Accurate, transparent, and scalable Measurement, Reporting, and Verification (MRV) of greenhouse-gas emissions is foundational to credible climate governance, yet prevailing systems remain fragmented, low-frequency, and vulnerable to manipulation. This paper proposes a hybrid IoTâHadoopâblockchain architecture that reconceptualizes carbon data as a continuously governed digital asset rather than a static compliance artifact. High-frequency operational data are collected through IoT infrastructures, stored and pre-processed in Hadoop for scalability and data sovereignty, and anchored on a Hyperledger Fabric consortium blockchain using Merkle-tree commitments to ensure immutability and traceability. A Carbon Data Interface Standard (CDIS) harmonizes heterogeneous data sources, while Decentralized Autonomous Organization (DAO)-based governance distributes authority across individual and institutional stakeholders. A Dynamic Authority Selection Mechanism (DASM) aligns participation in the consensus process with verifiable performance, institutionalizing a coopetitive model of data stewardship. The architecture further integrates with a public-chain value layer, enabling tokenization pathways and interoperability with emerging Web3 and Real-World Asset (RWA) climate-finance mechanisms. The results demonstrate how decentralized infrastructure, cryptographic verification, and polycentric governance can jointly improve data integrity, transparency, and market relevance in MRV systems. The paper concludes by outlining empirical pilot pathways and future research directions in AI-assisted verification, dynamic standardization, and climate-linked digital finance.
As blockchain technology evolves from specialized financial tools to foundational infrastructure for Web3, the necessity for rigorous performance validation becomes paramount. Stress testingâdefined as the evaluation of system stability under extreme workloadsâis critical for identifying bottlenecks in consensus mechanisms and peer-to-peer communication. This survey provides an exhaustive analysis of web-based stress testing frameworks. Unlike traditional CLI-based tools, web-based frameworks provide real-time telemetry and distributed orchestration capabilities essential for modern decentralized applications. We categorize existing literature into three generations of benchmarking, evaluate ten prominent frameworks based on a multi-dimensional rubric, and identify significant research gaps including the lack of standardized cross-chain stress protocols and AI-integrated anomaly detection. This work aims to provide a roadmap for researchers and DevOps engineers to select and implement robust testing environments for enterprise-grade blockchain deployments.
Blockchain technology has emerged as a foundational element of the digital economy, enabling secure, transparent, and decentralized transactions across diverse domains. Although numerous consensus algorithms have been proposed, existing studies often examine them in isolation or from a limited set of metrics. This paper presents a comprehensive and integrated comparative analysis of major consensus mechanismsâProof of Work (PoW), Proof of Stake (PoS), Delegated PoS (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Federated BFT (FBFT). We systematically evaluate their verification processes, performance metrics, security trade-offs, and application contexts. Our contribution lies in consolidating these aspects into a unified framework that highlights critical design trade-offs and decision criteria for selecting appropriate consensus protocols. This work aims to support researchers and practitioners in developing and deploying more efficient and secure blockchain systems.
In decentralized finance (DeFi), accidental cryptocurrency transfers to incorrect wallet addresses are a large usability and security issue, frequently causing permanent loss of funds. We present CryptoSafeSend, a smart contract-based safety protocol for transactions featuring a cryptographically secure passcode verification scheme supporting conditional transfers. This work addresses higher-level security issues by introducing a PBKDF2-based key derivation function, which generates strong encryption keys based on Firebase's Firebase Unique Identifier. Secret passcodes are encrypted in Advanced Encryption Standard operating in Galois/Counter Mode functions deployed underneath the Web Crypto API, and the initialization vector and ciphertext are Base64 encoded for off-chain reliable storage and Firebase Firestore-based messaging. The protocol prevents unauthorized exploitation by safely binding off-chain passcode transmission to a matching on-chain verification, reinforcing user trust without undermining decentralization. Also, to ensure recoverability, CryptoSafeSend integrates a 7-day on-chain escrow lock, after which unclaimed funds become permissionlessly refundable to the sender, preserving decentralization while eliminating reliance on trusted intermediaries. Testing on an Ethereum testnet confirms negligible gas overhead, immunity against double claims, and strong security guarantees, qualifying CryptoSafeSend as a valuable constituent in next-generation secure digital asset protocols.
This thesis, submitted at the Institute for Law and Finance at Goethe University Frankfurt, provides a critical legal and technological analysis of the effectiveness of the Financial Action Task Force framework in addressing money laundering risks arising from decentralized finance. It examines how decentralized blockchain-based systems fundamentally challenge the assumptions underlying traditional anti-money laundering regulation.The study argues that FATF Recommendations, originally designed for centralized financial systems, are structurally incompatible with decentralized architectures that operate without identifiable intermediaries such as Virtual Asset Service Providers. Through an integrated legal and technological assessment, the research demonstrates how privacy-enhancing tools, including non-custodial wallets, cryptocurrency mixers, zero-knowledge proof mechanisms, and cross-chain bridges, obscure ownership trails and significantly impair regulatory oversight.While these technologies are designed to enhance user privacy, they simultaneously enable sophisticated money laundering techniques, including chain hopping, transaction obfuscation, and the untraceable movement of assets across blockchain networks. The thesis further identifies critical regulatory gaps in the application of core FATF standards, particularly in relation to customer due diligence, beneficial ownership transparency, and the implementation of the Travel Rule.A case study of Bosnia and Herzegovina illustrates the practical consequences of fragmented regulatory implementation. Divergent adoption of FATF standards across its entities reflects the broader âSunrise Issue,â whereby asynchronous global implementation of the Travel Rule generates cross-border inconsistencies and enforcement challenges.To address these structural deficiencies, the thesis proposes a reinterpretation of FATF standards based on the principle of functional equivalence, extending AML obligations to any actor or protocol exercising effective control over financial transactions, irrespective of formal legal classification. It further advocates for the integration of RegTech, tokenization, and machine learning as tools to reconcile regulatory oversight with technological innovation.The research concludes that the current FATF framework remains fundamentally misaligned with the operational realities of decentralized finance. Ensuring the continued integrity of the global financial system will require the adoption of technologically adaptive, risk-based, and internationally coordinated regulatory approaches. Only through such innovation can AML enforcement remain effective in an increasingly decentralized digital economy.
Decentralized Autonomous Organizations (DAOs) allow for novel collective governance. However, their reliance on conventional forms of cryptography raises fundamental security concerns, as well as paradoxes in their governance. With the emergence of fault-tolerant quantum computers threatening critical IoT-Blockchain ecosystems, which will shatter today's monetary encryption, this study proposes the first holistic, comprehensive, and conceptualization of a Quantum-Secured DAO (Q-DAO). Such entities will have their core functions organically designed around the foundational elements of quantum theory. Q-DAO design conceptualization transcends the mere addition of post-quantum cryptography and addresses the re-invention of the trustlessness paradigm. It will transform current reliance on a computational assumption to a physical guarantee of trustlessness as defined by the immutable and unassailable laws of nature. The designed system conceptualization will revolve around four pillars: The first is quantum-state governance designed tokens exploiting the no-cloning theorem towards Sybil attacks. The second focuses on a secure and private voting stratagem induced by quantum entanglement. The third introduces a hybrid onchain/quantum channel governance system designed to ensure simultaneous transparency and security of communication. Finally, the fourth emphasizes a novel quantum interference for dispute resolution that overcomes the Code is Law rigidity.
Maximal Extractable Value, or MEV, remains a structural threat to blockchain fairness because a block producer can often observe pending transactions and unilaterally decide their ordering or inclusion. Existing mitigations hide transaction contents or outsource ordering, but they often leave two gaps unresolved. First, commitments are not authenticated by slashable identities. Second, inclusion obligations are not backed by transferable evidence that other validators can verify. This paper presents MEV ACE, a fair ordering protocol for proposer controlled ordering MEV. MEV ACE combines three mechanisms. First, it uses registered economic identities whose authentication keys are deterministically derived from the ACE GF framework and bonded on chain. Second, it uses authenticated commit and open messages with validator receipt thresholds, which make admissibility and inclusion obligations independently auditable. Third, it uses verifiable delay based randomness to determine transaction order only after the admissible commitment set is fixed. We formalize the protocol in a Byzantine fault tolerant validator model with threshold receipts and show three properties under standard assumptions: order unpredictability after the admissible set is locked, commitment authenticity under signature unforgeability, and accountable inclusion for transactions that obtain threshold commit and open receipts. Under these conditions, and when producer and user bonds exceed the one slot gain from invalid execution or selective non opening, MEV ACE removes unilateral proposer discretion over front running, sandwich attacks, and censorship against admitted transactions. The protocol remains single slot in structure, requires no threshold decryption committee, and is compatible with post quantum signature schemes such as ML DSA 44.
Autonomous AI agents are beginning to operate across organizational boundaries on the open internet -- discovering, transacting with, and delegating to agents owned by other parties without centralized oversight. When agents from different human principals collaborate at scale, the collective becomes opaque: no single human can observe, audit, or govern the emergent behavior. We term this the Logic Monopoly -- the agent society's unchecked monopoly over the entire logic chain from planning through execution to evaluation. We propose the Separation of Power (SoP) model, a constitutional governance architecture deployed on public blockchain that breaks this monopoly through three structural separations: agents legislate operational rules as smart contracts, deterministic software executes within those contracts, and humans adjudicate through a complete ownership chain binding every agent to a responsible principal. In this architecture, smart contracts are the law itself -- the actual legislative output that agents produce and that governs their behavior. We instantiate SoP in AgentCity on an EVM-compatible layer-2 blockchain (L2) with a three-tier contract hierarchy (foundational, meta, and operational). The core thesis is alignment-through-accountability: if each agent is aligned with its human owner through the accountability chain, then the collective converges on behavior aligned with human intent -- without top-down rules. A pre-registered experiment evaluates this thesis in a commons production economy -- where agents share a finite resource pool and collaboratively produce value -- at 50-1,000 agent scale.
With the rapid advancement of decentralized applications, smart contract security faces severe challenges, particularly regarding atomicity violations in complex logic such as Oracle and NFT contracts. Rigid rule sets often limit traditional static analyzers and lack deep contextual awareness, leading to high false-positive and false-negative rates when identifying vulnerabilities that depend on intermediate state inconsistencies. To address these limitations, this paper proposes PSR\textsuperscript{2}, a novel collaborative static analysis framework that integrates structural path searching with deterministic semantic reasoning. PSR\textsuperscript{2} utilizes a Graph Structure Analysis Module (GSAM) to identify suspicious execution sequences in control flow graphs and a Semantic Context Analysis Module (SCAM) to extract data dependencies and state facts from abstract syntax trees. A Fusion Decision Module (FDM) then performs formal cross validation to confirm vulnerabilities based on a unified atomicity inconsistency model. Experimental results on 1,600 contract samples demonstrate that PSR\textsuperscript{2} significantly outperforms pattern-matching baselines, achieving an F1-score of 94.69\% in complex ERC-721 scenarios compared to 51.86\% for existing tools. Ablation studies further confirm that our fusion logic effectively reduces the false-positive rate by nearly half compared to single module analysis.
Junliang Luo, Xihan Xiong, Zonglun Li, Hong Kang ¡ 7 authors
The global financial architecture is undergoing a shift from intermediary centric-settlement to programmable infrastructure, to transmute trillions in static illiquid capital into active, high-velocity instruments. We argue that Real World Asset (RWA) tokenization represents a conceptual evolution beyond mere digitization, converting passive ledger entries into programmable economic agents capable of autonomous settlement and algorithmic collateralization. However, achieving such seamless capital efficiency necessitates resolving the fundamental friction between deterministic on-chain code and probabilistic off-chain reality, navigating the oracle problem and jurisdictional interoperability. This systematization of knowledge presents a taxonomy for the RWA lifecycle and deconstructs the multi-layered architecture, spanning legal custody, technical standards, and cryptoeconomic valuation, required to enforce off-chain rights within on-chain environments. We study systemic constraints such as latency and regulatory fragmentation through a comparative overview of sovereign debt, private credit, and real estate protocols, complemented by an empirical case study of on-chain U.S. Treasuries. We synthesize these findings to propose a prognostic outlook, positing that while asset tokenization provides a transitional bridge, it is not necessarily the inevitable shift compared to the emergence of unified, programmable ledgers.
Luigi Crisci, Lorenz Schuler, Herbert Jordan, Bernhard Scholz
The Ethereum state database uses Merkle Patricia Trie (MPT), which suffers from large witness proof sizes and high storage overhead. Verkle Tries have been proposed as a replacement, offering witness proofs below 150 bytes through vector commitments and Inner Product Argument aggregation. However, deploying a Verkle Trie in a high-throughput, short block-time blockchain such as Sonic, which produces a block every 300 milliseconds, introduces substantial engineering challenges related to storage efficiency, commitment computation costs, and the need to serve both live and historical state queries in real time. We present SonicDB S6, a production-grade Rust Verkle Trie database for the Sonic blockchain, which leverages its non-forking property to enable aggressive storage optimizations. Occupancy-aware node specializations, selected via an $\mathcal{O}(k n^2)$ dynamic program, reduce live storage by 97.8\%. Delta nodes that record only changed slots reduce archive storage by 95\%. Batched updates, multi-threaded commitment computation, and homomorphic Pedersen caching yield $3.2\times$ higher throughput than a persistent Geth Verkle baseline while sustaining production block-rate performance.
In this study, a novel theoretical framework is provided for the potential effect of smart contracts on reducing transaction costs in financial markets to increase corporate investment. The theoretical model considered in this paper is based on Williamsonâs transaction cost approach and considers how an important blockchain technology such as smart contracts can induce corporate investment through its potential transaction cost reduction effect. The findings obtained show that smart contracts minimize transaction costs and increase future growth opportunities for firms to corporate investment. This study sheds new light on smart contract-based finance and its impact on business investment within the Williamsonâs transaction cost framework.
Swarm Learning (SL) offers a transformative solution to the challenges posed by growing data security regulations and privacy concerns. It creates new opportunities for research in fields such as healthcare, finance, and smart technologies. This decentralized machine learning framework harnesses the collective intelligence of distributed nodes, each holding private data, and uses blockchain technology to ensure data privacy. The framework constructs a shared model by aggregating insights from each node without compromising the security of local data. Motivated by the goals of enhancing model performance and deepening the understanding of model aggregation, this study systematically tested various merging strategies on three datasetsâMNIST, BloodMNIST, and Blood Cell Cancer (ALL)âwithin a simulated Swarm Learning environment. As a result, we developed the Adaptive Performance-Based Merge Strategy (AP-BMS), a novel method that dynamically selects the optimal merging algorithm within the Swarm network based on continuous model evaluations. This strategy improved performance by approximately 1% on the MNIST dataset, 6% on BloodMNIST and 4% on the Blood Cell Cancer (ALL) dataset. The AP-BMS marks a significant advancement in local model aggregation and further accelerates the evolution of Swarm Learning and its application in secure, decentralized machine learning environments.
Faithful, Stable, Complete: Pick Two The Problem in Plain Language When a machine learning model makes a prediction â approving a loan, diagnosing a disease, flagging a transaction â practitioners use a tool called SHAP to answer "which input features mattered most?" SHAP is the most widely used explanation method in machine learning. Here is the problem: retrain the same model on the same data with a different random seed, and the explanation changes. The model's predictions barely move, but the "most important feature" can flip entirely. In 68% of 77 public datasets, the top feature is not stable across retrains. This is not a software bug. This is not fixable by tuning hyperparameters. We prove it is a mathematical impossibility. What We Prove No feature ranking can simultaneously be: Faithful â it reflects what the model actually learned Stable â it doesn't change when you retrain Complete â it ranks every pair of features âŚwhen features are correlated with similar importance. You must give up one. The proof is four lines long. It requires no assumptions about the model, the data, or the explanation method â only that correlated features admit models ranking them in opposite orders (the Rashomon property), which is true for every standard ML algorithm. How Bad Is It? We trained 50 XGBoost models on Breast Cancer Wisconsin â the dataset used in every SHAP tutorial â and counted how many different "top 3 most important features" appeared. Twenty-four. At 100 models: thirty-five. The "most common" answer appeared in only 12% of runs. Two randomly chosen models agree on the top-3 only 4.2% of the time. Every tutorial, textbook, and blog post showing SHAP on this dataset is showing one of two dozen equally valid answers. Three other datasets (California Housing, Heart Disease, Wine Quality) produce exactly one ranking every time â because their top features have clearly different importance. The theory correctly predicts which datasets are affected and which are safe. Dataset Distinct top-3 rankings (50 models) Two models agree? Breast Cancer 24 4.2% Diabetes 2 88.5% Wine Quality 1 100% (stable) Heart Disease 1 100% (stable) California Housing 1 100% (stable) It Gets Worse for Yes/No Questions For ranking questions (which feature is MORE important?), there is a fix: average across multiple models. But for binary questions â "does this feature contribute positively or negatively?", "is this feature selected?" â no fix exists. Even averaging doesn't help, because there's no middle ground between "positive" and "negative." We call this the bilemma. Real-World Consequences For loan applicants. We trained 30 models on German Credit data. Under standard settings, 45% of applicants receive a different "most important reason" for their decision depending on which model happens to be deployed. One applicant received six different top reasons across 30 models. For biomarker discovery. On a dataset of 10,935 genes distinguishing colon from kidney tissue, the "#1 most important gene" alternates between TSPAN8 (involved in tumor invasion) and CEACAM5/CEA (involved in immune evasion) depending on the random seed. A drug discovery pipeline targeting one gene makes a different bet than one targeting the other â and which bet gets made depends on a random number. For fairness audits. A SHAP-based audit checking whether a model relies on a protected attribute (like race or gender) reaches its conclusion with the reliability of a coin flip when the protected attribute is correlated with other features. The Fix DASH (Diversified Aggregation for Stable Hypotheses): train 25 models with different seeds, average their SHAP values. This is provably the best possible approach â no method can do better. Features that genuinely differ in importance get stable rankings. Features that are interchangeable get reported as tied, which is the honest answer. We also provide a 7-line diagnostic that identifies which features are at risk, requiring no statistical expertise and no assumptions about the data distribution. It outperforms the standard formula by 2Ă on real data. The practical workflow: Screen your model (1 model, seconds) Run the minority fraction diagnostic (7 lines of code) For flagged features, train 5 models and run a Z-test If unstable, use DASH with 25+ models Machine Verification Every mathematical claim is checked by a computer. The proofs are written in Lean 4 (a programming language for mathematics) and verified by its type-checker: 357 theorems, all machine-verified 6 axioms (the minimal assumptions the theory needs) Zero unproved claims across 58 files During the formalization, the computer caught two logical errors and one type mismatch that human reviewers missed. To our knowledge, this is the first formally verified impossibility result in explainable AI. Technical Details Architecture-dependent bounds Gradient boosting (XGBoost, LightGBM): instability diverges as correlation increases. At Ď = 0.9, the dominant feature gets 5Ă its fair share. Lasso: the ratio is infinite â one correlated feature gets everything, the other gets zero. Neural networks: 87% of feature pairs are unstable. Model instability dominates SHAP estimation noise by 8:1. Random forests: instability converges with more trees â the contrast case showing that parallel (not sequential) training helps. Cross-implementation. XGBoost, LightGBM, and Random Forest all show the same instability pattern. It is not specific to any one software package. Subsample sensitivity. Even at subsample = 0.95 (minimal randomness), 17 distinct rankings remain. Only fully deterministic training (subsample = 1.0) produces one ranking â but this sacrifices the regularization that makes the model accurate. Mechanistic interpretability. Preliminary evidence suggests the impossibility extends beyond feature importance to neural network circuit analysis. 10 transformers trained on modular addition (all achieving 100% accuracy) agree on only 36% of the top-3 circuit components. Design Space The achievable set of explanation methods has exactly two families: Family A (single model): faithful and complete, but unstable. Rankings flip up to 50% of the time. This is what standard SHAP does. Family B (DASH ensemble): faithful and stable, but reports ties for indistinguishable features. This is what DASH does. No third option exists. DASH is provably the best method in Family B. Associated Papers Companion paper (TMLR, under review). First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution.arXiv: https://arxiv.org/abs/2603.22346DOI: https://doi.org/10.5281/zenodo.19446088 Companion implementation: https://github.com/DrakeCaraker/dash-shap
Proof of Concept on the β-Catenin/TCF4 Interface in Wnt-Driven Oncogenesis and Gardner Syndrome Protein-protein interactions (PPIs) govern nearly every biological signalling pathway, yet their large, flat contact surfaces have resisted conventional drug design for decades. Existing computational approaches either require prohibitive molecular simulation resources or prior knowledge of a reference inhibitorâbarriers that have left many therapeutically important targets inaccessible. This repository presents GeoSol-ιι, a two-stage computational pipeline that generates macrocyclic drug scaffolds against PPI surfaces from first principlesâwithout molecular dynamics simulation, without a crystallographic inhibitor reference, and without prior chemical knowledge of the target. The engine couples deterministic rigid-body SO(3) Fibonacci sampling with directed chemical evolution (genetic algorithms). In milliseconds, the pipeline successfully converged on a novel: 15-atom macrocyclic scaffold (1,4-dioxacyclopentadecane) that achieves a thermodynamic optimum with zero desolvation penalty against the β-catenin interface. This repository establishes formal prior art for both the identified chemical entity and the underlying high-throughput methodology.
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Wnt/β-catenin signaling in development and cancer
Cloud computing underpins modern IT infrastructure by delivering scalable, on-demand resource provisioning, yet controlling cloud expenditure remains a pressing challenge. Dynamic pricing structures, unpredictable workloads, and billing pipelines that lack real-time visibility create conditions in which unauthorized consumption and anomalous usage spikes routinely escape timely detection. This paper presents CloudPay, a blockchain-integrated cloud storage billing system that unifies unsupervised machine learning with smart contract execution to deliver verifiable, fine-grained, and fraud-resistant cost governance. The system converts user storage activity into time-series representations and applies the Isolation Forest algorithm to detect abnormal consumption spikes without any labelled training data. Flagged events are routed through an owner confirmation protocol that validates suspicious uploads before billing proceeds, preventing unauthorized charges from entering the settlement pipeline. Smart contracts autonomously compute GB-time-based charges, execute tokenized payments, and anchor every transaction to an immutable SHA-256 blockchain ledger. Experimental results confirm that the system achieves 94.4% anomaly detection accuracy, 99.7% billing precision, and an 18.4% reduction in overall cloud expenditure relative to static allocation baselines. These results demonstrate that integrating unsupervised anomaly detection with cryptographically enforced billing logic is a viable path toward tamper-evident, real-time cost governance in multi-tenant cloud environments.
Derived from original PMR research written by Bastien Buchwalter, Jean-Michel Maeso, and Vincent Milhau using AI and an editor
Quickly apply original, key PMR-published papers with Snapshotsâa short article companion that distills PMR research into compressed, digestible takeaways, so you can put the paperâs core ideas to work in your investment processâfast. This Snapshot is based on an article about cleaning cryptocurrency data so researchers and investors can build more reliable investable universes. It presents a three-step protocol for fixing market-cap spikes, Bitcoin-dominance dips, and volume anomalies in CoinMarketCap data while preserving prices and returns and improving aggregate market indicators for analysis.
Abdullah Abdullah, Nida Hafeez, Maryam Shabbir, Muhammad Ateeb Ather ¡ 6 authors
The integration of blockchain technology with the Internet of Things (IoT) presents a paradigm shift in securing decentralized networks, yet it introduces critical trade-offs among security, privacy, and scalability. This systematic analytical review examines the inherent tensions within blockchain-enabled IoT systems, focusing on how consensus mechanisms, cryptographic primitives, and architectural choices affect these three pillars. Through a comprehensive analysis of the contemporary literature, we identify that no single blockchain configuration simultaneously optimizes security, privacy, and scalability. Instead, these properties exist in a triadic relationship where enhancing one dimension typically compromises at least one other. Our review categorizes existing solutions based on their approach to balancing these trade-offs, including sharding, layer-2 protocols, zero-knowledge proofs, and hybrid architectures. We further analyze the applicability of these solutions across different IoT domains, identifying context-specific optimal configurations. The findings reveal that while significant progress has been made in addressing individual challenges, integrated frameworks that holistically consider all three dimensions remain underdeveloped. This review contributes a novel analytical framework for evaluating blockchainâIoT systems and identifies critical research directions, including adaptive consensus mechanisms, privacy-preserving scalability solutions, and domain-specific architectural patterns. Unlike prior studies that primarily focus on conceptual discussions of blockchainâIoT integration, this work synthesizes insights from systematically reviewed literature to propose a conceptual lightweight blockchain framework tailored for resource-constrained IoT environments. This study combines a SLR with a conceptual and experimentally evaluated framework, where the review findings and the proposed solution are presented as distinct but complementary contributions.
In an era where data integrity and secure verification are paramount, especially in sectors such as governance, healthcare and education, traditional centralized document verification systems fall short due to vulnerabilities like single points of failure, limited traceability, and lack of accountability. This study proposes a LRDDV (A layered Ledger approach for Robust Digital Documents Verification system using blockchain) model to create a multi-level method for verifying documents. This would solve these issues. It adds a lightweight consensus model that is led by validators and a way to lock information based on role to do this. People who have jobs at different hierarchy levels can add information to papers more quickly. This makes sure that the changes are safe and can be made all the way through. If 51 % of validators agree on something, it works like a real board of directors. It makes people trust each other and be open without having to do mining, which takes a lot of resources. It works better, costs less, and is easier to keep track of than centralized models, according to tests especially useful for small and medium-sized businesses (SMEs) as well as for government sector. Right now, things work fine in a controlled environment. Although, in the future, it will be safer and more scalable because it will be connected to group blockchain systems, use self-sovereign identification standards, and have built-in zero- knowledge proofs. The suggested answer allows document checking to happen in public places with lots of people in a safe, open, and spread-out manner.
Smart contracts, essential to Blockchain functionality, can be compromised by vulnerabilities like reentrancy attacks, allowing unscrupulous entities to misappropriate funds. A universal and efficient multi-modal vulnerability detection framework is created to tackle detection issues that exceed the capability of standard methods such as fuzzy testing and symbolic execution. The methodology incorporates BiLSTM, EfficientNet, and Transformer architectures, augmented by CNN2D and BiGRU for better feature extraction and sequence modeling. The SMARTBUG dataset is employed in two formats: compiled OPCODES and features extracted via Word2Vec from smart contract source code. Preprocessing entails utilizing Word2Vec to produce N-gram numerical representations, succeeded by an 80-20 division for training and testing. The system analyzes multi-modal inputs, such as grayscale image attributes, opcode frequency statistics, and source code sequences, facilitating comprehensive vulnerability characterisation. The experimental assessment assesses the proposed model in comparison to existing algorithms, including MLP, GRU, and BiLSTM, utilizing criteria such as accuracy, precision, recall, and F-score. The CNN2D + BiGRU + EfficientNet + Transformer setup attains the greatest detection accuracy of 91.9%, surpassing all benchmarks. The system reduces dependence on domain knowledge by automating feature extraction, enabling adaptation across diverse smart contract forms and improving security in blockchain contexts
Chirag Sathish, Arshad Khan, Deepesh Haldankar, Nikhita G ¡ 5 authors
The increasing adoption of telemedicine has amplified concerns regarding the security of patient data, particularly in the context of remote authentication and the growing threat of advanced cyber and quantum-enabled attacks. Traditional telehealth security mechanisms rely on static authentication and cryptographic protections, which fail to adapt to changing risk conditions and provide limited resilience against credential compromise and future quantum threats. This paper proposes TAPQ-Health, a Threat-Adaptive Post-Quantum Authentication Pipeline that dynamically adjusts the strength of authentication and cryptographic hardness in real time based on contextual and behavioral risk. The proposed framework integrates four components a lightweight context-bound zero-knowledge proof authentication mechanism, a federated machine learning-based risk assessment model, threat-triggered escalation to lattice-based post-quantum cryptography with adaptive re-encryption, and decentralized, tamper-evident storage using IPFS. A fully implemented prototype was evaluated using 200 real telemedicine sessions and a large-scale analysis of 1.3 million authentication records. Experimental results demonstrate a mean end-to-end latency of 102.84 ms, 100 percent authentication success, and a 61 percent reduction in cryptographic overhead compared to static post-quantum configurations, while achieving 96 percent risk detection accuracy. These results indicate that threat-adaptive post-quantum authentication can significantly enhance telemedicine security without compromising usability or scalability.
Blockchain technology has emerged as one of the most transformative innovations in the financial sector, enabling secure, transparent, and decentralized transaction systems. Among its key applications, smart contracts have gained significant attention for automating financial agreements and reducing the need for intermediaries. Smart contracts are self-executing digital agreements embedded within blockchain networks that automatically enforce contractual terms when predefined conditions are met. The present study examines the role of blockchain-based smart contracts in financial transactions and evaluates their impact on efficiency, transparency, security, and cost reduction in financial systems. The study is based on secondary data collected from industry reports, academic publications, and financial technology databases. Analytical methods including descriptive analysis and regression-based conceptual modeling are used to examine the relationship between smart contract adoption and financial transaction efficiency. The findings indicate that smart contracts significantly enhance transaction speed, reduce operational costs, minimize fraud risk, and improve transparency in financial systems. The study concludes that blockchain-based smart contracts have the potential to transform financial transactions by improving efficiency, reliability, and trust in digital financial ecosystems.
Recent advancements, specifically the 2026 whitepaper by Google Quantum AI, Stanford University, and the Ethereum Foundation (arXiv:2603.28846), have demonstrated the resource feasibility of breaking secp256k1 elliptic curve cryptography using fault-tolerant quantum computation (⤠1200 logical qubits and ⤠90 million Toffoli gates). While their work validates this capability via zero-knowledge STARK proofs without disclosing explicit circuits, we provide the continuous operator-theoretic framework that explains the exact physical collapse mechanism underlying their discrete resource results. By modeling cryptographic hardness as a stable, invariant computational manifold, we show that quantum vulnerability is a manifestation of a Birman-Schwinger instability. We prove that, within this model, the introduction of a transverse quantum operator (e.g., Shor's algorithm implemented via Quantum Phase Estimation) forces a resolvent singularity in the classical generator when the resource perturbation parameter crosses a critical threshold (Ο_c). We establish a strict Hardness Phase Transition, demonstrating that cryptographic security is equivalent to the point 1 remaining outside the spectrum of the Birman-Schwinger kernel. Furthermore, we formalize zero-knowledge proofs (such as the Groth16-wrapped STARK artifacts published by Babbush et al.) as highly constrained Boolean projectors. We show that these proofs trigger an epistemic spectral collapse via Zeno stabilization, certifying the non-invertible regime without decohering the raw computational state into the public domain. The manuscript includes an exact analytic toy model demonstrating bound-state collapse into the continuum, explicitly mapping the destruction of exponential cryptographic isolation to a polynomial scattering state. This formalization transitions cryptographic failure from a domain of discrete computational estimates to a continuous framework of operator-theoretic necessity.