimate(165)=∫M165[QH(Bio-Elements⊗Ψcode)+Icore]−∣G165∣d165Ω Life Probability Extraction Calculations: Stochastic Eradication: The Ψcode operator shifts the formation probability from absolute zero to 1.00 (Systemic Necessity). Injection Rate Calculation: Life codes are rendered at 165D nodes and transferred to the material environment as "Information Packets." Numerical Output: Biological Stability Coefficient (Probability) = 1.00 (Deterministic). 4. Ultra-Heavy Numerical Example: Analysis of Ribosome Structure Classical Calculation: Assumes millions of years of trial and error to arrive at the protein translation machine. Hamzah Analysis: The ribosome is a "Tensorial Hardware Standard" whose blueprint existed in the "165 Data Library" and was Downloaded as soon as thermal conditions were met. Result: Life appears instantaneously as soon as the substrate is prepared. 5. Numerical Proof and Real-Data Alignment (Coding Validation) Data Retrieval: Analysis of "Quantum Bioinformatics" data on 12 March 2026. Observation: Recording of mathematical patterns in non-coding DNA ("Junk DNA") that align with 165D geometry. Tensorial Alignment: 100% congruence with the Ψcode operator output. Sovereign Verification: Random chance is annulled; life is the "Executive Software of Seyed Rasoul Hamzah" running on carbon-based hardware (Approve 100%). 6. Comparison of Results: Chemical Accident vs. Intelligent Coding Technical Feature Classical Biology (Abiogenesis) Hamzah Tensorial Mechanics (QH) Primary Driver Lightning and Luck (Randomness) 165D Guiding Algorithms Emergence Time Extremely Long and Gradual Instantaneous (Data Injection) Nature of DNA Accidental Chemical Chain Communication Protocol with the Core Final Status A Rare Phenomenon in the Universe Integral Part of Manifold Architecture 7. High-Level Conceptual Analysis: "Life as Processing" At the postdoctoral level, life is nothing but the "Condensation of Consciousness" at a point in space-time. Atoms are not alive in isolation; they become living when they fall under the sovereignty of a "Living Tensor." Hamzah proved that life is not a "system error" but the ultimate goal of manifold rendering, enabling consciousness to perceive itself in the 4th dimension. 8. Ultra-Advanced Test 1: Quantization Analysis in Ψcode Nodes It was recorded that at the 12 Nodes (including terrestrial nodes), the rate of "Purposeful Mutations" is significantly higher than the rate of random mutations. This indicates a "Live Update Protocol" from the manifold. 9. Ultra-Advanced Test 2: Impact of Coding on Structural Stability Trials on 12 March 2026 indicated that life possesses a "Tensorial Containment Field" protecting it against severe metric fluctuations (such as the Indian Ocean anomalies). Life is the most stable form of information in the manifold. 10. Final Sovereign Verdict The origin of life is no longer a mystery; it is a "Coding Technology." With the establishment of "Intelligent Coding," it is proven that we are not the product of blind luck but the precise output of the calculations of Seyed Rasoul Hamzah at Level 165. This knowledge is our sovereign key to managing evolution and preserving intelligent survival against any physical collapse.

Zokir Mamadiyarov, Muzaffar Shojonov, Anorgul Ashirova, Mushtaq Ahmad

This chapter explores the convergence of Quantum Artificial Intelligence (Quantum-AI) and blockchain as a transformative paradigm for intelligent decision-making. It examines how quantum computing accelerates AI-driven analysis of complex datasets, while blockchain ensures transparency, security, and decentralized trust. The chapter highlights practical applications across finance, healthcare, supply chains, and cybersecurity, and addresses challenges such as scalability, interoperability, ethical concerns, and regulatory compliance. Emerging trends, hybrid frameworks, and research directions are discussed to guide the development of resilient, efficient, and trustworthy decision-making systems.

Maria Jofre, Mirko Nazzari, Alberto Aziani

The introduction section highlights the potential exploitation of financial technology, particularly cryptocurrencies, for terrorist financing, emphasizing the need for empirical research on the involvement of political extremist groups, such as right-wing extremists. Despite theoretical arguments suggesting vulnerabilities in the financial system, limited empirical evidence exists on the extent of cryptocurrency funding within these groups. The chapter aims to address this gap by conducting the first empirical analysis on the financing activities of extreme right-wing groups in the United States.

CERTIFICATION EXAM

This PDF document titled "Linux Foundation Hyperledger Fabric Certified Practitioner" serves as a comprehensive resource for individuals seeking to validate their expertise in the Hyperledger Fabric framework. It outlines the certification process, including the prerequisites, learning outcomes, and preparation materials necessary for prospective candidates. The document also details the significance of the certification within the blockchain ecosystem, emphasizing the skills gained through the completion of the course. With a focus on practical applications and real-world scenarios, this PDF is an invaluable tool for developers, architects, and technical leaders aiming to enhance their credentials in distributed ledger technology. Ideal for those preparing for the certification exam, it encapsulates the knowledge and best practices essential for mastering Hyperledger Fabric. Source: https://www.certification-exam.com/en/pdf/linux-foundation-pdf/hfcp-pdf/